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@@ -57,6 +57,7 @@ def _check_data(y_true, y_pred): |
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''' |
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check if y_true and y_pred is same type of data e.g both binary or multiclass |
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''' |
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y_true, y_pred = _conver_numpy(y_true), _conver_numpy(y_pred) |
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if not _check_same_len(y_true, y_pred): |
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raise ValueError('cannot accept data with different shape {0}, {1}'.format(y_true, y_pred)) |
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type_true, y_true = _label_types(y_true) |
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@@ -100,9 +101,10 @@ def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary'): |
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if y_type != 'binary': |
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raise ValueError("data type is {} but use average type {}".format(y_type, average)) |
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else: |
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pos = y_true == pos_label |
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tp = np.logical_and((y_true == y_pred), pos) |
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return tp.sum() / pos.sum() |
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pos = (y_true == pos_label) |
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tp = np.logical_and((y_true == y_pred), pos).sum() |
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pos_sum = pos.sum() |
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return tp / pos_sum if pos_sum > 0 else 0 |
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elif average == None: |
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y_labels = set(list(np.unique(y_true))) |
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if labels is None: |
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@@ -111,25 +113,67 @@ def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary'): |
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for i in labels: |
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if i not in y_labels: |
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warnings.warn('label {} is not contained in data'.format(i), UserWarning) |
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if y_type in ['binary', 'multiclass']: |
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y_pred_right = y_true == y_pred |
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pos_list = [y_true == i for i in labels] |
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return [np.logical_and(y_pred_right, pos_i).sum() / pos_i.sum() if pos_i.sum() != 0 else 0 for pos_i in pos_list] |
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pos_sum_list = [pos_i.sum() for pos_i in pos_list] |
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return np.array([np.logical_and(y_pred_right, pos_i).sum() / sum_i if sum_i > 0 else 0 \ |
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for pos_i, sum_i in zip(pos_list, pos_sum_list)]) |
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elif y_type == 'multilabel': |
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y_pred_right = y_true == y_pred |
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pos = y_true == pos_label |
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tp = np.logical_and(y_pred_right, pos) |
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return [tp[:,i].sum() / pos[:,i].sum() if pos[:,i].sum() != 0 else 0 for i in labels] |
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pos = (y_true == pos_label) |
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tp = np.logical_and(y_pred_right, pos).sum(0) |
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pos_sum = pos.sum(0) |
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return np.array([tp[i] / pos_sum[i] if pos_sum[i] > 0 else 0 for i in labels]) |
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else: |
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raise ValueError('not support targets type {}'.format(y_type)) |
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raise ValueError('not support for average type {}'.format(average)) |
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def precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary'): |
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raise NotImplementedError |
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y_type, y_true, y_pred = _check_data(y_true, y_pred) |
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if average == 'binary': |
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if y_type != 'binary': |
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raise ValueError("data type is {} but use average type {}".format(y_type, average)) |
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else: |
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pos = (y_true == pos_label) |
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tp = np.logical_and((y_true == y_pred), pos).sum() |
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pos_pred = (y_pred == pos_label).sum() |
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return tp / pos_pred if pos_pred > 0 else 0 |
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elif average == None: |
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y_labels = set(list(np.unique(y_true))) |
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if labels is None: |
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labels = list(y_labels) |
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else: |
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for i in labels: |
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if i not in y_labels: |
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warnings.warn('label {} is not contained in data'.format(i), UserWarning) |
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if y_type in ['binary', 'multiclass']: |
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y_pred_right = y_true == y_pred |
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pos_list = [y_true == i for i in labels] |
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pos_sum_list = [(y_pred == i).sum() for i in labels] |
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return np.array([np.logical_and(y_pred_right, pos_i).sum() / sum_i if sum_i > 0 else 0 \ |
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for pos_i, sum_i in zip(pos_list, pos_sum_list)]) |
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elif y_type == 'multilabel': |
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y_pred_right = y_true == y_pred |
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pos = (y_true == pos_label) |
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tp = np.logical_and(y_pred_right, pos).sum(0) |
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pos_sum = (y_pred == pos_label).sum(0) |
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return np.array([tp[i] / pos_sum[i] if pos_sum[i] > 0 else 0 for i in labels]) |
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else: |
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raise ValueError('not support targets type {}'.format(y_type)) |
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raise ValueError('not support for average type {}'.format(average)) |
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def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary'): |
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raise NotImplementedError |
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precision = precision_score(y_true, y_pred, labels=labels, pos_label=pos_label, average=average) |
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recall = recall_score(y_true, y_pred, labels=labels, pos_label=pos_label, average=average) |
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if isinstance(precision, np.ndarray): |
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res = 2 * precision * recall / (precision + recall) |
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res[(precision + recall) <= 0] = 0 |
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return res |
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return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0 |
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def classification_report(y_true, y_pred, labels=None, target_names=None, digits=2): |
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raise NotImplementedError |
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