def experiment(model_definition,
model_definition_file=None,
data_csv=None,
data_train_csv=None,
data_validation_csv=None,
data_test_csv=None,
data_hdf5=None,
data_train_hdf5=None,
data_validation_hdf5=None,
data_test_hdf5=None,
train_set_metadata_json=None,
experiment_name='experiment',
model_name='run',
model_load_path=None,
model_resume_path=None,
skip_save_model=False,
skip_save_progress=False,
skip_save_log=False,
skip_save_processed_input=False,
skip_save_unprocessed_output=False,
output_directory='results',
gpus=None,
gpu_fraction=1.0,
use_horovod=False,
random_seed=default_random_seed,
debug=False,
**kwargs):
"""Trains a model on a dataset's training and validation splits and
uses it to predict on the test split.
It saves the trained model and the statistics of training and testing.
:param model_definition: Model definition which defines the different
parameters of the model, features, preprocessing and training.
:type model_definition: Dictionary
:param model_definition_file: The file that specifies the model definition.
It is a yaml file.
:type model_definition_file: filepath (str)
:param data_csv: A CSV file contanining the input data which is used to
train, validate and test a model. The CSV either contains a
split column or will be split.
:type data_csv: filepath (str)
:param data_train_csv: A CSV file contanining the input data which is used
to train a model.
:type data_train_csv: filepath (str)
:param data_validation_csv: A CSV file contanining the input data which is used
to validate a model..
:type data_validation_csv: filepath (str)
:param data_test_csv: A CSV file contanining the input data which is used
to test a model.
:type data_test_csv: filepath (str)
:param data_hdf5: If the dataset is in the hdf5 format, this is used instead
of the csv file.
:type data_hdf5: filepath (str)
:param data_train_hdf5: If the training set is in the hdf5 format, this is
used instead of the csv file.
:type data_train_hdf5: filepath (str)
:param data_validation_hdf5: If the validation set is in the hdf5 format,
this is used instead of the csv file.
:type data_validation_hdf5: filepath (str)
:param data_test_hdf5: If the test set is in the hdf5 format, this is
used instead of the csv file.
:type data_test_hdf5: filepath (str)
:param train_set_metadata_json: If the dataset is in hdf5 format, this is
the associated json file containing metadata.
:type train_set_metadata_json: filepath (str)
:param experiment_name: The name for the experiment.
:type experiment_name: Str
:param model_name: Name of the model that is being used.
:type model_name: Str
:param model_load_path: If this is specified the loaded model will be used
as initialization (useful for transfer learning).
:type model_load_path: filepath (str)
:param model_resume_path: Resumes training of the model from the path
specified. The difference with model_load_path is that also training
statistics like the current epoch and the loss and performance so
far are also resumed effectively cotinuing a previously interrupted
training process.
:type model_resume_path: filepath (str)
:param skip_save_model: Disables
saving model weights and hyperparameters each time the model
improves. By default Ludwig saves model weights after each epoch
the validation measure imrpvoes, but if the model is really big
that can be time consuming if you do not want to keep
the weights and just find out what performance can a model get
with a set of hyperparameters, use this parameter to skip it,
but the model will not be loadable later on.
:type skip_save_model: Boolean
:param skip_save_progress: Disables saving
progress each epoch. By default Ludwig saves weights and stats
after each epoch for enabling resuming of training, but if
the model is really big that can be time consuming and will uses
twice as much space, use this parameter to skip it, but training
cannot be resumed later on.
:type skip_save_progress: Boolean
:param skip_save_log: Disables saving TensorBoard
logs. By default Ludwig saves logs for the TensorBoard, but if it
is not needed turning it off can slightly increase the
overall speed..
:type skip_save_log: Boolean
:param skip_save_processed_input: If a CSV dataset is provided it is
preprocessed and then saved as an hdf5 and json to avoid running
the preprocessing again. If this parameter is False,
the hdf5 and json file are not saved.
