How to calculate F1 Macro in Keras?

since Keras 2.0 metrics f1, precision, and recall have been removed. The solution is to use a custom metric function:

from keras import backend as K

def f1(y_true, y_pred):
    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 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
    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))


model.compile(loss='binary_crossentropy',
          optimizer= "adam",
          metrics=[f1])

The return line of this function

return 2*((precision*recall)/(precision+recall+K.epsilon()))

was modified by adding the constant epsilon, in order to avoid division by 0. Thus NaN will not be computed.

Using a Keras metric function is not the right way to calculate F1 or AUC or something like that.

The reason for this is that the metric function is called at each batch step at validation. That way the Keras system calculates an average on the batch results. And that is not the right F1 score.

Thats the reason why F1 score got removed from the metric functions in keras. See here:

• https://github.com/keras-team/keras/commit/a56b1a55182acf061b1eb2e2c86b48193a0e88f7
• https://github.com/keras-team/keras/issues/5794

The right way to do this is to use a custom callback function in a way like this:

• https://github.com/PhilipMay/mltb#module-keras
• https://medium.com/@thongonary/how-to-compute-f1-score-for-each-epoch-in-keras-a1acd17715a2

This is a streaming custom f1_score metric that I made using subclassing. It works for TensorFlow 2.0 beta but I haven't tried it on other versions. What it's doing it keeping track of true positives, predicted positives, and all possible positives throughout the whole epoch and then calculating the f1 score at the end of the epoch. I think the other answers are only giving the f1 score for each batch which isn't really the best metric when we really want the f1 score of the all the data.

I got a raw unedited copy of Aurélien Geron new book Hands-On Machine Learning with Scikit-Learn & Tensorflow 2.0 and highly recommend it. This is how I learned how to this f1 custom metric using sub-classes. It's hands down the most comprehensive TensorFlow book I've ever seen. TensorFlow is seriously a pain in the butt to learn and this guy lays down the coding groundwork to learn a lot.

FYI: In the Metrics, I had to put the parenthesis in f1_score() or else it wouldn't work.

pip install tensorflow==2.0.0-beta1

from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
import numpy as np

def create_f1():
    def f1_function(y_true, y_pred):
        y_pred_binary = tf.where(y_pred>=0.5, 1., 0.)
        tp = tf.reduce_sum(y_true * y_pred_binary)
        predicted_positives = tf.reduce_sum(y_pred_binary)
        possible_positives = tf.reduce_sum(y_true)
        return tp, predicted_positives, possible_positives
    return f1_function


class F1_score(keras.metrics.Metric):
    def __init__(self, **kwargs):
        super().__init__(**kwargs) # handles base args (e.g., dtype)
        self.f1_function = create_f1()
        self.tp_count = self.add_weight("tp_count", initializer="zeros")
        self.all_predicted_positives = self.add_weight('all_predicted_positives', initializer='zeros')
        self.all_possible_positives = self.add_weight('all_possible_positives', initializer='zeros')

    def update_state(self, y_true, y_pred,sample_weight=None):
        tp, predicted_positives, possible_positives = self.f1_function(y_true, y_pred)
        self.tp_count.assign_add(tp)
        self.all_predicted_positives.assign_add(predicted_positives)
        self.all_possible_positives.assign_add(possible_positives)

    def result(self):
        precision = self.tp_count / self.all_predicted_positives
        recall = self.tp_count / self.all_possible_positives
        f1 = 2*(precision*recall)/(precision+recall)
        return f1

X = np.random.random(size=(1000, 10))     
Y = np.random.randint(0, 2, size=(1000,))
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)

model = keras.models.Sequential([
    keras.layers.Dense(5, input_shape=[X.shape[1], ]),
    keras.layers.Dense(1, activation='sigmoid')
])

model.compile(loss='binary_crossentropy', optimizer='SGD', metrics=[F1_score()])

history = model.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test))

As what @Pedia has said in his comment above, on_epoch_end,as stated in the github.com/fchollet/keras/issues/5400 is the best approach.

I also suggest this work-around

• install keras_metrics package by ybubnov
• call model.fit(nb_epoch=1, ...) inside a for loop taking advantage of the precision/recall metrics outputted after every epoch

Something like this:

    for mini_batch in range(epochs):
        model_hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=1,
                            verbose=2, validation_data=(X_val, Y_val))

        precision = model_hist.history['val_precision'][0]
        recall = model_hist.history['val_recall'][0]
        f_score = (2.0 * precision * recall) / (precision + recall)
        print 'F1-SCORE {}'.format(f_score)