regression wrapper + CSSVC

config.py

@@ -2,38 +2,40 @@
 
 from orca_python.utilities import Utilities
 
-ex = Experiment('Experiment Configuration')
+ex = Experiment("Experiment Configuration")
+
 
 @ex.config
 def default_config():
+    # Giving default values
+    general_conf = {
+        "basedir": "",
+        "dataset": "",
+        "input_preprocessing": "",
+        "hyperparam_cv_nfolds": 3,
+        "jobs": 1,
+        "metrics": "mae",
+        "cv_metric": "mae",
+        "output_folder": "my_runs/",
+    }
 
-	# Giving default values
-	general_conf = {"basedir": "",
-					"dataset": "",
-					"input_preprocessing": "",
-					"hyperparam_cv_nfolds": 3,
-					"jobs": 1,
-					"metrics": "ccr",
-					"cv_metric": "ccr",
-					"output_folder": "my_runs/"
-					}
+    configurations = {}
 
-	configurations = {}
 
 @ex.automain
 def main(general_conf, configurations):
-
-	if not general_conf['basedir'] or not general_conf['datasets']:
-
-		raise RuntimeError('A dataset has to be defined to run this program.\n' +
-							'For more information about using this framework, please refer to the README.')
-
-	if not configurations:
-
-		raise RuntimeError('No configuration was defined.\n' + 
-							'For more information about using this framework, please refer to the README.')
-
-
-	interface = Utilities(general_conf, configurations)
-	interface.run_experiment()
-	interface.write_report()
+    if not general_conf["basedir"] or not general_conf["datasets"]:
+        raise RuntimeError(
+            "A dataset has to be defined to run this program.\n"
+            + "For more information about using this framework, please refer to the README."
+        )
+
+    if not configurations:
+        raise RuntimeError(
+            "No configuration was defined.\n"
+            + "For more information about using this framework, please refer to the README."
+        )
+
+    interface = Utilities(general_conf, configurations)
+    interface.run_experiment()
+    interface.write_report()

orca_python/classifiers/CSSVC.py

@@ -0,0 +1,109 @@
+# encoding: utf-8
+import numpy as np
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
+from sklearn.svm import SVC
+
+
+class CSSVC(BaseEstimator, ClassifierMixin):
+    def __init__(
+        self,
+        kernel="rbf",
+        degree=3,
+        gamma=1,
+        coef0=0,
+        C=1,
+        cache_size=200,
+        tol=1e-3,
+        shrinking=True,
+        probability_estimates=False,
+        weight=None,
+        random_state=None,
+    ):
+        self.kernel = kernel
+        self.degree = degree
+        self.gamma = gamma
+        self.coef0 = coef0
+        self.C = C
+        self.cache_size = cache_size
+        self.tol = tol
+        self.shrinking = shrinking
+        self.probability_estimates = probability_estimates
+        self.weight = weight
+        self.random_state = random_state
+
+        self.models_ = []
+
+    def fit(self, X, y):
+        """
+        Fit the model with the training data
+        Parameters
+        ----------
+        X: {array-like, sparse matrix}, shape (n_samples, n_features)
+                Training patterns array, where n_samples is the number of samples
+                and n_features is the number of features
+        y: array-like, shape (n_samples)
+                Target vector relative to X
+
+        p: Label of the pattern which is choose for 1vsALL
+        Returns
+        -------
+        self: object
+        """
+        X, y = check_X_y(X, y)
+
+        for c in np.unique(y):
+            patterns_class = np.where(y == c, 1, 0)
+
+            self.classifier_ = SVC(
+                C=self.C,
+                kernel=self.kernel,
+                degree=self.degree,
+                gamma=self.gamma,
+                coef0=self.coef0,
+                shrinking=self.shrinking,
+                probability=self.probability_estimates,
+                tol=self.tol,
+                cache_size=self.cache_size,
+                class_weight=self.weight,
+                random_state=self.random_state,
+            )
+
+            w = self.ordinalWeights(c, y)
+            self.models_.append(
+                self.classifier_.fit(X, patterns_class, sample_weight=w)
+            )
+        return self
+
+    def predict(self, X):
+        """
+        Performs classification on samples in X
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+        Returns
+        -------
+        predicted_y : array, shape (n_samples, n_samples)
+        Class labels for samples in X.
+        """
+        check_is_fitted(self, "models_")
+
+        decfuncs = np.zeros((len(X), len(self.models_)))
+
+        X = check_array(X)
+
+        for idx, model in enumerate(self.models_):
+            decfuncs[:, idx] = model.decision_function(X)
+
+        preds = np.argmax(decfuncs, axis=1) + 1
+
+        return preds
+
+    def ordinalWeights(self, p, targets):
+        w = np.ones(len(targets))
+        w[targets != p] = (
+            (abs(p - targets[targets != p]) + 1)
+            * len(targets[targets != p])
+            / sum(abs(p - targets[targets != p]) + 1)
+        )
+        return w