In [1]:
from scipy.stats.mstats import mode
import pandas as pd
import numpy as np
import time
from sklearn.preprocessing import LabelEncoder
"""
Read Data
"""
train=pd.read_csv('../input/train.csv')
test=pd.read_csv('../input/test.csv')
target=train['SalePrice']
train=train.drop(['SalePrice'],axis=1)
trainlen=train.shape[0]
In [2]:
alldata=pd.concat([train, test], axis=0, join='outer',ignore_index=True)
alldata=alldata.drop(['Id','Utilities'],axis=1)
alldata.ix[:,(alldata.dtypes=='int64') & (alldata.columns!='MSSubClass')]=alldata.ix[:,(alldata.dtypes=='int64') & (alldata.columns!='MSSubClass')].astype('float64')
In [3]:
fMedlist=['LotFrontage']
fArealist=['MasVnrArea','TotalBsmtSF','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','BsmtFullBath', 'BsmtHalfBath','MasVnrArea','Fireplaces','GarageArea','GarageYrBlt','GarageCars']
for i in fArealist:
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=0
for i in fMedlist:
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=np.nanmedian(alldata.ix[:,i])
### Transforming Data
le=LabelEncoder()
nacount_category=np.array(alldata.columns[((alldata.dtypes=='int64') | (alldata.dtypes=='object')) & (pd.isnull(alldata).sum()>0)])
category=np.array(alldata.columns[((alldata.dtypes=='int64') | (alldata.dtypes=='object'))])
Bsmtset=set(['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2'])
MasVnrset=set(['MasVnrType'])
Garageset=set(['GarageType','GarageYrBlt','GarageFinish','GarageQual','GarageCond'])
Fireplaceset=set(['FireplaceQu'])
Poolset=set(['PoolQC'])
NAset=set(['Fence','MiscFeature','Alley'])
for i in nacount_category:
if i in Bsmtset:
alldata.ix[pd.isnull(alldata.ix[:,i]) & (alldata['TotalBsmtSF']==0),i]='Empty'
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=alldata.ix[:,i].value_counts().index[0]
elif i in MasVnrset:
alldata.ix[pd.isnull(alldata.ix[:,i]) & (alldata['MasVnrArea']==0),i]='Empty'
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=alldata.ix[:,i].value_counts().index[0]
elif i in Garageset:
alldata.ix[pd.isnull(alldata.ix[:,i]) & (alldata['GarageArea']==0),i]='Empty'
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=alldata.ix[:,i].value_counts().index[0]
elif i in Fireplaceset:
alldata.ix[pd.isnull(alldata.ix[:,i]) & (alldata['Fireplaces']==0),i]='Empty'
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=alldata.ix[:,i].value_counts().index[0]
elif i in Poolset:
alldata.ix[pd.isnull(alldata.ix[:,i]) & (alldata['PoolArea']==0),i]='Empty'
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=alldata.ix[:,i].value_counts().index[0]
elif i in NAset:
alldata.ix[pd.isnull(alldata.ix[:,i]),i]='Empty'
else:
alldata.ix[pd.isnull(alldata.ix[:,i]),i]=alldata.ix[:,i].value_counts().index[0]
for i in category:
alldata.ix[:,i]=le.fit_transform(alldata.ix[:,i])
train=alldata.ix[0:trainlen-1,:]
test=alldata.ix[trainlen:alldata.shape[0],:]
In [4]:
import xgboost as xgb
from sklearn.cross_validation import ShuffleSplit
from sklearn.metrics import mean_squared_error
from sklearn.utils import shuffle
In [5]:
o=[30,462,523,632,968,970, 1298, 1324]
train=train.drop(o,axis=0)
target=target.drop(o,axis=0)
train.index=range(0,train.shape[0])
target.index=range(0,train.shape[0])
In [6]:
est=xgb.XGBRegressor(colsample_bytree=0.4,
gamma=0.045,
learning_rate=0.07,
max_depth=20,
min_child_weight=1.5,
n_estimators=300,
reg_alpha=0.65,
reg_lambda=0.45,
subsample=0.95)
In [7]:
n=200
scores=pd.DataFrame(np.zeros([n,train.shape[1]]))
scores.columns=train.columns
ct=0
for train_idx, test_idx in ShuffleSplit(train.shape[0], n, .25):
ct+=1
X_train, X_test = train.ix[train_idx,:], train.ix[test_idx,:]
Y_train, Y_test = target.ix[train_idx], target.ix[test_idx]
r = est.fit(X_train, Y_train)
acc = mean_squared_error(Y_test, est.predict(X_test))
for i in range(train.shape[1]):
X_t = X_test.copy()
X_t.ix[:,i]=shuffle(np.array(X_t.ix[:, i]))
shuff_acc = mean_squared_error(Y_test, est.predict(X_t))
scores.ix[ct-1,i]=((acc-shuff_acc)/acc)
In [8]:
fin_score=pd.DataFrame(np.zeros([train.shape[1],4]))
fin_score.columns=['Mean','Median','Max','Min']
fin_score.index=train.columns
fin_score.ix[:,0]=scores.mean()
fin_score.ix[:,1]=scores.median()
fin_score.ix[:,2]=scores.min()
fin_score.ix[:,3]=scores.max()
In [9]:
pd.set_option('display.max_rows', None)
fin_score.sort_values('Mean',axis=0)
Out[9]:
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