def test_no_text_column_raise():
data = [('😁😁😁😁😁😁', 'grin')] * 2000 + [('😃😃😃😃😃😃😃😃', 'smile')
] * 1000 + [('😉😉😉', 'wink')] * 1000
df = pd.DataFrame(data, columns=['data', 'label'])
with pytest.raises(AssertionError):
predictor = TextPredictor(label='label', verbosity=4)
predictor.fit(df, hyperparameters=get_test_hyperparameters(), seed=123)
def test_empty_text_item():
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/sts/train.parquet')
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
train_data = train_data.iloc[train_perm[:100]]
train_data.iat[0, 0] = None
train_data.iat[10, 0] = None
predictor = TextPredictor(label='score', verbosity=4)
predictor.fit(train_data, hyperparameters=get_test_hyperparameters(), time_limit=30)
def test_no_job_finished_raise():
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/train.parquet')
with pytest.raises(RuntimeError):
# Setting a very small time limits to trigger the bug
predictor = TextPredictor(label='label')
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=1,
num_gpus=1,
seed=123)
def test_cpu_only_warning():
train_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/train.parquet')
dev_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/dev.parquet')
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
valid_perm = rng_state.permutation(len(dev_data))
train_data = train_data.iloc[train_perm[:100]]
dev_data = dev_data.iloc[valid_perm[:10]]
predictor = TextPredictor(label='label', eval_metric='acc')
with pytest.warns(UserWarning):
predictor.fit(train_data, hyperparameters=get_test_hyperparameters(),
num_gpus=0, seed=123)
def test_cpu_only_raise(set_env_train_without_gpu):
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/train.parquet')
dev_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sst/dev.parquet')
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
valid_perm = rng_state.permutation(len(dev_data))
train_data = train_data.iloc[train_perm[:100]]
dev_data = dev_data.iloc[valid_perm[:10]]
predictor = TextPredictor(label='label', eval_metric='acc')
if set_env_train_without_gpu is None:
with pytest.raises(RuntimeError):
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
num_gpus=0,
seed=123)
elif set_env_train_without_gpu is True:
os.environ['AUTOGLUON_TEXT_TRAIN_WITHOUT_GPU'] = '1'
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
num_gpus=0,
time_limit=30,
seed=123)
verify_predictor_save_load(predictor, dev_data, verify_proba=True)
else:
with pytest.raises(RuntimeError):
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
num_gpus=0,
seed=123)
def test_sst(hyperparameters):
train_data = load_pd.load(
'https://autogluon-text-data.s3-accelerate.amazonaws.com/'
'glue/sst/train.parquet')
dev_data = load_pd.load(
'https://autogluon-text-data.s3-accelerate.amazonaws.com/'
'glue/sst/dev.parquet')
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
valid_perm = rng_state.permutation(len(dev_data))
train_data = train_data.iloc[train_perm[:100]]
dev_data = dev_data.iloc[valid_perm[:10]]
predictor = TextPredictor(label='label', eval_metric='acc')
predictor.fit(train_data, hyperparameters=hyperparameters)
dev_acc = predictor.evaluate(dev_data, metrics=['acc'])
verify_predictor_save_load(predictor, dev_data, verify_proba=True)
def test_load_old_checkpoint():
dataset = AmazonReviewSentimentCrossLingualDataset()
sha1sum_id = "4ba096cdf6bd76c06386f2c27140db055e59c91b"
checkpoint_name = "mdeberta-v3-base-checkpoint"
save_path = os.path.join(get_home_dir(), "checkpoints")
file_path = os.path.join(save_path, f"{checkpoint_name}.zip")
checkpoint_path = os.path.join(get_home_dir(), "checkpoints",
checkpoint_name)
if os.path.exists(save_path):
shutil.rmtree(save_path)
download(
url=
f"s3://automl-mm-bench/unit-tests-0.4/checkpoints/{checkpoint_name}.zip",
path=file_path,
sha1_hash=sha1sum_id,
)
protected_zip_extraction(
file_path,
sha1_hash=sha1sum_id,
folder=save_path,
)
predictor = TextPredictor.load(checkpoint_path)
verify_predictor_save_load(predictor, dataset.test_df)
# continuous training
predictor.fit(
dataset.train_df,
presets="multilingual",
time_limit=10,
hyperparameters={"optimization.top_k_average_method": "uniform_soup"},
)
verify_predictor_save_load(predictor, dataset.test_df)
def train(args):
set_seed(args.seed)
if args.task is not None:
feature_columns, label_column, eval_metric, all_metrics = TASKS[args.task]
else:
raise NotImplementedError
if args.exp_dir is None:
args.exp_dir = 'autogluon_text_{}'.format(args.task)
train_df = load_pd.load(args.train_file)
dev_df = load_pd.load(args.dev_file)
test_df = load_pd.load(args.test_file)
train_df = train_df[feature_columns + [label_column]]
dev_df = dev_df[feature_columns + [label_column]]
test_df = test_df[feature_columns]
if args.task == 'mrpc' or args.task == 'sts':
# Augmenting the un-ordered set manually.
train_df_other_part = pd.DataFrame({feature_columns[0]: train_df[feature_columns[1]],
feature_columns[1]: train_df[feature_columns[0]],
label_column: train_df[label_column]})
real_train_df = pd.concat([train_df, train_df_other_part])
real_dev_df = dev_df
else:
real_train_df = train_df
real_dev_df = dev_df
if args.mode == 'stacking':
predictor = TabularPredictor(label=label_column,
eval_metric=eval_metric,
path=args.exp_dir)
predictor.fit(train_data=real_train_df,
tuning_data=real_dev_df,
hyperparameters='multimodal',
num_bag_folds=5,
num_stack_levels=1)
elif args.mode == 'weighted':
predictor = TabularPredictor(label=label_column,
eval_metric=eval_metric,
path=args.exp_dir)
predictor.fit(train_data=real_train_df,
tuning_data=real_dev_df,
hyperparameters='multimodal')
elif args.mode == 'single':
