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