46%

2013-06-16 23:21:54 +02:00 · 2013-06-16 23:21:54 +02:00 · 24c6264397
commit 24c6264397
parent 3126edc599
3 changed files with 224 additions and 144 deletions
--- a/Mining/Uebungen/4_Uebung/G22_predictions.txt
+++ b/Mining/Uebungen/4_Uebung/G22_predictions.txt
@ -1,165 +1,165 @@
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-f7099ffdcda8a3e231652cdfbdfe1d26.txt	editorial
-fc97d173fc6d18448bd334ccdbf36e4c.txt	hobbies
-fdcc797bb8b504885a2ce07017555f33.txt	news
+f2b173d5ffa6eda874a71aea5ba076d2.txt	belles_lettres
+f3b16a0072a6afc3a64e592f6c8ab78b.txt	belles_lettres
+f433e3a3fdf6455b68183790d72f7fd8.txt	belles_lettres
+f7099ffdcda8a3e231652cdfbdfe1d26.txt	belles_lettres
+fc97d173fc6d18448bd334ccdbf36e4c.txt	mystery
+fdcc797bb8b504885a2ce07017555f33.txt	lore
--- a/Mining/Uebungen/4_Uebung/code/confusion_matrix.py
+++ b/Mining/Uebungen/4_Uebung/code/confusion_matrix.py
@ -62,7 +62,7 @@ def accuracy():

    #print ok_recognized
    #print document_count  
-    if wrong_recognized + ok_recognized <> 0:      
+    if wrong_recognized + ok_recognized != 0:      
        accuracy = float(ok_recognized) / float(ok_recognized+wrong_recognized)
    else:
        accuracy = 0
@ -140,7 +140,7 @@ def recall():
            else:
                not_okvalues += conf[i]            
            j += 1
-        if not_okvalues + ok_values <> 0:
+        if not_okvalues + ok_values != 0:
            recalls[i] = float(ok_values) / float(ok_values+not_okvalues)
        #else:
        #    recalls[i] = 0        
@ -166,7 +166,7 @@ def prec_micro():
        i += 1
    
    global precision_micro
-    if result[0]+result[1] <> 0:
+    if result[0]+result[1] != 0:
        precision_micro = float(result[0]) / float(result[0]+result[1])
    else:
        precision_micro = 0
@ -209,7 +209,7 @@ def recall_micro():
        i += 1
    
    global recall_micro
-    if result[0]+result[2] <> 0:
+    if result[0]+result[2] != 0:
        recall_micro = float(result[0]) / float(result[0]+result[2])
    else:
        recall_micro = 0
--- a/Mining/Uebungen/4_Uebung/code/naive_bayes.py
+++ b/Mining/Uebungen/4_Uebung/code/naive_bayes.py
@ -119,7 +119,7 @@ class multiclassClassifier:
                thisline = line.split(" ");                
                for word in thisline:                    
                    word = self.clean_word(word)
-                    if word <> "":
+                    if word != "":
                        if dictonary.has_key(word):
                            dictonary[str(word)] += 1
                        else:
@ -128,14 +128,15 @@ class multiclassClassifier:
            return dictonary      
        