# When no embedding is used,
# we will just use TextPredictor that will train a single model internally.
predictor = TextPredictor(label=label_column,
eval_metric=eval_metric,
path=args.exp_dir)
predictor.fit(train_data=real_train_df,
tuning_data=real_dev_df,
seed=args.seed)
else:
raise NotImplementedError
dev_metric_score = predictor.evaluate(dev_df)
dev_predictions = predictor.predict(dev_df, as_pandas=True)
test_predictions = predictor.predict(test_df, as_pandas=True)
dev_predictions.to_csv(os.path.join(args.exp_dir, 'dev_prediction.csv'))
test_predictions.to_csv(os.path.join(args.exp_dir, 'test_prediction.csv'))
with open(os.path.join(args.exp_dir, 'final_model_scores.json'), 'w') as of:
json.dump({f'valid_{eval_metric}': dev_metric_score}, of)
def predict(args):
if args.use_tabular:
predictor = TabularPredictor.load(args.model_dir)
else:
predictor = TextPredictor.load(args.model_dir)
test_prediction = predictor.predict(args.test_file, as_pandas=True)
if args.exp_dir is None:
args.exp_dir = '.'
test_prediction.to_csv(os.path.join(args.exp_dir, 'test_prediction.csv'))
def test_emoji():
data = []
for i in range(50 * 3):
data.append(('😁' * (i + 1), 'grin'))
for i in range(30 * 3):
data.append(('😃' * (i + 1), 'smile'))
for i in range(20 * 3):
data.append(('😉' * (i + 1), 'wink'))
df = pd.DataFrame(data, columns=['data', 'label'])
predictor = TextPredictor(label='label', verbosity=3)
predictor.fit(df,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
assert set(predictor.class_labels) == {'grin', 'smile', 'wink'}
assert predictor.class_labels_internal == [0, 1, 2]
verify_predictor_save_load(predictor, df)
def test_predictor_fit(key):
train_data = load_pd.load(DATA_INFO[key]['train'])
dev_data = load_pd.load(DATA_INFO[key]['dev'])
label = DATA_INFO[key]['label']
eval_metric = DATA_INFO[key]['metric']
verify_proba = DATA_INFO[key]['verify_proba']
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
valid_perm = rng_state.permutation(len(dev_data))
train_data = train_data.iloc[train_perm[:100]]
dev_data = dev_data.iloc[valid_perm[:10]]
predictor = TextPredictor(label=label, eval_metric=eval_metric)
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_score = predictor.evaluate(dev_data)
verify_predictor_save_load(predictor, dev_data, verify_proba=verify_proba)
def load(cls, path: str, reset_paths=True, verbose=True):
model = super().load(path=path,
reset_paths=reset_paths,
verbose=verbose)
if model._load_model:
try_import_autogluon_text()
from autogluon.text import TextPredictor
model.model = TextPredictor.load(
os.path.join(path, cls.nn_model_name))
model._load_model = None
return model
def load(cls, path: str, reset_paths=True, verbose=True):
try:
from autogluon.text import TextPredictor
except ImportError:
raise ImportError(AG_TEXT_IMPORT_ERROR)
model = super().load(path=path,
reset_paths=reset_paths,
verbose=verbose)
model.model = TextPredictor.load(os.path.join(path, cls.nn_model_name))
return model
def verify_predictor_save_load(predictor,
df,
verify_proba=False,
verify_embedding=True):
with tempfile.TemporaryDirectory() as root:
predictor.save(root)
predictions = predictor.predict(df)
loaded_predictor = TextPredictor.load(root)
predictions2 = loaded_predictor.predict(df)
npt.assert_equal(predictions, predictions2)
if verify_proba:
predictions_prob = predictor.predict_proba(df)
predictions2_prob = loaded_predictor.predict_proba(df)
npt.assert_equal(predictions_prob, predictions2_prob)
if verify_embedding:
embeddings = predictor.predict_features(df)
assert embeddings.shape[0] == len(df)
def verify_predictor_save_load(predictor, df, verify_proba=False,
verify_embedding=True):
with tempfile.TemporaryDirectory() as root:
predictor.save(root)
predictions = predictor.predict(df, as_pandas=False)
loaded_predictor = TextPredictor.load(root)
predictions2 = loaded_predictor.predict(df, as_pandas=False)
predictions2_df = loaded_predictor.predict(df, as_pandas=True)
npt.assert_equal(predictions, predictions2)
npt.assert_equal(predictions2,
predictions2_df.to_numpy())
if verify_proba:
predictions_prob = predictor.predict_proba(df, as_pandas=False)
predictions2_prob = loaded_predictor.predict_proba(df, as_pandas=False)
predictions2_prob_df = loaded_predictor.predict_proba(df, as_pandas=True)
npt.assert_equal(predictions_prob, predictions2_prob)
npt.assert_equal(predictions2_prob, predictions2_prob_df.to_numpy())
if verify_embedding:
embeddings = predictor.extract_embedding(df)
assert embeddings.shape[0] == len(df)
def test_standalone_with_emoji():
import tempfile
from unittest import mock
requests_gag = mock.patch(
'requests.Session.request',
mock.Mock(side_effect=RuntimeError(
'Please use the `responses` library to mock HTTP in your tests.'
