Kaggle: House Prices: Advanced Regression Techniques

notebook来自https://www.kaggle.com/neviadomski/how-to-get-to-top-25-with-simple-model-sklearn

思路流程:

1.导入数据,查看数据结构和缺失值情况
重点在于查看缺失值情况的写法:
NAs = pd.concat([train.isnull().sum(), test.isnull().sum()], axis = 1, keys = ['train', 'test'])
NAs[NAs.sum(axis=1) > 0]

2.数据预处理(删除无用特征,特征转化,缺失值填充,构造新特征,特征值标准化,转化为dummy)
Q:什么样的特征需要做转化?
A:如某些整型数据只表示类别,其数值本身没有意义,则应转化为dummy
重点学习手动将特征转化为dummy的方法(这里情况稍微还要复杂一点,因为存在同一特征对应两列的情况,如Condition1,Condition2)

3.随机打乱数据,分离训练集和测试集

4.构建多个单一模型

5.模型融合

问题:

1.如何判断一个特征是否是无用特征?

2.模型融合的方法?这里为什是np.exp(GB_model.predict(test_features)) + np.exp(ENS_model.predict(test_features_std))?

3.为什么label分布偏斜需要做转化?

 
In [33]:
#Kaggle: House Prices: Advanced Regression Techniques
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import ensemble, linear_model, tree
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.utils import shuffle

%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

train = pd.read_csv('downloads/train.csv')
test = pd.read_csv('downloads/test.csv')
In [8]:
train.head()
Out[8]:
  Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000

5 rows × 81 columns

In [9]:
#检查缺失值
NAs = pd.concat([train.isnull().sum(), test.isnull().sum()], axis = 1, keys = ['train', 'test']) #sum()默认的axis=0,即跨行
NAs[NAs.sum(axis=1) > 0] #只显示有缺失值的特征
Out[9]:
  train test
Alley 1369 1352.0
BsmtCond 37 45.0
BsmtExposure 38 44.0
BsmtFinSF1 0 1.0
BsmtFinSF2 0 1.0
BsmtFinType1 37 42.0
BsmtFinType2 38 42.0
BsmtFullBath 0 2.0
BsmtHalfBath 0 2.0
BsmtQual 37 44.0
BsmtUnfSF 0 1.0
Electrical 1 0.0
Exterior1st 0 1.0
Exterior2nd 0 1.0
Fence 1179 1169.0
FireplaceQu 690 730.0
Functional 0 2.0
GarageArea 0 1.0
GarageCars 0 1.0
GarageCond 81 78.0
GarageFinish 81 78.0
GarageQual 81 78.0
GarageType 81 76.0
GarageYrBlt 81 78.0
KitchenQual 0 1.0
LotFrontage 259 227.0
MSZoning 0 4.0
MasVnrArea 8 15.0
MasVnrType 8 16.0
MiscFeature 1406 1408.0
PoolQC 1453 1456.0
SaleType 0 1.0
TotalBsmtSF 0 1.0
Utilities 0 2.0
 
In [10]:
#打印R2和RMSE得分
def print_score (prediction, labels):
print('R2: {}'.format(r2_score(prediction, labels)))
print('RMSE: {}'.format(np.sqrt(mean_squared_error(prediction, labels))))

#对给定的模型进行评估,分别打印训练集上的得分和测试集上的得分
def train_test_score(estimator, x_train, x_test, y_train, y_test):
train_predictions = estimator.predict(x_train)
print('------------train-----------')
print_score(train_predictions, y_train)
print('------------test------------')
test_predictions = estimator.predict(x_test)
print_score(test_predictions, y_test)
In [11]:
#将标签从训练集中分离出来
train_label = train.pop('SalePrice')

#将训练集特征和测试集特征拼在一起,便于一起删除无用的特征
features = pd.concat([train, test], keys = ['train', 'test'])

#删除无用特征(为什么说它们是无用特征并没有解释)
features.drop(['Utilities', 'RoofMatl', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'Heating', 'LowQualFinSF',
'BsmtFullBath', 'BsmtHalfBath', 'Functional', 'GarageYrBlt', 'GarageArea', 'GarageCond', 'WoodDeckSF',
'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC', 'Fence', 'MiscFeature', 'MiscVal'],
axis=1, inplace=True)
print(features.shape)
(2919, 56)
In [12]:
#将series数据转化为str
#问题:什么样的数据需要转化为str
#答:将原来的某些整型数据转化为str,这些整型数据数字大小本身并没有含义,而只是代表一个类,所以转化为str后,后续再转化为dummy
features['MSSubClass'] = features['MSSubClass'].astype(str)
#pandas调用特征的两种方法:.feature和['feature'],两者效果相同,下面就是.feature方法
features.OverallCond = features.OverallCond.astype(str)
features['KitchenAbvGr'] = features['KitchenAbvGr'].astype(str)
features['YrSold'] = features['YrSold'].astype(str)
features['MoSold'] = features['MoSold'].astype(str)

