From e0c3a309872b936d766a1b72c02f3e1cbe7cd381 Mon Sep 17 00:00:00 2001 From: nerofeeva2001 <144069512+nerofeeva2001@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:25:44 +0300 Subject: [PATCH 1/6] Delete lecture_1_intro_knn directory --- lecture_1_intro_knn/README.md | 1 - lecture_1_intro_knn/homework/KNN.ipynb | 778 ------- lecture_1_intro_knn/homework/knn.py | 143 -- lecture_1_intro_knn/homework/metrics.py | 87 - lecture_1_intro_knn/homework/requirements.txt | 5 - .../WorldHappiness_Corruption_2015_2020.csv | 793 ------- .../lecture/ml_01_intro_knn_annotated.pdf | Bin 2902148 -> 0 bytes .../lecture/sklearn_seminar_inclass.ipynb | 1954 ----------------- 8 files changed, 3761 deletions(-) delete mode 100644 lecture_1_intro_knn/README.md delete mode 100644 lecture_1_intro_knn/homework/KNN.ipynb delete mode 100644 lecture_1_intro_knn/homework/knn.py delete mode 100644 lecture_1_intro_knn/homework/metrics.py delete mode 100644 lecture_1_intro_knn/homework/requirements.txt delete mode 100644 lecture_1_intro_knn/lecture/WorldHappiness_Corruption_2015_2020.csv delete mode 100644 lecture_1_intro_knn/lecture/ml_01_intro_knn_annotated.pdf delete mode 100644 lecture_1_intro_knn/lecture/sklearn_seminar_inclass.ipynb diff --git a/lecture_1_intro_knn/README.md b/lecture_1_intro_knn/README.md deleted file mode 100644 index 61dd9a3..0000000 --- a/lecture_1_intro_knn/README.md +++ /dev/null @@ -1 +0,0 @@ -Introduction in ML and KNN algorithm. diff --git a/lecture_1_intro_knn/homework/KNN.ipynb b/lecture_1_intro_knn/homework/KNN.ipynb deleted file mode 100644 index 10332fb..0000000 --- a/lecture_1_intro_knn/homework/KNN.ipynb +++ /dev/null @@ -1,778 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "39a37345-99a6-4b16-9be7-ebdca1414c7f", - "metadata": {}, - "source": [ - "# Домашнее задание №1 - Метод К-ближайших соседей (K-neariest neighbors)\n", - "\n", - "Сегодня мы с вами реализуем наш первый алгоритм машинного обучения, метод К-ближайших соседей. Мы попытаемся решить с помощью него задачи:\n", - "- бинарной классификации (то есть, только двум классам)\n", - "- многоклассовой классификации (то есть, нескольким классам)\n", - "- регрессии (когда зависимая переменная - натуральное число)\n", - "\n", - "Так как методу необходим гиперпараметр (hyperparameter) - количество соседей, то нам нужно научиться подбирать этот параметр. Мы постараемся научиться пользовать numpy для векторизованных вычислений, а также посмотрим на несколько метрик, которые используются в задачах классификации и регрессии.\n", - "\n", - "Перед выполнением задания:\n", - "- установите все необходимые библиотеки, запустив `pip install -r requirements.txt`\n", - "\n", - "Если вы раньше не работали с numpy или позабыли его, то можно вспомнить здесь: \n", - "http://cs231n.github.io/python-numpy-tutorial/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9638c464-806f-41b5-9dfe-1ea2048a1fa1", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import random\n", - "import pandas as pd\n", - "\n", - "\n", - "from sklearn.datasets import fetch_openml\n", - "from sklearn.model_selection import train_test_split\n", - "from knn import KNNClassifier\n", - "from metrics import binary_classification_metrics, multiclass_accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43bd8dc9-c430-4313-a6a1-4d3e5a7e9c47", - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams[\"figure.figsize\"] = 12, 9\n", - "sns.set_style(\"whitegrid\")\n", - "\n", - "SEED = 111\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)" - ] - }, - { - "cell_type": "markdown", - "id": "2867b963-214c-49ea-9460-5b427b56544d", - "metadata": {}, - "source": [ - "## Задание 1. KNN на датасете Fashion-MNIST (10 баллов)" - ] - }, - { - "cell_type": "markdown", - "id": "60a90da7-87ac-42e6-b376-bb34dac2b10b", - "metadata": {}, - "source": [ - "В этом задании вам предстоит поработать с картинками одежды, среди которых можно выделить 10 классов. Данные уже загружены за вас: в переменной X лежат 70000 картинок размером 28 на 28 пикселей, вытянутые в вектор размерностью 784 (28 * 28). Так как данных довольно много, а наш KNN будет весьма медленный, то возьмем случайно 1000 наблюдений (в реальности в зависимости от вашей реализации можно будет взять больше, но если будет не зватать ОЗУ, то берите меньше)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54fa4253-ea6a-4ec4-b914-f7cb2346b195", - "metadata": {}, - "outputs": [], - "source": [ - "X, y = fetch_openml(name=\"Fashion-MNIST\", return_X_y=True, as_frame=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a188c83-6bf3-485d-9995-9f71d0868d30", - "metadata": {}, - "outputs": [], - "source": [ - "idx_to_stay = np.random.choice(np.arange(X.shape[0]), replace=False, size=1000)\n", - "X = X[idx_to_stay]\n", - "y = y[idx_to_stay]" - ] - }, - { - "cell_type": "markdown", - "id": "4a9e7f89-97f9-4257-94aa-989826258726", - "metadata": {}, - "source": [ - "Давайте посмотрим на какое-нибудь изображение из наших данных:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "277e132c-b89f-4dbb-8efd-cbcea015876d", - "metadata": {}, - "outputs": [], - "source": [ - "# возьмем случайную картинку и сделаем reshape\n", - "# 28, 28, 1 = H, W, C (число каналов, в данном случае 1)\n", - "image = X[np.random.choice(np.arange(X.shape[0]))].reshape(28, 28, 1)\n", - "plt.imshow(image)\n", - "plt.axis(\"off\");" - ] - }, - { - "cell_type": "markdown", - "id": "236e593f-595e-45f1-a794-4069a16637d7", - "metadata": {}, - "source": [ - "### 1.1. Посмотрим на все классы (0.5 баллов)" - ] - }, - { - "cell_type": "markdown", - "id": "8cdf3ab2-47a4-492f-bf9a-25c4b00eb945", - "metadata": {}, - "source": [ - "Возьмите по одной картинке каждого класса и изобразите их (например, сделайте subplots 5 на 2)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "362137fb-4577-4a21-8088-98cba79206f2", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "YOUR CODE\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "866ea214-4de8-41b8-a2aa-c86ac0a04b74", - "metadata": {}, - "source": [ - "### 1.2. Сделайте небольшой EDA (1 балл)" - ] - }, - { - "cell_type": "markdown", - "id": "1fe3abdf-2c95-4ce8-8fd3-2445d815ea3c", - "metadata": {}, - "source": [ - "Посмотрите на баланс классов. В дальнейших домашках делайте EDA, когда считаете нужным, он нужен почти всегда, но оцениваться это уже не будет, если не будет указано иное. Делайте EDA, чтобы узнать что-то новое о данных!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74595ef7-06ab-4700-b9d9-42db3fdd36d5", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "YOUR CODE\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "68e8f61e-32d5-4ad7-9f2c-f7e0de050d32", - "metadata": {}, - "source": [ - "### 1.3. Разделите данные на train и test (0.5 баллов)" - ] - }, - { - "cell_type": "markdown", - "id": "25cf3d30-6bd6-4bbb-bba6-4e249da33475", - "metadata": {}, - "source": [ - "Разделите данные на тренировочную и тестовую выборки, размеры тестовой выборки выберите сами. Здесь вам может помочь функция `train_test_split`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1932bd43-16d6-4201-8950-7dbe720a9fa1", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "YOUR CODE\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "7c8e4cd4-b3d7-49b7-9b10-be02991ecfa7", - "metadata": {}, - "source": [ - "### 1.4. KNN для бинарной классификации (6 баллов)" - ] - }, - { - "cell_type": "markdown", - "id": "aac2e121-639a-4b0c-8e9c-471ad5a8fac6", - "metadata": {}, - "source": [ - "Давайте возьмем для задачи бинарной классификации только объекты с метками классов 0 и 1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f40beae7-54a4-4323-b467-3173737dfd84", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "YOUR CODE\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "7df7db35-8832-47ec-9955-d0656695e7cd", - "metadata": {}, - "source": [ - "И вот мы подготовили данные, но модели у нас пока что нет. В нескольких занятиях нашего курса вам придется самостоятельно реализовывать какие-то алгоритмы машинного обучения, а потом сравнивать их с готовыми библиотечными решениями. В остальных заданиях реализовывать алгоритмы будет не обязательно, но может быть полезно, поэтому часто это будут задания на дополнительные баллы, но главное не это, а понимание работы алгоритма после его реализации с нуля на простом numpy. Также это все потом можно оформить в виде репозитория ml_from_scratch и хвастаться перед друзьями." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44d468e9-2a00-4268-bfdc-fbae3857ce90", - "metadata": {}, - "outputs": [], - "source": [ - "knn_classifier = KNNClassifier(k=1)\n", - "knn_classifier.fit(binary_train_X, binary_train_y)" - ] - }, - { - "cell_type": "markdown", - "id": "c5817a1d-161e-4242-bea5-821f416b3eec", - "metadata": {}, - "source": [ - "### Настало время писать код!" - ] - }, - { - "cell_type": "markdown", - "id": "61c760bb-63c9-426d-9f4e-8fab02536da5", - "metadata": {}, - "source": [ - "В KNN нам нужно для каждого тестового примера найти расстояния до всех точек обучающей выборки. Допустим у нас 1000 примеров в train'е и 100 в test'е, тогда в итоге мы бы хотели получить матрицу попарных расстояний (например, размерностью 100 на 1000). Это можно сделать несколькими способами, и кому-то наверняка, в голову приходит идея с двумя вложенными циклами (надеюсь, что не больше:). Так можно делать, то можно и эффективнее. Вообще, в реальном KNN используется структура данных [k-d-tree](https://ru.wikipedia.org/wiki/K-d-%D0%B4%D0%B5%D1%80%D0%B5%D0%B2%D0%BE), которая позволяет производить поиск за log(N), а не за N, как будем делать мы (по сути это такое расширение бинарного поиска на многомерное пространство).\n", - "\n", - "Вам нужно будет последовательно реализовать методы `compute_distances_two_loops`, `compute_distances_one_loop` и `compute_distances_no_loops` класса `KNN` в файле `knn.py`.\n", - "\n", - "Эти функции строят массив расстояний между всеми векторами в тестовом наборе и в тренировочном наборе. \n", - "В результате они должны построить массив размера `(num_test, num_train)`, где координата `[i][j]` соотвествует расстоянию между i-м вектором в test (`test[i]`) и j-м вектором в train (`train[j]`).\n", - "\n", - "**Обратите внимание** Для простоты реализации мы будем использовать в качестве расстояния меру L1 (ее еще называют [Manhattan distance](https://ru.wikipedia.org/wiki/%D0%A0%D0%B0%D1%81%D1%81%D1%82%D0%BE%D1%8F%D0%BD%D0%B8%D0%B5_%D0%B3%D0%BE%D1%80%D0%BE%D0%B4%D1%81%D0%BA%D0%B8%D1%85_%D0%BA%D0%B2%D0%B0%D1%80%D1%82%D0%B0%D0%BB%D0%BE%D0%B2)).\n", - "\n", - "$d_{1}(\\mathbf {p} ,\\mathbf {q} )=\\|\\mathbf {p} -\\mathbf {q} \\|_{1}=\\sum _{i=1}^{n}|p_{i}-q_{i}|$" - ] - }, - { - "cell_type": "markdown", - "id": "c32db2d0-355c-4d74-961e-22d6f42aa11b", - "metadata": {}, - "source": [ - "В начале я буду иногда писать разные assert'ы, чтобы можно было проверить правильность реализации, в дальнейшем вам нужно будет их писать самим, если нужно будет проверять корректность каких-то вычислений." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01b1ef27-4284-4d6c-978b-25f0fefd39be", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: compute_distances_two_loops\n", - "dists = knn_classifier.compute_distances_two_loops(binary_test_X)\n", - "assert np.isclose(dists[0, 100], np.sum(np.abs(binary_test_X[0] - binary_train_X[100])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "505e2c4b-1cfe-4e4a-8002-d9ce089d100e", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: compute_distances_one_loop\n", - "dists = knn_classifier.compute_distances_one_loop(binary_test_X)\n", - "assert np.isclose(dists[0, 100], np.sum(np.abs(binary_test_X[0] - binary_train_X[100])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd81b766-5de2-4f62-82cf-8b6bd52002db", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: compute_distances_no_loops\n", - "dists = knn_classifier.compute_distances_no_loops(binary_test_X)\n", - "assert np.isclose(dists[0, 100], np.sum(np.abs(binary_test_X[0] - binary_train_X[100])))" - ] - }, - { - "cell_type": "markdown", - "id": "64d108b7-b132-42b5-bdca-bc91af8ed3d7", - "metadata": {}, - "source": [ - "Проверим скорость работы реализованных методов" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ed08354-d0ef-497a-9d9c-b17c92b7bef9", - "metadata": {}, - "outputs": [], - "source": [ - "%timeit knn_classifier.compute_distances_two_loops(binary_test_X)\n", - "%timeit knn_classifier.compute_distances_one_loop(binary_test_X)\n", - "%timeit knn_classifier.compute_distances_no_loops(binary_test_X)" - ] - }, - { - "cell_type": "markdown", - "id": "8180ecc3-8c24-4564-8963-1a0123b06043", - "metadata": {}, - "source": [ - "Реализуем метод для предсказания меток класса" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ca2679e-c731-467b-94b5-91a3a0641d7e", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: predict_labels_binary in knn.py\n", - "prediction = knn_classifier.predict(binary_test_X)" - ] - }, - { - "cell_type": "markdown", - "id": "d746796d-6ca0-4828-be8a-8b9e22999f54", - "metadata": {}, - "source": [ - "### Метрика" - ] - }, - { - "cell_type": "markdown", - "id": "c29f2abf-be34-4273-a7cd-d2c62ba1eecc", - "metadata": {}, - "source": [ - "Теперь нужно реализовать несколько метрик для бинарной классификации. Не забудьте подумать о численной нестабильности (деление на 0)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94268969-5334-4672-a939-65733068a89a", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: binary_classification_metrics in metrics.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7842978b-a328-4035-b9c4-eb3711cb58c4", - "metadata": {}, - "outputs": [], - "source": [ - "binary_classification_metrics(prediction, binary_test_y)" - ] - }, - { - "cell_type": "markdown", - "id": "71dbca1f-9ad4-4059-9b1c-0c0c85d7d816", - "metadata": {}, - "source": [ - "Все ли хорошо с моделью? Можно проверить свою реализацию с функциями из библиотеки `sklearn`:" - ] - }, - { - "cell_type": "markdown", - "id": "5982d081-ddf9-4522-99b0-459a5cee087c", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5caeb94f-6464-4adf-b3b6-8e12bf4a50b4", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score" - ] - }, - { - "cell_type": "markdown", - "id": "6e9b0e1a-3c67-4cc3-bf75-6848f43e8256", - "metadata": {}, - "source": [ - "### Подбор оптимального k" - ] - }, - { - "cell_type": "markdown", - "id": "4069069e-f200-4673-a99c-745e7a5b6b36", - "metadata": {}, - "source": [ - "Чтобы подрбрать оптимальное значение параметра k можно сделать следующее: задать область допустимых значений k, например, `[1, 3, 5, 10]`. Дальше для каждого k обучить модель на тренировочных данных, сделать предсказания на тестовых и посчитать какую-нибудь метрику (метрику выберите сами исходя из задачи, но постарайтесь обосновать выбор). В конце нужно посмотреть на зависимость метрики на train'е и test'е от k и выбрать подходящее значение.\n", - "\n", - "Реализуйте функцию `choose_best_k` прямо в ноутбуке." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "374d6fcf-21f2-433c-b011-76019201ce52", - "metadata": {}, - "outputs": [], - "source": [ - "def find_best_k(X_train, y_train, X_test, y_test, params, metric):\n", - " \"\"\"\n", - " Choose the best k for KKNClassifier\n", - " Arguments:\n", - " X_train, np array (num_train_samples, num_features) - train data\n", - " y_train, np array (num_train_samples) - train labels\n", - " X_test, np array (num_test_samples, num_features) - test data\n", - " y_test, np array (num_test_samples) - test labels\n", - " params, list of hyperparameters for KNN, here it is list of k values\n", - " metric, function for metric calculation\n", - " Returns:\n", - " train_metrics the list of metric values on train data set for each k in params\n", - " test_metrics the list of metric values on test data set for each k in params\n", - " \"\"\"\n", - " \n", - " \"\"\"\n", - " YOUR CODE IS HERE\n", - " \"\"\"\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a2418dd8-93f1-4a11-8488-a8bce98e7d5f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "params = [1, 2, 4, 5, 8, 10, 30]\n", - "train_metrics, test_metrics = find_best_k(binary_train_X, binary_train_y, binary_test_X, binary_test_y, params, accuracy_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7053ce06-854c-412b-8559-833595b1d6c0", - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(params, train_metrics, label=\"train\")\n", - "plt.plot(params, test_metrics, label=\"test\")\n", - "plt.legend()\n", - "plt.xlabel(\"K in KNN\")\n", - "plt.ylabel(\"YOUR METRIC\");" - ] - }, - { - "cell_type": "markdown", - "id": "73fdecd7-a4b6-4ca9-8688-d1ae2195647f", - "metadata": {}, - "source": [ - "На самом деле, это не самый лучший способ подбирать гиперпараметры, но способы получше мы рассмотрим в следующий раз, а пока что выберите оптимальное значение k, сделайте предсказания и посмотрите, насколько хорошо ваша модель предсказывает каждый из классов." - ] - }, - { - "cell_type": "markdown", - "id": "0bc98c29-3217-407c-a466-58072bb7b8cc", - "metadata": {}, - "source": [ - "### 1.5. Многоклассоввая классификация (2 балла)" - ] - }, - { - "cell_type": "markdown", - "id": "aa0fa9bf-3002-4b0e-8e52-1d9e70795092", - "metadata": {}, - "source": [ - "Теперь нужно научиться предсказывать все 10 классов. Для этого в начале напишем соответствующий метод у нашего классификатора." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88b37ef1-fa15-4cc5-8558-0254890c899d", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: predict_labels_multiclass in knn.py\n", - "knn_classifier = KNNClassifier(k=1)\n", - "knn_classifier.fit(X_train, y_train)\n", - "predictions = knn_classifier.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "fa8ed7ad-e347-4f78-b34f-88febee9e94b", - "metadata": {}, - "source": [ - "Осталось реализовать метрику качества для многоклассовой классификации, для этого реализуйте функцию `multiclass_accuracy` в `metrics.py`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65887922-a799-4042-902e-f63f69508beb", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: multiclass_accuracy in metrics.py\n", - "multiclass_accuracy(predictions, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "a6fe44f6-e056-4f17-beb6-0ce65fab268f", - "metadata": {}, - "source": [ - "Снова выберите оптимальное значение K как мы делали для бинарной классификации." - ] - }, - { - "cell_type": "markdown", - "id": "daa8ee4a-88c1-4967-84f1-6f7ec223db7e", - "metadata": {}, - "source": [ - "## Задание 2. KNN на датасете diabetes (10 баллов)" - ] - }, - { - "cell_type": "markdown", - "id": "d3dac1f9-42ef-406f-988c-2d663b8b2806", - "metadata": {}, - "source": [ - "Теперь попробуем применить KNN к задаче регрессии. Будем работать с [данными](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) о диабете. В этом задании будем использовать класс `KNeighborsRegressor` из библиотеки `sklearn`. Загрузим необходимые библиотеки:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ab4c84e-6036-4fe2-b81f-8115bf0a4774", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import load_diabetes\n", - "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neighbors import KNeighborsRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4136b3bb-e482-4102-ad5f-af5b3b1a030a", - "metadata": {}, - "outputs": [], - "source": [ - "X, y = load_diabetes(as_frame=True, return_X_y=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c884e687-964a-4b00-9f10-232c45705f12", - "metadata": {}, - "outputs": [], - "source": [ - "X.head()" - ] - }, - { - "cell_type": "markdown", - "id": "9ce6cd71-dd17-40af-b7cc-c48e61d31719", - "metadata": {}, - "source": [ - "### 2.1. EDA (2 обязательных балла + 2 доп. балла за Pipeline)" - ] - }, - { - "cell_type": "markdown", - "id": "4397b3ad-63f9-4b25-ab7e-d572216e21c5", - "metadata": {}, - "source": [ - "Сделайте EDA, предобработайте данные так, как считаете нужным, нужна ли в данном случае стандартизация и почему? Не забудте, что если вы стандартизуете данные, то нужно считать среднее и сдандартное отклонение на тренировочной части и с помощью них трансформировать и train, и test (**если не поняли это предложение, то обязательно разберитесь**).\n", - "\n", - "**Дополнительно**:\n", - "Попробуйте разобраться с [`Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), чтобы можно было создать класс, который сразу проводит стандартизацию и обучает модель (или делает предсказание). Пайплайны очень удобны, когда нужно применять различные методы предобработки данных (в том числе и к разным столбцам), а также они позволяют правильно интегрировать предобработку данных в различные классы для поиска наилучших гиперпараметров модели (например, `GridSearchCV`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4aa413d-d830-4355-a063-9c5d6d5a6c9a", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.pipeline import Pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "8e1fa5ec-18db-4e7b-88f1-6d010f449488", - "metadata": {}, - "source": [ - "### 2.2. Регрессионная модель (1 балл)" - ] - }, - { - "cell_type": "markdown", - "id": "3d79f583-5feb-4eeb-b748-9b4cdea9150d", - "metadata": {}, - "source": [ - "Создайте модель `KNeighborsRegressor`, обучите ее на треноровочных данных и сделайте предсказания." - ] - }, - { - "cell_type": "markdown", - "id": "5ed4a2ea-4f42-43cb-95d7-50cdeab5f284", - "metadata": {}, - "source": [ - "### 2.3. Метрики регресии (3 балла)" - ] - }, - { - "cell_type": "markdown", - "id": "cbf563ad-1b71-464c-8359-75fa4da268d3", - "metadata": {}, - "source": [ - "Реализуйте метрики $R^2$, MSE и MAE в `metrics.py`. Примените их для оценки качества полученной модели. Все ли хорошо?\n", - "\n", - "Напомню, что:\n", - "\n", - "$R^2 = 1 - \\frac{\\sum_i^n{(y_i - \\hat{y_i})^2}}{\\sum_i^n{(y_i - \\overline{y})^2}}$\n", - "\n", - "$MSE = \\frac{1}{n}\\sum_i^n{(y_i - \\hat{y_i})^2}$\n", - "\n", - "$MAE = \\frac{1}{n}\\sum_i^n{|y_i - \\hat{y_i}|}$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "580f604f-e9c5-4109-8b9f-235369836057", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: r_squared, mse, mae in metrics.py" - ] - }, - { - "cell_type": "markdown", - "id": "a6568d8d-a9ec-4639-aac1-0ea18a644f9e", - "metadata": {}, - "source": [ - "### 2.4. Подбор оптимального числа соседей (2 балла)" - ] - }, - { - "cell_type": "markdown", - "id": "d82f145d-41f5-4bbe-b553-1d05d90ec2a2", - "metadata": {}, - "source": [ - "Мы почти дошли до конца. Теперь осталось при помощи реализованных нами метрик выбрать лучшее количество соседей для нашей модели.\n", - "\n", - "!!! Обратите внимание на то, что значат наши метрики, для некоторых хорошо, когда они уменьшаются, для других наоборот." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "138a7969-8079-4a38-b92e-8eae5d246325", - "metadata": {}, - "outputs": [], - "source": [ - "from metrics import r_squared, mse, mae" - ] - }, - { - "cell_type": "markdown", - "id": "1b4bbef7-35a4-4f05-abf8-5d2b450c86f2", - "metadata": {}, - "source": [ - "Для поиска лучшего k вы можете воспользоваться функцией `find_best_k`, которую вы реализовали выше." - ] - }, - { - "cell_type": "markdown", - "id": "2cb77960-fa30-4a29-9a1b-7c195cc867cb", - "metadata": {}, - "source": [ - "### 3. Социализация (0.5 доп. балла)\n", - "\n", - "Так как у нас теперь большая группа, то было бы здорово всем познакомиться получше (так как выпускной не за горами). Соберитесь с одногруппниками в зуме, познакомьтесь, а сюда прикрепите скриншот с камерами всех участников." - ] - }, - { - "cell_type": "markdown", - "id": "e116a42f-fae8-499c-a985-dc09e66a29b0", - "metadata": {}, - "source": [ - "## Therapy time" - ] - }, - { - "cell_type": "markdown", - "id": "031c493b-26f3-4622-a1f4-affee64f81db", - "metadata": {}, - "source": [ - "Напишите здесь ваши впечатления о задании: было ли интересно, было ли слишком легко или наоборот сложно и тд. Также сюда можно написать свои идеи по улучшению заданий, а также предложить данные, на основе которых вы бы хотели построить следующие дз. " - ] - }, - { - "cell_type": "markdown", - "id": "6d1c75b8", - "metadata": {}, - "source": [ - "**Ваши мысли:**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/lecture_1_intro_knn/homework/knn.py b/lecture_1_intro_knn/homework/knn.py deleted file mode 100644 index c73ba6e..0000000 --- a/lecture_1_intro_knn/homework/knn.py +++ /dev/null @@ -1,143 +0,0 @@ -import numpy as np - - -class KNNClassifier: - """ - K-neariest-neighbor classifier using L1 loss - """ - - def __init__(self, k=1): - self.k = k - - - def fit(self, X, y): - self.train_X = X - self.train_y = y - - - def predict(self, X, n_loops=0): - """ - Uses the KNN model to predict clases for the data samples provided - - Arguments: - X, np array (num_samples, num_features) - samples to run - through the model - num_loops, int - which implementation to use - - Returns: - predictions, np array of ints (num_samples) - predicted class - for each sample - """ - - if n_loops == 0: - distances = self.compute_distances_no_loops(X) - elif n_loops == 1: - distances = self.compute_distances_one_loops(X) - else: - distances = self.compute_distances_two_loops(X) - - if len(np.unique(self.train_y)) == 2: - return self.predict_labels_binary(distances) - else: - return self.predict_labels_multiclass(distances) - - - def compute_distances_two_loops(self, X): - """ - Computes L1 distance from every sample of X to every training sample - Uses simplest implementation with 2 Python loops - - Arguments: - X, np array (num_test_samples, num_features) - samples to run - - Returns: - distances, np array (num_test_samples, num_train_samples) - array - with distances between each test and each train sample - """ - - """ - YOUR CODE IS HERE - """ - pass - - - def compute_distances_one_loop(self, X): - """ - Computes L1 distance from every sample of X to every training sample - Vectorizes some of the calculations, so only 1 loop is used - - Arguments: - X, np array (num_test_samples, num_features) - samples to run - - Returns: - distances, np array (num_test_samples, num_train_samples) - array - with distances between each test and each train sample - """ - - """ - YOUR CODE IS HERE - """ - pass - - - def compute_distances_no_loops(self, X): - """ - Computes L1 distance from every sample of X to every training sample - Fully vectorizes the calculations using numpy - - Arguments: - X, np array (num_test_samples, num_features) - samples to run - - Returns: - distances, np array (num_test_samples, num_train_samples) - array - with distances between each test and each train sample - """ - - """ - YOUR CODE IS HERE - """ - pass - - - def predict_labels_binary(self, distances): - """ - Returns model predictions for binary classification case - - Arguments: - distances, np array (num_test_samples, num_train_samples) - array - with distances between each test and each train sample - Returns: - pred, np array of bool (num_test_samples) - binary predictions - for every test sample - """ - - n_train = distances.shape[1] - n_test = distances.shape[0] - prediction = np.zeros(n_test) - - """ - YOUR CODE IS HERE - """ - pass - - - def predict_labels_multiclass(self, distances): - """ - Returns model predictions for multi-class classification case - - Arguments: - distances, np array (num_test_samples, num_train_samples) - array - with distances between each test and each train sample - Returns: - pred, np array of int (num_test_samples) - predicted class index - for every test sample - """ - - n_train = distances.shape[0] - n_test = distances.shape[0] - prediction = np.zeros(n_test, np.int) - - """ - YOUR CODE IS HERE - """ - pass diff --git a/lecture_1_intro_knn/homework/metrics.py b/lecture_1_intro_knn/homework/metrics.py deleted file mode 100644 index b65c588..0000000 --- a/lecture_1_intro_knn/homework/metrics.py +++ /dev/null @@ -1,87 +0,0 @@ -import numpy as np - - -def binary_classification_metrics(y_pred, y_true): - """ - Computes metrics for binary classification - Arguments: - y_pred, np array (num_samples) - model predictions - y_true, np array (num_samples) - true labels - Returns: - precision, recall, f1, accuracy - classification metrics - """ - - # TODO: implement metrics! - # Some helpful links: - # https://en.wikipedia.org/wiki/Precision_and_recall - # https://en.wikipedia.org/wiki/F1_score - - """ - YOUR CODE IS HERE - """ - pass - - -def multiclass_accuracy(y_pred, y_true): - """ - Computes metrics for multiclass classification - Arguments: - y_pred, np array of int (num_samples) - model predictions - y_true, np array of int (num_samples) - true labels - Returns: - accuracy - ratio of accurate predictions to total samples - """ - - """ - YOUR CODE IS HERE - """ - pass - - -def r_squared(y_pred, y_true): - """ - Computes r-squared for regression - Arguments: - y_pred, np array of int (num_samples) - model predictions - y_true, np array of int (num_samples) - true values - Returns: - r2 - r-squared value - """ - - """ - YOUR CODE IS HERE - """ - pass - - -def mse(y_pred, y_true): - """ - Computes mean squared error - Arguments: - y_pred, np array of int (num_samples) - model predictions - y_true, np array of int (num_samples) - true values - Returns: - mse - mean squared error - """ - - """ - YOUR CODE IS HERE - """ - pass - - -def mae(y_pred, y_true): - """ - Computes mean absolut error - Arguments: - y_pred, np array of int (num_samples) - model predictions - y_true, np array of int (num_samples) - true values - Returns: - mae - mean absolut error - """ - - """ - YOUR CODE IS HERE - """ - pass - \ No newline at end of file diff --git a/lecture_1_intro_knn/homework/requirements.txt b/lecture_1_intro_knn/homework/requirements.txt deleted file mode 100644 index cc26140..0000000 --- a/lecture_1_intro_knn/homework/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -numpy -matplotlib -pandas -sklearn -seaborn \ No newline at end of file diff --git a/lecture_1_intro_knn/lecture/WorldHappiness_Corruption_2015_2020.csv b/lecture_1_intro_knn/lecture/WorldHappiness_Corruption_2015_2020.csv deleted file mode 100644 index f85236e..0000000 --- a/lecture_1_intro_knn/lecture/WorldHappiness_Corruption_2015_2020.csv +++ /dev/null @@ -1,793 +0,0 @@ -Country,happiness_score,gdp_per_capita,family,health,freedom,generosity,government_trust,dystopia_residual,continent,Year,social_support,cpi_score -Norway,7.537000179,1.616463184,1.53352356,0.796666503,0.635422587,0.362012237,0.315963835,2.277026653,Europe,2015,0.0,88 -Denmark,7.521999836,1.482383013,1.551121593,0.792565525,0.626006722,0.355280489,0.400770068,2.313707352,Europe,2015,0.0,91 -Iceland,7.504000187,1.48063302,1.610574007,0.833552122,0.627162635,0.475540221,0.153526559,2.322715282,Europe,2015,0.0,79 -Switzerland,7.493999958,1.564979553,1.516911745,0.858131289,0.620070577,0.290549278,0.367007285,2.276716232,Europe,2015,0.0,86 -Finland,7.468999863,1.443571925,1.540246725,0.80915767,0.617950857,0.245482773,0.382611543,2.430181503,Europe,2015,0.0,90 -Netherlands,7.376999855,1.503944635,1.428939223,0.810696125,0.585384488,0.47048983,0.282661825,2.294804096,Europe,2015,0.0,84 -Canada,7.315999985,1.479204416,1.481348991,0.834557652,0.611100912,0.435539722,0.287371516,2.187264442,North America,2015,0.0,83 -New Zealand,7.31400013,1.405706048,1.548195124,0.816759706,0.61406213,0.500005126,0.382816702,2.046456337,Australia,2015,0.0,91 -Sweden,7.28399992,1.494387269,1.478162169,0.830875158,0.612924099,0.385399252,0.384398729,2.097537994,Europe,2015,0.0,89 -Australia,7.28399992,1.484414935,1.510041952,0.843886793,0.601607382,0.47769925,0.30118373,2.065210819,Australia,2015,0.0,79 -Israel,7.212999821,1.375382423,1.376289964,0.838404,0.405988604,0.330082655,0.0852421,2.801757336,Asia,2015,0.0,61 -Costa Rica,7.078999996,1.109706283,1.416403651,0.759509265,0.58013165,0.214613229,0.100106589,2.898639202,South America,2015,0.0,55 -Austria,7.006000042,1.487097263,1.459944963,0.815328419,0.56776619,0.316472322,0.221060365,2.138506413,Europe,2015,0.0,76 -United