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基于隨機森林算法的房屋價格預測模型
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 25 17:17:16 2018@author: yaowu
"""
#==============================================================================
# 導入模塊 ： 導入所需要的模塊
# 數據清洗 ： 在研究變量的基礎上進行數據清洗
# 變量篩選 ：
# 建立模型 ： 完成數據建模
# 評估模型 ： 最終評估模型質量
#==============================================================================
## 1.1 導入模塊
# 導入一些數據分析和數據挖掘常用的包
import numpy as np,pandas as pd,os,seaborn as sns,matplotlib.pyplot as plt
from statsmodels.stats.outliers_influence import variance_inflation_factor
#statsmodels,統計模型，計量經濟學是一個包含統計模型、統計測試和統計數據挖掘python模塊
#outliers_influence，
#variance_inflation_factor，方差膨脹因子(Variance Inflation Factor,VIF):是指解釋變量之間存在多重共線性時的方差與不存在多重共線性時的方差之比
from sklearn.preprocessing import StandardScaler
#sklearn.preprocessing,數據預處理模塊
#StandardScaler,去均值和方差歸一化模塊
from sklearn.decomposition import PCA
#分解，降維
## 1.2 研究變量的情況 導入包之后加載一下數據集，查看一下數據的基礎情況，再考慮下一步的處理
# 加載一下數據，并打印部分數據，查看一下數據的情況
os.chdir(r'C:\Users\A3\Desktop\2：項目\項目\項目19：基于隨機森林算法的房屋價格預測模型\房屋價格預測')
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
print(data_train.head())
print(data_test.head())
# 查看數據的列名和每列的數據格式，方便后面對數據進行處理
#data_train.columns
#data_train.info
#查看數據結構
data_train_dtypes = data_train.dtypes
#print(data_train_dtypes)
'''
#==============================================================================
# 對因變量進行具體情況具體分析，主要查看因變量的統計情況，包含偏度和峰度等。
# 峰度：峰度（Kurtosis）是描述某變量所有取值分布形態陡緩程度的統計量。
# 它是和正態分布相比較的。
# Kurtosis=0 與正態分布的陡緩程度相同。
# Kurtosis>0 比正態分布的高峰更加陡峭——尖頂峰
# Kurtosis<0 比正態分布的高峰來得平穩——平頂峰
# 計算公式：β = M_4 /σ^4
# 偏度：偏度（Skewness）是描述某變量取值分布對稱性的統計量。
# Skewness=0 分布形態與正態分布偏度相同
# Skewness>0 正偏差數值較大，為正偏或右偏。長尾巴拖在右邊。
# Skewness<0 負偏差數值較大，為負偏或左偏。長尾巴拖在左邊。
# 計算公式： S= (X^ - M_0)/δ Skewness 越大，分布形態偏移程度越大。
#==============================================================================
'''
# 查看因變量價格的情況，進行基礎分析
sns.distplot(data_train['SalePrice'])
plt.show()
# 對房屋價格金額的數值圖形化，查看一下
sns.set(style="darkgrid")
titanic = pd.DataFrame(data_train['SalePrice'].value_counts())
titanic.columns = ['SalePrice_count']
ax = sns.countplot(x="SalePrice_count", data=titanic)
plt.show()# 從房屋價格的正態分布情況，查看房屋價格的峰度和偏度情況
print('房屋價格偏度：%f' % (data_train['SalePrice'].skew()))
print('房屋價格峰度：%f' % (data_train['SalePrice'].kurt()))
'''
#==============================================================================
# 分析數據中的缺失值情況，如果超過閾值15%，則刪除這個變量，其他變量根據類別或者是數值型變量進行填充。
# 具體得到的情況如下：
#
# 有缺失的對應的變量名稱
# * PoolQC Pool quality 游泳池質量
# * MiscFeature Miscellaneous feature not covered in other categories 其他雜項，例如網球場、第二車庫等
# * Alley Type of alley access to property 胡同小路，是碎石鋪就的還是其他等等
# * Fence Fence quality 護欄質量
# * FireplaceQu Fireplace quality 壁爐的質量
# * LotFrontage Linear feet of street connected to property 街道情況
# * GarageFinish Interior finish of the garage 車庫完成情況
# * GarageQual Garage quality 車庫質量
# * GarageType Garage location 車庫的位置
# * GarageYrBlt Year garage was built 車庫的建筑年齡
# * GarageCond Garage condition 車庫的條件
# * BsmtExposure Refers to walkout or garden level walls 花園的墻壁情況
# * BsmtFinType2 Rating of basement finished area (if multiple types) 地下室的完工面積
# * BsmtQual Evaluates the height of the basement 地下室的高度
# * BsmtCond Evaluates the general condition of the basement 地下室的質量情況
# * BsmtFinType1 Rating of basement finished area 地下室完工面積
# * MasVnrType Masonry veneer type 表層砌體類型
# * MasVnrArea Masonry veneer area in square feet 磚石鑲面面積平方英尺
# * Electrical Electrical system 電氣系統
#
#==============================================================================
