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71 lines
2.0 KiB
71 lines
2.0 KiB
import pandas as pd |
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import pytest |
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from dataclasses import dataclass |
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from sklearn import linear_model |
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@dataclass |
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class Regression: |
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intercept: float |
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coefficient: float |
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score: float |
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@property |
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def coeff(self) -> float: |
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return self.coefficient |
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@property |
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def r2(self) -> float: |
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return self.score |
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def predict(self, x: int | float = None, y: int | float = None) -> float: |
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"""predict a value if x or y is given""" |
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if x is not None: |
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return self.intercept + x * self.coefficient |
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if y is not None: |
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return (y - self.intercept) / self.coefficient |
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msg = "predict() expects 1 argument, got 0" |
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raise TypeError(msg) |
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def linear_regression(data: pd.DataFrame, *, x: str, y: str) -> Regression: |
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"""calculates a linear regression for two columns of a DataFrame""" |
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x_values = data[x].values.reshape(-1, 1) |
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y_values = data[y].values.reshape(-1, 1) |
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fit = linear_model.LinearRegression().fit(x_values, y_values) |
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score = fit.score(x_values, y_values) |
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return Regression(fit.intercept_[0], fit.coef_[0][0], score) |
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# tests |
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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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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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result = linear_regression(example_data, x="A", y="B") |
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prediction = result.predict(10) |
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assert pytest.approx(30.7) == prediction |
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assert pytest.approx(10) == result.predict(y=prediction) |
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with pytest.raises(TypeError, match="expects 1 argument"): |
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result.predict()
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