#!/usr/bin/env python
# ===============================================================================
# Copyright 2021 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============================================================================

import numpy as np
from numpy.testing import assert_allclose
from sklearn.datasets import make_regression
from daal4py.sklearn._utils import daal_check_version


def test_sklearnex_import_linear():
    from sklearnex.linear_model import LinearRegression
    X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
    y = np.dot(X, np.array([1, 2])) + 3
    linreg = LinearRegression().fit(X, y)
    assert 'daal4py' in linreg.__module__
    assert linreg.n_features_in_ == 2
    assert_allclose(linreg.intercept_, 3.)
    assert_allclose(linreg.coef_, [1., 2.])


def test_sklearnex_import_ridge():
    from sklearnex.linear_model import Ridge
    X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
    y = np.dot(X, np.array([1, 2])) + 3
    ridgereg = Ridge().fit(X, y)
    assert 'daal4py' in ridgereg.__module__
    assert_allclose(ridgereg.intercept_, 4.5)
    assert_allclose(ridgereg.coef_, [0.8, 1.4])


def test_sklearnex_import_lasso():
    from sklearnex.linear_model import Lasso
    X = [[0, 0], [1, 1], [2, 2]]
    y = [0, 1, 2]
    lasso = Lasso(alpha=0.1).fit(X, y)
    assert 'daal4py' in lasso.__module__
    assert_allclose(lasso.intercept_, 0.15)
    assert_allclose(lasso.coef_, [0.85, 0.0])


def test_sklearnex_import_elastic():
    from sklearnex.linear_model import ElasticNet
    X, y = make_regression(n_features=2, random_state=0)
    elasticnet = ElasticNet(random_state=0).fit(X, y)
    assert 'daal4py' in elasticnet.__module__
    assert_allclose(elasticnet.intercept_, 1.451, atol=1e-3)
    assert_allclose(elasticnet.coef_, [18.838, 64.559], atol=1e-3)
