文章目錄
一、特徵工程
#核心代碼舉例
# 統計特徵
#計算均值
gp = train.groupby(by)[fea].mean()
#計算中位數
gp = train.groupby(by)[fea].median()
#計算方差
gp = train.groupby(by)[fea].std()
#計算最大值
gp = train.groupby(by)[fea].max()
#計算最小值
gp = train.groupby(by)[fea].min()
#計算出現次數
gp = train.groupby(by)[fea].size()
# groupby生成統計特徵:mean,std
# 按照communityName分組計算面積的均值和方差
temp = data.groupby('communityName')['area'].agg({'com_area_mean': 'mean', 'com_area_std': 'std'})
# 特徵拆分
# 將houseType轉爲'Room','Hall','Bath'
def Room(x):
Room = int(x.split('室')[0])
return Room
def Hall(x):
Hall = int(x.split("室")[1].split("廳")[0])
return Hall
def Bath(x):
Bath = int(x.split("室")[1].split("廳")[1].split("衛")[0])
return Bath
data['Room'] = data['houseType'].apply(lambda x: Room(x))
data['Hall'] = data['houseType'].apply(lambda x: Hall(x))
data['Bath'] = data['houseType'].apply(lambda x: Bath(x))
#特徵合併
# 合併部分配套設施特徵
data['trainsportNum'] = 5 * data['subwayStationNum'] / data['subwayStationNum'].mean() + data['busStationNum'] / \
data[
'busStationNum'].mean()
# 交叉生成特徵:特徵之間交叉+ - * /
data['Room_Bath'] = (data['Bath']+1) / (data['Room']+1)
# 聚類特徵
from sklearn.mixture import GaussianMixture 使用GaussianMixture做聚類特徵
gmm = GaussianMixture(n_components=4, covariance_type='full', random_state=0)
gmm.fit_predict(data)
# 特徵編碼
from sklearn.preprocessing import LabelEncoder
data['communityName'] = LabelEncoder().fit_transform(data['communityName'])
from sklearn import preprocessing.OneHotEncoder
data['communityName'] = OneHotEncoder().fit_transform(data['communityName'])
# 過大量級值取log平滑(針對線性模型有效)
data[feature]=np.log1p(data[feature])
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import LabelEncoder
train = pd.read_csv('./train_data.csv')
test = pd.read_csv('./test_a.csv')
target_train = train.pop('tradeMoney')
target_test = test.pop('tradeMoney')
1.1 特徵合併
def newfeature(data):
# 將houseType轉爲'Room','Hall','Bath'
def Room(x):
Room = int(x.split('室')[0])
return Room
def Hall(x):
Hall = int(x.split("室")[1].split("廳")[0])
return Hall
def Bath(x):
Bath = int(x.split("室")[1].split("廳")[1].split("衛")[0])
return Bath
data['Room'] = data['houseType'].apply(lambda x: Room(x))
data['Hall'] = data['houseType'].apply(lambda x: Hall(x))
data['Bath'] = data['houseType'].apply(lambda x: Bath(x))
data['Room_Bath'] = (data['Bath']+1) / (data['Room']+1)
# 填充租房類型
data.loc[(data['rentType'] == '未知方式') & (data['Room'] <= 1), 'rentType'] = '整租'
# print(data.loc[(data['rentType']=='未知方式')&(data['Room_Bath']>1),'rentType'])
data.loc[(data['rentType'] == '未知方式') & (data['Room_Bath'] > 1), 'rentType'] = '合租'
data.loc[(data['rentType'] == '未知方式') & (data['Room'] > 1) & (data['area'] < 50), 'rentType'] = '合租'
data.loc[(data['rentType'] == '未知方式') & (data['area'] / data['Room'] < 20), 'rentType'] = '合租'
# data.loc[(data['rentType']=='未知方式')&(data['area']>60),'rentType']='合租'
data.loc[(data['rentType'] == '未知方式') & (data['area'] <= 50) & (data['Room'] == 2), 'rentType'] = '合租'
data.loc[(data['rentType'] == '未知方式') & (data['area'] > 60) & (data['Room'] == 2), 'rentType'] = '整租'
data.loc[(data['rentType'] == '未知方式') & (data['area'] <= 60) & (data['Room'] == 3), 'rentType'] = '合租'
data.loc[(data['rentType'] == '未知方式') & (data['area'] > 60) & (data['Room'] == 3), 'rentType'] = '整租'
data.loc[(data['rentType'] == '未知方式') & (data['area'] >= 100) & (data['Room'] > 3), 'rentType'] = '整租'
