New confusion natrix #392
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New confusion natrix #392
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NoaShapira8
requested review from
leondavi,
ohad123 and
GuyPerets106
as code owners
leondavi
requested changes
Aug 12, 2024
src_py/apiServer/stats.py
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| if has_multiple_max: | ||
| LOG_INFO(f"Worker {worker_name} has at least one sample with multiple predicted labels") | ||
| #max_column_predict_index = is_max.apply(lambda row: list(row[row].index), axis=1).tolist() # Generate a list of lists' each sublist has the index of the maximum value in the row |
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Why this line is commented out. Decide to remove or to add it with condition.
| max_column_predict_index =[[int(predict_label) - num_of_labels for predict_label in prdict_indexes_sublist] for prdict_indexes_sublist in max_column_predict_index] # fix the index to original labels index | ||
| max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1).tolist() # Get the index of the maximum actual value in each row | ||
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| else: # No sample with multiple predicted labels |
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that means that in each sample there is only 1 predicted label
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I still don't understand why no sample.
src_py/apiServer/stats.py
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| max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1).tolist() # Get the index of the maximum actual value in each row | ||
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| else: # No sample with multiple predicted labels | ||
| # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix |
src_py/apiServer/stats.py
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| class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index] # 1 if the label is belong to the class, 0 otherwise | ||
| confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list) | ||
| labels = [0, 1] | ||
| confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list, labels=labels) | ||
| #confusion_matrix_np = confusion_matrix.to_numpy() |
leondavi
approved these changes
Aug 12, 2024
| max_column_predict_index =[[int(predict_label) - num_of_labels for predict_label in prdict_indexes_sublist] for prdict_indexes_sublist in max_column_predict_index] # fix the index to original labels index | ||
| max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1).tolist() # Get the index of the maximum actual value in each row | ||
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| else: # No sample with multiple predicted labels |
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I still don't understand why no sample.
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fixing bugs