forked from duxuhao/JData-2018
/
Run_12.py
341 lines (322 loc) · 19.7 KB
/
Run_12.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
from History_detailB import order_hist,order_price_total_hist,action_hist
from utility import Merge,readlog
import xgboost as xgb
import lightgbm as lgbm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
order = pd.read_csv('./B/jdata_user_order.csv', parse_dates=['o_date'])
sku = pd.read_csv('./B/jdata_sku_basic_info.csv', )
order = pd.merge(order, sku, on='sku_id', how='left')
target_order = order[(order.cate == 101) | (order.cate == 30)].reset_index(drop=True)
first_day = datetime.datetime.strptime('2016-08-31 00:00:00', '%Y-%m-%d %H:%M:%S')
target_order['o_day_series'] = (target_order['o_date'] - first_day).apply(lambda x: x.days)
def score(pred, real): #评分系统，感谢herhert，只要s1
pred['index'] = np.arange(pred.shape[0]) + 1
pred['wi'] = 1 / (1 + np.log(pred['index']))
compare = pd.merge(pred, real, how='left', on='user_id')
compare = compare[:40000]
compare.fillna(0, inplace=True)
S10 = np.sum(compare['buy'] * compare['wi']) / compare['wi'].sum()
print(S10)
compare = compare[:34000]
S1 = np.sum(compare['buy'] * compare['wi']) / compare['wi'].sum()
print(S1)
return S1
def predict22(X_all, X_new, features, clf, score, v = False, esr=50, sk=3, fn='submission'):
first_day = datetime.datetime.strptime('2017-08-31 00:00:00', '%Y-%m-%d %H:%M:%S')
temp_user = target_order[(target_order.o_day_series < 336) & (target_order.o_day_series >= 274)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 336
print('before delete: {}'.format(X_all.shape))
X = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('after delete: {}'.format(X.shape))
temp_user = target_order[(target_order.o_day_series < 366) & \
(target_order.o_day_series >= 366 - 74)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 366
print('before delete: {}'.format(X_new.shape))
X_new = temp_user.merge(X_new,on=['user_id','CreateGroup'],how = 'left')
temp_user = target_order[(target_order.o_day_series < 306) & (target_order.o_day_series >= 215)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 306
print('before delete: {}'.format(X_all.shape))
X2 = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('Train: {}'.format(X_new.shape))
kf = KFold(n_splits=sk)
print(len(features))
Performance = []
X_new['Prob'] = 0
X_new['Prob_x'] = 0
X['Prob_x'] = 0
for train_index, test_index in kf.split(X2):
X_train, X_test = X2.ix[train_index,:], X2.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X2.ix[train_index,:].buy, X2.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
X_new['Prob_x'] = X_new['Prob_x'] + clf.predict_proba(X_new[features])[:,1]/sk
X['Prob_x'] = X['Prob_x'] + clf.predict_proba(X[features])[:,1]/sk
features.append('Prob_x')
for train_index, test_index in kf.split(X):
X_train, X_test = X.ix[train_index,:], X.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X.ix[train_index,:].buy, X.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
pred = clf.predict_proba(X_test)[:,1]
X_new['Prob'] = X_new['Prob'] + clf.predict_proba(X_new[features])[:,1]/sk
Performance.append(roc_auc_score(y_test,pred))
print("Mean Score: {}".format(np.mean(Performance)))
X_new['Days'] = np.random.randint(15,size=len(X_new))
X_new['pred_date'] = X_new['Days'].apply(lambda x: (datetime.timedelta(days=x) + first_day).strftime("%Y-%m-%d"))
X_new.sort_values(by = ['Prob'], ascending = False, inplace = True)