#用众数填充缺失值
features['MSZoning'] = features['MSZoning'].fillna(features['MSZoning'].mode()[0])
features['MasVnrType'] = features['MasVnrType'].fillna(features['MasVnrType'].mode()[0])
features['Electrical'] = features['Electrical'].fillna(features['Electrical'].mode()[0])
features['KitchenQual'] = features['KitchenQual'].fillna(features['KitchenQual'].mode()[0])
features['SaleType'] = features['SaleType'].fillna(features['SaleType'].mode()[0])

#用某个特定值填充缺失值
features['LotFrontage'] = features['LotFrontage'].fillna(features['LotFrontage'].mean())
features['Alley'] = features['Alley'].fillna('NOACCESS')
for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):
features[col] = features[col].fillna('NoBSMT')
features['TotalBsmtSF'] = features['TotalBsmtSF'].fillna(0)
features['FireplaceQu'] = features['FireplaceQu'].fillna('NoFP')
for col in ('GarageType', 'GarageFinish', 'GarageQual'):
features[col] = features[col].fillna('NoGRG')
features['GarageCars'] = features['GarageCars'].fillna(0.0)

#构造新特征
features['TotalSF'] = features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF']
features.drop(['TotalBsmtSF', '1stFlrSF', '2ndFlrSF'], axis=1, inplace=True)
print(features.shape)
(2919, 54)
 
In [13]:
#查看房价分布情况
ax = sns.distplot(train_label)
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" 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In [14]:
#发现图像整体向左倾斜,所以做log转变
train_label = np.log(train_label)
ax = sns.distplot(train_label)
aaarticlea/png;base64,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" alt="" />
 
In [15]:
#对数字特征做标准化处理
num_features = features.loc[:,['LotFrontage', 'LotArea', 'GrLivArea', 'TotalSF']]
num_features_standarized = (num_features - num_features.mean()) / num_features.std()
num_features_standarized.head()
Out[15]:
    LotFrontage LotArea GrLivArea TotalSF
train 0 -0.202033 -0.217841 0.413476 0.022999
1 0.501785 -0.072032 -0.471810 -0.029167
2 -0.061269 0.137173 0.563659 0.196886
3 -0.436639 -0.078371 0.427309 -0.092511
4 0.689469 0.518814 1.377806 0.988072
 
In [16]:
ax = sns.pairplot(num_features_standarized)
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" alt="" />
 
In [17]:
#重点
#convert categorical data to dummies
#将所有condition不重复的记录在一个set中
conditions = set([x for x in features['Condition1']] + [x for x in features['Condition2']])
#自定义dummy变量,行数为阳历数,列数为原condition数据转化为dummy后的维数
dummies = pd.DataFrame(data = np.zeros((len(features.index), len(conditions))), index = features.index, columns = conditions)
#遍历所有样例,将原来的condition信息转化为对应的dummy信息
for i, cond in enumerate(zip(features['Condition1'], features['Condition2'])):
#用ix找到位置,注意cond可能包含Condition1和Condition2两个位置的信息,对应dummies数组的两个点,所以需要用ix而不能简单的直接用dummies[i,cond]
dummies.ix[i, cond] = 1
#将dummy后的特征数据拼接到原features后面,并给dummy特征的index增加前缀
features = pd.concat([features, dummies.add_prefix('Cond_')], axis = 1)
#最后就可以删除原来的Condition特征
features.drop(['Condition1', 'Condition2'], axis = 1, inplace =True)
print(features.shape)
(2919, 61)
 
In [18]:
features.head()
 
Out[18]:
    Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour LotConfig ... TotalSF Cond_PosA Cond_Artery Cond_PosN Cond_RRAn Cond_RRAe Cond_Feedr Cond_Norm Cond_RRNn Cond_RRNe
train 0 1 60 RL 65.0 8450 Pave NOACCESS Reg Lvl Inside ... 2566.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
1 2 20 RL 80.0 9600 Pave NOACCESS Reg Lvl FR2 ... 2524.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0
2 3 60 RL 68.0 11250 Pave NOACCESS IR1 Lvl Inside ... 2706.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
3 4 70 RL 60.0 9550 Pave NOACCESS IR1 Lvl Corner ... 2473.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
4 5 60 RL 84.0 14260 Pave NOACCESS IR1 Lvl FR2 ... 3343.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0

5 rows × 61 columns

 
In [19]:
#convert Exterior to dummies
Exterior = set([x for x in features['Exterior1st']] + [x for x in features['Exterior2nd']])
dummies = pd.DataFrame(data = np.zeros([len(features.index), len(Exterior)]), index = features.index, columns = Exterior)
for i, ext in enumerate(zip(features['Exterior1st'], features['Exterior2nd'])):
dummies.ix[i, ext] = 1
features = pd.concat([features, dummies.add_prefix('Ext_')], axis = 1)
features.drop(['Exterior1st', 'Exterior2nd', 'Ext_nan'], axis = 1, inplace = True)
print(features.shape)
(2919, 78)
 
