From 7c7759b248de596fe0fe7bcc514548b2de3fb524 Mon Sep 17 00:00:00 2001
From: rylon <rylon@web.de>
Date: Sat, 15 Jun 2013 22:46:27 +0200
Subject: [PATCH] webmining u4 update

---
 .../4_Uebung/code/confusion_matrix.py         | 42 ++++++++++++++++---
 1 file changed, 37 insertions(+), 5 deletions(-)

diff --git a/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py b/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py
index 3f7ffacf..55537129 100644
--- a/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py	
+++ b/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py	
@@ -16,14 +16,22 @@ document_count = 0
 precision = [0,0,0,0,0,0,0,0,0,0,0]
 precision_avg = 0
 
-def load_predictionfile():
+def load_predictionfile(file):
     global document_count
-    for line in open(predfile,'r'):
+    for line in open(file,'r'):
         document_count += 1
         str = line.split("\t")
         preds.append([str[0],str[1].split("\n")[0]])
     
 def confusion_matrix():     
+    '''
+      c1 c2 ... c11
+    c1
+    c2
+    c3
+    ...
+    c11
+    '''
     for pred in preds:
         for c in classes:
             if os.path.isfile(trainDir +"/"+ c[0]+"/"+ pred[0]):
@@ -32,6 +40,7 @@ def confusion_matrix():
                         conf_matr[p[1]][c[1]] += 1            
                 
 def accuracy():
+    #alle werte auf der diagonale addiert / n (anzahl der dokumente)
     global accuracy
     global document_count
     i = 0
@@ -69,15 +78,38 @@ def avg_prec():
     for p in precision:
         precision_avg += p
     precision_avg = float(precision_avg) / 11
-        
+
+       
+def pp_confusionmatrix():
+    print "\tc1\tc2\tc3\tc4\tc5\tc6\tc7\tc8\tc9\tc10\tc11"
+    lines = ["c1\t","c2\t","c3\t","c4\t","c5\t","c6\t","c7\t","c8\t","c9\t","c10\t","c11\t"]
+    for column in conf_matr:
+        lines[0] += str(column[0]) + "\t"
+        lines[1] += str(column[1]) + "\t"
+        lines[2] += str(column[2]) + "\t"
+        lines[3] += str(column[3]) + "\t"
+        lines[4] += str(column[4]) + "\t"
+        lines[5] += str(column[5]) + "\t"
+        lines[6] += str(column[6]) + "\t"
+        lines[7] += str(column[7]) + "\t"
+        lines[8] += str(column[8]) + "\t"
+        lines[9] += str(column[9]) + "\t"
+        lines[10] += str(column[10]) + "\t"
+
+    for l in lines:
+        print l
+
+def pp_accuracy():
+    print "Accuracy: "+str(round(accuracy*100,4))+"%"
 
 if __name__ == '__main__':
-    load_predictionfile()
+    load_predictionfile(predfile)
     #print preds
     confusion_matrix()
+    pp_confusionmatrix()
     #print conf_matr
     accuracy()
-    #print "Accuracy: "+str(accuracy)
+    pp_accuracy()
     prec()
     print precision
     avg_prec()