))
)
data = []
for i in range(50 * 3):
data.append(('😁' * (i + 1), 'grin'))
for i in range(30 * 3):
data.append(('😃' * (i + 1), 'smile'))
for i in range(20 * 3):
data.append(('😉' * (i + 1), 'wink'))
df = pd.DataFrame(data, columns=['data', 'label'])
predictor = TextPredictor(label='label', verbosity=3)
predictor.fit(
df,
hyperparameters=get_test_hyperparameters(),
time_limit=5,
seed=123,
)
predictions1 = predictor.predict(df, as_pandas=False)
with tempfile.TemporaryDirectory() as root:
predictor.save(root, standalone=True)
with requests_gag: # no internet connections
offline_predictor = TextPredictor.load(root)
predictions2 = offline_predictor.predict(df, as_pandas=False)
npt.assert_equal(predictions1, predictions2)
def main(args):
tasks = {
'cola': ['CoLA.tsv', 'glue/cola/test.tsv'],
'sst': ['SST-2.tsv', 'glue/sst/test.tsv'],
'mrpc': ['MRPC.tsv', 'glue/mrpc/test.tsv'],
'sts': ['STS-B.tsv', 'glue/sts/test.tsv'],
'qqp': ['QQP.tsv', 'glue/qqp/test.tsv'],
'mnli_m': ['MNLI-m.tsv', 'glue/mnli/test_matched.tsv'],
'mnli_mm': ['MNLI-mm.tsv', 'glue/mnli/test_mismatched.tsv'],
'qnli': ['QNLI.tsv', 'glue/qnli/test.tsv'],
'rte': ['RTE.tsv', 'glue/rte/test.tsv'],
'wnli': ['WNLI.tsv', 'glue/wnli/test.tsv'],
'ax': ['AX.tsv', 'glue/rte_diagnostic/diagnostic.tsv']
}
os.makedirs(args.save_dir, exist_ok=True)
for task, (save_name, test_file_path) in tasks.items():
if task == 'ax':
# For AX, we need to load the mnli-m checkpoint and run inference
test_df = pd.read_csv(test_file_path, sep='\t', header=0)
test_index = test_df['index']
predictor = TextPredictor.load(f'{args.prefix}_mnli_m')
label_column = predictor.label
predictions = predictor.predict(test_df)
else:
test_index = get_test_index(test_file_path)
prediction_df = pd.read_csv(
f'{args.prefix}_{task}/test_prediction.csv', index_col=0)
label_column = prediction_df.columns[0]
predictions = prediction_df[label_column]
if task == 'sts':
predictions = np.clip(predictions, 0, 5)
with open(os.path.join(args.save_dir, save_name), 'w') as of:
of.write('index\t{}\n'.format(label_column))
for i in range(len(predictions)):
of.write('{}\t{}\n'.format(test_index[i], predictions[i]))
def extract_pretrained_embedding(dataset):
hyperparameters = ag_text_presets.create('default')
hyperparameters['models']['MultimodalTextModel']['search_space'][
'model.num_trainable_layers'] = 0
hyperparameters['models']['MultimodalTextModel']['search_space'][
'model._disable_update'] = True
hyperparameters['models']['MultimodalTextModel']['search_space'][
'optimization.num_train_epochs'] = 1
hyperparameters['models']['MultimodalTextModel']['search_space'][
'preprocessing.categorical.convert_to_text'] = True
hyperparameters['models']['MultimodalTextModel']['search_space']['optimization.lr'] = 0.
seed = 123
train_dataset = dataset_registry.create(dataset, 'train')
test_dataset = dataset_registry.create(dataset, 'test')
train_data1, tuning_data1 = sklearn.model_selection.train_test_split(
train_dataset.data,
test_size=0.05,
random_state=np.random.RandomState(seed))
column_types, inferred_problem_type = infer_column_problem_types(train_data1,
tuning_data1,
label_columns=train_dataset.label_columns,
problem_type=train_dataset.problem_type)
text_feature_columns = [col_name for col_name in train_dataset.feature_columns if
column_types[col_name] == 'text']
train_text_only_data = train_dataset.data[text_feature_columns + train_dataset.label_columns]
test_text_only_data = test_dataset.data[text_feature_columns + test_dataset.label_columns]
sampled_train_data = train_text_only_data.sample(10)
predictor = TextPredictor(label=train_dataset.label_columns)
predictor.fit(train_data=sampled_train_data,
column_types=column_types,
hyperparameters=hyperparameters)
train_features = predictor.extract_embedding(train_text_only_data)
test_features = predictor.extract_embedding(test_text_only_data)
save_base_dir = f'embeddings/{dataset}/pretrain_text_embedding'
os.makedirs(save_base_dir, exist_ok=True)
np.save(os.path.join(save_base_dir, 'train.npy'), train_features)
np.save(os.path.join(save_base_dir, 'test.npy'), test_features)
with open(os.path.join(save_base_dir, 'text_columns.json'), 'w') as in_f:
json.dump(text_feature_columns, in_f)
def test_predictor_fit(key):
train_data = load_pd.load(DATA_INFO[key]['train'])
dev_data = load_pd.load(DATA_INFO[key]['dev'])
label = DATA_INFO[key]['label']
eval_metric = DATA_INFO[key]['metric']
verify_proba = DATA_INFO[key]['verify_proba']
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
valid_perm = rng_state.permutation(len(dev_data))
train_data = train_data.iloc[train_perm[:100]]
dev_data = dev_data.iloc[valid_perm[:10]]
predictor = TextPredictor(label=label, eval_metric=eval_metric)
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_score = predictor.evaluate(dev_data)
verify_predictor_save_load(predictor, dev_data, verify_proba=verify_proba)
# Test for continuous fit
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
verify_predictor_save_load(predictor, dev_data, verify_proba=verify_proba)
# Saving to folder, loading the saved model and call fit again (continuous fit)
with tempfile.TemporaryDirectory() as root:
predictor.save(root)
predictor = TextPredictor.load(root)
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
def test_mixed_column_type():
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sts/train.parquet')
dev_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/'
'glue/sts/dev.parquet')
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
valid_perm = rng_state.permutation(len(dev_data))
train_data = train_data.iloc[train_perm[:1000]]
dev_data = dev_data.iloc[valid_perm[:10]]
# Add more columns as feature
train_data = pd.DataFrame({
'sentence1': train_data['sentence1'],
'sentence2': train_data['sentence2'],
'sentence3': train_data['sentence2'],
'categorical0': train_data['genre'],
'numerical0': train_data['score'],
'genre': train_data['genre'],
'score': train_data['score']
})
dev_data = pd.DataFrame({
'sentence1': dev_data['sentence1'],
'sentence2': dev_data['sentence2'],
'sentence3': dev_data['sentence2'],
'categorical0': dev_data['genre'],
'numerical0': dev_data['score'],
'genre': dev_data['genre'],
'score': dev_data['score']
})
# Train Regression
predictor1 = TextPredictor(label='score', verbosity=4)
predictor1.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_rmse = predictor1.evaluate(dev_data, metrics=['rmse'])
verify_predictor_save_load(predictor1, dev_data)
# Train Classification
predictor2 = TextPredictor(label='genre', verbosity=4)
