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63 lines
1.9 KiB
63 lines
1.9 KiB
1 year ago
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import pandas as pd
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import pytest
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@pytest.fixture()
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def example_data() -> pd.DataFrame:
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x = list(range(1, 6))
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y = [4.1, 6.9, 10.1, 12.9, 15.9]
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return pd.DataFrame({"A": x, "B": y})
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def test_linear_regression(example_data):
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from conda_helpers import linear_regression
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from conda_helpers.linear_regression import Regression
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result = linear_regression(example_data, x="A", y="B")
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assert isinstance(result, Regression)
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assert pytest.approx(2.96) == result.coefficient
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assert pytest.approx(2.96) == result.coeff
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assert pytest.approx(1.1) == result.intercept
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assert pytest.approx(0.9996349) == result.score
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assert pytest.approx(0.9996349) == result.r2
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def test_regression_predict(example_data):
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from conda_helpers import linear_regression
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regression = linear_regression(example_data, x="A", y="B")
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prediction = regression.predict(x=10)
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assert pytest.approx(30.7) == prediction
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assert pytest.approx(10) == regression.predict(y=prediction)
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def test_regression_predict_exceptions(example_data):
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from conda_helpers import linear_regression
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regression = linear_regression(example_data, x="A", y="B")
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with pytest.raises(TypeError, match="expects a keyword"):
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regression.predict()
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with pytest.raises(TypeError, match="expects one keyword"):
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regression.predict(x=1, y=2)
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with pytest.raises(TypeError, match="takes 1 positional argument but"):
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regression.predict(1)
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def test_regression_to_dict(example_data):
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from conda_helpers import linear_regression
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regression = linear_regression(example_data, x="A", y="B")
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result = regression.to_dict()
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assert sorted(result.keys()) == ["coefficient", "intercept", "score"]
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assert pytest.approx(2.96) == result["coefficient"]
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assert pytest.approx(1.1) == result["intercept"]
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assert pytest.approx(0.9996349) == result["score"]
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