def preprocess_for_prediction(model_path,
split,
dataset_type='generic',
data_csv=None,
data_hdf5=None,
train_set_metadata=None,
only_predictions=False):
"""Preprocesses the dataset to parse it into a format that is usable by the
Ludwig core
:param model_path: The input data that is joined with the model
hyperparameter file to create the model definition file
:type model_path: Str
:param dataset_type: Generic
:type: Str
:param split: Splits the data into the train and test sets
:param data_csv: The CSV input data file
:param data_hdf5: The hdf5 data file if there is no csv data file
:param train_set_metadata: Train set metadata for the input features
:param only_predictions: If False does not load output features
:returns: Dataset, Train set metadata
"""
model_definition = load_json(
os.path.join(model_path, MODEL_HYPERPARAMETERS_FILE_NAME))
preprocessing_params = merge_dict(default_preprocessing_parameters,
model_definition['preprocessing'])
# Check if hdf5 and json already exist
if data_csv is not None:
data_hdf5_fp = os.path.splitext(data_csv)[0] + '.hdf5'
if os.path.isfile(data_hdf5_fp):
logging.info(
'Found hdf5 with the same filename of the csv, using it instead'
)
data_csv = None
data_hdf5 = data_hdf5_fp
# Load data
_, _, build_dataset, _ = get_dataset_fun(dataset_type)
train_set_metadata = load_metadata(train_set_metadata)
features = (
model_definition['input_features'] +
([] if only_predictions else model_definition['output_features']))
if split == 'full':
if data_hdf5 is not None:
dataset = load_data(data_hdf5,
model_definition['input_features'],
[] if only_predictions else
model_definition['output_features'],
split_data=False,
shuffle_training=False)
else:
dataset, train_set_metadata = build_dataset(
data_csv,
features,
preprocessing_params,
train_set_metadata=train_set_metadata)
else:
if data_hdf5 is not None:
training, test, validation = load_data(
data_hdf5,
model_definition['input_features'], []
if only_predictions else model_definition['output_features'],
shuffle_training=False)
if split == 'training':
dataset = training
elif split == 'validation':
dataset = validation
else: # if split == 'test':
dataset = test
else:
dataset, train_set_metadata = build_dataset(
data_csv,
features,
preprocessing_params,
train_set_metadata=train_set_metadata)
replace_text_feature_level(model_definition, [dataset])
dataset = Dataset(
dataset,
model_definition['input_features'],
[] if only_predictions else model_definition['output_features'],
data_hdf5,
)
return dataset, train_set_metadata
def export_neuropod(ludwig_model_path,
neuropod_path,
neuropod_model_name="ludwig_model"):
try:
from neuropod.backends.python.packager import create_python_neuropod
except ImportError:
logger.error(
'The "neuropod" package is not installed in your environment.')
sys.exit(-1)
data_paths = [
{
"path":
os.path.join(ludwig_model_path, MODEL_HYPERPARAMETERS_FILE_NAME),
"packaged_name":
MODEL_HYPERPARAMETERS_FILE_NAME
},
{
"path": os.path.join(ludwig_model_path,
TRAIN_SET_METADATA_FILE_NAME),
"packaged_name": TRAIN_SET_METADATA_FILE_NAME
},
{
"path": os.path.join(ludwig_model_path, 'checkpoint'),
"packaged_name": 'checkpoint'
},
]
for filename in os.listdir(ludwig_model_path):
if (MODEL_WEIGHTS_FILE_NAME in filename
and MODEL_WEIGHTS_PROGRESS_FILE_NAME not in filename):
data_paths.append({
"path": os.path.join(ludwig_model_path, filename),
"packaged_name": filename
})
logger.debug('data_paths: {}'.format(data_paths))
ludwig_model_definition = load_json(
os.path.join(ludwig_model_path, MODEL_HYPERPARAMETERS_FILE_NAME))
training_set_metadata = load_json(
os.path.join(ludwig_model_path, TRAIN_SET_METADATA_FILE_NAME))
input_spec = []
for feature in ludwig_model_definition['input_features']:
input_spec.append({
"name": feature['name'],
"dtype": "str",
"shape": (None, 1)
})
logger.debug('input_spec: {}'.format(input_spec))
output_spec = []
for feature in ludwig_model_definition['output_features']:
feature_type = feature['type']
feature_name = feature['name']
if feature_type == BINARY:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "str",
"shape": (None, 1)
})
output_spec.append({
"name": feature['name'] + '_probabilities',
"dtype": "float32",
"shape": (None, 1)
})
elif feature_type == NUMERICAL:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "float32",
