guzhujushi
319b16b17c
Codex 直接改的（格式 / 死代码 / 纯 bug，不动接口与语义）： - winsorize(Series) 直接崩：select_dtypes 在 Series 上不存在，Series 分支提到最前并加数值 dtype 守卫 - winsorize 的分位数改为只在数值列上算，含文本列的赛题表不再崩（与已有 numeric_cols 判断对齐） - 删掉 detect_outliers 里已成空操作的 df = pd.DataFrame(data)，去掉 df 别名 - clean_missing 的嵌套 if 合并（ruff SIM102），清掉 W293 行尾空白与函数内连续空行 测试： - 5 条 xfail 经 XPASS 信号确认已修好，摘掉 mark 转正式用例 （detect_outliers 4 条 + winsorize 混合类型 1 条） - 53 passed / 7 xfailed -> 58 passed / 2 xfailed - 剩 2 条 xfail 保留，且不是漏改而是设计没定： detect_outliers 遇文本列返回什么、impute_missing 的 mode 分支要不要就地改调用方的表 - ruff: 1 error -> All checks passed；pyright: 0 errors 不变 文档： - STATUS.md：L0 进度 4/12、验证结果表、下一步重写、新增决策 31 （Codex 可直接改 src 的范围：纯 bug / 格式 / 死代码；要选语义的留给用户） - CHANGELOG.md：本轮变更，并修掉 [Unreleased] 里被重复追加撑坏的一句 - docs/dev/personal-pitfalls.md：只追加第 7 条（守卫子句要排在类型分派之后）
347 lines
12 KiB
Python
347 lines
12 KiB
Python
"""data 包测试。
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L0 阶段的约定：**只测已实现的行为**。未实现的算法用逐条 skip 标出，
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不要用模块级 pytestmark —— 那会把同文件里已实现的函数一起跳过，
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让 load_table 这种已经写好的函数变成零覆盖。
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"""
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from __future__ import annotations
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from pathlib import Path
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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 L0/L1"
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# ---------------------------------------------------------------- io: load_table
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def test_load_table_detects_gbk(tmp_path):
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"""国内赛题数据常见 GBK，编码探测必须能兜住。"""
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from mcm_toolkit.data import load_table
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p = tmp_path / "gbk.csv"
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p.write_bytes("城市,人口\n北京,2189\n".encode("gbk"))
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df = load_table(p)
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assert list(df.columns) == ["城市", "人口"]
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def test_load_table_keeps_utf8_readable(tmp_path):
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"""UTF-8 文件必须按 UTF-8 读出，不能先试 GBK 变成乱码。"""
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from mcm_toolkit.data import load_table
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p = tmp_path / "utf8.csv"
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p.write_text("省份,GDP增速\n广东,5.8\n", encoding="utf-8")
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df = load_table(p)
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assert list(df.columns) == ["省份", "GDP增速"]
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assert df.loc[0, "省份"] == "广东"
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def test_load_table_maps_na_like_tokens_to_nan(tmp_path):
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"""赛题里的缺失值长得五花八门，默认 na_values 要全部吃成 NaN。"""
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from mcm_toolkit.data import load_table
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p = tmp_path / "na.csv"
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p.write_text("a,b,c\n1,NA,\n2,-,#N/A\n", encoding="utf-8")
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df = load_table(p)
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# 外面套 bool()：df["b"] 的类型是 Series | DataFrame，直接当条件用 pyright 会报
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# "条件操作数无效"；套上 bool() 既满足类型检查，又能在它真返回 Series 时当场炸掉。
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assert bool(df["b"].isna().all())
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assert bool(df["c"].isna().all())
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assert bool(df["a"].notna().all())
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def test_load_table_respects_explicit_encoding(tmp_path):
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"""显式给了 encoding 就照办，不再做探测。"""
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from mcm_toolkit.data import load_table
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p = tmp_path / "gbk.csv"
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p.write_bytes("城市,人口\n上海,2487\n".encode("gbk"))
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df = load_table(p, encoding="gbk")
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assert list(df.columns) == ["城市", "人口"]
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def test_load_table_rejects_unknown_suffix(tmp_path):
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"""不支持的扩展名要抛 ValueError，而且要带上后缀名。"""
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from mcm_toolkit.data import load_table
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p = tmp_path / "data.txt"
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p.write_text("a,b\n1,2\n", encoding="utf-8")
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with pytest.raises(ValueError, match=r"\.txt"):
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load_table(p)
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def test_load_table_missing_file_raises(tmp_path):
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"""文件不存在时要把路径原样带出来，方便定位。"""
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from mcm_toolkit.data import load_table
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missing = tmp_path / "nope.csv"
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with pytest.raises(FileNotFoundError, match=r"nope\.csv"):