'''
# 在進行圖形分析之前，先分析一下數據中缺失值的情況
miss_data = data_train.isnull().sum().sort_values(ascending=False) # 缺失值數量
total = data_train.isnull().count() # 總數量
miss_data_tmp = (miss_data / total).sort_values(ascending=False) # 缺失值占比
# 添加百分號
def precent(X):X = '%.2f%%' % (X * 100)return X
miss_precent = miss_data_tmp.map(precent)
# 根據缺失值占比倒序排序
miss_data_precent = pd.concat([total, miss_precent, miss_data_tmp], axis=1, keys=['total', 'Percent', 'Percent_tmp']).sort_values(by='Percent_tmp', ascending=False)
# 有缺失值的變量打印出來
print(miss_data_precent[miss_data_precent['Percent'] != '0.00%'])#* 將缺失值比例大于15%的數據全部刪除，剩余數值型變量用眾數填充、類別型變量用None填充。*drop_columns = miss_data_precent[miss_data_precent['Percent_tmp'] > 0.15].index
data_train = data_train.drop(drop_columns, axis=1)
data_test = data_test.drop(drop_columns, axis=1)
# 類別型變量
class_variable = [col for col in data_train.columns if data_train[col].dtypes == 'O']
# 數值型變量
numerical_variable = [col for col in data_train.columns if data_train[col].dtypes != 'O'] # 大寫o
print('類別型變量:%s' % class_variable, '數值型變量:%s' % numerical_variable)# 數值型變量用中位數填充，test集中最后一列為預測價格，所以不可以填充
from sklearn.preprocessing import Imputer
#Imputer填充模塊
padding = Imputer(strategy='median')
data_train[numerical_variable] = padding.fit_transform(data_train[numerical_variable])
data_test[numerical_variable[:-1]] = padding.fit_transform(data_test[numerical_variable[:-1]])
# 類別變量用None填充
data_train[class_variable] = data_train[class_variable].fillna('None')
data_test[class_variable] = data_test[class_variable].fillna('None')# 對所有的數據圖形化，主要判別一下數據和因變量之間的關聯性，后期我們再采用方差驗證來選擇變量。
# 用圖形化只是為了大致查看數據情況，構建簡單模型使用。對變量的選擇并不準確，對于較多變量，用圖形先判斷一下，也是一個辦法；
# 如果變量較多，這種辦法效率低，并且準確度也差，下面隨機選擇了幾個變量畫圖看一下狀況，后期并不使用這種辦法來選擇變量# 根據變量，選擇相關變量查看與因變量之間的關系
# CentralAir 中央空調
data = pd.concat([data_train['SalePrice'], data_train['CentralAir']], axis=1)
fig = sns.boxplot(x='CentralAir', y="SalePrice", data=data)
plt.title('CentralAir')
plt.show()
# 有中央空調的房價更高# MSSubClass 房屋類型
data = pd.concat([data_train['SalePrice'], data_train['MSSubClass']], axis=1)
fig = sns.boxplot(x='MSSubClass', y="SalePrice", data=data)
plt.title('MSSubClass')
plt.show()
# 房屋類型方面和房屋價格之間關聯度不大# MSZoning 房屋區域，例如有高密度住宅、低密度住宅，猜想這個變量和房屋價格之間的關系比較密切
data = pd.concat([data_train['SalePrice'], data_train['MSZoning']], axis=1)
fig = sns.boxplot(x='MSZoning', y="SalePrice", data=data)
plt.title('MSZoning')
plt.show()
# 實際結果顯示和房屋價格的關系不大# LotArea 這個變量猜想會與房屋價格有直接的關系
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x=data_train['SalePrice'], y=data_train['LotArea'])
plt.xlabel('SalePrice')
plt.ylabel('LotArea')
plt.title('LotArea')
plt.show()
# 地塊面積與房屋價格有較高相關性# Street
data = pd.concat([data_train['SalePrice'], data_train['Street']], axis=1)
fig = sns.boxplot(x='Street', y="SalePrice", data=data)
