def _predict(
self,
data_df=None,
data_csv=None,
data_dict=None,
return_type=pd.DataFrame,
batch_size=128,
evaluate_performance=False,
skip_save_unprocessed_output=False,
gpus=None,
gpu_fraction=1,
):
if (self.model is None or self.model_definition is None
or self.train_set_metadata is None):
raise ValueError('Model has not been trained or loaded')
if data_df is None:
data_df = self._read_data(data_csv, data_dict)
logger.debug('Preprocessing {} datapoints'.format(len(data_df)))
# Added [:] to next line, before I was just assigning,
# this way I'm copying the list. If you don't do it, you are actually
# modifying the input feature list when you add output features,
# which you definitely don't want to do
features_to_load = self.model_definition['input_features'][:]
if evaluate_performance:
output_features = self.model_definition['output_features']
else:
output_features = []
features_to_load += output_features
num_overrides = override_in_memory_flag(
self.model_definition['input_features'], True)
if num_overrides > 0:
logger.warning(
'Using in_memory = False is not supported for Ludwig API.')
preprocessed_data = build_data(data_df, features_to_load,
self.train_set_metadata,
self.model_definition['preprocessing'])
replace_text_feature_level(features_to_load, [preprocessed_data])
dataset = Dataset(preprocessed_data,
self.model_definition['input_features'],
output_features, None)
logger.debug('Predicting')
predict_results = self.model.predict(
dataset,
batch_size,
evaluate_performance=evaluate_performance,
gpus=gpus,
gpu_fraction=gpu_fraction,
session=getattr(self.model, 'session', None))
if evaluate_performance:
calculate_overall_stats(predict_results,
self.model_definition['output_features'],
dataset, self.train_set_metadata)
logger.debug('Postprocessing')
if (return_type == 'dict' or return_type == 'dictionary'
or return_type == dict):
postprocessed_predictions = postprocess(
predict_results,
self.model_definition['output_features'],
self.train_set_metadata,
experiment_dir_name=self.exp_dir_name,
skip_save_unprocessed_output=skip_save_unprocessed_output,
)
elif (return_type == 'dataframe' or return_type == 'df'
or return_type == pd.DataFrame):
postprocessed_predictions = postprocess_df(
predict_results,
self.model_definition['output_features'],
self.train_set_metadata,
experiment_dir_name=self.exp_dir_name,
skip_save_unprocessed_output=skip_save_unprocessed_output,
)
else:
logger.warning('Unrecognized return_type: {}. '
'Returning DataFrame.'.format(return_type))
postprocessed_predictions = postprocess(
predict_results,
self.model_definition['output_features'],
self.train_set_metadata,
experiment_dir_name=self.exp_dir_name,
skip_save_unprocessed_output=skip_save_unprocessed_output,
)
return postprocessed_predictions, predict_results
def _predict(
self,
data_df=None,
data_csv=None,
data_dict=None,
return_type=pd.DataFrame,
batch_size=128,
gpus=None,
gpu_fraction=1,
only_predictions=True,
logging_level=logging.ERROR,
):
logging.getLogger().setLevel(logging_level)
if logging_level in {logging.WARNING, logging.ERROR, logging.CRITICAL}:
set_disable_progressbar(True)
if (self.model is None or self.model_definition is None
or self.train_set_metadata is None):
raise ValueError('Model has not been trained or loaded')
if data_df is None:
data_df = self._read_data(data_csv, data_dict)
logging.debug('Preprocessing {} datapoints'.format(len(data_df)))
features_to_load = self.model_definition['input_features']
if not only_predictions:
features_to_load += self.model_definition['output_features']
preprocessed_data = build_data(data_df, features_to_load,
self.train_set_metadata,
self.model_definition['preprocessing'])
replace_text_feature_level(
self.model_definition['input_features'] +
([] if only_predictions else
self.model_definition['output_features']), [preprocessed_data])
dataset = Dataset(preprocessed_data,
self.model_definition['input_features'],
[] if only_predictions else
self.model_definition['output_features'], None)
logging.debug('Predicting')
predict_results = self.model.predict(dataset,
batch_size,
only_predictions=only_predictions,
gpus=gpus,
gpu_fraction=gpu_fraction,
session=getattr(
self.model, 'session', None))
if not only_predictions:
calculate_overall_stats(predict_results,
self.model_definition['output_features'],
dataset, self.train_set_metadata)
logging.debug('Postprocessing')
if (return_type == 'dict' or return_type == 'dictionary'
or return_type == dict):
postprocessed_predictions = postprocess(
predict_results, self.model_definition['output_features'],
self.train_set_metadata)
elif (return_type == 'dataframe' or return_type == 'df'
or return_type == pd.DataFrame):
postprocessed_predictions = postprocess_df(
predict_results, self.model_definition['output_features'],
self.train_set_metadata)
else:
logging.warning('Unrecognized return_type: {}. '
'Returning DataFrame.'.format(return_type))
postprocessed_predictions = postprocess(
predict_results, self.model_definition['output_features'],
self.train_set_metadata)
return postprocessed_predictions, predict_results
def _predict(
self,
data_df=None,
data_csv=None,
data_dict=None,
return_type=pd.DataFrame,
batch_size=128,
gpus=None,
gpu_fraction=1,
evaluate_performance=False,
logging_level=logging.ERROR,
):
logging.getLogger('ludwig').setLevel(logging_level)
if logging_level in {logging.WARNING, logging.ERROR, logging.CRITICAL}:
set_disable_progressbar(True)
if (self.model is None or self.model_definition is None
or self.train_set_metadata is None):
raise ValueError('Model has not been trained or loaded')
if data_df is None:
data_df = self._read_data(data_csv, data_dict)
logger.debug('Preprocessing {} datapoints'.format(len(data_df)))
# Added [:] to next line, before I was just assigning,
# this way I'm copying the list. If you don't do it, you are actually
# modifying the input feature list when you add output features,
# which you definitely don't want to do
features_to_load = self.model_definition['input_features'][:]
if evaluate_performance:
output_features = self.model_definition['output_features']
else:
output_features = []
features_to_load += output_features
preprocessed_data = build_data(data_df, features_to_load,
self.train_set_metadata,
self.model_definition['preprocessing'])
replace_text_feature_level(features_to_load, [preprocessed_data])
dataset = Dataset(preprocessed_data,
self.model_definition['input_features'],
output_features, None)
logger.debug('Predicting')
predict_results = self.model.predict(
dataset,
batch_size,
evaluate_performance=evaluate_performance,
gpus=gpus,
gpu_fraction=gpu_fraction,
session=getattr(self.model, 'session', None))
if evaluate_performance:
calculate_overall_stats(predict_results,
self.model_definition['output_features'],
dataset, self.train_set_metadata)
logger.debug('Postprocessing')
if (return_type == 'dict' or return_type == 'dictionary'
or return_type == dict):
postprocessed_predictions = postprocess(
predict_results, self.model_definition['output_features'],
self.train_set_metadata)
elif (return_type == 'dataframe' or return_type == 'df'
or return_type == pd.DataFrame):
postprocessed_predictions = postprocess_df(
predict_results, self.model_definition['output_features'],
self.train_set_metadata)
else:
logger.warning('Unrecognized return_type: {}. '
'Returning DataFrame.'.format(return_type))
postprocessed_predictions = postprocess(
predict_results, self.model_definition['output_features'],
self.train_set_metadata)
return postprocessed_predictions, predict_results