"shape": (None, 1)
})
elif feature_type == CATEGORY:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "str",
"shape": (None, 1)
})
output_spec.append({
"name": feature['name'] + '_probability',
"dtype": "float32",
"shape": (None, 1)
})
output_spec.append({
"name":
feature['name'] + '_probabilities',
"dtype":
"float32",
"shape":
(None, training_set_metadata[feature_name]['vocab_size'])
})
elif feature_type == SEQUENCE:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "str",
"shape": (None, 1)
})
elif feature_type == TEXT:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "str",
"shape": (None, 1)
})
elif feature_type == SET:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "str",
"shape": (None, 1)
})
output_spec.append({
"name": feature['name'] + '_probability',
"dtype": "str",
"shape": (None, 1)
})
output_spec.append({
"name":
feature['name'] + '_probabilities',
"dtype":
"float32",
"shape":
(None, training_set_metadata[feature_name]['vocab_size'])
})
elif feature_type == VECTOR:
output_spec.append({
"name":
feature['name'] + '_predictions',
"dtype":
"float32",
"shape":
(None, training_set_metadata[feature_name]['vector_size'])
})
else:
output_spec.append({
"name": feature['name'] + '_predictions',
"dtype": "str",
"shape": (None, 1)
})
logger.debug('output_spec: {}'.format(output_spec))
if os.path.exists(neuropod_path):
if os.path.isfile(neuropod_path):
logger.warning('Removing file: {}'.format(neuropod_path))
os.remove(neuropod_path)
else:
logger.warning('Removing directory: {}'.format(neuropod_path))
shutil.rmtree(neuropod_path, ignore_errors=True)
from pathlib import Path
path = Path(ludwig_path)
logger.debug('python_root: {}'.format(path.parent.parent))
create_python_neuropod(
neuropod_path=neuropod_path,
model_name=neuropod_model_name,
data_paths=data_paths,
code_path_spec=[{
"python_root":
path.parent.parent,
"dirs_to_package": [
"ludwig" # Package everything in the python_root
],
}],
entrypoint_package="ludwig.neuropod_export",
entrypoint="get_model",
# test_deps=['torch', 'numpy'],
skip_virtualenv=True,
input_spec=input_spec,
output_spec=output_spec)
logger.info('Neuropod saved to: {}'.format(neuropod_path))
def load_metadata(metadata_file_path):
logging.info('Loading metadata from: {0}'.format(metadata_file_path))
return data_utils.load_json(metadata_file_path)
def preprocess_for_prediction(model_path,
split,
data_csv=None,
data_hdf5=None,
train_set_metadata=None,
evaluate_performance=True):
"""Preprocesses the dataset to parse it into a format that is usable by the
Ludwig core
:param model_path: The input data that is joined with the model
hyperparameter file to create the model definition file
:type model_path: Str
:param split: Splits the data into the train and test sets
:param data_csv: The CSV input data file
:param data_hdf5: The hdf5 data file if there is no csv data file
:param train_set_metadata: Train set metadata for the input features
:param evaluate_performance: If False does not load output features
:returns: Dataset, Train set metadata
"""
model_definition = load_json(
os.path.join(model_path, MODEL_HYPERPARAMETERS_FILE_NAME))
for input_feature in model_definition['input_features']:
if 'preprocessing' in input_feature:
if 'in_memory' in input_feature['preprocessing']:
if not input_feature['preprocessing']['in_memory']:
logger.warning(
'WARNING: When running predict in_memory flag should '
'be true. Overriding and setting it to true for '
'feature <{}>'.format(input_feature['name']))
input_feature['preprocessing']['in_memory'] = True
preprocessing_params = merge_dict(default_preprocessing_parameters,
model_definition['preprocessing'])
output_features = model_definition[
'output_features'] if evaluate_performance else []
features = model_definition['input_features'] + output_features
# Check if hdf5 file already exists
if data_csv is not None:
data_hdf5_fp = replace_file_extension(data_csv, 'hdf5')
if os.path.isfile(data_hdf5_fp):
logger.info('Found hdf5 with the same filename of the csv, '
'using it instead')
data_csv = None
data_hdf5 = data_hdf5_fp
else:
data_hdf5_fp = None
# Load data
train_set_metadata = load_metadata(train_set_metadata)
if split == 'full':
if data_hdf5 is not None:
dataset = load_data(data_hdf5,
model_definition['input_features'],
output_features,
split_data=False,
shuffle_training=False)
