#coding=utf8

import numpy as np
import pandas as pd
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, AdaBoostRegressor
from xgboost import XGBRegressor

#
 
不要第一列id，只是作為索引
train_df = pd.read_csv(‘./input/train.csv‘, index_col=0)
test_df = pd.read_csv(‘./input/test.csv‘, index_col=0)
prices = pd.DataFrame({‘price‘:train_df[‘SalePrice‘], ‘log(price + 1)‘:np.log1p(train_df[‘SalePrice‘])})
#print train_df.columns
#prices.hist()
#print ‘ok‘
#print train_df.index
#
 
print test_df.index

y_train = np.log1p(train_df.pop(‘SalePrice‘))
#print y_train.shape
#print train_df.index

all_df = pd.concat((train_df,test_df), axis=0)
#變量轉換
print train_df.index
print test_df.index

#print all_df[‘MSSubClass‘].dtypes
all_df[‘MSSubClass‘] = all_df[‘MSSubClass‘].astype(str)
#print all_df.shape
 

#print all_df[‘MSSubClass‘].value_counts()
#print all_df[‘MSSubClass‘].dtypes
#print pd.get_dummies(all_df[‘MSSubClass‘], prefix=‘MSSubClass‘).head()
#當我們用numerical來表達categorical的時候，要註意，數字本身有大小的含義，所以亂用數字會給之後的模型學習帶來麻煩。於是我們可以用One-Hot的方法來表達category。
#pandas自帶的get_dummies方法，一鍵做到One-Hot。
#把所有的category數據，都給One-Hot了
all_dummy_df = pd.get_dummies(all_df)
#print all_dummy_df.head()
#print all_dummy_df.isnull().sum().sort_values(ascending=False).head(10)
#處理缺失值
mean_cols = all_dummy_df.mean()
#print mean_cols
all_dummy_df = all_dummy_df.fillna(mean_cols)
#print all_dummy_df.isnull().sum().sum()
#標準化numerical數據,這裏，我們當然不需要把One-Hot的那些0/1數據給標準化。我們的目標應該是那些本來就是numerical的數據：
#先來看看 哪些是numerical的
numeric_cols = all_df.columns[all_df.dtypes != ‘object‘]
#print numeric_cols
#print train_df.index
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

dummy_train_df = all_dummy_df.loc[train_df.index]
dummy_test_df = all_dummy_df.loc[test_df.index]
#print train_df.index
#print test_df.index
#print dummy_train_df.shape
#print dummy_test_df.shape
#print type(dummy_train_df)

X_train = dummy_train_df.values
X_test = dummy_test_df.values
#print type(X_train)

print X_train.shape
alphas = np.logspace(-3, 2, 50)
test_scores = []
for alpha in alphas:
    clf = Ridge(alpha)
    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(alphas, test_scores)
plt.title(‘Alpha vs CV Error‘)

max_features = [.1, .3, .5, .7, .9, .99]
test_scores = []
for max_feat in max_features:
    clf = RandomForestRegressor(n_estimators=200, max_features=max_feat)
    test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=5, scoring=‘neg_mean_squared_error‘))
    test_scores.append(np.mean(test_score))

plt.plot(max_features, test_scores)
plt.title("Max Features vs CV Error")

#做一點高級的Ensemble
#這裏，可以不必輸入Base_estimator，使用自帶的，但是結果不及已經調好的 base_estimator，通過作圖可以驗證。
ridge = Ridge(alpha=15)

#Bagging
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))

plt.plot(params, test_scores)
plt.title("n_estimator vs CV Error")

#Boosting
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")

#XGBoost
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))

plt.plot(params, test_scores)
plt.title("max_depth vs CV Error")


"""
rf = RandomForestRegressor(n_estimators=500, max_features=.3)

ridge.fit(X_train, y_train)
rf.fit(X_train, y_train)

y_ridge = np.expm1(ridge.predict(X_test))
y_rf = np.expm1(rf.predict(X_test))
y_final = (y_ridge + y_rf) / 2
"""

房價預測《進階版，測試》