import copy
import pickle
import warnings

import numpy as np
import pytest
from scipy.special import expit

import xlearn
from xlearn.datasets import make_regression
from xlearn.isotonic import (
    IsotonicRegression,
    _make_unique,
    check_increasing,
    isotonic_regression,
)
from xlearn.utils import shuffle
from xlearn.utils._testing import (
    assert_allclose,
    assert_array_almost_equal,
    assert_array_equal,
)
from xlearn.utils.validation import check_array


def test_permutation_invariance():
    # check that fit is permutation invariant.
    # regression test of missing sorting of sample-weights
    ir = IsotonicRegression()
    x = [1, 2, 3, 4, 5, 6, 7]
    y = [1, 41, 51, 1, 2, 5, 24]
    sample_weight = [1, 2, 3, 4, 5, 6, 7]
    x_s, y_s, sample_weight_s = shuffle(x, y, sample_weight, random_state=0)
    y_transformed = ir.fit_transform(x, y, sample_weight=sample_weight)
    y_transformed_s = ir.fit(x_s, y_s, sample_weight=sample_weight_s).transform(x)

    assert_array_equal(y_transformed, y_transformed_s)


def test_check_increasing_small_number_of_samples():
    x = [0, 1, 2]
    y = [1, 1.1, 1.05]

    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        is_increasing = check_increasing(x, y)

    assert is_increasing


def test_check_increasing_up():
    x = [0, 1, 2, 3, 4, 5]
    y = [0, 1.5, 2.77, 8.99, 8.99, 50]

    # Check that we got increasing=True and no warnings
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        is_increasing = check_increasing(x, y)

    assert is_increasing


def test_check_increasing_up_extreme():
    x = [0, 1, 2, 3, 4, 5]
    y = [0, 1, 2, 3, 4, 5]

    # Check that we got increasing=True and no warnings
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        is_increasing = check_increasing(x, y)

    assert is_increasing


def test_check_increasing_down():
    x = [0, 1, 2, 3, 4, 5]
    y = [0, -1.5, -2.77, -8.99, -8.99, -50]

    # Check that we got increasing=False and no warnings
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        is_increasing = check_increasing(x, y)

    assert not is_increasing


def test_check_increasing_down_extreme():
    x = [0, 1, 2, 3, 4, 5]
    y = [0, -1, -2, -3, -4, -5]

    # Check that we got increasing=False and no warnings
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        is_increasing = check_increasing(x, y)

    assert not is_increasing


def test_check_ci_warn():
    x = [0, 1, 2, 3, 4, 5]
    y = [0, -1, 2, -3, 4, -5]

    # Check that we got increasing=False and CI interval warning
    msg = "interval"
    with pytest.warns(UserWarning, match=msg):
        is_increasing = check_increasing(x, y)

    assert not is_increasing


def test_isotonic_regression():
    y = np.array([3, 7, 5, 9, 8, 7, 10])
    y_ = np.array([3, 6, 6, 8, 8, 8, 10])
    assert_array_equal(y_, isotonic_regression(y))

    y = np.array([10, 0, 2])
    y_ = np.array([4, 4, 4])
    assert_array_equal(y_, isotonic_regression(y))

    x = np.arange(len(y))
    ir = IsotonicRegression(y_min=0.0, y_max=1.0)
    ir.fit(x, y)
    assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
    assert_array_equal(ir.transform(x), ir.predict(x))

    # check that it is immune to permutation
    perm = np.random.permutation(len(y))
    ir = IsotonicRegression(y_min=0.0, y_max=1.0)
    assert_array_equal(ir.fit_transform(x[perm], y[perm]), ir.fit_transform(x, y)[perm])
    assert_array_equal(ir.transform(x[perm]), ir.transform(x)[perm])

    # check we don't crash when all x are equal:
    ir = IsotonicRegression()
    assert_array_equal(ir.fit_transform(np.ones(len(x)), y), np.mean(y))


def test_isotonic_regression_ties_min():
    # Setup examples with ties on minimum
    x = [1, 1, 2, 3, 4, 5]
    y = [1, 2, 3, 4, 5, 6]
    y_true = [1.5, 1.5, 3, 4, 5, 6]