X_new[['user_id','Prob']].to_csv('prob_{}.csv'.format(fn), index = None)
X_new[['user_id','pred_date']][:50000].to_csv('{}.csv'.format(fn), index = None)
return np.mean(Performance),clf
def predict2(X_all, X_new, features, clf, score, v = False, esr=50, sk=3, fn='submission'):
temp_user = target_order[(target_order.o_day_series < 336) & (target_order.o_day_series >= 274)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 336
print('before delete: {}'.format(X_all.shape))
X = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('after delete: {}'.format(X.shape))
temp_user = target_order[(target_order.o_day_series < 366) & \
(target_order.o_day_series >= 366 - 74)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 366
print(-1 in temp_user.user_id)
print(4366 in temp_user.user_id)
print('before delete: {}'.format(X_new.shape))
X_new = temp_user.merge(X_new,on=['user_id','CreateGroup'],how = 'left')
temp_user = target_order[(target_order.o_day_series < 306) & (target_order.o_day_series >= 215)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 306
print('before delete: {}'.format(X_all.shape))
X2 = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('Train: {}'.format(X_new.shape))
kf = KFold(n_splits=sk)
print(len(features))
Performance = []
X_new['Prob'] = 0
X_new['Prob_x'] = 0
X['Prob_x'] = 0
for train_index, test_index in kf.split(X2):
X_train, X_test = X2.ix[train_index,:], X2.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X2.ix[train_index,:].buy, X2.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
X_new['Prob_x'] = X_new['Prob_x'] + clf.predict_proba(X_new[features])[:,1]/sk
X['Prob_x'] = X['Prob_x'] + clf.predict_proba(X[features])[:,1]/sk
features.append('Prob_x')
for train_index, test_index in kf.split(X):
X_train, X_test = X.ix[train_index,:], X.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X.ix[train_index,:].buy, X.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
pred = clf.predict_proba(X_test)[:,1]
X_new['Prob'] = X_new['Prob'] + clf.predict_proba(X_new[features])[:,1]/sk
Performance.append(roc_auc_score(y_test,pred))
print("Mean Score: {}".format(np.mean(Performance)))
importantlist = []
for i, j in zip(features,clf.feature_importances_):
importantlist.append([j,i])
print(sorted(importantlist)[::-1])
first_day = datetime.datetime.strptime('2017-08-31 00:00:00', '%Y-%m-%d %H:%M:%S')
X_new['Days'] = np.random.randint(15,size=len(X_new))
X_new['pred_date'] = X_new['Days'].apply(lambda x: (datetime.timedelta(days=x) + first_day).strftime("%Y-%m-%d"))
X_new.sort_values(by = ['Prob'], ascending = False, inplace = True)
X_new[['user_id','Prob']].to_csv('prob_{}.csv'.format(fn), index = None)
X_new[['user_id','pred_date']][:50000].to_csv('{}.csv'.format(fn), index = None)
return np.mean(Performance),clf
def predict(X_all, X_new, features, clf, score, v = False, esr=50, sk=3, fn='submission'):
first_day = datetime.datetime.strptime('2017-08-31 00:00:00', '%Y-%m-%d %H:%M:%S')
temp_user = target_order[(target_order.o_day_series < 336) & (target_order.o_day_series >= 274)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 336
print('before delete: {}'.format(X_all.shape))
X = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('after delete: {}'.format(X.shape))
temp_user = target_order[(target_order.o_day_series < 366) & \
(target_order.o_day_series >= 366 - 75)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 366
print('before delete: {}'.format(X_new.shape))