States,6.993000031,1.546259284,1.419920564,0.774286628,0.505740523,0.392578781,0.135638788,2.218113422,North America,2015,0.0,76 -Ireland,6.977000237,1.535706639,1.558231115,0.809782624,0.573110342,0.427858323,0.298388153,1.773869038,Europe,2015,0.0,75 -Germany,6.951000214,1.487923384,1.472520351,0.798950732,0.562511384,0.33626917,0.276731938,2.015769958,Europe,2015,0.0,81 -Belgium,6.890999794,1.463780761,1.462312698,0.818091869,0.539770722,0.231503338,0.251343131,2.124210358,Europe,2015,0.0,77 -Luxembourg,6.862999916,1.741943598,1.457583666,0.845089495,0.596627891,0.283180982,0.318834424,1.619512081,Europe,2015,0.0,85 -United Kingdom,6.714000225,1.44163394,1.49646008,0.805335939,0.508190036,0.492774159,0.265428066,1.704143524,Europe,2015,0.0,81 -Chile,6.65199995,1.25278461,1.284024954,0.819479704,0.376895279,0.326662421,0.082287982,2.509585857,South America,2015,0.0,70 -United Arab Emirates,6.647999763,1.626343369,1.266410232,0.726798236,0.60834527,0.360941947,0.324489564,1.734703541,Asia,2015,0.0,70 -Brazil,6.635000229,1.10735321,1.431306005,0.616552353,0.437453747,0.162349895,0.111092761,2.769267082,South America,2015,0.0,38 -Argentina,6.598999977,1.185295463,1.440451145,0.695137084,0.494519204,0.109457061,0.059739888,2.614005327,South America,2015,0.0,32 -Mexico,6.578000069,1.153183818,1.21086216,0.709978998,0.412730008,0.120990433,0.132774115,2.837154865,North America,2015,0.0,31 -Singapore,6.572000027,1.69227767,1.353814363,0.949492395,0.549840569,0.345965981,0.464307785,1.216362,Asia,2015,0.0,85 -Malta,6.52699995,1.343279839,1.488411665,0.821944237,0.588767052,0.574730575,0.153066069,1.556862831,Europe,2015,0.0,60 -Guatemala,6.453999996,0.872001946,1.255585194,0.54023999,0.531310618,0.283488393,0.077223279,2.893891096,South America,2015,0.0,28 -Uruguay,6.453999996,1.217559695,1.412227869,0.719216824,0.579392254,0.175096929,0.178061873,2.172409534,South America,2015,0.0,74 -Panama,6.452000141,1.233748436,1.373192549,0.706156135,0.550026834,0.210556939,0.070983924,2.307199955,South America,2015,0.0,39 -France,6.441999912,1.430923462,1.387776852,0.844465852,0.470222116,0.129762307,0.172502428,2.005954742,Europe,2015,0.0,70 -Thailand,6.423999786,1.127868772,1.425792456,0.647239029,0.580200732,0.57212311,0.031612735,2.039508343,Asia,2015,0.0,38 -Spain,6.402999878,1.384397864,1.532090902,0.8889606,0.40878123,0.190133572,0.070914097,1.92775774,Europe,2015,0.0,58 -Colombia,6.356999874,1.070622325,1.402182937,0.595027924,0.477487415,0.149014473,0.046668742,2.616068125,South America,2015,0.0,37 -Saudi Arabia,6.343999863,1.530623555,1.286677599,0.59014833,0.449750572,0.147616014,0.273432255,2.065429688,Asia,2015,0.0,52 -Kuwait,6.105000019,1.632952452,1.259698749,0.632105708,0.496337593,0.228289798,0.21515955,1.640425205,Asia,2015,0.0,49 -Slovakia,6.09800005,1.325393558,1.505059242,0.712732911,0.295817465,0.136544481,0.024210852,2.097776651,Europe,2015,0.0,51 -Bahrain,6.086999893,1.488412261,1.323110461,0.653133035,0.536746919,0.172668487,0.25704217,1.656149387,Asia,2015,0.0,51 -Malaysia,6.084000111,1.29121542,1.284646034,0.618784428,0.402264982,0.41660893,0.065600708,2.004448891,Asia,2015,0.0,50 -Nicaragua,6.071000099,0.737299204,1.28721571,0.653095961,0.447551847,0.301674217,0.130687982,2.513930559,South America,2015,0.0,27 -Ecuador,6.007999897,1.000820398,1.286168814,0.685636222,0.455198199,0.150112465,0.140134647,2.290352583,South America,2015,0.0,32 -El Salvador,6.002999783,0.909784496,1.182125092,0.596018553,0.43245253,0.078257985,0.08998096,2.714593887,South America,2015,0.0,39 -Poland,5.97300005,1.291787863,1.44571197,0.699475348,0.520342112,0.158465967,0.059307806,1.797722816,Europe,2015,0.0,63 -Uzbekistan,5.971000195,0.786441088,1.54896915,0.498272628,0.658248663,0.415983647,0.246528223,1.816913605,Asia,2015,0.0,19 -Italy,5.964000225,1.395066619,1.444923282,0.853144348,0.256450713,0.172789648,0.028028091,1.813312054,Europe,2015,0.0,44 -Russia,5.962999821,1.281778097,1.469282389,0.547349334,0.373783112,0.052263822,0.032962881,2.205607414,Europe,2015,0.0,29 -Japan,5.920000076,1.416915178,1.436337829,0.913475871,0.505625546,0.120572768,0.163760737,1.363223553,Asia,2015,0.0,75 -Lithuania,5.90199995,1.314582348,1.473516107,0.62894994,0.234231785,0.010164657,0.011865643,2.228440523,Europe,2015,0.0,59 -Algeria,5.872000217,1.091864467,1.146217465,0.617584646,0.233335808,0.069436647,0.14609611,2.567603827,Africa,2015,0.0,36 -Latvia,5.849999905,1.260748625,1.404714942,0.638566971,0.325707912,0.153074786,0.073842727,1.993655205,Europe,2015,0.0,56 -Moldova,5.837999821,0.72887063,1.251825571,0.589465201,0.240729049,0.208779126,0.010091286,2.807808399,Europe,2015,0.0,33 -Romania,5.824999809,1.217683911,1.15009129,0.685158312,0.457003742,0.133519918,0.004387901,2.176831484,Europe,2015,0.0,46 -Bolivia,5.822999954,0.833756566,1.227619052,0.47363025,0.558732927,0.225560725,0.060477726,2.443279028,South America,2015,0.0,34 -Turkmenistan,5.822000027,1.130776763,1.493149161,0.43772608,0.418271929,0.249924988,0.25927034,1.832909822,Asia,2015,0.0,18 -Kazakhstan,5.818999767,1.28455627,1.384369016,0.606041551,0.437454283,0.201964423,0.119282886,1.784892559,Asia,2015,0.0,28 -Slovenia,5.757999897,1.341205955,1.452518821,0.790828228,0.572575808,0.242649093,0.045128979,1.313317299,Europe,2015,0.0,60 -Peru,5.715000153,1.035225272,1.218770385,0.630166113,0.450002879,0.126819715,0.047049087,2.20726943,South America,2015,0.0,36 -Mauritius,5.629000187,1.189395547,1.20956099,0.638007462,0.491247326,0.360933751,0.042181555,1.697583914,Africa,2015,0.0,53 -Cyprus,5.620999813,1.355938077,1.131363273,0.844714701,0.355111539,0.271254301,0.041237976,1.621249199,Asia,2015,0.0,61 -Estonia,5.611000061,1.32087934,1.4766711,0.695168316,0.47913143,0.098890811,0.183248922,1.357508659,Europe,2015,0.0,70 -Belarus,5.568999767,1.15655756,1.444945216,0.637714267,0.295400262,0.155137509,0.156313822,1.723232985,Europe,2015,0.0,32 -Libya,5.525000095,1.101803064,1.35756433,0.52016902,0.46573323,0.152073666,0.09261021,1.835011244,Africa,2015,0.0,16 -Turkey,5.5,1.198274374,1.337753177,0.637605608,0.3007406,0.046693042,0.09967158,1.879277945,Asia,2015,0.0,42 -Paraguay,5.493000031,0.932537317,1.50728488,0.579250693,0.473507792,0.224150658,0.091065913,1.68533349,South America,2015,0.0,27 -Philippines,5.429999828,0.857699215,1.253917575,0.468009055,0.585214674,0.193513423,0.099331893,1.972604752,Asia,2015,0.0,35 -Serbia,5.394999981,1.069317579,1.258189797,0.650784671,0.208715528,0.220125884,0.040903781,1.947084427,Europe,2015,0.0,40 -Jordan,5.335999966,0.991012394,1.239088893,0.604590058,0.418421149,0.17217046,0.119803272,1.791176558,Asia,2015,0.0,53 -Hungary,5.323999882,1.286011934,1.343133092,0.687763453,0.175863519,0.078401662,0.036636937,1.716459274,Europe,2015,0.0,51 -Jamaica,5.31099987,0.925579309,1.368218064,0.641022384,0.474307239,0.233818337,0.055267781,1.612325668,South America,2015,0.0,41 -Croatia,5.293000221,1.222556233,0.967983007,0.701288521,0.255772293,0.248002976,0.04310311,1.854492426,Europe,2015,0.0,51 -Kosovo,5.278999805,0.951484382,1.137853503,0.54145205,0.260287941,0.319931448,0.057471618,2.010540724,Europe,2015,0.0,33 -China,5.272999763,1.081165791,1.160837412,0.741415501,0.472787708,0.028806841,0.022794275,1.764938593,Asia,2015,0.0,37 -Pakistan,5.269000053,0.726883531,0.67269069,0.402047783,0.235215262,0.315446019,0.124348067,2.79248929,Asia,2015,0.0,30 -Indonesia,5.262000084,0.995538592,1.274444699,0.492345721,0.443323463,0.611704588,0.015317135,1.429476976,Asia,2015,0.0,36 -Venezuela,5.25,1.128431201,1.431337595,0.617144227,0.153997123,0.06501963,0.064491123,1.789463758,South America,2015,0.0,17 -Montenegro,5.236999989,1.121129036,1.238376498,0.667464674,0.194989055,0.197911024,0.088174194,1.729191542,Europe,2015,0.0,44 -Morocco,5.235000134,0.878114581,0.774864435,0.597710669,0.408158332,0.032209955,0.087763183,2.456189394,Africa,2015,0.0,36 -Azerbaijan,5.234000206,1.153601766,1.152400255,0.540775776,0.398155838,0.04526934,0.180987507,1.762481689,Asia,2015,0.0,29 -Dominican Republic,5.230000019,1.079373837,1.402416706,0.574873745,0.552589834,0.18696785,0.113945253,1.31946516,South America,2015,0.0,33 -Greece,5.227000237,1.289487481,1.239414573,0.810198903,0.095731251,0.0,0.043289777,1.749221563,Europe,2015,0.0,46 -Lebanon,5.224999905,1.074987531,1.129624248,0.735081077,0.288515985,0.264450759,0.03751383,1.695073843,Asia,2015,0.0,28 -Portugal,5.195000172,1.315175295,1.367043018,0.795843542,0.4984653,0.095102713,0.015869452,1.107682705,Europe,2015,0.0,64 -Bosnia and Herzegovina,5.18200016,0.982409418,1.069335938,0.705186307,0.204403177,0.328867495,0.0,1.892172575,Europe,2015,0.0,38 -Honduras,5.181000233,0.730573118,1.143944979,0.58256948,0.34807986,0.236188874,0.073345453,2.065811157,South America,2015,0.0,31 -Nigeria,5.073999882,0.783756256,1.215770483,0.05691573,0.394952565,0.230947196,0.026121566,2.365390539,Africa,2015,0.0,26 -Vietnam,5.073999882,0.788547575,1.277491331,0.652168989,0.571055591,0.234968051,0.087633237,1.462318659,Asia,2015,0.0,31 -Tajikistan,5.040999889,0.524713635,1.271463275,0.529235125,0.471566707,0.248997644,0.146377146,1.84904933,Asia,2015,0.0,26 -Kyrgyzstan,5.004000187,0.596220076,1.394238591,0.553457797,0.454943389,0.428580374,0.039439179,1.536723137,Asia,2015,0.0,28 -Nepal,4.961999893,0.479820192,1.179283261,0.504130781,0.440305948,0.394096166,0.072975546,1.891241074,Asia,2015,0.0,27 -Mongolia,4.954999924,1.027235866,1.493011236,0.557783484,0.394143969,0.33846423,0.032902289,1.111292362,Asia,2015,0.0,39 -South Africa,4.828999996,1.054698706,1.384788632,0.18708007,0.479246736,0.13936238,0.072509497,1.510908604,Africa,2015,0.0,44 -Tunisia,4.804999828,1.007265806,0.86835146,0.613212049,0.28968069,0.049693357,0.086723149,1.89025116,Africa,2015,0.0,38 -Egypt,4.735000134,0.989701807,0.997471392,0.520187259,0.282110155,0.128631443,0.114381365,1.702161074,Africa,2015,0.0,36 -Bulgaria,4.714000225,1.161459088,1.434379458,0.70821768,0.289231718,0.113177694,0.011051531,0.996139288,Europe,2015,0.0,41 -Sierra Leone,4.709000111,0.368420929,0.984136045,0.005564754,0.318697691,0.293040901,0.071095176,2.668459892,Africa,2015,0.0,29 -Cameroon,4.695000172,0.564305365,0.946018219,0.132892117,0.430388749,0.236298457,0.051306631,2.333645582,Africa,2015,0.0,27 -Iran,4.691999912,1.156873107,0.711551249,0.639333189,0.249322608,0.387242913,0.048761073,1.498734951,Asia,2015,0.0,27 -Albania,4.644000053,0.996192753,0.803685248,0.731159747,0.381498635,0.201312944,0.039864216,1.490441561,Europe,2015,0.0,36 -Bangladesh,4.607999802,0.586682975,0.735131741,0.533241034,0.478356659,0.172255352,0.123717859,1.978736162,Asia,2015,0.0,25 -Kenya,4.552999973,0.560479462,1.067950726,0.30998835,0.452763766,0.444860309,0.064641319,1.651902199,Africa,2015,0.0,25 -Myanmar,4.545000076,0.36711055,1.123235941,0.397522569,0.514492035,0.838075161,0.188816205,1.115290403,Asia,2015,0.0,22 -Senegal,4.534999847,0.479309022,1.179691911,0.409362853,0.377922267,0.183468893,0.115460448,1.789646149,Africa,2015,0.0,44 -Zambia,4.513999939,0.636406779,1.003187299,0.257835895,0.461603492,0.249580145,0.07821355,1.826705456,Africa,2015,0.0,38 -Iraq,4.497000217,1.102710485,0.978613198,0.50118047,0.288555533,0.199637264,0.107215755,1.318907261,Asia,2015,0.0,16 -Gabon,4.465000153,1.198210239,1.155620217,0.356578588,0.312328577,0.043785378,0.076046787,1.322916269,Africa,2015,0.0,34 -Ethiopia,4.460000038,0.339233845,0.864669204,0.353409708,0.408842742,0.31265074,0.165455714,2.015743732,Africa,2015,0.0,33 -Sri Lanka,4.440000057,1.009850144,1.259976387,0.625130832,0.561213255,0.490863562,0.073653966,0.419389248,Asia,2015,0.0,37 -Armenia,4.375999928,0.900596738,1.007483721,0.637524426,0.198303267,0.083488092,0.026674422,1.521499157,Asia,2015,0.0,35 -India,4.315000057,0.792221248,0.754372597,0.455427617,0.469987005,0.231538489,0.092226885,1.519117117,Asia,2015,0.0,38 -Mauritania,4.291999817,0.648457289,1.27203083,0.28534928,0.096098043,0.201870024,0.136957005,1.651637316,Africa,2015,0.0,31 -Georgia,4.285999775,0.950612664,0.570614934,0.649546981,0.309410036,0.054008815,0.251666635,1.500137806,Asia,2015,0.0,52 -Mali,4.190000057,0.476180494,1.281473398,0.169365674,0.306613743,0.183354199,0.104970247,1.668190956,Africa,2015,0.0,35 -Cambodia,4.168000221,0.601765096,1.006238341,0.429783404,0.633375823,0.385922968,0.068105951,1.042941093,Asia,2015,0.0,21 -Ghana,4.119999886,0.667224824,0.873664737,0.295637727,0.423026294,0.256923944,0.02533637,1.577867508,Africa,2015,0.0,47 -Ukraine,4.096000195,0.894651949,1.394537568,0.575903952,0.122974776,0.270061463,0.023029471,0.814382315,Europe,2015,0.0,27 -Uganda,4.080999851,0.381430715,1.129827738,0.217632607,0.443185955,0.325766057,0.057069719,1.526362658,Africa,2015,0.0,25 -Burkina Faso,4.032000065,0.350227714,1.043280005,0.215844259,0.324367851,0.250864685,0.120328106,1.727212906,Africa,2015,0.0,38 -Niger,4.027999878,0.161925331,0.993025005,0.268505007,0.363658696,0.228673846,0.138572946,1.873983383,Africa,2015,0.0,34 -Malawi,3.970000029,0.233442038,0.512568831,0.315089583,0.466914654,0.28717047,0.072711654,2.081786156,Africa,2015,0.0,31 -Chad,3.936000109,0.438012987,0.953855872,0.041134715,0.162342027,0.21611385,0.053581882,2.071238041,Africa,2015,0.0,22 -Zimbabwe,3.875,0.375846535,1.083095908,0.196763754,0.336384207,0.189143494,0.095375381,1.597970247,Africa,2015,0.0,21 -Afghanistan,3.79399991,0.401477218,0.581543326,0.180746779,0.10617952,0.311870933,0.06115783,2.150801182,Asia,2015,0.0,11 -Botswana,3.766000032,1.122094154,1.221554995,0.341755509,0.505196333,0.099348448,0.098583199,0.377913713,Africa,2015,0.0,63 -Benin,3.657000065,0.431085408,0.435299844,0.209930211,0.425962776,0.207948461,0.060929015,1.885630965,Africa,2015,0.0,37 -Madagascar,3.644000053,0.305808693,0.913020372,0.375223309,0.189196765,0.20873253,0.067231975,1.584612608,Africa,2015,0.0,28 -Haiti,3.602999926,0.368610263,0.640449822,0.27732113,0.030369857,0.489203781,0.09987215,1.697167635,South America,2015,0.0,17 -Yemen,3.592999935,0.591683447,0.935382247,0.310080916,0.249463722,0.104125209,0.056767423,1.345600605,Asia,2015,0.0,18 -Liberia,3.532999992,0.119041793,0.872117937,0.229918197,0.332881182,0.266549885,0.038948249,1.673285961,Africa,2015,0.0,37 -Guinea,3.506999969,0.24454993,0.791244686,0.194129139,0.348587513,0.264815092,0.110937618,1.552311897,South America,2015,0.0,25 -Togo,3.494999886,0.305444717,0.43188253,0.247105569,0.380426139,0.196896151,0.095665015,1.837229252,Africa,2015,0.0,32 -Rwanda,3.470999956,0.368745893,0.945707023,0.326424807,0.581843853,0.252756029,0.455220014,0.540061235,Africa,2015,0.0,54 -Tanzania,3.348999977,0.511135876,1.041989803,0.364509284,0.390017778,0.354256362,0.066035107,0.621130466,Africa,2015,0.0,30 -Burundi,2.904999971,0.091622569,0.629793584,0.151610792,0.059900753,0.204435185,0.084147945,1.683024168,Africa,2015,0.0,21 -Switzerland,7.587,1.39651,1.34951,0.94143,0.66557,0.29678,0.41978,2.51738,Europe,2016,0.0,86 -Iceland,7.561,1.30232,1.40223,0.94784,0.62877,0.4363,0.14145,2.70201,Europe,2016,0.0,78 -Denmark,7.527,1.32548,1.36058,0.87464,0.64938,0.34139,0.48357,2.49204,Europe,2016,0.0,90 -Norway,7.522,1.459,1.33095,0.88521,0.66973,0.34699,0.36503,2.46531,Europe,2016,0.0,85 -Canada,7.427,1.32629,1.32261,0.90563,0.63297,0.45811,0.32957,2.45176,North America,2016,0.0,82 -Finland,7.406,1.29025,1.31826,0.88911,0.64169,0.23351,0.41372,2.61955,Europe,2016,0.0,89 -Netherlands,7.378,1.32944,1.28017,0.89284,0.61576,0.4761,0.31814,2.4657,Europe,2016,0.0,83 -Sweden,7.364,1.33171,1.28907,0.91087,0.6598,0.36262,0.43844,2.37119,Europe,2016,0.0,88 -New Zealand,7.286,1.25018,1.31967,0.90837,0.63938,0.47501,0.42922,2.26425,Australia,2016,0.0,90 -Australia,7.284,1.33358,1.30923,0.93156,0.65124,0.43562,0.35637,2.26646,Australia,2016,0.0,79 -Israel,7.278,1.22857,1.22393,0.91387,0.41319,0.33172,0.07785,3.08854,Asia,2016,0.0,64 -Costa Rica,7.226,0.95578,1.23788,0.86027,0.63376,0.25497,0.10583,3.17728,South America,2016,0.0,58 -Austria,7.2,1.33723,1.29704,0.89042,0.62433,0.33088,0.18676,2.5332,Europe,2016,0.0,75 -Mexico,7.187,1.02054,0.91451,0.81444,0.48181,0.14074,0.21312,3.60214,North America,2016,0.0,30 -United States,7.119,1.39451,1.24711,0.86179,0.54604,0.40105,0.1589,2.51011,North America,2016,0.0,74 -Brazil,6.983,0.98124,1.23287,0.69702,0.49049,0.14574,0.17521,3.26001,South America,2016,0.0,40 -Luxembourg,6.946,1.56391,1.21963,0.91894,0.61583,0.28034,0.37798,1.96961,Europe,2016,0.0,81 -Ireland,6.94,1.33596,1.36948,0.89533,0.61777,0.45901,0.28703,1.9757,Europe,2016,0.0,73 -Belgium,6.937,1.30782,1.28566,0.89667,0.5845,0.2225,0.2254,2.41484,Europe,2016,0.0,77 -United Arab Emirates,6.901,1.42727,1.12575,0.80925,0.64157,0.26428,0.38583,2.24743,Asia,2016,0.0,66 -United Kingdom,6.867,1.26637,1.28548,0.90943,0.59625,0.51912,0.32067,1.96994,Europe,2016,0.0,81 -Venezuela,6.81,1.04424,1.25596,0.72052,0.42908,0.05841,0.11069,3.19131,South America,2016,0.0,17 -Singapore,6.798,1.52186,1.02,1.02525,0.54252,0.31105,0.4921,1.88501,Asia,2016,0.0,84 -Panama,6.786,1.06353,1.1985,0.79661,0.5421,0.24434,0.0927,2.84848,South America,2016,0.0,38 -Germany,6.75,1.32792,1.29937,0.89186,0.61477,0.28214,0.21843,2.11569,Europe,2016,0.0,81 -Chile,6.67,1.10715,1.12447,0.85857,0.44132,0.33363,0.12869,2.67585,South America,2016,0.0,66 -France,6.575,1.27778,1.26038,0.94579,0.55011,0.12332,0.20646,2.21126,Europe,2016,0.0,69 -Argentina,6.574,1.05351,1.24823,0.78723,0.44974,0.11451,0.08484,2.836,South America,2016,0.0,36 -Uruguay,6.485,1.06166,1.2089,0.8116,0.60362,0.2324,0.24558,2.32142,South America,2016,0.0,71 -Colombia,6.477,0.91861,1.24018,0.69077,0.53466,0.18401,0.0512,2.85737,South America,2016,0.0,37 -Thailand,6.455,0.9669,1.26504,0.7385,0.55664,0.5763,0.03187,2.31945,Asia,2016,0.0,35 -Saudi Arabia,6.411,1.39541,1.08393,0.72025,0.31048,0.13706,0.32524,2.43872,Asia,2016,0.0,46 -Spain,6.329,1.23011,1.31379,0.95562,0.45951,0.18227,0.06398,2.12367,Europe,2016,0.0,58 -Malta,6.302,1.2074,1.30203,0.88721,0.60365,0.51752,0.13586,1.6488,Europe,2016,0.0,55 -Kuwait,6.295,1.55422,1.16594,0.72492,0.55499,0.16228,0.25609,1.87634,Asia,2016,0.0,41 -El Salvador,6.13,0.76454,1.02507,0.67737,0.4035,0.10692,0.11776,3.035,South America,2016,0.0,36 -Guatemala,6.123,0.74553,1.04356,0.64425,0.57733,0.27489,0.09472,2.74255,South America,2016,0.0,28 -Uzbekistan,6.003,0.63244,1.34043,0.59772,0.65821,0.22837,0.30826,2.23741,Asia,2016,0.0,21 -Slovakia,5.995,1.16891,1.26999,0.78902,0.31751,0.16893,0.03431,2.24639,Europe,2016,0.0,51 -Japan,5.987,1.27074,1.25712,0.99111,0.49615,0.10705,0.1806,1.68435,Asia,2016,0.0,72 -Ecuador,5.975,0.86402,0.99903,0.79075,0.48574,0.11541,0.1809,2.53942,South America,2016,0.0,31 -Bahrain,5.96,1.32376,1.21624,0.74716,0.45492,0.17362,0.306,1.73797,Asia,2016,0.0,43 -Italy,5.948,1.25114,1.19777,0.95446,0.26236,0.22823,0.02901,2.02518,Europe,2016,0.0,47 -Bolivia,5.89,0.68133,0.97841,0.5392,0.57414,0.20536,0.088,2.82334,South America,2016,0.0,33 -Moldova,5.889,0.59448,1.01528,0.61826,0.32818,0.20951,0.01615,3.10712,Europe,2016,0.0,30 -Paraguay,5.878,0.75985,1.30477,0.66098,0.53899,0.3424,0.08242,2.18896,South America,2016,0.0,30 -Kazakhstan,5.855,1.12254,1.12241,0.64368,0.51649,0.11827,0.08454,2.24729,Asia,2016,0.0,29 -Slovenia,5.848,1.18498,1.27385,0.87337,0.60855,0.25328,0.03787,1.61583,Europe,2016,0.0,61 -Lithuania,5.833,1.14723,1.25745,0.73128,0.21342,0.02641,0.01031,2.44649,Europe,2016,0.0,59 -Nicaragua,5.828,0.59325,1.14184,0.74314,0.55475,0.27815,0.19317,2.32407,South America,2016,0.0,26 -Peru,5.824,0.90019,0.97459,0.73017,0.41496,0.14982,0.05989,2.5945,South America,2016,0.0,35 -Belarus,5.813,1.03192,1.23289,0.73608,0.37938,0.11046,0.1909,2.1309,Europe,2016,0.0,40 -Poland,5.791,1.12555,1.27948,0.77903,0.53122,0.16759,0.04212,1.86565,Europe,2016,0.0,62 -Malaysia,5.77,1.12486,1.07023,0.72394,0.53024,0.33075,0.10501,1.88541,Asia,2016,0.0,49 -Croatia,5.759,1.08254,0.79624,0.78805,0.25883,0.05444,0.0243,2.75414,Europe,2016,0.0,49 -Libya,5.754,1.13145,1.11862,0.7038,0.41668,0.18295,0.11023,2.09066,Africa,2016,0.0,14 -Russia,5.716,1.13764,1.23617,0.66926,0.36679,0.00199,0.03005,2.27394,Europe,2016,0.0,29 -Jamaica,5.709,0.81038,1.15102,0.68741,0.50442,0.2123,0.02299,2.32038,South America,2016,0.0,39 -Cyprus,5.689,1.20813,0.89318,0.92356,0.40672,0.30638,0.06146,1.88931,Asia,2016,0.0,55 -Algeria,5.605,0.93929,1.07772,0.61766,0.28579,0.07822,0.17383,2.43209,Africa,2016,0.0,34 -Kosovo,5.589,0.80148,0.81198,0.63132,0.24749,0.2831,0.04741,2.76579,Europe,2016,0.0,36 -Turkmenistan,5.548,0.95847,1.22668,0.53886,0.4761,0.16979,0.30844,1.86984,Asia,2016,0.0,22 -Mauritius,5.477,1.00761,0.98521,0.7095,0.56066,0.37744,0.07521,1.76145,Africa,2016,0.0,54 -Estonia,5.429,1.15174,1.22791,0.77361,0.44888,0.0868,0.15184,1.58782,Europe,2016,0.0,70 -Indonesia,5.399,0.82827,1.08708,0.63793,0.46611,0.51535,0.0,1.86399,Asia,2016,0.0,37 -Vietnam,5.36,0.63216,0.91226,0.74676,0.59444,0.1686,0.10441,2.20173,Asia,2016,0.0,33 -Turkey,5.332,1.06098,0.94632,0.73172,0.22815,0.12253,0.15746,2.08528,Asia,2016,0.0,41 -Kyrgyzstan,5.286,0.47428,1.15115,0.65088,0.43477,0.3003,0.04232,2.2327,Asia,2016,0.0,28 -Nigeria,5.268,0.65435,0.90432,0.16007,0.34334,0.27233,0.0403,2.89319,Africa,2016,0.0,28 -Azerbaijan,5.212,1.02389,0.93793,0.64045,0.3703,0.07799,0.16065,2.00073,Asia,2016,0.0,30 -Pakistan,5.194,0.59543,0.41411,0.51466,0.12102,0.33671,0.10464,3.10709,Asia,2016,0.0,32 -Jordan,5.192,0.90198,1.05392,0.69639,0.40661,0.11053,0.14293,1.87996,Asia,2016,0.0,48 -Montenegro,5.192,0.97438,0.90557,0.72521,0.1826,0.1614,0.14296,2.10017,Europe,2016,0.0,45 -China,5.14,0.89012,0.94675,0.81658,0.51697,0.08185,0.02781,1.8604,Asia,2016,0.0,40 -Zambia,5.129,0.47038,0.91612,0.29924,0.48827,0.19591,0.12468,2.6343,Africa,2016,0.0,38 -Romania,5.124,1.04345,0.88588,0.7689,0.35068,0.13748,0.00649,1.93129,Europe,2016,0.0,48 -Serbia,5.123,0.92053,1.00964,0.74836,0.20107,0.19231,0.02617,2.025,Europe,2016,0.0,42 -Portugal,5.102,1.15991,1.13935,0.87519,0.51469,0.13719,0.01078,1.26462,Europe,2016,0.0,62 -Latvia,5.098,1.11312,1.09562,0.72437,0.29671,0.18226,0.06332,1.62215,Europe,2016,0.0,57 -Philippines,5.073,0.70532,1.03516,0.58114,0.62545,0.24991,0.12279,1.7536,Asia,2016,0.0,35 -Morocco,5.013,0.73479,0.64095,0.60954,0.41691,0.07172,0.08546,2.45373,Africa,2016,0.0,37 -Albania,4.959,0.87867,0.80434,0.81325,0.35733,0.14272,0.06413,1.89894,Europe,2016,0.0,39 -Bosnia and Herzegovina,4.949,0.83223,0.91916,0.79081,0.09245,0.24808,0.00227,2.06367,Europe,2016,0.0,39 -Dominican Republic,4.885,0.89537,1.17202,0.66825,0.57672,0.21684,0.14234,1.21305,South America,2016,0.0,31 -Mongolia,4.874,0.82819,1.3006,0.60268,0.43626,0.3323,0.02666,1.34759,Asia,2016,0.0,38 -Greece,4.857,1.15406,0.92933,0.88213,0.07699,0.0,0.01397,1.80101,Europe,2016,0.0,44 -Lebanon,4.839,1.02564,0.80001,0.83947,0.33916,0.21854,0.04582,1.57059,Asia,2016,0.0,28 -Hungary,4.8,1.12094,1.20215,0.75905,0.32112,0.128,0.02758,1.24074,Europe,2016,0.0,48 -Honduras,4.788,0.59532,0.95348,0.6951,0.40148,0.23027,0.06825,1.84408,South America,2016,0.0,30 -Tajikistan,4.786,0.39047,0.85563,0.57379,0.47216,0.22974,0.15072,2.11399,Asia,2016,0.0,25 -Tunisia,4.739,0.88113,0.60429,0.73793,0.26268,0.06431,0.06358,2.12466,Africa,2016,0.0,41 -Bangladesh,4.694,0.39753,0.43106,0.60164,0.4082,0.21222,0.12569,2.51767,Asia,2016,0.0,26 -Iran,4.686,1.0088,0.54447,0.69805,0.30033,0.38086,0.05863,1.6944,Asia,2016,0.0,29 -Ukraine,4.681,0.79907,1.20278,0.6739,0.25123,0.15275,0.02961,1.5714,Europe,2016,0.0,29 -Iraq,4.677,0.98549,0.81889,0.60237,0.0,0.17922,0.13788,1.95335,Asia,2016,0.0,17 -South Africa,4.642,0.92049,1.18468,0.27688,0.33207,0.11973,0.08884,1.71956,Africa,2016,0.0,45 -Ghana,4.633,0.54558,0.67954,0.40132,0.42342,0.23087,0.04355,2.30919,Africa,2016,0.0,43 -Zimbabwe,4.61,0.271,1.03276,0.33475,0.25861,0.18987,0.08079,2.44191,Africa,2016,0.0,22 -Liberia,4.571,0.0712,0.78968,0.34201,0.28531,0.24362,0.06232,2.77729,Africa,2016,0.0,37 -India,4.565,0.64499,0.38174,0.51529,0.39786,0.26475,0.08492,2.27513,Asia,2016,0.0,40 -Haiti,4.518,0.26673,0.74302,0.38847,0.24425,0.46187,0.17175,2.24173,South America,2016,0.0,20 -Nepal,4.514,0.35997,0.86449,0.56874,0.38282,0.32296,0.05907,1.95637,Asia,2016,0.0,29 -Ethiopia,4.512,0.19073,0.60406,0.44055,0.4345,0.24325,0.15048,2.44876,Africa,2016,0.0,34 -Sierra Leone,4.507,0.33024,0.95571,0.0,0.4084,0.21488,0.08786,2.51009,Africa,2016,0.0,30 -Mauritania,4.436,0.45407,0.86908,0.35874,0.24232,0.219,0.17461,2.11773,Africa,2016,0.0,27 -Kenya,4.419,0.36471,0.99876,0.41435,0.42215,0.37542,0.05839,1.78555,Africa,2016,0.0,26 -Armenia,4.35,0.76821,0.77711,0.7299,0.19847,0.07855,0.039,1.75873,Asia,2016,0.0,33 -Botswana,4.332,0.99355,1.10464,0.04776,0.49495,0.10461,0.12474,1.46181,Africa,2016,0.0,60 -Myanmar,4.307,0.27108,0.70905,0.48246,0.44017,0.79588,0.19034,1.41805,Asia,2016,0.0,28 -Georgia,4.297,0.7419,0.38562,0.72926,0.40577,0.05547,0.38331,1.59541,Asia,2016,0.0,57 -Malawi,4.292,0.01604,0.41134,0.22562,0.43054,0.33128,0.06977,2.80791,Africa,2016,0.0,31 -Sri Lanka,4.271,0.83524,1.01905,0.70806,0.53726,0.40828,0.09179,0.67108,Asia,2016,0.0,36 -Cameroon,4.252,0.4225,0.88767,0.23402,0.49309,0.20618,0.05786,1.95071,Africa,2016,0.0,26 -Bulgaria,4.218,1.01216,1.10614,0.76649,0.30587,0.11921,0.00872,0.89991,Europe,2016,0.0,41 -Egypt,4.194,0.8818,0.747,0.61712,0.17288,0.11291,0.06324,1.59927,Africa,2016,0.0,34 -Yemen,4.077,0.54649,0.68093,0.40064,0.35571,0.09131,0.07854,1.92313,Asia,2016,0.0,14 -Mali,3.995,0.26074,1.03526,0.20583,0.38857,0.18798,0.12352,1.79293,Africa,2016,0.0,32 -Uganda,3.931,0.21102,1.13299,0.33861,0.45727,0.29066,0.07267,1.42766,Africa,2016,0.0,25 -Senegal,3.904,0.36498,0.97619,0.4354,0.36772,0.20843,0.10713,1.44395,Africa,2016,0.0,45 -Gabon,3.896,1.06024,0.90528,0.43372,0.31914,0.06822,0.11091,0.99895,Africa,2016,0.0,35 -Niger,3.845,0.0694,0.77265,0.29707,0.47692,0.19387,0.15639,1.87877,Africa,2016,0.0,35 -Cambodia,3.819,0.46038,0.62736,0.61114,0.66246,0.40359,0.07247,0.98195,Asia,2016,0.0,21 -Tanzania,3.781,0.2852,1.00268,0.38215,0.32878,0.34377,0.05747,1.38079,Africa,2016,0.0,32 -Madagascar,3.681,0.20824,0.66801,0.46721,0.19184,0.21333,0.08124,1.851,Africa,2016,0.0,26 -Chad,3.667,0.34193,0.76062,0.1501,0.23501,0.18386,0.05269,1.94296,Africa,2016,0.0,20 -Guinea,3.656,0.17417,0.46475,0.24009,0.37725,0.28657,0.12139,1.99172,South America,2016,0.0,27 -Burkina Faso,3.587,0.25812,0.85188,0.27125,0.39493,0.21747,0.12832,1.46494,Africa,2016,0.0,42 -Afghanistan,3.575,0.31982,0.30285,0.30335,0.23414,0.3651,0.09719,1.9521,Asia,2016,0.0,15 -Rwanda,3.465,0.22208,0.7737,0.42864,0.59201,0.22628,0.55191,0.67042,Africa,2016,0.0,54 -Benin,3.34,0.28665,0.35386,0.3191,0.4845,0.1826,0.0801,1.63328,Africa,2016,0.0,36 -Burundi,2.905,0.0153,0.41587,0.22396,0.1185,0.19727,0.10062,1.83302,Africa,2016,0.0,20 -Togo,2.839,0.20868,0.13995,0.28443,0.36453,0.16681,0.10731,1.56726,Africa,2016,0.0,32 -Finland,7.769,1.34,0.0,0.986,0.596,0.153,0.393,0.0,Europe,2017,1.587,85 -Denmark,7.6,1.383,0.0,0.996,0.592,0.252,0.41,0.0,Europe,2017,1.573,88 -Norway,7.554,1.488,0.0,1.028,0.603,0.271,0.341,0.0,Europe,2017,1.582,85 -Iceland,7.494,1.38,0.0,1.026,0.591,0.354,0.118,0.0,Europe,2017,1.624,77 -Netherlands,7.488,1.396,0.0,0.999,0.557,0.322,0.298,0.0,Europe,2017,1.522,82 -Switzerland,7.48,1.452,0.0,1.052,0.572,0.263,0.343,0.0,Europe,2017,1.526,85 -Sweden,7.343,1.387,0.0,1.009,0.574,0.267,0.373,0.0,Europe,2017,1.487,84 -New Zealand,7.307,1.303,0.0,1.026,0.585,0.33,0.38,0.0,Australia,2017,1.557,89 -Canada,7.278,1.365,0.0,1.039,0.584,0.285,0.308,0.0,North America,2017,1.505,82 -Austria,7.246,1.376,0.0,1.016,0.532,0.244,0.226,0.0,Europe,2017,1.475,75 -Australia,7.228,1.372,0.0,1.036,0.557,0.332,0.29,0.0,Australia,2017,1.548,77 -Costa Rica,7.167,1.034,0.0,0.963,0.558,0.144,0.093,0.0,South America,2017,1.441,59 -Israel,7.139,1.276,0.0,1.029,0.371,0.261,0.082,0.0,Asia,2017,1.455,62 -Luxembourg,7.09,1.609,0.0,1.012,0.526,0.194,0.316,0.0,Europe,2017,1.479,82 -United Kingdom,7.054,1.333,0.0,0.996,0.45,0.348,0.278,0.0,Europe,2017,1.538,82 -Ireland,7.021,1.499,0.0,0.999,0.516,0.298,0.31,0.0,Europe,2017,1.553,74 -Germany,6.985,1.373,0.0,0.987,0.495,0.261,0.265,0.0,Europe,2017,1.454,81 -Belgium,6.923,1.356,0.0,0.986,0.473,0.16,0.21,0.0,Europe,2017,1.504,75 -United States,6.892,1.433,0.0,0.874,0.454,0.28,0.128,0.0,North America,2017,1.457,75 -United Arab Emirates,6.825,1.503,0.0,0.825,0.598,0.262,0.182,0.0,Asia,2017,1.31,71 -Malta,6.726,1.3,0.0,0.999,0.564,0.375,0.151,0.0,Europe,2017,1.52,56 -Mexico,6.595,1.07,0.0,0.861,0.433,0.074,0.073,0.0,North America,2017,1.323,29 -France,6.592,1.324,0.0,1.045,0.436,0.111,0.183,0.0,Europe,2017,1.472,70 -Chile,6.444,1.159,0.0,0.92,0.357,0.187,0.056,0.0,South America,2017,1.369,67 -Guatemala,6.436,0.8,0.0,0.746,0.535,0.175,0.078,0.0,South America,2017,1.269,28 -Saudi Arabia,6.375,1.403,0.0,0.795,0.439,0.08,0.132,0.0,Asia,2017,1.357,49 -Spain,6.354,1.286,0.0,1.062,0.362,0.153,0.079,0.0,Europe,2017,1.484,57 -Panama,6.321,1.149,0.0,0.91,0.516,0.109,0.054,0.0,South America,2017,1.442,37 -Brazil,6.3,1.004,0.0,0.802,0.39,0.099,0.086,0.0,South America,2017,1.439,37 -Uruguay,6.293,1.124,0.0,0.891,0.523,0.127,0.15,0.0,South America,2017,1.465,70 -Singapore,6.262,1.572,0.0,1.141,0.556,0.271,0.453,0.0,Asia,2017,1.463,84 -El Salvador,6.253,0.794,0.0,0.789,0.43,0.093,0.074,0.0,South America,2017,1.242,33 -Italy,6.223,1.294,0.0,1.039,0.231,0.158,0.03,0.0,Europe,2017,1.488,50 -Bahrain,6.199,1.362,0.0,0.871,0.536,0.255,0.11,0.0,Asia,2017,1.368,36 -Slovakia,6.198,1.246,0.0,0.881,0.334,0.121,0.014,0.0,Europe,2017,1.504,50 -Poland,6.182,1.206,0.0,0.884,0.483,0.117,0.05,0.0,Europe,2017,1.438,60 -Uzbekistan,6.174,0.745,0.0,0.756,0.631,0.322,0.24,0.0,Asia,2017,1.529,22 -Lithuania,6.149,1.238,0.0,0.818,0.291,0.043,0.042,0.0,Europe,2017,1.515,59 -Colombia,6.125,0.985,0.0,0.841,0.47,0.099,0.034,0.0,South America,2017,1.41,37 -Slovenia,6.118,1.258,0.0,0.953,0.564,0.144,0.057,0.0,Europe,2017,1.523,61 -Nicaragua,6.105,0.694,0.0,0.835,0.435,0.2,0.127,0.0,South America,2017,1.325,26 -Kosovo,6.1,0.882,0.0,0.758,0.489,0.262,0.006,0.0,Europe,2017,1.232,39 -Argentina,6.086,1.092,0.0,0.881,0.471,0.066,0.05,0.0,South America,2017,1.432,39 -Romania,6.07,1.162,0.0,0.825,0.462,0.083,0.005,0.0,Europe,2017,1.232,48 -Cyprus,6.046,1.263,0.0,1.042,0.406,0.19,0.041,0.0,Asia,2017,1.223,57 -Ecuador,6.028,0.912,0.0,0.868,0.498,0.126,0.087,0.0,South America,2017,1.312,32 -Kuwait,6.021,1.5,0.0,0.808,0.493,0.142,0.097,0.0,Asia,2017,1.319,39 -Thailand,6.008,1.05,0.0,0.828,0.557,0.359,0.028,0.0,Asia,2017,1.409,37 -Latvia,5.94,1.187,0.0,0.812,0.264,0.075,0.064,0.0,Europe,2017,1.465,58 -Estonia,5.893,1.237,0.0,0.874,0.495,0.103,0.161,0.0,Europe,2017,1.528,71 -Jamaica,5.89,0.831,0.0,0.831,0.49,0.107,0.028,0.0,South America,2017,1.478,44 -Mauritius,5.888,1.12,0.0,0.798,0.498,0.215,0.06,0.0,Africa,2017,1.402,50 -Japan,5.886,1.327,0.0,1.088,0.445,0.069,0.14,0.0,Asia,2017,1.419,73 -Honduras,5.86,0.642,0.0,0.828,0.507,0.246,0.078,0.0,South America,2017,1.236,29 -Kazakhstan,5.809,1.173,0.0,0.729,0.41,0.146,0.096,0.0,Asia,2017,1.508,31 -Bolivia,5.779,0.776,0.0,0.706,0.511,0.137,0.064,0.0,South America,2017,1.209,33 -Hungary,5.758,1.201,0.0,0.828,0.199,0.081,0.02,0.0,Europe,2017,1.41,45 -Paraguay,5.743,0.855,0.0,0.777,0.514,0.184,0.08,0.0,South America,2017,1.475,29 -Peru,5.697,0.96,0.0,0.854,0.455,0.083,0.027,0.0,South America,2017,1.274,37 -Portugal,5.693,1.221,0.0,0.999,0.508,0.047,0.025,0.0,Europe,2017,1.431,63 -Pakistan,5.653,0.677,0.0,0.535,0.313,0.22,0.098,0.0,Asia,2017,0.886,32 -Russia,5.648,1.183,0.0,0.726,0.334,0.082,0.031,0.0,Europe,2017,1.452,29 -Philippines,5.631,0.807,0.0,0.657,0.558,0.117,0.107,0.0,Asia,2017,1.293,34 -Serbia,5.603,1.004,0.0,0.854,0.282,0.137,0.039,0.0,Europe,2017,1.383,41 -Moldova,5.529,0.685,0.0,0.739,0.245,0.181,0.0,0.0,Europe,2017,1.328,31 -Libya,5.525,1.044,0.0,0.673,0.416,0.133,0.152,0.0,Africa,2017,1.303,17 -Montenegro,5.523,1.051,0.0,0.871,0.197,0.142,0.08,0.0,Europe,2017,1.361,46 -Tajikistan,5.467,0.493,0.0,0.718,0.389,0.23,0.144,0.0,Asia,2017,1.098,21 -Croatia,5.432,1.155,0.0,0.914,0.296,0.119,0.022,0.0,Europe,2017,1.266,49 -Dominican