-
Notifications
You must be signed in to change notification settings - Fork 3
/
light_gbm.py
141 lines (98 loc) · 4.42 KB
/
light_gbm.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
import pandas as pd
import numpy as np
import lightgbm as lgbm
from lightgbm import LGBMClassifier, LGBMRegressor
from file_io import *
emotion_list = ['anger', 'brain dysfunction (forget)', 'emptiness', 'hopelessness', 'loneliness', 'sadness', 'suicide intent', 'worthlessness']
def convert_labels(labels):
labels2 = []
for idx, label in enumerate(labels):
try:
label = str(label[0])
except:
label = str(label)
pass
temp = []
num = len(emotion_list) - len(label)
if (num == 0):
temp = [int(x) for x in label]
else:
temp = ''.join(['0']*num) + str(label)
temp = [int(x) for x in temp]
labels2.append(temp)
return labels2
train_set = read_list_from_jsonl_file('dataset/train.json')
val_set = read_list_from_jsonl_file('dataset/val.json')
test_set = read_list_from_jsonl_file('dataset/test.json')
train_set = train_set + test_set + val_set
data_labels = [item['label_id'] for item in train_set]
df_labels = pd.DataFrame(data_labels, columns=['labels'])
print('df_labels: ', df_labels)
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import re
#from transformers import AutoTokenizer
# Download necessary NLTK corpora
nltk.download('stopwords')
nltk.download('wordnet')
stop_words = set(stopwords.words('english'))
lemma = WordNetLemmatizer()
#tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
def clean_text(text):
'''text = tokenizer.encode_plus(text, max_length=256, add_special_tokens=True, return_token_type_ids=False, padding = "max_length", return_attention_mask=True, return_tensors='pt')
text = ' '.join(str(x) for x in text['input_ids'].tolist()[0])'''
'''text = re.sub(r'http\S+', '', text)
text = re.sub(r'[^\w\s]', '', text, re.UNICODE) # remove punctuation
# cleaning everything except alphabetical and numerical characters
text = re.sub("[^a-zA-Z0-9]"," ",text)
# tokenizing and lemmatizing
text = nltk.word_tokenize(text.lower())
text = [lemma.lemmatize(word) for word in text]
# removing stopwords
text = [word for word in text if word not in stop_words]
# joining
text = " ".join(text)'''
return text
for item in train_set: item['text'] = clean_text(item['text'])
for i in range(0,5): print(train_set[i]['text'],end="\n\n")
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
#vectorizer = TfidfVectorizer(max_features=5000)
data_features = vectorizer.fit_transform([item['text'] for item in train_set])
data_features = data_features.toarray()
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_features, np.asarray(df_labels), test_size=0.3, train_size = 0.7, random_state=0)
x_test, x_val, y_test, y_val = train_test_split(x_test, y_test, test_size=0.5, random_state=0)
print(x_train.shape)
print(y_train.shape)
print(x_val.shape)
print(y_val.shape)
print(x_test.shape)
print(y_test.shape)
model = lgbm.LGBMClassifier(objective='multiclass', learning_rate = 0.5, verbose = -1)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train = le.fit_transform(y_train.ravel())
model.fit(x_train, y_train)
predictions = model.predict(x_test)
predictions = le.inverse_transform(predictions)
predictions = convert_labels(predictions)
print('predictions: ', predictions)
y_test = convert_labels(y_test)
print('y_test: ', y_test)
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
f1_mi = f1_score(y_true=y_test, y_pred=predictions, average='micro')
re_mi = recall_score(y_true=y_test, y_pred=predictions, average='micro')
pre_mi = precision_score(y_true=y_test, y_pred=predictions, average='micro')
f1_mac = f1_score(y_true=y_test, y_pred=predictions, average='macro')
re_mac = recall_score(y_true=y_test, y_pred=predictions, average='macro')
pre_mac = precision_score(y_true=y_test, y_pred=predictions, average='macro')
result = {}
result['f1_micro'] = f1_mi
result['recall_micro'] = re_mi
result['precision_micro'] = pre_mi
result['f1_macro'] = f1_mac
result['recall_macro'] = re_mac
result['precision_macro'] = pre_mac
print(result)