我正在降维。我正在使用随机森林来查找与目标 SalePrice 列相关性最高的列。
问题是输出太大。绝对不是我想要的。它返回 259 列。其中一些列是对分类变量进行一次热编码并将它们添加回数据框中的结果，这在逻辑上增加了数据集的维度。但是，我只想返回与目标变量“SalePrice”相关性最高的列。不是整个该死的数据框。
这是输出：
0 1 2 3 4 5 6 ... 252 253 254 255 256 257 258
0 1 RL 65.0 8450 Pave NaN Reg ... 0 1 0 0 1 0 1
1 2 RL 80.0 9600 Pave NaN Reg ... 0 1 0 0 1 0 1
2 3 RL 68.0 11250 Pave NaN IR1 ... 0 1 0 0 1 0 1
3 4 RL 60.0 9550 Pave NaN IR1 ... 0 0 0 0 1 0 1
4 5 RL 84.0 14260 Pave NaN IR1 ... 0 1 0 0 1 0 1
... ... .. ... ... ... ... ... ... .. .. .. .. .. .. ..
1455 1456 RL 62.0 7917 Pave NaN Reg ... 0 1 0 0 1 0 1
1456 1457 RL 85.0 13175 Pave NaN Reg ... 0 1 0 0 1 0 1
1457 1458 RL 66.0 9042 Pave NaN Reg ... 0 1 0 0 1 0 1
1458 1459 RL 68.0 9717 Pave NaN Reg ... 0 1 0 0 1 0 1
1459 1460 RL 75.0 9937 Pave NaN Reg ... 0 1 0 0 1 0 1
[1460 rows x 259 columns]
这是我的代码：
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
train = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/test.csv")
categorical_columns = ['MSSubClass', 'MSZoning', 'LotShape', 'LandContour', 'LotConfig', 'Neighborhood', 'Condition1',
'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd',
'Foundation', 'Heating', 'Electrical', 'Functional', 'GarageType', 'PavedDrive', 'Fence',
'MiscFeature', 'SaleType', 'SaleCondition', 'Street', 'CentralAir']
ranked_columns = ['Utilities', 'LandSlope', 'ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'BsmtExposure',
'BsmtFinType1', 'BsmtFinType2', 'HeatingQC', 'KitchenQual', 'FireplaceQu', 'GarageQual', 'GarageCond',
'PoolQC', 'OverallQual', 'OverallCond']
numerical_columns = ['LotArea', 'LotFrontage', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
'BsmtUnfSF','TotalBsmtSF', '1stFlrSF', '2ndFlrSf', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',
'BsmtHalfBath', 'FullBath', 'HalfBath', 'Bedroom', 'Kitchen', 'TotRmsAbvGrd', 'Fireplaces',
'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch',
'3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']
def feature_encoding(df, categorical_list):
# take one-hot encoding
OHE_sdf = pd.get_dummies(df[categorical_list])
# drop the old categorical column from original df
df.drop(columns = categorical_list, inplace = True)
# attach one-hot encoded columns to original dataframe
df = pd.concat([df, OHE_sdf], axis = 1, ignore_index = True)
# Integer Encoding
df['Utilities'] = df['Utilities'].replace(['AllPub', 'NoSeWa'], [2, 1]) # Utilities
df['ExterQual'] = df['ExterQual'].replace(['Ex', 'Gd', 'TA', 'Fa'], [4, 3, 2, 1]) # Exterior Quality
df['LandSlope'] = df['LandSlope'].replace(['Gtl', 'Mod', 'Sev'], [3, 2, 1]) # Land Slope
df['ExterCond'] = df['ExterCond'].replace(['Ex', 'Gd', 'TA', 'Fa', 'Po'], [4, 3, 2, 1, 0]) # Exterior Condition
df['HeatingQC'] = df['HeatingQC'].replace(['Ex', 'Gd', 'TA', 'Fa', 'Po'], [4, 3, 2, 1, 0]) # Heating Quality and Condition
df['KitchenQual'] = df['KitchenQual'].replace(['Ex', 'Gd', 'TA', 'Fa'], [3, 2, 1, 0]) # Kitchen Quality
# Replacing the NA values of each column with XX to avoid pandas from listing them as NaN
na_data = ['Alley', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'FireplaceQu',
'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature']
for i in na_data:
df[i] = df[i].fillna('XX')
# Replaced the NaN values of LotFrontage and MasVnrArea with the mean of their column
df['LotFrontage'] = df['LotFrontage'].fillna(df['LotFrontage'].mean())
df['MasVnrArea'] = df['MasVnrArea'].fillna(df['MasVnrArea'].mean())
x_train, x_test, y_train, y_test = train_test_split(df, df['SalePrice'], test_size = 0.3, random_state = 42)
sel = SelectFromModel(RandomForestClassifier(n_estimators = 100), threshold = 300 * "mean")
sel.fit(x_train, y_train)
sel.get_support()
selected_feat = x_train.columns[sel.get_support()]
return selected_feat
print(feature_encoding(train, categorical_columns))
随机森林的代码就在训练测试拆分之后。
更新
将代码更改为上述版本后，出现以下错误：
Traceback (most recent call last):
File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\pandas\core\indexes\base.py", line 2657, in get_loc
return self._engine.get_loc(key)
File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 129, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index_class_helper.pxi", line 91, in pandas._libs.index.Int64Engine._check_type
KeyError: 'Utilities'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/Users/security/Downloads/AP/Boston-Kaggle/Boston.py", line 66, in <module>
print(feature_encoding(train, categorical_columns))
File "C:/Users/security/Downloads/AP/Boston-Kaggle/Boston.py", line 37, in feature_encoding
df['Utilities'] = df['Utilities'].replace(['AllPub', 'NoSeWa'], [2, 1]) # Utilities
File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\pandas\core\frame.py", line 2927, in __getitem__
indexer = self.columns.get_loc(key)
File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\pandas\core\indexes\base.py", line 2659, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 129, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index_class_helper.pxi", line 91, in pandas._libs.index.Int64Engine._check_type
KeyError: 'Utilities'