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model_full_url.py
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model_full_url.py
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from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pickle
from sklearn import metrics
import time
#48000 bad queries
#8700 good queries
i = 200
while i < 8700:
start = time.time()
#print('started')
badQueries = open('badQueries_full_url.txt', 'r', encoding='utf-8')
validQueries = open('goodQueries_full_url.txt', 'r', encoding='utf-8')
badQueries = list(set(badQueries))
validQueries = list(set(validQueries))
num_samples = i
badQueries = badQueries[:num_samples]
validQueries = validQueries[:num_samples]
#print(len(badQueries))
#print(len(validQueries))
allQueries = badQueries + validQueries
yBad = [1 for i in range(0, len(badQueries))]
yGood = [0 for i in range(0, len(validQueries))]
y = yBad + yGood
queries = allQueries
#print('vectorizing')
vectorizer = TfidfVectorizer(min_df=0.0, analyzer="char", sublinear_tf=True, ngram_range=(1, 3))
X = vectorizer.fit_transform(queries)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
badCount = len(badQueries)
validCount = len(validQueries)
#print('training')
lgs = LogisticRegression(class_weight={1: 2 * validCount / badCount, 0: 1.0}, solver='lbfgs') # class_weight='balanced')
lgs.fit(X_train, y_train)
#print('saving')
with open('model_full_url.pkl', 'wb') as model_file:
pickle.dump(lgs, model_file)
with open('vectorizer_full_url.pkl', 'wb') as vectorizer_file:
pickle.dump(vectorizer, vectorizer_file)
end = time.time()
predicted = lgs.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_test, (lgs.predict_proba(X_test)[:, 1]))
auc = metrics.auc(fpr, tpr)
print("%d" % badCount, end=',')
print("%d" % validCount, end=',')
print("%.6f" % (validCount / (validCount + badCount)), end=',')
print("%f" % lgs.score(X_test, y_test), end=',') # checking the accuracy
print("%f" % metrics.precision_score(y_test, predicted), end=',')
print("%f" % metrics.recall_score(y_test, predicted), end=',')
print("%f" % metrics.f1_score(y_test, predicted), end=',')
print("%f" % auc, end=',')
print("{0}".format(end - start))
i += 200