def evaluate(prediction,labels_test):
labels_test = [0 if x=="neutral" else 1 if x=="positive" else -1 for x in labels_test]
#logistic regression evaluation
print "Average F1 : " +str(measures.avgF1(labels_test,prediction,-1,1))
#print "Baseline AverageF1 : " +str(measures.avgF1(labels_test,baseline_prediction))
print "Accuracy : " +str(measures.accuracy(labels_test,prediction))
#print "Baseline Accuracy : "+str(measures.accuracy(labels_test,baseline_prediction))
print "F1 negative : " +str(measures.F1(labels_test,prediction,-1))
print "F1 positive : " +str(measures.F1(labels_test,prediction,1))
print "Precision negative: " +str(measures.precision(labels_test,prediction,-1))
print "Precision positive: " +str(measures.precision(labels_test,prediction,1))
print "Recall negative : " +str(measures.recall(labels_test,prediction,-1))
print "Recall positive : " +str(measures.recall(labels_test,prediction,1))
def plot_recall_precision(length,features_train,labels_train,features_test,labels_test):
#threshold=[0.1 ,0.2 ,0.3 ,0.4,0.5,0.6,0.7,0.8,0.9]
threshold = [x / 1000.0 for x in range(0, 1001, 1)]
step = length/3
colors=['b','r','g']
for i in range(0,3):
#((i+1)*(step)) percent of train data
f = features_train[0:((i+1)*(step))]
l=labels_train[0:((i+1)*(step))]
#train classifier for the specific subset of training set
model = LogisticRegression.train(f,l)
#recall-precision for every threshold value
recall = []
precision=[]
for t in threshold :
prediction = LogisticRegression.predict(features_test,model,t)
recall.append(measures.recall(labels_test,prediction,0))
precision.append(measures.precision(labels_test,prediction,0))
plt.plot(recall,precision,linewidth="2.0",label=str((i+1)*33)+"% of train data",color=colors[i])
plt.xlim(0,1)
plt.ylim(0,1)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Negative tweets')
plt.legend()
plt.show()