In [20]:
features.dtypes[features.dtypes == 'object'].index
 
Out[20]:
Index(['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour',
'LotConfig', 'LandSlope', 'Neighborhood', 'BldgType', 'HouseStyle',
'OverallCond', 'RoofStyle', 'MasVnrType', 'ExterQual', 'ExterCond',
'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',
'BsmtFinType2', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenAbvGr',
'KitchenQual', 'FireplaceQu', 'GarageType', 'GarageFinish',
'GarageQual', 'PavedDrive', 'MoSold', 'YrSold', 'SaleType',
'SaleCondition'],
dtype='object')
 
In [21]:
#遍历特定类型数据的方法:for col in features.dtypes[features.dtypes == 'object'].index
#convert all other categorical vars to dummies
for col in features.dtypes[features.dtypes == 'object'].index:
for_dummy = features.pop(col)
features = pd.concat([features, pd.get_dummies(for_dummy, prefix = col)], axis = 1)
print(features.shape)
(2919, 263)
 
In [22]:
#用之前几个标准化的数据更新features

features_standardized = features.copy()
features_standardized.update(num_features_standarized)
In [23]:
#重新分离训练集和测试集

#首先分离没有标准化的features
train_features = features.loc['train'].drop(['Id'], axis=1).select_dtypes(include=[np.number]).values
test_features = features.loc['test'].drop(['Id'], axis=1).select_dtypes(include=[np.number]).values

#再分离标准化的数据
train_features_std = features_standardized.loc['train'].drop(['Id'], axis=1).select_dtypes(include=[np.number]).values
test_features_std = features_standardized.loc['test'].drop(['Id'], axis=1).select_dtypes(include=[np.number]).values
print(train_features.shape)
print(train_features_std.shape)
(1460, 262)
(1460, 262)
 
In [24]:
#shuffle train dataset
train_features_std, train_features, train_label = shuffle(train_features_std, train_features, train_label, random_state = 5)
In [25]:
#split train and test data
x_train, x_test, y_train, y_test = train_test_split(train_features, train_label, test_size = 0.1, random_state = 200)
x_train_std, x_test_std, y_train_std, y_test_std = train_test_split(train_features_std, train_label, test_size = 0.1, random_state = 200)
In [26]:
#构建第一个模型:ElasticNet
ENSTest = linear_model.ElasticNetCV(alphas=[0.0001, 0.0005, 0.001, 0.01, 0.1, 1, 10], l1_ratio=[.01, .1, .5, .9, .99], max_iter=5000).fit(x_train_std, y_train_std)
train_test_score(ENSTest, x_train_std, x_test_std, y_train_std, y_test_std)
------------train-----------
R2: 0.9009283127352861
RMSE: 0.11921419084690392
------------test------------
R2: 0.8967299522701895
RMSE: 0.11097042840114624
 
In [27]:
#测试模型的交叉验证得分
score = cross_val_score(ENSTest, train_features_std, train_label, cv = 5)
print('Accurary: %0.2f +/- %0.2f' % (score.mean(), score.std()*2))
Accurary: 0.88 +/- 0.10
 
In [28]:
#构建第二个模型:GradientBoosting
GB = ensemble.GradientBoostingRegressor(n_estimators=3000, learning_rate = 0.05, max_depth = 3, max_features = 'sqrt',
min_samples_leaf = 15,
min_samples_split = 10, loss = 'huber').fit(x_train_std, y_train_std)
train_test_score(GB, x_train_std, x_test_std, y_train_std, y_test_std)
------------train-----------
R2: 0.9607778449577035
RMSE: 0.07698826081848897
------------test------------
R2: 0.9002871760789876
RMSE: 0.10793269100940146
 
In [29]:
#构建第二个模型:GradientBoosting
GB = ensemble.GradientBoostingRegressor(n_estimators=3000, learning_rate = 0.05, max_depth = 3, max_features = 'sqrt',
min_samples_leaf = 15,
min_samples_split = 10, loss = 'huber').fit(x_train_std, y_train_std)
train_test_score(GB, x_train_std, x_test_std, y_train_std, y_test_std)
Accurary: 0.90 +/- 0.04
 
In [30]:
#模型融合
GB_model = GB.fit(train_features, train_label)
ENS_model = ENSTest.fit(train_features_std, train_label)
In [31]:
#为什么模型融合公式是这样的?
Final_score = (np.exp(GB_model.predict(test_features)) + np.exp(ENS_model.predict(test_features_std))) / 2
 
In [32]:
#写入csv文件
pd.DataFrame({'Id':test.Id, 'SalePrice':Final_score}).to_csv('submit.csv', index=False)

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