# data.drop('Room_Bath', axis=1, inplace=True)
# 提升0.0001
def month(x):
month = int(x.split('/')[1])
return month
# def day(x):
# day = int(x.split('/')[2])
# return day
# 結果變差
# 分割交易時間
# data['year']=data['tradeTime'].apply(lambda x:year(x))
data['month'] = data['tradeTime'].apply(lambda x: month(x))
# data['day'] = data['tradeTime'].apply(lambda x: day(x))# 結果變差
# data['pv/uv'] = data['pv'] / data['uv']
# data['房間總數'] = data['室'] + data['廳'] + data['衛']
# 合併部分配套設施特徵
data['trainsportNum'] = 5 * data['subwayStationNum'] / data['subwayStationNum'].mean() + data['busStationNum'] / \
data[
'busStationNum'].mean()
data['all_SchoolNum'] = 2 * data['interSchoolNum'] / data['interSchoolNum'].mean() + data['schoolNum'] / data[
'schoolNum'].mean() \
+ data['privateSchoolNum'] / data['privateSchoolNum'].mean()
data['all_hospitalNum'] = 2 * data['hospitalNum'] / data['hospitalNum'].mean() + \
data['drugStoreNum'] / data['drugStoreNum'].mean()
data['all_mall'] = data['mallNum'] / data['mallNum'].mean() + \
data['superMarketNum'] / data['superMarketNum'].mean()
data['otherNum'] = data['gymNum'] / data['gymNum'].mean() + data['bankNum'] / data['bankNum'].mean() + \
data['shopNum'] / data['shopNum'].mean() + 2 * data['parkNum'] / data['parkNum'].mean()
data.drop(['subwayStationNum', 'busStationNum',
'interSchoolNum', 'schoolNum', 'privateSchoolNum',
'hospitalNum', 'drugStoreNum', 'mallNum', 'superMarketNum', 'gymNum', 'bankNum', 'shopNum', 'parkNum'],
axis=1, inplace=True)
# 提升0.0005
# data['houseType_1sumcsu']=data['Bath'].map(lambda x:str(x))+data['month'].map(lambda x:str(x))
# data['houseType_2sumcsu']=data['Bath'].map(lambda x:str(x))+data['communityName']
# data['houseType_3sumcsu']=data['Bath'].map(lambda x:str(x))+data['plate']
data.drop('houseType', axis=1, inplace=True)
data.drop('tradeTime', axis=1, inplace=True)
data["area"] = data["area"].astype(int)
# categorical_feats = ['rentType', 'houseFloor', 'houseToward', 'houseDecoration', 'communityName','region', 'plate']
categorical_feats = ['rentType', 'houseFloor', 'houseToward', 'houseDecoration', 'region', 'plate','cluster']
return data, categorical_feats
1.2 計算統計特徵
#計算統計特徵
def featureCount(train,test):
train['data_type'] = 0
test['data_type'] = 1
data = pd.concat([train, test], axis=0, join='outer')
def feature_count(data, features=[]):
new_feature = 'count'
for i in features:
new_feature += '_' + i
temp = data.groupby(features).size().reset_index().rename(columns={0: new_feature})
data = data.merge(temp, 'left', on=features)
return data
data = feature_count(data, ['communityName'])
data = feature_count(data, ['buildYear'])
data = feature_count(data, ['totalFloor'])
data = feature_count(data, ['communityName', 'totalFloor'])
data = feature_count(data, ['communityName', 'newWorkers'])
data = feature_count(data, ['communityName', 'totalTradeMoney'])
new_train = data[data['data_type'] == 0]
new_test = data[data['data_type'] == 1]
new_train.drop('data_type', axis=1, inplace=True)
new_test.drop(['data_type'], axis=1, inplace=True)
return new_train, new_test
train, test = featureCount(train, test)
1.3 groupby方法生成統計特徵
#groupby生成統計特徵:mean,std等
def gourpby(train,test):
train['data_type'] = 0
test['data_type'] = 1
data = pd.concat([train, test], axis=0, join='outer')
columns = ['rentType', 'houseFloor', 'houseToward', 'houseDecoration', 'communityName', 'region', 'plate']
