def evaluate(self, gold_labels, pred_labels, target_names):
overall_accuracy = accuracy_score(gold_labels, pred_labels)
#class_report = classification_report(gold_labels, pred_labels, target_names=target_names)
precision = metrics.precision_score(gold_labels, pred_labels)
recall = metrics.recall_score(gold_labels, pred_labels)
f1_score = metrics.f1_score(gold_labels, pred_labels)
return overall_accuracy, precision, recall, f1_score
def SVM(X_train,y_train,X_test,y_test):
'''fit a SVM model to the data
'''
t0 = time()
# normalize
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
X_test = min_max_scaler.fit_transform(X_test)
model = SVC(kernel = "rbf")
model.fit(X_train, y_train)
print ("training time:", round(time()-t0, 3), "s")
# make predictions
t0 = time()
expected = y_test
predicted = model.predict(X_test)
print ("predicting time:", round(time()-t0, 3), "s")
# summarize the fit of the model
score = metrics.accuracy_score(expected, predicted)
print(score)
print(metrics.recall_score(expected,predicted))
return model, score
def DTree(X_train, y_train, X_test, y_test):
model = tree.DecisionTreeClassifier(min_samples_split=40)
t0 = time()
model.fit(X_train, y_train)
print("training time:", round(time() - t0, 3), "s")
t0 = time()
expected = y_test
predicted = model.predict(X_test)
print("predicting time:", round(time() - t0, 3), "s")
# summarize the fit of the model
score = metrics.accuracy_score(expected, predicted)
print(score)
print(metrics.recall_score(expected, predicted))
return model, score
def SVM(X_train, y_train, X_test, y_test):
# fit a SVM model to the data
t0 = time()
model = SVC(kernel="linear")
model.fit(X_train, y_train)
print("training time:", round(time() - t0, 3), "s")
#print(model)
# make predictions
t0 = time()
expected = y_test
predicted = model.predict(X_test)
print("predicting time:", round(time() - t0, 3), "s")
# summarize the fit of the model
score = metrics.accuracy_score(expected, predicted)
print(score)
print(metrics.recall_score(expected, predicted))
return model, score
