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model10.py
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model10.py
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# coding:utf-8
'''
利用特征 最后一天对物品点击数，最后一天对品牌点击数， 用户转化率，商品最后一天热门程度
'''
import sklearn,pandas, pickle, os, summary, util
import numpy as np
from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
from sklearn.metrics import f1_score
from sklearn.grid_search import GridSearchCV
__fname__ = os.path.basename(__file__)
__fname__ = __fname__[:__fname__.rindex('.')]
def GetData():
data = pandas.read_csv('data.train.csv')
Y=data['buy']
X=GetFeature(data)
#rand = np.random.rand(len(Y))<0.0001
#idx = (Y==1) | ((Y==0) & rand)
#X = X[idx]
#Y = Y[idx]
return X, Y
_feature_names = [
"user_action_count",
"user_lastday_count",
"user_buy_count",
"item_click_count",
"item_lastday_count",
"item_buy_count",
"cat_click_count",
"cat_buy_count",
"user_cat_count",
"user_cat_lastday_count",
"user_item_count",
"user_item_lastday_count",
"user_add_car",
"user_add_star",
"item_added_car",
"item_added_start",
"user_item_lasttime",
"cat_add_car",
"cat_add_star",
"user_item_buy",
"user_item_lastweek_star",
"user_item_before_halfmonth_click",
"user_item_before_halfmonth_star",
"user_item_before_halfmonth_add_car",
"user_item_before_halfmonth_buy",
"user_cat_lastweek_star",
"user_cat_halfmonth_buy",
"user_cat_before_halfmonth_click",
"user_cat_before_halfmonth_star",
"user_cat_before_halfmonth_add_car",
"user_cat_before_halfmonth_buy",
"user_lastday_add_star",
"user_item_lastday_add_star",
"user_cat_lastday_add_star",
"user_lastday_add_cart",
"user_item_lastday_add_cart",
"user_cat_lastday_add_cart",
"user_lastday_buy",
"user_item_lastday_buy",
"user_cat_lastday_buy",
"item_convert_rate",
"user_item_click_nobuy",
"user_item_star_nobuy",
"user_item_cart_nobuy",
"user_item_buy_again",
]
def GetFeature(data):
nolog = ['user_id','item_id', 'buy', 'user_convert_rate', 'item_convert_rate']
factor_features = [
"user_item_click_nobuy",
"user_item_star_nobuy",
"user_item_cart_nobuy",
"user_item_buy_again"
]
feature_names = [i for i in _feature_names if i not in nolog and i not in factor_features]
X1 = np.log(0.3+data[feature_names])
X2 = dict()
X2['user_convert_rate'] = data['user_buy_count'] / (1+data['user_action_count'])
X2['item_convert_rate'] = data['item_buy_count'] / (1+data['item_click_count'])
X2 = pandas.DataFrame(X2)
X3 = data[factor_features]
X = pandas.concat([X1, X2, X3], axis=1)
return X[_feature_names]
def GetModel():
f = open('%s.model' % __fname__,'rb')
clf = pickle.load(f)
f.close()
return clf
def TestModel():
summary.TestModel(__fname__)
if __name__ == '__main__':
X, Y = GetData()
feature_names = X.columns
parms = {
'C': np.logspace(-2,1,10), # 0.5是最好的
# 'class_weight':[{0:1,1:r} for r in np.linspace(1,10,10)] #[{0:1,1:50},{0:1,1:70},{0:1,1:85},{0:1,1:100},{0:1,1:120},{0:1,1:150}]
}
lr = LogisticRegression(penalty='l1')
clf = GridSearchCV(lr, parms, scoring='f1', n_jobs=10)
clf.fit(X,Y)
import pickle
f = open('%s.model' % __fname__,'wb')
pickle.dump(clf, f)
f.close()
pred = clf.predict(X)
summary.clf_summary(clf, feature_names)
summary.summary(Y, pred)
TestModel()
util.notify_me('%s is finished' % __fname__)