:type skip_save_processed_input: Boolean
:param skip_save_unprocessed_output: By default predictions and
their probabilities are saved in both raw unprocessed numpy files
contaning tensors and as postprocessed CSV files
(one for each output feature). If this parameter is True,
only the CSV ones are saved and the numpy ones are skipped.
:type skip_save_unprocessed_output: Boolean
:param output_directory: The directory that will contanin the training
statistics, the saved model and the training procgress files.
:type output_directory: filepath (str)
:param gpus: List of GPUs that are available for training.
:type gpus: List
:param gpu_fraction: Fraction of the memory of each GPU to use at
the beginning of the training. The memory may grow elastically.
:type gpu_fraction: Integer
:param random_seed: Random seed used for weights initialization,
splits and any other random function.
:type random_seed: Integer
:param debug: If true turns on tfdbg with inf_or_nan checks.
:type debug: Boolean
"""
# set input features defaults
if model_definition_file is not None:
with open(model_definition_file, 'r') as def_file:
model_definition = merge_with_defaults(yaml.load(def_file))
else:
model_definition = merge_with_defaults(model_definition)
# setup directories and file names
experiment_dir_name = None
if model_resume_path is not None:
if os.path.exists(model_resume_path):
experiment_dir_name = model_resume_path
else:
if is_on_master():
logging.info('Model resume path does not exists, '
'starting training from scratch')
model_resume_path = None
if model_resume_path is None:
if is_on_master():
experiment_dir_name = get_experiment_dir_name(
output_directory, experiment_name, model_name)
else:
experiment_dir_name = '/'
description_fn, training_stats_fn, model_dir = get_file_names(
experiment_dir_name)
# save description
description = get_experiment_description(
model_definition, data_csv, data_train_csv, data_validation_csv,
data_test_csv, data_hdf5, data_train_hdf5, data_validation_hdf5,
data_test_hdf5, train_set_metadata_json, random_seed)
if is_on_master():
save_json(description_fn, description)
# print description
logging.info('Experiment name: {}'.format(experiment_name))
logging.info('Model name: {}'.format(model_name))
logging.info('Output path: {}'.format(experiment_dir_name))
logging.info('')
for key, value in description.items():
logging.info('{}: {}'.format(key, pformat(value, indent=4)))
logging.info('')
# preprocess
(training_set, validation_set, test_set,
train_set_metadata) = preprocess_for_training(
model_definition,
data_csv=data_csv,
data_train_csv=data_train_csv,
data_validation_csv=data_validation_csv,
data_test_csv=data_test_csv,
data_hdf5=data_hdf5,
data_train_hdf5=data_train_hdf5,
data_validation_hdf5=data_validation_hdf5,
data_test_hdf5=data_test_hdf5,
train_set_metadata_json=train_set_metadata_json,
skip_save_processed_input=skip_save_processed_input,
preprocessing_params=model_definition['preprocessing'],
random_seed=random_seed)
if is_on_master():
logging.info('Training set: {0}'.format(training_set.size))
if validation_set is not None:
logging.info('Validation set: {0}'.format(validation_set.size))
if test_set is not None:
logging.info('Test set: {0}'.format(test_set.size))
# update model definition with metadata properties
update_model_definition_with_metadata(model_definition, train_set_metadata)
# run the experiment
model, training_results = train(training_set=training_set,
validation_set=validation_set,
test_set=test_set,
model_definition=model_definition,
save_path=model_dir,
model_load_path=model_load_path,
resume=model_resume_path is not None,
skip_save_model=skip_save_model,
skip_save_progress=skip_save_progress,
skip_save_log=skip_save_log,
gpus=gpus,
gpu_fraction=gpu_fraction,
use_horovod=use_horovod,
random_seed=random_seed,
debug=debug)
(train_trainset_stats, train_valisest_stats,
train_testset_stats) = training_results
if is_on_master():
if not skip_save_model:
# save train set metadata
save_json(os.path.join(model_dir, TRAIN_SET_METADATA_FILE_NAME),
train_set_metadata)