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load_table(missing)
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# ---------------------------------------------------------------- io: save_table
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def test_save_table_round_trip(tmp_path):
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"""存进去再读出来，内容要一致，并且返回实际写入的 Path。"""
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from mcm_toolkit.data import load_table, save_table
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df = pd.DataFrame({"城市": ["北京", "上海"], "人口": [2189.0, 2487.0]})
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out = save_table(df, tmp_path / "out.csv")
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assert isinstance(out, Path)
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assert out.exists()
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back = load_table(out)
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assert list(back.columns) == ["城市", "人口"]
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assert back["人口"].tolist() == [2189.0, 2487.0]
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def test_save_table_rejects_non_dataframe(tmp_path):
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"""传错类型要当场报错，不要写出一个奇怪的文件。"""
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from mcm_toolkit.data import save_table
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with pytest.raises(TypeError):
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save_table([1, 2, 3], tmp_path / "bad.csv") # type: ignore[arg-type] # 就是要传错
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def test_save_table_validates_before_touching_disk(tmp_path):
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"""校验必须早于副作用：参数不合法时，一个目录、一个文件都不能留下。"""
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from mcm_toolkit.data import save_table
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with pytest.raises(ValueError, match=r"\.txt"):
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save_table(pd.DataFrame({"a": [1]}), tmp_path / "d1" / "x.txt")
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with pytest.raises(TypeError):
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save_table([1, 2, 3], tmp_path / "d2" / "x.csv") # type: ignore[arg-type] # 就是要传错
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assert sorted(p.name for p in tmp_path.iterdir()) == []
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# --------------------------------------------------------------- data: clean
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def test_clean_missing_default_drops_any_missing():
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"""不传 threshold 时是"只要有缺失就删"。"""
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from mcm_toolkit.data import clean_missing
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df = pd.DataFrame({"a": [1.0, np.nan, 3.0], "b": [1.0, 2.0, np.nan]})
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out = clean_missing(df)
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assert len(out) == 1
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assert list(out.columns) == ["a", "b"]
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def test_clean_missing_threshold_keeps_rows_within_limit():
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"""四行三列、阈值 0.5：缺 2/3 的两行要删，剩 2 行。
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3 列时 min_non_na 必须是 ceil(3*0.5)=2；写成 int() 截断就会漏删。
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"""
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from mcm_toolkit.data import clean_missing
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df = pd.DataFrame(
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{
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"a": [1.0, np.nan, np.nan, 4.0],
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"b": [1.0, np.nan, np.nan, 4.0],
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"c": [1.0, 2.0, np.nan, 4.0],
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}
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)
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out = clean_missing(df, axis=0, threshold=0.5)
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assert len(out) == 2
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assert out["a"].tolist() == [1.0, 4.0]
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def test_clean_missing_axis1_drops_empty_column():
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"""axis=1 按列删：整列全空（缺失率 1.0 > 0.5）要掉。"""
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from mcm_toolkit.data import clean_missing
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df = pd.DataFrame({"x": [1.0, 2.0, 3.0], "y": [np.nan, np.nan, np.nan]})
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assert list(clean_missing(df, axis=1, threshold=0.5).columns) == ["x"]
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def test_clean_missing_rejects_bad_axis():
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from mcm_toolkit.data import clean_missing
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with pytest.raises(ValueError, match="axis"):
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clean_missing(pd.DataFrame({"a": [1.0]}), axis=2)
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def test_clean_missing_rejects_non_dataframe():
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from mcm_toolkit.data import clean_missing
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with pytest.raises(TypeError):