    # Check that we get identical results for fit/transform and fit_transform
    ir = IsotonicRegression()
    ir.fit(x, y)
    assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
    assert_array_equal(y_true, ir.fit_transform(x, y))


def test_isotonic_regression_ties_max():
    # Setup examples with ties on maximum
    x = [1, 2, 3, 4, 5, 5]
    y = [1, 2, 3, 4, 5, 6]
    y_true = [1, 2, 3, 4, 5.5, 5.5]

    # Check that we get identical results for fit/transform and fit_transform
    ir = IsotonicRegression()
    ir.fit(x, y)
    assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
    assert_array_equal(y_true, ir.fit_transform(x, y))


def test_isotonic_regression_ties_secondary_():
    """
    Test isotonic regression fit, transform  and fit_transform
    against the "secondary" ties method and "pituitary" data from R
     "isotone" package, as detailed in: J. d. Leeuw, K. Hornik, P. Mair,
     Isotone Optimization in R: Pool-Adjacent-Violators Algorithm
    (PAVA) and Active Set Methods

    Set values based on pituitary example and
     the following R command detailed in the paper above:
    > library("isotone")
    > data("pituitary")
    > res1 <- gpava(pituitary$age, pituitary$size, ties="secondary")
    > res1$x

    `isotone` version: 1.0-2, 2014-09-07
    R version: R version 3.1.1 (2014-07-10)
    """
    x = [8, 8, 8, 10, 10, 10, 12, 12, 12, 14, 14]
    y = [21, 23.5, 23, 24, 21, 25, 21.5, 22, 19, 23.5, 25]
    y_true = [
        22.22222,
        22.22222,
        22.22222,
        22.22222,
        22.22222,
        22.22222,
        22.22222,
        22.22222,
        22.22222,
        24.25,
        24.25,
    ]

    # Check fit, transform and fit_transform
    ir = IsotonicRegression()
    ir.fit(x, y)
    assert_array_almost_equal(ir.transform(x), y_true, 4)
    assert_array_almost_equal(ir.fit_transform(x, y), y_true, 4)


def test_isotonic_regression_with_ties_in_differently_sized_groups():
    """
    Non-regression test to handle issue 9432:
    https://github.com/chenxingqiang/jax-sklearn/issues/9432

    Compare against output in R:
    > library("isotone")
    > x <- c(0, 1, 1, 2, 3, 4)
    > y <- c(0, 0, 1, 0, 0, 1)
    > res1 <- gpava(x, y, ties="secondary")
    > res1$x

    `isotone` version: 1.1-0, 2015-07-24
    R version: R version 3.3.2 (2016-10-31)
    """
    x = np.array([0, 1, 1, 2, 3, 4])
    y = np.array([0, 0, 1, 0, 0, 1])
    y_true = np.array([0.0, 0.25, 0.25, 0.25, 0.25, 1.0])
    ir = IsotonicRegression()
    ir.fit(x, y)
    assert_array_almost_equal(ir.transform(x), y_true)
    assert_array_almost_equal(ir.fit_transform(x, y), y_true)


def test_isotonic_regression_reversed():
    y = np.array([10, 9, 10, 7, 6, 6.1, 5])
    y_result = np.array([10, 9.5, 9.5, 7, 6.05, 6.05, 5])

    y_iso = isotonic_regression(y, increasing=False)
    assert_allclose(y_iso, y_result)

    y_ = IsotonicRegression(increasing=False).fit_transform(np.arange(len(y)), y)
    assert_allclose(y_, y_result)
    assert_array_equal(np.ones(y_[:-1].shape), ((y_[:-1] - y_[1:]) >= 0))


def test_isotonic_regression_auto_decreasing():
    # Set y and x for decreasing
    y = np.array([10, 9, 10, 7, 6, 6.1, 5])
    x = np.arange(len(y))

    # Create model and fit_transform
    ir = IsotonicRegression(increasing="auto")
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        y_ = ir.fit_transform(x, y)
        # work-around for pearson divide warnings in scipy <= 0.17.0
        assert all(["invalid value encountered in " in str(warn.message) for warn in w])

