-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathmodel_ama_logit.py
127 lines (94 loc) · 4.5 KB
/
model_ama_logit.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
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 06 16:24:41 2017
@author: mimar
"""
import numpy as np
from sklearn.metrics import roc_auc_score
from scipy.stats import pearsonr
import lightgbm as lgb
from sklearn.preprocessing import OneHotEncoder
import os
from sklearn.externals import joblib
from sklearn.cross_validation import StratifiedKFold
from sklearn.utils import shuffle
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
def bagged_set(X_ts,y_cs, seed, estimators, xt,yt=None):
# create array object to hold predictions
baggedpred=np.array([ 0.0 for d in range(0, xt.shape[0])])
#loop for as many times as we want bags
for n in range (0, estimators):
model=KNeighborsClassifier(n_neighbors=20, weights='uniform', algorithm='brute', leaf_size=30, metric='cityblock', n_jobs=20)
model.fit(X_ts,y_cs)
preds=model.predict_proba(xt)[:,1]
# update bag's array
baggedpred+=preds
print("completed: " + str(n) )
# divide with number of bags to create an average estimate
baggedpred/= estimators
return baggedpred
#train = np.loadtxt('first_level_train.csv')
#test = np.loadtxt('first_level_test.csv')
#train=np.loadtxt('train.csv',usecols=[k for k in range(1,9)], skiprows=1, delimiter=",")
#test = np.loadtxt('test.csv',usecols=[k for k in range(1,9)], skiprows=1, delimiter=",")
X=np.loadtxt('train_munged.csv',usecols=[k for k in range(0,55)], skiprows=1, delimiter=",")
y =np.loadtxt("train.csv", delimiter=',',usecols=[0], skiprows=1)
stda=StandardScaler()
X=stda.fit_transform(X)
meta_folder="meta_folder/"
if not os.path.exists(meta_folder): #if it does not exists, we create it
os.makedirs(meta_folder)
output_name="lightgbm_logit_v1"
print(X.shape, y.shape)
############### Params section #####################
bagging=1 # number of models trained with different seeds
number_of_folds = 5 # number of folds in strattified cv
kfolder=StratifiedKFold(y, n_folds= number_of_folds,shuffle=True, random_state=1)
#model to use
#modelextra= ExtraTreesClassifier(n_estimators=1000, criterion='entropy', max_depth=40,
#min_samples_split=4,min_samples_leaf=2, max_features=0.7,n_jobs=25, random_state=1)
train_stacker=[ 0.0 for k in range (0,X.shape[0]) ]
#create target variable
mean_kapa = 0.0
kfolder=StratifiedKFold(y, n_folds=number_of_folds,shuffle=True, random_state=1)
#X,y=shuffle(X,y, random_state=SEED) # Shuffle since the data is ordered by time
i=0 # iterator counter
print ("starting cross validation with %d kfolds " % (number_of_folds))
for train_index, test_index in kfolder:
# creaning and validation sets
X_train, X_cv = X[train_index], X[test_index]
y_train, y_cv = np.array(y)[train_index], np.array(y)[test_index]
print (" train size: %d. test size: %d, cols: %d " % ((X_train.shape[0]) ,(X_cv.shape[0]) ,(X_train.shape[1]) ))
preds= bagged_set(X_train,y_train, 1, bagging, X_cv,yt=None )# y_cv
auc=roc_auc_score(y_cv, preds)
print ("size train: %d size cv: %d auc (fold %d/%d): %f" % ((X_train.shape[0]), (X_cv.shape[0]), i + 1, number_of_folds,auc))
mean_kapa += auc
#save the results
no=0
for real_index in test_index:
train_stacker[real_index]=(preds[no])
no+=1
i+=1 # increment cv iterator
if (number_of_folds)>0:
mean_kapa/=number_of_folds
print (" Average auc : %f" % (mean_kapa) )
np.savetxt(meta_folder+output_name+ ".train.csv",np.array(train_stacker), fmt='%.6f')
print (" printing train datasets ")
X_test = np.loadtxt('test_munged.csv',usecols=[k for k in range(0,55)], skiprows=1, delimiter=",")
X_test=stda.transform(X_test)
preds= bagged_set(X,y, 1, bagging, X_test,yt=None)
# === Predictions === #
print (" printing test datasets ")
np.savetxt(meta_folder+output_name+ ".test.csv",np.array(preds), fmt='%.6f')
print("Write results...")
output_file = "submission_"+str( (mean_kapa ))+".csv"
print("Writing submission to %s" % output_file)
f = open(output_file, "w")
f.write("id,action\n")# the header
for g in range(0, len(preds)) :
f.write("%d,%f\n" % (g+1,preds[g]))
f.close()
print("Done.")