#!/users/dsc/bin/Python3
#-*-coding: UTF-8 -*-
from typing_extensions import Self
from ml_model import models
from Serialization import Serialization,Deserialization
import numpy as np
from pyml.pyml import logger
class ModelsAPI(object):
    def __init__(self) -> None:
        self.models_={}
    def train(self,x_train,y_train,model_info):
        ruleid=model_info.ruleid
        model_type=model_info.type#int 1.分类；2.回归
        model_name=model_info.name
        #根据类型而定
        # 分类器
        # 1.多层感知机 MLP
        # 2.逻辑斯蒂回归 LR
        # 3.随机森林 RFC
        # 4.决策树 DTC
        # 回归模型
        # 1.多层感知机 MLPR
        # 2.逻辑斯蒂回归 LRR
        # 3.随机森林 RFR
        # 4.决策树 DTR
        if(model_type==1):
            try:
                now_model=models.model(model_name)
                now_model.fit(x_train, y_train)
                key=ruleid+"_"+model_type+"_"+model_name
                Serialization(key,now_model)
                self.models_[key]=now_model
                now_score=now_model.score(x_train, y_train)
                logger.info(key+",训练完成！score:"+ str(now_score))
                return now_score
            except:
                 logger.error(key+",训练失败")
    def predict(self,x_test,model_info):
        ruleid=model_info.ruleid
        model_type=model_info.type#int 1.分类；2.回归
        model_name=model_info.name
        #根据类型而定
        # 分类器
        # 1.多层感知机 MLP
        # 2.逻辑斯蒂回归 LR
        # 3.随机森林 RFC
        # 4.决策树 DTC
        # 回归模型
        # 1.多层感知机 MLPR
        # 2.逻辑斯蒂回归 LRR
        # 3.随机森林 RFR
        # 4.决策树 DTR
        key=ruleid+"_"+model_type+"_"+model_name
        if(key in self.models_):
            pass
        else:
            try:
                now_model=Deserialization(key)
                self.models_[key]=now_model
            except:
                logger.error("predict ERROR! Deserialization ERROR!")
        try:
            return self.models_[key].predict(x_test)
        except:
            logger.error("predict ERROR! model.predict ERROR! ")
            return None