else:
dataset, train_set_metadata = build_dataset(
data_csv,
features,
preprocessing_params,
train_set_metadata=train_set_metadata)
else:
if data_hdf5 is not None:
training, test, validation = load_data(
data_hdf5,
model_definition['input_features'],
output_features,
shuffle_training=False)
if split == 'training':
dataset = training
elif split == 'validation':
dataset = validation
else: # if split == 'test':
dataset = test
else:
dataset, train_set_metadata = build_dataset(
data_csv,
features,
preprocessing_params,
train_set_metadata=train_set_metadata)
replace_text_feature_level(features, [dataset])
dataset = Dataset(dataset, model_definition['input_features'],
output_features,
train_set_metadata.get(DATA_TRAIN_HDF5_FP))
return dataset, train_set_metadata
def test_kfold_cv_cli(features_to_use: FeaturesToUse):
# k-fold cross validation cli
num_folds = 3
# setup temporary directory to run test
with tempfile.TemporaryDirectory() as tmpdir:
training_data_fp = os.path.join(tmpdir, 'train.csv')
config_fp = os.path.join(tmpdir, 'config.yaml')
results_dir = os.path.join(tmpdir, 'results')
statistics_fp = os.path.join(results_dir,
'kfold_training_statistics.json')
indices_fp = os.path.join(results_dir, 'kfold_split_indices.json')
# generate synthetic data for the test
input_features = features_to_use.input_features
output_features = features_to_use.output_features
generate_data(input_features, output_features, training_data_fp)
# generate config file
config = {
'input_features': input_features,
'output_features': output_features,
'combiner': {
'type': 'concat',
'fc_size': 14
},
'training': {
'epochs': 2
}
}
with open(config_fp, 'w') as f:
yaml.dump(config, f)
# run k-fold cv
kfold_cross_validate_cli(k_fold=num_folds,
config_file=config_fp,
dataset=training_data_fp,
output_directory=results_dir,
logging_level='warn')
# check for expected results
# check for existence and structure of statistics file
assert os.path.isfile(statistics_fp)
# check for required keys
cv_statistics = load_json(statistics_fp)
for key in ['fold_' + str(i + 1)
for i in range(num_folds)] + ['overall']:
assert key in cv_statistics
# check for existence and structure of split indices file
assert os.path.isfile(indices_fp)
# check for required keys
cv_indices = load_json(indices_fp)
for key in ['fold_' + str(i + 1) for i in range(num_folds)]:
assert key in cv_indices
import pprint
import sys
from ludwig.utils.data_utils import load_json
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Display K-fold cross validation results",
prog="display_kfold_cv_results",
usage="%(prog)s [options]",
)
# ----------------------------
# Experiment naming parameters
# ----------------------------
parser.add_argument("--results_directory",
type=str,
default="results",
help="directory that contains the K-fold cv results")
args = parser.parse_args(sys.argv[1:])
results_directory = args.results_directory
print("Retrieving results from ", results_directory)
kfold_cv_stats = load_json(
os.path.join(results_directory, "kfold_training_statistics.json"))
print("#\n# K-fold Cross Validation Results\n#")
pprint.pprint(kfold_cv_stats["overall"])
def preprocess_for_prediction(model_path,
split,
data_csv=None,
data_hdf5=None,
train_set_metadata=None,
evaluate_performance=True):
"""Preprocesses the dataset to parse it into a format that is usable by the
Ludwig core
:param model_path: The input data that is joined with the model
hyperparameter file to create the model definition file
:type model_path: Str
:param split: Splits the data into the train and test sets
:param data_csv: The CSV input data file
:param data_hdf5: The hdf5 data file if there is no csv data file
:param train_set_metadata: Train set metadata for the input features
:param evaluate_performance: If False does not load output features
:returns: Dataset, Train set metadata
"""
model_definition = load_json(
os.path.join(model_path, MODEL_HYPERPARAMETERS_FILE_NAME))
for input_feature in model_definition['input_features']:
if 'preprocessing' in input_feature:
if 'in_memory' in input_feature['preprocessing']:
if not input_feature['preprocessing']['in_memory']:
logger.warning(
'WARNING: When running predict in_memory flag should '
'be true. Overriding and setting it to true for '
'feature <{}>'.format(input_feature['name']))
