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svm_classifier.py
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"""
python svm_classifier.py --set_name1 kenya --set_type Country
python svm_classifier.py --set_name1 ek --set_type User
"""
import os
import argparse, data_split
import pandas as pd
import numpy as np
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, accuracy_score
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
def f1_multiclass(labels, preds):
return f1_score(labels, preds, average='micro')
parser = argparse.ArgumentParser()
parser.add_argument('--set_name1', required=True)
parser.add_argument('--set_name2', default='default')
parser.add_argument('--set_type', required=True)
parser.add_argument('--danger_label', default=2, type=int)
parser.add_argument('--max_features', default=5000, type=int)
args = parser.parse_args()
TASK = 'extreme' if args.danger_label == 2 else 'acceptable'
DATA_PATH = 'data_splits'
f = open('res_svm_{}_{}.txt'.format(args.set_name1, TASK), 'w')
f.close()
def read_data():
if ',' in args.set_name1:
train_data = []
for name in args.set_name1.split(','):
train_data.append(pd.read_csv('{}/{}_train.csv'.format(DATA_PATH, name), engine='python'))
train_df = pd.concat(train_data)
else:
train_df = pd.read_csv('{}/{}_train.csv'.format(DATA_PATH, args.set_name1), engine='python')
if args.set_name2 == 'default':
if ',' in args.set_name1: print('ERROR! set_name1 must be a list')
dev_df = pd.read_csv('{}/{}_dev.csv'.format(DATA_PATH, args.set_name1), engine='python')
test_df = pd.read_csv('{}/{}_test.csv'.format(DATA_PATH, args.set_name1), engine='python')
elif ',' in args.set_name2:
dev_data, test_data = [], []
for name in args.set_name2.split(','):
dev_data.append(pd.read_csv('{}/{}_dev.csv'.format(DATA_PATH, name), engine='python'))
test_data.append(pd.read_csv('{}/{}_test.csv'.format(DATA_PATH, name), engine='python'))
dev_df = pd.concat(dev_data)
test_df = pd.concat(test_data)
else:
dev_df = pd.read_csv('{}/{}_dev.csv'.format(DATA_PATH, args.set_name2), engine='python')
test_df = pd.read_csv('{}/{}_test.csv'.format(DATA_PATH, args.set_name2), engine='python')
return clean_data(train_df, dev_df, test_df)
def clean_data(train_df, dev_df, test_df):
train_df = train_df[pd.notnull(train_df['label'])]
dev_df = dev_df[pd.notnull(dev_df['label'])]
test_df = test_df[pd.notnull(test_df['label'])]
train_df = train_df[pd.notnull(train_df['Text'])].reset_index()
dev_df = dev_df[pd.notnull(dev_df['Text'])].reset_index()
test_df = test_df[pd.notnull(test_df['Text'])].reset_index()
train_df['label'] = train_df['label'].replace({2: args.danger_label}).astype(int)
dev_df['label'] = dev_df['label'].replace({2: args.danger_label}).astype(int)
test_df['label'] = test_df['label'].replace({2: args.danger_label}).astype(int)
train_df['text'] = train_df['text'].astype(str)
dev_df['text'] = dev_df['text'].astype(str)
test_df['text'] = test_df['text'].astype(str)
train_df = train_df[['text', 'label']]
dev_df = dev_df[['text', 'label']]
test_df = test_df[['text', 'label']]
return train_df, dev_df, test_df
def format_data(data, max_features, count_vect=None, tf_transformer=None):
data = data.sample(frac=1).reset_index(drop=True)
data['text'] = data['text'].apply(lambda x: str(x))
Y = data['label'].values
X = data['text']
if not tf_transformer:
count_vect = CountVectorizer(max_features=max_features, lowercase=False)
X = count_vect.fit_transform(X)
tf_transformer = TfidfTransformer(use_idf=False).fit(X)
X = tf_transformer.transform(X)
else:
X = count_vect.transform(X)
X = tf_transformer.transform(X)
return X, Y, count_vect, tf_transformer
train_df, dev_df, test_df = read_data()
def make_and_print_results(model, X, Y):
preds = model.predict(X)
acc = accuracy_score(preds, Y)
print(acc)
f = open('res_svm_{}_{}.txt'.format(args.set_name1, TASK), 'a+')
f.write('{}'.format(acc))
f.write('\n')
f.close()
def main():
max_features = args.max_features
X_train, Y_train, cv, tf = format_data(train_df, max_features)
X_dev, Y_dev, _, _ = format_data(dev_df, max_features, cv, tf)
X_dev0, Y_dev0, _, _ = format_data(dev_df[dev_df['label'] == 0], max_features, cv, tf)
X_dev1, Y_dev1, _, _ = format_data(dev_df[dev_df['label'] == 1], max_features, cv, tf)
X_dev2, Y_dev2, _, _ = format_data(dev_df[dev_df['label'] == args.danger_label], max_features, cv, tf)
svc = svm.LinearSVC().fit(X_train, Y_train)
print(len(dev_df[dev_df['label'] == 0]) / len(test_df))
print(len(dev_df[dev_df['label'] == 1]) / len(test_df))
print(len(dev_df[dev_df['label'] == args.danger_label]) / len(dev_df))
make_and_print_results(svc, X_dev, Y_dev)
make_and_print_results(svc, X_dev0, Y_dev0)
make_and_print_results(svc, X_dev1, Y_dev1)
make_and_print_results(svc, X_dev2, Y_dev2)
if __name__ == '__main__':
main()