- df = self.data.copy()
- le = LabelEncoder()
- y = le.fit_transform(y)
- joblib.dump(le, '/tmp/ad/LabelEncoder')
- X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.20, stratify=y, random_state=42)
- rf = RandomForestClassifier(max_depth=9, random_state=0)
- rf.fit(X_train, y_train)
-
- joblib.dump(rf, '/tmp/ad/RF')
- print('--------------------------- Training Score------------------------------------')
- score(X_test, y_test, rf)
- print('--------------------------- Test Score------------------------------------')
- test = pd.read_csv('/tmp/ad/ue_test.csv')
- test = test[UEKeyList]
- y = category(test, [sigstr, PRB, tput])
- y =le.transform(y)
- ps = preprocess(test)
- ps.process()
- test = ps.data.drop(['UEID', 'MeasTimestampRF'], axis = 1)
- score(test, y, rf)
+ iso = IsolationForest(contamination=outliers_fraction, random_state=random_state)
+ md = iso.fit(self.data, None)
+ if push_model:
+ joblib.dump(self.cols, 'params')
+ joblib.dump(md, 'model')
+ return test(self, md)