plt.title('Street')
plt.show()
# 街道類型和房屋價格有較高相關性# LotShape
data = pd.concat([data_train['SalePrice'], data_train['LotShape']], axis=1)
fig = sns.boxplot(x='LotShape', y="SalePrice", data=data)
plt.title('LotShape')
plt.show()
# 房屋形狀和房屋價格相關不明顯# LandContour
data = pd.concat([data_train['SalePrice'], data_train['LandContour']], axis=1)
fig = sns.boxplot(x='LandContour', y="SalePrice", data=data)
plt.title('LandContour')
plt.show()
# 房屋所在地是否平整和房屋價格關系較弱# Utilities
data = pd.concat([data_train['SalePrice'], data_train['Utilities']], axis=1)
fig = sns.boxplot(x='Utilities', y="SalePrice", data=data)
plt.title('Utilities')
plt.show()
# 公共設施和房屋價格基本無關系# LotConfig
data = pd.concat([data_train['SalePrice'], data_train['LotConfig']], axis=1)
fig = sns.boxplot(x='LotConfig', y="SalePrice", data=data)
plt.title('LotConfig')
plt.show()
'''
#==============================================================================
# 因為變量較多，直接采用關系矩陣，查看各個變量和因變量之間的關系，使用的時候采用spearman系數，原因:
# Pearson 線性相關系數只是許多可能中的一種情況，為了使用Pearson 線性相關系數必須假設數
# 據是成對地從正態分布中取得的，并且數據至少在邏輯范疇內必須是等間距的數據。如果這兩條件
# 不符合，一種可能就是采用Spearman 秩相關系數來代替Pearson 線性相關系數。Spearman 秩相關系
# 數是一個非參數性質（與分布無關）的秩統計參數，由Spearman 在1904 年提出，用來度量兩個變
# 量之間聯系的強弱(Lehmann and D’Abrera 1998)。Spearman 秩相關系數可以用于R 檢驗，同樣可以
# 在數據的分布使得Pearson 線性相關系數不能用來描述或是用來描述或導致錯誤的結論時，作為變
# 量之間單調聯系強弱的度量。
# Spearman對原始變量的分布不作要求，屬于非參數統計方法，適用范圍要廣些。
# 理論上不論兩個變量的總體分布形態、樣本容量的大小如何，都可以用斯皮爾曼等級相關來進行研究 。
#==============================================================================
'''
#==============================================================================
# 變量處理 ：
#
# 在變量處理期間，我們先考慮處理更簡單的數值型變量，再考慮處理復雜的類別型變量；
#
# 其中數值型變量，需要先考慮和因變量的相關性，其次考慮變量兩兩之間的相關性，再考慮變量的多重共線性；
#
# 類別型變量除了考慮相關性之外，需要進行編碼。
#==============================================================================
# 繪制熱力圖，查看一下數值型變量之間的關系
corrmat = data_train[numerical_variable].corr('spearman')
f, ax = plt.subplots(figsize=(12, 9))
ax.set_xticklabels(corrmat, rotation='horizontal')
sns.heatmap(np.fabs(corrmat), square=False, center=1)
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360)
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=90)
plt.show()# 計算變量之間的相關性
numerical_variable_corr = data_train[numerical_variable].corr('spearman')
numerical_corr = numerical_variable_corr[numerical_variable_corr['SalePrice'] > 0.5]['SalePrice']
print(numerical_corr.sort_values(ascending=False))
index0 = numerical_corr.sort_values(ascending=False).index
# 結合考慮兩兩變量之間的相關性
print(data_train[index0].corr('spearman'))#==============================================================================
# 結合上述情況，選擇出如下的變量：
# Variable 相關性
# OverallQual 0.809829
# GrLivArea 0.731310
# GarageCars 0.690711
# YearBuilt 0.652682
# FullBath 0.635957
# TotalBsmtSF 0.602725
# YearRemodAdd 0.571159
# Fireplaces 0.519247
#==============================================================================#在這個基礎上再考慮變量之間的多重共線性
new_numerical = ['OverallQual', 'GrLivArea', 'GarageCars','YearBuilt', 'FullBath', 'TotalBsmtSF', 'YearRemodAdd', 'Fireplaces']
X = np.matrix(data_train[new_numerical])
VIF_list = [variance_inflation_factor(X, i) for i in range(X.shape[1])]
