李宏毅机器学习-task3

大作业：

• 按照Homework1_introduction.txt的要求完成这次作业

• 访问我的百度云可以下载数据

链接：https://pan.baidu.com/s/1IykfA4Z0-JLLXx9MvDcesg 

提取码：esel 

 

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
path = "~/Downloads/week1/"
train = pd.read_csv(path + 'train.csv', engine='python', encoding='gbk')
test = pd.read_csv(path + 'test.csv', engine='python', encoding='gbk')
train = train[train['observation'] == 'PM2.5']
test = test[test['AMB_TEMP'] == 'PM2.5']
train = train.drop(['Date', 'stations', 'observation'], axis=1)
test_x = test.iloc[:, 2:]

train_x = []
train_y = []
for i in range(15):
    x = train.iloc[:, i:i + 9]
    x.columns = np.array(
        range(9))  # notice if we don't set columns name, it will have different columns name in each iteration
    y = train.iloc[:, i + 9]
    y.columns = np.array(range(1))
    train_x.append(x)
    train_y.append(y)
train_x = pd.concat(train_x)
train_y = pd.concat(train_y)
train_y = np.array(train_y, float)
test_x = np.array(test_x, float)

ss = StandardScaler()
ss.fit(train_x)
train_x = ss.transform(train_x)

ss.fit(test_x)
test_x = ss.transform(test_x)

def r2_score(y_true, y_predict):
    MSE = np.sum((y_true - y_predict) ** 2) / len(y_true)
    return 1 - MSE / np.var(y_true)

class LinearRegression:

    def __init__(self):
        self.coef_ = None
        self.intercept_ = None
        self._theta = None

    def fit_normal(self, X_train, y_train):
        assert X_train.shape[0] == y_train.shape[0]
        X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
        self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)

        self.intercept_ = self._theta[0]
        self.coef_ = self._theta[1:]

        return self

    def fit_gd(self, X_train, y_train, eta=0.01, n_iters=1e4):
       
        assert X_train.shape[0] == y_train.shape[0], \
            "the size of X_train must be equal to the size of y_train"

        
        def J(theta, X_b, y):
            try:
                return np.sum((y - X_b.dot(theta)) ** 2) / len(y)
            except:
                return float('inf')
        
        def dJ(theta, X_b, y):
            return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)

        def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
            '''
            :param X_b:
            :param y: lebel
            :param initial_theta: 
            :param eta: 
            :param n_iters:
            :param epsilon: theta
            :return:
            '''
            theta = initial_theta
            cur_iter = 0

            while cur_iter < n_iters:
                gradient = dJ(theta, X_b, y)
                last_theta = theta
                theta = theta - eta * gradient
                if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
                    break

                cur_iter += 1

            return theta

        X_b = np.hstack([np.ones((len(X_train), 1)), X_train])


        initial_theta = np.zeros(X_b.shape[1])
        self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)

        self.intercept_ = self._theta[0]
        self.coef_ = self._theta[1:]

        return self

    def predict(self, X_predict):
       
        assert self.intercept_ is not None and self.coef_ is not None, \
            "must fit before predict!"
        assert X_predict.shape[1] == len(self.coef_), \
            "the feature number of X_predict must be equal to X_train"

        X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
        return X_b.dot(self._theta)

    def score(self, X_test, y_test):

        y_predict = self.predict(X_test)
        return r2_score(y_test, y_predict)

    def __repr__(self):
        return "LR()"



LR = LinearRegression().fit_gd(train_x, train_y)

LR.score(train_x, train_y)
result = LR.predict(test_x)


sampleSubmission = pd.read_csv(path + 'sampleSubmission.csv', engine='python', encoding='gbk')
sampleSubmission['value'] = result
sampleSubmission.to_csv(path + 'result.csv')

参考：

1、https://docs.qq.com/sheet/DRU54aFRwQ29ZS2RD?opendocxfrom=admin&id=DRU54aFRwQ29ZS2RD&tab=BB08J2&coord=D40%24D40%240%240%240%240

2、