predictor2.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_rmse = predictor2.evaluate(dev_data, metrics=['acc'])
verify_predictor_save_load(predictor2, dev_data, verify_proba=True)
# Specify the feature column
predictor3 = TextPredictor(label='score', verbosity=4)
predictor3.fit(
train_data[['sentence1', 'sentence3', 'categorical0', 'score']],
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_rmse = predictor3.evaluate(dev_data, metrics=['rmse'])
verify_predictor_save_load(predictor3, dev_data)
def train_model(dataset_name,
text_presets,
save_dir,
model,
tabular_presets,
num_gpus=None,
get_competition_results=False,
seed=123):
set_seed(seed)
if get_competition_results:
train_dataset = dataset_registry.create(dataset_name, 'train')
test_dataset = dataset_registry.create(dataset_name, 'competition')
else:
train_dataset = dataset_registry.create(dataset_name, 'train')
test_dataset = dataset_registry.create(dataset_name, 'test')
feature_columns = train_dataset.feature_columns
label_columns = train_dataset.label_columns
metric = train_dataset.metric
problem_type = train_dataset.problem_type
train_data1, tuning_data1 = sklearn.model_selection.train_test_split(
train_dataset.data,
test_size=0.05,
random_state=np.random.RandomState(seed))
train_data = train_dataset.data
test_data = test_dataset.data
column_types, inferred_problem_type = infer_column_problem_types(
train_data1,
tuning_data1,
label_columns=label_columns,
problem_type=problem_type)
train_data = train_data[feature_columns + label_columns]
# tuning_data = tuning_data[feature_columns + label_columns]
if not get_competition_results:
test_data = test_data[feature_columns + label_columns]
train_tic = time.time()
if model == 'ag_tabular_quick':
MAX_NGRAM = 300
feature_generator = AutoMLPipelineFeatureGenerator(
vectorizer=CountVectorizer(min_df=30,
ngram_range=(1, 3),
max_features=MAX_NGRAM,
dtype=np.uint8))
predictor = TabularPredictor(label=label_columns[0],
path=save_dir,
problem_type=problem_type)
predictor.fit(train_data,
time_limit=30,
feature_generator=feature_generator)
elif model == 'ag_tabular_without_text':
no_text_feature_columns = []
for col_name in feature_columns:
if column_types[col_name] != _TEXT:
no_text_feature_columns.append(col_name)
train_data = train_data[no_text_feature_columns + label_columns]
# tuning_data = tuning_data[no_text_feature_columns + label_columns]
test_data = test_data[no_text_feature_columns + label_columns]
predictor = TabularPredictor(path=save_dir,
label=label_columns[0],
problem_type=problem_type,
eval_metric=metric)
if tabular_presets in ['best_quality']:
predictor.fit(train_data=train_data,
excluded_model_types=TABULAR_EXCLUDE_MODELS,
presets=tabular_presets)
elif tabular_presets == '5fold_1stack':
predictor.fit(train_data=train_data,
excluded_model_types=TABULAR_EXCLUDE_MODELS,
num_bag_folds=5,
num_stack_levels=1)
elif tabular_presets == 'no':
predictor.fit(train_data=train_data,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
else:
raise NotImplementedError
elif model == 'ag_tabular_old':
predictor = TabularPredictor(path=save_dir,
label=label_columns[0],
problem_type=problem_type,
eval_metric=metric)
if tabular_presets == 'best_quality':
predictor.fit(train_data=train_data,
presets=tabular_presets,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == '5fold_1stack':
predictor.fit(train_data=train_data,
num_bag_folds=5,
num_stack_levels=1,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == 'no':
predictor.fit(train_data=train_data,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
else:
raise NotImplementedError
elif model == 'ag_text_only':
text_feature_columns = [
col_name for col_name in feature_columns
if column_types[col_name] == _TEXT
]
train_data = train_data[text_feature_columns + label_columns]
test_data = test_data[text_feature_columns + label_columns]
predictor = TextPredictor(path=save_dir,
label=label_columns[0],
problem_type=problem_type,
eval_metric=metric)
hparams = ag_text_presets.create(text_presets)
if len(train_data) > 500000:
hparams = set_epoch3(hparams)
predictor.fit(train_data=train_data,
hyperparameters=hparams,
num_gpus=num_gpus,
seed=seed)
elif model == 'ag_text_multimodal':
predictor = TextPredictor(path=save_dir,
label=label_columns[0],
problem_type=problem_type,
eval_metric=metric)
hparams = ag_text_presets.create(text_presets)
if len(train_data) > 500000:
hparams = set_epoch3(hparams)
predictor.fit(train_data=train_data,
hyperparameters=hparams,
num_gpus=num_gpus,
seed=seed)
elif model == 'pre_embedding' or model == 'tune_embedding_multimodal' or model == 'tune_embedding_text':
feature_generator = AutoMLPipelineFeatureGenerator(
enable_text_special_features=False,
enable_text_ngram_features=False)
pre_embedding_folder = os.path.join(_CURR_DIR,
'pre_computed_embeddings')
if model == 'pre_embedding':
train_features = np.load(
os.path.join(pre_embedding_folder, dataset_name,
'pretrain_text_embedding', 'train.npy'))
test_features = np.load(
os.path.join(pre_embedding_folder, dataset_name,
'pretrain_text_embedding', 'test.npy'))
elif model == 'tune_embedding_multimodal':
train_features = np.load(
os.path.join(pre_embedding_folder, dataset_name,
'multimodal_embedding', 'train.npy'))
test_features = np.load(
os.path.join(pre_embedding_folder, dataset_name,
'multimodal_embedding', 'test.npy'))
elif model == 'tune_embedding_text':
train_features = np.load(
os.path.join(pre_embedding_folder, dataset_name,
'tuned_text_embedding', 'train.npy'))
test_features = np.load(
os.path.join(pre_embedding_folder, dataset_name,
'tuned_text_embedding', 'test.npy'))
else:
raise NotImplementedError
train_data = train_data.join(
pd.DataFrame(train_features,
columns=[
f'pre_feat{i}'
for i in range(train_features.shape[1])
]))
train_data.reset_index(drop=True, inplace=True)
test_data = test_data.join(
pd.DataFrame(test_features,
columns=[
f'pre_feat{i}'
for i in range(test_features.shape[1])
]))
test_data.reset_index(drop=True, inplace=True)
predictor = TabularPredictor(path=save_dir,
label=label_columns[0],
problem_type=problem_type,
eval_metric=metric)
if tabular_presets == 'best_quality':
predictor.fit(train_data=train_data,
presets=tabular_presets,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == '5fold_1stack':
predictor.fit(train_data=train_data,
num_bag_folds=5,
num_stack_levels=1,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == 'no':
predictor.fit(train_data=train_data,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
else:
raise NotImplementedError
