def zero_one_accuracy(y_true, y_pred):
y_true, y_pred = tensorify(y_true), tensorify(y_pred)
n_instances, n_objects = get_instances_objects(y_true)
equal_ranks = K.cast(K.all(K.equal(y_pred, y_true), axis=1), dtype="float32")
denominator = K.cast(n_instances, dtype="float32")
zero_one_loss = K.sum(equal_ranks) / denominator
return zero_one_loss
def zero_one_accuracy_for_scores(y_true, y_pred):
y_true, y_pred = tensorify(y_true), tensorify(y_pred)
n_instances, n_objects = get_instances_objects(y_true)
predicted_rankings = scores_to_rankings(n_objects, y_pred)
y_true = K.cast(y_true, dtype='float32')
equal_ranks = K.cast(K.all(K.equal(predicted_rankings, y_true), axis=1),
dtype='float32')
denominator = K.cast(n_instances, dtype='float32')
zero_one_loss = K.sum(equal_ranks) / denominator
return zero_one_loss
def spearman_correlation_for_scores(y_true, y_pred):
y_true, y_pred = tensorify(y_true), tensorify(y_pred)
n_instances, n_objects = get_instances_objects(y_true)
predicted_rankings = scores_to_rankings(n_objects, y_pred)
y_true = K.cast(y_true, dtype='float32')
sum_of_squared_distances = tf.constant(0.0)
for i in np.arange(K.int_shape(y_pred)[1]):
objects_pred = predicted_rankings[:, i]
objects_true = y_true[:, i]
t = (objects_pred - objects_true)**2
sum_of_squared_distances = sum_of_squared_distances + tf.reduce_sum(t)
denominator = K.cast(n_objects * (n_objects**2 - 1) * n_instances,
dtype='float32')
spearman_correlation = 1 - (6 * sum_of_squared_distances) / denominator
return spearman_correlation