Republic,5.425,1.015,0.0,0.779,0.497,0.113,0.101,0.0,South America,2017,1.401,29 -Bosnia and Herzegovina,5.386,0.945,0.0,0.845,0.212,0.263,0.006,0.0,Europe,2017,1.212,38 -Turkey,5.373,1.183,0.0,0.808,0.195,0.083,0.106,0.0,Asia,2017,1.36,40 -Malaysia,5.339,1.221,0.0,0.828,0.508,0.26,0.024,0.0,Asia,2017,1.171,47 -Belarus,5.323,1.067,0.0,0.789,0.235,0.094,0.142,0.0,Europe,2017,1.465,44 -Greece,5.287,1.181,0.0,0.999,0.067,0.0,0.034,0.0,Europe,2017,1.156,48 -Mongolia,5.285,0.948,0.0,0.667,0.317,0.235,0.038,0.0,Asia,2017,1.531,36 -Nigeria,5.265,0.696,0.0,0.245,0.426,0.215,0.041,0.0,Africa,2017,1.111,27 -Kyrgyzstan,5.261,0.551,0.0,0.723,0.508,0.3,0.023,0.0,Asia,2017,1.438,29 -Turkmenistan,5.247,1.052,0.0,0.657,0.394,0.244,0.028,0.0,Asia,2017,1.538,19 -Algeria,5.211,1.002,0.0,0.785,0.086,0.073,0.114,0.0,Africa,2017,1.16,33 -Morocco,5.208,0.801,0.0,0.782,0.418,0.036,0.076,0.0,Africa,2017,0.782,40 -Azerbaijan,5.208,1.043,0.0,0.769,0.351,0.035,0.182,0.0,Asia,2017,1.147,31 -Lebanon,5.197,0.987,0.0,0.815,0.216,0.166,0.027,0.0,Asia,2017,1.224,28 -Indonesia,5.192,0.931,0.0,0.66,0.491,0.498,0.028,0.0,Asia,2017,1.203,37 -China,5.191,1.029,0.0,0.893,0.521,0.058,0.1,0.0,Asia,2017,1.125,41 -Vietnam,5.175,0.741,0.0,0.851,0.543,0.147,0.073,0.0,Asia,2017,1.346,35 -Cameroon,5.044,0.549,0.0,0.331,0.381,0.187,0.037,0.0,Africa,2017,0.91,25 -Bulgaria,5.011,1.092,0.0,0.815,0.311,0.081,0.004,0.0,Europe,2017,1.513,42 -Ghana,4.996,0.611,0.0,0.486,0.381,0.245,0.04,0.0,Africa,2017,0.868,40 -Nepal,4.913,0.446,0.0,0.677,0.439,0.285,0.089,0.0,Asia,2017,1.226,31 -Jordan,4.906,0.837,0.0,0.815,0.383,0.11,0.13,0.0,Asia,2017,1.225,48 -Benin,4.883,0.393,0.0,0.397,0.349,0.175,0.082,0.0,Africa,2017,0.437,39 -Gabon,4.799,1.057,0.0,0.571,0.295,0.043,0.055,0.0,Africa,2017,1.183,32 -South Africa,4.722,0.96,0.0,0.469,0.389,0.13,0.055,0.0,Africa,2017,1.351,43 -Albania,4.719,0.947,0.0,0.874,0.383,0.178,0.027,0.0,Europe,2017,0.848,38 -Venezuela,4.707,0.96,0.0,0.805,0.154,0.064,0.047,0.0,South America,2017,1.427,18 -Cambodia,4.7,0.574,0.0,0.637,0.609,0.232,0.062,0.0,Asia,2017,1.122,21 -Senegal,4.681,0.45,0.0,0.571,0.292,0.153,0.072,0.0,Africa,2017,1.134,45 -Niger,4.628,0.138,0.0,0.366,0.318,0.188,0.102,0.0,Africa,2017,0.774,33 -Burkina Faso,4.587,0.331,0.0,0.38,0.255,0.177,0.113,0.0,Africa,2017,1.056,42 -Armenia,4.559,0.85,0.0,0.815,0.283,0.095,0.064,0.0,Asia,2017,1.055,35 -Iran,4.548,1.1,0.0,0.785,0.305,0.27,0.125,0.0,Asia,2017,0.842,30 -Guinea,4.534,0.38,0.0,0.375,0.332,0.207,0.086,0.0,South America,2017,0.829,27 -Georgia,4.519,0.886,0.0,0.752,0.346,0.043,0.164,0.0,Asia,2017,0.666,56 -Kenya,4.509,0.512,0.0,0.581,0.431,0.372,0.053,0.0,Africa,2017,0.983,28 -Mauritania,4.49,0.57,0.0,0.489,0.066,0.106,0.088,0.0,Africa,2017,1.167,28 -Tunisia,4.461,0.921,0.0,0.815,0.167,0.059,0.055,0.0,Africa,2017,1.0,42 -Bangladesh,4.456,0.562,0.0,0.723,0.527,0.166,0.143,0.0,Asia,2017,0.928,28 -Iraq,4.437,1.043,0.0,0.574,0.241,0.148,0.089,0.0,Asia,2017,0.98,18 -Mali,4.39,0.385,0.0,0.308,0.327,0.153,0.052,0.0,Africa,2017,1.105,31 -Sierra Leone,4.374,0.268,0.0,0.242,0.309,0.252,0.045,0.0,Africa,2017,0.841,30 -Sri Lanka,4.366,0.949,0.0,0.831,0.47,0.244,0.047,0.0,Asia,2017,1.265,38 -Myanmar,4.36,0.71,0.0,0.555,0.525,0.566,0.172,0.0,Asia,2017,1.181,30 -Chad,4.35,0.35,0.0,0.192,0.174,0.198,0.078,0.0,Africa,2017,0.766,20 -Ukraine,4.332,0.82,0.0,0.739,0.178,0.187,0.01,0.0,Europe,2017,1.39,30 -Ethiopia,4.286,0.336,0.0,0.532,0.344,0.209,0.1,0.0,Africa,2017,1.033,35 -Uganda,4.189,0.332,0.0,0.443,0.356,0.252,0.06,0.0,Africa,2017,1.069,26 -Egypt,4.166,0.913,0.0,0.644,0.241,0.076,0.067,0.0,Africa,2017,1.039,32 -Zambia,4.107,0.578,0.0,0.426,0.431,0.247,0.087,0.0,Africa,2017,1.058,37 -Togo,4.085,0.275,0.0,0.41,0.293,0.177,0.085,0.0,Africa,2017,0.572,32 -India,4.015,0.755,0.0,0.588,0.498,0.2,0.085,0.0,Asia,2017,0.765,40 -Liberia,3.975,0.073,0.0,0.443,0.37,0.233,0.033,0.0,Africa,2017,0.922,31 -Madagascar,3.933,0.274,0.0,0.555,0.148,0.169,0.041,0.0,Africa,2017,0.916,24 -Burundi,3.775,0.046,0.0,0.38,0.22,0.176,0.18,0.0,Africa,2017,0.447,22 -Zimbabwe,3.663,0.366,0.0,0.433,0.361,0.151,0.089,0.0,Africa,2017,1.114,22 -Haiti,3.597,0.323,0.0,0.449,0.026,0.419,0.11,0.0,South America,2017,0.688,22 -Botswana,3.488,1.041,0.0,0.538,0.455,0.025,0.1,0.0,Africa,2017,1.145,61 -Malawi,3.41,0.191,0.0,0.495,0.443,0.218,0.089,0.0,Africa,2017,0.56,31 -Yemen,3.38,0.287,0.0,0.463,0.143,0.108,0.077,0.0,Asia,2017,1.163,16 -Rwanda,3.334,0.359,0.0,0.614,0.555,0.217,0.411,0.0,Africa,2017,0.711,55 -Tanzania,3.231,0.476,0.0,0.499,0.417,0.276,0.147,0.0,Africa,2017,0.885,36 -Afghanistan,3.203,0.35,0.0,0.361,0.0,0.158,0.025,0.0,Asia,2017,0.517,15 -Finland,7.632,1.305,0.0,0.874,0.681,0.202,0.393,0.0,Europe,2018,1.592,85 -Norway,7.594,1.456,0.0,0.861,0.686,0.286,0.34,0.0,Europe,2018,1.582,84 -Denmark,7.555,1.351,0.0,0.868,0.683,0.284,0.408,0.0,Europe,2018,1.59,88 -Iceland,7.495,1.343,0.0,0.914,0.677,0.353,0.138,0.0,Europe,2018,1.644,76 -Switzerland,7.487,1.42,0.0,0.927,0.66,0.256,0.357,0.0,Europe,2018,1.549,85 -Netherlands,7.441,1.361,0.0,0.878,0.638,0.333,0.295,0.0,Europe,2018,1.488,82 -Canada,7.328,1.33,0.0,0.896,0.653,0.321,0.291,0.0,North America,2018,1.532,81 -New Zealand,7.324,1.268,0.0,0.876,0.669,0.365,0.389,0.0,Australia,2018,1.601,87 -Sweden,7.314,1.355,0.0,0.913,0.659,0.285,0.383,0.0,Europe,2018,1.501,85 -Australia,7.272,1.34,0.0,0.91,0.647,0.361,0.302,0.0,Australia,2018,1.573,77 -United Kingdom,7.19,1.244,0.0,0.888,0.464,0.262,0.082,0.0,Europe,2018,1.433,80 -Austria,7.139,1.341,0.0,0.891,0.617,0.242,0.224,0.0,Europe,2018,1.504,76 -Costa Rica,7.072,1.01,0.0,0.817,0.632,0.143,0.101,0.0,South America,2018,1.459,56 -Ireland,6.977,1.448,0.0,0.876,0.614,0.307,0.306,0.0,Europe,2018,1.583,73 -Germany,6.965,1.34,0.0,0.861,0.586,0.273,0.28,0.0,Europe,2018,1.474,80 -Belgium,6.927,1.324,0.0,0.894,0.583,0.188,0.24,0.0,Europe,2018,1.483,75 -Luxembourg,6.91,1.576,0.0,0.896,0.632,0.196,0.321,0.0,Europe,2018,1.52,81 -United States,6.886,1.398,0.0,0.819,0.547,0.291,0.133,0.0,North America,2018,1.471,71 -Israel,6.814,1.301,0.0,0.883,0.533,0.354,0.272,0.0,Asia,2018,1.559,61 -United Arab Emirates,6.774,2.096,0.0,0.67,0.284,0.186,0.312,0.0,Asia,2018,0.776,70 -Malta,6.627,1.27,0.0,0.884,0.645,0.376,0.142,0.0,Europe,2018,1.525,54 -France,6.489,1.293,0.0,0.908,0.52,0.098,0.176,0.0,Europe,2018,1.466,72 -Mexico,6.488,1.038,0.0,0.761,0.479,0.069,0.095,0.0,North America,2018,1.252,28 -Chile,6.476,1.131,0.0,0.808,0.431,0.197,0.061,0.0,South America,2018,1.331,67 -Panama,6.43,1.112,0.0,0.759,0.597,0.125,0.063,0.0,South America,2018,1.438,37 -Brazil,6.419,0.986,0.0,0.675,0.493,0.11,0.088,0.0,South America,2018,1.474,35 -Argentina,6.388,1.073,0.0,0.744,0.57,0.062,0.054,0.0,South America,2018,1.468,40 -Guatemala,6.382,0.781,0.0,0.608,0.604,0.179,0.071,0.0,South America,2018,1.268,27 -Uruguay,6.379,1.093,0.0,0.771,0.625,0.13,0.155,0.0,South America,2018,1.459,70 -Saudi Arabia,6.371,1.379,0.0,0.633,0.509,0.098,0.127,0.0,Asia,2018,1.331,49 -Singapore,6.343,1.529,0.0,1.008,0.631,0.261,0.457,0.0,Asia,2018,1.451,85 -Malaysia,6.322,1.161,0.0,0.669,0.356,0.311,0.059,0.0,Asia,2018,1.258,47 -Spain,6.31,1.251,0.0,0.965,0.449,0.142,0.074,0.0,Europe,2018,1.538,58 -Colombia,6.26,0.96,0.0,0.635,0.531,0.099,0.039,0.0,South America,2018,1.439,36 -Slovakia,6.173,1.21,0.0,0.776,0.354,0.118,0.014,0.0,Europe,2018,1.537,50 -El Salvador,6.167,0.806,0.0,0.639,0.461,0.065,0.082,0.0,South America,2018,1.231,35 -Nicaragua,6.141,0.668,0.0,0.7,0.527,0.208,0.128,0.0,South America,2018,1.319,25 -Poland,6.123,1.176,0.0,0.781,0.546,0.108,0.064,0.0,Europe,2018,1.448,60 -Bahrain,6.105,1.338,0.0,0.698,0.594,0.243,0.123,0.0,Asia,2018,1.366,36 -Uzbekistan,6.096,0.719,0.0,0.605,0.724,0.328,0.259,0.0,Asia,2018,1.584,23 -Kuwait,6.083,1.474,0.0,0.675,0.554,0.167,0.106,0.0,Asia,2018,1.301,41 -Thailand,6.072,1.016,0.0,0.707,0.637,0.364,0.029,0.0,Asia,2018,1.417,36 -Italy,6.0,1.264,0.0,0.946,0.281,0.137,0.028,0.0,Europe,2018,1.501,52 -Ecuador,5.973,0.889,0.0,0.736,0.556,0.114,0.12,0.0,South America,2018,1.33,34 -Lithuania,5.952,1.197,0.0,0.716,0.35,0.026,0.006,0.0,Europe,2018,1.527,59 -Slovenia,5.948,1.219,0.0,0.856,0.633,0.16,0.051,0.0,Europe,2018,1.506,60 -Romania,5.945,1.116,0.0,0.726,0.528,0.088,0.001,0.0,Europe,2018,1.219,47 -Latvia,5.933,1.148,0.0,0.671,0.363,0.092,0.066,0.0,Europe,2018,1.454,58 -Japan,5.915,1.294,0.0,0.988,0.553,0.079,0.15,0.0,Asia,2018,1.462,73 -Mauritius,5.891,1.09,0.0,0.684,0.584,0.245,0.05,0.0,Africa,2018,1.387,51 -Jamaica,5.89,0.819,0.0,0.693,0.575,0.096,0.031,0.0,South America,2018,1.493,44 -Russia,5.81,1.151,0.0,0.599,0.399,0.065,0.025,0.0,Europe,2018,1.479,28 -Kazakhstan,5.79,1.143,0.0,0.631,0.454,0.148,0.121,0.0,Asia,2018,1.516,31 -Cyprus,5.762,1.229,0.0,0.909,0.423,0.202,0.035,0.0,Asia,2018,1.191,59 -Bolivia,5.752,0.751,0.0,0.508,0.606,0.141,0.054,0.0,South America,2018,1.223,29 -Estonia,5.739,1.2,0.0,0.737,0.553,0.086,0.174,0.0,Europe,2018,1.532,73 -Paraguay,5.681,0.835,0.0,0.615,0.541,0.162,0.074,0.0,South America,2018,1.522,29 -Peru,5.663,0.934,0.0,0.674,0.53,0.092,0.034,0.0,South America,2018,1.249,35 -Kosovo,5.662,0.855,0.0,0.578,0.448,0.274,0.023,0.0,Europe,2018,1.23,37 -Moldova,5.64,0.657,0.0,0.62,0.232,0.171,0.0,0.0,Europe,2018,1.301,33 -Turkmenistan,5.636,1.016,0.0,0.517,0.417,0.199,0.037,0.0,Asia,2018,1.533,20 -Hungary,5.62,1.171,0.0,0.732,0.259,0.061,0.022,0.0,Europe,2018,1.401,46 -Libya,5.566,0.985,0.0,0.553,0.496,0.116,0.148,0.0,Africa,2018,1.35,17 -Philippines,5.524,0.775,0.0,0.513,0.643,0.12,0.105,0.0,Asia,2018,1.312,36 -Honduras,5.504,0.62,0.0,0.622,0.459,0.197,0.074,0.0,South America,2018,1.205,29 -Belarus,5.483,1.039,0.0,0.7,0.307,0.101,0.154,0.0,Europe,2018,1.498,44 -Turkey,5.483,1.148,0.0,0.686,0.324,0.106,0.109,0.0,Asia,2018,1.38,41 -Pakistan,5.472,0.652,0.0,0.424,0.334,0.216,0.113,0.0,Asia,2018,0.81,33 -Portugal,5.41,1.188,0.0,0.884,0.562,0.055,0.017,0.0,Europe,2018,1.429,64 -Serbia,5.398,0.975,0.0,0.685,0.288,0.134,0.043,0.0,Europe,2018,1.369,39 -Lebanon,5.358,0.965,0.0,0.785,0.503,0.214,0.136,0.0,Asia,2018,1.179,28 -Greece,5.358,1.154,0.0,0.879,0.131,0.0,0.044,0.0,Europe,2018,1.202,45 -Montenegro,5.347,1.017,0.0,0.729,0.259,0.111,0.081,0.0,Europe,2018,1.279,45 -Croatia,5.321,1.115,0.0,0.737,0.38,0.12,0.039,0.0,Europe,2018,1.161,48 -Dominican Republic,5.302,0.982,0.0,0.614,0.578,0.12,0.106,0.0,South America,2018,1.441,30 -Algeria,5.295,0.979,0.0,0.687,0.077,0.055,0.135,0.0,Africa,2018,1.154,35 -Morocco,5.254,0.779,0.0,0.669,0.46,0.026,0.074,0.0,Africa,2018,0.797,43 -China,5.246,0.989,0.0,0.799,0.597,0.029,0.103,0.0,Asia,2018,1.142,39 -Azerbaijan,5.201,1.024,0.0,0.603,0.43,0.031,0.176,0.0,Asia,2018,1.161,25 -Tajikistan,5.199,0.474,0.0,0.598,0.292,0.187,0.034,0.0,Asia,2018,1.166,25 -Jordan,5.161,0.822,0.0,0.645,0.468,0.13,0.134,0.0,Asia,2018,1.265,49 -Nigeria,5.155,0.689,0.0,0.048,0.462,0.201,0.032,0.0,Africa,2018,1.172,27 -Kyrgyzstan,5.131,0.53,0.0,0.594,0.54,0.281,0.035,0.0,Asia,2018,1.416,29 -Bosnia and Herzegovina,5.129,0.915,0.0,0.758,0.28,0.216,0.0,0.0,Europe,2018,1.078,38 -Mongolia,5.125,0.914,0.0,0.575,0.395,0.253,0.032,0.0,Asia,2018,1.517,37 -Vietnam,5.103,0.715,0.0,0.702,0.618,0.177,0.079,0.0,Asia,2018,1.365,33 -Indonesia,5.093,0.899,0.0,0.522,0.538,0.484,0.018,0.0,Asia,2018,1.215,38 -Cameroon,4.975,0.535,0.0,0.182,0.454,0.183,0.043,0.0,Africa,2018,0.891,25 -Bulgaria,4.933,1.054,0.0,0.712,0.359,0.064,0.009,0.0,Europe,2018,1.515,43 -Nepal,4.88,0.425,0.0,0.539,0.526,0.302,0.078,0.0,Asia,2018,1.228,31 -Venezuela,4.806,0.996,0.0,0.657,0.133,0.056,0.052,0.0,South America,2018,1.469,18 -Gabon,4.758,1.036,0.0,0.404,0.356,0.032,0.052,0.0,Africa,2018,1.164,31 -South Africa,4.724,0.94,0.0,0.33,0.516,0.103,0.056,0.0,Africa,2018,1.41,43 -Iran,4.707,1.059,0.0,0.691,0.459,0.282,0.129,0.0,Asia,2018,0.771,28 -Ghana,4.657,0.592,0.0,0.337,0.499,0.212,0.029,0.0,Africa,2018,0.896,41 -Senegal,4.631,0.429,0.0,0.433,0.406,0.138,0.082,0.0,Africa,2018,1.117,45 -Tunisia,4.592,0.9,0.0,0.69,0.271,0.04,0.063,0.0,Africa,2018,0.906,43 -Albania,4.586,0.916,0.0,0.79,0.419,0.149,0.032,0.0,Europe,2018,0.817,36 -Sierra Leone,4.571,0.256,0.0,0.0,0.355,0.238,0.053,0.0,Africa,2018,0.813,30 -Bangladesh,4.5,0.532,0.0,0.579,0.58,0.153,0.144,0.0,Asia,2018,0.85,26 -Sri Lanka,4.471,0.918,0.0,0.672,0.585,0.307,0.05,0.0,Asia,2018,1.314,38 -Iraq,4.456,1.01,0.0,0.536,0.304,0.148,0.095,0.0,Asia,2018,0.971,18 -Mali,4.447,0.37,0.0,0.152,0.367,0.139,0.056,0.0,Africa,2018,1.233,32 -Cambodia,4.433,0.549,0.0,0.457,0.696,0.256,0.065,0.0,Asia,2018,1.088,20 -Burkina Faso,4.424,0.314,0.0,0.254,0.312,0.175,0.128,0.0,Africa,2018,1.097,41 -Egypt,4.419,0.885,0.0,0.553,0.312,0.092,0.107,0.0,Africa,2018,1.025,35 -Kenya,4.41,0.493,0.0,0.454,0.504,0.352,0.055,0.0,Africa,2018,1.048,27 -Zambia,4.377,0.562,0.0,0.295,0.503,0.221,0.082,0.0,Africa,2018,1.047,35 -Mauritania,4.356,0.557,0.0,0.292,0.129,0.134,0.093,0.0,Africa,2018,1.245,27 -Ethiopia,4.35,0.308,0.0,0.391,0.452,0.22,0.146,0.0,Africa,2018,0.95,34 -Georgia,4.34,0.853,0.0,0.643,0.375,0.038,0.215,0.0,Asia,2018,0.592,58 -Armenia,4.321,0.816,0.0,0.666,0.26,0.077,0.028,0.0,Asia,2018,0.99,35 -Myanmar,4.308,0.682,0.0,0.429,0.58,0.598,0.178,0.0,Asia,2018,1.174,29 -Chad,4.301,0.358,0.0,0.053,0.189,0.181,0.06,0.0,Africa,2018,0.907,19 -India,4.19,0.721,0.0,0.485,0.539,0.172,0.093,0.0,Asia,2018,0.747,41 -Niger,4.166,0.131,0.0,0.221,0.39,0.175,0.099,0.0,Africa,2018,0.867,34 -Uganda,4.161,0.322,0.0,0.237,0.45,0.259,0.061,0.0,Africa,2018,1.09,26 -Benin,4.141,0.378,0.0,0.24,0.44,0.163,0.067,0.0,Africa,2018,0.372,40 -Ukraine,4.103,0.793,0.0,0.609,0.163,0.187,0.011,0.0,Europe,2018,1.413,32 -Togo,3.999,0.259,0.0,0.253,0.434,0.158,0.101,0.0,Africa,2018,0.474,30 -Guinea,3.964,0.344,0.0,0.211,0.394,0.185,0.094,0.0,South America,2018,0.792,28 -Madagascar,3.774,0.262,0.0,0.402,0.221,0.155,0.049,0.0,Africa,2018,0.908,25 -Zimbabwe,3.692,0.357,0.0,0.248,0.406,0.132,0.099,0.0,Africa,2018,1.094,22 -Afghanistan,3.632,0.332,0.0,0.255,0.085,0.191,0.036,0.0,Asia,2018,0.537,16 -Botswana,3.59,1.017,0.0,0.417,0.557,0.042,0.092,0.0,Africa,2018,1.174,61 -Malawi,3.587,0.186,0.0,0.306,0.531,0.21,0.08,0.0,Africa,2018,0.541,32 -Haiti,3.582,0.315,0.0,0.289,0.025,0.392,0.104,0.0,South America,2018,0.714,20 -Liberia,3.495,0.076,0.0,0.267,0.419,0.206,0.03,0.0,Africa,2018,0.858,32 -Rwanda,3.408,0.332,0.0,0.4,0.636,0.2,0.444,0.0,Africa,2018,0.896,56 -Yemen,3.355,0.442,0.0,0.343,0.244,0.083,0.064,0.0,Asia,2018,1.073,14 -Tanzania,3.303,0.455,0.0,0.381,0.481,0.27,0.097,0.0,Africa,2018,0.991,36 -Burundi,2.905,0.091,0.0,0.145,0.065,0.149,0.076,0.0,Africa,2018,0.627,17 -Denmark,7.526,1.44178,1.16374,0.79504,0.57941,0.36171,0.44453,2.73939,Europe,2019,0.0,87 -Switzerland,7.509,1.52733,1.14524,0.86303,0.58557,0.28083,0.41203,2.69463,Europe,2019,0.0,85 -Iceland,7.501,1.42666,1.18326,0.86733,0.56624,0.47678,0.14975,2.83137,Europe,2019,0.0,78 -Norway,7.498,1.57744,1.1269,0.79579,0.59609,0.37895,0.35776,2.66465,Europe,2019,0.0,84 -Finland,7.413,1.40598,1.13464,0.81091,0.57104,0.25492,0.41004,2.82596,Europe,2019,0.0,86 -Canada,7.404,1.44015,1.0961,0.8276,0.5737,0.44834,0.31329,2.70485,North America,2019,0.0,77 -Netherlands,7.339,1.46468,1.02912,0.81231,0.55211,0.47416,0.29927,2.70749,Europe,2019,0.0,82 -New Zealand,7.334,1.36066,1.17278,0.83096,0.58147,0.49401,0.41904,2.47553,Australia,2019,0.0,87 -Australia,7.313,1.44443,1.10476,0.8512,0.56837,0.47407,0.32331,2.5465,Australia,2019,0.0,77 -Sweden,7.291,1.45181,1.08764,0.83121,0.58218,0.38254,0.40867,2.54734,Europe,2019,0.0,85 -Israel,7.267,1.33766,0.99537,0.84917,0.36432,0.32288,0.08728,3.31029,Asia,2019,0.0,60 -Austria,7.119,1.45038,1.08383,0.80565,0.54355,0.32865,0.21348,2.69343,Europe,2019,0.0,77 -United States,7.104,1.50796,1.04782,0.779,0.48163,0.41077,0.14868,2.72782,North America,2019,0.0,69 -Costa Rica,7.087,1.06879,1.02152,0.76146,0.55225,0.22553,0.10547,3.35168,South America,2019,0.0,56 -Germany,6.994,1.44787,1.09774,0.81487,0.53466,0.30452,0.28551,2.50931,Europe,2019,0.0,80 -Brazil,6.952,1.08754,1.03938,0.61415,0.40425,0.15776,0.14166,3.50733,South America,2019,0.0,35 -Belgium,6.929,1.42539,1.05249,0.81959,0.51354,0.2424,0.26248,2.61355,Europe,2019,0.0,75 -Ireland,6.907,1.48341,1.16157,0.81455,0.54008,0.44963,0.29754,2.15988,Europe,2019,0.0,74 -Luxembourg,6.871,1.69752,1.03999,0.84542,0.5487,0.27571,0.35329,2.11055,Europe,2019,0.0,80 -Mexico,6.778,1.11508,0.7146,0.71143,0.37709,0.11735,0.18355,3.55906,North America,2019,0.0,29 -Singapore,6.739,1.64555,0.86758,0.94719,0.4877,0.32706,0.46987,1.99375,Asia,2019,0.0,85 -United Kingdom,6.725,1.40283,1.08672,0.80991,0.50036,0.50156,0.27399,2.14999,Europe,2019,0.0,77 -Chile,6.705,1.2167,0.90587,0.81883,0.37789,0.31595,0.11451,2.95505,South America,2019,0.0,67 -Panama,6.701,1.18306,0.98912,0.70835,0.48927,0.2418,0.08423,3.00559,South America,2019,0.0,36 -Argentina,6.65,1.15137,1.06612,0.69711,0.42284,0.10989,0.07296,3.12985,South America,2019,0.0,45 -United Arab Emirates,6.573,1.57352,0.87114,0.72993,0.56215,0.26591,0.35561,2.21507,Asia,2019,0.0,71 -Uruguay,6.545,1.18157,1.03143,0.72183,0.54388,0.18056,0.21394,2.67139,South America,2019,0.0,71 -Malta,6.488,1.30782,1.09879,0.80315,0.54994,0.56237,0.17554,1.99032,Europe,2019,0.0,54 -Colombia,6.481,1.03032,1.02169,0.59659,0.44735,0.15626,0.05399,3.17471,South America,2019,0.0,37 -France,6.478,1.39488,1.00508,0.83795,0.46562,0.1216,0.17808,2.4744,Europe,2019,0.0,69 -Thailand,6.474,1.0893,1.04477,0.64915,0.49553,0.58696,0.02833,2.5796,Asia,2019,0.0,36 -Saudi Arabia,6.379,1.48953,0.84829,0.59267,0.37904,0.15457,0.30008,2.61482,Asia,2019,0.0,53 -Spain,6.361,1.34253,1.12945,0.87896,0.37545,0.17665,0.06137,2.39663,Europe,2019,0.0,62 -Algeria,6.355,1.05266,0.83309,0.61804,0.21006,0.07044,0.16157,3.40904,Africa,2019,0.0,35 -Guatemala,6.324,0.83454,0.87119,0.54039,0.50379,0.28808,0.08701,3.19863,South America,2019,0.0,26 -Kuwait,6.239,1.61714,0.87758,0.63569,0.43166,0.15965,0.23669,2.28085,Asia,2019,0.0,40 -Bahrain,6.218,1.44024,0.94397,0.65696,0.47375,0.17147,0.25772,2.27405,Asia,2019,0.0,42 -Venezuela,6.084,1.13367,1.03302,0.61904,0.19847,0.0425,0.08304,2.97468,South America,2019,0.0,16 -Slovakia,6.078,1.27973,1.08268,0.70367,0.23391,0.13837,0.02947,2.61065,Europe,2019,0.0,50 -El Salvador,6.068,0.8737,0.80975,0.596,0.37269,0.08877,0.10613,3.22134,South America,2019,0.0,34 -Malaysia,6.005,1.25142,0.88025,0.62366,0.39031,0.41474,0.09081,2.35384,Asia,2019,0.0,53 -Nicaragua,5.992,0.69384,0.89521,0.65213,0.46582,0.29773,0.16292,2.82428,South America,2019,0.0,22 -Uzbekistan,5.987,0.73591,1.1681,0.50163,0.60848,0.34326,0.28333,2.34638,Asia,2019,0.0,25 -Italy,5.977,1.35495,1.04167,0.85102,0.18827,0.16684,0.02556,2.34918,Europe,2019,0.0,53 -Ecuador,5.976,0.97306,0.85974,0.68613,0.4027,0.10074,0.18037,2.77366,South America,2019,0.0,38 -Japan,5.921,1.38007,1.06054,0.91491,0.46761,0.10224,0.18985,1.80584,Asia,2019,0.0,73 -Kazakhstan,5.919,1.22943,0.95544,0.57386,0.4052,0.15011,0.11132,2.49325,Asia,2019,0.0,34 -Moldova,5.897,0.69177,0.83132,0.52309,0.25202,0.19997,0.01903,3.38007,Europe,2019,0.0,32 -Russia,5.856,1.23228,1.05261,0.58991,0.32682,0.02736,0.03586,2.59115,Europe,2019,0.0,28 -Poland,5.835,1.24585,1.04685,0.69058,0.4519,0.14443,0.055,2.20035,Europe,2019,0.0,58 -Bolivia,5.822,0.79422,0.83779,0.4697,0.50961,0.21698,0.07746,2.91635,South America,2019,0.0,31 -Lithuania,5.813,1.2692,1.06411,0.64674,0.18929,0.02025,0.0182,2.60525,Europe,2019,0.0,60 -Belarus,5.802,1.13062,1.04993,0.63104,0.29091,0.13942,0.17457,2.38582,Europe,2019,0.0,45 -Slovenia,5.768,1.29947,1.05613,0.79151,0.53164,0.25738,0.03635,1.79522,Europe,2019,0.0,60 -Peru,5.743,0.99602,0.81255,0.62994,0.37502,0.14527,0.05292,2.73117,South America,2019,0.0,36 -Turkmenistan,5.658,1.08017,1.03817,0.44006,0.37408,0.22567,0.28467,2.21489,Asia,2019,0.0,19 -Mauritius,5.648,1.14372,0.75695,0.66189,0.46145,0.36951,0.05203,2.20223,Africa,2019,0.0,52 -Libya,5.615,1.06688,0.95076,0.52304,0.40672,0.17087,0.10339,2.39374,Africa,2019,0.0,18 -Latvia,5.56,1.21788,0.95025,0.63952,0.27996,0.17445,0.0889,2.20859,Europe,2019,0.0,56 -Cyprus,5.546,1.31857,0.70697,0.8488,0.29507,0.27906,0.05228,2.04497,Asia,2019,0.0,58 -Paraguay,5.538,0.89373,1.11111,0.58295,0.46235,0.25296,0.07396,2.16091,South America,2019,0.0,28 -Romania,5.528,1.1697,0.72803,0.67602,0.36712,0.12889,0.00679,2.45184,Europe,2019,0.0,44 -Estonia,5.517,1.27964,1.05163,0.68098,0.41511,0.08423,0.18519,1.81985,Europe,2019,0.0,74 -Jamaica,5.51,0.89333,0.96372,0.59469,0.43597,0.22245,0.04294,2.35682,South America,2019,0.0,43 -Croatia,5.488,1.18649,0.60809,0.70524,0.23907,0.18434,0.04002,2.52462,Europe,2019,0.0,47 -Kosovo,5.401,0.90145,0.66062,0.54,0.14396,0.27992,0.06547,2.80998,Europe,2019,0.0,36 -Turkey,5.389,1.16492,0.87717,0.64718,0.23889,0.04707,0.12348,2.29074,Asia,2019,0.0,39 -Indonesia,5.314,0.95104,0.87625,0.49374,0.39237,0.56521,0.00322,2.03171,Asia,2019,0.0,40 -Jordan,5.303,0.99673,0.86216,0.60712,0.36023,0.14262,0.13297,2.20142,Asia,2019,0.0,48 -Azerbaijan,5.291,1.12373,0.76042,0.54504,0.35327,0.0564,0.17914,2.2735,Asia,2019,0.0,30 -Philippines,5.279,0.81217,0.87877,0.47036,0.54854,0.21674,0.11757,2.23484,Asia,2019,0.0,34 -China,5.245,1.0278,0.79381,0.73561,0.44012,0.04959,0.02745,2.17087,Asia,2019,0.0,41 -Kyrgyzstan,5.185,0.56044,0.95434,0.55449,0.40212,0.38432,0.04762,2.28136,Asia,2019,0.0,30 -Serbia,5.177,1.03437,0.81329,0.6458,0.15718,0.20737,0.04339,2.27539,Europe,2019,0.0,39 -Bosnia and Herzegovina,5.163,0.93383,0.64367,0.70766,0.09511,0.29889,0.0,2.48406,Europe,2019,0.0,36 -Montenegro,5.161,1.07838,0.74173,0.63533,0.15111,0.17191,0.12721,2.25531,Europe,2019,0.0,45 -Dominican Republic,5.155,1.02787,0.99496,0.57669,0.52259,0.21286,0.12372,1.69626,South America,2019,0.0,28 -Morocco,5.151,0.84058,0.38595,0.59471,0.25646,0.04053,0.08404,2.94891,Africa,2019,0.0,41 -Hungary,5.145,1.24142,0.93164,0.67608,0.1977,0.099,0.04472,1.95473,Europe,2019,0.0,44 -Pakistan,5.132,0.68816,0.26135,0.40306,0.14622,0.31185,0.1388,3.18286,Asia,2019,0.0,32 -Lebanon,5.129,1.12268,0.64184,0.76171,0.26228,0.23693,0.03061,2.07339,Asia,2019,0.0,28 -Portugal,5.123,1.27607,0.94367,0.79363,0.44727,0.11691,0.01521,1.53015,Europe,2019,0.0,62 -Vietnam,5.061,0.74037,0.79117,0.66157,0.55954,0.25075,0.11556,1.9418,Asia,2019,0.0,37 -Tunisia,5.045,0.97724,0.43165,0.59577,0.23553,0.03936,0.0817,2.68413,Africa,2019,0.0,43 -Greece,5.033,1.24886,0.75473,0.80029,0.05822,0.0,0.04127,2.12944,Europe,2019,0.0,48 -Tajikistan,4.996,0.48835,0.75602,0.53119,0.43408,0.25998,0.13509,2.39106,Asia,2019,0.0,25 -Mongolia,4.907,0.98853,1.08983,0.55469,0.35972,0.34539,0.03285,1.53586,Asia,2019,0.0,35 -Nigeria,4.875,0.75216,0.64498,0.05108,0.27854,0.23219,0.0305,2.88586,Africa,2019,0.0,26 -Honduras,4.871,0.69429,0.75596,0.58383,0.26755,0.2044,0.06906,2.29551,South America,2019,0.0,26 -Iran,4.813,1.11758,0.38857,0.64232,0.22544,0.38538,0.0557,1.99817,Asia,2019,0.0,26 -Zambia,4.795,0.61202,0.6376,0.23573,0.42662,0.17866,0.11479,2.58991,Africa,2019,0.0,34 -Nepal,4.793,0.44626,0.69699,0.50073,0.37012,0.3816,0.07008,2.32694,Asia,2019,0.0,34 -Albania,4.655,0.9553,0.50163,0.73007,0.31866,0.1684,0.05301,1.92816,Europe,2019,0.0,35 -Bangladesh,4.643,0.54177,0.24749,0.52989,0.39778,0.19132,0.12583,2.60904,Asia,2019,0.0,26 -Sierra Leone,4.635,0.36485,0.628,0.0,0.30685,0.23897,0.08196,3.01402,Africa,2019,0.0,33 -Iraq,4.575,1.07474,0.59205,0.51076,0.24856,0.19589,0.13636,1.81657,Asia,2019,0.0,20 -Cameroon,4.513,0.52497,0.62542,0.12698,0.42736,0.2268,0.06126,2.5198,Africa,2019,0.0,25 -Ethiopia,4.508,0.29283,0.37932,0.34578,0.36703,0.29522,0.1717,2.65614,Africa,2019,0.0,37 -South Africa,4.459,1.02416,0.96053,0.18611,0.42483,0.13656,0.08415,1.64227,Africa,2019,0.0,44 -Sri Lanka,4.415,0.97318,0.84783,0.62007,0.50817,0.46978,0.07964,0.91681,Asia,2019,0.0,38 -India,4.404,0.74036,0.29247,0.45091,0.40285,0.25028,0.08722,2.18032,Asia,2019,0.0,41 -Myanmar,4.395,0.34112,0.69981,0.3988,0.42692,0.81971,0.20243,1.50655,Asia,2019,0.0,29 -Egypt,4.362,0.95395,0.49813,0.52116,0.18847,0.12706,0.10393,1.96895,Africa,2019,0.0,35 -Armenia,4.36,0.86086,0.62477,0.64083,0.14037,0.07793,0.03616,1.97864,Asia,2019,0.0,42 -Kenya,4.356,0.52267,0.7624,0.30147,0.40576,0.41328,0.06686,1.88326,Africa,2019,0.0,28 -Ukraine,4.324,0.87287,1.01413,0.58628,0.12859,0.20363,0.01829,1.50066,Europe,2019,0.0,30 -Ghana,4.276,0.63107,0.49353,0.29681,0.40973,0.21203,0.0326,2.2002,Africa,2019,0.0,41 -Georgia,4.252,0.83792,0.19249,0.64035,0.32461,0.06786,0.3188,1.87031,Asia,2019,0.0,56 -Senegal,4.219,0.44314,0.77416,0.40457,0.31056,0.19103,0.11681,1.97861,Africa,2019,0.0,45 -Bulgaria,4.217,1.11306,0.92542,0.67806,0.21219,0.12793,0.00615,1.15377,Europe,2019,0.0,43 -Mauritania,4.201,0.61391,0.84142,0.28639,0.1268,0.22686,0.17955,1.9263,Africa,2019,0.0,28 -Zimbabwe,4.193,0.35041,0.71478,0.1595,0.25429,0.18503,0.08582,2.4427,Africa,2019,0.0,24 -Malawi,4.156,0.08709,0.147,0.29364,0.4143,0.30968,0.07564,2.82859,Africa,2019,0.0,31 -Gabon,4.121,1.15851,0.72368,0.3494,0.28098,0.06244,0.09314,1.45332,Africa,2019,0.0,31 -Mali,4.073,0.31292,0.86333,0.16347,0.27544,0.21064,0.13647,2.11087,Africa,2019,0.0,29 -Haiti,4.028,0.34097,0.29561,0.27494,0.12072,0.47958,0.14476,2.37116,South America,2019,0.0,18 -Botswana,3.974,1.09426,0.89186,0.34752,0.44089,0.12425,0.10769,0.96741,Africa,2019,0.0,61 -Cambodia,3.907,0.55604,0.5375,0.42494,0.58852,0.40339,0.08092,1.31573,Asia,2019,0.0,20 -Niger,3.856,0.1327,0.6053,0.26162,0.38041,0.2097,0.17176,2.09469,Africa,2019,0.0,32 -Chad,3.763,0.42214,0.63178,0.03824,0.12807,0.18667,0.04952,2.30637,Africa,2019,0.0,20 -Burkina Faso,3.739,0.31995,0.63054,0.21297,0.3337,0.24353,0.12533,1.87319,Africa,2019,0.0,40 -Uganda,3.739,0.34719,0.90981,0.19625,0.43653,0.27102,0.06442,1.51416,Africa,2019,0.0,28 -Yemen,3.724,0.57939,0.47493,0.31048,0.2287,0.09821,0.05892,1.97295,Asia,2019,0.0,15 -Madagascar,3.695,0.27954,0.46115,0.37109,0.13684,0.2204,0.07506,2.15075,Africa,2019,0.0,24 -Tanzania,3.666,0.47155,0.77623,0.357,0.3176,0.31472,0.05099,1.37769,Africa,2019,0.0,37 -Liberia,3.622,0.10706,0.50353,0.23165,0.25748,0.24063,0.04852,2.23284,Africa,2019,0.0,28 -Guinea,3.607,0.22415,0.3109,0.18829,0.30953,0.29914,0.1192,2.15604,South America,2019,0.0,29 -Rwanda,3.515,0.32846,0.61586,0.31865,0.5432,0.23552,0.50521,0.96819,Africa,2019,0.0,53 -Benin,3.484,0.39499,0.10419,0.21028,0.39747,0.2018,0.06681,2.10812,Africa,2019,0.0,41 -Afghanistan,3.36,0.38227,0.11037,0.17344,0.1643,0.31268,0.07112,2.14558,Asia,2019,0.0,16 -Togo,3.303,0.28123,0.0,0.24811,0.34678,0.17517,0.11587,2.1354,Africa,2019,0.0,29 -Burundi,2.905,0.06831,0.23442,0.15747,0.0432,0.2029,0.09419,2.10404,Africa,2019,0.0,19 -Finland,7.808700085,1.285189509,0.0,0.961271405,0.66231674,0.159670442,0.477857262,2.762835026,Europe,2020,1.499525905,85 -Denmark,7.645599842,1.326948524,0.0,0.979332566,0.665039897,0.242793396,0.495260328,2.432740688,Europe,2020,1.503449202,88 -Switzerland,7.559899807,1.39077425,0.0,1.040533185,0.62895447,0.269055754,0.407945901,2.35026741,Europe,2020,1.472403407,85 -Iceland,7.504499912,1.326501608,0.0,1.000843406,0.661980748,0.362330228,0.144540772,2.460688114,Europe,2020,1.547567487,75 -Norway,7.487999916,1.42420733,0.0,1.008071899,0.670200884,0.287985086,0.434100568,2.168266296,Europe,2020,1.495172501,84 -Netherlands,7.448900223,1.338946342,0.0,0.975675344,0.61362648,0.336317569,0.368569762,2.352117062,Europe,2020,1.463645935,82 -Sweden,7.353499889,1.322235227,0.0,0.986470461,0.650297701,0.272827893,0.442066371,2.246299267,Europe,2020,1.433347702,85 -New Zealand,7.299600124,1.242317915,0.0,1.008138299,0.646789908,0.325726211,0.461268276,2.128108025,Australia,2020,1.48721838,88 -Austria,7.294199944,1.317285538,0.0,1.000933528,0.603368878,0.255509764,0.281256139,2.398446083,Europe,2020,1.437444925,76 -Luxembourg,7.237500191,1.536676049,0.0,0.986442685,0.610137045,0.19595392,0.367041469,2.153700352,Europe,2020,1.387528419,80 -Canada,7.23210001,1.301647663,0.0,1.022501945,0.644028127,0.28152892,0.351701856,2.195269108,North America,2020,1.435391903,77 -Australia,7.222799778,1.310396433,0.0,1.022607684,0.621877193,0.324973613,0.335996419,2.129804134,Australia,2020,1.477146268,77 -United Kingdom,7.164500237,1.273061037,0.0,0.97570008,0.525168657,0.373433441,0.322601646,2.236721992,Europe,2020,1.457844973,77 -Israel,7.128600121,1.216463685,0.0,1.008052945,0.420699477,0.266861796,0.09989845,2.713358402,Asia,2020,1.403256774,60 -Costa Rica,7.121399879,0.981107712,0.0,0.939635336,0.645017743,0.131266311,0.096362092,2.953135014,South America,2020,1.374853611,57 -Ireland,7.093699932,1.446886778,0.0,0.975670695,0.587779939,0.295426995,0.373433262,1.943878174,Europe,2020,1.470596433,72 -Germany,7.075799942,1.314184546,0.0,0.972114801,0.564274132,0.252037704,0.309362292,2.295249462,Europe,2020,1.368543744,80 -United States,6.939599991,1.37398684,0.0,0.831618011,0.534608245,0.298143059,0.152284741,2.344124794,North America,2020,1.404786706,67 -Belgium,6.863500118,1.295842767,0.0,0.964901149,0.499805421,0.146966159,0.208724052,2.348626614,Europe,2020,1.398677588,76 -United Arab Emirates,6.790800095,1.431086421,0.0,0.787814438,0.652936101,0.28065598,0.220213518,2.166965961,Asia,2020,1.251170993,71 -Malta,6.772799969,1.252513289,0.0,0.972042024,0.633239031,0.341180831,0.178864077,1.952012062,Europe,2020,1.442956924,53 -France,6.663799763,1.268129349,0.0,1.029714227,0.514050901,0.112607703,0.227303237,2.053198338,Europe,2020,1.458839178,69 -Mexico,6.465000153,1.024387479,0.0,0.831601024,0.553892553,0.083094485,0.083133668,2.662540197,North America,2020,1.226333499,31 -Uruguay,6.440100193,1.071000457,0.0,0.856928885,0.59426707,0.132143691,0.193425074,2.167276382,South America,2020,1.425081134,71 -Saudi Arabia,6.406499863,1.334328532,0.0,0.759818137,0.548477471,0.087440684,0.163322315,2.203118801,Asia,2020,1.309950113,53 -Spain,6.400899887,1.230535269,0.0,1.051343083,0.425983816,0.16530548,0.109579779,1.997088432,Europe,2020,1.421099186,62 -Guatemala,6.398900032,0.753815711,0.0,0.705952585,0.613146722,0.170611665,0.0983603,2.882723093,South America,2020,1.174267054,25 -Italy,6.38740015,1.236396074,0.0,1.022504926,0.321305573,0.170266211,0.040145598,2.249505997,Europe,2020,1.347296,53 -Singapore,6.377099991,1.519580126,0.0,1.137814283,0.635317206,0.218770906,0.533162236,0.937031746,Asia,2020,1.39545691,85 -Brazil,6.375599861,0.952679873,0.0,0.766119063,0.483292729,0.131674588,0.106518604,2.571860313,South America,2020,1.363464117,38 -Slovenia,6.363399982,1.208652496,0.0,0.932548046,0.646700144,0.145701498,0.076516323,1.888586521,Europe,2020,1.464677691,60 -El Salvador,6.34829998,0.748940408,0.0,0.752730012,0.524043918,0.1189363,0.117030352,2.937191725,South America,2020,1.149397612,36 -Kosovo,6.325200081,0.840481341,0.0,0.672709167,0.557280421,0.325286865,0.008559024,2.736902714,Europe,2020,1.183962822,36 -Panama,6.304800034,1.097667813,0.0,0.878546536,0.57984978,0.097207308,0.054230526,2.221176147,South America,2020,1.376149297,35 -Slovakia,6.280600071,1.194837689,0.0,0.853465259,0.423542529,0.116729774,0.011291441,2.256422043,Europe,2020,1.424331188,49 -Uzbekistan,6.257599831,0.696652949,0.0,0.716703713,0.693270326,0.363311023,0.280260265,2.073346615,Asia,2020,1.434020042,26 -Chile,6.228499889,1.096992493,0.0,0.889260828,0.417482227,0.155579001,0.06284935,2.283012867,South America,2020,1.323286891,67 -Bahrain,6.227300167,1.296692252,0.0,0.838836372,0.610399902,0.287453711,0.126697257,1.751917362,Asia,2020,1.31532371,42 -Lithuania,6.215499878,1.193559647,0.0,0.795421183,0.42046079,0.053691041,0.081350274,2.238146067,Europe,2020,1.432865739,60 -Poland,6.186299801,1.16922915,0.0,0.868038476,0.557903528,0.063374244,0.160541251,2.056797981,Europe,2020,1.310399771,56 -Colombia,6.163400173,0.93220371,0.0,0.810020149,0.526890039,0.092374094,0.04584837,2.421656609,South America,2020,1.33445096,39 -Cyprus,6.15899992,1.21279943,0.0,1.026124597,0.459385872,0.227932334,0.051207144,2.032334805,Asia,2020,1.149170756,57 -Nicaragua,6.13710022,0.620033145,0.0,0.803093255,0.560117304,0.212871477,0.174084574,2.496081114,South America,2020,1.27081275,22 -Romania,6.123700142,1.120401621,0.0,0.79229486,0.534852326,0.068181619,0.000829569,2.412749767,Europe,2020,1.1944381,44 -Kuwait,6.102099895,1.424833655,0.0,0.776468933,0.570261419,0.132751092,0.112814933,1.840167999,Asia,2020,1.244779825,42 -Mauritius,6.101299763,1.07366395,0.0,0.763389409,0.590838134,0.186894551,0.084088229,2.006720543,Africa,2020,1.395666838,53 -Kazakhstan,6.057899952,1.122594237,0.0,0.698788941,0.497432142,0.153713793,0.110463686,2.021602631,Asia,2020,1.453267694,38 -Estonia,6.021800041,1.192441225,0.0,0.842615008,0.576664805,0.125136748,0.201766819,1.629928112,Europe,2020,1.453232527,75 -Philippines,6.006000042,0.775120676,0.0,0.602189481,0.621915162,0.129260212,0.130385727,2.501741886,Asia,2020,1.245381713,34 -Hungary,6.000400066,1.164307117,0.0,0.806793869,0.386401802,0.070491239,0.027548173,2.121845722,Europe,2020,1.423009396,44 -Thailand,5.998799801,1.007029295,0.0,0.793855846,0.609449804,0.376709014,0.031837862,1.832384109,Asia,2020,1.347519517,36 -Argentina,5.974699974,1.028465629,0.0,0.849773705,0.520840347,0.070100471,0.060415059,2.072540998,South America,2020,1.372543693,42 -Honduras,5.953199863,0.598763585,0.0,0.791989982,0.568148077,0.256528199,0.086807102,2.464295387,South America,2020,1.186664104,24 -Latvia,5.949999809,1.14139545,0.0,0.777902424,0.329198807,0.075407945,0.090391524,2.121283054,Europe,2020,1.414398789,57 -Ecuador,5.925199986,0.853383601,0.0,0.838837743,0.555234551,0.115006477,0.086753383,2.254933834,South America,2020,1.221027613,39 -Portugal,5.910900116,1.168800831,0.0,0.979315281,0.589895189,0.053036947,0.027733466,1.752558708,Europe,2020,1.339530349,61 -Jamaica,5.889800072,0.779058397,0.0,0.788434088,0.553124607,0.116268493,0.030147785,2.214436531,South America,2020,1.408289194,44 -Japan,5.870800018,1.26672411,0.0,1.072881341,0.495465875,0.03571178,0.181439638,1.486200333,Asia,2020,1.332338691,74 -Peru,5.796800137,0.91854918,0.0,0.824444175,0.513210058,0.091611817,0.02703266,2.213499546,South America,2020,1.208405972,38 -Serbia,5.77820015,0.988181829,0.0,0.828403294,0.395428419,0.15028289,0.059447147,2.029040098,Europe,2020,1.327448964,38 -Bolivia,5.747499943,0.730976343,0.0,0.662445664,0.5744645,0.138375074,0.072942637,2.425928593,South America,2020,1.142350554,31 -Pakistan,5.69329977,0.616799474,0.0,0.469933242,0.405421734,0.228705063,0.122592121,2.976876736,Asia,2020,0.872979581,31 -Paraguay,5.692100048,0.897990823,0.0,0.735869646,0.586510062,0.204299241,0.065077379,1.83412528,South America,2020,1.36819756,28 -Dominican Republic,5.689199924,0.983191848,0.0,0.741901696,0.5628739,0.112196781,0.115945682,1.844246864,South America,2020,1.328888893,28 -Bosnia and Herzegovina,5.674099922,0.91839546,0.0,0.813928187,0.305365741,0.264005244,0.001172487,2.16724205,Europe,2020,1.203986526,35 -Moldova,5.607500076,0.707916796,0.0,0.713299453,0.389571488,0.174049184,0.014378744,2.370968342,Europe,2020,1.237312198,34 -Tajikistan,5.555699825,0.474874616,0.0,0.680594802,0.521141171,0.182417125,0.221779913,2.25650835,Asia,2020,1.218377709,25 -Montenegro,5.54610014,1.010150075,0.0,0.839028895,0.303223848,0.14901033,0.098435111,1.880565882,Europe,2020,1.265657902,45 -Russia,5.546000004,1.126999617,0.0,0.68044591,0.399499595,0.099041916,0.045699362,1.815716743,Europe,2020,1.378644109,30 -Kyrgyzstan,5.541500092,0.513180971,0.0,0.680645883,0.614617765,0.30137074,0.030466691,2.060206652,Asia,2020,1.341036677,31 -Belarus,5.539899826,1.018854499,0.0,0.75258857,0.290755868,0.08993306,0.193607435,1.8069911,Europe,2020,1.38713932,47 -Greece,5.514999866,1.128070116,0.0,0.979431748,0.173516348,0.0,0.048844352,2.016179085,Europe,2020,1.168973565,50 -Croatia,5.504700184,1.109024286,0.0,0.900575578,0.381456882,0.113998979,0.012325006,1.676029205,Europe,2020,1.311264873,47 -Libya,5.488800049,1.021913767,0.0,0.615626633,0.451354057,0.142757699,0.172258079,1.888563156,Africa,2020,1.196283698,17 -Mongolia,5.456200123,0.904872775,0.0,0.615788162,0.355703115,0.263885736,0.046533126,1.810527563,Asia,2020,1.458930612,35 -Malaysia,5.384300232,1.168421626,0.0,0.788511872,0.596941531,0.274886161,0.062163133,1.319420815,Asia,2020,1.17400229,51 -Vietnam,5.353499889,0.718092382,0.0,0.819133997,0.650835574,0.136488721,0.089848459,1.685977936,Asia,2020,1.253074765,36 -Indonesia,5.285600185,0.891720712,0.0,0.610437036,0.568161428,0.542646527,0.038278613,1.479573488,Asia,2020,1.154800892,37 -Benin,5.21600008,0.366244704,0.0,0.328062952,0.40583989,0.196670428,0.125931874,3.440809727,Africa,2020,0.352428436,41 -Azerbaijan,5.164800167,0.990272701,0.0,0.731134057,0.467734724,0.040113214,0.247307181,1.507632971,Asia,2020,1.180613041,30 -Ghana,5.147999763,0.575862467,0.0,0.432162255,0.477290064,0.261291206,0.056570381,2.378437281,Africa,2020,0.96636796,43 -Nepal,5.137199879,0.444050372,0.0,0.66887939,0.480608255,0.300971806,0.127502963,2.014386892,Asia,2020,1.100789309,33 -Turkey,5.131800175,1.127169251,0.0,0.781335294,0.25440076,0.085885569,0.120983243,1.564816713,Asia,2020,1.197159171,40 -China,5.123899937,0.990533412,0.0,0.867248535,0.601605117,0.079021044,0.117255554,1.336181879,Asia,2020,1.132080674,42 -Turkmenistan,5.119100094,1.008963585,0.0,0.612448037,0.515236676,0.323129326,0.03350389,1.115337372,Asia,2020,1.510476947,19 -Bulgaria,5.101500034,1.046554685,0.0,0.777776897,0.417820066,0.103833713,0.0,1.294961452,Europe,2020,1.460578918,44 -Morocco,5.094799995,0.75862211,0.0,0.745096922,0.450054139,0.040032551,0.077385604,2.378402472,Africa,2020,0.645208478,40 -Cameroon,5.084899902,0.503958046,0.0,0.270189553,0.439242482,0.198020101,0.054393422,2.719370842,Africa,2020,0.89972645,25 -Venezuela,5.053199768,0.770238638,0.0,0.76702553,0.271717221,0.087179154,0.063624777,1.74484086,South America,2020,1.348546863,15 -Algeria,5.005099773,0.943856001,0.0,0.745418549,0.083943799,0.118915014,0.129190654,1.840811729,Africa,2020,1.143003583,36 -Senegal,4.980800152,0.504061818,0.0,0.518391907,0.352400899,0.164397135,0.081865937,2.405123949,Africa,2020,0.95459342,45 -Guinea,4.949299812,0.390007734,0.0,0.333655238,0.371878058,0.249490842,0.112204559,2.740729809,South America,2020,0.75136596,28 -Niger,4.909599781,0.108330332,0.0,0.298816353,0.435311615,0.208176896,0.137554765,3.017630577,Africa,2020,0.703800142,32 -Albania,4.882699966,0.906653047,0.0,0.846329629,0.461945891,0.171027765,0.025361285,1.640897036,Europe,2020,0.830483913,36 -Cambodia,4.848400116,0.544634938,0.0,0.587904334,0.674940348,0.233342081,0.072837502,1.663300037,Asia,2020,1.071426034,21 -Bangladesh,4.832799911,0.556156278,0.0,0.694940507,0.604130566,0.176745117,0.176735908,1.755261898,Asia,2020,0.868800581,26 -Gabon,4.829299927,0.988044381,0.0,0.522574842,0.369459897,0.052013602,0.055804539,1.735027552,Africa,2020,1.10639751,30 -South Africa,4.814099789,0.902140439,0.0,0.407034069,0.43478182,0.126406848,0.05950214,1.625117302,Africa,2020,1.259086251,44 -Iraq,4.784800053,0.982018709,0.0,0.529350698,0.283588052,0.153002068,0.073164992,1.752173662,Asia,2020,1.011466622,21 -Lebanon,4.771500111,0.889232516,0.0,0.788671136,0.18551667,0.158524141,0.021518147,1.53554225,Asia,2020,1.19249332,25 -Burkina Faso,4.768700123,0.302467644,0.0,0.312833875,0.322398156,0.186390609,0.126408055,2.588826418,Africa,2020,0.9293859,40 -Mali,4.729300022,0.352462649,0.0,0.234981522,0.377534449,0.16966705,0.062146276,2.559335232,Africa,2020,0.97314173,30 -Nigeria,4.724100113,0.645901859,0.0,0.167835936,0.435079455,0.221328124,0.047589935,2.219635487,Africa,2020,0.986717939,25 -Armenia,4.676799774,0.808262408,0.0,0.77585727,0.378075808,0.107225738,0.104618184,1.468161583,Asia,2020,1.034576893,49 -Georgia,4.672599792,0.847198069,0.0,0.694657624,0.485494107,0.047609735,0.174088076,1.69239831,Asia,2020,0.7311939,56 -Iran,4.672399998,1.029322505,0.0,0.749053836,0.301195472,0.27697894,0.142651513,1.286969423,Asia,2020,0.886271179,25 -Jordan,4.633399963,0.785179198,0.0,0.777624726,0.424855083,0.091494769,0.151878625,1.262258053,Asia,2020,1.140118599,49 -Kenya,4.583000183,0.476413399,0.0,0.536312759,0.519180536,0.393902093,0.067201078,1.684904575,Africa,2020,0.905077755,31 -Ukraine,4.56069994,0.780434608,0.0,0.698674381,0.319423705,0.178551316,0.00965116,1.252669334,Europe,2020,1.321316481,33 -Liberia,4.557899952,0.174103007,0.0,0.392284274,0.405943096,0.226967871,0.051139876,2.386757851,Africa,2020,0.920733929,28 -Uganda,4.43200016,0.312337428,0.0,0.378311664,0.401682556,0.264807373,0.063818842,1.958671093,Africa,2020,1.052327394,27 -Chad,4.422699928,0.302287489,0.0,0.1087441,0.228601769,0.210805088,0.085755408,2.74742651,Africa,2020,0.739118278,21 -Tunisia,4.392199993,0.874742687,0.0,0.781156719,0.235860556,0.055881143,0.043899573,1.528496742,Africa,2020,0.872167706,44 -Mauritania,4.374599934,0.539684892,0.0,0.425184816,0.185714483,0.128899664,0.122257635,1.85955739,Africa,2020,1.113323569,29 -Sri Lanka,4.327000141,0.897986948,0.0,0.792036712,0.528632462,0.252666146,0.049444564,0.611288548,Asia,2020,1.19494009,38 -Myanmar,4.308000088,0.67809093,0.0,0.495443076,0.597478867,0.569813728,0.187530354,0.681462526,Asia,2020,1.098178267,28 -Togo,4.187200069,0.268116266,0.0,0.342731178,0.303539038,0.200774252,0.114826456,2.409595251,Africa,2020,0.547622859,29 -Ethiopia,4.186200142,0.315125644,0.0,0.483846247,0.41256687,0.227698103,0.117437035,1.628459215,Africa,2020,1.001103282,38 -Madagascar,4.165599823,0.244553208,0.0,0.500617027,0.192967549,0.191190064,0.076248638,2.136298418,Africa,2020,0.823694348,25 -Egypt,4.151400089,0.875228941,0.0,0.596911311,0.373684734,0.068801254,0.095461793,1.158818245,Africa,2020,0.982539535,33 -Sierra Leone,3.926399946,0.240560383,0.0,0.203953966,0.382027686,0.257647008,0.047940936,2.046271801,Africa,2020,0.747984946,33 -Burundi,3.775300026,0.0,0.0,0.295212835,0.275399059,0.187401786,0.212186828,2.401507378,Africa,2020,0.403575271,19 -Zambia,3.759399891,0.536833763,0.0,0.36359334,0.49131754,0.250620902,0.086705238,1.134339333,Africa,2020,0.896037281,33 -Haiti,3.720799923,0.284734428,0.0,0.37436673,0.169297516,0.463909656,0.161935672,1.619916916,South America,2020,0.646671355,18 -India,3.573299885,0.730576158,0.0,0.54057014,0.581142247,0.237072483,0.105587982,0.734130859,Asia,2020,0.644198656,40 -Malawi,3.538000107,0.176534727,0.0,0.446163297,0.487389833,0.213185057,0.131633952,1.552718282,Africa,2020,0.53036809,30 -Yemen,3.527400017,0.392701775,0.0,0.41500017,0.243721485,0.094689012,0.087352127,1.116472721,Asia,2020,1.177477121,15 -Botswana,3.478899956,0.997548997,0.0,0.494101733,0.50908941,0.033407487,0.101786368,0.257240534,Africa,2020,1.08569479,60 -Tanzania,3.476200104,0.457163125,0.0,0.442677855,0.509343088,0.27154091,0.203880861,0.718963385,Africa,2020,0.872674644,38 -Rwanda,3.312299967,0.343242675,0.0,0.572383285,0.604087889,0.235704988,0.485542476,0.548444986,Africa,2020,0.522876322,54 -Zimbabwe,3.299200058,0.425564021,0.0,0.375037611,0.37740472,0.151349187,0.080928579,0.841031075,Africa,2020,1.047835231,24 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    OneHotEncoder(drop='first', sparse=False)
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    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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"source": [] - }, - { - "cell_type": "markdown", - "id": "27e3d252-e331-4c70-91f8-a604b6c72e1f", - "metadata": {}, - "source": [ - "## LabelEncoder" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "395ade97-e454-4aba-8732-d99a5efbdb73", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['red', 'blue', 'green', 'red', 'pink', 'blabla', 'tratra'],\n", - " dtype='#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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    LabelEncoder()
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    " - ], - "text/plain": [ - "LabelEncoder()" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "le = LabelEncoder()\n", - "le.fit(colors)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "4097edad-dce3-4036-a5a8-c65878641949", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([4, 1, 2, 4, 3, 0, 5])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "le.transform(colors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3a67875-f006-401d-ac37-ccb6561c531f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1992c710-5b07-42a3-9aa6-9ca8dad230b3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "ecf722f2-7667-48b6-a41d-9338255e1926", - "metadata": {}, - "source": [ - "## Work with data" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "ad732be5-e016-40f5-be09-537e5d3ada35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Country', 'happiness_score', 'gdp_per_capita', 'family', 'health',\n", - " 'freedom', 'generosity', 'government_trust', 'dystopia_residual',\n", - " 'continent', 'Year', 'social_support', 'cpi_score'],\n", - " dtype='object')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "651aa052-92bc-46ee-aaa3-611642a137e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    Countryhappiness_scoregdp_per_capitafamilyhealthfreedomgenerositygovernment_trustdystopia_residualcontinentYearsocial_supportcpi_score
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    " - ], - "text/plain": [ - " Country happiness_score gdp_per_capita family health freedom \\\n", - "0 Norway 7.537 1.616463 1.533524 0.796667 0.635423 \n", - "1 Denmark 7.522 1.482383 1.551122 0.792566 0.626007 \n", - "2 Iceland 7.504 1.480633 1.610574 0.833552 0.627163 \n", - "3 Switzerland 7.494 1.564980 1.516912 0.858131 0.620071 \n", - "4 Finland 7.469 1.443572 1.540247 0.809158 0.617951 \n", - "\n", - " generosity government_trust dystopia_residual continent Year \\\n", - "0 0.362012 0.315964 2.277027 Europe 2015 \n", - "1 0.355280 0.400770 2.313707 Europe 2015 \n", - "2 0.475540 0.153527 2.322715 Europe 2015 \n", - "3 0.290549 0.367007 2.276716 Europe 2015 \n", - "4 0.245483 0.382612 2.430182 Europe 2015 \n", - "\n", - " social_support cpi_score \n", - "0 0.0 88 \n", - "1 0.0 91 \n", - "2 0.0 79 \n", - "3 0.0 86 \n", - "4 0.0 90 " - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "c83be377-3f8f-4c37-8685-70b8e4ecb31b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "747" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[\"happiness_score\"].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "099a5226-ce15-4594-b56f-c1e36bb07c50", - "metadata": {}, - "outputs": [], - "source": [ - "cat_cols = [\"Country\", \"continent\"]\n", - "num_cols = [\"gdp_per_capita\",\t\"family\",\n", - " \"health\",\t\"freedom\",\t\"generosity\",\n", - " \"government_trust\",\t\"dystopia_residual\",\n", - " \"Year\",\t\"social_support\",\t\"cpi_score\"]\n", - "all_features = cat_cols + num_cols\n", - "\n", - "target = data[\"happiness_score\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "8508c0f6-693f-4620-955c-f7a160c5b11c", - "metadata": {}, - "outputs": [], - "source": [ - "X = data[all_features]\n", - "y = target" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d409db5a-75e3-4278-bf8b-8235df6f2dc7", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0b39e3e-38ec-4091-b404-e68d70322302", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "e123a17a-399d-4229-8d70-f6cf9a6b10c5", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, \n", - " y, \n", - " test_size=0.15,\n", - " random_state=SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "537d8e35-4185-4b7a-9210-3d83243503c3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "a17357fb-d31d-40a3-83d2-0939ae131e89", - "metadata": {}, - "outputs": [], - "source": [ - "preprocessor = ColumnTransformer(transformers=[\n", - " (\"scaler\", StandardScaler(), num_cols),\n", - " (\"ohe\", OneHotEncoder(drop=\"first\"), cat_cols)\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "11454051-8037-44ac-833f-58b34fa4e7df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    " - ], - "text/plain": [ - "Pipeline(steps=[('preprocessor',\n", - " ColumnTransformer(transformers=[('scaler', StandardScaler(),\n", - " ['gdp_per_capita', 'family',\n", - " 'health', 'freedom',\n", - " 'generosity',\n", - " 'government_trust',\n", - " 'dystopia_residual', 'Year',\n", - " 'social_support',\n", - " 'cpi_score']),\n", - " ('ohe',\n", - " OneHotEncoder(drop='first'),\n", - " ['Country', 'continent'])])),\n", - " ('knn', KNeighborsRegressor(n_jobs=8, n_neighbors=8))])" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "knn_pipeline.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a51768c3-4dbf-4b6e-8ba0-e824db9cf57b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "2bb3e6a0-3958-48d3-94e9-754c7790284a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([7.31874998, 7.46487497, 7.10825006, 7.33774996, 7.40374995,\n", - " 7.09262509, 7.25375003, 7.32149998, 7.392875 , 7.19400004])" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "knn_pipeline.predict(X)[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "ee3f2792-c01f-45df-ab1b-29336c0ac67b", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, \n", - " y, \n", - " test_size=0.15,\n", - " random_state=SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4acdc3a4-f028-4cbc-9c65-50d461a28601", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "2cf1196f-b825-42ff-a28e-529c05310959", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 0.9016263201941989 0.11642944911889179\n", - "2 0.9217602000110323 0.09260014294342377\n", - "3 0.9216167217996469 0.09276995553094866\n", - "5 0.8978481879054924 0.12090102995169963\n", - "7 0.891164228620634 0.1288117810693301\n", - "10 0.8880385842110294 0.13251111464585388\n" - ] - } - ], - "source": [ - "k_neighbors = [1, 2, 3, 5, 7, 10]\n", - "r2_scores = []\n", - "mse_scores = []\n", - "\n", - "for k in k_neighbors:\n", - " knn_pipeline = Pipeline(steps=[\n", - " (\"preprocessor\", preprocessor),\n", - " (\"knn\", KNeighborsRegressor(n_neighbors=k, n_jobs=8))\n", - " ])\n", - " knn_pipeline.fit(X_train, y_train)\n", - " y_test_pred = knn_pipeline.predict(X_test)\n", - " r2 = r2_score(y_test, y_test_pred)\n", - " mse = mean_squared_error(y_test, y_test_pred)\n", - " \n", - " r2_scores += [r2]\n", - " mse_scores += [mse]\n", - " \n", - " print(k, r2, mse)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "5a788c59-5709-4454-95f1-8f822f96a746", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.lineplot(x=k_neighbors, y=mse_scores);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b50edb9-c977-4a4f-bf6b-0632b02c1b44", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34d20f55-08a6-4d32-8f95-7dffd57aff88", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "1afec2d3-03a8-4fb6-84f1-d173368082dd", - "metadata": {}, - "source": [ - "## Classification" - ] - }, - { - "cell_type": "markdown", - "id": "5cfb2153-8612-40d5-a954-540fdccf1e78", - "metadata": {}, - "source": [ - "#### Pikachu vs not Pikachu" - ] - }, - { - "cell_type": "markdown", - "id": "d4c61d53-f76c-4c2e-b962-a4b4ed96efd8", - "metadata": {}, - "source": [ - "[data](https://www.kaggle.com/datasets/hal0samuel/pikachu-classification-dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "86eac829-5455-4507-b7ed-00641a829d8a", - "metadata": {}, - "outputs": [], - "source": [ - "PIKA_FOLDER = \"pikachu_dataset\"" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "54a45d9b-04d9-4699-ba08-bf6fce5f4ec7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "img = Image.open(join(PIKA_FOLDER, \"train\", \"not_pikachu\", \"not_pikachu_00038.jpg\"))\n", - "img" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "8df0129e-fe1c-42ea-8b76-eebb5a0ef7e5", - "metadata": {}, - "outputs": [], - "source": [ - "labels = [\"pikachu\", \"not_pikachu\"]\n", - "modes = [\"train\", \"validation\", \"test\"]\n", - "pika_mats = {}\n", - "img_size = 128\n", - "num_features = 3 * img_size * img_size\n", - "\n", - "for label in labels:\n", - " n_samples = len(os.listdir(join(PIKA_FOLDER, modes[0], label))) + \\\n", - " len(os.listdir(join(PIKA_FOLDER, modes[1], label))) + \\\n", - " len(os.listdir(join(PIKA_FOLDER, modes[2], label)))\n", - " \n", - " X = np.zeros((n_samples, num_features))\n", - " i = 0\n", - " for mode in modes:\n", - " for img in os.listdir(join(PIKA_FOLDER, mode, label)):\n", - " image = Image.open(join(PIKA_FOLDER, mode, label, img)).resize((img_size, img_size))\n", - " image = np.array(image).reshape(num_features) # vector\n", - " X[i] += image\n", - " i += 1\n", - " \n", - " pika_mats[label] = X" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "77dc34a9-9747-463d-966b-5a118ed0db99", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(645, 49152)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pika_mats[\"pikachu\"].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "6f0e2134-9a72-4ae4-9b47-4df47addedde", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(645, 49152)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pika_mats[\"not_pikachu\"].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "48f0d291-4da1-45b1-bf1e-85e86535145a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(pika_mats[\"not_pikachu\"][120].astype(int).reshape((img_size, img_size, 3)))\n", - "plt.axis(\"off\");" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "6bb5ee54-2154-4336-a2b6-07f5793f4006", - "metadata": {}, - "outputs": [], - "source": [ - "X_merged = np.vstack([pika_mats[\"pikachu\"], pika_mats[\"not_pikachu\"]])\n", - "y = np.array([*np.repeat(\"pikachu\", len(pika_mats[\"pikachu\"]))] + \\\n", - " [*np.repeat(\"not_pikachu\", len(pika_mats[\"not_pikachu\"]))])\n", - "y_numeric = (y == \"pikachu\").astype(int)\n", - "\n", - "pika_X_train, pika_X_test, pika_y_train, pika_y_test = train_test_split(X_merged,\n", - " y_numeric,\n", - " test_size=0.2248,\n", - " random_state=SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "4d7bb791-f00e-4a9e-bcb1-0c42015492bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 0.7172413793103448\n", - "3 0.7551724137931034\n", - "5 0.7586206896551724\n", - "7 0.7655172413793103\n", - "11 0.7931034482758621\n", - "15 0.7793103448275862\n", - "25 0.7931034482758621\n" - ] - } - ], - "source": [ - "k_neighbors = [1, 3, 5, 7, 11, 15, 25]\n", - "accuracy_scores = []\n", - "\n", - "for k in k_neighbors:\n", - " knn_pipeline = Pipeline(steps=[\n", - " (\"preprocessor\", StandardScaler()),\n", - " (\"knn\", KNeighborsClassifier(n_neighbors=k, n_jobs=8))\n", - " ])\n", - " knn_pipeline.fit(pika_X_train, pika_y_train)\n", - " pika_y_test_pred = knn_pipeline.predict(pika_X_test)\n", - " accuracy = accuracy_score(pika_y_test, pika_y_test_pred) \n", - " accuracy_scores += [accuracy]\n", - "\n", - " print(k, accuracy)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "62949d01-2715-47c4-9ad2-5bad7ab380b7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.argmax(accuracy_scores)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "27b66a29-9045-4fbb-ab9e-1e8b70f021f7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k_neighbors[np.argmax(accuracy_scores)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9520675f-436a-49d6-8a90-4128cedee491", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "7d2a05be-9561-4a93-9d98-444d11d7b99a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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1 file changed, 1 deletion(-) delete mode 100644 data/ba diff --git a/data/ba b/data/ba deleted file mode 100644 index 8b13789..0000000 --- a/data/ba +++ /dev/null @@ -1 +0,0 @@ - From 2f7a88c1eaa612e0ae7746570831da18b4bcdb87 Mon Sep 17 00:00:00 2001 From: nerofeeva2001 <144069512+nerofeeva2001@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:27:11 +0300 Subject: [PATCH 4/6] Create one --- data/one | 1 + 1 file changed, 1 insertion(+) create mode 100644 data/one diff --git a/data/one b/data/one new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/data/one @@ -0,0 +1 @@ + From 515c92cafdf9855572eb4232c7ff1205c23bd9c3 Mon Sep 17 00:00:00 2001 From: nerofeeva2001 <144069512+nerofeeva2001@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:36:35 +0300 Subject: [PATCH 5/6] Add files via upload --- data/Ensembles.ipynb | 8817 ++++++++++++++++++++++++++++++++++++++++++ data/churn.csv | 3334 ++++++++++++++++ 2 files changed, 12151 insertions(+) create mode 100644 data/Ensembles.ipynb create mode 100644 data/churn.csv diff --git a/data/Ensembles.ipynb b/data/Ensembles.ipynb new file mode 100644 index 0000000..ff85b2d --- /dev/null +++ b/data/Ensembles.ipynb @@ -0,0 +1,8817 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 148, + "id": "9b21216d-37c8-406a-a8bf-5642cff16bb7", + "metadata": { + "id": "9b21216d-37c8-406a-a8bf-5642cff16bb7" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import warnings\n", + "import random\n", + "import math\n", + "import pandas as pd\n", + "import xgboost\n", + "import lightgbm\n", + "import catboost\n", + "\n", + "from matplotlib.colors import ListedColormap\n", + "from scipy.stats import pearsonr\n", + "from itertools import combinations\n", + "from sklearn.base import BaseEstimator\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import (RandomForestClassifier,\n", + " ExtraTreesClassifier,\n", + " VotingClassifier)\n", + "from sklearn.tree import (DecisionTreeRegressor,\n", + " DecisionTreeClassifier)\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import accuracy_score\n", + "from lightgbm import LGBMClassifier\n", + "from catboost import CatBoostClassifier\n", + "from sklearn.model_selection import GridSearchCV" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e0cbb7f2-2038-4e1b-8c39-ca20b7b332a2", + "metadata": { + "id": "e0cbb7f2-2038-4e1b-8c39-ca20b7b332a2" + }, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = 12, 9\n", + "sns.set_style(\"whitegrid\")\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "SEED = 111\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "5ab161b1-2231-4464-96f9-bb9ae0c3d1ed", + "metadata": { + "id": "5ab161b1-2231-4464-96f9-bb9ae0c3d1ed" + }, + "source": [ + "### Задание 1. Bias-variance trade-off\n", + "\n", + "**2 балла**\n", + "\n", + "Продемонстрируйте bias-variance trade-off для `DecisionTreeRegressor` при изменении глубины дерева. Постройте регрессионную модель функции от одной независимой переменной, представленной в ячейке ниже, используя функцию `plot_regression_predictions` (можете ее как-то поменять, если захочется). Попробуйте разные значения глубины деревьев, при каком значении, на ваш взгляд, модель оптимальна, при каком variance становится слишком большим?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "563b781e-78dd-4679-b0b0-3e70a064cfd7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 753 + }, + "id": "563b781e-78dd-4679-b0b0-3e70a064cfd7", + "outputId": "a2aa4ce2-edff-482c-cf08-6bbdadeae34b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Сгенерируем какую-нибудь необычную зависимость и научимся ее предсказывать\n", + "np.random.seed(42)\n", + "m = 300\n", + "X = np.linspace(-3, 3, m).reshape(-1, 1)\n", + "y = (3 + 2/np.pi * np.arcsin(np.cos(10 * X))) * X\n", + "y = y + np.random.randn(m, 1) / 3\n", + "plt.plot(X.reshape(-1), y.reshape(-1), \"b.\");" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "7d7c9c3d-5d4e-43a8-9946-3bde8c5b6e85", + "metadata": { + "id": "7d7c9c3d-5d4e-43a8-9946-3bde8c5b6e85" + }, + "outputs": [], + "source": [ + "# Функция для отрисовки предсказаний деревьев решений в случае регрессии (с видоизменениями)\n", + "def plot_regression_predictions(tree_reg, X, y, axes=[-3, 3, -10, 10], ylabel=\"$y$\", label=None):\n", + " x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n", + " y_pred = tree_reg.predict(x1)\n", + " plt.axis(axes)\n", + " plt.xlabel(\"$x_1$\", fontsize=18)\n", + " if ylabel:\n", + " plt.ylabel(ylabel, fontsize=18, rotation=0)\n", + " plt.plot(X.reshape(-1), y.reshape(-1), \"b.\", alpha=0.3) # Исходные данные\n", + " plt.plot(x1, y_pred, linewidth=2, label=label) # Предсказания модели" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "ff1a7668-6c5b-4817-98fd-78337f3e9685", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 788 + }, + "id": "ff1a7668-6c5b-4817-98fd-78337f3e9685", + "outputId": "51508727-8798-4a1d-fb6b-3a55acd1ecef" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Построение моделей с различной глубиной дерева и их визуализация\n", + "for depth in [1, 2, 3, 5, 10, 15]:\n", + " tree_reg = DecisionTreeRegressor(max_depth=depth, random_state=42)\n", + " tree_reg.fit(X, y)\n", + " plot_regression_predictions(tree_reg, X, y, ylabel=None if depth != 1 else \"$y$\", label=f\"depth={depth}\")\n", + " plt.legend(loc=\"upper center\", fontsize=14)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "На мой взгляд оптимально при глубине 10, при 15 слишком большое" + ], + "metadata": { + "id": "wMJiFajWCYRh" + }, + "id": "wMJiFajWCYRh" + }, + { + "cell_type": "markdown", + "id": "b185effa-2fd7-496a-a5f6-f802d6a32eea", + "metadata": { + "id": "b185effa-2fd7-496a-a5f6-f802d6a32eea" + }, + "source": [ + "### Задание 2. Random forest\n", + "\n", + "Теперь давайте немного подготовимся к тому, чтобы реализовать свой собственный случайный лес, а потом реализуем его." + ] + }, + { + "cell_type": "markdown", + "id": "573cfb4a-1fc3-4c0f-b165-a4ab6a341843", + "metadata": { + "id": "573cfb4a-1fc3-4c0f-b165-a4ab6a341843" + }, + "source": [ + "#### Задание 2. 1. Простое ансамблирование\n", + "\n", + "**1 балла**\n", + "\n", + "Представим, что у нас есть 101 классификатор. Каждый может с вероятностью `p` (равной для всех моделей) правильно предсказать класс объекта. Будем делать предсказания по большинству голосов (majority vote). Постройте зависимость вероятности правильно классифицировать объект от значения `p`. Вам может быть полезная следующая формула:" + ] + }, + { + "cell_type": "markdown", + "id": "9a47cc99-0337-4831-bc83-5dc09dc55e52", + "metadata": { + "id": "9a47cc99-0337-4831-bc83-5dc09dc55e52" + }, + "source": [ + "$$ \\large \\mu = \\sum_ {i = 51} ^ {101} C_{101} ^ ip ^ i (1-p) ^ {101-i} $$" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f398cc1a-c87a-4b6e-97fc-6c6355c6c4ab", + "metadata": { + "id": "f398cc1a-c87a-4b6e-97fc-6c6355c6c4ab" + }, + "outputs": [], + "source": [ + "# Функция для расчета биномиального коэффициента\n", + "def binom_coeff(n, k):\n", + " return math.factorial(n) / (math.factorial(k) * math.factorial(n - k))\n", + "# Функция для расчета вероятности голосования\n", + "def majority_vote_prob(p):\n", + " mu = 0\n", + " for i in range(51, 102):\n", + " mu += binom_coeff(101, i) * (p ** i) * ((1 - p) ** (101 - i))\n", + " return mu" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "33d1ecd5-b310-4106-915a-2f7afb26f608", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "33d1ecd5-b310-4106-915a-2f7afb26f608", + "outputId": "e42b26e6-4c76-4133-e50d-4c13b070c655" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Генерируем значения p от 0 до 1 с шагом 0.01\n", + "p_values = np.linspace(0, 1, 101)\n", + "# Рассчитываем вероятность правильной классификации для каждого значения p\n", + "probabilities = [majority_vote_prob(p) for p in p_values]\n", + "# Визуализация\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(p_values, probabilities, label='Вероятность правильной классификации')\n", + "plt.xlabel('Вероятность p правильной классификации одним классификатором')\n", + "plt.ylabel('Вероятность правильной классификации ансамблем')\n", + "plt.title('Зависимость вероятности правильной классификации от p')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b3d10f32-08d6-4a78-a167-82818f8acd93", + "metadata": { + "id": "b3d10f32-08d6-4a78-a167-82818f8acd93" + }, + "source": [ + "А теперь давайте посмотрим на другую ситуацию. У нас есть фиксированная вероятность того, что модель правильно классифицирует объект `p = 0.65`. Постройте зависимость вероятности правильно классифицировать объект от числа моделей в ансамбле." