-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathwhen_less_is_more.py
205 lines (186 loc) · 9.22 KB
/
when_less_is_more.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
"""
Code based on BreakfastPirate Forum post
__author__ : SRK
"""
import csv
import datetime
from operator import sub
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn import preprocessing, ensemble
import json
import os
#['data_date','']
mapping_dict = {
'ind_empleado' : {-99:0, 'N':1, 'B':2, 'F':3, 'A':4, 'S':5},
'sexo' : {'V':0, 'H':1, -99:2},
'ind_nuevo' : {'0':0, '1':1, -99:2},
'indrel' : {'1':0, '99':1, -99:2},
'indrel_1mes' : {-99:0, '1.0':1, '1':1, '2.0':2, '2':2, '3.0':3, '3':3, '4.0':4, '4':4, 'P':5},
'tiprel_1mes' : {-99:0, 'I':1, 'A':2, 'P':3, 'R':4, 'N':5},
'indresi' : {-99:0, 'S':1, 'N':2},
'indext' : {-99:0, 'S':1, 'N':2},
'conyuemp' : {-99:0, 'S':1, 'N':2},
'indfall' : {-99:0, 'S':1, 'N':2},
'tipodom' : {-99:0, '1':1},
'ind_actividad_cliente' : {'0':0, '1':1, -99:2},
'segmento' : {'02 - PARTICULARES':0, '03 - UNIVERSITARIO':1, '01 - TOP':2, -99:2},
'pais_residencia' : {'LV': 102, 'BE': 12, 'BG': 50, 'BA': 61, 'BM': 117, 'BO': 62, 'JP': 82, 'JM': 116, 'BR': 17, 'BY': 64, 'BZ': 113, 'RU': 43, 'RS': 89, 'RO': 41, 'GW': 99, 'GT': 44, 'GR': 39, 'GQ': 73, 'GE': 78, 'GB': 9, 'GA': 45, 'GN': 98, 'GM': 110, 'GI': 96, 'GH': 88, 'OM': 100, 'HR': 67, 'HU': 106, 'HK': 34, 'HN': 22, 'AD': 35, 'PR': 40, 'PT': 26, 'PY': 51, 'PA': 60, 'PE': 20, 'PK': 84, 'PH': 91, 'PL': 30, 'EE': 52, 'EG': 74, 'ZA': 75, 'EC': 19, 'AL': 25, 'VN': 90, 'ET': 54, 'ZW': 114, 'ES': 0, 'MD': 68, 'UY': 77, 'MM': 94, 'ML': 104, 'US': 15, 'MT': 118, 'MR': 48, 'UA': 49, 'MX': 16, 'IL': 42, 'FR': 8, 'MA': 38, 'FI': 23, 'NI': 33, 'NL': 7, 'NO': 46, 'NG': 83, 'NZ': 93, 'CI': 57, 'CH': 3, 'CO': 21, 'CN': 28, 'CM': 55, 'CL': 4, 'CA': 2, 'CG': 101, 'CF': 109, 'CD': 112, 'CZ': 36, 'CR': 32, 'CU': 72, 'KE': 65, 'KH': 95, 'SV': 53, 'SK': 69, 'KR': 87, 'KW': 92, 'SN': 47, 'SL': 97, 'KZ': 111, 'SA': 56, 'SG': 66, 'SE': 24, 'DO': 11, 'DJ': 115, 'DK': 76, 'DE': 10, 'DZ': 80, 'MK': 105, -99: 1, 'LB': 81, 'TW': 29, 'TR': 70, 'TN': 85, 'LT': 103, 'LU': 59, 'TH': 79, 'TG': 86, 'LY': 108, 'AE': 37, 'VE': 14, 'IS': 107, 'IT': 18, 'AO': 71, 'AR': 13, 'AU': 63, 'AT': 6, 'IN': 31, 'IE': 5, 'QA': 58, 'MZ': 27},
