liaozhaorun
192718095f
- StockPoolManager：每日独立筛选股票池，支持代码过滤和市值选择 - Trainer：整合训练完整流程，支持 processor 分阶段行为和模型持久化 - TrainingConfig：pydantic 配置管理，含必填字段和日期验证 - experiment 模块：预留结构 - 从计划中移除 metrics 组件 - 调整 commit 序号（7-10 → 6-9） - 更新 training/__init__.py 导出所有公开 API
236 lines
7.2 KiB
Python
236 lines
7.2 KiB
Python
"""测试 LightGBM 模型
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验证 LightGBMModel 的训练、预测、保存和加载功能。
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"""
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import os
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import tempfile
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import numpy as np
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import polars as pl
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import pytest
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from src.training.components.models.lightgbm import LightGBMModel
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class TestLightGBMModel:
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"""LightGBMModel 测试类"""
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def test_init_default(self):
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"""测试默认初始化"""
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model = LightGBMModel()
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assert model.name == "lightgbm"
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assert model.params["objective"] == "regression"
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assert model.params["metric"] == "rmse"
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assert model.params["num_leaves"] == 31
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assert model.params["learning_rate"] == 0.05
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assert model.n_estimators == 100
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assert model.model is None
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def test_init_custom(self):
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"""测试自定义参数"""
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model = LightGBMModel(
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objective="huber",
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metric="mae",
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num_leaves=50,
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learning_rate=0.1,
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n_estimators=200,
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)
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assert model.params["objective"] == "huber"
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assert model.params["metric"] == "mae"
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assert model.params["num_leaves"] == 50
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assert model.params["learning_rate"] == 0.1
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assert model.n_estimators == 200
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def test_fit_success(self):
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"""测试正常训练"""
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# 创建简单回归数据
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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],
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"feature2": [2.0, 4.0, 6.0, 8.0, 10.0],
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}
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)
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y = pl.Series("target", [1.5, 3.0, 4.5, 6.0, 7.5])
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model = LightGBMModel(n_estimators=10)
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result = model.fit(X, y)
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# 验证返回 self（支持链式调用）
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assert result is model
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# 验证模型已训练
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assert model.model is not None
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# 验证特征名称已保存
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assert model.feature_names_ == ["feature1", "feature2"]
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def test_predict_before_fit(self):
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"""测试未训练就预测"""
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X = pl.DataFrame(
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{
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"feature1": [1.0, 2.0],
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"feature2": [2.0, 4.0],
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}
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)
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model = LightGBMModel()
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with pytest.raises(RuntimeError, match="模型尚未训练"):
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model.predict(X)
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def test_predict_success(self):
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"""测试正常预测"""
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# 创建回归数据
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np.random.seed(42)
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n_samples = 100
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X_train = pl.DataFrame(
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{
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"feature1": np.random.randn(n_samples),
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"feature2": np.random.randn(n_samples),
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}
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)
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# y = 2*feature1 + 3*feature2 + noise
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y_train = pl.Series(
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"target",
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2 * X_train["feature1"]
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+ 3 * X_train["feature2"]
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+ np.random.randn(n_samples) * 0.1,
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)
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model = LightGBMModel(n_estimators=20, learning_rate=0.1)
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model.fit(X_train, y_train)
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# 预测新数据（使用明显不同的值）
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X_test = pl.DataFrame(
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{
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"feature1": [-2.0, 3.0],
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"feature2": [-1.0, 4.0],
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}
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)
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predictions = model.predict(X_test)
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# 验证预测结果格式
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assert isinstance(predictions, np.ndarray)
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assert len(predictions) == 2
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# 验证预测值是数值
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assert all(np.isfinite(predictions))
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# 验证单调性（第二个样本的 feature 值更大，预测值也应更大）
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assert predictions[1] > predictions[0]
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def test_feature_importance_before_fit(self):
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"""测试未训练就获取特征重要性"""
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model = LightGBMModel()
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assert model.feature_importance() is None
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def test_feature_importance_after_fit(self):
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"""测试训练后获取特征重要性"""
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X = pl.DataFrame(
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{
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"feature1": np.random.randn(100),
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"feature2": np.random.randn(100),
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}
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)
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y = pl.Series("target", X["feature1"] * 2 + X["feature2"] * 0.1)
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model = LightGBMModel(n_estimators=10)
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model.fit(X, y)
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importance = model.feature_importance()
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# 验证特征重要性格式
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assert importance is not None
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assert len(importance) == 2
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assert "feature1" in importance.index
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assert "feature2" in importance.index
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# feature1 的系数更大，重要性应该更高
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assert importance["feature1"] >= importance["feature2"]
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def test_save_before_fit(self):
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"""测试未训练就保存"""
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model = LightGBMModel()
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with pytest.raises(RuntimeError, match="模型尚未训练"):
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model.save("dummy.txt")
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def test_save_and_load(self):
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"""测试保存和加载"""
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# 训练模型
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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],
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"feature2": [2.0, 4.0, 6.0, 8.0, 10.0],
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}
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)
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y = pl.Series("target", [2.0, 4.0, 6.0, 8.0, 10.0])
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model = LightGBMModel(n_estimators=10, learning_rate=0.1)
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model.fit(X, y)
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# 保存前预测
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X_test = pl.DataFrame(
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{
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"feature1": [6.0],
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"feature2": [12.0],
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}
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)
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pred_before = model.predict(X_test)
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# 保存到临时文件
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with tempfile.TemporaryDirectory() as tmpdir:
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save_path = os.path.join(tmpdir, "model.txt")
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model.save(save_path)
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# 加载模型
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loaded_model = LightGBMModel.load(save_path)
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# 验证加载后预测结果相同
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pred_after = loaded_model.predict(X_test)
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assert pred_after[0] == pytest.approx(pred_before[0], rel=1e-5)
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# 验证元数据已恢复
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assert loaded_model.feature_names_ == ["feature1", "feature2"]
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assert loaded_model.n_estimators == 10
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def test_registration(self):
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"""测试模型已注册到 registry"""
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from src.training.registry import ModelRegistry
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# 重新导入模型模块以确保注册（处理其他测试 clear 注册表的情况）
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import importlib
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import src.training.components.models.lightgbm as lightgbm_module
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importlib.reload(lightgbm_module)
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from src.training.components.models.lightgbm import (
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LightGBMModel as ReloadedModel,
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)
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model_class = ModelRegistry.get_model("lightgbm")
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assert model_class is ReloadedModel
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def test_fit_predict_consistency(self):
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"""测试多次预测结果一致"""
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X = pl.DataFrame(
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{
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"feature1": np.random.randn(50),
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"feature2": np.random.randn(50),
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}
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)
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y = pl.Series("target", X["feature1"] + X["feature2"])
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model = LightGBMModel(n_estimators=10)
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model.fit(X, y)
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X_test = pl.DataFrame(
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{
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"feature1": [1.0, 2.0, 3.0],
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"feature2": [1.0, 2.0, 3.0],
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}
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)
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# 多次预测应该返回相同结果
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pred1 = model.predict(X_test)
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pred2 = model.predict(X_test)
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np.testing.assert_array_almost_equal(pred1, pred2)
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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