elif model == 'tabular_multimodal' or model == 'tabular_multimodal_just_table':
if model == 'tabular_multimodal':
MAX_NGRAM = 300
feature_generator = AutoMLPipelineFeatureGenerator(
vectorizer=CountVectorizer(min_df=30,
ngram_range=(1, 3),
max_features=MAX_NGRAM,
dtype=np.uint8),
enable_raw_text_features=True)
hyperparameters = get_multimodal_tabular_hparam_just_gbm(
text_presets=text_presets)
else:
MAX_NGRAM = 300
feature_generator = AutoMLPipelineFeatureGenerator(
vectorizer=CountVectorizer(min_df=30,
ngram_range=(1, 3),
max_features=MAX_NGRAM,
dtype=np.uint8),
enable_raw_text_features=True,
enable_text_special_features=False,
enable_text_ngram_features=False)
hyperparameters = multimodal_tabular_just_table_hparam(
text_presets=text_presets)
predictor = TabularPredictor(path=save_dir,
label=label_columns[0],
problem_type=problem_type,
eval_metric=metric)
if tabular_presets == 'best_quality':
predictor.fit(train_data=train_data,
presets=tabular_presets,
hyperparameters=hyperparameters,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == '5fold_1stack':
predictor.fit(train_data=train_data,
num_bag_folds=5,
num_stack_levels=1,
hyperparameters=hyperparameters,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == '3fold_1stack':
predictor.fit(train_data=train_data,
num_bag_folds=3,
num_stack_levels=1,
hyperparameters=hyperparameters,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
elif tabular_presets == 'no':
predictor.fit(train_data=train_data,
hyperparameters=hyperparameters,
feature_generator=feature_generator,
excluded_model_types=TABULAR_EXCLUDE_MODELS)
else:
raise NotImplementedError
else:
raise NotImplementedError
train_toc = time.time()
inference_tic = time.time()
predictions = predictor.predict(test_data, as_pandas=True)
predictor.save()
inference_toc = time.time()
if problem_type == MULTICLASS or problem_type == BINARY:
prediction_prob = predictor.predict_proba(test_data, as_pandas=True)
prediction_prob.to_csv(
os.path.join(save_dir, 'test_prediction_prob.csv'))
predictions.to_csv(os.path.join(save_dir, 'test_prediction.csv'))
gt = test_data[label_columns[0]]
gt.to_csv(os.path.join(save_dir, 'ground_truth.csv'))
if not get_competition_results:
score = predictor.evaluate(test_data)
with open(os.path.join(save_dir, 'test_score.json'), 'w') as of:
json.dump({metric: score}, of)
with open(os.path.join(save_dir, 'speed_stats.json'), 'w') as of:
json.dump(
{
'train_time': train_toc - train_tic,
'inference_time': inference_toc - inference_tic,
'cpuinfo': cpuinfo.get_cpu_info()
}, of)
class TextPredictionV1Model(AbstractModel):
nn_model_name = 'text_nn'
def __init__(self, **kwargs):
"""The TextPredictionV1Model.
The features can be a mix of
- text column
- categorical column
- numerical column
The labels can be categorical or numerical.
Parameters
----------
path
The directory to store the modeling outputs.
name
Name of subdirectory inside path where model will be saved.
problem_type
Type of problem that this model will handle.
Valid options: ['binary', 'multiclass', 'regression'].
eval_metric
The evaluation metric.
num_classes
The number of classes.
stopping_metric
The stopping metric.
model
The internal model object.
hyperparameters
The hyperparameters of the model
features
Names of the features.
feature_metadata
The feature metadata.
debug
Whether to turn on debug mode
"""
super().__init__(**kwargs)
self._label_column_name = None
def _get_default_auxiliary_params(self) -> dict:
default_auxiliary_params = super()._get_default_auxiliary_params()
extra_auxiliary_params = dict(get_features_kwargs=dict(
valid_raw_types=[R_INT, R_FLOAT, R_CATEGORY, R_OBJECT],
invalid_special_types=[
S_TEXT_NGRAM, S_TEXT_AS_CATEGORY, S_TEXT_SPECIAL
],
), )
default_auxiliary_params.update(extra_auxiliary_params)
return default_auxiliary_params
@classmethod
def _get_default_ag_args(cls) -> dict:
default_ag_args = super()._get_default_ag_args()
extra_ag_args = {'valid_stacker': False}
default_ag_args.update(extra_ag_args)
return default_ag_args
def _set_default_params(self):
try:
from autogluon.text import ag_text_presets
except ImportError:
raise ImportError(AG_TEXT_IMPORT_ERROR)
super()._set_default_params()
self.params = ag_text_presets.create('default')
def _fit(self,
X_train: pd.DataFrame,
y_train: pd.Series,
X_val: Optional[pd.DataFrame] = None,
y_val: Optional[pd.Series] = None,
time_limit: Optional[int] = None,
**kwargs):
"""The internal fit function
Parameters
----------
X_train
Features of the training dataset
y_train
Labels of the training dataset
X_val
Features of the validation dataset
y_val
Labels of the validation dataset
time_limit
The time limits for the fit function
kwargs
Other keyword arguments
"""
try:
import mxnet as mx
from autogluon.text import TextPredictor
except ImportError:
raise ImportError(AG_TEXT_IMPORT_ERROR)
# Decide name of the label column
if 'label' in X_train.columns:
label_col_id = 0
while True:
self._label_column_name = 'label{}'.format(label_col_id)
if self._label_column_name not in X_train.columns:
break
label_col_id += 1
else:
self._label_column_name = 'label'
X_train = self.preprocess(X_train, fit=True)
if X_val is not None:
X_val = self.preprocess(X_val)
# Get arguments from kwargs
verbosity = kwargs.get('verbosity', 2)
num_cpus = kwargs.get('num_cpus', None)
num_gpus = kwargs.get('num_gpus', None)
self.model = TextPredictor(label=self._label_column_name,
problem_type=self.problem_type,
path=self.path,
eval_metric=self.eval_metric,
verbosity=verbosity)
X_train.insert(len(X_train.columns), self._label_column_name, y_train)
if X_val is not None:
X_val.insert(len(X_val.columns), self._label_column_name, y_val)
assert self.params['hpo_params']['num_trials'] == 1 \
or self.params['hpo_params']['num_trials'] is None
params = copy.deepcopy(self.params)
params['models']['MultimodalTextModel']['search_space']['optimization.per_device_batch_size']\
= max(1,
params['models']['MultimodalTextModel']['search_space']['optimization.per_device_batch_size'] // 2)
self.model.fit(train_data=X_train,
tuning_data=X_val,
time_limit=time_limit,
num_gpus=num_gpus,
num_cpus=num_cpus,
hyperparameters=params,
seed=params.get('seed'))
def save(self, path: str = None, verbose=True) -> str:
model = self.model
self.model = None
# save this AbstractModel object without NN weights
path = super().save(path=path, verbose=verbose)
self.model = model
text_nn_path = os.path.join(path, self.nn_model_name)
model.save(text_nn_path)
logger.log(
15,
f"\tSaved Text NN weights and model hyperparameters to '{text_nn_path}'."