'canal_entrada' : {'013': 49, 'KHP': 160, 'KHQ': 157, 'KHR': 161, 'KHS': 162, 'KHK': 10, 'KHL': 0, 'KHM': 12, 'KHN': 21, 'KHO': 13, 'KHA': 22, 'KHC': 9, 'KHD': 2, 'KHE': 1, 'KHF': 19, '025': 159, 'KAC': 57, 'KAB': 28, 'KAA': 39, 'KAG': 26, 'KAF': 23, 'KAE': 30, 'KAD': 16, 'KAK': 51, 'KAJ': 41, 'KAI': 35, 'KAH': 31, 'KAO': 94, 'KAN': 110, 'KAM': 107, 'KAL': 74, 'KAS': 70, 'KAR': 32, 'KAQ': 37, 'KAP': 46, 'KAW': 76, 'KAV': 139, 'KAU': 142, 'KAT': 5, 'KAZ': 7, 'KAY': 54, 'KBJ': 133, 'KBH': 90, 'KBN': 122, 'KBO': 64, 'KBL': 88, 'KBM': 135, 'KBB': 131, 'KBF': 102, 'KBG': 17, 'KBD': 109, 'KBE': 119, 'KBZ': 67, 'KBX': 116, 'KBY': 111, 'KBR': 101, 'KBS': 118, 'KBP': 121, 'KBQ': 62, 'KBV': 100, 'KBW': 114, 'KBU': 55, 'KCE': 86, 'KCD': 85, 'KCG': 59, 'KCF': 105, 'KCA': 73, 'KCC': 29, 'KCB': 78, 'KCM': 82, 'KCL': 53, 'KCO': 104, 'KCN': 81, 'KCI': 65, 'KCH': 84, 'KCK': 52, 'KCJ': 156, 'KCU': 115, 'KCT': 112, 'KCV': 106, 'KCQ': 154, 'KCP': 129, 'KCS': 77, 'KCR': 153, 'KCX': 120, 'RED': 8, 'KDL': 158, 'KDM': 130, 'KDN': 151, 'KDO': 60, 'KDH': 14, 'KDI': 150, 'KDD': 113, 'KDE': 47, 'KDF': 127, 'KDG': 126, 'KDA': 63, 'KDB': 117, 'KDC': 75, 'KDX': 69, 'KDY': 61, 'KDZ': 99, 'KDT': 58, 'KDU': 79, 'KDV': 91, 'KDW': 132, 'KDP': 103, 'KDQ': 80, 'KDR': 56, 'KDS': 124, 'K00': 50, 'KEO': 96, 'KEN': 137, 'KEM': 155, 'KEL': 125, 'KEK': 145, 'KEJ': 95, 'KEI': 97, 'KEH': 15, 'KEG': 136, 'KEF': 128, 'KEE': 152, 'KED': 143, 'KEC': 66, 'KEB': 123, 'KEA': 89, 'KEZ': 108, 'KEY': 93, 'KEW': 98, 'KEV': 87, 'KEU': 72, 'KES': 68, 'KEQ': 138, -99: 6, 'KFV': 48, 'KFT': 92, 'KFU': 36, 'KFR': 144, 'KFS': 38, 'KFP': 40, 'KFF': 45, 'KFG': 27, 'KFD': 25, 'KFE': 148, 'KFB': 146, 'KFC': 4, 'KFA': 3, 'KFN': 42, 'KFL': 34, 'KFM': 141, 'KFJ': 33, 'KFK': 20, 'KFH': 140, 'KFI': 134, '007': 71, '004': 83, 'KGU': 149, 'KGW': 147, 'KGV': 43, 'KGY': 44, 'KGX': 24, 'KGC': 18, 'KGN': 11}
}
cat_cols = list(mapping_dict.keys())
target_cols = ['ind_ahor_fin_ult1','ind_aval_fin_ult1','ind_cco_fin_ult1','ind_cder_fin_ult1','ind_cno_fin_ult1','ind_ctju_fin_ult1','ind_ctma_fin_ult1','ind_ctop_fin_ult1','ind_ctpp_fin_ult1','ind_deco_fin_ult1','ind_deme_fin_ult1','ind_dela_fin_ult1','ind_ecue_fin_ult1','ind_fond_fin_ult1','ind_hip_fin_ult1','ind_plan_fin_ult1','ind_pres_fin_ult1','ind_reca_fin_ult1','ind_tjcr_fin_ult1','ind_valo_fin_ult1','ind_viv_fin_ult1','ind_nomina_ult1','ind_nom_pens_ult1','ind_recibo_ult1']
target_cols = target_cols[2:]
def getTarget(row):
tlist = []
for col in target_cols:
if row[col].strip() in ['', 'NA']:
target = 0
else:
target = int(float(row[col]))
tlist.append(target)
return tlist
def getIndex(row, col):
val = row[col].strip()
if val not in ['','NA']:
ind = mapping_dict[col][val]
else:
ind = mapping_dict[col][-99]
return ind
def getAge(row):
mean_age = 40.
min_age = 20.
max_age = 90.
range_age = max_age - min_age
age = row['age'].strip()
if age == 'NA' or age == '':
age = mean_age
else:
age = float(age)
if age < min_age:
age = min_age
elif age > max_age:
age = max_age
return round( (age - min_age) / range_age, 4)
def getCustSeniority(row):
min_value = 0.