# grab the results of the model with highest validation test performance
validation_field = model_definition['training']['validation_field']
validation_measure = model_definition['training']['validation_measure']
validation_field_result = train_valisest_stats[validation_field]
best_function = get_best_function(validation_measure)
# print results of the model with highest validation test performance
if is_on_master():
if validation_set is not None:
# max or min depending on the measure
epoch_best_vali_measure, best_vali_measure = best_function(
enumerate(validation_field_result[validation_measure]),
key=lambda pair: pair[1])
logging.info('Best validation model epoch: {0}'.format(
epoch_best_vali_measure + 1))
logging.info(
'Best validation model {0} on validation set {1}: {2}'.format(
validation_measure, validation_field, best_vali_measure))
if test_set is not None:
best_vali_measure_epoch_test_measure = train_testset_stats[
validation_field][validation_measure][
epoch_best_vali_measure]
logging.info(
'Best validation model {0} on test set {1}: {2}'.format(
validation_measure, validation_field,
best_vali_measure_epoch_test_measure))
# save training statistics
if is_on_master():
save_json(
training_stats_fn, {
'train': train_trainset_stats,
'validation': train_valisest_stats,
'test': train_testset_stats
})
if test_set is not None:
# predict
test_results = predict(test_set,
train_set_metadata,
model,
model_definition,
model_definition['training']['batch_size'],
only_predictions=False,
gpus=gpus,
gpu_fraction=gpu_fraction,
debug=debug)
# postprocess
postprocessed_output = postprocess(
test_results, model_definition['output_features'],
train_set_metadata, experiment_dir_name,
skip_save_unprocessed_output or not is_on_master())
if is_on_master():
print_prediction_results(test_results)
save_prediction_outputs(postprocessed_output, experiment_dir_name)
save_prediction_statistics(test_results, experiment_dir_name)
model.close_session()
if is_on_master():
logging.info('\nFinished: {0}_{1}'.format(experiment_name, model_name))
logging.info('Saved to: {}'.format(experiment_dir_name))
return experiment_dir_name
def experiment(model_definition,
model_definition_file=None,
data_csv=None,
data_train_csv=None,
data_validation_csv=None,
data_test_csv=None,
data_hdf5=None,
data_train_hdf5=None,
data_validation_hdf5=None,
data_test_hdf5=None,
train_set_metadata_json=None,
experiment_name='experiment',
model_name='run',
model_load_path=None,
model_resume_path=None,
skip_save_model=False,
skip_save_progress=False,
skip_save_log=False,
skip_save_processed_input=False,
skip_save_unprocessed_output=False,
output_directory='results',
gpus=None,
gpu_fraction=1.0,
use_horovod=False,
random_seed=default_random_seed,
debug=False,
**kwargs):
"""Trains a model on a dataset's training and validation splits and
uses it to predict on the test split.
It saves the trained model and the statistics of training and testing.
:param model_definition: Model definition which defines the different
parameters of the model, features, preprocessing and training.
:type model_definition: Dictionary
:param model_definition_file: The file that specifies the model definition.
It is a yaml file.
:type model_definition_file: filepath (str)
:param data_csv: A CSV file contanining the input data which is used to
train, validate and test a model. The CSV either contains a
split column or will be split.
:type data_csv: filepath (str)
:param data_train_csv: A CSV file contanining the input data which is used
to train a model.
:type data_train_csv: filepath (str)
:param data_validation_csv: A CSV file contanining the input data which is used
to validate a model..
:type data_validation_csv: filepath (str)
:param data_test_csv: A CSV file contanining the input data which is used
to test a model.
:type data_test_csv: filepath (str)
:param data_hdf5: If the dataset is in the hdf5 format, this is used instead
of the csv file.
:type data_hdf5: filepath (str)
:param data_train_hdf5: If the training set is in the hdf5 format, this is
used instead of the csv file.
