[Issue-Id:RICAPP-155]Code changes to support for JJB and Unit testing for AD xApp

[ric-app/ad.git] / ad / ad_model / processing.py

diff --git a/ad/ad_model/processing.py b/ad/ad_model/processing.py
index 300dc71..dab328f 100644 (file)
--- a/ad/ad_model/processing.py
+++ b/ad/ad_model/processing.py
@@ -1,46 +1,28 @@
-# ==================================================================================
-#  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 pandas as pd
 import numpy as np
-from scipy.stats import skew
-import json
 import joblib
-from sklearn.decomposition import PCA
 from sklearn.preprocessing import StandardScaler
 
+
 class preprocess(object):
-    
-    def __init__(self,data):
+
+    def __init__(self, data):
         """
            Columns that are not useful for the prediction will be dropped(UEID, Category, & Timestamp)
-        """        
+        """
         self.id = data.UEID
         self.time = data.MeasTimestampRF
-        self.data = data.drop(['UEID','MeasTimestampRF'], axis = 1)
+        self.data = data.drop(['UEID', 'MeasTimestampRF'], axis=1)
 
     def variation(self):
         """ drop the constant parameters """
-        self.data =  self.data.loc[:,self.data.apply(pd.Series.nunique) != 1]
-    
-    
+        self.data = self.data.loc[:, self.data.apply(pd.Series.nunique) != 1]
+
     def numerical_data(self):
         """  Filters only numeric data types """
         numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
-        self.data =  self.data.select_dtypes(include=numerics)
-    
+        self.data = self.data.select_dtypes(include=numerics)
+
     def drop_na(self):
         """ drop observations having nan values """
         self.data = self.data.dropna(axis=0)
@@ -48,32 +30,28 @@ class preprocess(object):
     def correlation(self):
         """  check and drop high correlation parameters  """
         corr = self.data.corr().abs()
-        corr = pd.DataFrame(np.tril(corr, k =-1), columns = self.data.columns)
+        corr = pd.DataFrame(np.tril(corr, k=-1), columns=self.data.columns)
         drop = [column for column in corr.columns if any(corr[column] > 0.98)]
-        self.data = self.data.drop(drop,axis=1)
-    
-    #check skewness of all parameters and use log transform if half of parameters are enough skewd
-    #otherwise use standardization
+        self.data = self.data.drop(drop, axis=1)
+
+    # check skewness of all parameters and use log transform if half of parameters are enough skewd
+    # otherwise use standardization
     def transform(self):
-        """ Use standard scalar and save the scale """
+        """ use log transform for skewed data """
         scale = StandardScaler()
         data = scale.fit_transform(self.data)
-        self.data = pd.DataFrame(data, columns = self.data.columns)
-        joblib.dump(scale, '/tmp/ad/scale')
+        self.data = pd.DataFrame(data, columns=self.data.columns)
+        joblib.dump(scale, 'ad/scale')
 
-    def normalize(self):
-        """ normalize the data  """
-        upper = self.data.max()
-        lower = self.data.min()
-        self.data = (self.data - lower)/(upper-lower)  
-    
     def process(self):
-        """  Calls the modules for the data preprocessing like dropping columns, normalization etc.,  """
+        """
+          Calls the modules for the data preprocessing like dropping columns, normalization etc.,
+        """
         self.numerical_data()
         self.drop_na()
         self.variation()
 #        self.correlation()
         self.transform()
-        self.data.loc[:,'UEID'] = self.id      
-        self.data.loc[:,'MeasTimestampRF'] = self.time
+        self.data.loc[:, 'UEID'] = self.id
+        self.data.loc[:, 'MeasTimestampRF'] = self.time
         return self.data