       
-        def bayes(self, text, termfrequenciesOfClasses, termCount, percentage):
+        def bayes(self, text, termfrequenciesOfClasses, termCount, percentage, cl):
            result = 1.0
            wordcount = 0.0
+            notwordcount = 0.0
            for line in text:
                thisline = line.split(" ");
                for word in thisline:                    
                    word = self.clean_word(word)                    
-                    if word <> "":
+                    if word != "":
                        '''
                        Accuracy: 21.2121%
                        Precision per class: adventure:40.0% belles_lettres:22.2222% editorial:17.6471% fiction:36.3636% government:0.0% hobbies:11.1111% learned:0.0% lore:17.5439% mystery:0.0% news:23.4043% romance:0.0% 
@ -163,6 +164,79 @@ class multiclassClassifier:
                        result += math.log(percentage)
                        result += math.log(wordcount)
                        return result
+                        
+                        Accuracy: 33.9394%
+                        Precision per class: adventure:0.0% belles_lettres:36.8421% editorial:0.0% fiction:0.0% government:20.0% hobbies:0.0% learned:38.9831% lore:0.0% mystery:36.8421% news:0.0% romance:52.9412% 
+                        Precision Macroavg: 16.8735%
+                        Precision Microavg: 33.9394%
+                        Recall per class: adventure:0.0% belles_lettres:25.9259% editorial:0.0% fiction:0.0% government:90.9091% hobbies:0.0% learned:76.6667% lore:0.0% mystery:87.5% news:0.0% romance:81.8182% 
+                        Recall Microavg: 4.8866%
+                        
+                        if termfrequenciesOfClasses.has_key(str(word)):
+                            wordcount += 1                            
+                            result += termfrequenciesOfClasses[word]/(termCount+1)
+                            #print "known word: "+word
+                        else:
+                            result -= 1./(termCount+1)
+                            #print "new word: "+word
+                        ...    
+                        result /= len(termfrequenciesOfClasses)
+                        print cl +" "+str(result)
+                        return math.log(result)
+                        
+                        Accuracy: 37.5758%
+                        Precision per class: adventure:66.6667% belles_lettres:36.5385% editorial:0.0% fiction:0.0% government:28.0% hobbies:0.0% learned:36.8421% lore:0.0% mystery:50.0% news:100.0% romance:37.5% 
+                        Precision Macroavg: 32.3225%
+                        Precision Microavg: 37.5758%
+                        Recall per class: adventure:20.0% belles_lettres:70.3704% editorial:0.0% fiction:0.0% government:63.6364% hobbies:0.0% learned:70.0% lore:0.0% mystery:50.0% news:18.75% romance:54.5455% 
+                        Recall Microavg: 5.6777%
+                        
+                        if termfrequenciesOfClasses.has_key(str(word)):
+                            wordcount += 1
+                            result += termfrequenciesOfClasses[word]/(termCount+1)
+                        else:
+                            result -= 1./(termCount+1)
+                        ...
+                        result *= wordcount
+                        result /= len(termfrequenciesOfClasses)
+                        #return result
+                        print cl +" "+str(result)
+                        return math.log(result)
+                        
+                        Accuracy: 40.6061%
+                        Precision per class: adventure:40.0% belles_lettres:44.7368% editorial:0.0% fiction:0.0% government:23.6842% hobbies:66.6667% learned:40.0% lore:0.0% mystery:46.1538% news:100.0% romance:47.3684% 
+                        Precision Macroavg: 37.1464%
+                        Precision Microavg: 40.6061%
+                        Recall per class: adventure:20.0% belles_lettres:62.963% editorial:0.0% fiction:0.0% government:81.8182% hobbies:15.3846% learned:60.0% lore:0.0% mystery:75.0% news:25.0% romance:81.8182% 
+                        Recall Microavg: 6.3992%
+                        
+                        if termfrequenciesOfClasses.has_key(str(word)):
+                            wordcount += 1
+                            result += (termfrequenciesOfClasses[word]/(termCount+1))*(1-percentage)
+                        ...
+                        result *= wordcount
+                        result /= len(termfrequenciesOfClasses)
+                        print cl +" "+str(result)
+                        return math.log(result)
+                        
+                        Accuracy: 46.0606%
+                        Precision per class: adventure:25.0% belles_lettres:35.3846% editorial:0.0% fiction:31.25% government:40.0% hobbies:66.6667% learned:72.0% lore:20.0% mystery:66.6667% news:70.5882% romance:25.0% 
+                        Precision Macroavg: 41.1415%
+                        Precision Microavg: 46.0606%
+                        Recall per class: adventure:20.0% belles_lettres:85.1852% editorial:0.0% fiction:45.4545% government:36.3636% hobbies:46.1538% learned:60.0% lore:5.5556% mystery:50.0% news:75.0% romance:9.0909% 
+                        Recall Microavg: 7.8675%
+                        
+                        if termfrequenciesOfClasses.has_key(str(word)):
+                            wordcount += 1
+                            result += (termfrequenciesOfClasses[word]/(termCount+1))
+                        else:
+                            notwordcount += 1
+                            result += (1./(termCount+1))
+                        ...    
+                        result *= (1-percentage)*wordcount
+                        result /= percentage*notwordcount                        
+                        print cl +" "+str(result)
+                        return math.log(result)
                        '''
                        #result = 1.0
                        #for word in text:
@ -171,17 +245,23 @@ class multiclassClassifier:
                            #result += math.log(1./(termfrequenciesOfClasses[word]+1.))
                            #result += math.log((termfrequenciesOfClasses[word]+1.)/(termCount+1)) #gewichte häufig auftretende terme am stärksten
                            #result += math.log(1./((termfrequenciesOfClasses[word]+1.)/(termCount+1))) #gewichte häufig auftretende worter am wenigsten, wenigauftretende am stärksten + termcount -> was ist das?
-                            result += termfrequenciesOfClasses[word]
+                            result += (termfrequenciesOfClasses[word]/(termCount+1))
                            #print "known word: "+word
-                        #else:
+                        else:
+                            notwordcount += 1
+                            result += (1./(termCount+1))
                            #result += math.log(1./(termCount+1))
                            #result += math.log(1.)
                            #print "new word: "+word
            #result += math.log(percentage)
-            result *= percentage
+            #result /= percentage
            #result += math.log(wordcount)
-            result *= wordcount
+            #result *= (wordcount/(wordcount + notwordcount))            
+            result *= (1-percentage)*wordcount
+            result /= percentage*notwordcount
+            #result /= len(termfrequenciesOfClasses)
            #return result
+            print cl +" "+str(result)
            return math.log(result)
            
        def clean_word(self, word):
@ -249,7 +329,7 @@ if __name__ == '__main__':
            # check all possible classes
            for cl in mc.percentage.keys():
                f = open(currentPath, 'r')                
-                temp = mc.bayes(f.readlines(), mc.termfrequenciesOfClasses[cl], sumOfClasses, mc.percentage[cl])
+                temp = mc.bayes(f.readlines(), mc.termfrequenciesOfClasses[cl], sumOfClasses, mc.percentage[cl], cl)
                #class_matches.append([infile,cl,temp])
                #print class_matches
                if (temp >= maxRes):