)
return path
@classmethod
def load(cls, path: str, reset_paths=True, verbose=True):
try:
from autogluon.text import TextPredictor
except ImportError:
raise ImportError(AG_TEXT_IMPORT_ERROR)
model = super().load(path=path,
reset_paths=reset_paths,
verbose=verbose)
model.model = TextPredictor.load(os.path.join(path, cls.nn_model_name))
return model
def get_memory_size(self) -> int:
"""Return the memory size by calculating the total number of parameters.
Returns
-------
memory_size
The total memory size in bytes.
"""
total_size = 0
for k, v in self.model._model.net.collect_params().items():
total_size += np.dtype(v.dtype).itemsize * np.prod(v.shape)
return total_size
def _get_default_resources(self):
num_cpus = get_cpu_count()
num_gpus = get_gpu_count()
return num_cpus, num_gpus
def _fit(self,
X_train: pd.DataFrame,
y_train: pd.Series,
X_val: Optional[pd.DataFrame] = None,
y_val: Optional[pd.Series] = None,
time_limit: Optional[int] = None,
**kwargs):
"""The internal fit function
Parameters
----------
X_train
Features of the training dataset
y_train
Labels of the training dataset
X_val
Features of the validation dataset
y_val
Labels of the validation dataset
time_limit
The time limits for the fit function
kwargs
Other keyword arguments
"""
try:
import mxnet as mx
from autogluon.text import TextPredictor
except ImportError:
raise ImportError(AG_TEXT_IMPORT_ERROR)
# Decide name of the label column
if 'label' in X_train.columns:
label_col_id = 0
while True:
self._label_column_name = 'label{}'.format(label_col_id)
if self._label_column_name not in X_train.columns:
break
label_col_id += 1
else:
self._label_column_name = 'label'
X_train = self.preprocess(X_train, fit=True)
if X_val is not None:
X_val = self.preprocess(X_val)
# Get arguments from kwargs
verbosity = kwargs.get('verbosity', 2)
num_cpus = kwargs.get('num_cpus', None)
num_gpus = kwargs.get('num_gpus', None)
self.model = TextPredictor(label=self._label_column_name,
problem_type=self.problem_type,
path=self.path,
eval_metric=self.eval_metric,
verbosity=verbosity)
X_train.insert(len(X_train.columns), self._label_column_name, y_train)
if X_val is not None:
X_val.insert(len(X_val.columns), self._label_column_name, y_val)
assert self.params['hpo_params']['num_trials'] == 1 \
or self.params['hpo_params']['num_trials'] is None
params = copy.deepcopy(self.params)
params['models']['MultimodalTextModel']['search_space']['optimization.per_device_batch_size']\
= max(1,
params['models']['MultimodalTextModel']['search_space']['optimization.per_device_batch_size'] // 2)
self.model.fit(train_data=X_train,
tuning_data=X_val,
time_limit=time_limit,
num_gpus=num_gpus,
num_cpus=num_cpus,
hyperparameters=params,
seed=params.get('seed'))
class TextPredictorModel(AbstractModel):
nn_model_name = 'text_nn'
def __init__(self, **kwargs):
"""Wrapper of autogluon.text.TextPredictor.
The features can be a mix of
- text column
- categorical column
- numerical column
The labels can be categorical or numerical.
Parameters
----------
path
The directory to store the modeling outputs.
name
Name of subdirectory inside path where model will be saved.
problem_type
Type of problem that this model will handle.
Valid options: ['binary', 'multiclass', 'regression'].
eval_metric
The evaluation metric.
num_classes
The number of classes.
stopping_metric
The stopping metric.
model
The internal model object.
hyperparameters
The hyperparameters of the model
features
Names of the features.
feature_metadata
The feature metadata.
"""
super().__init__(**kwargs)
self._label_column_name = None
self._load_model = None # Whether to load inner model when loading.