VIF_list
#可以明顯看到數據有很強的多重共線性，對數據進行標準化和降維Scaler = StandardScaler()
data_train_numerical = Scaler.fit_transform(data_train[new_numerical])
pca = PCA(n_components=7)
newData_train = pca.fit_transform(data_train_numerical)
newData_trainScaler = StandardScaler()
data_test_numerical = Scaler.fit_transform(data_test[new_numerical])
pca = PCA(n_components=7)
newData_test = pca.fit_transform(data_test_numerical)
newData_testnewData_train = pd.DataFrame(newData_train)
# newData
y = np.matrix(newData_train)
VIF_list = [variance_inflation_factor(y, i) for i in range(y.shape[1])]
print(newData_train, VIF_list)#從上面的數據標準化和降維之后，已經消除了多重共線性了。接下來處理類別數據
# 單因素方差分析from statsmodels.formula.api import ols
#單因素方差分析模塊
from statsmodels.stats.anova import anova_lm
#雙因素方差分析模塊
a = '+'.join(class_variable)
formula = 'SalePrice~ %s' % a
anova_results = anova_lm(ols(formula, data_train).fit())
print(anova_results.sort_values(by='PR(>F)'))
#我們需要看的是單個自變量對因變量SalePrice的影響，因此這里使用單因素方差分析。
#分析結果中 P_values（PR(>F)）越小，說明該變量對目標變量的影響越大。通常我們只選擇 P_values 小于 0.05 的變量
# 從變量列表和數據中剔除 P 值大于 0.05 的變量
del_var = list(anova_results[anova_results['PR(>F)'] > 0.05].index)
del_var
# 移除變量
for each in del_var:class_variable.remove(each)
# 移除變量數據
data_train = data_train.drop(del_var, axis=1)
data_test = data_test.drop(del_var, axis=1)
# 對類別型變量進行編碼
def factor_encode(data):map_dict = {}for each in data.columns[:-1]:piv = pd.pivot_table(data, values='SalePrice',index=each, aggfunc='mean')piv = piv.sort_values(by='SalePrice')piv['rank'] = np.arange(1, piv.shape[0] + 1)map_dict[each] = piv['rank'].to_dict()return map_dict# 調用上面的函數，對名義特征進行編碼轉換
class_variable.append('SalePrice')
map_dict = factor_encode(data_train[class_variable])
for each_fea in class_variable[:-1]:data_train[each_fea] = data_train[each_fea].replace(map_dict[each_fea])data_test[each_fea] = data_test[each_fea].replace(map_dict[each_fea])
#因為上面已經完成編碼，這里我們再根據相關性判斷和選擇變量
class_coding_corr=data_train[class_variable].corr('spearman')['SalePrice'].sort_values(ascending=False)
print(class_coding_corr[class_coding_corr>0.5])
class_0=class_coding_corr[class_coding_corr>0.5].index
data_train[class_0].corr('spearman')
#==============================================================================
# SalePrice Neighborhood ExterQual BsmtQual KitchenQual GarageFinish GarageType Foundation
# SalePrice 1.000000 0.755779 0.684014 0.678026 0.672849 0.633974 0.598814 0.573580
# Neighborhood 0.755779 1.000000 0.641588 0.650639 0.576106 0.542172 0.520204 0.584784
# ExterQual 0.684014 0.641588 1.000000 0.645766 0.725266 0.536103 0.444759 0.609009
# BsmtQual 0.678026 0.650639 0.645766 1.000000 0.575112 0.555535 0.468710 0.669723
# KitchenQual 0.672849 0.576106 0.725266 0.575112 1.000000 0.480438 0.412784 0.546736
# GarageFinish 0.633974 0.542172 0.536103 0.555535 0.480438 1.000000 0.663870 0.516078
# GarageType 0.598814 0.520204 0.444759 0.468710 0.412784 0.663870 1.000000 0.445793
# Foundation 0.573580 0.584784 0.609009 0.669723 0.546736 0.516078 0.445793 1.000000
#==============================================================================
#查找兩兩之間的共線性之后，我們保留如下變量Neighborhood，ExterQual，BsmtQual，GarageFinish，GarageType，GarageType；