X_new = temp_user.merge(X_new,on=['user_id','CreateGroup'],how = 'left')
print('Train: {}'.format(X_new.shape))
kf = KFold(n_splits=sk)
print(len(features))
Performance = []
X_new['Prob'] = 0
for train_index, test_index in kf.split(X):
X_train, X_test = X.ix[train_index,:], X.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X.ix[train_index,:].buy, X.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
pred = clf.predict_proba(X_test)[:,1]
X_new['Prob'] = X_new['Prob'] + clf.predict_proba(X_new[features])[:,1]/sk
Performance.append(roc_auc_score(y_test,pred))
print("Mean Score: {}".format(np.mean(Performance)))
X_new['Days'] = np.random.randint(15,size=len(X_new))
X_new['pred_date'] = X_new['Days'].apply(lambda x: (datetime.timedelta(days=x) + first_day).strftime("%Y-%m-%d"))
X_new.sort_values(by = ['Prob'], ascending = False, inplace = True)
X_new[['user_id','Prob']].to_csv('prob_{}.csv'.format(fn), index = None)
X_new[['user_id','pred_date']][:50000].to_csv('{}.csv'.format(fn), index = None)
return np.mean(Performance),clf
def validateseq2(X_all, y, features, clf, score, v = False, esr=50, sk=5):
temp_user = target_order[(target_order.o_day_series < 336) & (target_order.o_day_series >= 274)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 336
print('before delete: {}'.format(X_all.shape))
X = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('after delete: {}'.format(X.shape))
temp_user = target_order[(target_order.o_day_series < 306) & (target_order.o_day_series >= 215)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 306
print('before delete: {}'.format(X_all.shape))
X2 = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('after delete: {}'.format(X.shape))
kf = KFold(n_splits=sk)
print(len(features))
X['Prob_x'] = 0
for train_index, test_index in kf.split(X2):
X_train, X_test = X2.ix[train_index,:], X2.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X2.ix[train_index,:].buy, X2.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
X['Prob_x'] = X['Prob_x'] + clf.predict_proba(X[features])[:,1]/sk
Performance = []
features.append('Prob_x')
for train_index, test_index in kf.split(X):
X_train, X_test = X.ix[train_index,:], X.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X.ix[train_index,:].buy, X.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
pred = clf.predict_proba(X_test)[:,1]
Performance.append(roc_auc_score(y_test,pred))
print("Mean Score: {}".format(np.mean(Performance)))
return np.mean(Performance),clf
def read6810():
mergeindex = ['user_id','CreateGroup']
mergefeatures = []
# train = pd.read_csv('traina_8month_more.csv')
key = ['_o_area','_sku_id']
mainfn = 'trainb_8month_final.csv'
mergefn = ['trainb_1month_final.csv']
train = Merge(mainfn, mergefn, key)
train_other = pd.read_csv('trainb_8month_other_final.csv')
for i in train_other.columns:
if ('cnt' in i) | ('median' in i) | ('Difference' in i) | ('sum' in i) | ('Plux' in i) | ('series' in i):
mergefeatures.append(i)
train_other = train_other[mergeindex + mergefeatures]
mergefeatures2 = [i+'_other' for i in mergefeatures]
train_other.columns = mergeindex + mergefeatures2
train = pd.merge(train, train_other, on = mergeindex, how = 'left')
# pre = pd.read_csv('testa_8month_more.csv')
print(np.unique(train.CreateGroup))
mainfn = 'testb_8month_final.csv'
mergefn = ['testb_1month_final.csv']
pre = Merge(mainfn, mergefn, key)
pre_other = pd.read_csv('testb_8month_other_final.csv')