def _get_default_auxiliary_params(self) -> dict:
default_auxiliary_params = super()._get_default_auxiliary_params()
extra_auxiliary_params = dict(get_features_kwargs=dict(
valid_raw_types=[R_INT, R_FLOAT, R_CATEGORY, R_OBJECT],
invalid_special_types=[
S_TEXT_NGRAM, S_TEXT_AS_CATEGORY, S_TEXT_SPECIAL
],
), )
default_auxiliary_params.update(extra_auxiliary_params)
return default_auxiliary_params
@classmethod
def _get_default_ag_args(cls) -> dict:
default_ag_args = super()._get_default_ag_args()
extra_ag_args = {'valid_stacker': False}
default_ag_args.update(extra_ag_args)
return default_ag_args
def _set_default_params(self):
super()._set_default_params()
try_import_autogluon_text()
from autogluon.text import ag_text_presets
self.params = ag_text_presets.create('default')
def _fit(self,
X: pd.DataFrame,
y: pd.Series,
X_val: Optional[pd.DataFrame] = None,
y_val: Optional[pd.Series] = None,
time_limit: Optional[int] = None,
sample_weight=None,
**kwargs):
"""The internal fit function
Parameters
----------
X
Features of the training dataset
y
Labels of the training dataset
X_val
Features of the validation dataset
y_val
Labels of the validation dataset
time_limit
The time limits for the fit function
kwargs
Other keyword arguments
"""
try_import_mxnet()
try_import_autogluon_text()
from autogluon.text import TextPredictor
# Decide name of the label column
if 'label' in X.columns:
label_col_id = 0
while True:
self._label_column_name = 'label{}'.format(label_col_id)
if self._label_column_name not in X.columns:
break
label_col_id += 1
else:
self._label_column_name = 'label'
X_train = self.preprocess(X, fit=True)
if X_val is not None:
X_val = self.preprocess(X_val)
# Get arguments from kwargs
verbosity = kwargs.get('verbosity', 2)
num_cpus = kwargs.get('num_cpus', None)
num_gpus = kwargs.get('num_gpus', None)
if sample_weight is not None: # TODO: support
logger.log(
15,
"sample_weight not yet supported for TextPredictorModel, this model will ignore them in training."
)
X_train.insert(len(X_train.columns), self._label_column_name, y)
if X_val is not None:
X_val.insert(len(X_val.columns), self._label_column_name, y_val)
assert self.params['tune_kwargs']['num_trials'] == 1 \
or self.params['tune_kwargs']['num_trials'] is None,\
'Currently, you cannot nest the hyperparameter search in text neural network ' \
'and the AutoGluon Tabular.'
verbosity_text = max(0, verbosity - 1)
root_logger = logging.getLogger()
root_log_level = root_logger.level
self.model = TextPredictor(label=self._label_column_name,
problem_type=self.problem_type,
path=self.path,
eval_metric=self.eval_metric,
verbosity=verbosity_text)
self.model.fit(train_data=X_train,
tuning_data=X_val,
time_limit=time_limit,
num_gpus=num_gpus,
num_cpus=num_cpus,
hyperparameters=self.params,
seed=self.params.get('seed', 0))
self.model.set_verbosity(verbosity)
root_logger.setLevel(root_log_level) # Reset log level
def save(self, path: str = None, verbose=True) -> str:
self._load_model = self.model is not None
__model = self.model
self.model = None
# save this AbstractModel object without NN weights
path = super().save(path=path, verbose=verbose)
self.model = __model
if self._load_model:
text_nn_path = os.path.join(path, self.nn_model_name)
self.model.save(text_nn_path)
logger.log(
15,
f"\tSaved Text NN weights and model hyperparameters to '{text_nn_path}'."
)
self._load_model = None
return path
@classmethod
def load(cls, path: str, reset_paths=True, verbose=True):
model = super().load(path=path,
reset_paths=reset_paths,
verbose=verbose)
if model._load_model:
try_import_autogluon_text()
from autogluon.text import TextPredictor
model.model = TextPredictor.load(
os.path.join(path, cls.nn_model_name))
model._load_model = None
return model
def get_memory_size(self) -> int:
"""Return the memory size by calculating the total number of parameters.
Returns
-------
memory_size
The total memory size in bytes.
"""
total_size = 0
for k, v in self.model._model.net.collect_params().items():
total_size += np.dtype(v.dtype).itemsize * np.prod(v.shape)
return total_size
def _get_default_resources(self):
num_cpus = get_cpu_count()
num_gpus = get_gpu_count()
return num_cpus, num_gpus
def _predict_proba(self, X, **kwargs):
X = self.preprocess(X, **kwargs)
if self.problem_type == REGRESSION:
return self.model.predict(X, as_pandas=False)
y_pred_proba = self.model.predict_proba(X, as_pandas=False)
return self._convert_proba_to_unified_form(y_pred_proba)
def _fit(self,
X: pd.DataFrame,
y: pd.Series,
X_val: Optional[pd.DataFrame] = None,
y_val: Optional[pd.Series] = None,
time_limit: Optional[int] = None,
sample_weight=None,
**kwargs):
"""The internal fit function
Parameters
----------
X
Features of the training dataset
y
Labels of the training dataset
X_val
Features of the validation dataset
y_val
Labels of the validation dataset
time_limit
The time limits for the fit function
kwargs
Other keyword arguments
"""
try_import_mxnet()
try_import_autogluon_text()
from autogluon.text import TextPredictor
# Decide name of the label column
if 'label' in X.columns:
label_col_id = 0
while True:
self._label_column_name = 'label{}'.format(label_col_id)
if self._label_column_name not in X.columns:
break
label_col_id += 1
else:
self._label_column_name = 'label'
X_train = self.preprocess(X, fit=True)
if X_val is not None:
X_val = self.preprocess(X_val)
# Get arguments from kwargs
verbosity = kwargs.get('verbosity', 2)
num_cpus = kwargs.get('num_cpus', None)
num_gpus = kwargs.get('num_gpus', None)
if sample_weight is not None: # TODO: support
logger.log(
15,
"sample_weight not yet supported for TextPredictorModel, this model will ignore them in training."
