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[:100]]
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 = task.fit(train_data,
hyperparameters=test_hyperparameters,
label='score', num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./sts_score',
plot_results=False)
dev_rmse = predictor1.evaluate(dev_data, metrics=['rmse'])
dev_prediction = predictor1.predict(dev_data)
# Tran Classification
predictor2 = task.fit(train_data,
hyperparameters=test_hyperparameters,
label='genre', num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./sts_genre',
plot_results=False)
dev_rmse = predictor2.evaluate(dev_data, metrics=['acc'])
dev_prediction = predictor2.predict(dev_data)
# Specify the feature column
predictor3 = task.fit(train_data,
hyperparameters=test_hyperparameters,
feature_columns=['sentence1', 'sentence3', 'categorical0'],
label='score', num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./sts_score',
plot_results=False)
dev_rmse = predictor1.evaluate(dev_data, metrics=['rmse'])
dev_prediction = predictor1.predict(dev_data)
def test_no_text_column_raise():
data = [('😁😁😁😁😁😁', 'grin')] * 20 + [('😃😃😃😃😃😃😃😃', 'smile')
] * 50 + [('😉😉😉', 'wink')] * 30
df = pd.DataFrame(data, columns=['data', 'label'])
with pytest.raises(NotImplementedError):
predictor = task.fit(df, label='label', verbosity=4)
def test_emoji():
data = []
for i in range(50):
data.append(('😁' * (i + 1), 'grin'))
for i in range(30):
data.append(('😃' * (i + 1), 'smile'))
for i in range(20):
data.append(('😉' * (i + 1), 'wink'))
df = pd.DataFrame(data, columns=['data', 'label'])
predictor = task.fit(df, label='label', verbosity=3)
def test_no_job_finished_raise():
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')
with pytest.raises(RuntimeError):
# Setting a very small time limits to trigger the bug
predictor = task.fit(train_data, hyperparameters=test_hyperparameters,
label='label', num_trials=1,
ngpus_per_trial=0,
verbosity=4,
time_limits=10,
output_directory='./sst_raise',
plot_results=False)
def test_sts():
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[:100]]
dev_data = dev_data.iloc[valid_perm[:10]]
predictor = task.fit(train_data, hyperparameters=test_hyperparameters,
label='score', num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./sts',
plot_results=False)
dev_rmse = predictor.evaluate(dev_data, metrics=['rmse'])
dev_prediction = predictor.predict(dev_data)
def test_mrpc():
train_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/train.parquet'
)
dev_data = load_pd.load(
'https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/dev.parquet'
)
train_data = train_data.iloc[:100]
dev_data = dev_data.iloc[:10]
predictor = task.fit(train_data,
hyperparameters=test_hyperparameters,
label='label',
num_trials=1,
verbosity=4,
ngpus_per_trial=1,
output_directory='./mrpc',
plot_results=False)
dev_acc = predictor.evaluate(dev_data, metrics=['acc'])
dev_prediction = predictor.predict(dev_data)
dev_pred_prob = predictor.predict_proba(dev_data)
