machine_learn/linear/train.py

#!/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 draw import draw_util


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,y=zip(*lines)
    X=np.array(X)
    y=np.array(y)
    return X,y


def demo():
    X_train,y_train=read_data(config.get('application', 'train_data_path'))
    X_test,y_test=read_data(config.get('application', 'test_data_path'))

	#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(X_train, y_train)
    #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(X_train)
    #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(y_train, 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(X_test)
    print(y_pred_test)
    test_mse = metrics.mean_squared_error(y_test, y_pred_test)
    print("测试集MSE:",test_mse)

    # train_curve = curce_data(X_train,y_train,y_pred_train)
    test_curve = curce_data(X_test,y_test,y_pred_test)
    print("推广mse差", test_mse-train_mse)

    '''
	with open("train_curve.csv","w") as f :
		f.writelines("\n".join(train_curve))
	保存数据
	with open("test_curve.csv","w") as f :
		f.writelines("\n".join(test_curve))
	'''
    for x in test_curve:
        print(x)

    return X_train,y_train, model.coef_, model.intercept_


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__':
	# draw_line()
	p, q, w,b = demo()
	p = [i[0] for i in p.tolist()]
	q = [i[0] for i in q.tolist()]
	w = w[0]
	b = b[0]
	# draw_util.drawScatterAndLine(p, q, w, b)