def test():
# load tf-idf
with open(os.path.join(root_path, "models/pretrained/tfidf.pkl"),
"rb") as f:
vectorizer = pickle.load(f)
# load validation data
val_ques, val_lab = VQADataProvider.label_ques("val")
val_ques_matrix = vectorizer.transform(val_ques)
# load model
with open(os.path.join(root_path, "models/pretrained/pretrained_svm.pkl"),
"rb") as f:
clf = pickle.load(f)
# compute validation accuracy
val_acc = clf.score(val_ques_matrix, val_lab)
print("validation accuracy")
print(val_acc)
# compute validation confusion matrix
preds = clf.predict(val_ques_matrix)
val_c_m = confusion_matrix(val_lab, preds)
print("validation confusion matrix")
print(val_c_m)
def train():
# prepare the data
# utils folder is in the root path, which is /home/leishi/vqa2019
questions, labels = VQADataProvider.label_ques("train_val")
vectorizer = TfidfVectorizer()
ques_matrix = vectorizer.fit_transform(questions)
# build classifier
# svc
classifier = SVC(gamma="scale", verbose=False)
# # rf
# classifier = RandomForestClassifier()
# # gb
# classifier = GradientBoostingClassifier()
# train
classifier.fit(ques_matrix, labels)
# # training accuracy
train_acc = classifier.score(ques_matrix, labels)
print("training accuracy: ", train_acc)
# save tf-idf
with open(os.path.join(root_path, "models/pretrained/tfidf_train_val.pkl"),
"wb") as f:
pickle.dump(vectorizer, f)
# save classifier
with open(
os.path.join(root_path,
"models/pretrained/pretrained_svm_train_val.pkl"),
"wb") as f:
pickle.dump(classifier, f)
# # debug
# print("\nraw questions")
# print(questions[:5])
# print("question matrix")
# print(vectorizer.transform(questions[:5]).todense())
# # print("corresponding words of transformed questions")
# # print(vectorizer.inverse_transform(vectorizer.transform(questions[:5])))
# print("terms used")
# print(vectorizer.get_feature_names())
return vectorizer, classifier