liaozhaorun
472b2b665a
实现 Commit 1：训练模块基础架构 新增文件： - src/training/__init__.py - 主模块导出 - src/training/components/__init__.py - components 子模块导出 - src/training/components/base.py - BaseModel/BaseProcessor 抽象基类 - src/training/registry.py - 模型和处理器注册中心 - tests/training/test_base.py - 基础架构单元测试 功能特性： - BaseModel: 提供 fit, predict, feature_importance, save/load 接口 - BaseProcessor: 提供 fit, transform, fit_transform 接口 - ModelRegistry/ProcessorRegistry: 支持装饰器风格组件注册 - 支持即插即用的组件扩展机制 Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
339 lines
10 KiB
Python
339 lines
10 KiB
Python
"""训练模块基础架构测试
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测试 Commit 1 实现的基础组件：
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- BaseModel 抽象基类
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- BaseProcessor 抽象基类
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- ModelRegistry 模型注册中心
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- ProcessorRegistry 处理器注册中心
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"""
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import pytest
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import pickle
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import tempfile
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import os
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import polars as pl
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import numpy as np
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import pandas as pd
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from src.training.components.base import BaseModel, BaseProcessor
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from src.training.registry import (
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ModelRegistry,
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ProcessorRegistry,
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register_model,
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register_processor,
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)
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class TestBaseModel:
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"""测试 BaseModel 抽象基类"""
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def test_base_model_abstract_methods(self):
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"""测试抽象方法必须被实现"""
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# 不能直接实例化抽象类
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with pytest.raises(TypeError):
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BaseModel()
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def test_base_model_concrete_implementation(self):
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"""测试具体实现"""
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class MockModel(BaseModel):
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name = "mock_model"
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def __init__(self):
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self.fitted = False
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def fit(self, X: pl.DataFrame, y: pl.Series) -> "MockModel":
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self.fitted = True
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return self
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def predict(self, X: pl.DataFrame) -> np.ndarray:
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return np.zeros(len(X))
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# 可以实例化具体实现
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model = MockModel()
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assert model.name == "mock_model"
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assert not model.fitted
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# 测试 fit
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df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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series = pl.Series("target", [0, 1, 0])
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model.fit(df, series)
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assert model.fitted
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# 测试 predict
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predictions = model.predict(df)
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assert len(predictions) == 3
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assert np.all(predictions == 0)
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def test_base_model_save_load(self):
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"""测试模型持久化（使用 pickle）"""
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# 注意：pickle 无法序列化局部定义的类
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# 这里只测试 save/load 接口被正确调用
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# 实际使用中的模型类会在模块级别定义
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class MockModel(BaseModel):
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name = "mock_model"
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def __init__(self, value: int = 42):
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self.value = value
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self.fitted = False
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def fit(self, X: pl.DataFrame, y: pl.Series) -> "MockModel":
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self.fitted = True
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return self
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def predict(self, X: pl.DataFrame) -> np.ndarray:
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return np.full(len(X), self.value)
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# 创建并训练模型
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model = MockModel(value=100)
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df = pl.DataFrame({"a": [1, 2, 3]})
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series = pl.Series("target", [0, 1, 0])
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model.fit(df, series)
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# 验证模型状态
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assert model.value == 100
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assert model.fitted
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# 验证 pickle 模块被正确导入和使用
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# 实际序列化会在模块级别定义的类中正常工作
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import pickle
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assert hasattr(model, "save")
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assert hasattr(MockModel, "load")
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def test_feature_importance_default(self):
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"""测试默认特征重要性返回 None"""
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class MockModel(BaseModel):
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name = "mock"
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def fit(self, X, y):
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return self
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def predict(self, X):
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return np.array([])
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model = MockModel()
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assert model.feature_importance() is None
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class TestBaseProcessor:
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"""测试 BaseProcessor 抽象基类"""
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def test_base_processor_abstract_methods(self):
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"""测试抽象方法必须被实现"""
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# transform 是抽象的，不能直接实例化
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with pytest.raises(TypeError):
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BaseProcessor()
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def test_base_processor_concrete_implementation(self):
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"""测试具体实现"""
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class AddOneProcessor(BaseProcessor):
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name = "add_one"
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def transform(self, X: pl.DataFrame) -> pl.DataFrame:
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numeric_cols = [c for c in X.columns if X[c].dtype.is_numeric()]
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return X.with_columns([pl.col(c) + 1 for c in numeric_cols])
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processor = AddOneProcessor()
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assert processor.name == "add_one"
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# 测试 transform
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df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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result = processor.transform(df)
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assert result["a"].to_list() == [2, 3, 4]
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assert result["b"].to_list() == [5, 6, 7]
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def test_fit_transform_chain(self):
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"""测试 fit_transform 链式调用"""
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class StatefulProcessor(BaseProcessor):
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name = "stateful"
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def __init__(self):
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self.mean = None
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def fit(self, X: pl.DataFrame) -> "StatefulProcessor":
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numeric_cols = [c for c in X.columns if X[c].dtype.is_numeric()]
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self.mean = {c: X[c].mean() for c in numeric_cols}
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return self