pre_other = pre_other[mergeindex + mergefeatures]
pre_other.columns = mergeindex + mergefeatures2
pre = pd.merge(pre, pre_other, on = mergeindex, how = 'left')
print(np.unique(pre.CreateGroup))
df = pd.concat([train, pre]).reset_index(drop=True)
df2 = pd.read_csv('D2b.csv')
df2_other = pd.read_csv('D2b_other.csv')
print(np.unique(df2.CreateGroup))
cold = list(df2.columns)
cold.remove('user_id')
cold.remove('CreateGroup')
df2_other = df2_other[['user_id','CreateGroup'] + cold]
for i in range(len(cold)):
cold[i] = cold[i] + '_other'
df2_other.columns = ['user_id','CreateGroup'] + cold
df2 = df2.merge(df2_other,on = ['user_id','CreateGroup'])
col2 = list(df2.columns)
for i in col2[:]:
if ('price' in i) | ('para' in i) | ('o_month' in i) | ('o_date' in i)| ('age' in i)| ('sex' in i) | ('user_lv_cd' in i):
col2.remove(i)
df = df.merge(df2[col2],on=['user_id','CreateGroup'],how = 'left')
df = df.merge(order_hist([246,276,306,336,366],5,'o_sku_num') ,on=['user_id','CreateGroup'],how = 'left')
df = df.merge(order_hist([246,276,306,336,366],5,'o_day_series') ,on=['user_id','CreateGroup'],how = 'left')
df = df.merge(order_hist([246,276,306,336,366],5,'para_1') ,on=['user_id','CreateGroup'],how = 'left')
df = df.merge(order_price_total_hist([246,276,306,336,366],5),on=['user_id','CreateGroup'],how = 'left')
col = list(df.columns)
last = pd.read_csv('last3b.csv')
print(np.unique(last.CreateGroup))
df = df.merge(last,on = ['user_id','CreateGroup'],how = 'left')
# print(df[['user_id','CreateGroup','nextbuy_1','nextbuy_2','nextbuy_3','nextbuy_4']])
print(df.shape)
return df
def validate(X_all, y, features, clf, score, v = False, esr=50, sk=5):
temp_user = target_order[(target_order.o_day_series < 336) & (target_order.o_day_series >= 273)][['user_id']].drop_duplicates().reset_index(drop=True)
temp_user['CreateGroup'] = 336
print('before delete: {}'.format(X_all.shape))
X = temp_user.merge(X_all,on=['user_id','CreateGroup'],how = 'left')
print('after delete: {}'.format(X.shape))
kf = KFold(n_splits=sk)
print(len(features))
Performance = []
for train_index, test_index in kf.split(X):
X_train, X_test = X.ix[train_index,:], X.ix[test_index,:]
X_train, X_test = X_train[features], X_test[features]
y_train, y_test = X.ix[train_index,:].buy, X.ix[test_index,:].buy
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='auc', verbose=v, early_stopping_rounds=esr)
pred = clf.predict_proba(X_test)[:,1]
Performance.append(roc_auc_score(y_test,pred))
print("Mean Score: {}".format(np.mean(Performance)))
return np.mean(Performance),clf
df = read6810()
df.ix[df.CreateGroup == 367, 'CreateGroup'] = 366
df = df.drop_duplicates(subset=['user_id','CreateGroup'],keep='last').reset_index(drop = True)
print(df.shape)
t3 = pd.read_csv('trainb_3month_3level_userid_2.csv')
t3p = pd.read_csv('testb_3month_3level_userid_2.csv')
t3 = pd.concat([t3,t3p])
notusable = ['buy','nextbuy','o_date','a_date','PredictDays','user_id','CreateGroup']
month = [i for i in t3.columns if i not in notusable]
tt = t3[['user_id','CreateGroup'] + month]
tt.columns = ['user_id','CreateGroup'] + ['{}_3month'.format(i) for i in month]
df2 = df.merge(tt, on = ['user_id','CreateGroup'], how = 'left')
df3 = df2[:]
for i in ['o_day_series_o_ave','o_day_series_o_sum','o_day_series_o_median','o_day_series_o_min','o_day_series_o_max','o_day_series',
'c_day_series_o_ave','c_day_series_o_sum','c_day_series_o_median','c_day_series_o_min','c_day_series_o_max','c_day_series',
