easy-ml/pipeline/pipeline.py

# -*- coding: utf-8 -*-
"""
@author: yq
@time: 2024/11/1
@desc: 模型训练管道
"""
from typing import List

import pandas as pd

from entitys import DataSplitEntity, MlConfigEntity, DataFeatureEntity
from enums import ConstantEnum, ModelEnum
from feature import FeatureStrategyFactory, FeatureStrategyBase
from init import init
from model import ModelBase, ModelFactory, f_add_rules, f_get_model_score_bin, f_calcu_model_psi
from monitor import ReportWord

init()


class Pipeline():
    def __init__(self, ml_config: MlConfigEntity = None, data: DataSplitEntity = None, *args, **kwargs):
        if ml_config is not None:
            self._ml_config = ml_config
        else:
            self._ml_config = MlConfigEntity(*args, **kwargs)
        feature_strategy_clazz = FeatureStrategyFactory.get_strategy(self._ml_config.feature_strategy)
        self._feature_strategy: FeatureStrategyBase = feature_strategy_clazz(self._ml_config)
        model_clazz = ModelFactory.get_model(self._ml_config.model_type)
        self._model: ModelBase = model_clazz(self._ml_config)
        self._data = data

    def train(self, ):
        # 特征筛选
        self._feature_strategy.feature_search(self._data)
        metric_feature = self._feature_strategy.feature_report(self._data)

        # 生成训练数据
        train_data = self._feature_strategy.feature_generate(self._data.train_data)
        train_data = DataFeatureEntity(data_x=train_data, data_y=self._data.train_data[self._ml_config.y_column])
        test_data = self._feature_strategy.feature_generate(self._data.test_data)
        test_data = DataFeatureEntity(data_x=test_data, data_y=self._data.test_data[self._ml_config.y_column])
        self._model.train(train_data, test_data)
        metric_model = self._model.train_report(self._data)

        self.metric_value_dict = {**metric_feature, **metric_model}

    def prob(self, data: pd.DataFrame):
        feature = self._feature_strategy.feature_generate(data)
        prob = self._model.prob(feature)
        return prob

    def score(self, data: pd.DataFrame):
        return self._model.score(data)

    def score_rule(self, data: pd.DataFrame):
        return self._model.score_rule(data)

    def psi(self, x1: pd.DataFrame, x2: pd.DataFrame, points: List[float] = None) -> pd.DataFrame:
        if self._ml_config.model_type == ModelEnum.XGB.value:
            y1 = self.prob(x1)
            y2 = self.prob(x2)
            sort_ascending = False
        else:
            sort_ascending = True
            if len(self._ml_config.rules) != 0:
                y1 = self.score_rule(x1)
                y2 = self.score_rule(x2)
            else:
                y1 = self.score(x1)
                y2 = self.score(x2)
        x1_score_bin, score_bins = f_get_model_score_bin(x1, y1, points)
        x2_score_bin, _ = f_get_model_score_bin(x2, y2, score_bins)
        model_psi = f_calcu_model_psi(x1_score_bin, x2_score_bin, sort_ascending)
        print(f"模型psi: {model_psi['psi'].sum()}")
        return model_psi

    def report(self, ):
        save_path = self._ml_config.f_get_save_path("模型报告.docx")
        ReportWord.generate_report(self.metric_value_dict, self._model.get_report_template_path(), save_path=save_path)
        print(f"模型报告文件储存路径:{save_path}")

    def save(self):
        self._ml_config.config_save()
        self._feature_strategy.feature_save()
        self._model.model_save()

    @staticmethod
    def load(path: str):
        ml_config = MlConfigEntity.from_config(path)
        pipeline = Pipeline(ml_config=ml_config)
        pipeline._feature_strategy.feature_load(path)
        pipeline._model.model_load(path)
        return pipeline

    def variable_analyse(self, column: str, format_bin=None):
        self._feature_strategy.variable_analyse(self._data, column, format_bin)

    def rules_test(self, ):
        rules = self._ml_config.rules
        df = self._data.train_data.copy()
        df[ConstantEnum.SCORE.value] = [0] * len(df)
        f_add_rules(df, rules)


if __name__ == "__main__":
    pass