for feature in columns:
data[feature] = LabelEncoder().fit_transform(data[feature])
temp = data.groupby('communityName')['area'].agg({'com_area_mean': 'mean', 'com_area_std': 'std'})
temp.fillna(0, inplace=True)
data = data.merge(temp, on='communityName', how='left')
data['price_per_area'] = data.tradeMeanPrice / data.area * 100
temp = data.groupby('communityName')['price_per_area'].agg(
{'comm_price_mean': 'mean', 'comm_price_std': 'std'})
temp.fillna(0, inplace=True)
data = data.merge(temp, on='communityName', how='left')
temp = data.groupby('plate')['price_per_area'].agg(
{'plate_price_mean': 'mean', 'plate_price_std': 'std'})
temp.fillna(0, inplace=True)
data = data.merge(temp, on='plate', how='left')
data.drop('price_per_area', axis=1, inplace=True)
temp = data.groupby('plate')['area'].agg({'plate_area_mean': 'mean', 'plate_area_std': 'std'})
temp.fillna(0, inplace=True)
data = data.merge(temp, on='plate', how='left')
temp = data.groupby(['plate'])['buildYear'].agg({'plate_year_mean': 'mean', 'plate_year_std': 'std'})
data = data.merge(temp, on='plate', how='left')
data.plate_year_mean = data.plate_year_mean.astype('int')
data['comm_plate_year_diff'] = data.buildYear - data.plate_year_mean
data.drop('plate_year_mean', axis=1, inplace=True)
temp = data.groupby('plate')['trainsportNum'].agg('sum').reset_index(name='plate_trainsportNum')
data = data.merge(temp, on='plate', how='left')
temp = data.groupby(['communityName', 'plate'])['trainsportNum'].agg('sum').reset_index(name='com_trainsportNum')
data = data.merge(temp, on=['communityName', 'plate'], how='left')
data['trainsportNum_ratio'] = list(map(lambda x, y: round(x / y, 3) if y != 0 else -1,
data['com_trainsportNum'], data['plate_trainsportNum']))
data = data.drop(['com_trainsportNum', 'plate_trainsportNum'], axis=1)
temp = data.groupby('plate')['all_SchoolNum'].agg('sum').reset_index(name='plate_all_SchoolNum')
data = data.merge(temp, on='plate', how='left')
temp = data.groupby(['communityName', 'plate'])['all_SchoolNum'].agg('sum').reset_index(name='com_all_SchoolNum')
data = data.merge(temp, on=['communityName', 'plate'], how='left')
data = data.drop(['com_all_SchoolNum', 'plate_all_SchoolNum'], axis=1)
temp = data.groupby(['communityName', 'plate'])['all_mall'].agg('sum').reset_index(name='com_all_mall')
data = data.merge(temp, on=['communityName', 'plate'], how='left')
temp = data.groupby('plate')['otherNum'].agg('sum').reset_index(name='plate_otherNum')
data = data.merge(temp, on='plate', how='left')
temp = data.groupby(['communityName', 'plate'])['otherNum'].agg('sum').reset_index(name='com_otherNum')
data = data.merge(temp, on=['communityName', 'plate'], how='left')
data['other_ratio'] = list(map(lambda x, y: round(x / y, 3) if y != 0 else -1,
data['com_otherNum'], data['plate_otherNum']))
data = data.drop(['com_otherNum', 'plate_otherNum'], axis=1)
temp = data.groupby(['month', 'communityName']).size().reset_index(name='communityName_saleNum')
data = data.merge(temp, on=['month', 'communityName'], how='left')
temp = data.groupby(['month', 'plate']).size().reset_index(name='plate_saleNum')
data = data.merge(temp, on=['month', 'plate'], how='left')
data['sale_ratio'] = round((data.communityName_saleNum + 1) / (data.plate_saleNum + 1), 3)