#接下來嘗試查看多重共線性
class_variable = ['Neighborhood', 'ExterQual', 'BsmtQual','GarageFinish', 'GarageType', 'Foundation']
X = np.matrix(data_train[class_variable])
VIF_list = [variance_inflation_factor(X, i) for i in range(X.shape[1])]
VIF_list
#==============================================================================
# [9.613821793463067,
# 31.39188664149662,
# 33.53637481741086,
# 22.788724064203514,
# 18.362389057630754,
# 15.022566626297733]
#==============================================================================
Scaler = StandardScaler()
data_train_class = Scaler.fit_transform(data_train[class_variable])
pca = PCA(n_components=3)
newData_train_class = pca.fit_transform(data_train_class)
newData_train_class
#==============================================================================
# array([[-1.77187082, 0.39144961, -0.00666812],
# [-0.56662675, -0.71029462, -0.51616971],
# [-1.77187082, 0.39144961, -0.00666812],
# ...,
# [-1.53107491, 0.09189354, -1.97872919],
# [ 1.08133883, -0.73280347, -0.2622237 ],
# [-0.15886914, -1.39847287, -0.0633631 ]])
#==============================================================================
Scaler = StandardScaler()
data_test_class = Scaler.fit_transform(data_test[class_variable])
pca = PCA(n_components=3)
newData_test_class = pca.fit_transform(data_test_class)
newData_test_class
#==============================================================================
# array([[ 1.02960083, -0.69269663, 0.29882836],
# [ 1.02960083, -0.69269663, 0.29882836],
# [-1.36691619, -0.77507848, -1.26800226],
# ...,
# [ 1.62092967, 0.49991231, 0.28578798],
# [ 1.25970992, 2.79139405, -1.00467437],
# [-1.16601113, -0.81964858, -1.47794992]])
#==============================================================================
newData_train_class = pd.DataFrame(newData_train_class)
y = np.matrix(newData_train_class)
VIF_list = [variance_inflation_factor(y, i) for i in range(y.shape[1])]
print(VIF_list)
#==============================================================================
# [1.0, 0.9999999999999993, 0.9999999999999996]
#==============================================================================
#訓練集
newData_train_class = pd.DataFrame(newData_train_class)
newData_train_class.columns = ['降維后類別A','降維后類別B','降維后類別C']
newData_train = pd.DataFrame(newData_train)
newData_train.columns = ['降維后數值A','降維后數值B','降維后數值C','降維后數值D','降維后數值E','降維后數值F','降維后數值G']
target = data_train['SalePrice']
target = pd.DataFrame(target)
train = pd.concat([newData_train_class,newData_train],axis=1, ignore_index=True)#測試集
newData_test_class = pd.DataFrame(newData_test_class)
newData_test_class.columns = ['降維后類別A','降維后類別B','降維后類別C']
newData_test = pd.DataFrame(newData_test)
newData_test.columns = ['降維后數值A','降維后數值B','降維后數值C','降維后數值D','降維后數值E','降維后數值F','降維后數值G']
test = pd.concat([newData_test_class,newData_test],axis=1, ignore_index=True)from sklearn.model_selection import train_test_split
#train_test_split函數用于將矩陣隨機劃分為訓練子集和測試子集,并返回劃分好的訓練集測試集樣本和訓練集測試集標簽
from sklearn.ensemble import RandomForestRegressor
#隨機森林
from sklearn.linear_model import LogisticRegression