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "3f6736e1-bfaf-4a90-bc8f-a2eae5e09dee", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "3f6736e1-bfaf-4a90-bc8f-a2eae5e09dee", + "outputId": "9af69d15-201c-44d9-fedc-f06023d9522c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Фиксированная вероятность правильной классификации одной моделью\n", + "p = 0.65\n", + "# Диапазон количества моделей в ансамбле\n", + "n_models = range(1, 102, 2) # Используем нечетное количество моделей для избежания ничьих\n", + "# Рассчитываем вероятность правильной классификации для каждого количества моделей\n", + "probabilities = [ensemble_vote_prob(n, p) for n in n_models]\n", + "# Визуализация\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(n_models, probabilities, label='Вероятность правильной классификации')\n", + "plt.xlabel('Количество моделей в ансамбле')\n", + "plt.ylabel('Вероятность правильной классификации ансамблем')\n", + "plt.title('Зависимость вероятности правильной классификации от числа моделей в ансамбле')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a3d929c1-3c47-4c09-ac74-d5a5f206b66e", + "metadata": { + "id": "a3d929c1-3c47-4c09-ac74-d5a5f206b66e" + }, + "source": [ + "Опишите ваши наблюдения:\n", + "\n", + "* С увеличением p вероятность правильной классификации ансамблем значительно возрастает, особенно когда p превышает 0.5\n", + "* Поэтому важно иметь классификаторы, которые работают лучше случайного угадывания, поскольку даже небольшое улучшение в производительности отдельных классификаторов может значительно увеличить общую производительность ансамбля\n", + "* С увеличением количества моделей в ансамбле вероятность правильной классификации ансамблем увеличивается\n", + "* Если каждый классификатор в ансамбле имеет лишь умеренную производительность, их комбинация может достичь значительно более высокой точности" + ] + }, + { + "cell_type": "markdown", + "id": "a8914245-d761-4ce3-9836-7d832aea7005", + "metadata": { + "id": "a8914245-d761-4ce3-9836-7d832aea7005" + }, + "source": [ + "#### Задание 2. 2. Реализация простого RF\n", + "\n", + "**4 балла**\n", + "\n", + "Реализуйте свой собственный класс `RandomForestClassifierCustom`, используя в качестве базовой модели `DecisionTreeClassifier` из `sklearn`.\n", + "\n", + "Небольшое описание:\n", + "- Используйте приведенный ниже код\n", + "- В методе `fit` в цикле (`i` от 0 до `n_estimators-1`):\n", + " * Зафиксируйте генератор случайных чисел следующим образом np.random.seed(`random_state + i`). Идея в том, что на каждой итерации у нас будет новое значение для генератора случайных чисел, что добавит побольше \"случайности\", но в то же время мы сможем иметь воспроизводимые результаты\n", + " * После чего выберите `max_features` признаков **без возвращения/without replacement**, сохраните список выбранных признаков (их индексов) в `self.feat_ids_by_tree`\n", + " * Также создайте псевдовыборку при помощи бутстрэпа (выбор **с возвращением/with replacement**) из тренировочных данных. Может помочь функция `np.random.choice` и ее аргумент `replace`\n", + " * Обучите дерево решений с параметрами, заданными в конструкторе класса `max_depth`, `max_features` и `random_state` на полученной псевдовыборке.\n", + "- Метод `fit` должен возвращать текущий экземпляр класса `RandomForestClassifierCustom`, то есть `self` (все по-взрослому, как в `sklearn`)\n", + "- В методе `predict_proba` мы должны пройти циклом по всем деревьям. Для каждого предсказания, нам нужно будет брать только те признаки, на которых училось изначальное дерево, поэтому мы и сохраняли эту информацию в артрибуте `self.feat_ids_by_tree`. Этот метод должен возвращать предсказанные вероятности (можно делать двумя способами: для каждого дерева предсказывать значение при помощи метода `predict_proba` и потом усреднять эти вероятности, или к примеру пользоваться методом `predict` и также считать среднее." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "c19fa91b-815f-4e65-bac7-5f218ccd4dfe", + "metadata": { + "id": "c19fa91b-815f-4e65-bac7-5f218ccd4dfe" + }, + "outputs": [], + "source": [ + "class RandomForestClassifierCustom(BaseEstimator):\n", + " def __init__(\n", + " self, n_estimators=10, max_depth=None, max_features=None, random_state=SEED\n", + " ):\n", + " self.n_estimators = n_estimators\n", + " self.max_depth = max_depth\n", + " self.max_features = max_features\n", + " self.random_state = random_state\n", + "\n", + " self.trees = []\n", + " self.feat_ids_by_tree = []\n", + "\n", + " def fit(self, X, y):\n", + " self.classes_ = np.unique(y)\n", + "\n", + " n_samples, n_features = X.shape\n", + " self.feat_ids_by_tree = []\n", + "\n", + " for i in range(self.n_estimators):\n", + " np.random.seed(self.random_state + i)\n", + "\n", + " # Выбор признаков без возвращения\n", + " if self.max_features is None:\n", + " self.max_features = n_features\n", + " feat_ids = np.random.choice(n_features, self.max_features, replace=False)\n", + " self.feat_ids_by_tree.append(feat_ids)\n", + "\n", + " # Бутстрэп выборка\n", + " sample_ids = np.random.choice(n_samples, n_samples, replace=True)\n", + " X_sample, y_sample = X[sample_ids][:, feat_ids], y[sample_ids]\n", + "\n", + " # Обучение дерева\n", + " tree = DecisionTreeClassifier(max_depth=self.max_depth, random_state=self.random_state)\n", + " tree.fit(X_sample, y_sample)\n", + " self.trees.append(tree)\n", + "\n", + " return self\n", + "\n", + " def predict_proba(self, X):\n", + " probas = np.array([tree.predict_proba(X[:, feat_ids]) for tree, feat_ids in zip(self.trees, self.feat_ids_by_tree)])\n", + " avg_proba = np.mean(probas, axis=0)\n", + " return avg_proba\n", + "\n", + " def predict(self, X):\n", + " probas = self.predict_proba(X)\n", + " predictions = np.argmax(probas, axis=1)\n", + " return predictions\n" + ] + }, + { + "cell_type": "markdown", + "id": "2d011ba4-85f2-453a-b284-78e711ab1733", + "metadata": { + "id": "2d011ba4-85f2-453a-b284-78e711ab1733" + }, + "source": [ + "Протестируем нашу реализацию на искусственных данных. Визуализируйте разделяющую границу, которую рисует ваша модель при помощи функции `plot_decision_boundary` (см. примеры в лекции)." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "4e67ff2b-62a9-4f94-8bb2-7f5c1636999d", + "metadata": { + "id": "4e67ff2b-62a9-4f94-8bb2-7f5c1636999d" + }, + "outputs": [], + "source": [ + "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n", + " x1s = np.linspace(axes[0], axes[1], 100)\n", + " x2s = np.linspace(axes[2], axes[3], 100)\n", + " x1, x2 = np.meshgrid(x1s, x2s)\n", + " X_new = np.c_[x1.ravel(), x2.ravel()]\n", + " y_pred = clf.predict(X_new).reshape(x1.shape)\n", + " custom_cmap = ListedColormap([\"#ffdab9\",\"#9898ff\", \"#4B0082\"])\n", + " plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n", + " if contour:\n", + " custom_cmap2 = ListedColormap([\"#ffdab9\", \"#4c4c7f\", \"#4B0082\"])\n", + " plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n", + " plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n", + " plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n", + " plt.axis(axes)\n", + " plt.xlabel(r\"$x_1$\", fontsize=18)\n", + " plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "a76e9b76-94c2-47b5-9ef9-150bc798a307", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 522 + }, + "id": "a76e9b76-94c2-47b5-9ef9-150bc798a307", + "outputId": "31cd0e95-9a1c-437f-d7d6-6d1497257491" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "X, y = datasets.make_moons(n_samples=500, noise=0.30, random_state=SEED)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n", + "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "cb4de83e-dfe0-48f4-8bd8-f0f9fdc36e80", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + }, + "id": "cb4de83e-dfe0-48f4-8bd8-f0f9fdc36e80", + "outputId": "57473ce4-c5ff-4871-8f1a-e1247dfa6d2c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestClassifierCustom(max_features=2, n_estimators=100, random_state=42)" + ], + "text/html": [ + "
    RandomForestClassifierCustom(max_features=2, n_estimators=100, random_state=42)
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    " + ] + }, + "metadata": {}, + "execution_count": 71 + } + ], + "source": [ + "# Создание и обучение модели\n", + "rf_custom = RandomForestClassifierCustom(n_estimators=100, max_depth=None, max_features=None, random_state=42)\n", + "rf_custom.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "c59e2333-fb8d-4fa4-9405-a0c186596b19", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 557 + }, + "id": "c59e2333-fb8d-4fa4-9405-a0c186596b19", + "outputId": "1ae5b6fb-aedd-4881-d6dd-8925aba59e9b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Визуализация\n", + "plt.figure(figsize=(8, 6))\n", + "plot_decision_boundary(rf_custom, X, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "da484a87-bb76-43a7-8953-1af94efbd0aa", + "metadata": { + "id": "da484a87-bb76-43a7-8953-1af94efbd0aa" + }, + "source": [ + "Подберите наилучшие гиперпараметры, при которых разделяющая граница будет, на ваш взгляд, оптимальной с точки зрения bias-variance. Можно также подключить какие-то метрики для выбора лучшей модели." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "fccbd1cc-740c-4411-9b27-680a9cf73c9f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fccbd1cc-740c-4411-9b27-680a9cf73c9f", + "outputId": "f6b8caad-1b22-40af-95d8-0352d641bf27" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Лучшая точность: 0.9279999999999999\n", + "Лучшие гиперпараметры: {'n_estimators': 100, 'max_depth': None, 'max_features': None}\n" + ] + } + ], + "source": [ + "def search_best_params(X, y, n_estimators_list, max_depth_list, max_features_list):\n", + " best_score = 0\n", + " best_params = {}\n", + " n_features = X.shape[1]\n", + " for n_estimators in n_estimators_list:\n", + " for max_depth in max_depth_list:\n", + " for max_features in max_features_list:\n", + " # Преобразование строковых значений max_features в числа\n", + " if max_features == 'sqrt':\n", + " max_features_num = int(np.sqrt(n_features))\n", + " elif max_features == 'log2':\n", + " max_features_num = int(np.log2(n_features))\n", + " else:\n", + " max_features_num = max_features\n", + "\n", + " rf_custom = RandomForestClassifierCustom(n_estimators=n_estimators,\n", + " max_depth=max_depth,\n", + " max_features=max_features_num,\n", + " random_state=42)\n", + " scores = cross_val_score(rf_custom, X, y, cv=5, scoring='accuracy')\n", + " mean_score = scores.mean()\n", + " if mean_score > best_score:\n", + " best_score = mean_score\n", + " best_params = {'n_estimators': n_estimators, 'max_depth': max_depth, 'max_features': max_features}\n", + " return best_score, best_params\n", + "# Параметры для перебора\n", + "n_estimators_list = [10, 50, 100]\n", + "max_depth_list = [None, 5, 10, 20]\n", + "max_features_list = [None, 'sqrt', 'log2'] # Теперь корректно обрабатываются строковые значения\n", + "# Поиск лучших гиперпараметров\n", + "best_score, best_params = search_best_params(X, y, n_estimators_list, max_depth_list, max_features_list)\n", + "print(\"Лучшая точность:\", best_score)\n", + "print(\"Лучшие гиперпараметры:\", best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "293aac4e-a7c9-400a-a939-e3b1122d5517", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 557 + }, + "id": "293aac4e-a7c9-400a-a939-e3b1122d5517", + "outputId": "8a406460-8348-47fa-8b7a-746050b648e5" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Создание экземпляра с оптимальными параметрами\n", + "rf_custom_optimal = RandomForestClassifierCustom(n_estimators=100, max_depth=None, max_features=None, random_state=42)\n", + "# Генерация искусственных данных\n", + "X, y = datasets.make_moons(n_samples=500, noise=0.30, random_state=42)\n", + "# Обучение модели\n", + "rf_custom_optimal.fit(X, y)\n", + "# Визуализация разделяющей границы для оптимальной модели\n", + "plt.figure(figsize=(8, 6))\n", + "plot_decision_boundary(rf_custom_optimal, X, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Вроде неплохо выглядит" + ], + "metadata": { + "id": "lmO0vEiZfKjE" + }, + "id": "lmO0vEiZfKjE" + }, + { + "cell_type": "markdown", + "id": "5fad02ca-af42-4142-8201-93602cfea49f", + "metadata": { + "id": "5fad02ca-af42-4142-8201-93602cfea49f" + }, + "source": [ + "#### Задание 2. 3. Корреляция базовых моделей\n", + "\n", + "**3 балла**\n", + "\n", + "Как мы выянили на лекции, для того, чтобы bagging работал хорошо, предсказания наших моделей не должны сильно коррелировать. Для этого в случайном лесе применяются различные подходы, в том числе и RSM. Давайте посмотрим, как влияет параметр `max_features` на корреляцию базовых моделей в случайном лесу из `sklearn`. В качестве примера будем использовать датасет `breast_cancer`. Для расчета корреляций используйте приведенную ниже функцию `base_model_pair_correlation`. Для каждой модели у вас будет получаться набор значений (попарные корреляции всех деревьев), дальше можно изобразить их в виде боксплотов, как мы на лекции рисовали распределение метрик." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c755182a-f385-4f0b-a153-60ef6e572f3b", + "metadata": { + "id": "c755182a-f385-4f0b-a153-60ef6e572f3b" + }, + "outputs": [], + "source": [ + "# Функция для расчета попарных корреляций базовых моделей в случайном лесу\n", + "def base_model_pair_correlation(ensemble, X):\n", + " corrs = []\n", + " for (i, est1), (j, est2) in combinations(enumerate(ensemble.estimators_), 2):\n", + " Xi_test = X\n", + " Xj_test = X\n", + "\n", + " ypred_t1 = est1.predict_proba(Xi_test)[:, 1]\n", + " ypred_t2 = est2.predict_proba(Xj_test)[:, 1]\n", + "\n", + " corrs.append(pearsonr(ypred_t1, ypred_t2)[0])\n", + " return np.array(corrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "65ab9ab7-311a-4bb1-8adc-f66c063e827e", + "metadata": { + "id": "65ab9ab7-311a-4bb1-8adc-f66c063e827e" + }, + "outputs": [], + "source": [ + "# Загрузим данные\n", + "breast_cancer = datasets.load_breast_cancer()\n", + "X = breast_cancer.data\n", + "y = breast_cancer.target\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "3d26f651-5f5c-41a5-a80a-b37be6e1802c", + "metadata": { + "id": "3d26f651-5f5c-41a5-a80a-b37be6e1802c" + }, + "outputs": [], + "source": [ + "# Функция для расчет попарных корреляций\n", + "def base_model_pair_correlation(ensemble, X):\n", + " corrs = []\n", + " for (i, est1), (j, est2) in combinations(enumerate(ensemble.estimators_), 2):\n", + " ypred_t1 = est1.predict_proba(X)[:, 1]\n", + " ypred_t2 = est2.predict_proba(X)[:, 1]\n", + " corrs.append(pearsonr(ypred_t1, ypred_t2)[0])\n", + " return np.array(corrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "d34e1758-d2dc-4820-afe5-2abdd850b590", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 718 + }, + "id": "d34e1758-d2dc-4820-afe5-2abdd850b590", + "outputId": "344bd4a8-aa59-4428-9120-d8f132a59745" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Значения max_features для анализа\n", + "max_features_values = [None, 'sqrt', 'log2', 0.5, 0.7, 0.9]\n", + "correlations = []\n", + "for max_features in max_features_values:\n", + " # Создание и обучение случайного леса\n", + " rf = RandomForestClassifier(n_estimators=100, max_features=max_features, random_state=42)\n", + " rf.fit(X_train, y_train)\n", + " # Расчет попарных корреляций\n", + " corrs = base_model_pair_correlation(rf, X_test)\n", + " correlations.append(corrs)\n", + "# Визуализация результатов\n", + "plt.figure(figsize=(12, 8))\n", + "sns.boxplot(data=correlations)\n", + "plt.xticks(range(len(max_features_values)), labels=[str(mf) for mf in max_features_values])\n", + "plt.xlabel('max_features')\n", + "plt.ylabel('Pairwise Base Model Correlation')\n", + "plt.title('Impact of max_features on Base Model Correlation in Random Forest')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "511f49f6-b433-449d-b27c-522098ef7a32", + "metadata": { + "id": "511f49f6-b433-449d-b27c-522098ef7a32" + }, + "source": [ + "Теперь давайте посмотрим, как на это влияет параметр `max_depth`:" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "26d98e98-b0ba-480b-944c-a27486b8b7c5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 718 + }, + "id": "26d98e98-b0ba-480b-944c-a27486b8b7c5", + "outputId": "8400308c-19b1-4b88-89a5-cdd3f223ec79" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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+ }, + "metadata": {} + } + ], + "source": [ + "# Значения max_depth для анализа\n", + "max_depth_values = [None, 1, 2, 3, 5, 10, 15]\n", + "correlations_depth = []\n", + "\n", + "for max_depth in max_depth_values:\n", + " # Создание и обучение случайного леса\n", + " rf = RandomForestClassifier(n_estimators=100, max_depth=max_depth, random_state=42)\n", + " rf.fit(X_train, y_train)\n", + "\n", + " # Расчет попарных корреляций\n", + " corrs = base_model_pair_correlation(rf, X_test)\n", + " correlations_depth.append(corrs)\n", + "\n", + "# Визуализация результатов\n", + "plt.figure(figsize=(12, 8))\n", + "sns.boxplot(data=correlations_depth)\n", + "plt.xticks(range(len(max_depth_values)), labels=[str(md) if md is not None else 'None' for md in max_depth_values])\n", + "plt.xlabel('max_depth')\n", + "plt.ylabel('Pairwise Base Model Correlation')\n", + "plt.title('Impact of max_depth on Base Model Correlation in Random Forest')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "efafb748-a14d-4fd0-9995-4eee6e092445", + "metadata": { + "id": "efafb748-a14d-4fd0-9995-4eee6e092445" + }, + "source": [ + "Опишите ваши наблюдения:\n", + "\n", + "* При использовании деревьев с небольшой глубиной (например, max_depth=1) корреляция между предсказаниями отдельных деревьев ниже\n", + "* По мере увеличения max_depth корреляция между предсказаниями возрастает, а затем немного снижается" + ] + }, + { + "cell_type": "markdown", + "id": "aef2bd46-a98e-4f8f-ba17-0bdfe51ba395", + "metadata": { + "id": "aef2bd46-a98e-4f8f-ba17-0bdfe51ba395" + }, + "source": [ + "### Задание 3. Строим большой ансамбль\n", + "\n", + "**4 балла + 3 дополнительных за скор выше 0.87**\n", + "\n", + "В данной задаче вам нужно диагностировать сердечное заболевание у людей по медицинским показателям." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "475ce2d1-1a24-4731-a424-df36cef9b270", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "475ce2d1-1a24-4731-a424-df36cef9b270", + "outputId": "2dfecb9b-ec57-446b-f80b-620cb5738400" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:138: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "!gdown --id 1VFbDK-Ad-hpf0_GGCBzn4thdn9mkQ-Y- -O heart.csv -q\n", + "heart_dataset = pd.read_csv(\"heart.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "8bde25c5-2da7-4fb9-93e1-bd347cecef8e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "8bde25c5-2da7-4fb9-93e1-bd347cecef8e", + "outputId": "6e8248e0-cb4a-426c-9176-f53f37a72d4a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", + "178 43 1 0 120 177 0 0 120 1 2.5 \n", + "298 57 0 0 140 241 0 1 123 1 0.2 \n", + "201 60 1 0 125 258 0 0 141 1 2.8 \n", + "246 56 0 0 134 409 0 0 150 1 1.9 \n", + "153 66 0 2 146 278 0 0 152 0 0.0 \n", + "\n", + " slope ca thal \n", + "178 1 0 3 \n", + "298 1 0 3 \n", + "201 1 1 3 \n", + "246 1 2 3 \n", + "153 1 1 2 " + ], + "text/html": [ + "\n", + "
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    \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "X_train", + "summary": "{\n \"name\": \"X_train\",\n \"rows\": 227,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9,\n \"min\": 29,\n \"max\": 76,\n \"num_unique_values\": 40,\n \"samples\": [\n 65,\n 61,\n 55\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 2,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"trestbps\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 17,\n \"min\": 100,\n \"max\": 200,\n \"num_unique_values\": 45,\n \"samples\": [\n 108,\n 115\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"chol\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48,\n \"min\": 131,\n \"max\": 417,\n \"num_unique_values\": 133,\n \"samples\": [\n 286,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fbs\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"restecg\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 2,\n \"num_unique_values\": 3,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"thalach\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 23,\n \"min\": 88,\n \"max\": 202,\n \"num_unique_values\": 86,\n \"samples\": [\n 164,\n 120\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"exang\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"oldpeak\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.2266336979069916,\n \"min\": 0.0,\n \"max\": 6.2,\n \"num_unique_values\": 40,\n \"samples\": [\n 1.6,\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"slope\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 2,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ca\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"thal\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 2,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 85 + } + ], + "source": [ + "X = heart_dataset.drop(\"target\", axis=1)\n", + "y = heart_dataset[\"target\"]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + "X_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b144a808-145d-4370-9b6c-4859ee231d91", + "metadata": { + "id": "b144a808-145d-4370-9b6c-4859ee231d91" + }, + "source": [ + "Обучите разнообразные классификаторы, приведенные ниже, а также ансамбль `VotingClassifier` из `sklearn.ensemble`, объединяющий эти классификаторы с помощью жесткого или мякого голосования (параметр `voting =` `'hard'` или `'soft'` соответственно). Оцените качество моделей с помощью кросс-валидации на тренировочном наборе, используя функцию `cross_val_score` и метрику `f1`. Часть моделей отсюда мы не проходили, о них можно почитать дополнительно, но в принципе для задания не очень важно знать принципы их работы (но, если есть время, то почитайте, там интересно)." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "099f6e1a-641c-4acc-8334-e4b304aa8810", + "metadata": { + "id": "099f6e1a-641c-4acc-8334-e4b304aa8810" + }, + "outputs": [], + "source": [ + "dt = DecisionTreeClassifier(random_state=SEED, max_depth=10, min_samples_leaf=10)\n", + "rf = RandomForestClassifier(n_estimators=50, random_state=SEED)\n", + "etc = ExtraTreesClassifier(random_state=SEED)\n", + "knn = KNeighborsClassifier(n_neighbors=5, weights=\"distance\")\n", + "svc_lin = SVC(kernel='linear', probability=True, random_state=SEED)\n", + "svc_rbf = SVC(kernel='rbf', probability=True, random_state=SEED)\n", + "cat = catboost.CatBoostClassifier(verbose=0, random_seed=SEED)\n", + "lgbm = lightgbm.LGBMClassifier(random_state=SEED)\n", + "lgbm_rf = lightgbm.LGBMClassifier(boosting_type=\"rf\", bagging_freq=1, bagging_fraction=0.7, random_state=SEED)\n", + "xgb = xgboost.XGBClassifier(random_state=SEED)\n", + "xgb_rf = xgboost.XGBRFClassifier(random_state=SEED)\n", + "lr = LogisticRegression(solver='liblinear', max_iter=10000)\n", + "nb = GaussianNB()\n", + "base_models = [(\"DT\", dt), (\"RF\", rf),\n", + " (\"ETC\", etc), (\"KNN\", knn),\n", + " (\"SVC_LIN\", svc_lin), (\"SVC_RBF\", svc_rbf),\n", + " (\"CAT\", cat), (\"LGBM\", lgbm),\n", + " (\"LGBM_RF\", lgbm_rf), (\"XGB\", xgb),\n", + " (\"XGB_RF\", xgb_rf), (\"LR\", lr), (\"NB\", nb)]" + ] + }, + { + "cell_type": "markdown", + "id": "afb6e5b6-da19-48e8-9882-63e0c5cafdde", + "metadata": { + "id": "afb6e5b6-da19-48e8-9882-63e0c5cafdde" + }, + "source": [ + "Здесь могут возникать различные предупреждения при обучении бустингов, не волнуйтесь, все нормально, просто они обычно очень разговорчивые)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "03d7be2e-568b-47ce-b29c-7221ba2d5bee", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "03d7be2e-568b-47ce-b29c-7221ba2d5bee", + "outputId": "67cd166b-da48-43d6-c968-68a676adafdb" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "DecisionTreeClassifier: 0.797997226792219\n", + "RandomForestClassifier: 0.8328751280279528\n", + "CatBoostClassifier: 0.8342715174922052\n", + "ExtraTreesClassifier: 0.828174603174603\n", + "KNeighborsClassifier: 0.6493313763861709\n", + "SVC: 0.8403098469098905\n", + "SVC: 0.6973119072190279\n", + "XGBClassifier: 0.8134522115571786\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] 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initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000064 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "LGBMClassifier: 0.8170106316447779\n", + "XGBRFClassifier: 0.8499478840942255\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000062 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000097 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000061 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "LGBMClassifier: 0.8132478632478634\n", + "LogisticRegression: 0.8500073681108163\n", + "GaussianNB: 0.8140676625250128\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000073 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000060 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000053 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "VotingClassifier: 0.8421927307508369\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000065 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: 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[binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "VotingClassifier: 0.8503633581946834\n" + ] + } + ], + "source": [ + "voting_hard = VotingClassifier(estimators=[('dt', dt), ('rf', rf), ('cat', cat), ('etc', etc), ('knn', knn),\n", + " ('svc_lin', svc_lin), ('svc_rbf', svc_rbf), ('xgb', xgb), ('lgbm', lgbm),\n", + " ('lr', lr), ('nb', nb)], voting='hard')\n", + "voting_soft = VotingClassifier(estimators=[('dt', dt), ('rf', rf), ('cat', cat), ('etc', etc), ('knn', knn),\n", + " ('svc_lin', svc_lin), ('svc_rbf', svc_rbf), ('xgb', xgb), ('lgbm', lgbm),\n", + " ('lr', lr), ('nb', nb)], voting='soft')\n", + "\n", + "# Оценка качества моделей с помощью кросс-валидации\n", + "for model in [dt, rf, cat, etc, knn, svc_lin, svc_rbf, xgb, lgbm, xgb_rf, lgbm_rf, lr, nb, voting_hard, voting_soft]:\n", + " scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"f1\")\n", + " print(f\"{model.__class__.__name__}: {scores.mean()}\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "VotingClassifier: 0.8503633581946834" + ], + "metadata": { + "id": "m2k8ruLI_eXG" + }, + "id": "m2k8ruLI_eXG" + }, + { + "cell_type": "markdown", + "id": "a95d780c-3960-47e0-84b1-496a128d5a5b", + "metadata": { + "id": "a95d780c-3960-47e0-84b1-496a128d5a5b" + }, + "source": [ + "Вы можете заметить, что ансамбль показывает хорошее, но не лучшее качество предсказания, попробуем его улучшить. Как вы знаете, ансамбли работают лучше, когда модели, входящие в них не скоррелированы друг с другом. Определите корреляцию предсказаний базовых моделей в ансамбле на тестовом наборе данных, и удалите из ансамбля те модели, чьи предсказания будут сильнее коррелировать с остальными. Воспользуйтесь функцией `base_model_pair_correlation_for_voting_clf`. **Спойлер**: далеко не факт, что если вы удалите две модели с корреляцией 0.95, то все станет сильно лучше, здесь все будет немного сложнее. Чтобы добиться максимального качества может понадобиться долгий перебор различных комбинаций моделей. Наилучший скор, который мне удалось достичь, это 0.915, но он получен весьма странной комбинацией алгоритмов, а еще и простым перебором всех вариантов)" + ] + }, + { + "cell_type": "code", + "source": [ + "voting_soft.fit(X_train, y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "YHT8IU8FkqnN", + "outputId": "62c2fbd2-7674-4a75-91e1-4fc89d278ca1" + }, + "id": "YHT8IU8FkqnN", + "execution_count": 93, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[LightGBM] [Info] Number of positive: 118, number of negative: 109\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000073 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 236\n", + "[LightGBM] [Info] Number of data points in the train set: 227, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519824 -> initscore=0.079337\n", + "[LightGBM] [Info] Start training from score 0.079337\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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missing=nan,\n", + " monotone_constraints=None,\n", + " multi_strategy=None,\n", + " n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None,\n", + " random_state=111, ...)),\n", + " ('lgbm', LGBMClassifier(random_state=111)),\n", + " ('lr',\n", + " LogisticRegression(max_iter=10000,\n", + " solver='liblinear')),\n", + " ('nb', GaussianNB())],\n", + " voting='soft')" + ], + "text/html": [ + "
    VotingClassifier(estimators=[('dt',\n",
    +              "                              DecisionTreeClassifier(max_depth=10,\n",
    +              "                                                     min_samples_leaf=10,\n",
    +              "                                                     random_state=111)),\n",
    +              "                             ('rf',\n",
    +              "                              RandomForestClassifier(n_estimators=50,\n",
    +              "                                                     random_state=111)),\n",
    +              "                             ('cat',\n",
    +              "                              <catboost.core.CatBoostClassifier object at 0x787a215deec0>),\n",
    +              "                             ('etc', ExtraTreesClassifier(random_state=111)),\n",
    +              "                             ('knn', KNeighborsClassifier(weights='distance')),\n",
    +              "                             ('svc_lin',\n",
    +              "                              SVC(...\n",
    +              "                                            max_cat_to_onehot=None,\n",
    +              "                                            max_delta_step=None, max_depth=None,\n",
    +              "                                            max_leaves=None,\n",
    +              "                                            min_child_weight=None, missing=nan,\n",
    +              "                                            monotone_constraints=None,\n",
    +              "                                            multi_strategy=None,\n",
    +              "                                            n_estimators=None, n_jobs=None,\n",
    +              "                                            num_parallel_tree=None,\n",
    +              "                                            random_state=111, ...)),\n",
    +              "                             ('lgbm', LGBMClassifier(random_state=111)),\n",
    +              "                             ('lr',\n",
    +              "                              LogisticRegression(max_iter=10000,\n",
    +              "                                                 solver='liblinear')),\n",
    +              "                             ('nb', GaussianNB())],\n",
    +              "                 voting='soft')
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    " + ] + }, + "metadata": {}, + "execution_count": 93 + } + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "6b132bb4-e551-4791-88e7-95eaa7df909c", + "metadata": { + "id": "6b132bb4-e551-4791-88e7-95eaa7df909c" + }, + "outputs": [], + "source": [ + "def base_model_pair_correlation_for_voting_clf(ensemble, X):\n", + " corrs = []\n", + " base_model_names = [f\"{est.__class__.__name__}\" for est in ensemble.estimators_]\n", + " for (i, est1), (j, est2) in combinations(enumerate(ensemble.estimators_), 2):\n", + " Xi_test = X\n", + " Xj_test = X\n", + " if not isinstance(est1, SVC):\n", + " ypred_t1 = est1.predict_proba(Xi_test)[:, 1]\n", + " else:\n", + " ypred_t1 = est1.decision_function(Xi_test)\n", + " if not isinstance(est2, SVC):\n", + " ypred_t2 = est2.predict_proba(Xi_test)[:, 1]\n", + " else:\n", + " ypred_t2 = est2.decision_function(Xi_test)\n", + " corrs.append((est1, est2, pearsonr(ypred_t1, ypred_t2)[0]))\n", + " return corrs" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "21fb93e4-8065-473c-a11a-cd423baf71d6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + }, + "id": "21fb93e4-8065-473c-a11a-cd423baf71d6", + "outputId": "d1c93346-3c90-4012-e28f-9baa59a64355" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Model 1 Model 2 Correlation\n", + "49 XGBClassifier LGBMClassifier 0.970839\n", + "24 CatBoostClassifier LGBMClassifier 0.955278\n", + "19 CatBoostClassifier ExtraTreesClassifier 0.952720\n", + "43 SVC LogisticRegression 0.952159\n", + "10 RandomForestClassifier CatBoostClassifier 0.945070\n", + "23 CatBoostClassifier XGBClassifier 0.933990\n", + "11 RandomForestClassifier ExtraTreesClassifier 0.921565\n", + "16 RandomForestClassifier LGBMClassifier 0.914687\n", + "15 RandomForestClassifier XGBClassifier 0.896641\n", + "31 ExtraTreesClassifier LGBMClassifier 0.892875" + ], + "text/html": [ + "\n", + "
