Merge pull request #31 from olest/master

python implementation of a naive bayes

Author: bricef

06-Naive-Bayes/nbayes.py

@@ -0,0 +1,102 @@
+#!/usr/bin/python
+
+# Cambridge Programmer Study Group
+#
+# Naive bayes implementation of a spam filter
+# Keeps 1000 text messages as test data set
+# Write incorrectly classified text messages
+# to a file for further inspection
+#
+# Ole Schulz-Trieglaff
+
+from collections import defaultdict
+import math
+import string
+
+def tokenize(ls):
+    # remove some frequent words, convert to lower case and remove
+    # punctuation characters
+    forbidden = ["and","to", "i","a", "you", "the", "your", "is"]
+    ls = [ w.lower() for w in ls ]
+    ls = [ w.translate(None, string.punctuation) for w in ls ]
+    ls = [ w for w in ls if w not in forbidden ]
+    return ls
+
+def main():
+
+    datafile = "corpus/SMSSpamCollection.txt"
+    data = []
+    with open(datafile) as input:
+        for line in input:
+            fields = line.split()
+            label = fields[0]
+            text  = tokenize(fields[1:])
+            data.append([label,text])
+
+    print "Have",len(data)," examples"
+
+    # let's keep 1000 examples separate as test data
+    num_test = 1000
+    test = data[:num_test]
+    train = data[(num_test+1):]
+
+    # P(word|label)
+    word_llhoods = defaultdict(lambda: defaultdict(lambda: 0.0001))
+    # P(label)
+    prior = defaultdict(float)
+    num_train = len(train)
+    for d in train:
+        label = d[0]
+        text = d[1]
+        prior[label]+=1
+        for t in text:
+            word_llhoods[label][t]+=1
+
+    # normalize to get probabilities
+    for k in prior:
+        prior[k] /= num_train
+
+    spam_sum = sum(word_llhoods["spam"].itervalues())
+    for w in word_llhoods["spam"]:
+        word_llhoods["spam"][w] /= spam_sum
+    ham_sum = sum(word_llhoods["ham"].itervalues())
+    for w in word_llhoods["ham"]:
+        word_llhoods["ham"][w] /= ham_sum
+
+    # debugging
+    print "prior=",prior
+    maxSpam = sorted(word_llhoods["spam"].iteritems(), key=lambda x: x[1])[0:5]
+    print "5 most freqent spam word",maxSpam
+    maxHam = sorted(word_llhoods["ham"].iteritems(), key=lambda x: x[1])[0:5]
+    print "5 most frequent ham word",maxHam
+
+    # read test data
+    correct = 0
+    mistakesFile = "mistakes" # write incorrectly classified messages to a file
+    with open(mistakesFile,"w") as mistakesOut:
+        for d in test:
+            label = d[0]
+            text  = d[1]
+            llhood_spam = 0.0
+            llhood_ham = 0.0
+            for w in text:
+                #print w," ",math.log10(word_llhoods["ham"][w])," ", math.log10(word_llhoods["spam"][w])
+                llhood_spam += math.log10(word_llhoods["spam"][w])
+                llhood_ham += math.log10(word_llhoods["ham"][w])
+
+            llhood_spam += math.log10(prior["spam"])
+            llhood_ham += math.log10(prior["ham"])
+
+            guess = "spam" if llhood_spam > llhood_ham else "ham"
+            if label == guess:
+                correct+=1
+            else:
+                print >> mistakesOut, text
+                print >> mistakesOut, "llhood_spam=",llhood_spam
+                print >> mistakesOut, "llhood_ham=",llhood_ham
+                print >> mistakesOut, "true label=",label
+
+    print "correct={} out of {} test cases".format(correct,num_test)
+
+if __name__ == "__main__":
+	main()