import numpy as np
import keras.backend as K
import tensorflow as tf
from scipy.stats import pearsonr
def pearsons_correlation(y_true, y_pred):
y_true_mean = K.mean(y_true)
y_pred_mean = K.mean(y_pred)
true_diff_mean = y_true - y_true_mean
pred_diff_mean = y_pred - y_pred_mean
sum_prod_diff_mean = K.sum(true_diff_mean * pred_diff_mean)
true_diff_mean_sq = K.sum(K.square(true_diff_mean))
pred_diff_mean_sq = K.sum(K.square(pred_diff_mean))
r = sum_prod_diff_mean / (K.sqrt(true_diff_mean_sq) * K.sqrt(pred_diff_mean_sq) + K.epsilon())
return r
def f1_score_keras(y_true, y_pred):
# convert probas to 0,1
y_pred_ones = K.zeros_like(y_true)
# y_pred_ones[:, K.argmax(y_pred, axis=-1)] = 1
# indices_x = K.arange(start=0, stop=y_true.get_shape()[0])
indices_x = K.expand_dims(K.arange(start=0, stop=tf.shape(y_true)[0], dtype='int64'), dim=-1)
indices_y = K.expand_dims(K.argmax(y_pred, axis=-1), dim=-1)
indices = K.concatenate((indices_x, indices_y))
values = K.sum(K.ones_like(indices_x, dtype='float32'), axis=-1)
shape = K.cast(tf.shape(y_pred_ones), dtype='int64')
delta = tf.SparseTensor(indices, values, shape)
y_pred_ones = y_pred_ones + tf.sparse_tensor_to_dense(delta)
# where y_ture=1 and y_pred=1 -> true positive
y_true_pred = K.sum(y_true * y_pred_ones, axis=0)
# for each class: how many where classified as said class
pred_cnt = K.sum(y_pred_ones, axis=0)
# for each class: how many are true members of said class
gold_cnt = K.sum(y_true, axis=0)
# precision for each class
precision = tf.select(K.equal(pred_cnt, 0), K.zeros_like(y_true_pred), y_true_pred / pred_cnt,
name='precision_f1_semeval')
# recall for each class
recall = tf.select(K.equal(gold_cnt, 0), K.zeros_like(y_true_pred), y_true_pred / gold_cnt,
name='racall_f1_semeval')
# f1 for each class
f1_class = tf.select(K.equal(precision + recall, 0), K.zeros_like(y_true_pred),
2 * (precision * recall) / (precision + recall), name='precision_f1_semeval')
# return average f1 score over all classes
return K.mean(f1_class)
def f1_score_taskB(y_true, y_pred):
# convert probas to 0,1
y_pred_ones = K.zeros_like(y_true)
y_pred_ones[:, K.argmax(y_pred, axis=-1)] = 1
# where y_ture=1 and y_pred=1 -> true positive
y_true_pred = K.sum(y_true * y_pred_ones, axis=0)
# for each class: how many where classified as said class
pred_cnt = K.sum(y_pred_ones, axis=0)
# for each class: how many are true members of said class
gold_cnt = K.sum(y_true, axis=0)
# precision for each class
precision = K.switch(K.equal(pred_cnt, 0), 0, y_true_pred / pred_cnt)
# recall for each class
recall = K.switch(K.equal(gold_cnt, 0), 0, y_true_pred / gold_cnt)
# f1 for each class
f1_class = K.switch(K.equal(precision + recall, 0), 0, 2 * (precision * recall) / (precision + recall))
# return average f1 score over all classes
return f1_class
def f1_score_semeval(y_true, y_pred):
# convert probas to 0,1
y_pred_ones = K.zeros_like(y_true)
# y_pred_ones[:, K.argmax(y_pred, axis=-1)] = 1
# indices_x = K.arange(start=0, stop=y_true.get_shape()[0])
indices_x = K.expand_dims(K.arange(start=0, stop=tf.shape(y_true, name='get_indicec_x_shape')[0], dtype='int64'),
dim=-1)
indices_y = K.expand_dims(K.argmax(y_pred, axis=-1), dim=-1)
indices = K.concatenate((indices_x, indices_y))