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    Model 1Model 2Correlation
    49XGBClassifierLGBMClassifier0.970839
    24CatBoostClassifierLGBMClassifier0.955278
    19CatBoostClassifierExtraTreesClassifier0.952720
    43SVCLogisticRegression0.952159
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    23CatBoostClassifierXGBClassifier0.933990
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    \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "corrs_df", + "summary": "{\n \"name\": \"corrs_df\",\n \"rows\": 55,\n \"fields\": [\n {\n \"column\": \"Model 1\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"DecisionTreeClassifier\",\n \"CatBoostClassifier\",\n \"LogisticRegression\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Model 2\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"RandomForestClassifier\",\n \"ExtraTreesClassifier\",\n \"GaussianNB\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Correlation\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2596297739031848,\n \"min\": 0.2263393781495638,\n \"max\": 0.970838692549758,\n \"num_unique_values\": 55,\n \"samples\": [\n 0.7609652214109747,\n 0.9339896573127269,\n 0.7399817403132603\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 104 + } + ], + "source": [ + "corrs = base_model_pair_correlation_for_voting_clf(voting_soft, X_test)\n", + "corrs_df = pd.DataFrame(corrs, columns=['Model 1', 'Model 2', 'Correlation'])\n", + "corrs_df['Model 1'] = corrs_df['Model 1'].apply(lambda x: x.__class__.__name__)\n", + "corrs_df['Model 2'] = corrs_df['Model 2'].apply(lambda x: x.__class__.__name__)\n", + "# Сортировка результатов по убыванию корреляции\n", + "corrs_df = corrs_df.sort_values(by='Correlation', ascending=False)\n", + "# Вывод первых 10 пар моделей с наивысшей корреляцией\n", + "corrs_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Наиболее высокая корреляция наблюдается между XGBClassifier и LGBMClassifier, что указывает на то, что эти две модели делают очень похожие предсказания. Аналогично, CatBoostClassifier и LGBMClassifier также показывают высокую корреляцию." + ], + "metadata": { + "id": "HzY8rbeUnw_w" + }, + "id": "HzY8rbeUnw_w" + }, + { + "cell_type": "code", + "source": [ + "models_list = [(\"DT\", dt), (\"RF\", rf),\n", + " (\"ETC\", etc), (\"KNN\", knn),\n", + " (\"SVC_LIN\", svc_lin), (\"SVC_RBF\", svc_rbf),\n", + " (\"CAT\", cat), (\"LGBM\", lgbm),\n", + " (\"LGBM_RF\", lgbm_rf), (\"XGB\", xgb),\n", + " (\"XGB_RF\", xgb_rf), (\"LR\", lr), (\"NB\", nb)]\n", + "def evaluate_ensemble(models, X_train, y_train, X_test, y_test):\n", + " estimators = [(model.__class__.__name__ + str(i), model) for i, model in enumerate(models)]\n", + " voting_clf = VotingClassifier(estimators=estimators, voting='soft')\n", + " voting_clf.fit(X_train, y_train)\n", + " y_pred = voting_clf.predict(X_test)\n", + " score = f1_score(y_test, y_pred)\n", + " return score\n", + "best_score = 0\n", + "best_combination = None\n", + "for L in range(2, len(models_list) + 1):\n", + " for subset in combinations(models_list, L):\n", + " score = evaluate_ensemble(subset, X_train, y_train, X_test, y_test)\n", + " if score > best_score:\n", + " best_score = score\n", + " best_combination = subset\n", + "# Выводим наилучшую комбинацию и ее производительность\n", + "print(f\"Лучший F1-скор: {best_score}\")\n", + "print(\"Лучшая комбинация моделей:\")\n", + "for model in best_combination:\n", + " print(model.__class__.__name__)" + ], + "metadata": { + "id": "Oiabf__Tnjmy" + }, + "id": "Oiabf__Tnjmy", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "054b2673-1725-48a9-ae6a-4e348d50169d", + "metadata": { + "id": "054b2673-1725-48a9-ae6a-4e348d50169d" + }, + "source": [ + "### Задание 4. Определение оттока клиентов из телекома\n", + "\n", + "**6 баллов + 7 дополнительных за высокое качество модели и различные эксперименты**\n", + "\n", + "Будем предсказывать, уйдет ли от нас клиент (переменная `Churn?`). Данные можно скачать [здесь](https://www.kaggle.com/venky12347/churn-telecom). Это будет уже совсем взрослое задание, так как правильного ответа на него нет. Вам нужно будет разобраться с данными, правильно подготовить их для моделей, а также выбрать лучшую модель.\n", + "\n", + "Задача минимум:\n", + "\n", + "Выберите 2 модели — один случайный лес и один бустинг из приведенных ниже:\n", + "\n", + "1. `xgboost.XGBClassifier`\n", + "2. `xgboost.XGBRFClassifier` — случайный лес от xgboost\n", + "3. `lightgbm.LGBMClassifier`\n", + "4. `lightgbm.LGBMClassifier(boosting_type=\"rf\")` — случайный лес от lightgbm\n", + "5. `catboost.CatBoostClassifier`\n", + "\n", + "И попробуйте разобраться с тем, как для этих моделей правильно настраивать гиперпараметры. Советую гуглить примерно следующее `how to choose best hyperparameters for lightgbm`. Там вы найдете кучу сложного и непонятного кода, но если с ним разобраться и научиться обучать нестандартные бустинги, то в плане табличных данных равных вам не будет)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "47cd58e0-3c7b-4dc6-8eba-617eec699586", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "47cd58e0-3c7b-4dc6-8eba-617eec699586", + "outputId": "9e06dfa4-9fa7-45c7-8fbf-3eb413f23fca" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " State Account Length Area Code Phone Int'l Plan VMail Plan \\\n", + "0 KS 128 415 382-4657 no yes \n", + "1 OH 107 415 371-7191 no yes \n", + "2 NJ 137 415 358-1921 no no \n", + "3 OH 84 408 375-9999 yes no \n", + "4 OK 75 415 330-6626 yes no \n", + "\n", + " VMail Message Day Mins Day Calls Day Charge ... Eve Calls Eve Charge \\\n", + "0 25 265.1 110 45.07 ... 99 16.78 \n", + "1 26 161.6 123 27.47 ... 103 16.62 \n", + "2 0 243.4 114 41.38 ... 110 10.30 \n", + "3 0 299.4 71 50.90 ... 88 5.26 \n", + "4 0 166.7 113 28.34 ... 122 12.61 \n", + "\n", + " Night Mins Night Calls Night Charge Intl Mins Intl Calls Intl Charge \\\n", + "0 244.7 91 11.01 10.0 3 2.70 \n", + "1 254.4 103 11.45 13.7 3 3.70 \n", + "2 162.6 104 7.32 12.2 5 3.29 \n", + "3 196.9 89 8.86 6.6 7 1.78 \n", + "4 186.9 121 8.41 10.1 3 2.73 \n", + "\n", + " CustServ Calls Churn? \n", + "0 1 False. \n", + "1 1 False. \n", + "2 0 False. \n", + "3 2 False. \n", + "4 3 False. \n", + "\n", + "[5 rows x 21 columns]\n", + "\n", + "RangeIndex: 3333 entries, 0 to 3332\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 State 3333 non-null object \n", + " 1 Account Length 3333 non-null int64 \n", + " 2 Area Code 3333 non-null int64 \n", + " 3 Phone 3333 non-null object \n", + " 4 Int'l Plan 3333 non-null object \n", + " 5 VMail Plan 3333 non-null object \n", + " 6 VMail Message 3333 non-null int64 \n", + " 7 Day Mins 3333 non-null float64\n", + " 8 Day Calls 3333 non-null int64 \n", + " 9 Day Charge 3333 non-null float64\n", + " 10 Eve Mins 3333 non-null float64\n", + " 11 Eve Calls 3333 non-null int64 \n", + " 12 Eve Charge 3333 non-null float64\n", + " 13 Night Mins 3333 non-null float64\n", + " 14 Night Calls 3333 non-null int64 \n", + " 15 Night Charge 3333 non-null float64\n", + " 16 Intl Mins 3333 non-null float64\n", + " 17 Intl Calls 3333 non-null int64 \n", + " 18 Intl Charge 3333 non-null float64\n", + " 19 CustServ Calls 3333 non-null int64 \n", + " 20 Churn? 3333 non-null object \n", + "dtypes: float64(8), int64(8), object(5)\n", + "memory usage: 546.9+ KB\n", + "None\n", + " Account Length Area Code VMail Message Day Mins Day Calls \\\n", + "count 3333.000000 3333.000000 3333.000000 3333.000000 3333.000000 \n", + "mean 101.064806 437.182418 8.099010 179.775098 100.435644 \n", + "std 39.822106 42.371290 13.688365 54.467389 20.069084 \n", + "min 1.000000 408.000000 0.000000 0.000000 0.000000 \n", + "25% 74.000000 408.000000 0.000000 143.700000 87.000000 \n", + "50% 101.000000 415.000000 0.000000 179.400000 101.000000 \n", + "75% 127.000000 510.000000 20.000000 216.400000 114.000000 \n", + "max 243.000000 510.000000 51.000000 350.800000 165.000000 \n", + "\n", + " Day Charge Eve Mins Eve Calls Eve Charge Night Mins \\\n", + "count 3333.000000 3333.000000 3333.000000 3333.000000 3333.000000 \n", + "mean 30.562307 200.980348 100.114311 17.083540 200.872037 \n", + "std 9.259435 50.713844 19.922625 4.310668 50.573847 \n", + "min 0.000000 0.000000 0.000000 0.000000 23.200000 \n", + "25% 24.430000 166.600000 87.000000 14.160000 167.000000 \n", + "50% 30.500000 201.400000 100.000000 17.120000 201.200000 \n", + "75% 36.790000 235.300000 114.000000 20.000000 235.300000 \n", + "max 59.640000 363.700000 170.000000 30.910000 395.000000 \n", + "\n", + " Night Calls Night Charge Intl Mins Intl Calls Intl Charge \\\n", + "count 3333.000000 3333.000000 3333.000000 3333.000000 3333.000000 \n", + "mean 100.107711 9.039325 10.237294 4.479448 2.764581 \n", + "std 19.568609 2.275873 2.791840 2.461214 0.753773 \n", + "min 33.000000 1.040000 0.000000 0.000000 0.000000 \n", + "25% 87.000000 7.520000 8.500000 3.000000 2.300000 \n", + "50% 100.000000 9.050000 10.300000 4.000000 2.780000 \n", + "75% 113.000000 10.590000 12.100000 6.000000 3.270000 \n", + "max 175.000000 17.770000 20.000000 20.000000 5.400000 \n", + "\n", + " CustServ Calls \n", + "count 3333.000000 \n", + "mean 1.562856 \n", + "std 1.315491 \n", + "min 0.000000 \n", + "25% 1.000000 \n", + "50% 1.000000 \n", + "75% 2.000000 \n", + "max 9.000000 \n", + "State 0\n", + "Account Length 0\n", + "Area Code 0\n", + "Phone 0\n", + "Int'l Plan 0\n", + "VMail Plan 0\n", + "VMail Message 0\n", + "Day Mins 0\n", + "Day Calls 0\n", + "Day Charge 0\n", + "Eve Mins 0\n", + "Eve Calls 0\n", + "Eve Charge 0\n", + "Night Mins 0\n", + "Night Calls 0\n", + "Night Charge 0\n", + "Intl Mins 0\n", + "Intl Calls 0\n", + "Intl Charge 0\n", + "CustServ Calls 0\n", + "Churn? 0\n", + "dtype: int64\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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iInT48GGdOHFC0uU5cT08Ln/cMplM+uuvv1S5cmXdcccd+vXXX28YZ8E+t0qVKjfNyWQy6fvvv1eXLl3UoEEDy/ratWurZ8+e+umnnyyPt23bNoWGhlpdHVOzZk316tXL6jF37Nih7Oxs9ejRw2qs8vDwUMuWLQuNVQDgzAwGg3r06KHExEQdP37csr5g3vPw8PBr9vPz81OHDh20adMmSZfHmLCwMN16663XbH/lVSfnzp3TyZMnFRYWJrPZfNNxwVa2xLZv3z4dPnxYvXr10l9//WXZ5587d07h4eH64YcfLNNRVatWTT///LNlrLuar6+vJGn79u03nI73ytfizJkzOnnypO6++24dO3bMMuVWQkKCLl26ZDmzuMC1vjt+9tlnat26tapVq2Y1brVr104mk0k//PDDjV4uAHALlStX1tmzZy3LV+6LL168qJMnT1quNL9yisQHH3xQubm5VtNgbd682aYr3CWpffv28vPz06ZNm2Q2m7V582b16NHD5jwefvhhyzFD6fJxK5PJpD/++EOSLFcnfvPNN8rLy7P58eEemEoLNjOZTNq0aZPatm1r9aWhRYsWeu+995SQkKCIiAhJ0tGjR9W1a9cbPt7Ro0d1xx13yNPz+m/HP/74Q7Vr17Z8yC7QqFEjy/biqlevntVytWrVJEmnT58u9Hyl5ciRI/rtt9+u+wXr6rmN69atW6iNn5+fsrOzLe0vXLig2267rVC7a627GXu8JgBQHmwZwwpcPV3U0aNHZTabNW/ePM2bN++az5OVlaU6deooPz9fy5cv16pVq3T8+HGrwkv16tVvGGvB/vbKLy7Xc/LkSZ0/f1533HFHoW2NGjVSfn6+/ve//6lJkyZKS0uzfNm50tV9Dx8+LEl6/PHHbxgfALiKXr166f3339fGjRs1fPhw/fnnn/rxxx81aNCga857fmW/cePGKS0tTV9++aXGjBlz3bZpaWmaP3++vvrqK50+fdpqW1mchFTU2Ar2+ePHj7/uY505c0Z+fn4aM2aMJkyYoHvuuUfNmzdXZGSkevfubSnMN2jQQE8++aSWLVum+Ph4tWnTRp07d9YDDzxgOVAlXZ4ueMGCBUpKSipUQDlz5oyqVq2qtLQ0SYWn/q1evbr8/Pys1h05ckQpKSnX/Y519dzzAOCOzp07p1q1almWT506pTfffFObN28udCzqyvtCNWrUSCEhIYqPj9dDDz0k6XLBPTQ01KbjTl5eXvrnP/+pjRs3qkWLFvrf//5X6AStorjecauC42R33323unXrpjfffFPvv/++7r77bnXp0kW9evWSt7e3zc8H10RhBDbbuXOnMjIytGnTJsvZR1eKj48vdFCpvFxZLb7Stc4ALlBwJu/VzFfc1L205efnKzAw8Lo3fbryRrqSrvtFrKxitMdrAgDloThj2NVzyhecMfvUU09d96q8ggM4ixYt0rx589SvXz+NGjVKfn5+8vDw0IwZM266T73zzjslSSkpKWratGnREixFBfG9/vrrCggIKLT9RgcJAcAZBQcH684779SmTZs0fPhwbdy4UWaz+aYHbDp37iwvLy+NHz9eubm5uv/++6/ZzmQy6cknn9Tp06c1ZMgQ3XnnnapcubJOnDihCRMmlMkNwosaW8E+f9y4cdcdcypXrixJ6t69u9q0aaMtW7bo+++/19KlS7VkyRItWLBAkZGRkqQJEyaoT58++vLLL/X9999r+vTpWrx4sdasWaNbbrlFR48e1RNPPKE777xTEyZMUN26deXl5aVt27bp/fffL9ZrkZ+fr/bt22vIkCHX3H777bfb/JgA4Er+/PNPnTlzxqrY/PzzzysxMVFRUVFq2rSpKleurPz8fA0ZMqTQ95XevXvrtdde059//qnc3FwlJSXp1VdftTmOXr16KS4uTgsWLFBQUJAaN25s82Pc7LhVhQoVNH/+fCUlJenrr7/Wd999p4kTJ2rZsmX66KOPinRVPlwfhRHYLD4+XrVq1brmzm/Lli3asmWLpkyZokqVKqlhw4Y6ePDgDR+vYcOG+vnnn5WXlycvL69rtrn11luVkJCgnJwcqzNUDx06ZNkuyXLWUEGFuEDBmUaOomHDhtq/f7/Cw8OvW8yxRa1atVSxYkUdOXKk0LZrrQMAd2XLGHY9BWfEenl5qV27djd8vs8//1xt27bVjBkzrNZnZ2erRo0aN+zbsWNHGQwGxcfHq3fv3jdsW7NmTfn4+Oj3338vtO3QoUPy8PCwXH1Yr169a44NV/ctyLNWrVo3zRMAXEWvXr00b9487d+/Xxs3btTtt99uNfXgtVSqVEldunTRhg0b1LFjR6vpF6904MABHT58WLNnz7bar3///felmUKxYivY5/v6+hZpn1+7dm0NHDhQAwcOVFZWlvr06aNFixZZCiOSZDQaZTQaNWLECP2///f/9Mgjj2j16tV64YUX9NVXXyk3N1dvv/221Vm/V0/TWLDt6NGjVlNF/vXXX4WuuGnYsKHOnTvHmAUA1/Hf//5Xkiwngp0+fVoJCQl67rnnFB0dbWlXcBXh1bp3765Zs2Zp48aNunDhgry8vK5bcL+R1q1bq169etq9e/cNr7IsDaGhoQoNDdULL7yg+Ph4jRkzRps3b7Zc9QL3xj1GYJMLFy7oiy++0D333KN//vOfhX4GDhyos2fP6quvvpJ0eV72/fv3a8uWLYUeq6CK27VrV/3111/68MMPr9umY8eOMplMhdq8//77qlChgjp27Cjp8gf5GjVq6Mcff7Rqt2rVqhLlXbly5ULFlpK4//77deLECa1Zs6bQtgsXLujcuXM2PZ7BYFC7du305ZdfWs31e+TIEX333XeF2pd2PgDgDGwdw66nVq1auvvuu/XRRx8pPT290PYrp+owGAyFzrT69NNPrzsv+5Xq1q2rhx56SNu3b9eKFSsKbc/Pz9d7772nP//8UwaDQe3bt9eXX35pNUVYZmamNm7cqNatW1tOLIiMjFRSUpL27NljFXN8fLzV43fo0EG+vr5avHjxNeflZUoSAK6o4OqQ+fPna9++fUWe3iMqKkrR0dEaMWLEddsUnN165bhgNpu1fPnyEkRcOrEFBwerYcOGeu+99645hWPBPt9kMllNrSJdHhdr166t3NxcSZenBLt06ZJVm8DAQHl4eFjaFFx1eOVrcebMGa1du9aqX3h4uDw9PbV69Wqr9df67nj//fcrMTHxmt9/srOzC8UEAO4kISFBb731lurXr2+5J8j1rgD/4IMPrrm+Zs2a6tChgzZs2GC50v56BfcbqVChgiZNmqTo6Gg9+OCDNvcvitOnTxf6HlZwRWTBWARwxQhs8tVXX+ns2bPq3LnzNbeHhoaqZs2a2rBhg7p3766oqCh9/vnnGjVqlPr166fmzZvr9OnT+uqrrzRlyhQFBQWpd+/eWr9+vWbOnKk9e/aodevWOn/+vBISEvTII4+oS5cu6ty5s9q2bat///vf+uOPP2Q0GvX999/ryy+/1OOPP251GeBDDz2kd955R5MmTVJwcLB+/PHHa55Ba4vmzZsrISFBy5YtU+3atVW/fv1rzs9+pYSEBF28eLHQ+i5duujBBx/Up59+qsmTJ2vXrl1q1aqVTCaTDh06pM8++0zvvvuuQkJCbIoxOjpa27dv1yOPPKJHHnlE+fn5WrlypZo0aaJ9+/aVOB8AcHa2jmE3MnnyZD366KPq1auXHn74YTVo0ECZmZlKSkrSn3/+qQ0bNkiS7rnnHi1cuFAvvfSSwsLCdODAAcXHx1ud9XojEyZM0LFjxzR9+nR98cUX6tSpk6pVq6b//e9/+uyzz3To0CHLzQqff/557dixQ48++qgeffRRGQwGffTRR8rNzdXYsWMtjzlkyBD997//1ZAhQzR48GD5+PhozZo1qlevnlJSUiztfH19FRMTo3Hjxqlv377q3r27atasqbS0NG3btk2tWrUq1qXzAODIGjRooLCwMH355ZeSVOTCSFBQkIKCgm7Y5s4771TDhg01e/ZsnThxQr6+vvr888/L/ISlosTm4eGh6dOna+jQoerZs6f69u2rOnXq6MSJE9q1a5d8fX21aNEinT17VpGRkerWrZuCgoJUuXJl7dixQ8nJyZowYYKky9NWTp06Vf/85z91++23y2Qy6b///a8MBoO6desm6fLNd728vDR8+HANGDBAZ8+e1X/+8x/VqlVLGRkZlrj8/f01ePBgvffeexo+fLg6dOiglJQUffvtt6pRo4bV1fdRUVH66quvNHz4cPXp00fNmzfX+fPndeDAAX3++ef68ssvi3UADwCczbfffqtDhw7JZDIpMzNTu3bt0vfff6969erp7bffVsWKFSVd/rx/11136d1331VeXp7q1Kmj77//3upEq6v17t1bI0eOlCSNGjWq2DF26dJFXbp0KXb/m1m3bp1Wr16tLl26qGHDhjp79qzWrFkjX19fy8nVAIUR2GTDhg2qWLGi2rdvf83tHh4euueeexQfH6+//vpLNWrU0IcffqgFCxZoy5YtWrdunWrVqqXw8HDVqVNH0uUK9ZIlS/T2229r48aN+uKLL1S9enW1atVKRqPR8rhvv/225s+fr82bN+uTTz7RrbfeqnHjxumpp56yiuHZZ5/VyZMn9fnnn+vTTz9Vx44d9e677173JnxFMWHCBL366quaO3euLly4oD59+ty0kPDdd99d82ylW2+9VYGBgVq4cKHef/99/fe//9WWLVvk4+Oj+vXra9CgQde8ee7NBAcHa8mSJXr99dc1b9481a1bVyNHjtShQ4csU46VJB8AcHa2jmE30rhxY61du1Zvvvmm1q1bp1OnTqlmzZpq1qyZnn32WUu74cOH6/z584qPj9fmzZvVrFkzLV68WHPmzClSzD4+PlqyZIk++eQTrV+/Xm+99ZYuXLig2rVrq23btoqNjbWMp02aNNGHH36oOXPmaPHixTKbzWrRooX+9a9/We3ja9eureXLl2v69Ol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0YcMGvfzyy2ratKliY2PVvXt39evXT8uXL1eDBg3UoUMHbdu2TZ9++qnPfRUkqXPnznI6nXrxxRd15MgRhYaG6tJLL9WFF16ohx56SPfcc49GjhypIUOGKCoqSv/5z3/00UcfqVevXpo5c2a1PysAgHUo1AEAAevWW29VaWmp3nnnHeXk5CghIUELFy7UnDlzfPY7Vew8//zzWrFihVavXq1GjRqpV69e3mXqVe3f7XbrhRdeUElJiXr37q0//elP3plQSUpOTtZbb72l+fPna8mSJTp69Kiio6PVrVs3jRkzRtLJGdBT14bv2rVL99xzT4W+3nvvPTVo0EA9e/aUJE2ZMkVt27bVK6+8oueee07Syeu4+/btqyuvvFKS9MEHHygkJEQvvPCCUlNTf/H9zJ49WxdddJHefPNNPfXUU3I6nWrZsqXS0tLUq1evKn9ODodDM2bMOOs+QUFBeu655/TKK69o+fLleu+99xQWFqbY2FhNmDDhnJb4n6vbb79dUVFRWrJkiR599FFFRkbq+uuv11133fWLl0bcc889WrVqlT766CO99dZbKisrU4sWLTRu3DjddtttPs+vT09P18yZM/X000/r2LFjGjFihLp37677779fQUFBeuedd3T8+HH16tVLL7/8siZPnuzTV3R0tGbNmqWFCxfq/vvvl2EYeu2113ThhRdq2LBhatq0qV544QX93//9n06cOKGYmBhdcsklPl8WAAACk8O0w912AAAIILm5uZo4caKeeeaZKs8y/9QPP/yg/v376/3331dsbOxp95k3b54KCgqUkZFR7f4AAIC9cY06AAAAAAA2wtJ3AAAsFh4ermHDhp31Oefx8fFq2rRpLaYCAABWoVAHAMBiUVFRZ3x++SmDBg2qpTQAAMBqXKMOAAAAAICNcI06AAAAAAA2QqEOAAAAAICN1Nlr1D0ej8rLyxUUFCSHw2F1HAAAAADAec40TXk8HgUHByso6Mzz5nW2UC8vL9eOHTusjgEAAAAAqGO6du2q0NDQM26vs4X6qW8vunbtKqfTaXEaAAAAe/CYHgU5uDrSHwzD0I4dO/j3JmyHsWmdU5/92WbTpTpcqJ9a7p75ea72lBRanAYAAMB6MQ0idUP3VKtjnHecTifFEGyJsWmdX7r82naFenp6uoqKijR//nxv26pVq/SHP/xBd955p4YPH65nnnlGH330kQ4cOKDIyEh16tRJv/vd73TxxRdXur//lhSpoOigP98CAAAAAABVZrtC/efefvttzZo1S7NmzdKoUaN0ww03qKysTBkZGWrVqpV+/PFHbdiwQYcPH7Y6KgAAAAAA1WbrQv3FF1/UvHnz9NRTT2ngwIEqKirSpk2btHjxYiUlJUmSWrZsqW7dulmcFAAAAAAA/7DtnUKeeOIJzZ8/XwsXLtTAgQMlSeHh4QoPD9eaNWt04sQJixMCAAAAAOB/tizUP/74Yy1atEjz589X7969ve3BwcHKyMjQsmXLdMkll+jXv/61nnzySX355ZcWpgUAAAAAwH9sWajHx8erZcuWmjdvnkpKSny2DR48WOvWrdPzzz+v1NRU5eXlaeTIkcrKyrIoLQAAAAAA/mPLQj0mJkaLFy/Wvn37NHnyZBUXF/tsr1evnvr27aupU6fqzTff1IgRIzRv3jyL0gIAAAAA4D+2LNSlkzeJW7JkiQ4cOHDaYv2nOnTooKNHj9ZiOgAAAAAAaoat7/revHlzLV68WBMnTtTkyZM1Z84c3XfffRo1apTi4+NVv3597dy5U4sWLVL//v2r1EfT+g1lyPRzcgAAgMAT0yDS6ggAANm8UJekZs2aeYv1u+66S927d9err76q7777TuXl5WrWrJmuu+463XrrrVU6//Vd+8jpdPo5NQAAQGDymB4FOWy76BIA6gTbFeoZGRkV2mJiYvTuu+/WSH+GYVCow1YMw5Db7VZ8fDxjE7bC2IRdMTb9iyIdAKzH38QAAAAAANiI7WbUaxvfvMNunE6nEhISrI4BVMDYhF0xNlFTTI9HjiDmtQDUPtsU6nv27NHcuXO1bt06HT58WNHR0erfv7+mTp2qxo0bS5ImTJigTp066f777/c5NisrS4888og2bdpU6X6LPnTJPLTXL+8BAAAA5wdn46aKHDDW6hgA6ihbFOrff/+9xowZo7Zt2+rJJ59UbGysvv76az3xxBNat26d3nrrLTVq1KhG+jYO75fnQEGNnBsAAAAAgMqyxVqeWbNmKSQkRC+99JKSkpLUokULXX755Xr55Ze1b98+PfXUU1ZHBAAAAACgVlheqB8+fFjr16/XuHHjdMEFF/hsi46O1rBhw7Ry5UqZJs86BwAAAACc/yxf+v7tt9/KNE21b9/+tNvbt2+vwsJCHTx4UJK0dOlSuVwun33Ky8tVr169Gs8KAAAAAEBNs7xQP+VcZ8yHDRumW2+91adt9erVWrhwYU3EAgAAAACgVlleqLdu3VoOh0O7du3SwIEDK2zftWuXIiMjFRUVJUlq0KCB2rRp47PPhRdeWCtZAQAAAACoaZZfo964cWP17dtXb7zxho4dO+azbf/+/XrnnXd09dVXy+FwWJQQAAAAAIDaY3mhLkkPPvigTpw4oUmTJmnjxo3as2ePPv74Y918882KiYnRnXfeaXVEAAAAAABqheVL3yWpbdu2yszM1Lx58zR9+nQVFhaqSZMmGjBggKZOnVpjz1CXJGejaAWZRo2dHwAAAIHH2bip1REA1GEOs44+98wwDG3btk09evSQ0+m0Og4AAABsxvR45Aiq+gJU/r0Ju2JsWudcP3tbLH23kmEwmw57MQxD+fn5jE3YDmMTdsXYRE2pTpEOANVhi6XvVuIbJNiN0+lUXFyc1TGAChibsCvGpi+Px1RQEDfhBYBAVucL9Q82fK1DRaVWxwAAAKi2xg3DNTAl3uoYAIBqsn2hnp6eruzsbM2YMUNTpkzxtq9Zs0ZTp06V2+1Wbm6uJk6cqI0bN6phw4aVOv/hI0d14OBRf8cGAAAAAKBKAuLCm3r16unFF19UYWGh1VEAAAAAAKhRAVGo9+nTR02aNNHChQutjgIAAAAAQI0KiEI9KChId911l5YsWaK9e/daHQcAAAAAgBoTEIW6JA0cOFCdO3fW3LlzrY4CAAAAAECNCZhCXZLuvvtuLVu2TLt27bI6CgAAAAAANSKgCvXExESlpKRozpw5VkcBAAAAAKBG2P7xbD83Y8YMDR8+XO3atbM6CgAAAAAAfhdwhXp8fLyGDRumxYsX++V8jSLCZZoOv5wLAADASo0bhlsdAQDgBwFXqEvStGnTlJOT433t8XgkSU6ns9LnurL3r6p0HAAAgB15PKaCgpiEAIBAZvtCPSMjo0JbbGysdu7c6X198OBBhYeHq379+pU+v2EYFOqwFcMw5Ha7FR8fz9iErTA2YVeMTV8U6QAQ+GxfqJ/NiRMn9N1332nJkiXq3bu31XEAAAAAAKi2gLrr+899/PHHuu666xQeHq4HHnigSufgm3fYjdPpVEJCAmMTtsPYhF2dy9g0PWYtJgIAoHpsN6O+Z88ezZ07V+vWrdPhw4cVHR2t/v37a+rUqWrcuLEkacKECerUqZPuv/9+bd26VZL06quv6oknntBjjz2ma6655pz72/ehW8ah0hp5LwAAwHohjcPVbEBnq2MAAHDObFWof//99xozZozatm2rJ598UrGxsfr666/1xBNPaN26dXrrrbfUqFGjCsfNnTtXL730kubPn6/LLrusUn2WHS5V2YFiP70DAAAAAACqx1aF+qxZsxQSEqKXXnpJF1xwgSSpRYsWSkhI0MCBA/XUU09p1qxZ3v1N09Rf/vIX/f3vf9dLL72kXr16WRUdAAAAAAC/sM016ocPH9b69es1btw4b5F+SnR0tIYNG6aVK1fKNE9eY2YYhu6++269++67Wrx4MUU6AAAAAOC8YJsZ9W+//Vamaap9+/an3d6+fXsVFhbq4MGDkqS//e1vkqTly5ef8RgAAAAAAAKNbWbUTzk1Y/5LLr74YtWvX1/PPPOMysvLazgVAAAAAAC1wzaFeuvWreVwOLRr167Tbt+1a5ciIyMVFRUlSerYsaNeeeUV5ebm6s4776RYBwAAAACcF2xTqDdu3Fh9+/bVG2+8oWPHjvls279/v9555x1dffXVcjgc3vbOnTvr1Vdf1caNGzV9+nSVlZXVdmwAAAAAAPzKNoW6JD344IM6ceKEJk2apI0bN2rPnj36+OOPdfPNNysmJkZ33nlnhWM6deqkV199VZs3b6ZYBwAAAAAEPNvcTE6S2rZtq8zMTM2bN0/Tp09XYWGhmjRpogEDBmjq1KmnfYa6JMXHx+vVV1/VTTfdpDvuuENPP/20QkNDz6nPkEZhCjq3y+IBAEAACmkcbnUEAAAqxVaFuiS1bNlSGRkZZ91n8eLFFdo6duyoTz/9tNL9xVwRL6fTWenjAABA4DA9phxBjl/eEQAAG7DV0ncrGIZhdQTAh2EYys/PZ2zCdhibsKtzGZsU6bBKSEiI1REABCDbzajXNmbTYTdOp1NxcXFWxwAqYGzCrhibsCun06kuXRKsjgEgANX5Qn1D/qsqPPYfq2MAAADgPBMZ1kwpHSazEglApdm6UN+6davGjRun1NRUvfDCC972H374Qf3799eyZcvUuXPnavVRVPpfHSz9rrpRAQAAAADwC1tfo+5yuTR+/Hht3LhR+/btszoOAAAAAAA1zraFeklJiXJycjR27Fj169dP2dnZVkcCAAAAAKDG2bZQX7lypeLi4hQXF6e0tDRlZmbKNHngOQAAAADg/GbbQt3lciktLU2SlJqaqiNHjigvL8/iVAAAAAAA1CxbFur5+fnasWOHhg4dKkkKDg7WkCFD5HK5LE4GAAAAAEDNsuVd310ul8rLy5WamuptM01ToaGhmjlzpoXJAAAAAACoWbYr1MvLy7V8+XKlp6erb9++PtumTp2qFStW+BTwAAAAAACcT2xXqK9du1aFhYUaPXq0IiIifLYNGjRILpfLW6h/8803FY7v0KGDQkJCzrm/hmFNZTrKqxcaAAAA+JnIsGZWRwAQoGxXqLtcLvXp06dCkS5JgwcP1qJFi1RcXCxJuvPOOyvs89FHH6lZs3P/S7F33I1yOp1VDwwAAACcgWGUS3JYHQNAgLFdob5gwYIzbuvWrZvcbrckef+3ugzDoFCHrRiGIbfbrfj4eMYmbIWxCbtibMKuDMPQv/71ubp06WJ1FAABxpZ3fQcAAADOB2VlZVZHABCAbDejXtv45h1243Q6lZCQYHUMoALGJuwqkMamaXrkcDBPAgA4uzpfqB/+5ml5ju22OgYAADjPBYe1UuP291gdAwAQAGxRqKenpys7O1tjxozR7NmzfbbNmjVLb7zxhkaMGKGMjAxv+9atWzVu3DilpqbqhRdeqHLf5aUFMkp3Vfl4AAAAAAD8yTZrr5o3b66cnBwdO3bM23b8+HGtWLFCLVq0qLC/y+XS+PHjtXHjRu3bt682owIAAAAAUGNsU6gnJCSoefPmWr16tbdt9erVat68uTp37uyzb0lJiXJycjR27Fj169dP2dnZtR0XAAAAAIAaYZtCXZJGjRqlrKws7+vMzEyNHDmywn4rV65UXFyc4uLilJaWpszMTJmmWZtRAQAAAACoEbYq1NPS0rR582YVFBSooKBAW7ZsUVpaWoX9XC6Xtz01NVVHjhxRXl5ebccFAAAAAMDvbFWoR0VFeZeyZ2VlqV+/foqKivLZJz8/Xzt27NDQoUMlScHBwRoyZIhcLpcVkQEAAAAA8Ctb3PX9p0aNGuW98/uf/vSnCttdLpfKy8uVmprqbTNNU6GhoZo5c6YiIiJqLSsAAAAAAP5mu0I9NTVVZWVlcjgcSklJ8dlWXl6u5cuXKz09XX379vXZNnXqVK1YsUJjx46tzbgAAAAAAPiV7Qp1p9OplStXen/+qbVr16qwsFCjR4+uMHM+aNAguVyuShfqwWEtFeQoq15oAACAXxAc1srqCACAAGG7Ql2SGjRocNp2l8ulPn36nHZ5++DBg7Vo0SJ9+eWX6tSp0zn31ajd9ApfCAAAANQE0/TI4bDVLYIAADZki0I9IyPjrNvnz5//i+fo1q2b3G53pfs2DINCHbZiGIbcbrfi4+MZm7AVxibsKpDGJkU6AOBc8NsCAAAAAAAbscWMupXs/s076h6n06mEhASrYwAVMDZhV7U1Nlm2DgCoLZYW6unp6crOztaYMWO8j2Q7ZdasWXrjjTc0YsQI79L4PXv2aO7cuVq3bp0OHz6s6Oho9e/fX1OnTlXjxo2rlGHfV+tllBZW+70AAIDzV0h4pJp1utzqGACAOsLyGfXmzZsrJydHf/zjH3XBBRdIko4fP64VK1aoRYsW3v2+//57jRkzRm3bttWTTz6p2NhYff3113riiSe0bt06vfXWW2rUqFGl+y87WqSyoz/66+0AAAAAAFAtlq/fSkhIUPPmzbV69Wpv2+rVq9W8eXN17tzZ2zZr1iyFhITopZdeUlJSklq0aKHLL79cL7/8svbt26ennnrKivgAAAAAAPiV5YW6JI0aNUpZWVne15mZmRo5cqT39eHDh7V+/XqNGzfOO+t+SnR0tIYNG6aVK1fKNM1aywwAAAAAQE2wRaGelpamzZs3q6CgQAUFBdqyZYvS0tK827/99luZpqn27duf9vj27dursLBQBw8erK3IAAAAAADUCMuvUZekqKgo9evXT9nZ2TJNU/369VNUVFSF/ZgxBwAAAACc72wxoy79b/l7dna2Ro0a5bOtdevWcjgc2rVr12mP3bVrlyIjI09b3AMAAAAAEEhsU6inpqaqrKxM5eXlSklJ8dnWuHFj9e3bV2+88YaOHTvms23//v165513dPXVV8vhcNRmZAAAAAAA/M4WS98lyel0auXKld6ff+7BBx/Ur3/9a02aNEnTp0/3eTxbTEyM7rzzzir1GxLeUEEOltQDAIAzCwmPtDoCAKAOsU2hLkkNGjQ447a2bdsqMzNT8+bN0/Tp01VYWKgmTZpowIABmjp1apWeoS5JMR1TTvvFAAAAwE+ZpkcOh20WIwIAzmOWFuoZGRln3T5//nyf1y1btvzFYyrLMAwKddiKYRhyu92Kj49nbMJWGJuwq9oamxTpAIDawm8cAAAQ8EpLS62OAACA39hq6bsVmBWC3TidTiUkJFgdA6iAsVn7TNPkRqkAANRBdb5Q3759u4qLi62OAQCAj4iICPXs2dPqGAAAwAK2LNTT09NVVFRU4Rr1n9q7d68GDBigtm3basWKFVXuq7i4WEVFRVU+HgAAAAAAfwrYa9SzsrJ01VVXqbi4WNu3b7c6DgAAAAAAfhGQhbppmsrKytK1116roUOHyuVyWR0JAAAAAAC/CMhC/bPPPtOxY8fUp08fpaWl6R//+IeOHj1qdSwAAAAAAKotIAt1l8ulIUOGyOl0qmPHjmrVqpVWrVpldSwAAAAAAKot4Ar1oqIivffee0pLS/O2paWlsfwdAAAAAHBesOVd38/mnXfe0fHjx3X99dd720zTlMfj0TfffKN27dpZmA4AAAAAgOoJuEI9MzNTN998s0aMGOHTPmvWLGVmZuruu++2KBkAAAAAANVn20L9yJEj+uKLL3zaSkpK9K9//UtPPPGE2rdv77Ptmmuu0fz58zV9+nQFB5/722rQoIFf8gIA4E8RERFWRwAAABaxbaGel5en4cOH+7SNHDlSHTp0qFCkS9LAgQP15z//WR999JH69+9/zv10795dTqezunEBAPA70zTlcDisjgEAAGqZLQv1jIwMZWRkVOqY6OjoCjPw58IwDAp12IphGHK73YqPj2dswlYYm7WPIh0AgLop4O76DgAA8HNhYWFWRwBOi7EJu2Js2pvDNE3T6hBWMAxD27ZtU48ePZgZAgAAAIAA5DE9CnIEzvzzudahli19X7p0qR5//HFt3LjRe/O3kpISJSUlqVevXlq8eLF339zcXE2cOFENGzbULbfcoilTplQ433PPPafXX39dH330kUJCQs45x992fKo9JYXVf0MAAAAAgFoT0yBSN3RPtTpGjbCsUE9OTtbRo0e1c+dO9ejRQ5K0adMmNWnSRNu3b9fx48dVr149SScL9RYtWmjAgAHKzMysUKibpqns7Gxde+21lSrSJem/JUUqKDrol/cEAAAAAEB1WbZGIC4uTtHR0crLy/O25eXlqX///oqNjdW2bdt82pOTkzV69Gjt3r1bmzZt8jlXXl6evv/+e40ePbq24gMAAAAAUCMsXcyfnJys3Nxc7+vc3FwlJSUpMTHR237s2DFt375dycnJio+PV9euXZWZmelznqysLPXs2fO0j20DAAAAACCQWFqoX3rppdqyZYvKy8tVXFysL774wluon5pp37p1q06cOKHk5GRJ0ujRo7Vq1SqVlJRIkoqLi/Xuu+9q1KhRlr0PAAAAAAD8xdJCPSkpSUePHtWOHTu0efNmtW3bVlFRUUpMTPRep56Xl6dWrVqpRYsWkqShQ4fK4/Fo5cqVkqSVK1fK4XBoyJAhVr4VAAAAAAD8wtJCvU2bNmrWrJlyc3P12WefKTExUZIUExOj5s2ba8uWLcrNzdWll17qPaZBgwYaPHiwsrKyJEmZmZm6+uqrVb9+fUveAwAAAAAA/mT5A+eSk5OVl5envLw8JSUledsvueQSffzxx/rnP//pXfZ+yujRo7V582Z9+OGH2rp1KzeRAwAAAACcN2xRqG/evFlffvmlT6GelJSkt956S2VlZRUK9cTERLVp00b33nuv4uLi1KtXr9qODQAAAABAjbDsOeqnJCcn69ixY4qLi1OTJk287YmJiSopKVG7du3UtGlTn2McDodGjRqlJ598ssIz1Suraf2GMmRW6xwAAAAAgNoV0yDS6gg1xmGaZp2sUg3D0LZt29SjRw85nU6r4wAAAAAAKsljehTksHyh+Dk71zo0cN5RDTEMw+oIgA/DMJSfn8/YhO0wNmFXjE3YlWEY+vzzzxmbsJ3zaWwGUpFeGZYvfbcas+mwG6fTqbi4OKtjABUwNmFXjM3aZ3o8cgSdn/849rfS0lKrIwCnxdi0tzpfqBd96JJ5aK/VMQAAAAKCs3FTRQ4Ya3UMADiv2b5QT09PV1FRkebPn6/09HRlZ2drxowZPjeRW7NmjaZOnSq3213p8xuH98tzoMCfkQEAAAAAqLKAW7NUr149vfjiiyosLLQ6CgAAAAAAfhdwhXqfPn3UpEkTLVy40OooAAAAAAD4XcAV6kFBQbrrrru0ZMkS7d3LteUAAAAAgPNLwBXqkjRw4EB17txZc+fOtToKAAAAAAB+FZCFuiTdfffdWrZsmXbt2mV1FAAAAAAA/CZgC/XExESlpKRozpw5VkcBAAAAAMBvbP94trOZMWOGhg8frnbt2lkdBQAAAAAAvwjoQj0+Pl7Dhg3T4sWLq3wOZ6NoBZmGH1MBAACcv5yNm1odAQDOewFdqEvStGnTlJOTU+XjG14xWk6n04+JAAAAzm+mxyNHUMBeQQkAtmf7Qj0jI+O0P58SGxurnTt3Vvn8hmFQqMNWDMOQ2+1WfHw8YxO2wtiEXTE2ax9FOgDULP6WBQAAAADARmw/o17T+OYdduN0OpWQkGB1DKACxibsKpDGpsdjKijIYXUMAIDN1flC/YMNX+tQUanVMQAAwHmuccNwDUyJtzoGACAABEShnp6eruzsbElScHCwYmJidNVVV+mOO+5QvXr1JJ28A/xzzz2nAQMGVOrch48c1YGDR/2eGQAAAACAqgiIQl2SUlNT9eijj6q8vFz/+te/dO+998rhcOgPf/iD1dEAAAAAAPCbgCnUQ0NDFR0dLUlq3ry5+vTpo08//dTiVAAAAAAA+FdA3vX9q6++0tatWxUSEmJ1FAAAAAAA/CpgZtTXrl2rnj17qry8XCdOnFBQUJAefPBBq2MBAAAAAOBXAVOoJycn66GHHlJpaaleeeUVOZ1ODR482OpYAAAAAAD4VcAsfQ8LC1ObNm3UqVMnPfLII/rnP/+pt99+2+pYAAAAAAD4VcAU6j8VFBSk3/72t3rmmWd07Ngxq+MAAAAAAOA3AbP0/eeuuuoqPf7443r99dc1adKkKp+nUUS4TNPhx2QAAAAVNW4YbnUEAECACNhCPTg4WOPHj9eiRYs0duzYKp/nyt6/ktPp9GMyAACA0/N4TAUFMUEAADi7gCjUMzIyTts+ZcoUTZkyRZLkdrurdG7DMCjUYSuGYcjtdis+Pp6xCVthbMKuAmlsUqQDAM5FQF6jDgAA8FOlpaVWRwAAwG8CYka9Jtn9m3fUPU6nUwkJCVbHACqwy9g0PaYczEoCAIDzWEAU6unp6SoqKtL8+fO9bVu3btW4ceOUmpqqF154ocrn3vehW8YhvoUHgEAQ0jhczQZ0tjoGAABAjQqIQv10XC6Xxo8fL5fLpX379ikmJqZK5yk7XKqyA8V+TgcAAAAAQNUE5DXqJSUlysnJ0dixY9WvXz9lZ2dbHQkAAAAAAL8IyEJ95cqViouLU1xcnNLS0pSZmSnTNK2OBQAAAABAtQVkoe5yuZSWliZJSk1N1ZEjR5SXl2dxKgAAAAAAqi/gCvX8/Hzt2LFDQ4cOlSQFBwdryJAhcrlcFicDAAAAAKD6Au5mci6XS+Xl5UpNTfW2maap0NBQzZw5UxERERamAwAAAACgegKqUC8vL9fy5cuVnp6uvn37+mybOnWqVqxYobFjx1qUDgAAAACA6guoQn3t2rUqLCzU6NGjK8ycDxo0SC6Xq9KFekijMAVxHzoACAghjcOtjgAAAFDjAqpQd7lc6tOnz2mXtw8ePFiLFi3Sl19+qU6dOp3zOWOuiJfT6fRnTABADTI9phxBDqtjAAAA1JiAKNQzMjJ+cZ9u3brJ7XZX+tyGYVCow1YMw5Db7VZ8PF8iwV7sMjYp0gEAwPku4O76DgAA8HNhYWFWRwAAwG8CYka9JjFjCbtxOp1KSEiwOgZQAWMTdsXY9OUxPQpyMBcDAIGszhfqG/JfVeGx/1gdAwAAoNoiw5oppcNkq2MAAKrJNoV6enq6srOzNWbMGM2ePdtn26xZs/TGG29oxIgRys7OPut5fv/73+v2228/536LSv+rg6XfVSkzAAAAAAD+ZptCXZKaN2+unJwc/fGPf9QFF1wgSTp+/LhWrFihFi1aSJLWr1/v3T8nJ0dz587VqlWrvG3h4Ty6BwAAAAAQuGxVqCckJOj777/X6tWrlZaWJklavXq1mjdvrtjYWElSdHS0d/+IiAg5HA6fNgAAAAAAApnt7jQyatQoZWVleV9nZmZq5MiRFiYCAAAAAKD22K5QT0tL0+bNm1VQUKCCggJt2bLFO7sOAAAAAMD5zlZL3yUpKipK/fr1U3Z2tkzTVL9+/RQVFWV1LAAAAAAAaoXtZtSl/y1/z87O1qhRo6yOAwAAAABArbHdjLokpaamqqysTA6HQykpKVbHAQAAAACg1tiyUHc6nVq5cqX355rUMKypTEd5jfYBAABQGyLDmlkdAQDgB7Ys1CWpQYMGtdJP77gba/zLAAAAgNriMT0Kctjy6kYAwDmyTaGekZFx1u3z58+v0DZy5MhqP7rNMAwKddiKYRhyu92Kj49nbMJWGJuwK8amL4p0AAh8/E0OAAAA1JCwsDCrIwAIQLaZUbcK37zDbpxOpxISEqyOAVTA2IRdMTZhV7U1Nk3TIwcrKYDziq0K9fT0dGVnZ2vGjBmaMmWKt33NmjWaOnWqHnvsMT300ENavny52rRp492+b98+DR06VHfccYfGjx9fqT4Pf/O0PMd2++stAAAAALUmOKyVGre/x+oYAPzMVoW6JNWrV08vvviixowZo8jISJ9tw4cP15o1a5Senq7XX39dQUEnvzl88MEH1aVLF91www2V7q+8tEBG6S6/ZAcAAAAAoLpst0amT58+atKkiRYuXHja7bNnz9bu3bv18ssvS5KysrK0ZcsWPfroo3I4HLUZFQAAAAAAv7NdoR4UFKS77rpLS5Ys0d69eytsj4qK0p///Gc988wz+uSTT/Too4/q/vvvV/PmzS1ICwAAAACAf9muUJekgQMHqnPnzpo7d+5ptw8YMEBXX321Jk+erMTERI0YMaKWEwIAAAAAUDNsWahL0t13361ly5Zp167TXz/+u9/9Th6PR7fddlstJwMAAAAAoObYtlBPTExUSkqK5syZc9rtpx6rFhxsu/vhAQAAAABQZbaucmfMmKHhw4erXbt2VkcBAAAAAKBW2HZGXZLi4+M1bNgwLV682OooAAAAAADUClvPqEvStGnTlJOTU2PnDw5rqSBHWY2dHwAAAKgpwWGtrI4AoAY4TNM0rQ5hBcMwtG3bNvXo0cN7vTsAAAAQaEzTI4fD1gtlYTPUQtY518++zv8/2jAMqyMAPgzDUH5+PmMTtsPYhF0xNmFXhmHo888/r/GxSZEOnH9sv/S9pvENEuzG6XQqLi7O6hhABYxN2BVjMzDVlVng0tJSqyMACEB1vlDf99V6GaWFVscAAACoM0LCI9Ws0+VWxwAA27J1oZ6enq6ioiLNnz9f6enpys7OlnTy2emRkZGKj4/XNddco5EjRyooqGrfyJYdLVLZ0R/9GRsAAAAAgCqzdaH+c6mpqXr00Ufl8Xh04MABrVu3Tg8//LDeffddPf/88woODqi3AwAAAABABQFV2YaGhio6OlqSFBMToy5duqh79+666aablJ2dreuuu87ihAAAAAAAVE/A38Gjd+/e6tSpk1avXm11FAAAAAAAqi3gC3VJiouLU0FBgdUxAAAAAACotvOiUDdNUw6Hw+oYAAAAAABU23lRqO/atUuxsbFWxwAAAAAAoNoCvlDfsGGDvvrqKw0aNMjqKAAAAAAAVFtA3fX9xIkT2r9/v8/j2RYuXKgrrrhCw4cPr9I5Q8IbKshh+jcoAAAAzigkPNLqCABgawFVqK9bt04pKSkKDg5Ww4YN1alTJz3wwAMaMWKEgoKqtjggpmOKnE6nn5MCAADgbEzTI4cj4Bd3AkCNsHWhnpGR4fPzT1/7i2EYFOqwFcMw5Ha7FR8fz9iErTA2YVeMzcBEkQ4AZ8bfkAAAAAAA2IitZ9RrA9+8w26cTqcSEhKsjgFUwNiEXTE2AxOP1wWAM7NVoX7rrbeqrKxM//d//1dh26ZNm3TDDTf84jncbnel+ty+fbuKi4srdQwAAACqLiIiQj179rQ6BgDYlq0K9dGjR+v222/X3r171axZM59tmZmZ6ty5s1588UWf/a+//npdf/31Ve6zuLhYRUVFVT4eAAAAAAB/stU16v369VNUVJSysrJ82ktKSrRq1SqNGTNG0dHR3j9Op1P169f3aQMAAAAAIJDZqlAPDg7Wtddeq+zsbJnm/55tvmrVKnk8Hg0dOtTCdAAAAAAA1DxbFeqSNGrUKH333XfKy8vztmVlZWnQoEGKiIiwMBkAAAAAADXPdoV6+/bt1bNnT2VmZkqSvv32W23atEmjR4+2OBkAAAAAADXPdoW6dPImcatXr1ZxcbGysrLUunVrJSUlWR0LAAAAAIAaZ8tC/eqrr5bD4dCKFSu0bNkyjRo1iudsAgAAAADqBFs9nu2U+vXra8iQIXryySdVXFysESNGWB0JAAAAAIBaYctCXTq5/N3lcunyyy9XTExMjfXToEGDGjs3AAAAKuIGwQBwdrYt1Hv27Cm3233WfT744INq99O9e3c5nc5qnwcAAADnzjRNLm0EgDOw5TXqtckwDKsjAD4Mw1B+fj5jE7bD2IRdMTYDE0U6AJyZbWfUawuz6bAbp9OpuLg4q2MAFTA2YVeMTVSVx/QoyFHn560A2FCdL9T/tuNT7SkptDoGAAAAalFMg0jd0D3V6hgAcFq2KtRvvfVWlZWV6f/+7/8qbNu0aZNuuOEGLV++XJ06ddLMmTP19ttv68knn9TVV19d5T7/W1KkgqKD1YkNAAAAAIDf2Gqtz+jRo/Xpp59q7969FbZlZmbqoosuUqdOnVRaWqp//OMfmjx5sjIzMy1ICgAAAABAzbBVod6vXz9FRUUpKyvLp72kpESrVq3S6NGjJUmrVq1Shw4dNGXKFG3atEl79uyxIi4AAAAAAH5nq0I9ODhY1157rbKzs2Waprd91apV8ng8Gjp0qCTJ5XIpLS1NERERuuyyyyoU9gAAAAAABCpbFeqSNGrUKH333XfKy8vztmVlZWnQoEGKiIjQ7t27tX37du916WlpacrKyvIp7AEAAAAACFS2K9Tbt2+vnj17eq89//bbb7Vp0ybvsvfMzEylpKQoKipKknTZZZepuLhYn332mWWZAQAAAADwF9sV6tLJm8qtXr1axcXFysrKUuvWrZWUlCTDMJSdna21a9cqISFBCQkJ6tGjhw4fPiyXy2V1bAAAAAAAqs1Wj2c75eqrr9bDDz+sFStWaNmyZRo7dqwcDoc++ugjlZSUaNmyZQoK+t93DF9//bXuu+8+FRUVqWHDhhYmBwAAAACgemxZqNevX19DhgzRk08+qeLiYo0YMULSyZvI9evXT506dfLZv0OHDnr00Uf1zjvv6IYbbqhUX03rN5Qhrm8HAACoS2IaRFodAQDOyJaFunRy+bvL5dLll1+umJgYHThwQB999JH++te/Vtg3KChIAwYMkMvlqnShfn3XPnI6nf6KDQAAgADhMT0KctjySlAAdZxtC/WePXvK7XZ7Xzdp0kT/+te/zrj/Qw89VKV+DMOgUIetGIYht9ut+Ph4xiZshbEJu2Jsoqoo0gHYFX87AQAAAABgI7adUa8tfPMOu3E6nUpISLA6BlABYxN2xdi0H9PjkSOI+SAAqKo6X6gXfeiSeWiv1TEAAADOC87GTRU5YKzVMQAgoNmqUI+Pjz/r9t///vfq1q2bpk6dqrfeektdunTxbnvppZe0cOFCrVixQtHR0efcp3F4vzwHCqqcGQAAAAAAf7JVob5+/Xrvzzk5OZo7d65WrVrlbQsPD1f9+vV17bXX6t5771VWVpZCQ0P173//W08//bQyMjIqVaQDAAAAAGA3trp4KDo62vsnIiJCDofDp61+/fqSpPvuu09Hjx7V3LlzVV5ernvvvVdXXHGFhgwZYvE7AAAAAACgemw1o36uGjRooIcffliTJ0/WDz/8oL1792rRokVWxwIAAAAAoNoCslCXpN69e2vw4MH6xz/+oaeeekqNGze2OhIAAAAAANVmq6XvlbFv3z6tW7dOYWFh2rx5s9VxAAAAAADwi4At1B944AF16dJFCxYs0NKlS5WXl2d1JAAAAAAAqi0gC/W3335bmzdv1sMPP6xLL71UY8eO1R//+EcdPXrU6mgAAAAAAFRLwF2jXlBQoEcffVT33nuvWrZsKUm6++679fHHH2vOnDl68MEHK3U+Z6NoBZlGTUQFAACoc5yNm1odAQACXkAV6qZp6v7771fPnj01ZswYb3tYWJgeffRRTZw4UYMHD1ZSUtI5n7PhFaPldDprIi4AAECdZHo8cgQF5MJNALAF2xbqI0eO1MiRI33aHA6HXnnlldPuf8kll+jzzz+vdD+GYVCow1YMw5Db7VZ8fDxjE7bC2IRdMTbthyIdAKqHv0UBAAAAALAR286o1xa+eYfdOJ1OJSQkWB0DqICxCbuqrbHp8ZgKCnLUeD8AANi2UE9PT1d2dnaF9tWrV+v5559XUVGR5s+fX+1+PtjwtQ4VlVb7PAAA4PzVuGG4BqbEWx0DAFBH2LZQl6TU1FQ9+uijPm1RUVF+7ePwkaM6cJDHugEAAAAA7MHWhXpoaKiio6OtjgEAAAAAQK3hZnIAAAAAANiIrWfU165dq549e3pfp6amau7cuRYmAgAAAACgZtm6UE9OTtZDDz3kfR0WFmZdGAAAAAAAaoGtC/WwsDC1adPG6hgAAAAAANQarlEHAAAAAMBGbD2jfjZHjhzRF1984dPWqFEjNW/e3KJEAAAAAABUX8AW6nl5eRo+fLhP2+jRo/Xwww9X6jyNIsJlmg4/JgMAAOebxg3DrY4AAKhDHKZpmlaHsIJhGNq2bZt69Oghp9NpdRwAAGBzHo+poCC+3Me549+bsCvGpnXO9bOv89eoG4ZhdQTAh2EYys/PZ2zCdhibsKvaGpsU6QCA