def C_comparison(length,features_train,labels_train,features_test,labels_test):
C = [0.001,0.05,0.1,0.3,0.5,0.8,1,10,100,350,500,1000,3500,5000,10000,50000,100000]
scores = []
for c in C:
model = LogisticRegression.train(features_train,labels_train,c)
prediction = LogisticRegression.predict(features_test,model)
scores.append((measures.avgF1(labels_test,prediction,0,1)))
plt.plot(C,scores,color="blue",linewidth="2.0")
plt.xticks(C)
plt.ylabel("F1")
plt.xlabel("C")
plt.show()
def plot_learning_curve(features_train,labels_train,features_test,labels_test,C=1):
#run for every 10% of training set and compute training error and testing error
step = len(features_train)/10
train = []
test = []
maj_clas = []
for i in range(0,10):
print i
#train for (i+1)*10 percent of training set
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)
#model = SVM.train(f,l,c=C,k="linear")
#get training error
prediction = LogisticRegression.predict(f,model)
#prediction = SVM.predict(f,model)
train.append(measures.error(l,prediction))
#get testing error
prediction = LogisticRegression.predict(features_test,model)
#prediction = SVM.predict(features_test,model)
test.append(measures.error(labels_test,prediction))
#get error for majority classifier
prediction = MajorityClassifier.predictSubj(features_test)
maj_clas.append(measures.error(labels_test,prediction))
#karabatsis = [0.6431]*len(train)
x = np.arange(len(train))*10
plt.plot(x,train,color="blue",linewidth="2.0",label="Training Error")
plt.plot(x,test,color="blue",linestyle="dashed",linewidth="2.0",label="Testing Error")
plt.plot(x,maj_clas,color="red",linewidth="2.0",label="Majority Classifier Error")
#plt.plot(x,karabatsis,color="green",linewidth="2.0",label="Karabatsis 14")
plt.ylim(0,1)
plt.ylabel('Error')
plt.xlabel("% of messages")
plt.legend(loc="lower left")
plt.show()
def plotFeaturesF1(features_train,labels_train,features_test,labels_test):
x = list(np.arange(len(features_train[0])))
#x = list(np.arange(5))
y = []
for i in range(0,len(features_train[0])):
f_train = features_train[:,i]
f_test = features_test[:,i]
f_train = f_train.reshape(f_train.shape[0],1)
f_test = f_test.reshape(f_test.shape[0],1)
model = LogisticRegression.train(f_train,labels_train)
prediction = LogisticRegression.predict(f_test,model)
y.append(measures.avgF1(labels_test,prediction,0,1))
plt.plot(x,y,color="blue",linewidth="2.0")
plt.ylabel("F1")
plt.xlabel("# of Feature")
plt.xticks(x)
plt.show()
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()
