58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
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"""models 包测试。"""
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from __future__ import annotations
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import numpy as np
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import pytest
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pytestmark = pytest.mark.skip(reason="未实现：见 docs/03-roadmap.md L1")
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def test_fit_linear_recovers_known_coefficients(linear_data):
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"""真值 y = 2x + 1，拟合系数应在 1% 内。"""
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from mcm_toolkit.models import fit_linear
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x, y = linear_data
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r = fit_linear(x.reshape(-1, 1), y)
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assert np.isclose(r.params["coef"][0], 2.0, rtol=0.01)
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assert np.isclose(r.params["intercept"], 1.0, atol=0.05)
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def test_fit_polynomial_matches_numpy(linear_data):
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from mcm_toolkit.models import fit_polynomial
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x, y = linear_data
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r = fit_polynomial(x.reshape(-1, 1), y, degree=2)
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assert np.allclose(r.params["coef"], np.polyfit(x, y, 2), rtol=1e-6)
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def test_metrics_known_values():
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from mcm_toolkit.models import mae, r2_score, rmse
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y_true = np.array([1.0, 2.0, 3.0])
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y_pred = np.array([1.0, 2.0, 5.0])
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assert np.isclose(rmse(y_true, y_pred), np.sqrt(4 / 3))
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assert np.isclose(mae(y_true, y_pred), 2 / 3)
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assert r2_score(y_true, y_pred) < 1.0
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def test_r2_is_one_for_perfect_fit():
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from mcm_toolkit.models import r2_score
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y = np.array([1.0, 2.0, 3.0, 4.0])
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assert np.isclose(r2_score(y, y), 1.0)
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def test_compare_models_returns_table_sorted_by_first_metric(linear_data):
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from mcm_toolkit.models import compare_models, fit_linear, fit_polynomial
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x, y = linear_data
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X = x.reshape(-1, 1)
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tbl = compare_models(
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{"linear": fit_linear(X, y), "poly2": fit_polynomial(X, y, degree=2)},
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X,
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y,
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cv=0,
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)
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assert set(tbl.index) >= {"linear", "poly2"}
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assert "rmse" in tbl.columns
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