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
predictor = TextPredictor(label='score', verbosity=4)
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_rmse = predictor.evaluate(dev_data, metrics=['rmse'])
verify_predictor_save_load(predictor, dev_data)
# Train Classification
predictor = TextPredictor(label='genre', verbosity=4)
predictor.fit(train_data,
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_rmse = predictor.evaluate(dev_data, metrics=['acc'])
verify_predictor_save_load(predictor, dev_data, verify_proba=True)
# Specify the feature column
predictor = TextPredictor(label='score', verbosity=4)
predictor.fit(train_data[['sentence1', 'sentence3', 'categorical0', 'score']],
hyperparameters=get_test_hyperparameters(),
time_limit=30,
seed=123)
dev_rmse = predictor.evaluate(dev_data, metrics=['rmse'])
verify_predictor_save_load(predictor, dev_data)
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_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_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)