guzhujushi
9907289ec8
models/metrics.py（用户写、Codex 审）：6 个回归指标 + 共享入口 verify_yt_yp 落地， compare_models 仍是 TODO(L1)。 真 bug（决策 59，由用户亲手改定）： - r2_score 的 SS_tot 基准用了 np.mean(y_pred)，必须是 np.mean(y_true)。 探针实测：修前 0.8803827751196172、修后 0.875 = sklearn.metrics.r2_score 一致； adjusted_r2 复用它，一并修正。完美拟合（r2_score(y, y) == 1）与常量 y 都掩盖了这个错， 所以新测试特意用"非完美拟合"的数据。ss_tol 顺带改名 ss_tot。 - smape 的 np.where 掩码防不住零除（两条分支都会被求值），在 filterwarnings = ["error::RuntimeWarning"] 下任何含 0 的用例直接红 → 改 np.divide(where=)， 数值结果不变（决策 60）。 工程侧（决策 47）：报错文案统一为"参数名 + 实际值"；补 7 处 docstring （Parameters / Returns / Raises，把 NaN 报错、mape 遇 0、r2 常量 y、adjusted_r2 分母保护 写成契约；n_features 注明不含截距）；ruff 9 条清零（6 F541 + 3 W293）。 tests/test_models.py：5 条 → 23 条（2 条摘 skip 转正 + 18 条新增）。 三连：pytest 224 passed / 26 skipped（250 收集）、ruff 0、pyright 0。 文档：STATUS 决策 59 / 60 + 第九轮（含 4 条待拍板口径）；CHANGELOG 本轮条目； docs/dev/personal-pitfalls.md 追加易错点 31（基准量抄错对象）与 32（np.where 掩码防不住零除）。
213 lines
7.0 KiB
Python
213 lines
7.0 KiB
Python
"""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 pandas as pd
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import pytest
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_NOT_IMPLEMENTED = "未实现：见 docs/03-roadmap.md L1"
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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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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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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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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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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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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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def test_metrics_reject_mismatched_shapes():
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from mcm_toolkit.models import rmse
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with pytest.raises(ValueError, match="形状必须一致"):
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rmse([1.0, 2.0], [1.0, 2.0, 3.0])
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def test_metrics_reject_empty_input():
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from mcm_toolkit.models import rmse
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with pytest.raises(ValueError, match="不能为空"):
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rmse(np.array([]), np.array([]))
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def test_metrics_reject_nan_instead_of_dropping_it():
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"""含 NaN 一律报错，**绝不静默删点**：删了样本量就变了，指标不可比。"""
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from mcm_toolkit.models import rmse
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with pytest.raises(ValueError, match="NaN"):
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rmse([1.0, np.nan, 3.0], [1.0, 2.0, 3.0])
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with pytest.raises(ValueError, match="NaN"):
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rmse([1.0, 2.0, 3.0], [1.0, np.nan, 3.0])
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def test_metrics_reject_non_whitelisted_type():
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"""`str` 也是可迭代对象，必须被入口白名单挡住，不能被当成三个字符算下去。"""
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from mcm_toolkit.models import rmse
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with pytest.raises(TypeError):
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rmse("abc", "abc") # type: ignore[arg-type]
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@pytest.mark.parametrize("name", ["rmse", "mae", "mape", "smape", "r2_score"])
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def test_metrics_agree_across_input_types(name):
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"""`DataFrame` / `Series` / `ndarray` / `list` 四种入参必须给出同一个数。"""
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import mcm_toolkit.models as models
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yt = [1.0, 2.0, 3.0, 4.0]
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yp = [1.1, 1.9, 3.2, 3.8]
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fn = getattr(models, name)
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base = fn(yt, yp)
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assert np.isclose(fn(np.array(yt), np.array(yp)), base)
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assert np.isclose(fn(pd.Series(yt), pd.Series(yp)), base)
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assert np.isclose(fn(pd.DataFrame({"y": yt}), pd.DataFrame({"y": yp})), base)
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def test_mape_rejects_zero_in_y_true():
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"""`y_true` 含 0 时 MAPE 无定义——拦住并点名改用 smape，而不是返回 inf。"""
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from mcm_toolkit.models import mape
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with pytest.raises(ValueError, match="不能含 0"):
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mape([0.0, 2.0], [1.0, 2.0])
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def test_smape_is_silent_when_denominator_is_zero():
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"""分子分母同时为 0 的点记 0，且**不能漏出 RuntimeWarning**。
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本仓库 `pyproject.toml` 里开了 `filterwarnings = ["error::RuntimeWarning"]`，
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所以这条测试同时守着"零除"这个坑：`np.where` 的两条分支都会被求值，