data['sale_newworker_differ'] = 3 * data.plate_saleNum - data.newWorkers
data.drop(['communityName_saleNum', 'plate_saleNum'], axis=1, inplace=True)
new_train = data[data['data_type'] == 0]
new_test = data[data['data_type'] == 1]
new_train.drop('data_type', axis=1, inplace=True)
new_test.drop(['data_type'], axis=1, inplace=True)
return new_train, new_test
train, test = gourpby(train, test)
1.4 聚類方法
#聚類
def cluster(train,test):
from sklearn.mixture import GaussianMixture
train['data_type'] = 0
test['data_type'] = 1
data = pd.concat([train, test], axis=0, join='outer')
col = ['totalFloor',
'houseDecoration', 'communityName', 'region', 'plate', 'buildYear',
'tradeMeanPrice', 'tradeSecNum', 'totalNewTradeMoney',
'totalNewTradeArea', 'tradeNewMeanPrice', 'tradeNewNum', 'remainNewNum',
'landTotalPrice', 'landMeanPrice', 'totalWorkers',
'newWorkers', 'residentPopulation', 'lookNum',
'trainsportNum',
'all_SchoolNum', 'all_hospitalNum', 'all_mall', 'otherNum']
# EM
gmm = GaussianMixture(n_components=3, covariance_type='full', random_state=0)
data['cluster']= pd.DataFrame(gmm.fit_predict(data[col]))
col1 = ['totalFloor','houseDecoration', 'communityName', 'region', 'plate', 'buildYear']
col2 = ['tradeMeanPrice', 'tradeSecNum', 'totalNewTradeMoney',
'totalNewTradeArea', 'tradeNewMeanPrice', 'tradeNewNum', 'remainNewNum',
'landTotalPrice', 'landMeanPrice', 'totalWorkers',
'newWorkers', 'residentPopulation', 'lookNum',
'trainsportNum',
'all_SchoolNum', 'all_hospitalNum', 'all_mall', 'otherNum']
for feature1 in col1:
for feature2 in col2:
temp = data.groupby(['cluster',feature1])[feature2].agg('mean').reset_index(name=feature2+'_'+feature1+'_cluster_mean')
temp.fillna(0, inplace=True)
data = data.merge(temp, on=['cluster', feature1], how='left')
new_train = data[data['data_type'] == 0]
new_test = data[data['data_type'] == 1]
new_train.drop('data_type', axis=1, inplace=True)
new_test.drop(['data_type'], axis=1, inplace=True)
return new_train, new_test
train, test = cluster(train, test)
1.5 log平滑
# 過大量級值取log平滑(針對線性模型有效)
big_num_cols = ['totalTradeMoney','totalTradeArea','tradeMeanPrice','totalNewTradeMoney', 'totalNewTradeArea',
'tradeNewMeanPrice','remainNewNum', 'supplyNewNum', 'supplyLandArea',
'tradeLandArea','landTotalPrice','landMeanPrice','totalWorkers','newWorkers',
'residentPopulation','pv','uv']
for col in big_num_cols:
train[col] = train[col].map(lambda x: np.log1p(x))
test[col] = test[col].map(lambda x: np.log1p(x))
#對比特徵工程前後線性模型結果情況
test=test.fillna(0)
# Lasso迴歸
from sklearn.linear_model import Lasso
lasso=Lasso(alpha=0.1)
lasso.fit(train,target_train)
#預測測試集和訓練集結果
y_pred_train=lasso.predict(train)
y_pred_test=lasso.predict(test)
#對比結果
from sklearn.metrics import r2_score
score_train=r2_score(y_pred_train,target_train)
print("訓練集結果:",score_train)
score_test=r2_score(y_pred_test, target_test)
print("測試集結果:",score_test)
二、特徵選擇
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import LabelEncoder
#讀取數據
train = pd.read_csv('')
test = pd.read_csv('')
target_train = train.pop('tradeMoney')
target_test = test.pop('tradeMoney')
2.1 相關係數法
#相關係數法特徵選擇
from sklearn.feature_selection import SelectKBest
print(train.shape)
sk=SelectKBest(k=150)
new_train=sk.fit_transform(train,target_train)
print(new_train.shape)
# 獲取對應列索引
select_columns=sk.get_support(indices = True)