input_feature['preprocessing']['in_memory'] = True
preprocessing_params = merge_dict(default_preprocessing_parameters,
model_definition['preprocessing'])
output_features = model_definition[
'output_features'] if evaluate_performance else []
features = model_definition['input_features'] + output_features
# Check if hdf5 file already exists
if data_csv is not None:
data_hdf5_fp = replace_file_extension(data_csv, 'hdf5')
if os.path.isfile(data_hdf5_fp):
logger.info('Found hdf5 with the same filename of the csv, '
'using it instead')
data_csv = None
data_hdf5 = data_hdf5_fp
else:
data_hdf5_fp = None
# Load data
train_set_metadata = load_metadata(train_set_metadata)
if split == FULL:
if data_hdf5 is not None:
dataset = load_data(data_hdf5,
model_definition['input_features'],
output_features,
split_data=False,
shuffle_training=False)
else:
dataset, train_set_metadata = build_dataset(
data_csv,
features,
preprocessing_params,
train_set_metadata=train_set_metadata)
else:
if data_hdf5 is not None:
training_set, test_set, validation_set = load_data(
data_hdf5,
model_definition['input_features'],
output_features,
shuffle_training=False)
if split == TRAINING:
dataset = training_set
elif split == VALIDATION:
dataset = validation_set
else: # if split == TEST:
dataset = test_set
else:
dataset, train_set_metadata = build_dataset(
data_csv,
features,
preprocessing_params,
train_set_metadata=train_set_metadata)
# build_dataset adds a split column if there is none in the csv
# so if we want to check if the csv contained a split column
# we have to check in the csv not in the built dataset.
# The logic is that if there is no split in the original csv
# we treat the split parameter as if it was == full
if csv_contains_column(data_csv, SPLIT):
training_set, test_set, validation_set = split_dataset_tvt(
dataset, dataset[SPLIT])
if split == TRAINING:
dataset = training_set
elif split == VALIDATION:
dataset = validation_set
else: # if split == TEST:
dataset = test_set
else:
logger.warning('You requested the {} split, but the data CSV '
'does not contain a "split" column, so the '
'full data will be used instead')
replace_text_feature_level(features, [dataset])
dataset = Dataset(dataset, model_definition['input_features'],
output_features,
train_set_metadata.get(DATA_TRAIN_HDF5_FP))
return dataset, train_set_metadata
def test_kfold_cv_cli():
# k-fold cross validation cli
num_folds = 3
# setup temporary directory to run test
with tempfile.TemporaryDirectory() as tmpdir:
training_data_fp = os.path.join(tmpdir, 'train.csv')
model_definition_fp = os.path.join(tmpdir, 'model_definition.yaml')
results_dir = os.path.join(tmpdir, 'results')
statistics_fp = os.path.join(results_dir,
'kfold_training_statistics.json')
indices_fp = os.path.join(results_dir, 'kfold_split_indices.json')
# generate synthetic data for the test
input_features = [
numerical_feature(normalization='zscore'),
numerical_feature(normalization='zscore')
]
output_features = [category_feature(vocab_size=2, reduce_input='sum')]
generate_data(input_features, output_features, training_data_fp)
# generate model definition file
model_definition = {
'input_features': input_features,
'output_features': output_features,
'combiner': {
'type': 'concat',
'fc_size': 14
},
'training': {
'epochs': 2
}
}
with open(model_definition_fp, 'w') as f:
yaml.dump(model_definition, f)
# run k-fold cv
full_kfold_cross_validate(k_fold=num_folds,
model_definition_file=model_definition_fp,
data_csv=training_data_fp,
output_directory=results_dir,
logging_level='warn')
# check for expected results
# check for existence and structure of statistics file
assert os.path.isfile(statistics_fp)
# check for required keys
cv_statistics = load_json(statistics_fp)
for key in ['fold_' + str(i + 1)
for i in range(num_folds)] + ['overall']:
assert key in cv_statistics
# check for existence and structure of split indices file
assert os.path.isfile(indices_fp)
# check for required keys
cv_indices = load_json(indices_fp)
for key in ['fold_' + str(i + 1) for i in range(num_folds)]:
assert key in cv_indices
def load(model_dir,
logging_level=logging.ERROR,
use_horovod=None,
gpus=None,
gpu_memory_limit=None,
allow_parallel_threads=True):
"""This function allows for loading pretrained models
# Inputs
:param logging_level: Log level that will be sent to stderr.