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直接写除式就会把告警漏出来，测试会红。
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"""
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from mcm_toolkit.models import smape
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assert smape([0.0, 0.0], [0.0, 0.0]) == 0.0
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assert smape([0.0, 2.0], [0.0, 2.0]) == 0.0
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assert np.isclose(smape([1.0, 1.0], [0.0, 1.0]), 1.0)
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def test_adjusted_r2_rejects_invalid_n_features():
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from mcm_toolkit.models import adjusted_r2
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yt = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
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yp = np.array([1.5, 2.5, 2.5, 4.5, 5.5])
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for bad in (True, 1.0, np.float64(1.0)):
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with pytest.raises(TypeError):
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adjusted_r2(yt, yp, bad) # type: ignore[arg-type]
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with pytest.raises(ValueError, match="正整数"):
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adjusted_r2(yt, yp, 0)
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with pytest.raises(ValueError, match="样本量太小"):
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adjusted_r2(yt, yp, 4) # n = 5，分母 n - k - 1 = 0
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def test_adjusted_r2_accepts_numpy_integer():
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from mcm_toolkit.models import adjusted_r2
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yt = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
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yp = np.array([1.5, 2.5, 2.5, 4.5, 5.5])
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assert adjusted_r2(yt, yp, np.int64(1)) == adjusted_r2(yt, yp, 1)
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def test_adjusted_r2_penalises_extra_features():
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"""同一份数据、同一个 R²，自变量越多，调整 R² 越小（没用的变量只吃惩罚）。"""
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from mcm_toolkit.models import adjusted_r2
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yt = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
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yp = np.array([1.2, 2.1, 3.3, 3.8, 5.1, 6.2])
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assert adjusted_r2(yt, yp, 3) < adjusted_r2(yt, yp, 2) < adjusted_r2(yt, yp, 1)
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def test_r2_matches_analytic_formula():
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"""`R² = 1 - SS_res / SS_tot`，且 `SS_tot` 的基准必须是 **y_true 的均值**。
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这里刻意用**非完美拟合**的数据：完美拟合（R² = 1）和常量 y（R² = 0）都能把
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"基准量抄错对象"这类 bug 掩盖掉（个人易错点 31）。
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"""
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from mcm_toolkit.models import r2_score
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yt = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
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yp = np.array([1.5, 2.5, 2.5, 4.5, 5.5])
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ss_res = np.sum((yt - yp) ** 2)
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ss_tot = np.sum((yt - np.mean(yt)) ** 2)
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assert np.isclose(r2_score(yt, yp), 1 - ss_res / ss_tot)
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assert np.isclose(r2_score(yt, yp), 0.875)
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def test_r2_matches_sklearn():
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"""拿成熟库当**独立的参照物**：自实现指标最容易"自己算自己"。"""
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sk = pytest.importorskip("sklearn.metrics")
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from mcm_toolkit.models import r2_score
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rng = np.random.default_rng(0)
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yt = rng.normal(size=50)
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yp = yt + rng.normal(scale=0.5, size=50)
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assert np.isclose(r2_score(yt, yp), sk.r2_score(yt, yp))
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def test_r2_is_negative_when_worse_than_predicting_the_mean():
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from mcm_toolkit.models import r2_score
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assert np.isclose(r2_score([1.0, 2.0, 3.0], [3.0, 2.0, 1.0]), -3.0)
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def test_adjusted_r2_matches_analytic_formula():
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from mcm_toolkit.models import adjusted_r2, r2_score
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yt = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
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yp = np.array([1.2, 2.1, 3.3, 3.8, 5.1, 6.2])
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n, k = len(yt), 2
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r2 = r2_score(yt, yp)
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assert np.isclose(adjusted_r2(yt, yp, k), 1 - (1 - r2) * (n - 1) / (n - k - 1))
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