机器学习房价预测模改进

2019-04-14 16:23发布

站内文章 / 模拟电子
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运行环境：win10 64位 py 2.7 pycharm 2018.1.1 #!/usr/bin/python # -*- coding:utf-8 -*- import numpy as np import pandas as pd import matplotlib.pyplot as plt #导入房价数据 train_df = pd.read_csv('D:/MLCode/SalePrice/input/train.csv', index_col=0) test_df = pd.read_csv('D:/MLCode/SalePrice/input/test.csv', index_col=0) #查看前几行 print train_df.head() import matplotlib prices = pd.DataFrame({"price":train_df["SalePrice"], "log(price + 1)":np.log1p(train_df["SalePrice"])}) prices.hist() plt.show() 这里写代码片 #删除saleprice行，合并train,test数据 y_train = np.log1p(train_df.pop('SalePrice')) all_df = pd.concat((train_df, test_df), axis=0) y_train.head() #将数值转化成类型 all_df['MSSubClass'].dtypes all_df['MSSubClass'] = all_df['MSSubClass'].astype(str) all_df['MSSubClass'].value_counts() #进行one-hot编码 pd.get_dummies(all_df['MSSubClass'], prefix='MSSubClass').head() #我们把所有的category数据，都给One-Hot了 all_dummy_df = pd.get_dummies(all_df) all_dummy_df.head() #查看每一列的缺失 all_dummy_df.isnull().sum().sort_values(ascending=False).head(10) #我们用平均值来填满这些空缺。 mean_cols = all_dummy_df.mean() mean_cols.head(10) all_dummy_df = all_dummy_df.fillna(mean_cols) #看看是不是没有空缺了 all_dummy_df.isnull().sum().sum() #标准化numerical数据，找到哪些是numerical的列数 numeric_cols = all_df.columns[all_df.dtypes != 'object'] numeric_cols #归一化处理 numeric_col_means = all_dummy_df.loc[:, numeric_cols].mean() numeric_col_std = all_dummy_df.loc[:, numeric_cols].std() all_dummy_df.loc[:, numeric_cols] = (all_dummy_df.loc[:, numeric_cols] - numeric_col_means) / numeric_col_std #把数据集分回 训练/测试集 dummy_train_df = all_dummy_df.loc[train_df.index] dummy_test_df = all_dummy_df.loc[test_df.index] dummy_train_df.shape, dummy_test_df.shape X_train = dummy_train_df.values X_test = dummy_test_df.values from sklearn.linear_model import Ridge ridge = Ridge(15) from sklearn.ensemble import BaggingRegressor from sklearn.model_selection import cross_val_score params = [1, 10, 15, 20, 25, 30, 40] test_scores = [] for param in params: clf = BaggingRegressor(n_estimators=param, base_estimator=ridge) test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=10, scoring='neg_mean_squared_error')) test_scores.append(np.mean(test_score)) import matplotlib.pyplot as plt plt.plot(params, test_scores) plt.title("n_estimator vs CV Error") plt.show() 这里写图片描述 #Bagging自带的DecisionTree模型 params = [10, 15, 20, 25, 30, 40, 50, 60, 70, 100] test_scores = [] for param in params: clf = BaggingRegressor(n_estimators=param) test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=10, scoring='neg_mean_squared_error')) test_scores.append(np.mean(test_score)) import matplotlib.pyplot as plt plt.plot(params, test_scores) plt.title("n_estimator vs CV Error") plt.show() 这里写代码片 #最好的结果也就0.140 #采用Boosting，Boosting比Bagging理论上更高级点 from sklearn.ensemble import AdaBoostRegressor params = [10, 15, 20, 25, 30, 35, 40, 45, 50] test_scores = [] for param in params: clf = AdaBoostRegressor(n_estimators=param, base_estimator=ridge) test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=10, scoring='neg_mean_squared_error')) test_scores.append(np.mean(test_score)) plt.plot(params, test_scores) plt.title("n_estimator vs CV Error") plt.show() #也可以不必输入Base_estimator，使用Adaboost自带的DT params = [10, 15, 20, 25, 30, 35, 40, 45, 50] test_scores = [] for param in params: clf = BaggingRegressor(n_estimators=param) test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=10, scoring='neg_mean_squared_error')) test_scores.append(np.mean(test_score)) plt.plot(params, test_scores) plt.title("n_estimator vs CV Error") #最后，我们来看看巨牛逼的XGBoost from xgboost import XGBRegressor params = [1,2,3,4,5,6] test_scores = [] for param in params: clf = XGBRegressor(max_depth=param) test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=10, scoring='neg_mean_squared_error')) test_scores.append(np.mean(test_score)) import matplotlib.pyplot as plt plt.plot(params, test_scores) plt.title("max_depth vs CV Error") #深度为5的时候，错误率缩小到0.127 这里写图片描述

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