    # Check that relationship decreases
    is_increasing = y_[0] < y_[-1]
    assert not is_increasing


def test_isotonic_regression_auto_increasing():
    # Set y and x for decreasing
    y = np.array([5, 6.1, 6, 7, 10, 9, 10])
    x = np.arange(len(y))

    # Create model and fit_transform
    ir = IsotonicRegression(increasing="auto")
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        y_ = ir.fit_transform(x, y)
        # work-around for pearson divide warnings in scipy <= 0.17.0
        assert all(["invalid value encountered in " in str(warn.message) for warn in w])

    # Check that relationship increases
    is_increasing = y_[0] < y_[-1]
    assert is_increasing


def test_assert_raises_exceptions():
    ir = IsotonicRegression()
    rng = np.random.RandomState(42)

    msg = "Found input variables with inconsistent numbers of samples"
    with pytest.raises(ValueError, match=msg):
        ir.fit([0, 1, 2], [5, 7, 3], [0.1, 0.6])

    with pytest.raises(ValueError, match=msg):
        ir.fit([0, 1, 2], [5, 7])

    msg = "X should be a 1d array"
    with pytest.raises(ValueError, match=msg):
        ir.fit(rng.randn(3, 10), [0, 1, 2])

    msg = "Isotonic regression input X should be a 1d array"
    with pytest.raises(ValueError, match=msg):
        ir.transform(rng.randn(3, 10))


def test_isotonic_sample_weight_parameter_default_value():
    # check if default value of sample_weight parameter is one
    ir = IsotonicRegression()
    # random test data
    rng = np.random.RandomState(42)
    n = 100
    x = np.arange(n)
    y = rng.randint(-50, 50, size=(n,)) + 50.0 * np.log(1 + np.arange(n))
    # check if value is correctly used
    weights = np.ones(n)
    y_set_value = ir.fit_transform(x, y, sample_weight=weights)
    y_default_value = ir.fit_transform(x, y)

    assert_array_equal(y_set_value, y_default_value)


def test_isotonic_min_max_boundaries():
    # check if min value is used correctly
    ir = IsotonicRegression(y_min=2, y_max=4)
    n = 6
    x = np.arange(n)
    y = np.arange(n)
    y_test = [2, 2, 2, 3, 4, 4]
    y_result = np.round(ir.fit_transform(x, y))
    assert_array_equal(y_result, y_test)


def test_isotonic_sample_weight():
    ir = IsotonicRegression()
    x = [1, 2, 3, 4, 5, 6, 7]
    y = [1, 41, 51, 1, 2, 5, 24]
    sample_weight = [1, 2, 3, 4, 5, 6, 7]
    expected_y = [1, 13.95, 13.95, 13.95, 13.95, 13.95, 24]
    received_y = ir.fit_transform(x, y, sample_weight=sample_weight)

    assert_array_equal(expected_y, received_y)


def test_isotonic_regression_oob_raise():
    # Set y and x
    y = np.array([3, 7, 5, 9, 8, 7, 10])
    x = np.arange(len(y))

    # Create model and fit
    ir = IsotonicRegression(increasing="auto", out_of_bounds="raise")
    ir.fit(x, y)

    # Check that an exception is thrown
    msg = "in x_new is below the interpolation range"
    with pytest.raises(ValueError, match=msg):
        ir.predict([min(x) - 10, max(x) + 10])


def test_isotonic_regression_oob_clip():
    # Set y and x
    y = np.array([3, 7, 5, 9, 8, 7, 10])
    x = np.arange(len(y))

    # Create model and fit
    ir = IsotonicRegression(increasing="auto", out_of_bounds="clip")
    ir.fit(x, y)

    # Predict from  training and test x and check that min/max match.
    y1 = ir.predict([min(x) - 10, max(x) + 10])
    y2 = ir.predict(x)
    assert max(y1) == max(y2)
    assert min(y1) == min(y2)


def test_isotonic_regression_oob_nan():
    # Set y and x
    y = np.array([3, 7, 5, 9, 8, 7, 10])
    x = np.arange(len(y))