:param use_horovod: (bool) use Horovod for distributed training. Will be set
automatically if `horovodrun` is used to launch the training script.
:param model_dir: (string) path to the directory containing the model.
If the model was trained by the `train` or `experiment` command,
the model is in `results_dir/experiment_dir/model`.
:param gpus: (string, default: `None`) list of GPUs to use (it uses the
same syntax of CUDA_VISIBLE_DEVICES)
:param gpu_memory_limit: (int: default: `None`) maximum memory in MB to allocate
per GPU device.
:param allow_parallel_threads: (bool, default: `True`) allow TensorFlow to use
multithreading parallelism to improve performance at the cost of
determinism.
# Return
:return: (LudwigModel) a LudwigModel object
# Example usage
```python
ludwig_model = LudwigModel.load(model_dir)
```
"""
horovod = configure_horovod(use_horovod)
model_definition = broadcast_return(lambda: load_json(os.path.join(
model_dir,
MODEL_HYPERPARAMETERS_FILE_NAME
)), horovod)
# initialize model
ludwig_model = LudwigModel(
model_definition,
logging_level=logging_level,
use_horovod=use_horovod,
gpus=gpus,
gpu_memory_limit=gpu_memory_limit,
allow_parallel_threads=allow_parallel_threads,
)
# generate model from definition
ludwig_model.model = LudwigModel.create_model(model_definition)
# load model weights
ludwig_model.load_weights(model_dir)
# load train set metadata
ludwig_model.training_set_metadata = broadcast_return(
lambda: load_metadata(
os.path.join(
model_dir,
TRAIN_SET_METADATA_FILE_NAME
)
), horovod
)
return ludwig_model
def _init_vocab(self, vocab_file: str) -> Dict[str, str]:
"""Loads the vocab from the vocab file."""
str2idx = load_json(
torchtext.utils.get_asset_local_path(vocab_file))
_, idx2str = zip(*sorted((v, k) for k, v in str2idx.items()))
return str2idx, idx2str
def test_kfold_cv_cli(features_to_use: FeaturesToUse):
# k-fold cross validation cli
num_folds = 3
# setup temporary directory to run test
with tempfile.TemporaryDirectory() as tmpdir:
training_data_fp = os.path.join(tmpdir, "train.csv")
config_fp = os.path.join(tmpdir, "config.yaml")
results_dir = os.path.join(tmpdir, "results")
statistics_fp = os.path.join(results_dir,
"kfold_training_statistics.json")
indices_fp = os.path.join(results_dir, "kfold_split_indices.json")
# generate synthetic data for the test
input_features = features_to_use.input_features
output_features = features_to_use.output_features
generate_data(input_features, output_features, training_data_fp)
# generate config file
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {
"type": "concat",
"output_size": 14
},
TRAINER: {
"epochs": 2
},
}
with open(config_fp, "w") as f:
yaml.dump(config, f)
# run k-fold cv
kfold_cross_validate_cli(
k_fold=num_folds,
config=config_fp,
dataset=training_data_fp,
output_directory=results_dir,
logging_level="warn",
)
# check for expected results
# check for existence and structure of statistics file
assert os.path.isfile(statistics_fp)
# check for required keys
cv_statistics = load_json(statistics_fp)
for key in ["fold_" + str(i + 1)
for i in range(num_folds)] + ["overall"]:
assert key in cv_statistics
# check for existence and structure of split indices file
assert os.path.isfile(indices_fp)
# check for required keys
cv_indices = load_json(indices_fp)
for key in ["fold_" + str(i + 1) for i in range(num_folds)]:
assert key in cv_indices
def config(self):
return load_json(
os.path.join(self.lpath, MODEL_HYPERPARAMETERS_FILE_NAME))
