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Fix: Slide ids turned into floats in split csv when names consist of only number #228

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Fix: Slide ids turned into floats in split csv when names consist of only number #228

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9 changes: 5 additions & 4 deletions datasets/dataset_generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
from utils.utils import generate_split, nth

def save_splits(split_datasets, column_keys, filename, boolean_style=False):
splits = [split_datasets[i].slide_data['slide_id'] for i in range(len(split_datasets))]
splits = [split_datasets[i].slide_data['slide_id'].astype(str) for i in range(len(split_datasets))]
if not boolean_style:
df = pd.concat(splits, ignore_index=True, axis=1)
df.columns = column_keys
Expand Down Expand Up @@ -188,7 +188,7 @@ def set_splits(self,start_from=None):

def get_split_from_df(self, all_splits, split_key='train'):
split = all_splits[split_key]
split = split.dropna().reset_index(drop=True)
split = split.dropna().reset_index(drop=True).astype(self.slide_data['slide_id'].dtype)

if len(split) > 0:
mask = self.slide_data['slide_id'].isin(split.tolist())
Expand All @@ -203,7 +203,7 @@ def get_merged_split_from_df(self, all_splits, split_keys=['train']):
merged_split = []
for split_key in split_keys:
split = all_splits[split_key]
split = split.dropna().reset_index(drop=True).tolist()
split = split.dropna().reset_index(drop=True).astype(self.slide_data['slide_id'].dtype).tolist()
merged_split.extend(split)

if len(split) > 0:
Expand Down Expand Up @@ -244,7 +244,8 @@ def return_splits(self, from_id=True, csv_path=None):

else:
assert csv_path
all_splits = pd.read_csv(csv_path, dtype=self.slide_data['slide_id'].dtype) # Without "dtype=self.slide_data['slide_id'].dtype", read_csv() will convert all-number columns to a numerical type. Even if we convert numerical columns back to objects later, we may lose zero-padding in the process; the columns must be correctly read in from the get-go. When we compare the individual train/val/test columns to self.slide_data['slide_id'] in the get_split_from_df() method, we cannot compare objects (strings) to numbers or even to incorrectly zero-padded objects/strings. An example of this breaking is shown in https://github.com/andrew-weisman/clam_analysis/tree/main/datatype_comparison_bug-2021-12-01.
#all_splits = pd.read_csv(csv_path, dtype=self.slide_data['slide_id'].dtype) # Without "dtype=self.slide_data['slide_id'].dtype", read_csv() will convert all-number columns to a numerical type. Even if we convert numerical columns back to objects later, we may lose zero-padding in the process; the columns must be correctly read in from the get-go. When we compare the individual train/val/test columns to self.slide_data['slide_id'] in the get_split_from_df() method, we cannot compare objects (strings) to numbers or even to incorrectly zero-padded objects/strings. An example of this breaking is shown in https://github.com/andrew-weisman/clam_analysis/tree/main/datatype_comparison_bug-2021-12-01.
all_splits = pd.read_csv(csv_path, dtype=object)
train_split = self.get_split_from_df(all_splits, 'train')
val_split = self.get_split_from_df(all_splits, 'val')
test_split = self.get_split_from_df(all_splits, 'test')
Expand Down