def test_save_load_model(): """Test saving/loading a fitted model to disk""" X_train, y_train, X_dev, y_dev = sst2_test_data() model = BertClassifier() model.max_seq_length = 64 model.train_batch_size = 8 model.epochs = 1 model.fit(X_train, y_train) accy1 = model.score(X_dev, y_dev) savefile = './test_model_save.bin' print("\nSaving model to ", savefile) model.save(savefile) # load model from disk new_model = load_model(savefile) # predict with new model accy2 = new_model.score(X_dev, y_dev) # clean up print("Cleaning up model file: test_model_save.bin ") os.remove(savefile) assert accy1 == accy2
def test_bert_sklearn_accy(): """ Test bert_sklearn accuracy compare against huggingface run_classifier.py on 200 rows of SST-2 data. """ print("Running bert-sklearn...") X_train, y_train, X_dev, y_dev, label_list = toxic_test_data() # define model model = BertClassifier() model.validation_fraction = 0.0 model.learning_rate = 5e-5 model.gradient_accumulation_steps = 2 model.max_seq_length = 64 model.train_batch_size = 16 model.eval_batch_size = 8 model.epochs = 2 model.multilabel = True # for multi-label classification model.label_list = label_list model.fit(X_train, y_train) bert_sklearn_accy = model.score(X_dev, y_dev) bert_sklearn_accy /= 100 # run huggingface BERT run_classifier and check we get the same accuracy cmd = r"python tests/run_classifier.py --task_name sst-2 \ --data_dir ./tests/data/sst2 \ --do_train --do_eval \ --output_dir ./comptest \ --bert_model bert-base-uncased \ --do_lower_case \ --learning_rate 5e-5 \ --gradient_accumulation_steps 2 \ --max_seq_length 64 \ --train_batch_size 16 \ --eval_batch_size 8 \ --num_train_epochs 2" print("\nRunning huggingface run_classifier.py...\n") os.system(cmd) print("...finished run_classifier.py\n") # parse run_classifier.py output file and find the accy accy = open("comptest/eval_results.txt").read().split("\n")[ 0] # 'acc = 0.76' accy = accy.split("=")[1] accy = float(accy) print("bert_sklearn accy: %.02f, run_classifier.py accy : %0.02f" % (bert_sklearn_accy, accy)) # clean up print("\nCleaning up eval file: eval_results.txt") #os.remove("eval_results.txt") shutil.rmtree("comptest") assert bert_sklearn_accy == accy
def test_not_fitted_exception(): """Test predicting with a model that has not been fitted""" X_train, y_train, X_dev, y_dev = sst2_test_data() model = BertClassifier() model.max_seq_length = 64 model.train_batch_size = 8 model.epochs = 1 # model has not been fitted: model.fit(X_train, y_train) with pytest.raises(Exception): model.score(X_dev, y_dev)
def test_nonbinary_classify(): """Test non-binary classification with different inputs""" train = pd.read_csv(DATADIR + "/mnli/train.csv") X_train = train[['text_a', 'text_b']] y_train = train['label'] #X_train = list(X_train.values) #y_train = list(y_train.values) model = BertClassifier() model.validation_fraction = 0.0 model.max_seq_length = 64 model.train_batch_size = 16 model.eval_batch_size = 8 model.epochs = 1 model.fit(X_train, y_train) accy = model.score(X_train, y_train) # pandas df input X = X_train[:5] print("testing %s input" % (type(X))) y1 = model.predict(X) # numpy array input X = X_train[:5].values print("testing %s input" % (type(X))) y2 = model.predict(X) assert list(y2) == list(y1) # list input X = list(X_train[:5].values) print("testing %s input" % (type(X))) y3 = model.predict(X) assert list(y3) == list(y1)
train_df, dev_df = train_test_split(data_df_not_na_label, test_size=0.2, shuffle=True) ## 准备模型的数据 X_train, y_train = train_df['content'], train_df['label'] X_dev, y_dev = dev_df['content'], dev_df['label'] # define model model = BertClassifier('bert-base-uncased') model.validation_fraction = 0.0 model.learning_rate = 3e-5 model.gradient_accumulation_steps = 1 model.max_seq_length = 64 model.train_batch_size = 1 model.eval_batch_size = 1 model.epochs = 1 # fit model.fit(X_train, y_train) # score accy = model.score(X_dev, y_dev) test_df = pd.read_csv( 'data/nCov_10k_test.csv', skiprows=[0], names=['id', 'time', 'account', 'content', 'pic', 'video']) test_df_not_na = test_df[test_df['content'].notna()] ## 直接设定没有微博内容的label为0