liaozhaorun
593ec99466
- 存储层重构: HDF5 → DuckDB（UPSERT模式、线程安全存储） - Sync类迁移: DataSync从sync.py迁移到api_daily.py（职责分离） - 模型模块重构: src/models → src/pipeline（更清晰的命名） - 新增因子模块: factors/momentum (MA、收益率排名)、factors/financial - 新增API接口: api_namechange、api_bak_basic - 新增训练入口: training模块（main.py、pipeline配置） - 工具函数统一: get_today_date等移至utils.py - 文档更新: AGENTS.md添加架构变更历史
479 lines
14 KiB
Python
479 lines
14 KiB
Python
"""Pipeline 组件库核心测试
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测试核心抽象类、插件注册中心、处理器、模型和划分策略。
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"""
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import pytest
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import polars as pl
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import numpy as np
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from typing import List, Optional
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# 确保导入时注册所有组件
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from src.pipeline import (
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PluginRegistry,
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PipelineStage,
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BaseProcessor,
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BaseModel,
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BaseSplitter,
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ProcessingPipeline,
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)
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from src.pipeline.core import TaskType
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# ========== 测试核心抽象类 ==========
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class TestPipelineStage:
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"""测试阶段枚举"""
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def test_stage_values(self):
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assert PipelineStage.ALL.name == "ALL"
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assert PipelineStage.TRAIN.name == "TRAIN"
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assert PipelineStage.TEST.name == "TEST"
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assert PipelineStage.VALIDATION.name == "VALIDATION"
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class TestBaseProcessor:
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"""测试处理器基类"""
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def test_processor_initialization(self):
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"""测试处理器初始化"""
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class DummyProcessor(BaseProcessor):
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stage = PipelineStage.ALL
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def fit(self, data: pl.DataFrame) -> "DummyProcessor":
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self._is_fitted = True
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return self
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def transform(self, data: pl.DataFrame) -> pl.DataFrame:
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return data
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processor = DummyProcessor(columns=["col1", "col2"])
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assert processor.columns == ["col1", "col2"]
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assert processor.stage == PipelineStage.ALL
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assert not processor._is_fitted
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def test_processor_fit_transform(self):
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"""测试 fit_transform 方法"""
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class AddOneProcessor(BaseProcessor):
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stage = PipelineStage.ALL
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def fit(self, data: pl.DataFrame) -> "AddOneProcessor":
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self._is_fitted = True
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return self
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def transform(self, data: pl.DataFrame) -> pl.DataFrame:
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result = data.clone()
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for col in self.columns or []:
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result = result.with_columns((pl.col(col) + 1).alias(col))
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return result
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processor = AddOneProcessor(columns=["value"])
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df = pl.DataFrame({"value": [1, 2, 3]})
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result = processor.fit_transform(df)
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assert processor._is_fitted
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assert result["value"].to_list() == [2, 3, 4]
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class TestBaseModel:
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"""测试模型基类"""
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def test_model_initialization(self):
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"""测试模型初始化"""
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class DummyModel(BaseModel):
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def fit(self, X, y, X_val=None, y_val=None, **kwargs):
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self._is_fitted = True
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return self
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def predict(self, X):
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return np.zeros(len(X))
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model = DummyModel(
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task_type="regression", params={"lr": 0.01}, name="test_model"
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)
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assert model.task_type == "regression"
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assert model.params == {"lr": 0.01}
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assert model.name == "test_model"
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assert not model._is_fitted
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def test_predict_proba_not_implemented(self):
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"""测试未实现 predict_proba 时抛出异常"""
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class DummyModel(BaseModel):
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def fit(self, X, y, X_val=None, y_val=None, **kwargs):
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return self
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def predict(self, X):
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return np.zeros(len(X))
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model = DummyModel(task_type="regression")
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df = pl.DataFrame({"feature": [1, 2, 3]})
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with pytest.raises(NotImplementedError):
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model.predict_proba(df)
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class TestBaseSplitter:
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"""测试划分策略基类"""
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def test_splitter_interface(self):
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"""测试划分策略接口"""
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class DummySplitter(BaseSplitter):
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def split(self, data, date_col="trade_date"):
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yield [0, 1], [2, 3]
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def get_split_dates(self, data, date_col="trade_date"):
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return [("20200101", "20201231", "20210101", "20211231")]
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splitter = DummySplitter()
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df = pl.DataFrame(
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{"trade_date": ["20200101", "20200601", "20210101", "20210601"]}
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)
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splits = list(splitter.split(df))
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assert len(splits) == 1
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assert splits[0] == ([0, 1], [2, 3])
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dates = splitter.get_split_dates(df)
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assert dates == [("20200101", "20201231", "20210101", "20211231")]
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# ========== 测试插件注册中心 ==========
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class TestPluginRegistry:
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"""测试插件注册中心"""
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def setup_method(self):
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"""每个测试前清除注册"""
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PluginRegistry.clear_all()
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def test_register_and_get_processor(self):
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"""测试注册和获取处理器"""
