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 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
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
