在python中计算f-measure,Precision / Recall / F1 score

sklearn第三方库可以帮助我们快速完成任务，使用方法如下：

from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)
pred = multilayer_perceptron(x, weights, biases)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
for epoch in xrange(150):
        for i in xrange(total_batch):
                train_step.run(feed_dict = {x: train_arrays, y: train_labels})
                avg_cost += sess.run(cost, feed_dict={x: train_arrays, y: train_labels})/total_batch         
        if epoch % display_step == 0:
                print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)

#metrics
y_p = tf.argmax(pred, 1)
val_accuracy, y_pred = sess.run([accuracy, y_p], feed_dict={x:test_arrays, y:test_label})

print("validation accuracy:", val_accuracy)
y_true = np.argmax(test_label,1)
print("Precision", sk.metrics.precision_score(y_true, y_pred))
print( "Recall", sk.metrics.recall_score(y_true, y_pred))
print( "f1_score", sk.metrics.f1_score(y_true, y_pred))
print( "confusion_matrix")
print( sk.metrics.confusion_matrix(y_true, y_pred))
fpr, tpr, tresholds = sk.metrics.roc_curve(y_true, y_pred)