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- # -*- coding: utf-8 -*-
- """
- @author: yq
- @time: 2024/11/1
- @desc:
- """
- import pandas as pd
- from sklearn.linear_model import LogisticRegression
- from entitys import DataFeatureEntity
- from model_base import ModelBase
- class ModelLr(ModelBase):
- def __init__(self, ):
- self.lr = LogisticRegression(penalty='l1', C=0.9, solver='saga', n_jobs=-1)
- def train(self, data: DataFeatureEntity, *args, **kwargs):
- self.lr.fit(data.get_Xdata(), data.get_Ydata())
- def predict_prob(self, x: pd.DataFrame, *args, **kwargs):
- return self.lr.predict_proba(x)[:, 1]
- def predict(self, x: pd.DataFrame, *args, **kwargs):
- pass
- def export_model_file(self):
- pass
- if __name__ == "__main__":
- pass
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