87 lines
1.9 KiB
Python
87 lines
1.9 KiB
Python
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"""ProStock 模型训练框架
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组件化、低耦合、插件式的机器学习训练框架。
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示例:
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>>> from src.models import (
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... PluginRegistry, ProcessingPipeline,
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... PipelineStage, BaseProcessor
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... )
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>>> # 获取注册的处理器
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>>> scaler_class = PluginRegistry.get_processor("standard_scaler")
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>>> scaler = scaler_class()
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>>> # 创建处理流水线
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>>> pipeline = ProcessingPipeline([
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... PluginRegistry.get_processor("dropna")(),
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... PluginRegistry.get_processor("winsorizer")(lower=0.01, upper=0.99),
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... PluginRegistry.get_processor("standard_scaler")(),
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... ])
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"""
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# 导入核心抽象类和划分策略
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from src.models.core import (
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PipelineStage,
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TaskType,
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BaseProcessor,
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BaseModel,
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BaseSplitter,
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BaseMetric,
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TimeSeriesSplit,
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WalkForwardSplit,
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ExpandingWindowSplit,
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)
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# 导入注册中心
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from src.models.registry import PluginRegistry
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# 导入处理流水线
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from src.models.pipeline import ProcessingPipeline
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# 导入并注册内置处理器
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from src.models.processors.processors import (
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DropNAProcessor,
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FillNAProcessor,
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Winsorizer,
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StandardScaler,
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MinMaxScaler,
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RankTransformer,
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Neutralizer,
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)
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# 导入并注册内置模型
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from src.models.models.models import (
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LightGBMModel,
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CatBoostModel,
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)
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__all__ = [
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# 核心抽象
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"PipelineStage",
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"TaskType",
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"BaseProcessor",
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"BaseModel",
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"BaseSplitter",
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"BaseMetric",
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# 划分策略
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"TimeSeriesSplit",
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"WalkForwardSplit",
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"ExpandingWindowSplit",
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# 注册中心
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"PluginRegistry",
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# 处理流水线
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"ProcessingPipeline",
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# 处理器
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"DropNAProcessor",
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"FillNAProcessor",
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"Winsorizer",
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"StandardScaler",
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"MinMaxScaler",
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"RankTransformer",
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"Neutralizer",
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# 模型
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"LightGBMModel",
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"CatBoostModel",
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]
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