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- #!/usr/bin/python
- # -*- coding: UTF-8 -*-
- '''
- 最简单的mse
- '''
- import sys
- import os
- sys.path.append(os.path.abspath('..'))
- from util.config import config
- import numpy as np
- from sklearn.linear_model import LinearRegression
- from sklearn import metrics
- from sklearn.model_selection import train_test_split
- from draw import draw_util
- import joblib
- def curce_data(x,y,y_pred):
- x=x.tolist()
- y=y.tolist()
- y_pred=y_pred.tolist()
- results=zip(x,y,y_pred)
- results=["{},{},{}".format(s[0],s[1][0],s[2][0]) for s in results ]
- return results
- def read_data(path):
- with open(path) as f :
- lines=f.readlines()
- lines=[eval(line.strip()) for line in lines]
- X,z,y=zip(*lines)
- X=np.array(X)
- y=np.array(y)
- return X,y
- def demo(file, model_file):
- X_train,y_train=read_data(file)
- # X_test,y_test=read_data(config.get('application', 'test_data_path'))
- Xtrain, Xtest, Ytrain, Ytest = train_test_split(X_train, y_train, test_size=0.3)
- # 一个对象,它代表的线性回归模型,它的成员变量,就已经有了w,b. 刚生成w和b的时候 是随机的
- model = LinearRegression()
- # 一调用这个函数,就会不停地找合适的w和b 直到误差最小
- model.fit(Xtrain, Ytrain)
- # 打印W
- # print(model.coef_)
- # 打印b
- print(model.intercept_)
- # 模型已经训练完毕,用模型看下在训练集的表现
- y_pred_train = model.predict(Xtrain)
- # sklearn 求解训练集的mse
- # y_train 在训练集上 真实的y值
- # y_pred_train 通过模型预测出来的y值
- # 计算 (y_train-y_pred_train)^2/n
- train_mse = metrics.mean_squared_error(Ytrain, y_pred_train)
- print("训练集MSE:", train_mse)
- # 看下在测试集上的效果
- y_pred_test = model.predict(Xtest)
- # print(y_pred_test)
- test_mse = metrics.mean_squared_error(Ytest, y_pred_test)
- print("测试集MSE:",test_mse)
- # 保存模型
- joblib.dump(model, model_file)
- def draw_line():
- x_train, y_train = read_data("../bbztx/train_data")
- print(x_train.tolist())
- print(y_train.tolist())
- draw_util.drawScatter(x_train.tolist(), y_train.tolist())
- if __name__ == '__main__':
- root_dir = 'D:\\data\\quantization\\5d\\'
- model_dir = 'D:\\data\\quantization\\5d_lr_model\\'
- list = os.listdir(root_dir)
- for f in list:
- print(f)
- m = f.split('.')[0][-6:]
- demo(root_dir + str(f), model_dir + '' + str(m) + '.pkl')
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