New object to handle outliers inserted in the tool

dream/KnowledgeExtraction/DetectOutliers.py

+'''
+Created on 24 Sep 2014
+
+@author: Panos
+'''
+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+
+from StatisticalMeasures import BasicStatisticalMeasures 
+
+#The DetectOuliers object
+class HandleOutliers(BasicStatisticalMeasures):
+#Two different approaches to handle the outliers are included in this object, 
+#the first one delete both the mild and extreme outliers while the second approach delete only the extreme outliers in the given data set 
+    def DeleteOutliers(self,mylist):              #Delete the ouliers (both mild and extreme) in a given data set
+        A= BasicStatisticalMeasures()             #Call the BasicStatisticalMeasures to calculate the quantiles and interquartile range
+        Q1= A.quantile(mylist)[1]
+        Q3= A.quantile(mylist)[3]
+        IQ= A.IQR(mylist)
+        LIF= Q1 - 1.5*IQ                        #Calculate the lower inner fence
+        UIF= Q3 + 1.5*IQ                        #Calculate the upper inner fence
+        LOF= Q1 - 3*IQ                          #Calculate the lower outer fence
+        UOF= Q3 + 3*IQ                          #Calculate the upper outer fence
+        i=0
+        listx=[]
+        for value in mylist:                       
+            if not ((value<LOF or value>UOF) or (value<LIF or value>UIF)):    #If the value is beyond the inner fence ([LIF,UIF]) on either side (mild outlier) or beyond the outer fence ([LOF,UOF]) on either side (extreme outlier) doesn't pass the control and deleted  
+                listx.append(value)
+            i+=1 
+        return listx
+    
+    def DeleteExtremeOutliers(self,mylist):            #Delete only the  extreme ouliers in a given data set
+        A= BasicStatisticalMeasures()
+        Q1= A.quantile(mylist)[1]
+        Q3= A.quantile(mylist)[3]
+        IQ= A.IQR(mylist)
+        LOF= Q1 - 3*IQ 
+        UOF= Q3 + 3*IQ
+        i=0
+        listx=[]
+        for value in mylist:
+            if not (value<LOF or value>UOF):           #If the value is  beyond the outer fence ([LOF,UOF]) on either side (extreme outlier) doesn't pass the control and deleted  
+                listx.append(value)
+            i+=1
+        return listx
+        
+                
+                
+                
+            
+    
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