    # Create model and fit
    ir = IsotonicRegression(increasing="auto", out_of_bounds="nan")
    ir.fit(x, y)

    # Predict from  training and test x and check that we have two NaNs.
    y1 = ir.predict([min(x) - 10, max(x) + 10])
    assert sum(np.isnan(y1)) == 2


def test_isotonic_regression_pickle():
    y = np.array([3, 7, 5, 9, 8, 7, 10])
    x = np.arange(len(y))

    # Create model and fit
    ir = IsotonicRegression(increasing="auto", out_of_bounds="clip")
    ir.fit(x, y)

    ir_ser = pickle.dumps(ir, pickle.HIGHEST_PROTOCOL)
    ir2 = pickle.loads(ir_ser)
    np.testing.assert_array_equal(ir.predict(x), ir2.predict(x))


def test_isotonic_duplicate_min_entry():
    x = [0, 0, 1]
    y = [0, 0, 1]

    ir = IsotonicRegression(increasing=True, out_of_bounds="clip")
    ir.fit(x, y)
    all_predictions_finite = np.all(np.isfinite(ir.predict(x)))
    assert all_predictions_finite


def test_isotonic_ymin_ymax():
    # Test from @NelleV's issue:
    # https://github.com/chenxingqiang/jax-sklearn/issues/6921
    x = np.array(
        [
            1.263,
            1.318,
            -0.572,
            0.307,
            -0.707,
            -0.176,
            -1.599,
            1.059,
            1.396,
            1.906,
            0.210,
            0.028,
            -0.081,
            0.444,
            0.018,
            -0.377,
            -0.896,
            -0.377,
            -1.327,
            0.180,
        ]
    )
    y = isotonic_regression(x, y_min=0.0, y_max=0.1)

    assert np.all(y >= 0)
    assert np.all(y <= 0.1)

    # Also test decreasing case since the logic there is different
    y = isotonic_regression(x, y_min=0.0, y_max=0.1, increasing=False)

    assert np.all(y >= 0)
    assert np.all(y <= 0.1)

    # Finally, test with only one bound
    y = isotonic_regression(x, y_min=0.0, increasing=False)

    assert np.all(y >= 0)


def test_isotonic_zero_weight_loop():
    # Test from @ogrisel's issue:
    # https://github.com/chenxingqiang/jax-sklearn/issues/4297

    # Get deterministic RNG with seed
    rng = np.random.RandomState(42)

    # Create regression and samples
    regression = IsotonicRegression()
    n_samples = 50
    x = np.linspace(-3, 3, n_samples)
    y = x + rng.uniform(size=n_samples)

    # Get some random weights and zero out
    w = rng.uniform(size=n_samples)
    w[5:8] = 0
    regression.fit(x, y, sample_weight=w)

    # This will hang in failure case.
    regression.fit(x, y, sample_weight=w)


def test_fast_predict():
    # test that the faster prediction change doesn't
    # affect out-of-sample predictions:
    # https://github.com/chenxingqiang/jax-sklearn/pull/6206
    rng = np.random.RandomState(123)
    n_samples = 10**3
    # X values over the -10,10 range
    X_train = 20.0 * rng.rand(n_samples) - 10
    y_train = (
        np.less(rng.rand(n_samples), expit(X_train)).astype("int64").astype("float64")
    )

    weights = rng.rand(n_samples)
    # we also want to test that everything still works when some weights are 0
    weights[rng.rand(n_samples) < 0.1] = 0

    slow_model = IsotonicRegression(y_min=0, y_max=1, out_of_bounds="clip")
    fast_model = IsotonicRegression(y_min=0, y_max=1, out_of_bounds="clip")

    # Build interpolation function with ALL input data, not just the
    # non-redundant subset. The following 2 lines are taken from the
    # .fit() method, without removing unnecessary points
    X_train_fit, y_train_fit = slow_model._build_y(
        X_train, y_train, sample_weight=weights, trim_duplicates=False
    )
    slow_model._build_f(X_train_fit, y_train_fit)

    # fit with just the necessary data
    fast_model.fit(X_train, y_train, sample_weight=weights)

    X_test = 20.0 * rng.rand(n_samples) - 10
    y_pred_slow = slow_model.predict(X_test)
    y_pred_fast = fast_model.predict(X_test)

    assert_array_equal(y_pred_slow, y_pred_fast)


def test_isotonic_copy_before_fit():
    # https://github.com/chenxingqiang/jax-sklearn/issues/6628
    ir = IsotonicRegression()
    copy.copy(ir)