2hKwS9/9hW+QYDdOp1NxcXFWxwAqYGzCrs6nsWl6TDn4QgAA6rw6X6jv+9At41Cp1TEAAEAdF9I4XM0GdLY6BgDABmxVqO/fv18LFizQ2rVrtW/fPl144YXq3LmzbrzxRvXu3VuStGXLFj3//PPatm2bjh07prZt22rkyJGaOHFilWbHyw6XquxAsb/fCgAAAAAAVWKbQv2HH37Q2LFj1bBhQ91zzz3q2LGjysvLtX79es2aNUurVq3Se++9p+nTp2vkyJF67bXXFBERoQ0bNuiJJ57Q1q1b9cwzz8jhYLkYAAAAACBw2aZQnzVrlhwOh95++22Fh//vESi/+tWvNGrUKB09elQPPPCArrzySv35z3/2br/uuut04YUX6rbbbtPKlSs1ZMgQK+IDAAAAAOAXtrjr++HDh7Vu3TrdcMMNPkX6KQ0bNtQnn3yiw4cP6+abb66w/corr1Tbtm21YsWK2ogLAAAAAECNsUWh/t1338k0zbPesfWbb76RJLVv3/602+Pi4rR79+6aiAcAAAAAQK2xRaFummaN7AsAAAAAQKCxRaHepk0bORwO5efnn3Gfdu3aSZJ27dp12u35+flq27ZtTcQDAAAAAKDW2KJQb9SokVJSUvT666/r6NGjFbYXFRWpb9++atSokV5++eUK299//33t3r1bQ4cOrY24AAAAAADUGNvc9f1Pf/qTxo4dq+uuu07Tpk1TfHy8DMPQJ598oqVLl2rlypWaNWuW7rrrLj344IO64YYb1KBBA+/j2QYPHqyrr7660v2GNApTEKvpAQCAxUIaV7yhLgCgbrJNod6qVStlZWVpwYIFeuyxx/Tf//5XUVFR6tKlix566CFJ0lVXXaUmTZro+eef1w033KDjx4+rbdu2uvXWW3XjjTdW6RnqMVfEy+l0+vndAAAAVJ7pMeUIqvy/ZwAA5xeHWUfvzmYYhrZt26auXbsqNDTU6jiAl2EYcrvdio/nSyTYC2MTdsXYhF2d+vdmjx49GJuwFcamdc71s7fFNeoAAAAAAOAk2yx9twrfIMFunE6nEhISrI4BVMDYhF3ZZWx6TI+CHMyBAACqz5aF+tatWzVu3DilpqbqhRde8LZPmDBBeXl5Zzxu8eLFSkpKqlRfG/JfVeGx/1Q5KwAAQGRYM6V0mGx1DADAecKWhbrL5dL48ePlcrm0b98+xcTESJLmzZunsrIyn33Lysr029/+VqGhoerevXul+yoq/a8Oln7nl9wAAAAAAFSX7Qr1kpIS5eTkKDMzUwcOHFB2drZuvfVWSSeft/5zDzzwgA4dOiSXy6V69erVcloAAAAAAPzLdhdSrVy5UnFxcYqLi1NaWpoyMzN1phvTv/7661q2bJnmzp2rZs2a1XJSAAAAAAD8z3aFusvlUlpamiQpNTVVR44cOe116Rs3btSjjz6qP/3pT+rVq1dtxwQAAAAAoEbYqlDPz8/Xjh07NHToUElScHCwhgwZIpfL5bPff/7zH02bNk3XX3+9rrvuOiuiAgAAAABQI2x1jbrL5VJ5eblSU1O9baZpKjQ0VDNnzlRERISOHTum3//+9+rQoYP++Mc/WpgWAAAAAAD/s02hXl5eruXLlys9PV19+/b12TZ16lStWLFCY8eO1f3336/Dhw9r0aJFCg62TXwAAAAAAPzCNpXu2rVrVVhYqNGjRysiIsJn26BBg+RyuVRSUqJ3331Xzz//vAzD0P79+332i4iI0AUXXFCbsQEAAAAA8CvbFOoul0t9+vSpUKRL0uDBg7Vo0SLt3LlTkjR58uTTnuPRRx/VyJEjK9Vvw7CmMh3llQ8MAADw/0WG8fQZAID/2KZQX7BgwRm3devWTW63u0b67R13o5xOZ42cGwAA1B0e06Mgh63u0wsACFB1/reJYRhWRwB8GIah/Px8xiZsh7EJuzIMQ1999ZXlY5MiHQDgL7aZUbcKs+mwG6fTqbi4OKtjABUwNmFXTqdTHdrHybQ6CAAAflLnC/XD3zwtz7HdVscAAABVFBzWSo3b32P5jDoAAP5iu0L94MGDeuaZZ/TRRx/pwIEDioyMVKdOnfS73/1OF198sSTp888/14IFC7Rp0yYdOXJEzZs3V1JSkiZNmqR27dpVqr/y0gIZpbtq4q0AAAAAAFBptivUb7/9dpWVlSkjI0OtWrXSjz/+qA0bNujw4cOSpA8//FC33367UlJS9Ne//lWtWrXSwYMHtWrVKj3zzDN6+umnLc0PAAAAAEB12KpQLyoq0qZNm7R48WIlJSVJklq2bKlu3bpJkkpLS3Xffffp8ssv13PPPec9rlWrVurevbuKioosyQ0AAAAAgL/Y6vak4eHhCg8P15o1a3TixIkK29evX69Dhw6d8TnqDRs2rOmIAAAAAADUKFsV6sHBwcrIyNCyZct0ySWX6Ne//rWefPJJffnll5Kk3bt3SxJ3HQYAAAAAnLdsVahL0uDBg7Vu3To9//zzSk1NVV5enkaOHKmsrCyZJg9eAQAAAACc32xXqEtSvXr11LdvX02dOlVvvvmmRowYoXnz5nnv6J6fn29xQgAAAAAAaoYtC/Wf69Chg44ePaq+ffuqcePGWrRo0Wn342ZyAAAAAIBAZ6u7vh86dEh33HGHRo0apfj4eNWvX187d+7UokWL1L9/f4WHh+svf/mLpk+frltvvVUTJ05U69atdejQIa1cuVJ79uzRU089Vak+g8NaKshRVkPvCAAA1LTgsFZWRwAAwK9sVajXr19f3bt316uvvqrvvvtO5eXlatasma677jrdeuutkqQBAwZo6dKleuGFFzRjxgwVFxerefPmuvTSSzV9+vRK99mo3XQ5nU4/vxMAAFCbPEa5JIfVMQAA8AtbFeqhoaGaMWOGZsyYcdb9unbtqnnz5vmlT8MwKNRhK4ZhyO12Kz4+nrEJW2Fswq4Mw9C//vW5unTpYnUUAAD8IiCuUQcAADib4GBbzT0AAFAtdf63GrNCsBun06mEhASrYwAVMDZhV+cyNk3TI4eD+QkAQGCo84X6vq/WyygttDoGAACoISHhkWrW6XKrYwAAcM5sVainp6crOztbM2bM0JQpU7zta9as0dSpU+V2u5Wbm6uJEyee9vj169crOjq6Un2WHS1S2dEfq5UbAAAAAAB/sVWhLkn16tXTiy++qDFjxigyMvKM+61atUoNGjTwabvwwgtrOh4AAAAAADXKdhdr9enTR02aNNHChQvPut+FF16o6Ohonz9BQbZ7OwAAAAAAVIrtKtugoCDdddddWrJkifbu3Wt1HAAAAAAAapXtCnVJGjhwoDp37qy5c+eecZ/LL79cPXv29P655pprajEhAAAAAAA1w3bXqJ9y991368Ybb9SkSZNOu/31119X/fr1va95fioAAAAA4Hxg2+o2MTFRKSkpmjNnjkaOHFlhe2xsrBo2bGhBMgAAAAAAao5tC3VJmjFjhoYPH6527dpZHQUAAAAAgFph60I9Pj5ew4YN0+LFiyts+/HHH3X8+HGftkaNGikkJKRSfYSEN1SQw6xWTgAAYF8h4Wd+3CsAAHZk60JdkqZNm6acnJwK7VdddVWFtrfeeks9evSo1PljOqbI6XRWNR4AAAgApumRw2HLe+gCAFCBrQr1jIyMCm2xsbHauXOn93VycrLcbrff+jQMg0IdtmIYhtxut+Lj4xmbsBXGJuzqXMYmRToAIJDwWwsAAAAAABux1Yy6FZgVgt04nU4lJCRYHQOogLEJu2JsoqpM05TD4bA6BgBUYGmhvnTpUj3++OPauHGj9znoJSUlSkpKUq9evXxuIpebm6uJEyeqcePGuummm3Trrbf6nOuOO+7Qnj17tHTp0koV39u3b1dxcbF/3hAAAAACQkREhHr27Gl1DAA4LUsL9eTkZB09elQ7d+703gRu06ZNatKkibZv367jx4+rXr16kk4W6i1atNADDzygO+64Q1dccYXi4+MlSStXrtTatWuVnZ1d6Rny4uJiFRUV+fV9AQAAAABQVZZeox4XF6fo6Gjl5eV52/Ly8tS/f3/FxsZq27ZtPu3Jycnq37+/hg0bpvT0dJWVlengwYOaPXu2ZsyYobi4OAveBQAAAAAA/mP5zeSSk5OVm5vrfZ2bm6ukpCQlJiZ6248dO6bt27crOTlZknT//ffr8OHDmj9/vh566CH96le/0oQJEyzJDwAAAACAP1l+M7lLL71UjzzyiMrLy3Xs2DF98cUXSkpKUnl5ud58801J0tatW3XixAlvod6gQQM98sgjmjRpksLCwvT3v/+dG4EAAAAAAM4LlhfqSUlJOnr0qHbs2KGioiK1bdtWUVFRSkxM1H333afjx48rLy9PrVq1UosWLbzH9e7dW927d1fnzp3VsmVLC98BAAAAAAD+Y3mh3qZNGzVr1ky5ubkqLCxUYmKiJCkmJkbNmzfXli1blJubq0svvbTCscHBwTxeDQAAAABwXrH8GnXp5HXqeXl5ysvLU1JSkrf9kksu0ccff6x//vOf3mXvAAAAAACczyyfUZdOFuqzZ89WeXm5T6GelJSk2bNnq6ysrMYK9QYNGtTIeQEAAGBfERERVkcAgDOyTaF+7NgxxcXFqUmTJt72xMRElZSUqF27dmratGmN9N29e3eWzwMAANRBpmlyQ2IAtmSLQj02NlZut7tCe8uWLU/bfsrixYur3bdhGBTqsBXDMOR2uxUfH8/YhK0wNmFXjE1UFUU6ALuyxTXqAAAA1VFaWmp1BAAA/MYWM+pW4pt32I3T6VRCQoLVMYAKGJuBx2N6FOTgO3kAAAJNnS/U/7bjU+0pKbQ6BgAAfhXTIFI3dE+1OgYAAKgC2xfq6enpKioq0vz5833ac3NzNXHiRG3cuFFffPGF9+eGDRtW6vz/LSlSQdFBf0YGAAAAAKDKqrUe7sSJE8rPz1d5ebm/8gAAAAAAUKdVqVAvLS3VH//4R/Xo0UNDhw7Vnj17JEl//vOf9cILL/g1IAAAAAAAdUmVCvU5c+boyy+/1GuvvaZ69ep523v37q2cnBy/hQMAAAAAoK6p0jXq77//vp566in16NHDp/1Xv/qVvvvuO3/k8rF27Vr17NnTp80wDL/3AwAAAACA1apUqB88eFAXXnhhhfbS0lI5HI5qh/q55ORkPfTQQz5t27dv1x/+8Ae/9wUAAAAAgJWqtPT9oosu0tq1ayu0v/322xVm2f0hLCxMbdq08fkTExPj934AAAAAALBalWbU77zzTt1yyy3697//LcMw9Nprr2nXrl3aunWrFi9e7O+MAAAAAADUGVUq1C+55BItX75cL7zwgjp27KhPPvlECQkJevPNNxUfH+/vjDWqaf2GMmRaHQMAAL+KaRBpdQQAAFBFVSrUJal169b6y1/+4s8slri+ax85nU6rYwAA4Hce06MgR5WucgMAABaq0m/vzp0768cff6zQfujQIXXu3LnaoX4qIyND8+fPr9CenJwst9uthg0b+vxcWdw9HnZjGIY+//xzxiZsh7EZeCjSAQAITFX6DW6ap18qfuLECYWEhFQrEAAAAAAAdVmllr6/9tprkiSHw6G3335b4eHh3m0ej0cbN25UXFycfxPWMJa9w26cTqcSEhKsjgFUwNhETTE9HjmCmP0HAOCUShXqr7zyiqSTM+pvvvmmgn7ySzUkJESxsbGaNWuWXwOekp6erqKiIs2fP9/n5+oq+tAl89BePyQEAACV5WzcVJEDxlodAwAAW6lUof7BBx9IkiZMmKBnn31WkZGBf0dZ4/B+eQ4UWB0DAAAAAABJVbzrO89KBwAAAACgZlT58Wx79+7V+++/rz179qisrMxn23333VftYAAAAAAA1EVVKtQ3bNig2267Ta1atVJ+fr5+9atfqaCgQKZpcqMhAAAAAACqoUq3WJ0zZ45uvvlmvfPOOwoNDdW8efO0du1aJSYm6qqrrvJ3RgAAAAAA6owqFeq7du3S8OHDJUnBwcE6duyY6tevrzvuuEOLFi3yZz4AAAAAAOqUKhXq4eHh3uvSo6Oj9d1333m3HTp0yD/JAAAAAACog6p0jXr37t21efNmtW/fXpdffrkee+wxffXVV3rvvffUvXt3f2c8rSNHjuiLL77waWvUqJGaN29eK/0DAAAAAFATqlSo33fffSopKZEk3X777SopKVFOTo7atm2r9PR0vwY8k7y8PO/y+1NGjx6thx9+uFLncTaKVpBp+DEZAAA4V87GTa2OAACA7ThM0zStDmEFwzC0bds29ejRQ06n0+o4AADUWabHI0dQla7Gk8TvdNgXYxN2xdi0zrl+9lX6rdi/f//TXoteVFSk/v37V+WUljEMZtNhL4ZhKD8/n7EJ22FsoqZUp0gHAOB8VKWl7wUFBfJ4PBXaT5w4oX379lU7VG3iGyTYjdPpVFxcnNUxgAoYmzgdj8dUUJDD6hgAAJxXKlWov//++96f161bp4iICO9rj8ejDRs2qGXLlv5LVws+2PC1DhWVWh0DAICA07hhuAamxFsdAwCA806lCvWpU6dKkhwOR4WbxgUHB6tly5ZVuplcenq6srOzNWPGDE2ZMsXbvmbNGk2dOlVut1u5ubmaOHGiNm7cqIYNG/ocf+WVV2rixIm66aabKt334SNHdeDg0UofBwAAAABATahUof7ll19KOlkYu1wuRUVF+S1IvXr19OKLL2rMmDGKjIz023kBAAAAAAgklbp7y9atW/Xhhx/qgw8+8Bbpy5Yt05VXXqnevXvrwQcf1IkTJ6oUpE+fPmrSpIkWLlxYpeMBAAAAADgfVKpQf/bZZ/X11197X7vdbt1///3q06ePpkyZog8//LDKhXZQUJDuuusuLVmyRHv37q3SOQAAAAAACHSVWvrudrs1ffp07+ucnBx169ZNf/nLXyRJzZo107x583T77bdXKczAgQPVuXNnzZ07V4888shp97n88ssrtJWWcjM4AAAAAMD5oVKFemFhoZo0aeJ9nZeXp8suu8z7umvXrtqzZ0+1At1999268cYbNWnSpNNuf/3111W/fn2ftgkTJlSrTwAAAAAA7KJSS9+bNGmiH374QdLJZ6Z//vnn6tGjh3d7SUmJQkJCqhUoMTFRKSkpmjNnzmm3x8bGqk2bNj5/goOr9Dh4AAAAAABsp1KF+mWXXaY5c+Zo06ZNevLJJ3XBBRfo4osv9m53u91q1apVtUPNmDFDH374obZu3VrtcwEAAAAAEEgqNRV9xx136Pbbb9f48eMVHh6uxx57TKGhod7tmZmZSklJqXao+Ph4DRs2TIsXL672uX5Jo4hwmaajxvsBAOB807hhuNURAAA4L1WqUI+KitLrr7+uI0eOKDw8XE6n02f7M888o/Bw//zSnjZtmnJycvxyrrO5svevKrwPAABwbjweU0FBfOENAIA/Veni7oiIiNO2N2rUqEohMjIyKrTFxsZq586d3tfJyclyu92nPf6DDz6oUr+SZBgGhTpsxTAMud1uxcfHMzZhK4xNnA5FOgAA/lepa9QBAAAAAEDNqvO3S2dWCHbjdDqVkJBgdQygAsbm+cn0mHIwKw4AgK3U+UJ934duGYdKrY4BAECtC2kcrmYDOlsdAwAA/IytCvX9+/dr4cKF+uijj7R3715FRESodevWSktL04gRIxQWFubdd+HChXr66ac1Y8YMTZ48ucp9lh0uVdmBYn/EBwAAAACg2mxTqH///fcaO3asIiIidOeddyo+Pl6hoaFyu93629/+ppiYGPXv39+7f2ZmpiZPnuz9XwAAAAAAzge2KdQfeughOZ1OZWZm+jzirVWrVhowYIBM0/S25eXl6dixY5o2bZqWLVumLVu2qFevXlbEBgAAAADAr2xx1/dDhw7pk08+0Q033HDG57A7HP+70Y3L5dI111yjkJAQDR06VC6Xq7aiAgAAAABQo2xRqH/33XcyTVPt2rXzaU9OTlbPnj3Vs2dPPfHEE5Kk4uJivfvuu7r22mslSWlpaVq5cqVKSkpqPTcAAAAAAP5mi0L9TFwul5YtW6YOHTroxIkTkqQVK1aodevW6tSpkySpc+fOatmypXJycqyMCgAAAACAX9jiGvXWrVvL4XDom2++8Wlv1aqVJOmCCy7wtrlcLn399dc+z/L1eDzKzMzUddddVzuBAQAAAACoIbYo1Bs3bqy+fftqyZIlGj9+/BmvU3e73dq5c6cWL16syMhIb3thYaEmTJigXbt2qX379rUVGwAAAAAAv7NFoS5Jf/rTnzR27FiNGjVKt99+u+Lj4+VwOLRjxw7l5+erS5cucrlc6tatmxITEysc37VrV7lcLt17772V6jekUZiCzF/eDwCA801I49N/MQ4AAKxlm0K9devWys7O1sKFCzVnzhzt27dPISEh6tChg26++WaNGTNG/fv31y233HLa4wcNGqSXX35Zd911l0JCQs6535gr4uV0Ov31NgAACCimx5QjyPHLOwIAgFrjMH/6gPI6xDAMbdu2TV27dlVoaKjVcQAvwzDkdrsVH8+XSLAXxibs6tTv9B49ejA2YSuMTdgVY9M65/rZ2/qu7wAAAAAA1DW2WfpuFb5Bgt04nU6fpxoAdsHYtB+P6VGQg+/cAQA439iyUN+6davGjRun1NRUvfDCC972H374Qf3799eyZcvUuXNnSVJxcbFuu+02/fjjj3rppZfUrFmzSvW1If9VFR77j1/zAwBQ0yLDmimlw2SrYwAAgBpgy0Ld5XJp/Pjxcrlc2rdvn2JiYk6738GDBzV58mQFBQXp9ddfV+PGjSvdV1Hpf3Ww9LvqRgYAAAAAwC9st16upKREOTk5Gjt2rPr166fs7OzT7rdnzx6NGzdOERERevXVV6tUpAMAAAAAYDe2K9RXrlypuLg4xcXFKS0tTZmZmfr5jem/+eYbjR07Vh06dNCLL76o+vXrW5QWAAAAAAD/sl2h7nK5lJaWJklKTU3VkSNHlJeX57PPPffco9atW+uZZ57h0WoAAAAAgPOKrQr1/Px87dixQ0OHDpUkBQcHa8iQIXK5XD77XXnlldq8ebNWr15tRUwAAAAAAGqMrW4m53K5VF5ertTUVG+baZoKDQ3VzJkzvW233Xab4uPjdffdd8s0TQ0ZMsSKuAAAAAAA+J1tCvXy8nItX75c6enp6tu3r8+2qVOnasWKFT4F/NSpUxUUFKQ//OEPkkSxDgAAAAA4L9imUF+7dq0KCws1evRoRURE+GwbNGiQXC6XT6EunZxZdzqduvvuu+XxeLxL5iujYVhTmY7yamUHAKC2RYY1szoCAACoIbYp1F0ul/r06VOhSJekwYMHa9GiRSouLq6wbcqUKXI4HLrnnntkmqaGDRtWqX57x90op9NZ5dwAAFjFY3oU5LDV7WYAAIAf2KZQX7BgwRm3devWTW63W5K8//tTt9xyi2655ZYq9WsYBoU6bMUwDLndbsXHxzM2YSuMTfuhSAcA4PzEb3gAABDwwsLCrI4AAIDf2GZG3SrMCsFunE6nEhISrI4BVMDYhF0xNv3LND1ysFoDACxV5wv1w988Lc+x3VbHAAAAsFxwWCs1bn+P1TEAoM6ztFBPT09Xdna2xowZo9mzZ/tsmzVrlt544w2NGDFCGRkZ3n0lKTg4WJGRkYqPj9c111yjkSNHKiioat/8lpcWyCjdVe33AgAAAACAP1i+rql58+bKycnRsWPHvG3Hjx/XihUr1KJFC599U1NTtX79en3wwQd68cUXlZycrIcffli//e1vVV7OI9YAAAAAAIHP8kI9ISFBzZs31+rVq71tq1evVvPmzdW5c2effUNDQxUdHa2YmBh16dJFt956q+bPn6+PP/7YO9sOAAAAAEAgs7xQl6RRo0YpKyvL+zozM1MjR448p2N79+6tTp06+RT6AAAAAAAEKlsU6mlpadq8ebMKCgpUUFCgLVu2KC0t7ZyPj4uLU0FBQQ0mBAAAAACgdtjiru9RUVHq16+fsrOzZZqm+vXrp6ioqHM+3jRNORyOGkwIAAAAAEDtsMWMuvS/5e/Z2dkaNWpUpY7dtWuXYmNjaygZAAAAAAC1xzaFempqqsrKylReXq6UlJRzPm7Dhg366quvNGjQoBpMBwAAAABA7bDF0ndJcjqdWrlypffn0zlx4oT2798vj8ejAwcOaN26dVq4cKGuuOIKDR8+vEr9Boe1VJCjrKqxAQAAzhvBYa2sjgAAkI0KdUlq0KDBWbevW7dOKSkpCg4OVsOGDdWpUyc98MADGjFihIKCqrY4oFG76Wf8YgAAAKCuMU2PHA7bLLoEgDrJYZqmaXUIKxiGoW3btqlr164KDQ21Og7gZRiG3G634uPj+RIJtsLYhF0xNmFXp/692aNHD8YmbIWxaZ1z/ez5uhQAAAS80tJSqyMAAOA3tlr6bgW+QYLdOJ1OJSQkWB0DqICxWTexDBoAgNpny0J969atGjdunFJTU/XCCy9423/44Qf179/f+zoyMlIdO3bU9OnTdckll1Spr31frZdRWljtzAAAnG9CwiPVrNPlVscAAKDOsWWh7nK5NH78eLlcLu3bt08xMTE+21955RV16NBBhw4d0oIFC/Tb3/5W7777rpo0aVLpvsqOFqns6I/+ig4AAAAAQLXYbi1bSUmJcnJyNHbsWPXr10/Z2dkV9mnUqJGio6PVsWNH/fa3v1VxcbG2b99uQVoAAAAAAPzLdoX6ypUrFRcXp7i4OKWlpSkzM1NnujH9sWPHtGzZMklSSEhILaYEAAAAAKBm2G7pu8vlUlpamiQpNTVVR44cUV5enpKTk737/PrXv1ZQUJBKS0tlmqa6dOmi3r17WxUZAAAAAAC/sdWMen5+vnbs2KGhQ4dKkoKDgzVkyBC5XC6f/Z566illZ2dr3rx5atOmjTIyMphRBwAAAACcF2w1o+5yuVReXq7U1FRvm2maCg0N1cyZM71tzZs3V9u2bdW2bVuVl5fr97//vVasWKHQ0FArYgMAAAAA4De2mVEvLy/X8uXLlZ6ermXLlnn/LF++XE2bNtWKFStOe9xVV10lp9OpN954o5YTAwAAAADgf7Yp1NeuXavCwkKNHj1aHTt29PkzaNCgCsvfT3E4HJowYYJeeOEFlZaW1nJqAAAAAAD8yzZL310ul/r06aOIiIgK2wYPHqxFixapuLj4tMeOGDFCTz/9tJYsWaJbbrmlUv2GhDdUkOP0d5UHAKAuCwmPtDoCAAB1km0K9QULFpxxW7du3eR2uyXJ+78/FRYWpry8vCr1G9MxRU6ns0rHAgBwvjNNjxwO2yzAAwCgTqjzv3kNw7A6AuDDMAzl5+czNmE7jM26KVCK9LCwMKsjAADgN7aZUbcKs+mwG6fTqbi4OKtjABUwNmFXTqdTCQkJVsfAecg0TTkcDqtjAKiD6nyhvn379jNe+w4AAIC6KSIiQj179rQ6BoA6ynaF+tKlS/X4449r48aNCg4+Ga+kpERJSUnq1auXFi9e7N03NzdXEydO1HvvvaebbrpJEydO1E033VSp/oqLi1VUVOTPtwAAAAAAQJXZ7sKz5ORkHT16VDt37vS2bdq0SU2aNNH27dt1/Phxb3tubq5atGih1q1bWxEVAAAAAAC/s12hHhcXp+joaJ+7uOfl5al///6KjY3Vtm3bfNqTk5MtSAkAAAAAQM2wXaEunZxVz83N9b7Ozc1VUlKSEhMTve3Hjh3T9u3bKdQBAAAAAOcVWxbql156qbZs2aLy8nIVFxfriy++8Bbqp2bat27dqhMnTlCoAwAAAADOK7Ys1JOSknT06FHt2LFDmzdvVtu2bRUVFaXExETvdep5eXlq1aqVWrRoYXVcAAAAAAD8xnZ3fZekNm3aqFmzZsrNzVVhYaESExMlSTExMWrevLm2bNmi3NxcXXrppRYnBQAAAADAv2w5oy6dvE49Ly9PeXl5SkpK8rZfcskl+vjjj/XPf/6TZe8AAAAAgPOOLWfUpZOF+uzZs1VeXu5TqCclJWn27NkqKyvzS6HeoEGDap8DAAAA55eIiAirIwCow2xdqB87dkxxcXFq0qSJtz0xMVElJSVq166dmjZtWu1+unfvLqfTWe3zAAAA4PximqYcDofVMQDUQbYt1GNjY+V2uyu0t2zZ8rTtH3zwQZX6MQyDQh22YhiG3G634uPjGZuwFcYm7IqxiZpCkQ7AKra9Rh0AAAAAgLrItjPqtYVv3mE3TqdTCQkJVscAKmBswq4Ym6gpHtOjIAfzWgBqn60K9T179mju3Llat26dDh8+rOjoaPXv319Tp05V48aNJUkTJkxQXl6eJCk0NFQtWrTQyJEjNWXKlCotT/rbjk+1p6TQr+8DAAAAgS2mQaRu6J5qdQwAdZRtCvXvv/9eY8aMUdu2bfXkk08qNjZWX3/9tZ544gmtW7dOb731lho1aiRJuv766zVt2jSdOHFCn332mWbOnKmIiAiNGzeu0v3+t6RIBUUH/fxuAAAAAACoGtus5Zk1a5ZCQkL00ksvKSkpSS1atNDll1+ul19+Wfv27dNTTz3l3feCCy5QdHS0WrZsqVGjRik+Pl6ffvqphekBAAAAAPAPWxTqhw8f1vr16zVu3DhdcMEFPtuio6M1bNgwrVy5UqZp+mwzTVObNm1Sfn6+QkJCajMyAAAAAAA1whZL37/99luZpqn27dufdnv79u1VWFiogwdPLlFfunSpXC6XysrKVFZWpnr16mnChAm1GRkAAAAAgBphi0L9lJ/PmJ/JsGHDdOutt6qwsFDz5s1Tz5491atXrxpOBwAAAABAzbPF0vfWrVvL4XBo165dp92+a9cuRUZGKioqSpLUoEEDtWnTRt26ddPTTz+t119/nWvUAQAAAADnBVsU6o0bN1bfvn31xhtv6NixYz7b9u/fr3feeUdXX331aR+/Vr9+fU2cOFGPPfbYOc/IAwAAAABgV7Yo1CXpwQcf1IkTJzRp0iRt3LhRe/bs0ccff6ybb75ZMTExuvPOO8947JgxY7R79269++67tZgYAAAAAAD/s8016m3btlVmZqbmzZun6dOnq7CwUE2aNNGAAQM0depU7zPUT6dRo0a69tpr9eyzz2rQoEEKCjr37x+a1m8oQ8zEAwAA4H9iGkRaHQFAHeYw6+h6ccMwtG3bNvXo0UNOp9PqOAAAALAZj+lRkKPqC1D59ybsirFpnXP97G2z9N0qhmFYHQHwYRiG8vPzGZuwHcYm7IqxiZpSnSIdAKrDNkvfrcI3SLAbp9OpuLg4q2MAFTA2YVeMzcBkejxyVOJyRQCoS+p8oV70oUvmob1WxwAAAKgznI2bKnLAWKtjAIBt2bZQT09PV3Z2tsaMGaPZs2f7bJs1a5beeOMNjRgxQhkZGUpPT1dRUZHmz59f6X6Mw/vlOVDgr9gAAAAAAFSLrdcbNW/eXDk5OT7PVj9+/LhWrFihFi1aWJgMAAAAAICaYetCPSEhQc2bN9fq1au9batXr1bz5s3VuXNnC5MBAAAAAFAzbF2oS9KoUaOUlZXlfZ2ZmamRI0damAgAAAAAgJpj+0I9LS1NmzdvVkFBgQoKCrRlyxalpaVZHQsAAAAAgBph25vJnRIVFaV+/fopOztbpmmqX79+ioqKsjoWAAAAAAA1wvYz6tL/lr9nZ2dr1KhRVscBAAAAAKDG2H5GXZJSU1NVVlYmh8OhlJQUq+MAAAAAAFBjAqJQdzqdWrlypfdnv567UbSCTMOv5wQAAMCZORs3tToCANhaQBTqktSgQYMaOW/DK0b7vfgHAADA2ZkejxxBAXEVJgDUOtsW6hkZGWfdPn/+/HPe92wMw6BQh60YhiG32634+HjGJmyFsQm7YmwGJop0ADgz/oYEAAAAAMBGbDujXlv45h1243Q6lZCQYHUMoALGJuzqXMamx2MqKMhRS4kAAKieOl+of7Dhax0qKrU6BgAAqCGNG4ZrYEq81TEAADhntirU09PTlZ2dLUkKDg5WZGSk4uPjdc0112jkyJEK+v/XMl155ZUqKCiocPyMGTM0ZcqUSvV5+MhRHTh4tPrhAQAAAADwA1sV6tLJZ6Y/+uij8ng8OnDggNatW6eHH35Y7777rp5//nkFB5+MPG3aNF1//fU+x9avX9+KyAAAAAAA+I3tCvXQ0FBFR0dLkmJiYtSlSxd1795dN910k7Kzs3XddddJOlmUn9oPAAAAAIDzRUDc9b13797q1KmTVq9ebXUUAAAAAABqVEAU6pIUFxfnc136X//6V/Xs2dPnz6ZNmyxMCAAAAABA9dlu6fuZmKYph+N/j1WZNGmSRo4c6bNPTExMbccCAAAAAMCvAqZQ37Vrl2JjY72vGzdurDZt2liYCAAAAAAA/wuIpe8bNmzQV199pUGDBlkdBQAAAACAGmW7GfUTJ05o//79Po9nW7hwoa644goNHz7cu19JSYn279/vc2xYWJgaNGhQqf4aRYTLNB2/vCMAAAhIjRuGWx0BAIBKsV2hvm7dOqWkpCg4OFgNGzZUp06d9MADD2jEiBEKCvrfAoC5c+dq7ty5PseOGTNGs2fPrlR/V/b+lZxOp1+yAwAAe/J4TAUF8cU8ACAw2KpQz8jIUEZGxi/u98EHH/itT8MwKNRhK4ZhyO12Kz4+nrEJW2Fswq7OZWxSpAMAAklAXKMOAAAAAEBdYasZdSswKwS7cTqdSkhIsDoGUAFjE3bF2LQf02PKwSoGAKgyWxXqEyZMUKdOnXT//ff7tGdlZemRRx5Rw4YNVVBQcMbjR4wYcU5L539q34duGYdKq5QXAAAAvkIah6vZgM5WxwCAgGarQv2XuFwuGYYhSdq6datuv/12rVq1ynun9wsuuKDS5yw7XKqyA8V+zQkAAAAAQFUFVKEeFRXl/TkyMlKSdOGFF6phw4ZWRQIAAAAAwK+4mRwAAAAAADZCoQ4AAAAAgI1QqAMAAAAAYCO2KtTr16+v4uKKN3YrKipSRESEBYkAAAAAAKhdtirU27Vrp3/9618V2j///HO1bdu29gMBAAAAAFDLbFWojxs3Trt379Zf/vIXffnll8rPz9fLL7+sf/zjH/rNb35jdTwAAAAAAGqcrR7P1qpVKy1ZskRPP/20fvOb36isrExxcXF65plndNlll9VInyGNwhRk1sipAQAA6pyQxuFWRwCAgGerQl2SunXrppdeeukX90tOTpbb7a52fzFXxMvpdFb7PAAAADjJ9JhyBDmsjgEAActWS9+tYBiG1REAH4ZhKD8/n7EJ2zEMQ59//jljE7bD2LQfinQAqB7bzajXNmbTYTdOp1NxcXFWx4CfeUyPghyB/91oaWmp1RGA02JsAgDOJ3W+UN+Q/6oKj/3H6hgAzmORYc2U0mGy1TEAAAAQIGxXqO/fv18LFizQ2rVrtW/fPl144YXq3LmzbrzxRvXu3du738KFC/X0009rxowZmjy56v8ALir9rw6WfueP6AAAAAAAVJut1mH+8MMPGjlypD777DPdc889euedd7Ro0SIlJydr1qxZPvtmZmZq8uTJyszMtCgtAAAAAAD+Z6sZ9VmzZsnhcOjtt99WePj/Hu3xq1/9SqNGjfK+zsvL07FjxzRt2jQtW7ZMW7ZsUa9evayIDAAAAACAX9lmRv3w4cNat26dbrjhBp8i/ZSGDRt6f3a5XLrmmmsUEhKioUOHyuVy1WZUAAAAAABqjG0K9e+++06maf7i3a6Li4v17rvv6tprr5UkpaWlaeXKlSopKamNmAAAAAAA1CjbFOqmaZ7TfitWrFDr1q3VqVMnSVLnzp3VsmVL5eTk1GQ8AAAAAABqhW2uUW/Tpo0cDofy8/PPup/L5dLXX3+thIQEb5vH41FmZqauu+66mo4JAAAAAECNsk2h3qhRI6WkpOj111/XhAkTKlynXlRUpD179mjnzp1avHixIiMjvdsKCws1YcIE7dq1S+3bt6/t6AAAAAAA+I1tCnVJ+tOf/qSxY8fquuuu07Rp0xQfHy/DMPTJJ59o6dKlSklJUbdu3ZSYmFjh2K5du8rlcunee++tVJ8Nw5rKdJT76y0AQAWRYc2sjgAAAIAAYqtCvVWrVsrKytKCBQv02GOP6b///a+ioqLUpUsX3X///ZoxY4ZuueWW0x47aNAgvfzyy7rrrrsUEhJyzn32jrtRTqfTX28BAE7LY3oU5LDNbUEAAABgY7Yq1CWpadOmmjlzpmbOnFlhW25u7hmPu+WWW85YxJ+NYRgU6rAVwzDkdrsVHx/P2DyPUKQDAADgXPEvRwDAOQsLC7M6AnBajE3YFWMTQFXYbka9tjFjCbtxOp0+TzUA7IKxCbtibMKu7DI2TdMjByu7gIBieaGenp6u7OxsjRkzRrNnz/bZNmvWLL3xxhsaMWKEMjIyvPvOmDFDU6ZM8e63Zs0aTZ06VW63u9L9H/7maXmO7a7u2wAAAABsJzislRq3v8fqGAAqyfJCXZKaN2+unJwc/fGPf9QFF1wgSTp+/LhWrFihFi1a+Oxbr149vfjiixozZozPI9qqqry0QEbprmqfBwAAAAAAf7DFGpiEhAQ1b95cq1ev9ratXr1azZs3V+fOnX327dOnj5o0aaKFCxfWdkwAAAAAAGqcLQp1SRo1apSysrK8rzMzMzVy5MgK+wUFBemuu+7SkiVLtHfv3tqMCAAAAABAjbNNoZ6WlqbNmzeroKBABQUF2rJli9LS0k6778CBA9W5c2fNnTu3llMCAAAAAFCzbHGNuiRFRUWpX79+ys7Olmma6tevn6Kios64/913360bb7xRkyZNqsWUAAAAAADULNvMqEv/W/6enZ2tUaNGnXXfxMREpaSkaM6cObWUDgAAAACAmmebGXVJSk1NVVlZmRwOh1JSUn5x/xkzZmj48OFq165dLaQDAAAAAKDm2apQdzqdWrlypffnXxIfH69hw4Zp8eLFNR0NAAAAAIBaYatCXZIaNGhQqf2nTZumnJycKvcXHNZSQY6yKh8PAAAA2FVwWCurIwCoAodpmqbVIaxgGIa2bdumHj16nNPsPQAAABCITNMjh8NWt6aCxaiFrHOun32d/3+sYRhWRwB8GIah/Px8xiZsh7EJuzIMQ1999RVjE7ZjGIY+//xzy8cmRToQeGy39L228Q0S7MbpdCouLs7qGEAFjE3YldPpVPv27a2OAZxWaWmp1REABKA6X6jv+2q9jNJCq2MAAIAqCgmPVLNOl1s+awkAgL/YqlA/ePCgnnnmGX300Uc6cOCAIiMj1alTJ/3ud7/TxRdfrCuvvFIFBQWSpHr16qlJkybq2rWrfv3rX6t3795V6rPsaJHKjv7oz7cBAAAAAECV2apQv/3221VWVqaMjAy1atVKP/74ozZs2KDDhw9795k2bZquv/56lZWVqaCgQH//+9/1m9/8RnfccYduu+0268IDAAAAAOAHtinUi4qKtGnTJi1evFhJSUmSpJYtW6pbt24++9WvX1/R0dGSpBYtWigxMVHR0dGaO3euBg8ezPWTAAAAAICAZptbQIaHhys8PFxr1qzRiRMnKnXsxIkTZZqm3n///RpKBwAAAABA7bBNoR4cHKyMjAwtW7ZMl1xyiX7961/rySef1JdffvmLxzZq1EgXXnih9/p1AAAAAAAClW0KdUkaPHiw1q1bp+eff16pqanKy8vTyJEjlZWV9YvHmqYph8NRCykBAAAAAKg5tirUpZN3c+/bt6+mTp2qN998UyNGjNC8efPOesyhQ4d08OBBxcbG1lJKAAAAAABqhu0K9Z/r0KGDjh49etZ9XnvtNQUFBWnAgAG1lAoAAAAAgJphm7u+Hzp0SHfccYdGjRql+Ph41a9fXzt37tSiRYvUv39/734lJSXav3+/ysvL9cMPP+jvf/+73n77bd11111q06ZNpfsNCW+oIIfpz7cCAABqUUh4pNURAADwK9sU6vXr11f37t316quv6rvvvlN5ebmaNWum6667Trfeeqt3v7lz52ru3LkKCQlRdHS0unfvrldeeUWXXnpplfqN6Zgip9Ppr7cBAAAsYBiG1REAAPAb2xTqoaGhmjFjhmbMmHHGfT744AO/92sYBoU6bMUwDLndbsXHxzM2YSuMTdiVYRj617/+pS5dulgdBQAAv7D9NeoAAAC/JDjYNnMPAABUW53/rcasEOzG6XQqISHB6hhABYxN2BVj0xePrAWAwFfnC/Xt27eruLjY6hgAAADVFhERoZ49e1odAwBQTbYp1NPT05Wdna0ZM2ZoypQp3vY1a9Zo6tSpcrvdkk5+S/y3v/1NLpdL//73v+V0OtW6dWulpaVpzJgxCgsLq1S/xcXFKioq8ut7AQAAAACgqmx1jXq9evX04osvqrCw8Iz7/OEPf9Ajjzyi/v3769VXX9WyZcv0u9/9Tu+//74++eSTWkwLAAAAAID/2WZGXZL69Omjb7/9VgsXLtQ999xTYXtOTo7eeecdPffccxowYIC3PTY2Vv3792cJOwAAAAAg4NlqRj0oKEh33XWXlixZor1791bY/s4776hdu3Y+RfopDodDERERtRETAAAAAIAaY6tCXZIGDhyozp07a+7cuRW2ffvtt2rXrp0FqQAAAAAAqB22K9Ql6e6779ayZcu0a9cun3bTNC1KBAAAAABA7bBloZ6YmKiUlBTNmTPHp71t27bKz8+3KBUAAAAAADXPloW6JM2YMUMffvihtm7d6m0bNmyYdu/erTVr1lTY3zRNHTlypDYjAgAAAADgd7a66/tPxcfHa9iwYVq8eLG37eqrr9Z7772nGTNm6LbbblPfvn0VFRWlr776Sq+88oomTJhw2hvNnU2DBg38HR0AAMAS3FgXAM4Pti3UJWnatGnKycnxvnY4HJozZ47eeustZWZmasGCBXI6nWrTpo2GDx+ulJSUSvfRvXt3OZ1Of8YGAACwjGmacjgcVscAAFSDbQr1jIyMCm2xsbHauXOnT1tQUJDGjh2rsWPH+qVfwzAo1GErhmHI7XYrPj6esQlbYWzCrhibvijSASDw2fYadQAAAAAA6iLbzKhbhW/eYTdOp1MJCQlWxwAqYGzCrs6nsekxPQpyMI8CAHWdpYX60qVL9fjjj2vjxo0KDj4ZpaSkRElJSerVq5fPjeRyc3M1ceJEvffee2rdurW2bt2qcePGKTU1VS+88EKVM/xtx6faU1JY7fcCAABQHTENInVD91SrYwAAbMDSQj05OVlHjx7Vzp071aNHD0nSpk2b1KRJE23fvl3Hjx9XvXr1JJ0s1Fu0aKHWrVtLklwul8aPHy+Xy6V9+/YpJiamShn+W1KkgqKDfnk/AAAAAABUl6Vrq+Li4hQdHa28vDxvW15envr376/Y2Fht27bNpz05OVnSyVn3nJwcjR07Vv369VN2dnZtRwcAAAAAoEZYfhFUcnKycnNzva9zc3OVlJSkxMREb/uxY8e0fft2b6G+cuVKxcXFKS4uTmlpacrMzJRpmpbkBwAAAADAnywv1C+99FJt2bJF5eXlKi4u1hdffOEt1E/NtG/dulUnTpzwFuoul0tpaWmSpNTUVB05csRnVh4AAAAAgEBleaGelJSko0ePaseOHdq8ebPatm2rqKgoJSYmeq9Tz8vLU6tWrdSiRQvl5+drx44dGjp0qCQpODhYQ4YMkcvlsvidAAAAAABQfZY/nq1NmzZq1qyZcnNzVVhYqMTERElSTEyMmjdvri1btig3N1eXXnqppJOz6eXl5UpN/d9dUU3TVGhoqGbOnKmIiAhL3gcAAAAAAP5geaEunbxOPS8vT4WFhZo0aZK3/ZJLLtHHH3+sf/7znxo7dqzKy8u1fPlypaenq2/fvj7nmDp1qlasWKGxY8fWdnwAAAAAAPzGNoX67NmzVV5erqSkJG97UlKSZs+erbKyMiUnJ2vt2rUqLCzU6NGjK8ycDxo0SC6Xq9KFetP6DWWIG9EBAABrxTSItDoCAMAmbFOoHzt2THFxcWrSpIm3PTExUSUlJWrXrp2aNm0ql8ulPn36nHZ5++DBg7Vo0SJ9+eWX6tSp0zn3fX3XPnI6nX55HwAAANXhMT0Kclh+CyEAgMVsUajHxsbK7XZXaG/ZsqVP+4IFC854jm7dup32HL/EMAwKddiKYRhyu92Kj49nbMJWGJuwq/NpbFKkAwAkG9z1HQAAAAAA/I8tZtStFOjfvOP843Q6lZCQYHUMoALGJuzKX2PT9HjkCGIOAwBgvTpfqBd96JJ5aK/VMQAAgIWcjZsqcgBPjgEA2IPlhXp6erqys7M1Y8YMTZkyxdu+Zs0aTZ06VW63W7m5uZo4caIaNmyo9evXq169et79/vnPf+q6666TpKpdo354vzwHCqr/RgAAAAAA8ANbrO+qV6+eXnzxRRUWFp51v/r16+u9997zaXO5XGrRokVNxgMAAAAAoNbYolDv06ePmjRpooULF551v+HDhyszM9P7+tixY8rJydHw4cNrOCEAAAAAALXDFoV6UFCQ7rrrLi1ZskR79575evFrr71WmzZt0n/+8x9J0rvvvquWLVuqS5cutRUVAAAAAIAaZYtCXZIGDhyozp07a+7cuWfc58ILL9Rll12mrKwsSVJmZqZGjRpVWxEBAAAAAKhxtinUJenuu+/WsmXLtGvXrjPuM2rUKGVnZ+v777/Xtm3bNGzYsFpMCAAAAABAzbJVoZ6YmKiUlBTNmTPnjPtcdtllOn78uP74xz/qiiuuUOPGjWsxIQAAAAAANctWhbokzZgxQx9++KG2bt162u3BwcG69tprlZeXx7J3AAAAAMB5x/LnqP9cfHy8hg0bpsWLF59xnzvuuEOTJk3yy2y6s1G0gkyj2ucBAACBy9m4qdURAADwsl2hLknTpk1TTk7OGbeHhoYqKirKL301vGK0nE6nX84FAAACl+nxyBFku8WGAIA6yPJCPSMjo0JbbGysdu7c6X2dnJwst9t9xnMMGDDgrNvPxjAMCnXYimEYcrvdio+PZ2zCVhibsCt/jU2KdACAXfAbCQAAAAAAG7F8Rt1qzArBbpxOpxISEqyOAVTA2IRdnU9j0+MxFRTksDoGAMBitinU09PTlZ2drRkzZmjKlCne9jVr1mjq1KmaO3eupk+frrVr1+r/tXfvYVEV+B/HP8NwEeSiCF4xQ8tRAhSzUIJNTe1imkg3s0wfy/XRtFJ31dbylklrVlqWhmZG5RVxnwwvuWarqVmZmZlkkuulm5BKiAnMnN8fPc4vFiuRgXNw3q/n8Yk558ycz/B8n4bPnDNnGjVqVOH+PXv2VNeuXTVhwoRK7XfT9gM6UXimyvkBAACqon5okHokO8yOAQCwAMsUdUkKCAhQRkaG7rrrLoWFhZVb161bN9WrV0/Z2dkaNmxYuXUfffSR/vvf/+r222+v9D5P/lys/J+Kq5QbAAAAAABPsdRn1JOSkhQREaH58+dXWOfn56fbbrtN2dnZFdZlZWWpXbt2uvLKK2siJgAAAAAA1cZSRd3Hx0ejR4/WG2+8oe+//77C+ttvv12HDh3SRx995F52+vRprV+//qKOpgMAAAAAYDWWKuqS1KNHD7Vt21Zz5sypsO6KK65Q+/btlZWV5V62du1aGYahW265pSZjAgAAAABQLSxX1CVp7NixWr16tQ4ePFhhXVpamtavX6+ioiJJv572ftNNNyk4OLimYwIAAAAA4HGWLOrXXHONkpOTNWvWrArrzh05X7t2rQ4dOqRdu3Zx2jsAAAAA4JJhqau+/9aYMWPUt29fRUdHl1seHBysm266SVlZWTpy5Iguv/xydezY0aSUAAAAAAB4liWPqEuSw+FQ7969lZmZWWFdWlqaPv30Uy1dulRpaWkmpAMAAAAAoHpY9oi6JI0aNUo5OTkVlnfs2FHR0dE6fPiw+vbtW6V91AsJkmHYqvQYAAAAVVU/NMjsCAAAi7BMUU9PT6+wLCoqSnv37j3v9uvWrfPIfrt1vlJ2u90jjwUAAFAVLpchHx8OIACAt7Psqe81xel0mh0BKMfpdCovL4/ZhOUwm7Aqp9Opffv2XRKzSUkHAEgWOqJuFo6mw2rsdrtatmxpdgygAmbz0mS4DNkugXJ45swZsyMAAOAxXl/Uf3gvV84TvLgDALyPX/0gNe7e1uwYAADgf1imqI8fP17Z2dmSJF9fX4WFhcnhcKhXr17q16+ffHz+/yz9ffv2ad68efr444/1888/q0mTJrr22ms1ZMiQCl/n9mdKT55RaX6RR58LAAAAAAAXy1KfUU9JSdHWrVu1adMmZWRkKDExUdOnT9df//pXlZWVSZLee+893XnnnSopKdEzzzyjnJwczZw5UyEhIZo9e7bJzwAAAAAAgKqxzBF1SfL391dkZKQkqVGjRrrqqqvUrl07DRo0SNnZ2br11ls1YcIEXX/99Zo7d677fs2bN1e7du1UWFhoVnQAAAAAADzCUkX9fDp37qw2bdpow4YNqlevnk6cOKEHHnjgvNuGhobWcDoAAAAAADzLUqe+/56WLVvq2LFjOnTokPs2AAAAAACXolpR1A3DkM1mk2EYZkcBAAAAAKBa1YqifvDgQUVFRbmv6J6Xl2dyIgAAAAAAqofli/r27dv11VdfqWfPnrruuutUv359LViw4LzbcjE5AAAAAEBtZ6mLyZWUlOj48eNyuVzKz8/Xli1bNH/+fHXt2lV9+/aV3W7Xk08+qUceeUTDhg3TwIEDddlll+nEiRNau3atvvvuOz333HOV2qdfvUD5cEY9AMAL+dUPMjsCAAA4D0sV9S1btig5OVm+vr4KDQ1VmzZtNHHiRKWmpsrH59eD/927d9eSJUv0yiuvaMyYMSoqKlKTJk3UqVMnPfLII5XeZ6OuDtntdg8/EwAAagfDZcjmYzM7BgAA+A3LFPX09HSlp6df0LZxcXF64YUXPLJfp9NJUYelOJ1O5ebmyuHgTSRYC7N5aaKkAwBgPZb/jDoAAAAAAN7EMkfUzcJRIViN3W5XTEyM2TGACphNmMlluORj4/gCAMA7eH1R3563WKd++dbsGAAA4HeEBTZW8hUPmB0DAIAaY8miPn78eBUWFuqll15yL1u3bp0efvhh+fr6avHixerYsaN7XXFxsXr37q2ePXtq3LhxldpX4Zkf9dOZwx7LDgAAAABAVdSKc8hWrFihsWPH6qmnntK9996rCRMmqLi42L1+5syZqlOnzkVd9R0AAAAAACuxfFHPyMjQtGnT9NxzzyktLU2jR4+Wn5+fnnnmGUnSjh07tGLFCj399NMKCAgwOS0AAAAAAFVjyVPfz5k5c6beeustzZ8/X507d5YkBQQE6Omnn1b//v2VlJSkp556SsOGDVNsbKzJaQEAAAAAqDrLFvX//Oc/+ve//63XXnvNXdLPiYuL09ChQzVy5Ei1bdtWw4YNMyklAAAAAACeZdlT3x0Oh5o1a6YXXnhBp0+frrB++PDhcrlcGjp0qHx9Lft+AwAAAAAAlWLZot6oUSNlZmbqhx9+0AMPPKCioqJy68+Vc0o6AAAAAOBSYtmiLknNmjXTG2+8ofz8/POWdQAAAAAALjWWPxzdpEkTZWZmauDAgXrggQe0YMECBQcHe+zxQwMbyrCVeezxAACAZ4UFNjY7AgAANcryRV2SGjdu7C7rQ4YM0cKFCz1W1ju3vF92u90jjwUAAKqHy3DJx2bpEwEBAPAYSxb19PT0CssaNWqk9evXl1uWm5tb5X05nU6KOizF6XQqNzdXDoeD2YSlMJswEyUdAOBNeNUDAAC1XmBgoNkRAADwGEseUa9JHBWC1djtdsXExJgdA6iA2YRVeWo2DcMlG0fuAQAWYImiPn78eGVnZ+uuu+7S1KlTy62bMmWK3nrrLaWmpio9Pd297f9KTk7WwoULK73vk988L9cvhy42OgAAuAT4BjZX/VZ/NzsGAACSLFLUpV+v7p6Tk6PHHntMderUkSSdPXtWa9asUdOmTcttm5KSohkzZpRb5u/vf1H7LTtzTM4zBy8uNAAAAAAAHmaZoh4TE6MjR45ow4YN6tOnjyRpw4YNatKkiaKiospt6+/vr8jISDNiAgAAAABQrSz1Qay0tDStWrXKfTsrK0v9+vUzMREAAAAAADXLUkW9T58++uSTT3Ts2DEdO3ZMu3btch9d/63NmzcrISGh3L958+aZkBgAAAAAAM+yzKnvkhQeHq4uXbooOztbhmGoS5cuCg8Pr7BdYmKiJk+eXG5ZWFhYDaUEAAAAAKD6WKqoS7+e/n7uyu+TJk067zaBgYFq0aJFTcYCAAAAAKBGWK6op6SkqLS0VDabTcnJyWbHAQAAAACgRlmuqNvtdq1du9b98/mUlJTo+PHjFe53vtPk/4xvYDP52EorHxQAAFwyfAObmx0BAAA3yxV1SQoODv7D9Vu2bKlwtD06Olrr1q2r9L7qRT/yu28IAAAA72EYLtlslrrOLgDAS9kMwzDMDmEGp9Op3bt3Ky4uTv7+/mbHAdycTqdyc3PlcDh4EwmWwmzCqphNWNW5vzfbt2/PbMJSmE3zXOjvnreNAQAAAACwEEue+l6TeAcJVmO32xUTE2N2DKACZhNWVZtmk9PrAQAXwuuL+g9fbZXzzCmzYwAAgEucX1CYGre53uwYAIBawBJFffz48crOztZdd93l/g71c6ZMmaK33npLqampSk9P108//aTZs2fr/fffV35+vsLCwtSmTRsNHz5cV199daX3XVpcqNLiAk89FQAAAAAAqsQSRV2SmjRpopycHD322GOqU6eOJOns2bNas2aNmjZt6t5u5MiRKi0tVXp6upo3b66CggJt375dJ0+eNCk5AAAAAACeY5miHhMToyNHjmjDhg3q06ePJGnDhg1q0qSJoqKiJEmFhYX6+OOPlZmZqWuvvVaS1KxZM8XHx5uWGwAAAAAAT7LU1UzS0tK0atUq9+2srCz169fPfTsoKEhBQUHauHGjSkpKzIgIAAAAAEC1slRR79Onjz755BMdO3ZMx44d065du9xH1yXJ19dX6enpWr16tTp27Ki7775bzz77rPbv329iagAAAAAAPMdSRT08PFxdunRRdna2Vq1apS5duig8PLzcNjfeeKO2bNmil19+WSkpKdq5c6f69etX7kg8AAAAAAC1laWKuvT/p79nZ2crLS3tvNsEBATouuuu04gRI7R06VKlpqbqhRdeqOGkAAAAAAB4nuWKekpKikpLS1VWVqbk5OQLus8VV1yh4uLiak4GAAAAAED1s8xV38+x2+1au3at++ffOnHihB5++GGlpaXJ4XCobt262rt3rxYsWKAbbrjhovbnFxQqH5tR5dwAAAB/xC8ozOwIAIBawnJFXZKCg4PPu7xu3bpq166dFi9erMOHD6usrEyNGzfWHXfcoWHDhl3Uvhq1Tq7whgAAAEB1MAyXbDbLndAIALAYm2EYXnk42el0avfu3YqLi5O/v7/ZcQA3p9Op3NxcORwO3kSCpTCbsCpmE1Z17u/N9u3bM5uwFGbTPBf6u+ctXQAAUOudOXPG7AgAAHiMJU99r0m8gwSrsdvtiomJMTsGUAGzWfsYhiGbzWZ2DAAAUEmWKeoOh+MP1z/00ENKTU3VDTfcoPDwcL377rvlPst+2223qXv37ho5cmSl9vvZZ5+pqKjoojIDAGBVISEhSkhIMDsGAAC4CJYp6lu3bnX/nJOTozlz5mjdunXuZUFBQTpx4oQk6fTp03r11Vc1atSoKu+3qKhIhYWFVX4cAAAAAAA8wTKfUY+MjHT/CwkJkc1mK7esbt267m3vvfdeLVq0SAUFBSYmBgAAAADA8yxT1Cvj1ltvVYsWLTR37lyzowAAAAAA4FG1sqjbbDaNGTNGy5cv1+HDh82OAwAAAACAx9TKoi5JKSkp6tChg2bPnm12FAAAAAAAPKbWFnVJGjt2rHJycrRv3z6zowAAAAAA4BG1uqjHx8erR48emjVrltlRAAAAAADwCMt8PdvFevTRR3XrrbfKbrebHQUAAAAAgCqr9UU9OjpaaWlpWrZs2UXdPzg42MOJAAAwX0hIiNkRAADARbIZhmGYHcIMTqdTu3fvVvv27TkaDwC4JBmGIZvNZnaMasdrOqyK2YRVMZvmudDffa3+jLonOJ1OsyMA5TidTuXl5TGbsBxms/bxhpIOAMClqNaf+l5VvIMEq7Hb7WrZsqXZMYAKatNsugyXfGxe/140AACopby+qC//fJu+O33K7BgAAA9pFBymAe1SzI4BAABw0SxZ1MePH6/s7GxJkp+fn5o0aaLbbrtNw4YNk6/vr5GHDBmibdu2admyZYqPj7/off14ulDHCn/ySG4AAAAAAKrKsucFpqSkaOvWrVq/fr0GDx6sF198UQsXLpQkffvtt9q1a5cGDBigrKwsk5MCAAAAAOA5li3q/v7+ioyMVLNmzXTPPfcoKSlJmzZtkiStWrVKXbt2Vf/+/fXOO+/ol19+MTktAAAAAACeYdmi/r8CAgJUWloqwzC0atUq9enTR61atdJll12mdevWmR0PAAAAAACPsHxRNwxD27Zt09atW5WYmKht27bpzJkzSk5OliT16dOH098BAAAAAJcMyxb1zZs3KyEhQXFxcXrwwQd1yy23aOTIkcrKytItt9zivqjcrbfeql27dunw4cMmJwYAAAAAoOosedV3SUpMTNTkyZPl5+enhg0bytfXVydPntS7776rsrIyLVmyxL2t0+lUVlaWHn30URMTAwAAAABQdZYt6oGBgWrRokW5ZW+//bYaN26suXPnllv+wQcf6NVXX9WoUaNkt9trMiYAAAAAAB5l2aJ+PitXrtSNN96o1q1bl1vepEkTzZo1S1u2bFGXLl0q9ZgN64bKKcODKQEAZmoUHGZ2BAAAgCqpNUV979692r9/v6ZNm1ZhXUhIiDp37qyVK1dWuqjfGZfEUXgAuMS4DJd8bJa9DAsAAMAfsmRRT09Pr7AsNjZWubm5v3ufjIyMi9qX0+mkqMNSnE6ncnNz5XA4mE1YSm2aTUo6AACozfhLBgAA1HqBgYFmRwAAwGMseUS9Jln9qBC8j91uV0xMjNkxgAqYTVgVs3lpMlwu2Xw4pgTAO1myqB8/flzz5s3T5s2b9cMPP6hBgwZq27at7r//fg0aNOgP7/v6668rMTHxgvdV+N5KGSe+r2JiAAAAeIq9fkOFde9vdgwAMI3livrRo0fVv39/hYaG6u9//7tat26tsrIybd26VRMnTtTWrVvd206fPl1FRUWaMWOGe1lYWOWu9us8eVyu/GMeyw8AAAAAQFVYrqhPmTJFNptNK1asUFBQkHv5lVdeqbS0NIWGhrqX1alTRyUlJYqMjDQjKgAAAAAAHmepD/6cPHlSW7Zs0YABA8qV9HN+W9IBAAAAALgUWaqoHz58WIZhqGXLlmZHAQAAAADAFJYq6oZhmB0BAAAAAABTWaqot2jRQjabTXl5eWZHAQAAAADAFJYq6vXq1VNycrLefPNNFRcXV1hfWFhoQioAAAAAAGqOpYq6JE2aNEkul0t33HGH1q9fr0OHDungwYN6/fXXddddd5kdDwAAAACAamW5r2dr3ry5Vq1apXnz5unpp5/Wjz/+qPDwcF111VWaPHmyx/dnrxcpH8Pp8ccFAADAxbHXb2h2BAAwlc3w0iu4OZ1O7d69W+3bt5fdbjc7DgAAAH7DcLlk87HcyZ+Vwt+bsCpm0zwX+ruv3f/38wCnk6PpsBan06m8vDxmE5bDbMKqmM1LU20v6QBQFZY79b2m8Q4SrMZut6tly5ZmxwAqYDZhVZ6aTZfLkI+PzQOJAACoGq8v6pu2H9CJwjNmxwAAACaqHxqkHskOs2MAACDJIkV9/Pjxys7O1pgxYzR06FD38o0bN2rEiBHKzc3Vhx9+qIEDB0qSbDab6tatq+bNmyspKUmDBg1Sw4YXd9GRkz8XK/+nil8FBwAAAACAGSzz4Z+AgABlZGTo1KlTf7jdunXrtGXLFq1cuVIPPvigtm/frt69eys3N7eGkgIAAAAAUH0sU9STkpIUERGh+fPn/+F2DRo0UGRkpKKjo9WrVy8tWbJE9evXr5avbgMAAAAAoKZZpqj7+Pho9OjReuONN/T9999f8P3q1Kmju+++W7t27VJBQUE1JgQAAAAAoPpZpqhLUo8ePdS2bVvNmTOnUvc7d6XXY8eOVUcsAAAAAABqjKWKuiSNHTtWq1ev1sGDBy/4PoZhVGMiAAAAAABqjuWK+jXXXKPk5GTNmjXrgu+Tl5cnSYqKiqquWAAAAAAA1AhLfD3b/xozZoz69u2r6OjoP932l19+0bJly3TNNdcoPDy8BtIBAAAAAFB9LFnUHQ6HevfurczMzArrCgoKdPbsWZ0+fVpffPGFFixYoBMnTujFF1+8qH3VCwmSYdiqGhkAANRi9UODzI4AAICbJYu6JI0aNUo5OTkVlt90002y2WwKCgpS8+bNdd1112nw4MGKjIy8qP1063yl7HZ7VeMCAIBazuUy5OPDm/cAAPNZoqinp6dXWBYVFaW9e/e6bycmJio3N9dj+zx3AbqSkhL5+/t77HGBqnI6nTpw4ICuvJI3kWAtzCasypOz6XR6KBSgX2fzt/8FrILZNM+53/mfXRDdZnjpJdNLSkr0+eefmx0DAAAAAOBl4uLi/vCAsdcWdZfLpbKyMvn4+Mhm4zQ3AAAAAED1MgxDLpdLvr6+8vH5/S9h89qiDgAAAACAFVnue9QBAAAAAPBmFHUAAAAAACyEog4AAAAAgIVQ1AEAAAAAsBCKOgAAAAAAFkJRBwAAAADAQijqAAAAAABYCEUdAAAAAAAL8cqi/uabb6pbt26Ki4vTHXfcoT179pgdCV5m/vz5SktLU0JCgjp37qzhw4crLy+v3DZnz57VlClTlJiYqISEBI0cOVL5+fkmJYa3euWVV+RwODR9+nT3MmYTZvnhhx80duxYJSYmKj4+Xr1799bnn3/uXm8YhmbPnq3k5GTFx8dr0KBBOnTokHmB4RWcTqeef/55devWTfHx8erevbvmzp0rwzDc2zCbqAkfffSRhg0bpuTkZDkcDm3cuLHc+guZw5MnT2rMmDHq0KGDOnbsqMcee0ynT5+uwWeBc7yuqOfk5GjGjBkaMWKEsrOz1aZNGw0ZMkQFBQVmR4MX2blzpwYMGKDly5dr0aJFKisr05AhQ1RcXOze5qmnntJ7772n559/XpmZmfrxxx/10EMPmZga3mbPnj1aunSpHA5HueXMJsxw6tQp9e/fX35+fsrIyNA777yjcePGKSwszL1NRkaGMjMzNXnyZC1fvlyBgYEaMmSIzp49a2JyXOoyMjK0ZMkSPfHEE8rJydHYsWO1YMECZWZmltuG2UR1Ky4ulsPh0KRJk867/kLmcOzYsfr666+1aNEizZs3Tx9//LGeeOKJmnoK+C3Dy9x+++3GlClT3LedTqeRnJxszJ8/38RU8HYFBQVG69atjZ07dxqGYRiFhYXGVVddZaxdu9a9zddff220bt3a+PTTT01KCW9SVFRk9OzZ0/jggw+Me++913jyyScNw2A2YZ6ZM2ca/fv3/931LpfLuO6664wFCxa4lxUWFhqxsbHGmjVraiIivNTQoUONCRMmlFv20EMPGWPGjDEMg9mEOVq3bm28++677tsXMofnXs/37Nnj3ub99983HA6H8f3339dceBiGYRhedUS9pKREX3zxhZKSktzLfHx8lJSUpE8//dTEZPB2P//8syS5jwzt3btXpaWl5Wa1VatWatq0qXbv3m1GRHiZqVOn6vrrry83gxKzCfNs2rRJsbGxGjVqlDp37qy+fftq+fLl7vVHjx7V8ePHy81mSEiI2rVrx2s8qlVCQoJ27Nihb775RpK0f/9+ffLJJ/rLX/4iidmENVzIHH766acKDQ1VXFyce5ukpCT5+PjwUWET+JodoCadOHFCTqdTDRo0KLe8QYMGFT4fDNQUl8ulp556Sh06dFDr1q0lSfn5+fLz81NoaGi5bRs0aKDjx4+bERNe5J133tG+ffu0cuXKCuuYTZjlyJEjWrJkiQYPHqxhw4bp888/15NPPik/Pz+lpqa65+98r/FcQwHVaejQoSoqKtLNN98su90up9OpRx99VH369JEkZhOWcCFzmJ+fr/Dw8HLrfX19FRYWxmu8CbyqqANWNGXKFB04cEBvvfWW2VEAfffdd5o+fbpeffVVBQQEmB0HcDMMQ7GxsRo9erQkKSYmRgcOHNDSpUuVmppqcjp4s7Vr1+rtt9/WrFmzdMUVV+jLL7/UjBkz1LBhQ2YTwEXzqlPf69evL7vdXuHCcQUFBYqIiDApFbzZ1KlTtXnzZi1evFiNGzd2L4+IiFBpaakKCwvLbV9QUKDIyMiajgkv8sUXX6igoED9+vVTTEyMYmJitHPnTmVmZiomJobZhGkiIyPVqlWrcstatmypb7/91r1eEq/xqHH//Oc/NXToUPXq1UsOh0N9+/bV/fffr/nz50tiNmENFzKHERER+umnn8qtLysr06lTp3iNN4FXFXV/f39dddVV2r59u3uZy+XS9u3blZCQYGIyeBvDMDR16lS9++67Wrx4sZo3b15ufWxsrPz8/MrNal5enr799lu1b9++htPCm3Tq1Elvv/22Vq9e7f4XGxur3r17u39mNmGGDh06uD8DfM6hQ4fUrFkzSVJUVJQiIyPLzWZRUZE+++wzXuNRrX755RfZbLZyy+x2u/vr2ZhNWMGFzGFCQoIKCwu1d+9e9zY7duyQy+VSfHx8jWf2dl536vvgwYM1btw4xcbGKj4+XosXL9aZM2fUr18/s6PBi0yZMkVr1qzRSy+9pLp167o/9xMSEqI6deooJCREaWlpSk9PV1hYmIKDg/Xkk08qISGBMoRqFRwc7L5WwjlBQUGqV6+eezmzCTPcf//96t+/v+bNm6ebb75Ze/bs0fLlyzV16lRJks1m08CBA/Xyyy+rRYsWioqK0uzZs9WwYUN1797d5PS4lHXt2lXz5s1T06ZN3ae+L1q0SGlpaZKYTdSc06dP6/Dhw+7bR48e1ZdffqmwsDA1bdr0T+ewVatWSklJ0eOPP64pU6aotLRU06ZNU69evdSoUSOznpbXshnn3u7zIm+88YYWLlyo48ePq23btpo4caLatWtndix4kf/9XupzZsyY4X7T6OzZs0pPT9c777yjkpISJScna9KkSZx6hBp33333qU2bNvrHP/4hidmEed577z09++yzOnTokKKiojR48GDdeeed7vWGYWjOnDlavny5CgsLdfXVV2vSpEmKjo42MTUudUVFRZo9e7Y2btyogoICNWzYUL169dKIESPk7+8vidlEzfjwww81cODACstTU1OVnp5+QXN48uRJTZs2TZs2bZKPj4969uypiRMnqm7dujX5VCAvLeoAAAAAAFiVV31GHQAAAAAAq6OoAwAAAABgIRR1AAAAAAAshKIOAAAAAICFUNQBAAAAALAQijoAAAAAABZCUQcAAAAAwEIo6gAAAAAAWAhFHQAAAAAAC6GoAwCAGnP06FE5HA59+eWXZkcBAMCyKOoAAAAAAFgIRR0AAC/icrmUkZGhHj16KDY2Vl26dNHLL78sScrNzdXAgQMVHx+vxMREPf744zp9+rT7vvfdd5+mT59e7vGGDx+u8ePHu29369ZN8+bN04QJE5SQkKAuXbpo2bJl7vU33HCDJKlv375yOBy67777qvPpAgBQK1HUAQDwIrNmzVJGRoaGDx+unJwcPfPMM4qIiFBxcbGGDBmisLAwrVy5Us8//7y2bdumadOmVXofixYtUmxsrFavXq177rlHkydPVl5eniRpxYoVkqTXXntNW7du1QsvvODR5wcAwKWAog4AgJcoKirS66+/rr/97W9KTU3VZZddpo4dO+qOO+7QmjVrVFJSoqefflqtW7dW586d9cQTT+hf//qX8vPzK7Wfv/zlLxowYIBatGihBx98UPXr19eHH34oSQoPD5ck1atXT5GRkapXr56nnyYAALUeRR0AAC+Rl5enkpISderUqcK6gwcPyuFwKCgoyL2sQ4cOcrlc+uabbyq1H4fD4f7ZZrMpIiJCBQUFFx8cAAAvQ1EHAMBLBAQEVOn+NptNhmGUW1ZWVlZhO19f3z+9HwAA+H0UdQAAvMTll1+uOnXqaMeOHRXWtWrVSrm5uSouLnYv27Vrl3x8fBQdHS3p19PWjx8/7l7vdDp14MCBSmXw8/Nz3xcAAJwfRR0AAC8REBCgBx98UDNnztTq1at1+PBh7d69WytWrFDv3r3l7++v8ePH66uvvtKOHTs0bdo03XbbbYqIiJAkderUSe+//742b96sgwcPavLkySosLKxUhgYNGqhOnTrasmWL8vPz9fPPP1fHUwUAoFbz/fNNAADApWL48OGy2+2aM2eOfvzxR0VGRuruu+9WYGCgFi5cqOnTp+v2229XYGCgevbsWe6r19LS0rR//36NGzdOdrtdgwYNUmJiYqX27+vrq4kTJ2ru3LmaM2eOOnbsqMzMTE8/TQAAajWbwYfGAAAAAACwDE59BwAAAADAQijqAAAAAABYCEUdAAAAAAALoagDAAAAAGAhFHUAAAAAACyEog4AAAAAgIVQ1AEAAAAAsBCKOgAAAAAAFkJRBwAAAADAQijqAAAAAABYCEUdAAAAAAAL+T8BFecUr6psPgAAAABJRU5ErkJggg==\n" 