:type data_train_hdf5: filepath (str)
:param data_validation_hdf5: If the validation set is in the hdf5 format,
this is used instead of the csv file.
:type data_validation_hdf5: filepath (str)
:param data_test_hdf5: If the test set is in the hdf5 format, this is
used instead of the csv file.
:type data_test_hdf5: filepath (str)
:param train_set_metadata_json: If the dataset is in hdf5 format, this is
the associated json file containing metadata.
:type train_set_metadata_json: filepath (str)
:param experiment_name: The name for the experiment.
:type experiment_name: Str
:param model_name: Name of the model that is being used.
:type model_name: Str
:param model_load_path: If this is specified the loaded model will be used
as initialization (useful for transfer learning).
:type model_load_path: filepath (str)
:param model_resume_path: Resumes training of the model from the path
specified. The difference with model_load_path is that also training
statistics like the current epoch and the loss and performance so
far are also resumed effectively cotinuing a previously interrupted
training process.
:type model_resume_path: filepath (str)
:param skip_save_model: Disables
saving model weights and hyperparameters each time the model
improves. By default Ludwig saves model weights after each epoch
the validation measure imrpvoes, but if the model is really big
that can be time consuming if you do not want to keep
the weights and just find out what performance can a model get
with a set of hyperparameters, use this parameter to skip it,
but the model will not be loadable later on.
:type skip_save_model: Boolean
:param skip_save_progress: Disables saving
progress each epoch. By default Ludwig saves weights and stats
after each epoch for enabling resuming of training, but if
the model is really big that can be time consuming and will uses
twice as much space, use this parameter to skip it, but training
cannot be resumed later on.
:type skip_save_progress: Boolean
:param skip_save_log: Disables saving TensorBoard
logs. By default Ludwig saves logs for the TensorBoard, but if it
is not needed turning it off can slightly increase the
overall speed..
:type skip_save_log: Boolean
:param skip_save_processed_input: If a CSV dataset is provided it is
preprocessed and then saved as an hdf5 and json to avoid running
the preprocessing again. If this parameter is False,
the hdf5 and json file are not saved.
:type skip_save_processed_input: Boolean
:param skip_save_unprocessed_output: By default predictions and
their probabilities are saved in both raw unprocessed numpy files
contaning tensors and as postprocessed CSV files
(one for each output feature). If this parameter is True,
only the CSV ones are saved and the numpy ones are skipped.
:type skip_save_unprocessed_output: Boolean
:param output_directory: The directory that will contanin the training
statistics, the saved model and the training procgress files.
:type output_directory: filepath (str)
:param gpus: List of GPUs that are available for training.
:type gpus: List
:param gpu_fraction: Fraction of the memory of each GPU to use at
the beginning of the training. The memory may grow elastically.
:type gpu_fraction: Integer
:param use_horovod: Flag for using horovod
:type use_horovod: Boolean
:param random_seed: Random seed used for weights initialization,
splits and any other random function.
:type random_seed: Integer
:param debug: If true turns on tfdbg with inf_or_nan checks.