@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
def test_isotonic_dtype(dtype):
    y = [2, 1, 4, 3, 5]
    weights = np.array([0.9, 0.9, 0.9, 0.9, 0.9], dtype=np.float64)
    reg = IsotonicRegression()

    for sample_weight in (None, weights.astype(np.float32), weights):
        y_np = np.array(y, dtype=dtype)
        expected_dtype = check_array(
            y_np, dtype=[np.float64, np.float32], ensure_2d=False
        ).dtype

        res = isotonic_regression(y_np, sample_weight=sample_weight)
        assert res.dtype == expected_dtype

        X = np.arange(len(y)).astype(dtype)
        reg.fit(X, y_np, sample_weight=sample_weight)
        res = reg.predict(X)
        assert res.dtype == expected_dtype


@pytest.mark.parametrize("y_dtype", [np.int32, np.int64, np.float32, np.float64])
def test_isotonic_mismatched_dtype(y_dtype):
    # regression test for #15004
    # check that data are converted when X and y dtype differ
    reg = IsotonicRegression()
    y = np.array([2, 1, 4, 3, 5], dtype=y_dtype)
    X = np.arange(len(y), dtype=np.float32)
    reg.fit(X, y)
    assert reg.predict(X).dtype == X.dtype


def test_make_unique_dtype():
    x_list = [2, 2, 2, 3, 5]
    for dtype in (np.float32, np.float64):
        x = np.array(x_list, dtype=dtype)
        y = x.copy()
        w = np.ones_like(x)
        x, y, w = _make_unique(x, y, w)
        assert_array_equal(x, [2, 3, 5])


@pytest.mark.parametrize("dtype", [np.float64, np.float32])
def test_make_unique_tolerance(dtype):
    # Check that equality takes account of np.finfo tolerance
    x = np.array([0, 1e-16, 1, 1 + 1e-14], dtype=dtype)
    y = x.copy()
    w = np.ones_like(x)
    x, y, w = _make_unique(x, y, w)
    if dtype == np.float64:
        x_out = np.array([0, 1, 1 + 1e-14])
    else:
        x_out = np.array([0, 1])
    assert_array_equal(x, x_out)


def test_isotonic_make_unique_tolerance():
    # Check that averaging of targets for duplicate X is done correctly,
    # taking into account tolerance
    X = np.array([0, 1, 1 + 1e-16, 2], dtype=np.float64)
    y = np.array([0, 1, 2, 3], dtype=np.float64)
    ireg = IsotonicRegression().fit(X, y)
    y_pred = ireg.predict([0, 0.5, 1, 1.5, 2])

    assert_array_equal(y_pred, np.array([0, 0.75, 1.5, 2.25, 3]))
    assert_array_equal(ireg.X_thresholds_, np.array([0.0, 1.0, 2.0]))
    assert_array_equal(ireg.y_thresholds_, np.array([0.0, 1.5, 3.0]))


def test_isotonic_non_regression_inf_slope():
    # Non-regression test to ensure that inf values are not returned
    # see: https://github.com/chenxingqiang/jax-sklearn/issues/10903
    X = np.array([0.0, 4.1e-320, 4.4e-314, 1.0])
    y = np.array([0.42, 0.42, 0.44, 0.44])
    ireg = IsotonicRegression().fit(X, y)
    y_pred = ireg.predict(np.array([0, 2.1e-319, 5.4e-316, 1e-10]))
    assert np.all(np.isfinite(y_pred))


@pytest.mark.parametrize("increasing", [True, False])
def test_isotonic_thresholds(increasing):
    rng = np.random.RandomState(42)
    n_samples = 30
    X = rng.normal(size=n_samples)
    y = rng.normal(size=n_samples)
    ireg = IsotonicRegression(increasing=increasing).fit(X, y)
    X_thresholds, y_thresholds = ireg.X_thresholds_, ireg.y_thresholds_
    assert X_thresholds.shape == y_thresholds.shape

