src/HOL/Tools/Sledgehammer/MaSh/src/theoryStats.py

+#     Title:      HOL/Tools/Sledgehammer/MaSh/src/theoryStats.py
+#     Author:     Daniel Kuehlwein, ICIS, Radboud University Nijmegen
+#     Copyright   2012
+#
+# An updatable sparse naive Bayes classifier.
+
+'''
+Created on Dec 26, 2012
+
+@author: Daniel Kuehlwein
+'''
+
+from cPickle import load,dump
+import logging,string
+
+class TheoryStatistics(object):
+    '''
+    Stores statistics for theory lvl predictions
+    '''
+
+
+    def __init__(self):
+        '''
+        Constructor
+        '''
+        self.logger = logging.getLogger('TheoryStatistics')
+        self.count = 0
+        self.precision = 0.0
+        self.recall100 = 0
+        self.recall = 0.0
+        self.predicted = 0.0
+    
+    def update(self,currentTheory,predictedTheories,usedTheories):
+        self.count += 1
+        allPredTheories = predictedTheories.union([currentTheory])
+        if set(usedTheories).issubset(allPredTheories):
+            self.recall100 += 1
+        localPredicted = len(allPredTheories)
+        self.predicted += localPredicted 
+        localPrec = float(len(set(usedTheories).intersection(allPredTheories))) / localPredicted
+        self.precision += localPrec
+        localRecall = float(len(set(usedTheories).intersection(allPredTheories))) / len(set(usedTheories))
+        self.recall += localRecall
+        self.logger.info('Theory prediction results:')
+        self.logger.info('Problem: %s \t Recall100: %s \t Precision: %s \t Recall: %s \t PredictedTeories: %s',\
+                         self.count,self.recall100,round(localPrec,2),round(localRecall,2),localPredicted)
+        
+    def printAvg(self):
+        self.logger.info('Average theory results:')
+        self.logger.info('avgPrecision: %s \t avgRecall100: %s \t avgRecall: %s \t avgPredicted:%s', \
+                         round(self.precision/self.count,2),\
+                         round(float(self.recall100)/self.count,2),\
+                         round(self.recall/self.count,2),\
+                         round(self.predicted /self.count,2))
+        
+    def save(self,fileName):
+        oStream = open(fileName, 'wb')
+        dump((self.count,self.precision,self.recall100,self.recall,self.predicted),oStream)
+        oStream.close()
+    def load(self,fileName):
+        iStream = open(fileName, 'rb')
+        self.count,self.precision,self.recall100,self.recall,self.predicted = load(iStream)
+        iStream.close()