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_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_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)
plt.show()
if __name__ == "__main__":
#读取路径
train_path = 'F:/code/tfcode/school_code/dataclean_code/train_data_vec.npz'
test_path = 'F:/code/tfcode/school_code/dataclean_code/test_data_vec.npz'
#读取训练集与测试集
train_x , train_y = get_dataset(train_path)
test_x , test_y = get_dataset(test_path)
seed = 1234
random.seed(seed)
random.shuffle(train_x )
random.seed(seed)
random.shuffle(train_y)
seed = 2143
random.seed(seed)
random.shuffle(test_x )
random.seed(seed)
random.shuffle(test_y)
model = BertClassifier()
model.fit(train_x , train_y)
pre_y = model.predict(test_x)
score = model.score(pre_y , test_y)
print(score)
# 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
test_df_na = test_df[test_df['content'].isna()]
test_df_na['label'] = 0
X_test = test_df_not_na['content']
y_test_pred = model.predict(X_test)
test_df_not_na['label'] = y_test_pred
new_test_df = pd.concat([test_df_not_na, test_df_na]).sort_index()
temp_df = pd.DataFrame(columns=['id', 'y'])
train_sents, val_sents, train_label, val_labels = train_test_split(train_text, train_label, test_size=0.2)
train_sents.head()
model = BertClassifier(max_seq_length=128,
train_batch_size=32,
epochs=5,
bert_model='bert-base-multilingual-cased')
model
# Commented out IPython magic to ensure Python compatibility.
# %%time
# history = model.fit(train_text, train_label)
accy = model.score(val_sents, val_labels)
# make class probability predictions
y_prob = model.predict_proba(val_sents)
print("class prob estimates:\n", y_prob)
# make predictions
y_pred = model.predict(val_sents)
print("Accuracy: %0.2f%%"%(metrics.accuracy_score(y_pred, val_labels) * 100))
target_names = ['negative', 'positive']
print(classification_report(val_labels, y_pred, target_names=target_names))
X_test = test['comment']
test_id = test['id']
def bert(train_x, train_y, test_x, test_y):
bert = BertClassifier(**bert_params)
bert.fit(train_x, train_y.values.ravel())
print('BERT Accuracy:', bert.score(test_x, test_y.values.ravel()))