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clean_missing([[1.0, 2.0]])
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def test_impute_missing_mean_and_ffill():
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from mcm_toolkit.data import impute_missing
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df = pd.DataFrame({"a": [1.0, np.nan, 3.0]})
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assert impute_missing(df, method="mean")["a"].tolist() == [1.0, 2.0, 3.0]
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assert impute_missing(df, method="ffill")["a"].tolist() == [1.0, 1.0, 3.0]
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def test_impute_missing_mode_survives_all_nan_column():
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"""整列全 NaN 时 mode() 给不出众数：不能崩，也不能硬填一个值进去。"""
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from mcm_toolkit.data import impute_missing
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df = pd.DataFrame({"a": [np.nan, np.nan], "b": [1.0, np.nan]})
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out = impute_missing(df, method="mode")
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assert bool(out["a"].isna().all())
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assert out["b"].tolist() == [1.0, 1.0]
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def test_impute_missing_rejects_unknown_method():
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from mcm_toolkit.data import impute_missing
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with pytest.raises(ValueError, match="bogus"):
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impute_missing(pd.DataFrame({"a": [1.0]}), method="bogus")
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def test_impute_missing_rejects_non_dataframe():
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from mcm_toolkit.data import impute_missing
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with pytest.raises(TypeError):
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impute_missing([[1.0, 2.0]])
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def test_winsorize_clips_series_to_quantiles():
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from mcm_toolkit.data import winsorize
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s = pd.Series([1.0, 2.0, 3.0, 4.0, 1000.0])
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out = winsorize(s, lower=0.1, upper=0.9)
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assert float(out.iloc[0]) == pytest.approx(1.4)
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assert float(out.iloc[-1]) == pytest.approx(601.6)
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assert float(out.max()) < 1000.0
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def test_winsorize_rejects_bad_quantile_order():
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from mcm_toolkit.data import winsorize
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with pytest.raises(ValueError, match="lower"):
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winsorize(pd.Series([1.0, 2.0]), lower=0.9, upper=0.1)
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def test_winsorize_rejects_non_frame_with_typeerror():
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"""§4.7：类型不对必须抛 TypeError，不能抛 ValueError。"""
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from mcm_toolkit.data import winsorize
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with pytest.raises(TypeError):
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winsorize([1.0, 2.0, 3.0])
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def test_detect_outliers_rejects_unknown_method():
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"""方法名校验在取默认取值之前，所以这条现在就是绿的。"""
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from mcm_toolkit.data import detect_outliers
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with pytest.raises(ValueError, match="bogus"):
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detect_outliers([1.0, 2.0], method="bogus") # type: ignore[arg-type]
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# ------------------- 契约先写进 docstring，实现逐条跟上（剩余 2 条 xfail）
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#
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# 这些用例写的是**文档里已经承诺的行为**。实现没跟上时用 xfail，让它们照样出现在
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# 报告里（显示成 x）；修好之后会自动变成 XPASS —— 看到 XPASS 就把 mark 摘掉。
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#
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# 已摘掉：threshold 范围校验、IQR 的 UnboundLocalError、(n,1) 形状、zscore 默认阈值、
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# winsorize 的文本列。剩下 2 条不是漏改，是**设计还没定**：
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# - detect_outliers 遇到文本列返回什么（全 False？还是只对数值列算掩码？）
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# - impute_missing 的 mode 分支该不该就地改调用方的表（其余分支都返回新表）
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def test_detect_outliers_finds_planted_points():
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"""人工埋 3 个离群点，IQR 法应正好命中 3 个。"""
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from mcm_toolkit.data import detect_outliers
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x = np.concatenate([np.arange(100.0), [1e6, -1e6, 1e7]])
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mask = np.asarray(detect_outliers(x, method="iqr"))
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assert int(mask.sum()) == 3
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assert bool(mask[-3:].all())
