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- # Instantiate the label encoder
- le = LabelEncoder()
- # Fit the label encoder to our label series
- le.fit(list(y_classes))
- # Create integer based labels Series
- y_integers = le.transform(list(y_classes))
- #print y_integers
- # Create dict of labels : integer representation
- labels_and_integers = dict(zip(y_classes, y_integers))
- print labels_and_integers
- class_weights = compute_class_weight('balanced', np.unique(y_integers), y_integers)
- sample_weights = compute_sample_weight('balanced', y_integers)
- class_weights_dict = dict(zip(le.transform(list(le.classes_)), class_weights))
- class_sweights_dict = dict(zip(le.transform(list(le.classes_)), sample_weights))
- print class_weights_dict
- return class_weights_dict
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