import pytest
import numpy as np

from keras import metrics
from keras import backend as K

all_metrics = [
    metrics.binary_accuracy,
    metrics.categorical_accuracy,
    metrics.mean_squared_error,
    metrics.mean_absolute_error,
    metrics.mean_absolute_percentage_error,
    metrics.mean_squared_logarithmic_error,
    metrics.squared_hinge,
    metrics.hinge,
    metrics.categorical_crossentropy,
    metrics.binary_crossentropy,
    metrics.poisson,
    metrics.cosine_proximity,
]

all_sparse_metrics = [
    metrics.sparse_categorical_accuracy,
    metrics.sparse_categorical_crossentropy,
]


def test_metrics():
    y_a = K.variable(np.random.random((6, 7)))
    y_b = K.variable(np.random.random((6, 7)))
    for metric in all_metrics:
        output = metric(y_a, y_b)
        print(metric.__name__)
        assert K.eval(output).shape == (6,)


def test_sparse_metrics():
    for metric in all_sparse_metrics:
        y_a = K.variable(np.random.randint(0, 7, (6,)), dtype=K.floatx())
        y_b = K.variable(np.random.random((6, 7)), dtype=K.floatx())
        assert K.eval(metric(y_a, y_b)).shape == (6,)


def test_top_k_categorical_accuracy():
    y_pred = K.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([[0, 1, 0], [1, 0, 0]]))
    success_result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred,
                                                               k=3))
    assert success_result == 1
    partial_result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred,
                                                               k=2))
    assert partial_result == 0.5
    failure_result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred,
                                                               k=1))
    assert failure_result == 0


if __name__ == '__main__':
    pytest.main([__file__])
