Remove batchwise metrics

keras/metrics.py

@@ -74,110 +74,16 @@ def poisson(y_true, y_pred):
 def cosine_proximity(y_true, y_pred):
     y_true = K.l2_normalize(y_true, axis=-1)
     y_pred = K.l2_normalize(y_pred, axis=-1)
-    return -K.mean(y_true * y_pred)
+    return - K.mean(y_true * y_pred)
 
 
-def matthews_correlation(y_true, y_pred):
-    """Matthews correlation metric.
+# Aliases
 
-    It is only computed as a batch-wise average, not globally.
-
-    Computes the Matthews correlation coefficient measure for quality
-    of binary classification problems.
-    """
-    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
-    y_pred_neg = 1 - y_pred_pos
-
-    y_pos = K.round(K.clip(y_true, 0, 1))
-    y_neg = 1 - y_pos
-
-    tp = K.sum(y_pos * y_pred_pos)
-    tn = K.sum(y_neg * y_pred_neg)
-
-    fp = K.sum(y_neg * y_pred_pos)
-    fn = K.sum(y_pos * y_pred_neg)
-
-    numerator = (tp * tn - fp * fn)
-    denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
-
-    return numerator / (denominator + K.epsilon())
-
-
-def precision(y_true, y_pred):
-    """Precision metric.
-
-    Only computes a batch-wise average of precision.
-
-    Computes the precision, a metric for multi-label classification of
-    how many selected items are relevant.
-    """
-    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
-    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
-    precision = true_positives / (predicted_positives + K.epsilon())
-    return precision
-
-
-def recall(y_true, y_pred):
-    """Recall metric.
-
-    Only computes a batch-wise average of recall.
-
-    Computes the recall, a metric for multi-label classification of
-    how many relevant items are selected.
-    """
-    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
-    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
-    recall = true_positives / (possible_positives + K.epsilon())
-    return recall
-
-
-def fbeta_score(y_true, y_pred, beta=1):
-    """Computes the F score.
-
-    The F score is the weighted harmonic mean of precision and recall.
-    Here it is only computed as a batch-wise average, not globally.
-
-    This is useful for multi-label classification, where input samples can be
-    classified as sets of labels. By only using accuracy (precision) a model
-    would achieve a perfect score by simply assigning every class to every
-    input. In order to avoid this, a metric should penalize incorrect class
-    assignments as well (recall). The F-beta score (ranged from 0.0 to 1.0)
-    computes this, as a weighted mean of the proportion of correct class
-    assignments vs. the proportion of incorrect class assignments.
-
-    With beta = 1, this is equivalent to a F-measure. With beta < 1, assigning
-    correct classes becomes more important, and with beta > 1 the metric is
-    instead weighted towards penalizing incorrect class assignments.
-    """
-    if beta < 0:
-        raise ValueError('The lowest choosable beta is zero (only precision).')
-
-    # If there are no true positives, fix the F score at 0 like sklearn.
-    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
-        return 0
-
-    p = precision(y_true, y_pred)
-    r = recall(y_true, y_pred)
-    bb = beta ** 2
-    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
-    return fbeta_score
-
-
-def fmeasure(y_true, y_pred):
-    """Computes the f-measure, the harmonic mean of precision and recall.
-
-    Here it is only computed as a batch-wise average, not globally.
-    """
-    return fbeta_score(y_true, y_pred, beta=1)
-
-
-# aliases
 mse = MSE = mean_squared_error
 mae = MAE = mean_absolute_error
 mape = MAPE = mean_absolute_percentage_error
 msle = MSLE = mean_squared_logarithmic_error
 cosine = cosine_proximity
-fscore = f1score = fmeasure
 
 
 def get(identifier):