:type debug: Boolean
"""
(model, preprocessed_data, experiment_dir_name, _,
model_definition) = full_train(
model_definition,
model_definition_file=model_definition_file,
data_csv=data_csv,
data_train_csv=data_train_csv,
data_validation_csv=data_validation_csv,
data_test_csv=data_test_csv,
data_hdf5=data_hdf5,
data_train_hdf5=data_train_hdf5,
data_validation_hdf5=data_validation_hdf5,
data_test_hdf5=data_test_hdf5,
train_set_metadata_json=train_set_metadata_json,
experiment_name=experiment_name,
model_name=model_name,
model_load_path=model_load_path,
model_resume_path=model_resume_path,
skip_save_model=skip_save_model,
skip_save_progress=skip_save_progress,
skip_save_log=skip_save_log,
skip_save_processed_input=skip_save_processed_input,
output_directory=output_directory,
should_close_session=False,
gpus=gpus,
gpu_fraction=gpu_fraction,
use_horovod=use_horovod,
random_seed=random_seed,
debug=debug,
**kwargs)
(training_set, validation_set, test_set,
train_set_metadata) = preprocessed_data
if test_set is not None:
if model_definition['training']['eval_batch_size'] > 0:
batch_size = model_definition['training']['eval_batch_size']
else:
batch_size = model_definition['training']['batch_size']
# predict
test_results = predict(test_set,
train_set_metadata,
model,
model_definition,
batch_size,
evaluate_performance=True,
gpus=gpus,
gpu_fraction=gpu_fraction,
debug=debug)
# postprocess
postprocessed_output = postprocess(
test_results, model_definition['output_features'],
train_set_metadata, experiment_dir_name,
skip_save_unprocessed_output or not is_on_master())
if is_on_master():
print_test_results(test_results)
save_prediction_outputs(postprocessed_output, experiment_dir_name)
save_test_statistics(test_results, experiment_dir_name)
model.close_session()
if is_on_master():
logger.info('\nFinished: {0}_{1}'.format(experiment_name, model_name))
logger.info('Saved to: {}'.format(experiment_dir_name))
contrib_command("experiment_save", experiment_dir_name)
return experiment_dir_name
def experiment(
model_definition,
model_definition_file=None,
data_df=None,
data_train_df=None,
data_validation_df=None,
data_test_df=None,
data_csv=None,
data_train_csv=None,
data_validation_csv=None,
data_test_csv=None,
data_hdf5=None,
data_train_hdf5=None,
data_validation_hdf5=None,
data_test_hdf5=None,
train_set_metadata_json=None,
experiment_name='experiment',
model_name='run',
model_load_path=None,
model_resume_path=None,
skip_save_training_description=False,
skip_save_training_statistics=False,
skip_save_model=False,
skip_save_progress=False,
skip_save_log=False,
skip_save_processed_input=False,
skip_save_unprocessed_output=False, # skipcq: PYL-W0613
skip_save_test_predictions=False, # skipcq: PYL-W0613
skip_save_test_statistics=False, # skipcq: PYL-W0613
output_directory='results',
gpus=None,
gpu_memory_limit=None,
allow_parallel_threads=True,
use_horovod=None,
random_seed=default_random_seed,
debug=False,
**kwargs):
(model, preprocessed_data, experiment_dir_name, train_stats,
model_definition) = full_train(
model_definition,
model_definition_file=model_definition_file,
data_df=data_df,
data_train_df=data_train_df,
data_validation_df=data_validation_df,
data_test_df=data_test_df,
data_csv=data_csv,
data_train_csv=data_train_csv,
data_validation_csv=data_validation_csv,
data_test_csv=data_test_csv,
data_hdf5=data_hdf5,
data_train_hdf5=data_train_hdf5,
data_validation_hdf5=data_validation_hdf5,
data_test_hdf5=data_test_hdf5,
train_set_metadata_json=train_set_metadata_json,
experiment_name=experiment_name,
model_name=model_name,
model_load_path=model_load_path,
model_resume_path=model_resume_path,
skip_save_training_description=skip_save_training_description,
skip_save_training_statistics=skip_save_training_statistics,
skip_save_model=skip_save_model,
skip_save_progress=skip_save_progress,
skip_save_log=skip_save_log,
skip_save_processed_input=skip_save_processed_input,
output_directory=output_directory,
gpus=gpus,
gpu_memory_limit=gpu_memory_limit,