max_value = 256.
range_value = max_value - min_value
missing_value = 0.
cust_seniority = row['antiguedad'].strip()
if cust_seniority == 'NA' or cust_seniority == '':
cust_seniority = missing_value
else:
cust_seniority = float(cust_seniority)
if cust_seniority < min_value:
cust_seniority = min_value
elif cust_seniority > max_value:
cust_seniority = max_value
return round((cust_seniority-min_value) / range_value, 4)
def getRent(row):
min_value = 0.
max_value = 1500000.
range_value = max_value - min_value
missing_value = 101850.
rent = row['renta'].strip()
if rent == 'NA' or rent == '':
rent = missing_value
else:
rent = float(rent)
if rent < min_value:
rent = min_value
elif rent > max_value:
rent = max_value
return round((rent-min_value) / range_value, 6)
def processData(in_file_name, cust_dict):
x_vars_list = []
y_vars_list = []
for row in csv.DictReader(in_file_name):
# use only the four months as specified by breakfastpirate #
if row['fecha_dato'] not in ['2015-05-28', '2015-06-28', '2016-05-28', '2016-06-28']:
continue
cust_id = int(row['ncodpers'])
if row['fecha_dato'] in ['2015-05-28', '2016-05-28']:
target_list = getTarget(row)
cust_dict[cust_id] = target_list[:]
continue
x_vars = []
for col in cat_cols:
x_vars.append( getIndex(row, col) )
x_vars.append( getAge(row) )
x_vars.append( getCustSeniority(row) )
x_vars.append( getRent(row) )
if row['fecha_dato'] == '2016-06-28':
prev_target_list = cust_dict.get(cust_id, [0]*22)
x_vars_list.append(x_vars + prev_target_list)
elif row['fecha_dato'] == '2015-06-28':
prev_target_list = cust_dict.get(cust_id, [0]*22)
target_list = getTarget(row)
new_products = [max(x1 - x2,0) for (x1, x2) in zip(target_list, prev_target_list)]
if sum(new_products) > 0:
for ind, prod in enumerate(new_products):
if prod>0:
assert len(prev_target_list) == 22
x_vars_list.append(x_vars+prev_target_list)
y_vars_list.append(ind)
return x_vars_list, y_vars_list, cust_dict
def runXGB(train_X, train_y, seed_val=0):
param = {}
param['objective'] = 'multi:softprob'
param['eta'] = 0.05
param['max_depth'] = 8
param['silent'] = 1
param['num_class'] = 22
param['eval_metric'] = "mlogloss"
param['min_child_weight'] = 1
param['subsample'] = 0.7
param['colsample_bytree'] = 0.7
param['seed'] = seed_val
num_rounds = 50
plst = list(param.items())
xgtrain = xgb.DMatrix(train_X, label=train_y)
model = xgb.train(plst, xgtrain, num_rounds)
return model
if __name__ == "__main__":
start_time = datetime.datetime.now()
data_path = "./"
train_file = open(data_path + "train_ver2.csv")
x_vars_list, y_vars_list, cust_dict = processData(train_file, {})
train_X = np.array(x_vars_list)
train_y = np.array(y_vars_list)
print(np.unique(train_y))
del x_vars_list, y_vars_list
train_file.close()
print(train_X.shape, train_y.shape)
print(datetime.datetime.now()-start_time)
test_file = open(data_path + "test_ver2.csv")
x_vars_list, y_vars_list, cust_dict = processData(test_file, cust_dict)
test_X = np.array(x_vars_list)
del x_vars_list
test_file.close()
print(test_X.shape)
print(datetime.datetime.now()-start_time)
print("Building model..")
model = runXGB(train_X, train_y, seed_val=0)
del train_X, train_y
print("Predicting..")
xgtest = xgb.DMatrix(test_X)
preds = model.predict(xgtest)
del test_X, xgtest
print(datetime.datetime.now()-start_time)
config = model.save_config()
model.save_model(os.path.join('ckpt','xgb.model'))
with open('config.json','w') as f:
configJson=json.dumps(config)
f.write(configJson)
print(config)
print("Getting the top products..")
target_cols = np.array(target_cols)
preds = np.argsort(preds, axis=1)
preds = np.fliplr(preds)[:,:7]
test_id = np.array(pd.read_csv(data_path + "test_ver2.csv", usecols=['ncodpers'])['ncodpers'])
final_preds = [" ".join(list(target_cols[pred])) for pred in preds]
out_df = pd.DataFrame({'ncodpers':test_id, 'added_products':final_preds})
out_df.to_csv('sub_xgb_new.csv', index=False)
print(datetime.datetime.now()-start_time)