-
Notifications
You must be signed in to change notification settings - Fork 0
/
fnc_kfold.py
147 lines (123 loc) · 5.77 KB
/
fnc_kfold.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
from __future__ import print_function
import os
import sys
import numpy as np
import json
import pandas as pd
import time
from xgboost import XGBClassifier
from sklearn.ensemble import GradientBoostingClassifier
from feature_engineering import refuting_features, polarity_features, hand_features, gen_or_load_feats
from feature_engineering import word_overlap_features, NMF_cos_50, LDA_cos_25
from utils.dataset import DataSet
from utils.generate_test_splits import kfold_split, get_stances_for_folds
from utils.score import report_score, LABELS, score_submission
from utils.system import parse_params, check_version
#Model 2 dependencies
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten,BatchNormalization
from keras.layers import Conv2D, MaxPooling2D
from keras.callbacks import ModelCheckpoint
from sklearn.metrics import accuracy_score,confusion_matrix,f1_score
import matplotlib.pyplot as plt
train_feature_data = pd.DataFrame(columns=['headline','body_id','stance'])
comp_feature_data = pd.DataFrame(columns=['headline','body_id','stance'])
def generate_features(stances,dataset,name):
h, b, y = [],[],[]
rows = []
for stance in stances:
row = []
y.append(LABELS.index(stance['Stance']))
h.append(stance['Headline'])
b.append(dataset.articles[stance['Body ID']])
row.append(stance['Headline'])
row.append(dataset.articles[stance['Body ID']])
row.append(LABELS.index(stance['Stance']))
rows.append(row)
X_overlap = gen_or_load_feats(word_overlap_features, h, b, "features/overlap."+name+".npy")
X_refuting = gen_or_load_feats(refuting_features, h, b, "features/refuting."+name+".npy")
X_polarity = gen_or_load_feats(polarity_features, h, b, "features/polarity."+name+".npy")
X_hand = gen_or_load_feats(hand_features, h, b, "features/hand."+name+".npy")
######Topic Modelling - New Features Added######
X_NMF = gen_or_load_feats(NMF_cos_50, h, b, "features/nmf."+name+".npy")
X_LDA = gen_or_load_feats(LDA_cos_25, h, b, "features/lda-25."+name+".npy")
X = np.c_[X_hand, X_polarity, X_refuting, X_overlap, X_NMF, X_LDA]
if(name == "competition"):
if not (os.path.isfile('comp_feature_data.csv')):
comp_feature_data['stance'] = y
comp_feature_data['headline'] = h
comp_feature_data['body_id'] = b
for i in range(0,X.shape[1]):
comp_feature_data[i] = X[:,i]
if(name == "full"):
if not (os.path.isfile('train_feature_data.csv')):
train_feature_data['stance'] = y
train_feature_data['headline'] = h
train_feature_data['body_id'] = b
for i in range(0,X.shape[1]):
train_feature_data[i] = X[:,i]
return X,y
if __name__ == "__main__":
check_version()
parse_params()
#Load the training dataset and generate folds
d = DataSet()
X_full,y_full = generate_features(d.stances,d,"full")
#for binary classification - related and unrelated
y_full = [x if x==3 else 2 for x in y_full]
#removing folds return train and holdout split - check if distribution same - does it matter
folds,hold_out = kfold_split(d,n_folds=10)
fold_stances, hold_out_stances = get_stances_for_folds(d,folds,hold_out)
X_holdout,y_holdout = generate_features(hold_out_stances,d,"holdout")
y_holdout = [x if x==3 else 2 for x in y_holdout]
#load training data
X_train, y_train = generate_features(fold_stances, d, "train_n")
y_train = [x if x==3 else 2 for x in y_train]
# Load the competition dataset
competition_dataset = DataSet("competition_test")
X_competition, y_competition = generate_features(competition_dataset.stances, competition_dataset, "competition")
y_competition = [x if x==3 else 2 for x in y_competition]
#param = {'eta':1, 'objective' : 'multi:softmax' , 'num_class' : 4, 'n_estimators':150}
param = {'eta':1, 'objectve' : "binary:logistic" , 'n_estimators':150, 'seed':10}
clf = XGBClassifier(**param)
start = int(round(time.time()*1000))
end = int(round(time.time()*1000))
train_time = end - start
clf.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_holdout, y_holdout)], verbose=True)
y_pred_train = clf.predict(X_train)
y_pred = clf.predict(X_holdout)
y_pred_onfull = clf.predict(X_full)
if not (os.path.isfile('train_feature_data.csv')):
train_feature_data['predicted_stance'] = y_pred_onfull
train_feature_data.to_csv('train_feature_data.csv', index = False)
#check file
feature_df = pd.read_csv('train_feature_data.csv')
print("train data file size : ", feature_df.shape)
print("train data file: ", feature_df.head())
predicted = [LABELS[int(a)] for a in y_pred_train]
actual = [LABELS[int(a)] for a in y_train]
print("Scores on the train set")
report_score(actual,predicted)
print("")
print("")
predicted = [LABELS[int(a)] for a in y_pred]
actual = [LABELS[int(a)] for a in y_holdout]
print("Scores on the dev set")
report_score(actual,predicted)
print("")
print("")
test_pred = clf.predict(X_competition)
predicted = [LABELS[int(a)] for a in test_pred]
actual = [LABELS[int(a)] for a in y_competition]
print("Scores on the test set")
report_score(actual,predicted)
if not (os.path.isfile('comp_feature_data.csv')):
comp_feature_data['predicted_stance'] = test_pred
comp_feature_data.to_csv('comp_feature_data.csv', index = False)
#check file
feature_df = pd.read_csv('comp_feature_data.csv')
print("comp data file size : ", feature_df.shape)
print("comp data file: ", feature_df.head())
print("train time: ",train_time)