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def test_detect_outliers_returns_mask_not_filtered_data():
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"""约定：只标不删——返回与原数据同形的布尔掩码。"""
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from mcm_toolkit.data import detect_outliers
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x = np.arange(50.0)
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mask = np.asarray(detect_outliers(x, method="zscore"))
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assert mask.shape == x.shape
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assert mask.dtype == bool
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def test_detect_outliers_zscore_flags_planted_point():
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from mcm_toolkit.data import detect_outliers
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x = np.concatenate([np.zeros(99), [1e6]])
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mask = np.asarray(detect_outliers(x, method="zscore"))
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assert int(mask.sum()) == 1
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assert bool(mask[-1].all())
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@pytest.mark.xfail(reason="quantile 在全表上算，遇到文本列直接崩", strict=False)
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def test_detect_outliers_survives_text_columns():
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"""赛题表几乎都带文本列，分位数只能在数值列上算。"""
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from mcm_toolkit.data import detect_outliers
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df = pd.DataFrame({"city": ["a", "b", "c", "d"], "v": [1.0, 2.0, 3.0, 1000.0]})
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assert int(np.asarray(detect_outliers(df)).sum()) == 1
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def test_winsorize_survives_text_columns():
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"""文本列必须原样留着：分位数只在数值列上算。"""
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from mcm_toolkit.data import winsorize
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df = pd.DataFrame({"city": ["a", "b", "c", "d"], "v": [1.0, 2.0, 3.0, 1000.0]})
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out = winsorize(df)
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assert out["city"].tolist() == ["a", "b", "c", "d"]
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assert float(out["v"].iloc[-1]) < 1000.0
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def test_clean_missing_rejects_out_of_range_threshold():
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from mcm_toolkit.data import clean_missing
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with pytest.raises(ValueError, match="threshold"):
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clean_missing(pd.DataFrame({"a": [1.0, np.nan]}), threshold=1.5)
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@pytest.mark.xfail(reason="mode 分支在调用方的表上就地改，其余分支都返回新表", strict=False)
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def test_impute_missing_mode_does_not_mutate_input():
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from mcm_toolkit.data import impute_missing
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df = pd.DataFrame({"a": [1.0, np.nan, 1.0]})
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impute_missing(df, method="mode")
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assert bool(df["a"].isna().any())
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# ------------------------------------------------- 以下为未实现算法（L0/L1 待写）
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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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def test_normalize_minmax_range(rng):
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from mcm_toolkit.data import normalize
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x = rng.normal(size=(50, 3))
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out = np.asarray(normalize(x, method="minmax"))
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assert np.allclose(out.min(axis=0), 0.0)
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assert np.allclose(out.max(axis=0), 1.0)
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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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def test_standardize_has_zero_mean_unit_std(rng):
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from mcm_toolkit.data import standardize
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x = rng.normal(5, 3, size=1000)
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out = np.asarray(standardize(x))
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assert abs(out.mean()) < 1e-9
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assert abs(out.std() - 1.0) < 1e-9
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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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def test_train_test_split_sizes_and_no_overlap(rng):
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from mcm_toolkit.data import train_test_split
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X = rng.normal(size=(100, 2))
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Xtr, Xte = train_test_split(X, test_size=0.2, random_state=0)
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assert len(Xtr) == 80 and len(Xte) == 20
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assert len({tuple(r) for r in Xtr} & {tuple(r) for r in Xte}) == 0
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