[Issue-Id:RICAPP-204] Bump version to 1.0.0 and push to staging area

[ric-app/ad.git] / ad / ad_train.py

diff --git a/ad/ad_train.py b/ad/ad_train.py
deleted file mode 100644 (file)
index e6eb73d..0000000
--- a/ad/ad_train.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# ==================================================================================
-#  Copyright (c) 2020 HCL Technologies Limited.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-# ==================================================================================
-
-import joblib
-from ad_model.processing import PREPROCESS
-from sklearn.metrics import f1_score
-from sklearn.ensemble import IsolationForest
-from database import DATABASE, DUMMY
-import numpy as np
-
-
-class modelling(object):
-    r""" The modelling class takes input as dataframe or array and train Isolation Forest model
-
-    Paramteres
-    .........
-    data: DataFrame or array
-        input dataset
-    cols: list
-        list of parameters in input dataset
-
-    Attributes
-    ----------
-    actual:array
-        actual label for test data
-    X: DataFrame or array
-        transformed values of input data
-    """
-    def __init__(self, data):
-        self.data = data
-        self.cols = data.columns
-
-    def read_test(self, db):
-        """ Read test dataset for model validation"""
-
-        db.read_data('valid')
-        test = db.data
-        self.actual = test['Anomaly']
-        X = test[self.cols]
-        sc = joblib.load('scale')
-        self.X = sc.transform(X)
-
-    def isoforest(self, outliers_fraction=0.05, random_state=42, push_model=False):
-        """ Train isolation forest
-
-        Parameters
-        ----------
-        outliers_fraction: float between 0.01 to 0.5 (default=0.05)
-            percentage of anomalous available in input data
-        push_model: boolean (default=False)
-            return f_1 score if True else push model into repo
-        random_state: int (default=42)
-        """
-        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)
-
-
-def train(thread=False):
-    """
-     Main function to perform training on input data
-    """
-    if thread:
-        db = DUMMY()
-    else:
-        db = DATABASE('UEData')
-    db.read_data('train')
-    ps = PREPROCESS(db.data)
-    ps.process()
-    df = ps.data
-
-    mod = modelling(df)
-    mod.read_test(db)
-
-    scores = []
-    for of in np.arange(0.01, 0.4, 0.01):
-        scores.append(mod.isoforest(outliers_fraction=of))
-    opt_f1 = scores.index(max(scores)) + 1
-    mod.isoforest(outliers_fraction=opt_f1*0.01, push_model=True)
-    print("Optimum value of contamination : {}".format(opt_f1*0.01))
-    print('Training Ends : ')
-
-
-def test(self, model):
-    pred = model.predict(self.X)
-    if -1 in pred:
-        pred = [1 if p == -1 else 0 for p in pred]
-    return f1_score(self.actual, pred)