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@PluginRegistry.register_processor("test_processor")
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class TestProcessor(BaseProcessor):
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stage = PipelineStage.ALL
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def fit(self, data):
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return self
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def transform(self, data):
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return data
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processor_class = PluginRegistry.get_processor("test_processor")
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assert processor_class == TestProcessor
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assert "test_processor" in PluginRegistry.list_processors()
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def test_register_and_get_model(self):
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"""测试注册和获取模型"""
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@PluginRegistry.register_model("test_model")
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class TestModel(BaseModel):
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def fit(self, X, y, X_val=None, y_val=None, **kwargs):
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return self
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def predict(self, X):
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return np.zeros(len(X))
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model_class = PluginRegistry.get_model("test_model")
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assert model_class == TestModel
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assert "test_model" in PluginRegistry.list_models()
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def test_register_and_get_splitter(self):
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"""测试注册和获取划分策略"""
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@PluginRegistry.register_splitter("test_splitter")
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class TestSplitter(BaseSplitter):
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def split(self, data, date_col="trade_date"):
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yield [], []
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def get_split_dates(self, data, date_col="trade_date"):
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return []
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splitter_class = PluginRegistry.get_splitter("test_splitter")
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assert splitter_class == TestSplitter
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assert "test_splitter" in PluginRegistry.list_splitters()
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def test_get_nonexistent_processor(self):
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"""测试获取不存在的处理器时抛出异常"""
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with pytest.raises(KeyError) as exc_info:
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PluginRegistry.get_processor("nonexistent")
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assert "nonexistent" in str(exc_info.value)
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def test_register_with_default_name(self):
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"""测试使用默认名称注册"""
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@PluginRegistry.register_processor()
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class MyCustomProcessor(BaseProcessor):
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stage = PipelineStage.ALL
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def fit(self, data):
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return self
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def transform(self, data):
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return data
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assert "MyCustomProcessor" in PluginRegistry.list_processors()
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# ========== 测试内置处理器 ==========
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class TestBuiltInProcessors:
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"""测试内置处理器"""
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def test_dropna_processor(self):
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"""测试缺失值删除处理器"""
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from src.pipeline.processors import DropNAProcessor
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processor = DropNAProcessor(columns=["a", "b"])
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df = pl.DataFrame({"a": [1, None, 3], "b": [4, 5, None], "c": [7, 8, 9]})
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result = processor.fit_transform(df)
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# 只有第一行没有缺失值
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assert len(result) == 1
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assert result["a"].to_list() == [1]
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assert result["b"].to_list() == [4]
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def test_fillna_processor(self):
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"""测试缺失值填充处理器"""
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from src.pipeline.processors import FillNAProcessor
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processor = FillNAProcessor(columns=["a"], method="mean")
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df = pl.DataFrame({"a": [1.0, 2.0, None, 4.0]})
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result = processor.fit_transform(df)
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# 均值 = (1+2+4)/3 = 2.333...
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assert result["a"][2] == pytest.approx(2.333, rel=0.01)
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def test_standard_scaler(self):
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"""测试标准化处理器"""
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from src.pipeline.processors import StandardScaler
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processor = StandardScaler(columns=["value"])
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df = pl.DataFrame({"value": [1.0, 2.0, 3.0, 4.0, 5.0]})
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result = processor.fit_transform(df)
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# Z-score 标准化后均值为0，标准差为1
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assert result["value"].mean() == pytest.approx(0.0, abs=1e-10)
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assert result["value"].std() == pytest.approx(1.0, rel=0.01)
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def test_winsorizer(self):
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"""测试缩尾处理器"""
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from src.pipeline.processors import Winsorizer
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processor = Winsorizer(columns=["value"], lower=0.1, upper=0.9)
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df = pl.DataFrame(
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{
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"value": list(range(100)) # 0-99
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}
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)
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result = processor.fit_transform(df)
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# 10%和90%分位数应该是10和89（Polars的quantile行为）
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assert result["value"].min() == 10
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assert result["value"].max() == 89
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def test_rank_transformer(self):
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"""测试排名转换处理器"""
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from src.pipeline.processors import RankTransformer
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processor = RankTransformer(columns=["value"])
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df = pl.DataFrame(
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{"trade_date": ["20200101"] * 5, "value": [10, 30, 20, 50, 40]}
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)
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result = processor.fit_transform(df)
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# 排名应该是 1, 3, 2, 5, 4
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assert result["value"].to_list() == [1, 3, 2, 5, 4]
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def test_neutralizer(self):
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"""测试中性化处理器"""
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from src.pipeline.processors import Neutralizer
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processor = Neutralizer(columns=["value"], group_col="industry")
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df = pl.DataFrame(
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{
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"trade_date": ["20200101", "20200101", "20200101", "20200101"],
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"industry": ["A", "A", "B", "B"],