    # Input thresholds are a strict subset of the training set (unless
    # the data is already strictly monotonic which is not the case with
    # this random data)
    assert X_thresholds.shape[0] < X.shape[0]
    assert np.isin(X_thresholds, X).all()

    # Output thresholds lie in the range of the training set:
    assert y_thresholds.max() <= y.max()
    assert y_thresholds.min() >= y.min()

    assert all(np.diff(X_thresholds) > 0)
    if increasing:
        assert all(np.diff(y_thresholds) >= 0)
    else:
        assert all(np.diff(y_thresholds) <= 0)


def test_input_shape_validation():
    # Test from #15012
    # Check that IsotonicRegression can handle 2darray with only 1 feature
    X = np.arange(10)
    X_2d = X.reshape(-1, 1)
    y = np.arange(10)

    iso_reg = IsotonicRegression().fit(X, y)
    iso_reg_2d = IsotonicRegression().fit(X_2d, y)

    assert iso_reg.X_max_ == iso_reg_2d.X_max_
    assert iso_reg.X_min_ == iso_reg_2d.X_min_
    assert iso_reg.y_max == iso_reg_2d.y_max
    assert iso_reg.y_min == iso_reg_2d.y_min
    assert_array_equal(iso_reg.X_thresholds_, iso_reg_2d.X_thresholds_)
    assert_array_equal(iso_reg.y_thresholds_, iso_reg_2d.y_thresholds_)

    y_pred1 = iso_reg.predict(X)
    y_pred2 = iso_reg_2d.predict(X_2d)
    assert_allclose(y_pred1, y_pred2)


def test_isotonic_2darray_more_than_1_feature():
    # Ensure IsotonicRegression raises error if input has more than 1 feature
    X = np.arange(10)
    X_2d = np.c_[X, X]
    y = np.arange(10)

    msg = "should be a 1d array or 2d array with 1 feature"
    with pytest.raises(ValueError, match=msg):
        IsotonicRegression().fit(X_2d, y)

    iso_reg = IsotonicRegression().fit(X, y)
    with pytest.raises(ValueError, match=msg):
        iso_reg.predict(X_2d)

    with pytest.raises(ValueError, match=msg):
        iso_reg.transform(X_2d)


def test_isotonic_regression_sample_weight_not_overwritten():
    """Check that calling fitting function of isotonic regression will not
    overwrite `sample_weight`.
    Non-regression test for:
    https://github.com/chenxingqiang/jax-sklearn/issues/20508
    """
    X, y = make_regression(n_samples=10, n_features=1, random_state=41)
    sample_weight_original = np.ones_like(y)
    sample_weight_original[0] = 10
    sample_weight_fit = sample_weight_original.copy()

    isotonic_regression(y, sample_weight=sample_weight_fit)
    assert_allclose(sample_weight_fit, sample_weight_original)

    IsotonicRegression().fit(X, y, sample_weight=sample_weight_fit)
    assert_allclose(sample_weight_fit, sample_weight_original)


@pytest.mark.parametrize("shape", ["1d", "2d"])
def test_get_feature_names_out(shape):
    """Check `get_feature_names_out` for `IsotonicRegression`."""
    X = np.arange(10)
    if shape == "2d":
        X = X.reshape(-1, 1)
    y = np.arange(10)

    iso = IsotonicRegression().fit(X, y)
    names = iso.get_feature_names_out()
    assert isinstance(names, np.ndarray)
    assert names.dtype == object
    assert_array_equal(["isotonicregression0"], names)


def test_isotonic_regression_output_predict():
    """Check that `predict` does return the expected output type.

    We need to check that `transform` will output a DataFrame and a NumPy array
    when we set `transform_output` to `pandas`.

    Non-regression test for:
    https://github.com/chenxingqiang/jax-sklearn/issues/25499
    """
    pd = pytest.importorskip("pandas")
    X, y = make_regression(n_samples=10, n_features=1, random_state=42)
    regressor = IsotonicRegression()
    with xlearn.config_context(transform_output="pandas"):
        regressor.fit(X, y)
        X_trans = regressor.transform(X)
        y_pred = regressor.predict(X)

    assert isinstance(X_trans, pd.DataFrame)
    assert isinstance(y_pred, np.ndarray)