allow_parallel_threads=allow_parallel_threads,
use_horovod=use_horovod,
random_seed=random_seed,
debug=debug,
**kwargs)
(
_, # training_set
_, # validation_set
test_set,
train_set_metadata) = preprocessed_data
if test_set is not None:
if model_definition[TRAINING]['eval_batch_size'] > 0:
batch_size = model_definition[TRAINING]['eval_batch_size']
else:
batch_size = model_definition[TRAINING]['batch_size']
# predict
test_results = predict(test_set,
train_set_metadata,
model,
model_definition,
batch_size,
evaluate_performance=True,
debug=debug)
else:
test_results = None
return (model, preprocessed_data, experiment_dir_name, train_stats,
model_definition, test_results)
def train_and_eval_on_split(
model_definition,
eval_split=VALIDATION,
data_df=None,
data_train_df=None,
data_validation_df=None,
data_test_df=None,
data_csv=None,
data_train_csv=None,
data_validation_csv=None,
data_test_csv=None,
data_hdf5=None,
data_train_hdf5=None,
data_validation_hdf5=None,
data_test_hdf5=None,
train_set_metadata_json=None,
experiment_name="hyperopt",
model_name="run",
# model_load_path=None,
# model_resume_path=None,
skip_save_training_description=False,
skip_save_training_statistics=False,
skip_save_model=False,
skip_save_progress=False,
skip_save_log=False,
skip_save_processed_input=False,
skip_save_unprocessed_output=False,
skip_save_test_predictions=False,
skip_save_test_statistics=False,
output_directory="results",
gpus=None,
gpu_memory_limit=None,
allow_parallel_threads=True,
use_horovod=False,
random_seed=default_random_seed,
debug=False,
**kwargs
):
# Collect training and validation losses and metrics
# & append it to `results`
# ludwig_model = LudwigModel(modified_model_definition)
(model, preprocessed_data, experiment_dir_name, train_stats,
model_definition) = full_train(
model_definition=model_definition,
data_df=data_df,
data_train_df=data_train_df,
data_validation_df=data_validation_df,
data_test_df=data_test_df,
data_csv=data_csv,
data_train_csv=data_train_csv,
data_validation_csv=data_validation_csv,
data_test_csv=data_test_csv,
data_hdf5=data_hdf5,
data_train_hdf5=data_train_hdf5,
data_validation_hdf5=data_validation_hdf5,
data_test_hdf5=data_test_hdf5,
train_set_metadata_json=train_set_metadata_json,
experiment_name=experiment_name,
model_name=model_name,
# model_load_path=model_load_path,
# model_resume_path=model_resume_path,
skip_save_training_description=skip_save_training_description,
skip_save_training_statistics=skip_save_training_statistics,
skip_save_model=skip_save_model,
skip_save_progress=skip_save_progress,
skip_save_log=skip_save_log,
skip_save_processed_input=skip_save_processed_input,
output_directory=output_directory,
gpus=gpus,
gpu_memory_limit=gpu_memory_limit,
allow_parallel_threads=allow_parallel_threads,
use_horovod=use_horovod,
random_seed=random_seed,
debug=debug,
)
(training_set, validation_set, test_set,
train_set_metadata) = preprocessed_data
if model_definition[TRAINING]["eval_batch_size"] > 0:
batch_size = model_definition[TRAINING]["eval_batch_size"]
else:
batch_size = model_definition[TRAINING]["batch_size"]
eval_set = validation_set
if eval_split == TRAINING:
eval_set = training_set
elif eval_split == VALIDATION:
eval_set = validation_set
elif eval_split == TEST:
eval_set = test_set
test_results = predict(
eval_set,
train_set_metadata,
model,
model_definition,
batch_size,
evaluate_performance=True,
debug=debug
)
if not (
skip_save_unprocessed_output and skip_save_test_predictions and skip_save_test_statistics):
if not os.path.exists(experiment_dir_name):
os.makedirs(experiment_dir_name)
# postprocess
postprocessed_output = postprocess(
test_results,
model_definition["output_features"],
train_set_metadata,
experiment_dir_name,
skip_save_unprocessed_output,
)
print_test_results(test_results)
if not skip_save_test_predictions:
save_prediction_outputs(postprocessed_output, experiment_dir_name)
if not skip_save_test_statistics:
save_test_statistics(test_results, experiment_dir_name)
return train_stats, test_results