# print(select_columns)
# 獲取對應列名
# print(test.columns[select_columns])
select_columns_name=test.columns[select_columns]
new_test=test[select_columns_name]
print(new_test.shape)
# Lasso迴歸
from sklearn.linear_model import Lasso
lasso=Lasso(alpha=0.1)
lasso.fit(new_train,target_train)
#預測測試集和訓練集結果
y_pred_train=lasso.predict(new_train)
y_pred_test=lasso.predict(new_test)
#對比結果
from sklearn.metrics import r2_score
score_train=r2_score(y_pred_train,target_train)
print("訓練集結果:",score_train)
score_test=r2_score(y_pred_test, target_test)
print("測試集結果:",score_test)
2.2 Wrapper
# Wrapper
from sklearn.feature_selection import RFE
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
rfe = RFE(lr, n_features_to_select=160)
rfe.fit(train,target_train)
RFE(estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False),
n_features_to_select=40, step=1, verbose=0)
select_columns = [f for f, s in zip(train.columns, rfe.support_) if s]
print(select_columns)
new_train = train[select_columns]
new_test = test[select_columns]
# Lasso迴歸
from sklearn.linear_model import Lasso
lasso=Lasso(alpha=0.1)
lasso.fit(new_train,target_train)
#預測測試集和訓練集結果
y_pred_train=lasso.predict(new_train)
y_pred_test=lasso.predict(new_test)
#對比結果
from sklearn.metrics import r2_score
score_train=r2_score(y_pred_train,target_train)
print("訓練集結果:",score_train)
score_test=r2_score(y_pred_test, target_test)
print("測試集結果:",score_test)
2.3 Embedded
基於懲罰項的特徵選擇法:Lasso(l1)和Ridge(l2)
# Embedded
# 基於懲罰項的特徵選擇法
# Lasso(l1)和Ridge(l2)
from sklearn.linear_model import Ridge
ridge = Ridge(alpha=5)
ridge.fit(train,target_train)
Ridge(alpha=5, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,
random_state=None, solver='auto', tol=0.001)
# 特徵係數排序
coefSort = ridge.coef_.argsort()
print(coefSort)
# 特徵係數
featureCoefSore=ridge.coef_[coefSort]
print(featureCoefSore)
select_columns = [f for f, s in zip(train.columns, featureCoefSore) if abs(s)> 0.0000005 ]
# 選擇絕對值大於0.0000005的特徵
new_train = train[select_columns]
new_test = test[select_columns]
# Lasso迴歸
from sklearn.linear_model import Lasso
lasso=Lasso(alpha=0.1)
lasso.fit(new_train,target_train)
#預測測試集和訓練集結果
y_pred_train=lasso.predict(new_train)
y_pred_test=lasso.predict(new_test)
#對比結果
from sklearn.metrics import r2_score
score_train=r2_score(y_pred_train,target_train)
print("訓練集結果:",score_train)
score_test=r2_score(y_pred_test, target_test)
print("測試集結果:",score_test)
2.4 基於樹模型的特徵選擇法
隨機森林 平均不純度減少(mean decrease impurity)
# Embedded
# 基於樹模型的特徵選擇法
# 隨機森林 平均不純度減少(mean decrease impurity
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor()
# 訓練隨機森林模型,並通過feature_importances_屬性獲取每個特徵的重要性分數。rf = RandomForestRegressor()
rf.fit(train,target_train)
print("Features sorted by their score:")
print(sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), train.columns),
reverse=True))
select_columns = [f for f, s in zip(train.columns, rf.feature_importances_) if abs(s)> 0.00005 ]
# 選擇絕對值大於0.00005的特徵
new_train = train[select_columns]
new_test = test[select_columns]
# Lasso迴歸
from sklearn.linear_model import Lasso
lasso=Lasso(alpha=0.1)
lasso.fit(new_train,target_train)
#預測測試集和訓練集結果
y_pred_train=lasso.predict(new_train)
y_pred_test=lasso.predict(new_test)
#對比結果
from sklearn.metrics import r2_score
score_train=r2_score(y_pred_train,target_train)
print("訓練集結果:",score_train)
score_test=r2_score(y_pred_test, target_test)
print("測試集結果:",score_test)