def get_classification_result(true_labels, pred_labels):
"""Return classification resuls for one fold.
Return an array containing accuracy, precision, recall, and f1, based on the
given true and predicted labels.
Keyword arguments:
fold_no -- this fold's number
true_labels -- true labels
pred_labels -- predicted labels
"""
res = np.zeros((1, 4))
res[:] = calc_metrics(true_labels, pred_labels)
return res
def get_classification_result(true_labels, pred_labels):
"""Return classification resuls for one fold.
Return an array containing accuracy, precision, recall, and f1, based on the
given true and predicted labels.
Keyword arguments:
fold_no -- this fold's number
true_labels -- true labels
pred_labels -- predicted labels
"""
res = np.zeros((1, 4))
res[:] = calc_metrics(true_labels, pred_labels)
return res
def get_classification_result(fold_no, true_labels, pred_labels):
"""Return classification resuls for one fold.
Return an array containing accuracy, precision, recall, and f1, based on the
given true and predicted labels.
Parameters
----------
fold_no : int
this fold's number
true_labels list(int)
true labels
pred_labels list(int)
predicted labels
Returns
-------
ndarray
[fold number, accuracy, precision, recall, f1]
"""
res = calc_metrics(true_labels, pred_labels)
return np.asarray([fold_no] + [r for r in res])