def DamagePrediction(mutation):
FeatVec = []
for label in D_Labels:
FeatVec.append(mutation[label])
D_Tree = pickle.load(open(D_TreeFile))
Pred = DecisionTree.Classify(D_Tree, D_Labels, FeatVec)
return Pred
def ConservePrediction(mutation):
FeatVec = []
for label in C_Labels:
FeatVec.append(mutation[label])
C_Tree = pickle.load(open(C_TreeFile))
Pred = DecisionTree.Classify(C_Tree, C_Labels, FeatVec)
return Pred
C_Positive_t = GetPositiveFeatureVector('testing.xls',C_Labels,4)
D_Positive_t = GetPositiveFeatureVector('testing.xls',D_Labels,4)
C_Negative,C_TestVec = GetNegativeFeatureVector('10128.annotation.xls',C_Labels)
D_Negative,D_TestVec = GetNegativeFeatureVector('10128.annotation.xls',D_Labels)
#############################################################################
print len(D_Positive)
C_dataset = C_Negative + C_Positive
D_dataset = D_Negative + D_Positive
#print dataset
C_Tree = DecisionTree.CreatTree(C_dataset,C_Labels)
D_Tree = DecisionTree.CreatTree(D_dataset,D_Labels)
#TreePlotter.CreatPlot(D_Tree)
#TreePlotter.CreatPlot(C_Tree)
C_TreeFile = open('C_Tree','w')
D_TreeFile = open('D_Tree','w')
pickle.dump(C_Tree,C_TreeFile)
pickle.dump(D_Tree,D_TreeFile)
C_TreeFile.close();D_TreeFile.close()
ClassList = []
#print Positive[50:] # [50:]: #Positive: #TestVec:
for vec in C_TestVec:
vec = vec[0:3]
ClassList.append(DecisionTree.Classify(C_Tree,C_FeatLabels,vec))
print len(ClassList),ClassList.count('no')/len(ClassList)
