import pandas as pd
import pytest


@pytest.fixture()
def example_data() -> pd.DataFrame:
    x = list(range(1, 6))
    y = [4.1, 6.9, 10.1, 12.9, 15.9]
    return pd.DataFrame({"A": x, "B": y})


def test_linear_regression(example_data):
    from conda_helpers import linear_regression
    from conda_helpers.linear_regression import Regression

    result = linear_regression(example_data, x="A", y="B")

    assert isinstance(result, Regression)
    assert pytest.approx(2.96) == result.coefficient
    assert pytest.approx(2.96) == result.coeff
    assert pytest.approx(1.1) == result.intercept
    assert pytest.approx(0.9996349) == result.score
    assert pytest.approx(0.9996349) == result.r2


def test_regression_predict(example_data):
    from conda_helpers import linear_regression

    regression = linear_regression(example_data, x="A", y="B")

    prediction = regression.predict(x=10)

    assert pytest.approx(30.7) == prediction
    assert pytest.approx(10) == regression.predict(y=prediction)


def test_regression_predict_exceptions(example_data):
    from conda_helpers import linear_regression

    regression = linear_regression(example_data, x="A", y="B")

    with pytest.raises(TypeError, match="expects a keyword"):
        regression.predict()

    with pytest.raises(TypeError, match="expects one keyword"):
        regression.predict(x=1, y=2)

    with pytest.raises(TypeError, match="takes 1 positional argument but"):
        regression.predict(1)


def test_regression_to_dict(example_data):
    from conda_helpers import linear_regression

    regression = linear_regression(example_data, x="A", y="B")

    result = regression.to_dict()

    assert sorted(result.keys()) == ["coefficient", "intercept", "score"]
    assert pytest.approx(2.96) == result["coefficient"]
    assert pytest.approx(1.1) == result["intercept"]
    assert pytest.approx(0.9996349) == result["score"]