test(data): 拆掉模块级 skip，给 load_table/save_table 补 8 条行为测试
原来 tests/test_data.py 顶上一条 pytestmark 把整个文件跳过了， 连已经写好的 load_table 也一起跳——等于零覆盖。 - 改成逐条 @pytest.mark.skip，只标 5 个未实现的算法 - load_table 补：UTF-8 不被误读成 GBK、NA/-/#N/A 归 NaN、 显式 encoding 优先、未知后缀抛 ValueError、缺文件抛带路径的报错 - save_table 补：往返读写返回 Path、非 DataFrame 抛 TypeError - 测试数 31 passed / 45 skipped -> 39 passed / 44 skipped
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@@ -1,58 +1,22 @@
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"""data 包测试。"""
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"""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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pytestmark = pytest.mark.skip(reason="未实现：见 docs/03-roadmap.md L0/L1")
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_NOT_IMPLEMENTED = "未实现：见 docs/03-roadmap.md L0/L1"
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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 mask.sum() == 3
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assert 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_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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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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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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# ---------------------------------------------------------------- io: load_table
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def test_load_table_detects_gbk(tmp_path):
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@@ -63,3 +27,134 @@ def test_load_table_detects_gbk(tmp_path):
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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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assert df["b"].isna().all()
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assert df["c"].isna().all()
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assert 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")
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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_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 mask.sum() == 3
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assert mask[-3:].all()
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@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
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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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@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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