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$D40$0$0$0$0
2、