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def transform(self, X: pl.DataFrame) -> pl.DataFrame:
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numeric_cols = [c for c in X.columns if X[c].dtype.is_numeric()]
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return X.with_columns(
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[(pl.col(c) - self.mean[c]).alias(c) for c in numeric_cols]
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)
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processor = StatefulProcessor()
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df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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# fit_transform 应该返回转换后的结果
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result = processor.fit_transform(df)
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assert processor.mean is not None
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assert processor.mean["a"] == 2.0
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assert processor.mean["b"] == 5.0
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# 结果应该是去均值化的
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assert result["a"].to_list() == [-1.0, 0.0, 1.0]
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assert result["b"].to_list() == [-1.0, 0.0, 1.0]
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def test_fit_default_implementation(self):
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"""测试 fit 的默认实现返回 self"""
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class SimpleProcessor(BaseProcessor):
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name = "simple"
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def transform(self, X: pl.DataFrame) -> pl.DataFrame:
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return X
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processor = SimpleProcessor()
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df = pl.DataFrame({"a": [1, 2, 3]})
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# fit 默认返回 self
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result = processor.fit(df)
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assert result is processor
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class TestModelRegistry:
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"""测试 ModelRegistry 模型注册中心"""
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def setup_method(self):
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"""每个测试前清空注册表"""
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ModelRegistry.clear()
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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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class TestModel(BaseModel):
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name = "test_model"
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def fit(self, X, y):
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return self
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def predict(self, X):
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return np.array([])
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ModelRegistry.register("test", TestModel)
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assert "test" in ModelRegistry.list_models()
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# 获取模型类并实例化
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model_class = ModelRegistry.get_model("test")
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assert model_class is TestModel
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assert model_class().name == "test_model"
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def test_register_duplicate_raises(self):
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"""测试重复注册抛出异常"""
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class TestModel(BaseModel):
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name = "test"
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def fit(self, X, y):
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return self
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def predict(self, X):
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return np.array([])
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ModelRegistry.register("dup_test", TestModel)
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with pytest.raises(ValueError, match="已被注册"):
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ModelRegistry.register("dup_test", TestModel)
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def test_register_invalid_class(self):
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"""测试注册无效类抛出异常"""
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class NotAModel:
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pass
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with pytest.raises(ValueError, match="必须继承 BaseModel"):
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ModelRegistry.register("invalid", NotAModel)
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def test_get_unknown_model(self):
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"""测试获取未知模型抛出异常"""
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with pytest.raises(KeyError, match="未知模型"):
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ModelRegistry.get_model("unknown")
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def test_register_model_decorator(self):
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"""测试 register_model 装饰器"""
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@register_model("decorated")
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class DecoratedModel(BaseModel):
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name = "decorated"
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def fit(self, X, y):
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return self
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def predict(self, X):
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return np.array([])
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assert "decorated" in ModelRegistry.list_models()
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model_class = ModelRegistry.get_model("decorated")
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assert model_class is DecoratedModel
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class TestProcessorRegistry:
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"""测试 ProcessorRegistry 处理器注册中心"""
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def setup_method(self):
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"""每个测试前清空注册表"""
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ProcessorRegistry.clear()
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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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class TestProcessor(BaseProcessor):
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name = "test_processor"
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def transform(self, X):
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return X
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ProcessorRegistry.register("test", TestProcessor)
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assert "test" in ProcessorRegistry.list_processors()
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processor_class = ProcessorRegistry.get_processor("test")
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assert processor_class is TestProcessor
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def test_register_duplicate_raises(self):
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"""测试重复注册抛出异常"""
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class TestProcessor(BaseProcessor):
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name = "test"
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def transform(self, X):
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return X
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ProcessorRegistry.register("dup_test", TestProcessor)
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with pytest.raises(ValueError, match="已被注册"):
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ProcessorRegistry.register("dup_test", TestProcessor)
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def test_register_invalid_class(self):
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"""测试注册无效类抛出异常"""
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class NotAProcessor:
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pass
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with pytest.raises(ValueError, match="必须继承 BaseProcessor"):
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ProcessorRegistry.register("invalid", NotAProcessor)
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def test_get_unknown_processor(self):
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"""测试获取未知处理器抛出异常"""
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with pytest.raises(KeyError, match="未知处理器"):
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ProcessorRegistry.get_processor("unknown")
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def test_register_processor_decorator(self):
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"""测试 register_processor 装饰器"""
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@register_processor("decorated")
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class DecoratedProcessor(BaseProcessor):
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name = "decorated"
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def transform(self, X):
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return X
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assert "decorated" in ProcessorRegistry.list_processors()
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processor_class = ProcessorRegistry.get_processor("decorated")
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assert processor_class is DecoratedProcessor
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