import matplotlib
import numpy as np
import pandas as pd

matplotlib.use('Agg')
import shap  # pylint: disable=wrong-import-position


def test_dependence_one_string_feature():
    """ Test the dependence plot with a string feature.
    """
    X = _create_sample_dataset(string_features={"Sex"})

    shap.dependence_plot(
        "Sex",
        np.random.randn(*X.values.shape),
        X,
        interaction_index="Age",
        show=False
    )


def test_dependence_two_string_features():
    """ Test the dependence plot with two string features.
    """
    X = _create_sample_dataset(string_features={"Sex", "Blood group"})

    shap.dependence_plot(
        "Sex",
        np.random.randn(*X.values.shape),
        X,
        interaction_index="Blood group",
        show=False
    )


def test_dependence_one_string_feature_no_interaction():
    """ Test the dependence plot with no interactions.
    """
    X = _create_sample_dataset(string_features={"Sex"})

    shap.dependence_plot(
        "Sex",
        np.random.randn(*X.values.shape),
        X,
        interaction_index=None,
        show=False
    )


def test_dependence_one_string_feature_auto_interaction():
    """ Test the dependence plot with auto interaction detection.
    """
    X = _create_sample_dataset(string_features={"Sex"})

    shap.dependence_plot(
        "Sex",
        np.random.randn(*X.values.shape),
        X,
        interaction_index='auto',
        show=False
    )


def test_approximate_interactions():
    """ Test the approximate interaction detector.
    """
    X_no_string_features = _create_sample_dataset(string_features={})
    X_one_string_feature = _create_sample_dataset(string_features={"Sex"})
    X_two_string_features = _create_sample_dataset(string_features={"Sex", "Blood group"})

    shap_values = np.random.randn(*X_one_string_feature.values.shape)

    interactions_no_features = shap.approximate_interactions(0, shap_values, X_no_string_features)
    interactions_one_string_feature = shap.approximate_interactions(0, shap_values, X_one_string_feature)
    interactions_two_string_feature = shap.approximate_interactions(0, shap_values, X_two_string_features)

    assert (interactions_no_features == interactions_one_string_feature).all()
    assert (interactions_no_features == interactions_two_string_feature).all()


def _create_sample_dataset(string_features):
    if "Sex" in string_features:
        sex_values = ["Male", "Female", "Male", "Male", "Female", "Female"]
    else:
        sex_values = [0, 1, 0, 0, 1, 1]

    if "Blood group" in string_features:
        blood_values = ["A", "B", "A", "O", "O", "O"]
    else:
        blood_values = [1, 2, 1, 3, 3, 3]

    X = pd.DataFrame({
        "Sex": sex_values,
        "Blood group": blood_values,
        "Age": [10, 15, 28, 3, 84, 56],
        "Height": [130, 170, 185, 40, 150, 164]
    })
    return X
