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)
pred_labels = predictor.predict(df)
for ele in pred_labels:
assert ele in {'grin', 'smile', 'wink'}
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)
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)