'a_day_series_a_ave','a_day_series_a_sum','a_day_series_a_median','a_day_series_a_min','a_day_series_a_max','a_day_series',
'o_day_series_0','o_day_series_1','o_day_series_2','o_day_series_3','o_day_series_4',
'o_day_series_o_ave_o_user_id_3month','o_day_series_o_sum_o_user_id_3month','o_day_series_o_min_o_user_id_3month',
'o_day_series_o_max_o_user_id_3month','o_day_series_3month','c_day_series_o_ave_o_user_id_3month','c_day_series_o_sum_o_user_id_3month',
'c_day_series_o_min_o_user_id_3month','c_day_series_o_max_o_user_id_3month','c_day_series_3month',
'a_day_series_a_ave_3month','a_day_series_a_sum_3month','a_day_series_a_min_3month','a_day_series_a_max_3month','a_day_series_3month',
'o_day_series_o_median_other','c_day_series_o_median_other','a_day_series_a_median_other','o_day_series_other',
'a_day_series_other','c_day_series_other']:
df3[i] = df3['CreateGroup'] - df3[i]
for i in ['o_month_series_o_ave','o_month_series_o_sum','o_month_series_o_median','o_month_series_o_min','o_month_series_o_max','o_month_series',
'a_month_series_a_ave','a_month_series_a_sum','a_month_series_a_median','a_month_series_a_min','a_month_series_a_max','a_month_series',
'o_month_series_o_median_other','a_month_series_a_median_other',
'o_month_series_o_ave_o_user_id_3month','o_month_series_o_sum_o_user_id_3month','o_month_series_o_min_o_user_id_3month',
'o_month_series_o_max_o_user_id_3month','o_month_series_3month',
'a_month_series_a_ave_3month','a_month_series_a_sum_3month','a_month_series_a_min_3month','a_month_series_a_max_3month']:
df3[i] = df3['CreateGroup']//30 - df3[i]
clf = lgbm.LGBMClassifier(objective='binary', num_leaves=35, max_depth=-1,
learning_rate=0.02, seed=1, colsample_bytree=0.8, subsample=0.8, n_estimators=10000)
fa = readlog('record_imp_auc2.log',0.765255)
for i in fa[:]:
if ('series_o_sum' in i) | ('series_a_sum' in i):
fa.remove(i)
'''
different = ['price_o_ave_o_cate_o_ave_o_user_id_3month','price_o_ave_o_cate_o_min_o_user_id_3month',
'price_o_ave_o_cate_o_max_o_user_id_3month','price_o_ave_o_cate_3month','para_1_o_ave_o_cate_o_ave_o_user_id_3month',
'para_1_o_ave_o_cate_o_min_o_user_id_3month','para_1_o_ave_o_cate_o_max_o_user_id_3month',
'para_1_o_ave_o_cate_3month','para_2_o_ave_o_cate_o_ave_o_user_id_3month',
'para_2_o_ave_o_cate_o_min_o_user_id_3month','para_2_o_ave_o_cate_o_max_o_user_id_3month',
'para_2_o_ave_o_cate_3month','para_3_o_ave_o_cate_o_ave_o_user_id_3month',
'para_3_o_ave_o_cate_o_min_o_user_id_3month','para_3_o_ave_o_cate_o_max_o_user_id_3month',
'para_3_o_ave_o_cate_3month','price_a_ave_cate_a_min_3month','price_a_ave_cate_a_max_3month',
'price_a_ave_cate_3month','para_1_a_ave_cate_a_min_3month','para_1_a_ave_cate_a_max_3month',
'para_1_a_ave_cate_3month',]
fa = readlog('record_imp_auc2.log',0.765255)
fa = readlog('record_imp_auc2.log',0.765472)
#fa = readlog('record_imp_auc2.log',0.765895)
#fa = fa + [j for j in ['{}_3month'.format(i) for i in month] if (j not in fa) & ('Plux' in j)]
for i in fa[:]:
if ('series_o_sum' in i) | ('series_a_sum' in i)| ('c_day' in i) | (i in different):
fa.remove(i)
'''
#fa = [i for i in df3.columns if i not in notusable]
#_,t = validateseq2(df3, df3, fa, clf, score,v = False, sk=5)
#_,t = validate(df3, df3, fa, clf, score, sk=5, v = False)
predict22(df3[df3.CreateGroup < 337].reset_index(), df3[df3.CreateGroup > 337].reset_index(),
fa, clf, score, fn='submission_7627_5216', sk=5, v = False)