def compute_labels(train_contour_dict, valid_contour_dict, \ test_contour_dict, olap_thresh): """ """ # Compute Labels using Overlap Threshold train_contour_dict, valid_contour_dict, test_contour_dict = \ eu.label_all_contours(train_contour_dict, valid_contour_dict, \ test_contour_dict, olap_thresh=olap_thresh) x_train, y_train = cc.pd_to_sklearn(train_contour_dict) x_valid, y_valid = cc.pd_to_sklearn(valid_contour_dict) x_test, y_test = cc.pd_to_sklearn(test_contour_dict) return x_train, y_train, x_valid, y_valid, x_test, y_test, test_contour_dict
def contour_probs(clf, contour_data): """ Compute classifier probabilities for contours. Parameters ---------- clf : scikit-learn classifier Binary classifier. contour_data : DataFrame DataFrame with contour information. Returns ------- contour_data : DataFrame DataFrame with contour information and predicted probabilities. """ contour_data['mel prob'] = -1 features, _ = cc.pd_to_sklearn(contour_data) probs = clf.predict_proba(features) mel_probs = [p[1] for p in probs] contour_data['mel prob'] = mel_probs return contour_data