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"value": [10, 20, 30, 50],
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}
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)
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result = processor.fit_transform(df)
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# 分组去均值后，每组的均值为0
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group_a = result.filter(pl.col("industry") == "A")
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group_b = result.filter(pl.col("industry") == "B")
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assert group_a["value"].mean() == pytest.approx(0.0, abs=1e-10)
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assert group_b["value"].mean() == pytest.approx(0.0, abs=1e-10)
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# ========== 测试处理流水线 ==========
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class TestProcessingPipeline:
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"""测试处理流水线"""
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def test_pipeline_fit_transform(self):
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"""测试流水线的 fit_transform"""
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from src.pipeline.processors import StandardScaler
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scaler1 = StandardScaler(columns=["a"])
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scaler2 = StandardScaler(columns=["b"])
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pipeline = ProcessingPipeline([scaler1, scaler2])
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df = pl.DataFrame({"a": [1.0, 2.0, 3.0], "b": [10.0, 20.0, 30.0]})
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result = pipeline.fit_transform(df)
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# 两个列都应该被标准化
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assert result["a"].mean() == pytest.approx(0.0, abs=1e-10)
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assert result["b"].mean() == pytest.approx(0.0, abs=1e-10)
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def test_pipeline_transform_uses_fitted_params(self):
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"""测试 transform 使用已 fit 的参数"""
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from src.pipeline.processors import StandardScaler
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scaler = StandardScaler(columns=["value"])
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pipeline = ProcessingPipeline([scaler])
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# 训练数据
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train_df = pl.DataFrame(
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{
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"value": [1.0, 2.0, 3.0] # 均值=2，标准差=1
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}
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)
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# 测试数据（不同的分布）
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test_df = pl.DataFrame(
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{
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"value": [4.0, 5.0, 6.0] # 如果重新计算应该是均值=5
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}
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)
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pipeline.fit_transform(train_df)
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result = pipeline.transform(test_df)
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# 使用训练数据的均值=2和标准差=1进行标准化
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# 4 -> (4-2)/1 = 2
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assert result["value"].to_list()[0] == pytest.approx(2.0, abs=1e-10)
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# ========== 测试划分策略 ==========
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class TestSplitters:
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"""测试划分策略"""
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def test_time_series_split(self):
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"""测试时间序列划分"""
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from src.pipeline.core import TimeSeriesSplit
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splitter = TimeSeriesSplit(n_splits=2, gap=1, min_train_size=3)
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# 10天的数据
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df = pl.DataFrame(
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{
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"trade_date": [f"202001{i:02d}" for i in range(1, 11)],
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"value": list(range(10)),
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}
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)
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splits = list(splitter.split(df))
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# 应该有两折
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assert len(splits) == 2
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# 检查每折训练集在测试集之前
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for train_idx, test_idx in splits:
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assert max(train_idx) < min(test_idx)
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def test_walk_forward_split(self):
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"""测试滚动前向划分"""
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from src.pipeline.core import WalkForwardSplit
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splitter = WalkForwardSplit(train_window=5, test_window=2, gap=1)
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df = pl.DataFrame(
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{
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"trade_date": [f"202001{i:02d}" for i in range(1, 13)],
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"value": list(range(12)),
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}
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)
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splits = list(splitter.split(df))
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# 检查训练集大小固定
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for train_idx, test_idx in splits:
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assert len(train_idx) == 5
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assert len(test_idx) == 2
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def test_expanding_window_split(self):
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"""测试扩展窗口划分"""
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from src.pipeline.core import ExpandingWindowSplit
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splitter = ExpandingWindowSplit(initial_train_size=3, test_window=2, gap=1)
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df = pl.DataFrame(
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{
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"trade_date": [f"202001{i:02d}" for i in range(1, 15)],
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"value": list(range(14)),
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}
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)
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splits = list(splitter.split(df))
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# 训练集应该逐渐增大
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train_sizes = [len(train_idx) for train_idx, _ in splits]
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assert train_sizes[0] == 3
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assert train_sizes[1] == 5 # 3 + 2
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assert train_sizes[2] == 7 # 5 + 2
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# ========== 测试内置模型（可选，需要安装依赖） ==========
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class TestModels:
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"""测试内置模型（标记为跳过如果依赖未安装）"""
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@pytest.mark.skip(reason="需要安装 lightgbm")
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def test_lightgbm_model(self):
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"""测试 LightGBM 模型"""
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from src.pipeline.models import LightGBMModel
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model = LightGBMModel(task_type="regression", params={"n_estimators": 10})
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X = pl.DataFrame(
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{
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"feature1": [1.0, 2.0, 3.0, 4.0, 5.0] * 10,
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"feature2": [5.0, 4.0, 3.0, 2.0, 1.0] * 10,
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}
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)
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y = pl.Series("target", [1.0, 2.0, 3.0, 4.0, 5.0] * 10)
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model.fit(X, y)
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predictions = model.predict(X)
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assert len(predictions) == len(X)
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assert model._is_fitted
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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