values = K.sum(K.ones_like(indices_x, dtype='float32'), axis=-1)
shape = K.cast(tf.shape(y_pred_ones), dtype='int64')
delta = tf.SparseTensor(indices, values, shape)
y_pred_ones = y_pred_ones + tf.sparse_tensor_to_dense(delta)
# where y_ture=1 and y_pred=1 -> true positive
y_true_pred = K.sum(y_true * y_pred_ones, axis=0)
# for each class: how many where classified as said class
pred_cnt = K.sum(y_pred_ones, axis=0)
# for each class: how many are true members of said class
gold_cnt = K.sum(y_true, axis=0)
# precision for each class
precision = tf.select(K.equal(pred_cnt, 0), K.zeros_like(y_true_pred), y_true_pred / pred_cnt,
name='precision_f1_semeval')
# recall for each class
recall = tf.select(K.equal(gold_cnt, 0), K.zeros_like(y_true_pred), y_true_pred / gold_cnt,
name='racall_f1_semeval')
# f1 for each class
f1_class = tf.select(K.equal(precision + recall, 0), K.zeros_like(y_true_pred),
2 * (precision * recall) / (precision + recall), name='precision_f1_semeval')
# return average f1 score over all classes
return (f1_class[0] + f1_class[2]) / 2.0
def precision_keras(y_true, y_pred):
# convert probas to 0,1
y_pred_ones = K.zeros_like(y_true)
y_pred_ones[:, K.argmax(y_pred, axis=-1)] = 1
# where y_ture=1 and y_pred=1 -> true positive
y_true_pred = K.sum(y_true * y_pred_ones, axis=0)
# for each class: how many where classified as said class
pred_cnt = K.sum(y_pred_ones, axis=0)
# precision for each class
precision = K.switch(K.equal(pred_cnt, 0), 0, y_true_pred / pred_cnt)
# return average f1 score over all classes
return K.mean(precision)
def f1_score_task3(y_true, y_pred):
# convert probas to 0,1
y_pred_ones = K.zeros_like(y_true)
# y_pred_ones = K.T.set_subtensor(y_ppred[K.T.arange(y_true.shape[0]), K.argmax(y_pred, axis=-1)], 1)
indices_x = K.expand_dims(K.arange(start=0, stop=tf.shape(y_true, name='get_indicec_x_shape')[0], dtype='int64'),
dim=-1)
indices_y = K.expand_dims(K.argmax(y_pred, axis=-1), dim=-1)
indices = K.concatenate((indices_x, indices_y))
values = K.sum(K.ones_like(indices_x, dtype='float32'), axis=-1)
shape = K.cast(tf.shape(y_pred_ones), dtype='int64')
delta = tf.SparseTensor(indices, values, shape)
y_pred_ones = y_pred_ones + tf.sparse_tensor_to_dense(delta)
# where y_ture=1 and y_pred=1 -> true positive
y_true_pred = K.sum(y_true * y_pred_ones, axis=0)
# for each class: how many where classified as said class
pred_cnt = K.sum(y_pred_ones, axis=0)
# for each class: how many are true members of said class
gold_cnt = K.sum(y_true, axis=0)
# precision for each class
precision = tf.select(K.equal(pred_cnt, 0), K.zeros_like(y_true_pred), y_true_pred / pred_cnt,
name='precision_f1_semeval')
# recall for each class
recall = tf.select(K.equal(gold_cnt, 0), K.zeros_like(y_true_pred), y_true_pred / gold_cnt,
name='racall_f1_semeval')
# f1 for each class
f1_class = tf.select(K.equal(precision + recall, 0), K.zeros_like(y_true_pred),
2 * (precision * recall) / (precision + recall), name='precision_f1_semeval')
# return average f1 score over all classes
return f1_class[1]
if __name__ == '__main__':
y_true = np.array([0.0, 0.2, 0.4, 0.4])
y_pred = np.array([0.0, 0.2, 0.4, 0.4])
print(pearsonr(y_true, y_pred))
y_true = K.variable(value=y_true)
y_pred = K.variable(value=y_pred)
print(K.eval(pearsons_correlation(y_true, y_pred)))
