def scorePredictMethod(self):
df = pd.read_csv("CveScore.csv", encoding='ISO-8859-1')
df.head(5)
df.dropna(subset=["baseScore"], inplace=True)
random_state = 100
kfold = model_selection.StratifiedKFold(n_splits=10)
tfidf = TfidfVectorizer(sublinear_tf=True,
min_df=5,
norm='l2',
encoding='latin-1',
ngram_range=(1, 2),
stop_words='english')
features = tfidf.fit_transform(df.description).toarray()
labels = df.baseScore
features.shape
print(df.baseScore)
X_train, X_test, y_train, y_test = train_test_split(df['description'],
df['baseScore'],
random_state=0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
print(y_train)
# RandomForestRegressor = ensemble.RandomForestRegressor(random_state=100, n_jobs=1, verbose=1, oob_score=True)
# RandomForestRegressorAfterFit = RandomForestRegressor.fit(X_train_tfidf, y_train)
#path = 'C:/Users/tahsin.asif/OneDrive - CYFIRMA INDIA PRIVATE LIMITED/AI/CveScorePrediction/'
#joblib.dump(RandomForestRegressorAfterFit, os.path.join(path, 'regression_model-v4.pkl'))
# cross check the dumped model with load
classifier_loaded = joblib.load('regression_model-v4.pkl')
inputData = input("Please enter the input text::")
print(classifier_loaded.predict(count_vect.transform([inputData])))
output = classifier_loaded.predict(count_vect.transform([inputData]))
return output
def predict():
json_ = request.args.get('url')
print('json:', json_)
inputData = json_
df = pd.read_csv(
"CveScore.csv",
encoding='ISO-8859-1')
df.head(5)
df.dropna(subset=["baseScore"], inplace=True)
random_state = 100
kfold = model_selection.StratifiedKFold(n_splits=10)
# tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2),
# stop_words='english')
# features = tfidf.fit_transform(df.description).toarray()
# labels = df.baseScore
# features.shape
print(df.baseScore)
X_train, X_test, y_train, y_test = train_test_split(df['description'], df['baseScore'], random_state=0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
print(y_train)
# RandomForestRegressor = ensemble.RandomForestRegressor(random_state=100, n_jobs=1, verbose=1, oob_score=True)
# RandomForestRegressorAfterFit = RandomForestRegressor.fit(X_train_tfidf, y_train)
# path = '............../AI/CveScorePrediction/'
# joblib.dump(RandomForestRegressorAfterFit, os.path.join(path, 'regression_model-v4.pkl'))
# cross check the dumped model with load
classifier_loaded = joblib.load('regression_model-v4.pkl')
#inputData = input("Please enter the input text::")
print(classifier_loaded.predict(count_vect.transform([json_])))
output = classifier_loaded.predict(count_vect.transform([json_]))
return render_template('index.html', prediction_text='Predicted CVE SCore is {}'.format(output))
from pandas.tests.groupby.test_value_counts import df df.head(5)