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\n" 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Churn 1.000000\n", + "CustServ Calls 0.208750\n", + "Day Mins 0.205151\n", + "Day Charge 0.205151\n", + "Eve Mins 0.092796\n", + "Eve Charge 0.092786\n", + "Intl Charge 0.068259\n", + "Intl Mins 0.068239\n", + "Night Charge 0.035496\n", + "Night Mins 0.035493\n", + "Day Calls 0.018459\n", + "Account Length 0.016541\n", + "Eve Calls 0.009233\n", + "Area Code 0.006174\n", + "Night Calls 0.006141\n", + "Intl Calls -0.052844\n", + "VMail Message -0.089728\n", + "Name: Churn, dtype: float64\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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QIG3bti2Nm2tRt32u008//VvHTJ48eb6HyM+d0+TJk7PJJpt863rm/jGqXbt289y34YYbpqqqKu+991423njjRZj5olmUr9mPP/44V155ZR566KF5Xvfz+5wt6GiE77LO+VmUz01VVVVuvPHG3HLLLZk0aVJpz3Py1d7XhXn33XdTt27deUK5devWad68+TyvraVlcV9Pl112WcaMGZM6derkiy++WKzn+sc//pGXX345SRYay3M/J4v6/f2zzz7LNddck4MOOmieowfeeeedVFdX5/LLL1/gEV3Tpk2rcZrEonxvWRRzv1/M72tsgw02yOjRo2sse++992r8DGjdunUGDx682Kc5AMUg7IHvlbp166ZLly658cYb8/bbb9eIjClTpqR169aLvK7LLrss66+/fvr27TvfCxIddNBBmTBhQq6++upceeWVC13X2muvPc+F3h555JHcfvvt8x0/ffr0XHfddTniiCPm+wt+VVVVWrZsmYsvvni+j2/RosVC5zNX/fr1s/vuu2f48OH54osvstpqq+VHP/rRPGFfVVWVOnXq5Nprr61xPvtc3/yFesaMGTn//PPTp0+ftG7dOkOHDk3yVYxcffXVOe644zJs2LD5/kI8N0jWXnvtb53/z3/+81x33XV5/PHH06dPnzzwwAPZbrvt5hvGZ511Vo1fmD///PMce+yxNcacc845GT58eA488MB06tQpzZo1S506dfK73/2uRuQvK4uz7Uly9NFHz3NY+NdPlSi6KVOmfOvH4vjjj88LL7yQQw45JJtttlkaN26cqqqqHHroofP9nA0ePDhNmzYt3Z4wYULOPvvs77TO+VmUz80111yTyy+/PHvuuWd++9vfZtVVV03dunVz3nnnLfLzLK3z/ZeV//znPzn//PNz00035Q9/+EPuu+++Rd6z/uKLL2bvvfdOo0aN8uc//zk777zzPH8kS746779x48aLHPZzT4s55JBD8vHHH9e4r6qqKslXRz707Nlzvo//5h9TFuV7y7LQqlWr0qlOn332We6+++4ceuihueWWW+a5WClQfMIe+N6Zu9dr5syZNZa/+eabNS4StzCvvPJKHnzwwVx11VXzDdnkq8N1zznnnLzyyitp1qxZjjnmmIwdOzYXXHDBPGMbN26cbt261Vj26quvLvD5//znP6dJkyY54IAD5nv/uuuum1GjRmXLLbescdjwkthzzz2zxx575P3338/Pf/7z+YbCuuuum+rq6qyzzjrz3Zv0TYMHD86MGTNyyimnpFWrVnnjjTfy9NNP56qrrsqWW26ZAw44IIMHD87PfvazrL766jUeO3cP3eabb/6tz7PJJpukffv2GTFiRNZcc81Mnjw5Z5555nzHfvPojvkdGj33PPqvXwH8yy+/XOBe2rfffjs/+tGPSrdnzJiRKVOmlM75n3sKxfjx42scvj579uxMmjRpntfE4mx78tX2f3Md33y9tmnTJhMmTJjnsePHj68xx2/TokWLNGrUaIHrqlu3btZaa61FWteiqKioyDvvvLPAuEq+2os7atSoHHvssTUOqX7rrbcW+Jitt966xh++vvlHoCVZ5/wsyufm0UcfTZcuXXLeeefVWP7pp5+WLgK3IGuvvXaqqqry9ttv1zj0eurUqfn0008X+Y9Di2txX0/HHnts+vbtm8022yx77rlnrr766hx//PGL9Fzdu3fPgAED8uWXX+bxxx/PWWedVePClHONGzdukQ8///DDD3PjjTfmhBNOSNOmTecJ+7lfp/Xr15/n87cgi/K9ZVHM/dhNmDBhnqOxJkyYMM/HdpVVVqkxx969e2fbbbfNzTffPM8fq4Dic4498L1SUVGRf/7zn6lfv36NX/ReeumlvPPOOzUibGEuueSSbLnlltl+++0XOu7SSy/Ne++9l4suuijdunUrHXL+Xcy9Evuxxx67wGjfZZddUllZWeP89bnmzJmzwLdSm5+NN944P/zhDzNu3Lj07dt3vmN22mmnlJWV5corr5xnT2J1dXWNtwF7/fXXM2zYsBx77LFZffXVU7du3XTq1ClJsuWWWyZJjjnmmDRp0mSeUwySr2KnXbt2i/yL+h577JF//vOfueGGG/KDH/xgniuuL475/RFn2LBhNQ6R/rrbb7+9dMG7JLn11lszZ86c0hy6deuW+vXrZ9iwYTU+bnfddVc+++yzed4RYHG3fVH06tUrL774Yl544YXSspkzZ+aOO+7I2muvvcArjH9TWVlZunfvnieeeKLGhRynTp2aBx54IFtttVWNPeHf1RNPPJEvvvhioV+zC/qj2w033LDEz7ss1rmw5/rm19PDDz88zznc8zP3tfPNec09OmZB7zbxXS3u62nuUQubbrppfv3rX+cvf/nLfK9EPz+dO3dOWVlZGjdunIEDB+a5557LHXfcUWPMe++9l3//+9+L/L39qquuSsuWLfPLX/5yvve3bNky2267bW6//fZ8+OGH89y/pNG+KDbffPO0bNkyt912W423LHzyySfz5ptvZrvttlvo4ysqKlJZWVnjscDKwx57YKX2j3/8o7SnaPr06RkxYkTeeuutHH744aXIuPLKKzNs2LC0bdt2nquaL8jTTz+dW2+9daFjRo4cmeuvvz4XXnjhUt079uyzz2bDDTdMv379Fjhm2223zT777JMhQ4bk1VdfTffu3VO/fv289dZbeeSRR3LGGWcs0rnEc91www2ZPXv2As/rXXfddXP88cfnkksuybvvvpsddtghTZo0yaRJk/L4449n7733ziGHHJIkGThwYDbaaKP5XnBwriZNmuS0007L8ccfn7322itdu3bNxIkTc+211+bFF1/MTjvtVON0gLnvEz1y5Mi0adOmxt7vPn365KKLLspjjz2Wfffd91vf5m9htttuu9x3331p2rRpNtpoo4wZMyYjR45c4MeloqIiBx10UHbZZZdMmDAht9xyS7baaqvSH4RatGiRI444IldeeWUOPfTQ9O7duzSuQ4cOpYv8Lem2L4rDDz88Dz74YA477LD0798/q666au69995MmjQpgwcPnucK5gtz/PHHZ+TIkdlvv/2y3377paysLLfffntmz56dk046abHmtSCzZs3KFVdckVtvvTWdO3dOjx49Fji2adOm2WabbfKXv/wlFRUVWWONNfLPf/6zxh8eFteyWOeCbLfddrnqqqty2mmnpXPnznn99dczYsSIRfocb7rppunbt29uv/32fPrpp9lmm23y0ksv5Z577skOO+ywyKG7uL7L6+mYY47J3/72t/zhD3/IrbfeulivvZ49e2b33XfPRRddlJ/85CdZffXVc8stt+R///d/06hRo4V+v/m6p59+OhdffPFCTwf44x//mP322y+77bZb9t5777Rt2zZTp07NmDFj8v777+f+++9f5Hkvjvr16+fEE0/Maaedll/96lfZddddS293t/baa89z6tLMmTNL3ys+//zz3Hffffnyyy8XeJFBoNiEPbBSu+KKK0r/X2WVVbLBBhtkwIABNfbG3Hnnndl+++1z/PHHz/M+5Auy/fbbl/Yuz89HH32UU045Jbvuuut8r8D+XZ1wwgkL3HM419lnn53NN988t912W/70pz+lrKwsa6+9dnbfffeFzn1+FuX81MMPPzzrr79+rr/++tJ7ZK+55prp3r17evfunSS59957M3r06Nxyyy3fOv9ddtkld955Z84+++zcd999ee6550rXHPjb3/4233c2uOaaa7LeeuvVCJ9WrVqle/fuefLJJ+f79lyL44wzzkjdunUzYsSIfPnll9lyyy0zdOjQHHroofMdf9ZZZ2XEiBG54oorUlFRkV133TVnnnlmjUOFjz322LRo0SI33XRTBg0alFVXXTV77713TjjhhNIfIZZ02xdFq1atctttt5Xeju7LL79MeXl5rrnmmm/dA/hNG2+8cW6++eZccsklGTJkSKqrq9OxY8dcdNFFi/Qe9ovi008/zcMPP5y99947xx133LfG3yWXXJJzzjknt9xyS6qrq9O9e/dce+21Cz2E/9ssi3XOz5FHHplZs2ZlxIgReeihh9K+ffsMGTIkl1xyySI9/txzz80666yTe+65J48//nhatWqVI444Yp4rvS9N3+X1tMoqq+Scc87JAQcckJtuummBpxotyOmnn56nn346Z599dq688srcc8892WKLLfLb3/62xsXsFmazzTZLnz59Fjpmo402yt133116jo8//jgtWrRI+/btF/oWhEtDv3790rBhw1x77bW5+OKL07hx4+ywww456aST5nlv+o8++qj09o9z303hwgsvFPawkqpTvTyu9gMA39Hw4cNz5ZVX5v/+7/8WOKZ///7p27fvPEczHH300Xn99dfz2GOPLetpJvlqrqeddlruuuuuGufWfpf1Lem2AwArP+fYA7BS+/DDD5fK3noAgBWVQ/EBKIR11133Ww8h7datW+mtpiZOnJh///vfueuuu1KvXr3ss88+y2Oay8TibjsA8P3iUHwAVkpzD4dv06ZNTjnllMW6WODSeu6ldSg+AMDCCHsAAAAoMOfYAwAAQIEJewAAACgwF89bBFVVVZkzZ07q1q1b472HAQAAYFmorq5OVVVV6tWrl7p1F75PXtgvgjlz5uSll16q7WkAAADwPdOhQ4c0aNBgoWOE/SKY+9eRDh06pKysrJZnAwAAwMqusrIyL7300rfurU+E/SKZe/h9WVmZsAcAAGC5WZTTwV08DwAAAApM2AMAAECBCXsAAAAoMOfYAwAAsEDV1dWZM2dOKisra3sqK5369esvleu4CXsAAADma/bs2Xnvvfcyc+bM2p7KSqlOnTpZZ5110rRp0++0HmEPAADAPKqqqjJhwoSUlZWlTZs2adCgwSJdoZ1FU11dnSlTpmTSpEnZeOONv9Oee2EPAADAPGbPnp2qqqq0bds2jRs3ru3prJRat26dt956KxUVFd8p7F08DwAAgAWqW1c2LitL6wgInyEAAAAoMGEPAAAABSbsAQAAWOGUl5fn8ccfr+1pFIKL5wEAALDcTZkyJddcc03+/ve/54MPPkjLli2z2Wab5cADD0zXrl1rdW5jx47N4MGD8+9//zsVFRXp1q1bBgwYkBYtWtTqvBbEHnsAAACWq0mTJqVfv3555plncvLJJ2fEiBH5y1/+ki5dumTgwIHL7Hlnz569SONGjx6dLbfcMjfeeGOuu+66vPbaa7nooouW2by+K2EPAADAcjVw4MDUqVMnd955Z37605+mXbt22XjjjXPwwQfnjjvuKI376KOPcvTRR2eLLbbITjvtlCeeeKJ03/Dhw7P11lvXWO/jjz+e8vLy0u3Bgwdnjz32yJ133pnevXunY8eOSb46zP/OO+9c4Lr333//HHLIIdl4442zxRZbpGfPnnnvvfeW1YfjOxP2AAAALDcff/xxnnrqqey///5p3LjxPPc3b9689P8rr7wyu+yyS+6///78+Mc/zoknnpiPP/54sZ7vnXfeyaOPPporr7wy995772Kve+zYsbn33nuz5557LtbzLk/CHgAAgOXmnXfeSXV1dTbYYINvHdu3b9/06dMn6623Xk444YTMnDkzL7744mI9X0VFRS688MK0b98+m2666WKte+zYsTnggAPym9/8JrvttttiPe/yJOwBAABYbqqrqxd57NcPq2/cuHGaNm2a6dOnL9bztWnTZr4XvVuUdV966aXp0aNHDjnkkMV6zuVN2AMAALDcrLfeeqlTp07Gjx//rWPr169f43adOnVSVVWVJKlbt+48fySoqKiYZx2NGjVa7HXP9eGHH6Zdu3bfOs/aJuwBAABYbn7wgx+kR48eufnmmzNz5sx57v/0008XaT2rrbZaZsyYUWMdY8eOXWrzTJKLL744e++991Jd57Ig7AEAAFiu/vjHP6aqqip77bVXHn300bz11lt58803c+ONN2afffZZpHVsscUWadSoUS699NK88847GTFiRIYPH75U53nOOefkscceW6rrXBaEPQAAAMtV27ZtM3z48HTp0iUXXHBB+vTpk4MPPjijRo3KgAEDFmkdP/jBD3LRRRflH//4R3bbbbc8+OCDOfbYY5fqPCdOnJiPPvpoqa5zWahTvThXLvieqqyszJgxY9KpU6eUlZXV9nQAAACWuS+++CITJkxIu3bt0rBhw9qezkppYR/jxelQe+wBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7L8HKquqansKsMx5nQMA8H1Vr7YnwLJXVrduzrzlqUz48JPangosE+1WXzXn7teztqcBAPC9UVlVlbK6y28/8fJ6vuHDh+e8887L888/v8yfa2kS9t8TEz78JGPfnV7b0wAAAFYCy3Pn4ZLsxDn11FNzzz33zLP8b3/7W9Zbb72lNbUVhrAHAABgsa3oOw979uyZQYMG1VjWokWLWprNsuUcewAAAFY6DRo0SOvWrWv8u/HGG7PbbrulU6dO6dWrVwYMGJAZM2YscB1jx45N//7907lz52y55Zbp169fXnrppdL9zz//fPbbb7907NgxvXr1yrnnnpuZM2cuj82rQdgDAADwvVCnTp2cccYZeeCBB3L++efnmWeeyUUXXbTA8SeeeGLWXHPN3HXXXRk+fHgOO+yw1K9fP0nyzjvv5LDDDstOO+2U+++/P3/6058yevTonHPOOctrc0ocig8AAMBK5+9//3s6d+5cut2zZ89cccUVpdvrrLNOjj/++Pzxj3/MgAED5ruOyZMn55BDDsmGG26YJFl//fVL9w0ZMiS77bZbDjrooNJ9Z5xxRvr3758BAwZklVVWWerbtCDCHgAAgJVOly5dagR7o0aNMnLkyAwZMiTjx4/P559/nsrKynz55ZeZNWtWGjVqNM86Dj744Jx55pm577770q1bt+y8885Zd911k3x1mP5rr72WESNGlMZXV1enqqoqkyZNKv0xYHkQ9gAAAKx0GjVqVOMK+JMmTcoRRxyRfffdN7/73e+y6qqrZvTo0TnjjDNSUVEx37A/9thj06dPnzz55JP5xz/+kSuuuCJ/+tOfsuOOO2bmzJn55S9/mf79+8/zuLXWWmuZbts3CXsAAABWev/9739TXV2dU089NXXrfnW5uYcffvhbH9euXbu0a9cuBx10UE444YTcfffd2XHHHdO+ffuMGzduhXj7PGEPAADAYmu3+qqFep711lsvFRUVGTZsWHr37p3Ro0fntttuW+D4L774IhdeeGF++tOfZp111sn777+fl156KTvttFOS5LDDDss+++yTs88+O3vttVcaNWqUcePGZeTIkTnrrLOSJJdcckk++OCDXHjhhUtlGxZE2AMAALBYKquqcu5+PZfr85XV/W5v6rbpppvmtNNOy7XXXptLL700W2+9dU444YSccsop8x1ft27dfPzxxznllFMyderUrLbaatlpp51y3HHHldY3bNiwXHbZZdlvv/2SJG3bts3Pfvaz0jqmTJmS99577zvNe1HUqa6url7mz1JwlZWVGTNmTDp16pSysrLans4S2f+yBzL23em1PQ1YJjZdu0VuPr5PbU8DAGCl8sUXX2TChAlp165dGjZsWNvTWSkt7GO8OB3qfewBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAACLpbqqcqV+vqKpV9sTAAAAoFjq1C3L1OGnpmLq+GX+XPVbbZBW/c5f5PHl5eULvf+YY47Jscce+12ntUIR9gAAACy2iqnjU/H+q7U9jXk8/fTTpf8/9NBDueKKK/LII4+UljVu3Lj0/+rq6lRWVqZevWKnsUPxAQAAWGm0bt269K9Zs2apU6dO6fb48eOz5ZZb5sknn0y/fv3SoUOHjB49OqeeemqOOuqoGuv5n//5n/Tv3790u6qqKkOGDEnv3r3TsWPH7L777jX+YFCbiv1nCQAAAFhMl1xySU455ZS0bds2zZs3X6THDBkyJPfff38GDhyY9ddfP88991xOOumktGjRIttuu+0ynvHCCXsAAAC+V4477rh07959kcfPnj07Q4YMydChQ9O5c+ckSdu2bTN69Ojcfvvtwh4AAACWpw4dOizW+LfffjuzZs3Kr3/96xrLKyoqstlmmy3NqS0RYQ8AAMD3SqNGjWrcrlOnTqqrq2ssmzNnTun/M2fOTPLV4fhrrLFGjXENGjRYRrNcdMIeAACA77UWLVrkjTfeqLHs1VdfTf369ZMkG264YRo0aJDJkyfX+mH38yPsAQAAWGz1W22w0jzPj370o1x33XW5995706lTp9x///1544030r59+yRJ06ZN8+tf/zqDBg1KdXV1ttpqq3z22Wf597//naZNm6Zv375Jkp133jm///3vs+OOOy7zOX+dsAcAAGCxVFdVplW/85fr89WpW7bM1t+zZ88cddRRueiii/Lll19mzz33zM9//vO8/vrrpTHHH398WrRokSFDhmTSpElp1qxZ2rdvnyOPPLI0ZsKECfnss8+W2TwXpE71N08kYB6VlZUZM2ZMOnXqlLKyZfdiWpb2v+yBjH13em1PA5aJTddukZuP71Pb0wAAWKl88cUXmTBhQtq1a5eGDRvW9nRWSgv7GC9Oh9ZdlpMEAAAAli1hDwAAAAVWq2E/ZMiQ7LnnnuncuXO6du2ao446KuPHj68xpn///ikvL6/x76yzzqoxZvLkyTn88MOzxRZbpGvXrrngggtqvDVBkvzrX/9K3759s/nmm2fHHXfM8OHDl/n2AQAAwLJWqxfPe/bZZ7P//vunQ4cOqayszKWXXppDDjkkDz74YBo3blwat/fee+e4444r3f76ew5WVlbmiCOOSKtWrXLbbbflww8/zCmnnJL69evnhBNOSJJMnDgxRxxxRH75y1/m4osvzqhRo3LmmWemdevW6dmz5/LbYAAAAFjKajXsr7vuuhq3zz///HTt2jX//e9/s80225SWN2zYMK1bt57vOp5++umMGzcuQ4cOTatWrbLZZpvlt7/9bS6++OIcc8wxadCgQW677bass846OfXUU5N89R6Eo0ePzvXXXy/sAQAAFsL11pedpfWxXaHe7m7u2wKsuuqqNZaPGDEi999/f1q3bp2f/OQnOeqoo0p77ceMGZNNNtkkrVq1Ko3v0aNHBgwYkHHjxqV9+/YZM2ZMunbtWmOdPXr0yHnnnbdY86usrFySzap1Rb2SPyyuon6NAgCsiOrWrZvq6urMmDHDVfGXkdmzZ6e6ujp16tSZ53fZxfnddoUJ+6qqqpx33nnZcssts8kmm5SW9+nTJ23atMnqq6+e1157LRdffHEmTJiQK6+8MkkyderUGlGfpHR7ypQpCx3z+eef54svvljkF+lLL720xNtXWxo1apT27dvX9jRguXjttdcya9as2p4GAMBKZfLkyZk9e3ZWWWWV1KlTp7ans9Korq7Ohx9+mFmzZuXll1/+TutaYcJ+4MCBeeONN3LLLbfUWL7PPvuU/l9eXp7WrVvnoIMOyjvvvJN11113uc6xQ4cO9n7DCqy8vLy2pwAAsFKprq7OBx98kI8//ri2p7JSqlu3btq3b58GDRrMc19lZeUi71xeIcL+7LPPzt///vfcdNNNWXPNNRc6dosttkiSvP3221l33XXTqlWrvPjiizXGTJ06NUlK5+W3atWqtOzrY5o2bbpYh5SUlZUJe1iB+foEAFj61l577ay55pqpqKio7amsdBo0aJC6db/7m9XVathXV1fnnHPOyWOPPZZhw4albdu23/qYV199Ncn/H+2dOnXKNddck2nTpqVly5ZJkpEjR6Zp06bZaKONSmP+8Y9/1FjPyJEj06lTp6W4NQAAACsnOzlXbLX6PvYDBw7M/fffn0suuSRNmjTJlClTMmXKlHzxxRdJknfeeSdXXXVVXn755UyaNClPPPFETjnllGyzzTbZdNNNk3x1EbyNNtooJ598csaOHZunnnoql112Wfbff//S4Qy//OUvM3HixFx44YV58803c/PNN+fhhx/OQQcdVFubDgAAAEtFre6xv/XWW5Mk/fv3r7F80KBB6devX+rXr59Ro0blxhtvzMyZM7PWWmtlp512ylFHHVUaW1ZWlmuuuSYDBgzIPvvsk0aNGqVv37413ve+bdu2GTJkSAYNGpQbb7wxa665Zs4991xvdQcAAEDh1WrYv/baawu9f6211spNN930retZe+21c+211y50TJcuXXLvvfcuzvQAAABghVerh+IDAAAA342wBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABRYrYb9kCFDsueee6Zz587p2rVrjjrqqIwfP77GmC+//DIDBw5Mly5d0rlz5xx77LGZOnVqjTGTJ0/O4Ycfni222CJdu3bNBRdckDlz5tQY869//St9+/bN5ptvnh133DHDhw9f5tsHAAAAy1qthv2zzz6b/fffP3fccUeGDh2aOXPm5JBDDsnMmTNLY84777z8v//3/3LZZZdl2LBh+fDDD3PMMceU7q+srMwRRxyRioqK3HbbbTn//PNzzz335IorriiNmThxYo444oh06dIl9913Xw488MCceeaZeeqpp5br9gIAAMDSVq82n/y6666rcfv8889P165d89///jfbbLNNPvvss9x99925+OKL07Vr1yRfhf7PfvazjBkzJp06dcrTTz+dcePGZejQoWnVqlU222yz/Pa3v83FF1+cY445Jg0aNMhtt92WddZZJ6eeemqSZMMNN8zo0aNz/fXXp2fPnst9uwEAAGBpqdWw/6bPPvssSbLqqqsmSV5++eVUVFSkW7dupTEbbrhh2rRpUwr7MWPGZJNNNkmrVq1KY3r06JEBAwZk3Lhxad++fcaMGVP6w8DXx5x33nmLNb/Kysol3bRaVVZWVttTgOWiqF+jAADwTYvzu+0KE/ZVVVU577zzsuWWW2aTTTZJkkydOjX169dP8+bNa4xt2bJlpkyZUhrz9ahPUrr9bWM+//zzfPHFF2nYsOEizfGll15a/A2rZY0aNUr79u1rexqwXLz22muZNWtWbU8DAACWqxUm7AcOHJg33ngjt9xyS21PZYE6dOhg7zeswMrLy2t7CgAAsFRUVlYu8s7lFSLszz777Pz973/PTTfdlDXXXLO0vFWrVqmoqMinn35aY6/9tGnT0rp169KYF198scb65l41/+tjvnkl/alTp6Zp06aLvLc++eqQdmEPKy5fnwAAfB/V6lXxq6urc/bZZ+exxx7LDTfckLZt29a4f/PNN0/9+vUzatSo0rLx48dn8uTJ6dSpU5KkU6dOef311zNt2rTSmJEjR6Zp06bZaKONSmOeeeaZGuseOXJkaR0AAABQVLUa9gMHDsz999+fSy65JE2aNMmUKVMyZcqUfPHFF0mSZs2aZc8998z555+fZ555Ji+//HJOP/30dO7cuRTlPXr0yEYbbZSTTz45Y8eOzVNPPZXLLrss+++/fxo0aJAk+eUvf5mJEyfmwgsvzJtvvpmbb745Dz/8cA466KBa2nIAAABYOmr1UPxbb701SdK/f/8aywcNGpR+/folSU4//fTUrVs3xx13XGbPnp0ePXrkj3/8Y2lsWVlZrrnmmgwYMCD77LNPGjVqlL59++a4444rjWnbtm2GDBmSQYMG5cYbb8yaa66Zc88911vdAQAAUHh1qqurq2t7Eiu6ysrK0tvrFfUc3v0veyBj351e29OAZWLTtVvk5uP71PY0AABgqVmcDq3VQ/EBAACA70bYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAAqsVsP+ueeey5FHHpkePXqkvLw8jz/+eI37Tz311JSXl9f4d8ghh9QY8/HHH+f3v/99ttxyy2y99dY5/fTTM2PGjBpjxo4dm/322y8dOnRIr169cu211y7zbQMAAIDloV5tPvnMmTNTXl6ePffcM8ccc8x8x/Ts2TODBg0q3W7QoEGN+0888cRMmTIlQ4cOTUVFRU4//fScddZZueSSS5Ikn3/+eQ455JB07do1AwcOzOuvv57TTz89zZs3zz777LPsNg4AAACWg1oN+169eqVXr14LHdOgQYO0bt16vve9+eabeeqpp3LXXXelQ4cOSZIzzzwzhx9+eE4++eSsscYauf/++1NRUZHzzjsvDRo0yMYbb5xXX301Q4cOFfYAAAAUXq2G/aJ49tln07Vr1zRv3jw/+tGPcvzxx2e11VZLkrzwwgtp3rx5KeqTpFu3bqlbt25efPHF7LjjjhkzZky23nrrGnv6e/TokWuvvTaffPJJVl111UWeS2Vl5dLbsOWorKystqcAy0VRv0YBAOCbFud32xU67Hv27Jkdd9wx66yzTiZOnJhLL700hx12WG6//faUlZVl6tSpadGiRY3H1KtXL6uuumqmTJmSJJk6dWrWWWedGmNatWpVum9xwv6ll176jlu0/DVq1Cjt27ev7WnAcvHaa69l1qxZtT0NAABYrlbosN91111L/5978bwddtihtBd/eevQoYO937ACKy8vr+0pAADAUlFZWbnIO5dX6LD/prZt22a11VbL22+/na5du6ZVq1aZPn16jTFz5szJJ598Ujovv1WrVpk6dWqNMXNvz91zv6jKysqEPazAfH0CAPB9VKj3sX///ffz8ccfl6K9c+fO+fTTT/Pyyy+XxjzzzDOpqqpKx44dkySdOnXK888/n4qKitKYkSNHpl27dot1GD4AAACsiGo17GfMmJFXX301r776apJk0qRJefXVVzN58uTMmDEjF1xwQcaMGZNJkyZl1KhROeqoo7LeeuulZ8+eSZINN9wwPXv2zB/+8Ie8+OKLGT16dM4555zsuuuuWWONNZIku+22W+rXr58zzjgjb7zxRh566KHceOONOfjgg2ttuwEAAGBpqdVD8V9++eUccMABpdtz36++b9++GTBgQF5//fXce++9+eyzz7L66qune/fu+e1vf1vjCvcXX3xxzjnnnBx44IGpW7dudtppp5x55pml+5s1a5brrrsuZ599dvr165fVVlstRx11lLe6AwAAYKVQp7q6urq2J7Giq6yszJgxY9KpU6fCnsO7/2UPZOy70799IBTQpmu3yM3H96ntaQAAwFKzOB1aqHPsAQAAgJqWKOwPOOCAfPrpp/Ms//zzz2scWg8AAAAsW0sU9s8++2yNq8zP9eWXX2b06NHfeVIAAADAolmsi+eNHTu29P9x48ZlypQppdtVVVV56qmnSlejBwAAAJa9xQr7n//856lTp07q1KmTAw88cJ77GzZsWOOK9AAAAMCytVhh/8QTT6S6ujo77LBD7rzzzrRo0aJ0X/369dOyZcvCXjUeAAAAimixwn7ttddOUvOQfAAAAKD2LFbYf91bb72Vf/3rX5k2bVqqqqpq3HfMMcd854kBAAAA326Jwv6OO+7IgAEDstpqq6VVq1apU6dO6b46deoIewAAAFhOlijs//znP+f444/P4YcfvrTnAwAAACyGJXof+08++SS77LLL0p4LAAAAsJiWKOx33nnnPP3000t7LgAAAMBiWqJD8ddbb71cfvnl+c9//pNNNtkk9erVXM0BBxywVCYHAAAALNwShf3tt9+exo0b59lnn82zzz5b4746deoIewAAAFhOlijs/+///m9pzwMAAABYAkt0jj0AAACwYliiPfannXbaQu8fNGjQEk0GAAAAWDxLFPaffvppjdtz5szJG2+8kU8//TQ/+tGPlsrEAAAAgG+3RGF/1VVXzbOsqqoqAwYMSNu2bb/zpAAAAIBFs9TOsa9bt24OOuig3HDDDUtrlQAAAMC3WKoXz5s4cWLmzJmzNFcJAAAALMQSHYr/zYvjVVdXZ8qUKfn73/+evn37LpWJAQAAAN9uicL+lVdeqXG7bt26adGiRU499dTsueeeS2ViAAAAwLdborAfNmzY0p4HAAAAsASWKOznmj59esaPH58k2WCDDdKiRYulMikAAABg0SxR2M+cOTPnnHNO7rvvvlRVVSVJysrKsscee+QPf/hDGjVqtFQnCQAAAMzfEl0V//zzz89zzz2XP//5z3n++efz/PPP5+qrr85zzz2X888/f2nPEQAAAFiAJQr7Rx99NP/zP/+TXr16pWnTpmnatGl69eqVc845J48++ujSniMAAACwAEsU9l988UVatWo1z/KWLVvmiy+++M6TAgAAABbNEoV9p06dcsUVV+TLL78sLfviiy9y5ZVXplOnTktrbgAAAMC3WKKL551++uk59NBD8+Mf/zibbrppkmTs2LFp0KBB/vrXvy7VCQIAAAALtkRhX15enr/97W8ZMWJE6e3u+vTpk9122y0NGzZcqhMEAAAAFmyJwn7IkCFp2bJl9t577xrL77rrrkyfPj2HH374UpkcAAAAsHBLdI797bffng022GCe5RtvvHFuu+227zwpAAAAYNEsUdhPmTIlrVu3nmd5ixYtMmXKlO88KQAAAGDRLFHYr7XWWvn3v/89z/LRo0dn9dVX/86TAgAAABbNEp1jv9dee+W8887LnDlz8qMf/ShJMmrUqFx00UX59a9/vVQnCAAAACzYEoX9oYcemo8//jgDBw5MRUVFkmSVVVbJoYcemiOOOGKpThAAAABYsCUK+zp16uSkk07KUUcdlTfffDMNGzbM+uuvnwYNGizt+QEAAAALsURhP1eTJk3SsWPHpTUXAAAAYDEt0cXzAAAAgBWDsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABRYrYb9c889lyOPPDI9evRIeXl5Hn/88Rr3V1dX5/LLL0+PHj3SsWPHHHTQQXnrrbdqjPn444/z+9//PltuuWW23nrrnH766ZkxY0aNMWPHjs1+++2XDh06pFevXrn22muX9aYBAADAclGrYT9z5syUl5fnj3/843zvv/baazNs2LAMGDAgd9xxRxo1apRDDjkkX375ZWnMiSeemHHjxmXo0KG55ppr8vzzz+ess84q3f/555/nkEMOSZs2bTJ8+PCcfPLJufLKK3P77bcv8+0DAACAZa1ebT55r1690qtXr/neV11dnRtvvDG/+c1vssMOOyRJLrzwwnTr1i2PP/54dt1117z55pt56qmnctddd6VDhw5JkjPPPDOHH354Tj755Kyxxhq5//77U1FRkfPOOy8NGjTIxhtvnFdffTVDhw7NPvvss9y2FQAAAJaFWg37hZk0aVKmTJmSbt26lZY1a9YsW2yxRV544YXsuuuueeGFF9K8efNS1CdJt27dUrdu3bz44ovZcccdM2bMmGy99dZp0KBBaUyPHj1y7bXX5pNPPsmqq666yHOqrKxcOhu3nJWVldX2FGC5KOrXKAAAfNPi/G67wob9lClTkiQtW7assbxly5aZOnVqkmTq1Klp0aJFjfvr1auXVVddtfT4qVOnZp111qkxplWrVqX7FifsX3rppcXbiBVAo0aN0r59+9qeBiwXr732WmbNmlXb0wAAgOVqhQ37FVGHDh3s/YYVWHl5eW1PAQAAlorKyspF3rm8woZ969atkyTTpk3L6quvXlo+bdq0bLrppkm+2vM+ffr0Go+bM2dOPvnkk9LjW7VqVdrDP9fc23P33C+qsrIyYQ8rMF+fAAB8H62w72O/zjrrpHXr1hk1alRp2eeff57//Oc/6dy5c5Kkc+fO+fTTT/Pyyy+XxjzzzDOpqqpKx44dkySdOnXK888/n4qKitKYkSNHpl27dot1GD4AAACsiGo17GfMmJFXX301r776apKvLpj36quvZvLkyalTp04OOOCA/PnPf84TTzyR1157LSeffHJWX3310lXyN9xww/Ts2TN/+MMf8uKLL2b06NE555xzsuuuu2aNNdZIkuy2226pX79+zjjjjLzxxht56KGHcuONN+bggw+ute0GAACApaVWD8V/+eWXc8ABB5RuDxo0KEnSt2/fnH/++TnssMMya9asnHXWWfn000+z1VZb5S9/+UtWWWWV0mMuvvjinHPOOTnwwANTt27d7LTTTjnzzDNL9zdr1izXXXddzj777PTr1y+rrbZajjrqKG91BwAAwEqhTnV1dXVtT2JFV1lZmTFjxqRTp06FPYd3/8seyNh3p3/7QCigTddukZuP71Pb0wAAgKVmcTp0hT3HHgAAAPh2wh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYCt02A8ePDjl5eU1/u28886l+7/88ssMHDgwXbp0SefOnXPsscdm6tSpNdYxefLkHH744dliiy3StWvXXHDBBZkzZ87y3hQAAABYJurV9gS+zcYbb5yhQ4eWbpeVlZX+f9555+XJJ5/MZZddlmbNmuWcc87JMccck9tuuy1JUllZmSOOOCKtWrXKbbfdlg8//DCnnHJK6tevnxNOOGG5bwsAAAAsbSv0Hvvkq5Bv3bp16V+LFi2SJJ999lnuvvvunHrqqenatWs233zznHfeeXnhhRcyZsyYJMnTTz+dcePG5aKLLspmm22WXr165be//W1uvvnmzJ49uxa3CgAAAJaOFX6P/dtvv50ePXpklVVWSadOnfL73/8+bdq0ycsvv5yKiop069atNHbDDTdMmzZtMmbMmHTq1CljxozJJptsklatWpXG9OjRIwMGDMi4cePSvn37xZpLZWXlUtuu5enrRznAyqyoX6MAAPBNi/O77Qod9h07dsygQYPSrl27TJkyJVdddVX233//jBgxIlOnTk39+vXTvHnzGo9p2bJlpkyZkiSZOnVqjahPUro9d8zieOmll5ZwS2pPo0aNFvsPGFBUr732WmbNmlXb0wAAgOVqhQ77Xr16lf6/6aabZosttshPfvKTPPzww2nYsOFyn0+HDh3s/YYVWHl5eW1PAQAAlorKyspF3rm8Qof9NzVv3jzrr79+3nnnnXTr1i0VFRX59NNPa+y1nzZtWlq3bp3kq73zL774Yo11zL1q/twxi6OsrEzYwwrM1ycAAN9HK/zF875uxowZmThxYlq3bp3NN9889evXz6hRo0r3jx8/PpMnT06nTp2SJJ06dcrrr7+eadOmlcaMHDkyTZs2zUYbbbS8pw8AAABL3Qq9x/6CCy7IT37yk7Rp0yYffvhhBg8enLp166ZPnz5p1qxZ9txzz5x//vlZddVV07Rp05x77rnp3LlzKex79OiRjTbaKCeffHJOOumkTJkyJZdddln233//NGjQoHY3DgAAAJaCFTrs33///Zxwwgn5+OOP06JFi2y11Va54447Sm95d/rpp6du3bo57rjjMnv27PTo0SN//OMfS48vKyvLNddckwEDBmSfffZJo0aN0rdv3xx33HG1tUkAAACwVNWprq6uru1JrOgqKytLb6FX1HN497/sgYx9d3ptTwOWiU3XbpGbj+9T29MAAIClZnE6tFDn2AMAAAA1CXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeKLyWzRqmuqqytqcBy5zXOQAwP/VqewIA31Wzhg1Sp25Zpg4/NRVTx9f2dGCZqN9qg7Tqd35tTwMAWAEJe2ClUTF1fCref7W2pwEAAMuVQ/EBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMALEBlVVVtTwGWOa9zKL56tT0BAIAVVVndujnzlqcy4cNPansqsEy0W33VnLtfz9qeBvAdCXsAgIWY8OEnGfvu9NqeBgAskEPxAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAB8T7Vs1jDVVZW1PQ1Y5lb213m92p4AAABQO5o1bJA6dcsydfipqZg6vranA8tE/VYbpFW/82t7GsuUsAcAgO+5iqnjU/H+q7U9DWAJORQfAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9AAAAFJiwBwAAgAIT9gAAAFBgwh4AAAAKTNgDAABAgQl7AAAAKDBhDwAAAAUm7AEAAKDAhD0AAAAUmLAHAACAAhP2AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIEJewAAACiw71XY33zzzendu3c6dOiQvfbaKy+++GJtTwkAAAC+k+9N2D/00EMZNGhQjj766Nxzzz3ZdNNNc8ghh2TatGm1PTUAAABYYt+bsB86dGj23nvv7Lnnntloo40ycODANGzYMHfffXdtTw0AAACWWL3ansDyMHv27Pz3v//NEUccUVpWt27ddOvWLS+88MK3Pr66urq0nrKysmU2z2WlrKwsG6+5ahqU1antqcAy0bZlk1RWVqas9SapqtugtqcDy0RZy/VTWVmZysrK2p7K94qfoazs/Azl+6CoP0Pnzndujy7M9yLsP/roo1RWVqZly5Y1lrds2TLjx4//1sdXVVUlSV555ZVlMr/lYbeNGycbN67tacAyM2bMmGTdvsm6tT0TWHYmjhlT21P4XvIzlJWdn6F8HxT5Z+jcHl2Y70XYf1f16tVLhw4dUrdu3dSp4y/2AAAALFvV1dWpqqpKvXrfnu3fi7BfbbXVUlZWNs+F8qZNm5ZWrVp96+Pr1q2bBg0cmgQAAMCK53tx8bwGDRrkhz/8YUaNGlVaVlVVlVGjRqVz5861ODMAAAD4br4Xe+yT5OCDD84pp5ySzTffPB07dswNN9yQWbNmpV+/frU9NQAAAFhi35uw/9nPfpbp06fniiuuyJQpU7LZZpvlL3/5yyIdig8AAAArqjrVi3LtfAAAAGCF9L04xx4AAABWVsIeAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9UBj9+/fPueeemwsvvDDbbrttunfvnsGDB5funzx5cn7zm9+kc+fO2XLLLfPb3/42U6dOrcUZA8Dyd++996ZLly6ZPXt2jeVHHXVUTjrppCTJ448/nr59+6ZDhw7Zfvvtc+WVV2bOnDlJkurq6gwePDjbbbddNt988/To0SPnnnvuct8OYNEJe6BQ7rnnnjRu3Dh33HFHTjrppFx11VX55z//maqqqhx11FH55JNPMmzYsAwdOjQTJ07M7373u9qeMgAsVzvvvHMqKyvzxBNPlJZNmzYtTz75ZPbcc888//zzOeWUU3LAAQfkoYceytlnn53hw4fnmmuuSZI8+uijuf766zNw4MD87W9/y9VXX51NNtmktjYHWAT1ansCAIujvLw8xxxzTJJk/fXXz0033ZRRo0YlSV5//fU88cQTWWuttZIkF154YXbddde8+OKL6dixY63NGQCWp4YNG6ZPnz4ZPnx4dtlllyTJ/fffn7XWWitdunTJwQcfnMMPPzx9+/ZNkrRt2za//e1vc9FFF+WYY47Je++9l1atWqVbt26pX79+2rRp4+corOCEPVAo5eXlNW63bt0606ZNy5tvvpk111yzFPVJstFGG6V58+YZP368X0gA+F7Ze++984tf/CIffPBB1lhjjQwfPjx9+/ZNnTp1Mnbs2Pz73/8u7aFPksrKynz55ZeZNWtWdt5559xwww3ZYYcd0rNnz/Tq1Ss/+clPUq+edIAVla9OoFC++UtFnTp1Ul1dXUuzAYAVU/v27bPpppvm3nvvTffu3TNu3Lj069cvSTJz5swce+yx2WmnneZ53CqrrJK11lorjzzySEaOHJmRI0dm4MCBue666zJs2LDUr19/eW8KsAiEPbBS2HDDDfP+++/nvffeK+21HzduXD799NNsuOGGtTw7AFj+fvGLX+SGG27IBx98kG7dupV+PrZv3z4TJkzIeuutt8DHNmzYML17907v3r2z3377ZZdddsnrr7+eH/7wh8tr+sBicPE8YKXQrVu3bLLJJjnxxBPz3//+Ny+++GJOPvnkbLvttunQoUNtTw8AlrvddtstH3zwQe64447sueeepeVHH3107rvvvlx55ZV544038uabb+bBBx/Mn/70pyTJ8OHDc+edd+b111/PxIkTc//996dhw4Zp06ZNbW0K8C2EPbBSqFOnTq6++uo0b948v/rVr3LQQQelbdu2pV9SAOD7plmzZtlpp53SpEmT7LDDDqXlPXv2zDXXXJOnn346v/jFL7L33nvn+uuvz9prr50kad68ee68887su+++2X333TNq1Khcc801WW211WprU4BvUafayakAALBSOvDAA7PxxhvnzDPPrO2pAMuQPfYAALCS+eSTT/LYY4/l2WefzX777Vfb0wGWMRfPAwCAlUzfvn3zySef5MQTT8wGG2xQ29MBljGH4gMAAECBORQfAAAACkzYAwAAQIEJewAAACgwYQ8AAAAFJuwBAACgwIQ9APCdnXrqqTnqqKO+0zrKy8vz+OOPL6UZAcD3h7AHgO+RJQnw+QX38OHD079//0Vex7/+9a+Ul5eX/nXr1i3HHntsJk6cuFhzAQDmJewBgOXmkUceyVNPPZXLL788b7zxRo488shUVlbW9rQAoNCEPQB8j/Xv3z/nnntuLrzwwmy77bbp3r17Bg8eXLq/d+/eSZKjjz465eXlpdtLqmXLlll99dWzzTbb5Oijj864cePy9ttvz3fsRRddlJ/+9KfZYostsv322+eyyy5LRUVF6f7Bgwdnjz32yL333pvevXtnq622yu9+97t8/vnn32mOAFA0wh4AvufuueeeNG7cOHfccUdOOumkXHXVVfnnP/+ZJLnrrruSJIMGDcrTTz9dur00NGzYMElqxPrXNWnSJIMGDcqDDz6YM844I3feeWeuv/76GmPeeeedPPHEE7nmmmsyZMiQPPfcc7n22muX2hwBoAjq1fYEAIDaVV5enmOOOSZJsv766+emm27KqFGj0r1797Ro0SJJ0rx587Ru3br0mH79+qVfv35L/Jwffvhhrrvuuqyxxhpp167dfMd8/VoA66yzTiZMmJAHH3wwhx12WGl5dXV1Bg0alKZNmyZJdt9994waNSq/+93vlnhuAFA0wh4AvufKy8tr3G7dunWmTZu2TJ6rV69eqa6uzqxZs7Lppptm8ODBadCgwXzHPvTQQ7nxxhszceLEzJw5M3PmzCkF/Fxrr712jWWrr776Mps7AKyohD0AfM/Vq1fz14E6deqkurp6mTzXzTffnKZNm6ZFixbzRPrXvfDCCznxxBNz7LHHpkePHmnWrFkefPDBDB06dKFzT7LM5g4AKyphDwAsVP369ZfalevXWWedNG/e/FvHvfDCC2nTpk1+85vflJZNnjx5qcwBAFY2Lp4HACzU2muvnVGjRmXKlCn55JNPlstzrrfeennvvffy4IMP5p133smNN96Yxx9/fLk8NwAUjbAHABbqlFNOyciRI7Pddtulb9++y+U5t99++xx44IE5++yzs8cee+SFF16osfceAPj/1al2IhoAAAAUlj32AAAAUGDCHgAAAApM2AMAAECBCXsAAAAoMGEPAAAABSbsAQAAoMCEPQAAABSYsAcAAIACE/YAAABQYMIeAAAACkzYAwAAQIH9fzg/4h6SjWSrAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ], + "source": [ + "data = pd.read_csv(\"churn.csv\")\n", + "print(data.head())\n", + "print(data.info())\n", + "print(data.describe())\n", + "print(data.isnull().sum())\n", + "# Визуализация распределения целевой переменной\n", + "sns.countplot(x='Churn?', data=data)\n", + "plt.title('Распределение оттока клиентов')\n", + "plt.show()\n", + "# Гистограммы числовых признаков\n", + "data.hist(bins=20, figsize=(20,15))\n", + "plt.show()\n", + "# Визуализация категориальных признаков\n", + "for column in data.select_dtypes(include=['object']).columns:\n", + " if column != 'Churn?':\n", + " sns.countplot(y=column, data=data, palette=\"Set2\")\n", + " plt.title(f'Распределение по {column}')\n", + " plt.show()\n", + "# Корреляция числовых признаков с целевой переменной\n", + "numeric_data = data.select_dtypes(include=[np.number])\n", + "numeric_data['Churn'] = data['Churn?'].apply(lambda x: 1 if x == 'True.' else 0)\n", + "corr_matrix = numeric_data.corr()\n", + "print(corr_matrix[\"Churn\"].sort_values(ascending=False))\n", + "sns.heatmap(corr_matrix)\n", + "plt.show()\n", + "# Пример проверки гипотезы\n", + "sns.countplot(x='Int\\'l Plan', hue='Churn?', data=data)\n", + "plt.title('Влияние международного плана на отток клиентов')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "691d25ae-0bf3-4ca6-bc71-3ec9964f9906", + "metadata": { + "id": "691d25ae-0bf3-4ca6-bc71-3ec9964f9906" + }, + "outputs": [], + "source": [ + "# Преобразование категориальных признаков с помощью one-hot encoding\n", + "categorical_features = ['State', 'Phone', \"Int'l Plan\", 'VMail Plan']\n", + "data_encoded = pd.get_dummies(data, columns=categorical_features)\n", + "X = data_encoded.drop('Churn?', axis=1)\n", + "y = data_encoded['Churn?'].apply(lambda x: 1 if x == 'True.' else 0)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Настройка гиперпараметров для LGBMClassifier\n", + "param_grid_lgbm = {\n", + " 'n_estimators': [100, 200],\n", + " 'learning_rate': [0.01, 0.1],\n", + " 'max_depth': [3, 5, 7],\n", + "}\n", + "\n", + "lgbm = LGBMClassifier(random_state=42)\n", + "grid_search_lgbm = GridSearchCV(lgbm, param_grid_lgbm, cv=5, scoring='accuracy')\n", + "grid_search_lgbm.fit(X_train, y_train)\n", + "# Настройка гиперпараметров для CatBoostClassifier\n", + "param_grid_catboost = {\n", + " 'iterations': [100, 200],\n", + " 'learning_rate': [0.01, 0.1],\n", + " 'depth': [4, 6, 8],\n", + "}\n", + "catboost = CatBoostClassifier(verbose=0, random_state=42)\n", + "grid_search_catboost = GridSearchCV(catboost, param_grid_catboost, cv=5, scoring='accuracy')\n", + "grid_search_catboost.fit(X_train, y_train)\n", + "# Выбор лучшей модели и оценка на тестовом наборе\n", + "best_model = grid_search_lgbm if grid_search_lgbm.best_score_ > grid_search_catboost.best_score_ else grid_search_catboost\n", + "y_pred = best_model.predict(X_test)\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Лучшая модель: {best_model.best_estimator_}\")\n", + "print(f\"Точность на тестовом наборе: {accuracy}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5_c1PKeB50tB", + "outputId": "445630b5-5833-4534-98f1-4985268d8775" + }, + "id": "5_c1PKeB50tB", + "execution_count": 147, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] 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(num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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(num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000401 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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(num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000376 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000445 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000456 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000817 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000869 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000359 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000393 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000385 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000385 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000333 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000393 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000368 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000381 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000616 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000691 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000358 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2466\n", + "[LightGBM] [Info] Number of data points in the train set: 2132, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143058 -> initscore=-1.790119\n", + "[LightGBM] [Info] Start training from score -1.790119\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000360 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2467\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 306, number of negative: 1827\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2462\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143460 -> initscore=-1.786845\n", + "[LightGBM] [Info] Start training from score -1.786845\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 305, number of negative: 1828\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000348 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2454\n", + "[LightGBM] [Info] Number of data points in the train set: 2133, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142991 -> initscore=-1.790666\n", + "[LightGBM] [Info] Start training from score -1.790666\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] 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with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Number of positive: 382, number of negative: 2284\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001068 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2494\n", + "[LightGBM] [Info] Number of data points in the train set: 2666, number of used features: 71\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.143286 -> initscore=-1.788263\n", + "[LightGBM] [Info] Start training from score -1.788263\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "Лучшая модель: LGBMClassifier(max_depth=7, n_estimators=200, random_state=42)\n", + "Точность на тестовом наборе: 0.9490254872563718\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Лучшая модель: LGBMClassifier(max_depth=7, n_estimators=200, random_state=42)\n", + "Точность на тестовом наборе: 0.9490254872563718" + ], + "metadata": { + "id": "4EgajnrA-c_D" + }, + "id": "4EgajnrA-c_D" + }, + { + "cell_type": "markdown", + "id": "aed4b39e-db2b-4891-9c83-26cf78bcba8c", + "metadata": { + "id": "aed4b39e-db2b-4891-9c83-26cf78bcba8c" + }, + "source": [ + "### Задание 5. Рисуем\n", + "\n", + "**дополнительно 0.5 балла**\n", + "\n", + "Наверняка, в процессе выполнения этого задания вас переполняли какие-то эмоции. Нарисуйте что-то, что бы могло бы передать их (я сам не умею, так что, если это будет просто квадрат, тоже подойдет). Прикрепите сюда свой рисунок:" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Когда ждешь, пока оно все посчитает...![photo_2024-03-06_07-32-28.jpg](data:image/jpeg;base64,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)" + ], + "metadata": { + "id": "UlRJZZsUXmpm" + }, + "id": "UlRJZZsUXmpm" + }, + { + "cell_type": "markdown", + "id": "e6c52141-d648-49a7-8c2a-06724d3e88af", + "metadata": { + "id": "e6c52141-d648-49a7-8c2a-06724d3e88af" + }, + "source": [ + "### Therapy time\n", + "\n", + "Напишите здесь ваши впечатления о задании (можно и не о задании): было ли интересно, было ли слишком легко или наоборот сложно и тд. Также сюда можно написать свои идеи по улучшению заданий, а также предложить данные, на основе которых вы бы хотели построить следующие дз.\n", + "\n", + "**Ваши мысли:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3db0b95-89ca-48a2-9600-53a28090b241", + "metadata": { + "id": "e3db0b95-89ca-48a2-9600-53a28090b241" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/data/churn.csv b/data/churn.csv new file mode 100644 index 0000000..9a2de22 --- /dev/null +++ b/data/churn.csv @@ -0,0 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From 22c00b39b403bbd6f1d49f6dac08dfb9a1c23cab Mon Sep 17 00:00:00 2001 From: nerofeeva2001 <144069512+nerofeeva2001@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:36:52 +0300 Subject: [PATCH 6/6] Delete data/one --- data/one | 1 - 1 file changed, 1 deletion(-) delete mode 100644 data/one diff --git a/data/one b/data/one deleted file mode 100644 index 8b13789..0000000 --- a/data/one +++ /dev/null @@ -1 +0,0 @@ -

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