)
X_train.insert(len(X_train.columns), self._label_column_name, y)
if X_val is not None:
X_val.insert(len(X_val.columns), self._label_column_name, y_val)
assert self.params['tune_kwargs']['num_trials'] == 1 \
or self.params['tune_kwargs']['num_trials'] is None,\
'Currently, you cannot nest the hyperparameter search in text neural network ' \
'and the AutoGluon Tabular.'
verbosity_text = max(0, verbosity - 1)
root_logger = logging.getLogger()
root_log_level = root_logger.level
self.model = TextPredictor(label=self._label_column_name,
problem_type=self.problem_type,
path=self.path,
eval_metric=self.eval_metric,
verbosity=verbosity_text)
self.model.fit(train_data=X_train,
tuning_data=X_val,
time_limit=time_limit,
num_gpus=num_gpus,
num_cpus=num_cpus,
hyperparameters=self.params,
seed=self.params.get('seed', 0))
self.model.set_verbosity(verbosity)
root_logger.setLevel(root_log_level) # Reset log level
def run(args):
if args.task == 'product_sentiment':
train_df, test_df, label_column = load_machine_hack_product_sentiment(args.train_file,
args.test_file)
elif args.task == 'mercari_price':
train_df, test_df, label_column = load_mercari_price_prediction(args.train_file,
args.test_file)
elif args.task == 'price_of_books':
train_df, test_df, label_column = load_price_of_books(args.train_file, args.test_file)
elif args.task == 'data_scientist_salary':
train_df, test_df, label_column = load_data_scientist_salary(args.train_file, args.test_file)
else:
raise NotImplementedError
hyperparameters = get_hyperparameter_config('multimodal')
if args.preset is not None and args.mode in ['stacking', 'weighted']:
hyperparameters['AG_TEXT_NN']['presets'] = args.preset
if args.mode == 'stacking':
predictor = TabularPredictor(label=label_column,
eval_metric=args.eval_metric,
path=args.exp_dir)
predictor.fit(train_data=train_df,
hyperparameters=hyperparameters,
num_bag_folds=5,
num_stack_levels=1)
elif args.mode == 'weighted':
predictor = TabularPredictor(label=label_column,
eval_metric=args.eval_metric,
path=args.exp_dir)
predictor.fit(train_data=train_df,
hyperparameters=hyperparameters)
elif args.mode == 'single':
# When no embedding is used,
# we will just use TextPredictor that will train a single model internally.
predictor = TextPredictor(label=label_column,
eval_metric=args.eval_metric,
path=args.exp_dir)
predictor.fit(train_data=train_df,
presets=args.preset,
seed=args.seed)
else:
raise NotImplementedError
if args.task == 'product_sentiment':
test_probabilities = predictor.predict_proba(test_df, as_pandas=True, as_multiclass=True)
test_probabilities.to_csv(os.path.join(args.exp_dir, 'submission.csv'), index=False)
elif args.task == 'data_scientist_salary':
predictions = predictor.predict(test_df, as_pandas=False)
submission = pd.read_excel(args.sample_submission, engine='openpyxl')
submission.loc[:, label_column] = predictions
submission.to_excel(os.path.join(args.exp_dir, 'submission.xlsx'))
elif args.task == 'price_of_books':
predictions = predictor.predict(test_df, as_pandas=False)
submission = pd.read_excel(args.sample_submission, engine='openpyxl')
submission.loc[:, label_column] = np.power(10, predictions) - 1
submission.to_excel(os.path.join(args.exp_dir, 'submission.xlsx'))
elif args.task == 'mercari_price':
test_predictions = predictor.predict(test_df, as_pandas=False)
submission = pd.read_csv(args.sample_submission)
submission.loc[:, label_column] = np.exp(test_predictions) - 1
submission.to_csv(os.path.join(args.exp_dir, 'submission.csv'), index=False)
else:
raise NotImplementedError
predictor = TabularPredictor(path=os.path.join(args.save_dir,
args.model_type, time_str),
problem_type=train_dataset.problem_type,
eval_metric=train_dataset.metric,
label=label_columns[0])
if args.ensemble_type == 'weighted':
predictor.fit(concat_df[feature_columns + [label_columns[0]]],
feature_generator=feature_generator,
hyperparameters=tabular_hparams)
else:
predictor.fit(concat_df[feature_columns + [label_columns[0]]],
feature_generator=feature_generator,
num_bag_folds=5,
num_stack_levels=1,
hyperparameters=tabular_hparams)
predictor.save()
else:
predictor = TextPredictor(path=os.path.join(args.save_dir, args.model_type,
time_str),
problem_type=train_dataset.problem_type,
eval_metric=train_dataset.metric,
label=label_columns[0])
predictor.fit(concat_df[feature_columns + [label_columns[0]]],
presets='electra_base_late_fusion_concate_e10_avg3')
predictor.save(
os.path.join(args.save_dir, args.model_type, time_str,
'text_prediction'))
predictions = predictor.predict(competition_df, as_pandas=True)
predictions.to_csv(
os.path.join(args.save_dir, args.model_type, time_str, 'pred.csv'))
def test_distillation():
train_data = load_pd.load(
"https://autogluon-text.s3-accelerate.amazonaws.com/"
"glue/sst/train.parquet")
test_data = load_pd.load(
"https://autogluon-text.s3-accelerate.amazonaws.com/"
"glue/sst/dev.parquet")
rng_state = np.random.RandomState(123)
train_perm = rng_state.permutation(len(train_data))
test_perm = rng_state.permutation(len(test_data))
train_data = train_data.iloc[train_perm[:100]]
test_data = test_data.iloc[test_perm[:10]]
teacher_predictor = TextPredictor(label="label", eval_metric="acc")
hyperparameters = {
"model.hf_text.checkpoint_name": "prajjwal1/bert-tiny",
"env.num_workers": 0,
"env.num_workers_evaluation": 0,
}
teacher_save_path = os.path.join("sst", "teacher")
if os.path.exists(teacher_save_path):
shutil.rmtree(teacher_save_path)
teacher_predictor = teacher_predictor.fit(
train_data=train_data,
hyperparameters=hyperparameters,
time_limit=30,
save_path=teacher_save_path,
)
# test for distillation
predictor = TextPredictor(label="label", eval_metric="acc")
student_save_path = os.path.join("sst", "student")
if os.path.exists(student_save_path):
shutil.rmtree(student_save_path)
predictor = predictor.fit(
train_data=train_data,
teacher_predictor=teacher_predictor,
hyperparameters=hyperparameters,
time_limit=30,
save_path=student_save_path,
)
verify_predictor_save_load(predictor, test_data)
# test for distillation with teacher predictor path
predictor = TextPredictor(label="label", eval_metric="acc")
student_save_path = os.path.join("sst", "student")
if os.path.exists(student_save_path):
shutil.rmtree(student_save_path)
predictor = predictor.fit(
train_data=train_data,
teacher_predictor=teacher_predictor.path,
hyperparameters=hyperparameters,
time_limit=30,
save_path=student_save_path,
)
verify_predictor_save_load(predictor, test_data)