#邏輯回歸
from sklearn import svm
#支持向量機
train_data, test_data, train_target, test_target = train_test_split(train, target, test_size=0.2, random_state=0)
# 當前參數為默認參數
m = RandomForestRegressor()
m.fit(train_data, train_target)
from sklearn.metrics import r2_score
score = r2_score(test_target,m.predict(test_data))
print(score)
#==============================================================================
# 0.8407489786565608
# D:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
# after removing the cwd from sys.path.
#==============================================================================
lr = LogisticRegression(C=1000.0,random_state=0)
lr.fit(train_data, train_target)
from sklearn.metrics import r2_score
score = r2_score(test_target,lr.predict(test_data))
print(score)
#==============================================================================
# D:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
# y = column_or_1d(y, warn=True)
# 0.6380328421527758
#==============================================================================
clf = svm.SVC(kernel = 'poly')
clf.fit(train_data, train_target)
score = r2_score(test_target,clf.predict(test_data))
print(score)
#==============================================================================
# D:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
# y = column_or_1d(y, warn=True)
# 0.775319597309544
#==============================================================================
#==============================================================================
# 結論就是邏輯回歸等模型性能比較差，即使已經經過了正則化、PCA降維、去除多重共線性等。kaggle的比賽起手就應該用XGB。
# 下面嘗試使用一下網格搜索的方式看能否提高一下隨機森林的性能。
#==============================================================================
from sklearn.grid_search import GridSearchCV
#網格搜索功能
from sklearn.pipeline import Pipeline#==============================================================================
# D:\ProgramData\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
# "This module will be removed in 0.20.", DeprecationWarning)
# D:\ProgramData\Anaconda3\lib\site-packages\sklearn\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
# DeprecationWarning)
#==============================================================================
param_grid = {'n_estimators':[1,10,100,200,300,400,500,600,700,800,900,1000,1200],'max_features':('auto','sqrt','log2')}
m = GridSearchCV(RandomForestRegressor(),param_grid)
m=m.fit(train_data,train_target.values.ravel())
print(m.best_score_)
print(m.best_params_)
#==============================================================================
# 0.8642193810202421
# {'max_features': 'sqrt', 'n_estimators': 500}
#==============================================================================
#通過網格搜索找到最佳的參數后，代入模型，模型完成
m = RandomForestRegressor(n_estimators=200,max_features='sqrt')
m.fit(train_data, train_target.values.ravel())
predict = m.predict(test)
test = pd.read_csv('test.csv')['Id']
sub = pd.DataFrame()
sub['Id'] = test
sub['SalePrice'] = pd.Series(predict)
sub.to_csv('Predictions.csv', index=False)print('finished!')
總結
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