def evaluate(data_set_file_or_name,
data_format=None,
data_directory=None,
map_features=None,
feature_selection=None,
example_filter=None,
noisy_preprocessing_methods=None,
preprocessing_methods=None,
split_data_set=None,
splitting_method=None,
splitting_fraction=None,
model_type=None,
latent_size=None,
hidden_sizes=None,
number_of_importance_samples=None,
number_of_monte_carlo_samples=None,
inference_architecture=None,
latent_distribution=None,
number_of_classes=None,
parameterise_latent_posterior=False,
prior_probabilities_method=None,
generative_architecture=None,
reconstruction_distribution=None,
number_of_reconstruction_classes=None,
count_sum=None,
proportion_of_free_nats_for_y_kl_divergence=None,
minibatch_normalisation=None,
batch_correction=None,
dropout_keep_probabilities=None,
number_of_warm_up_epochs=None,
kl_weight=None,
minibatch_size=None,
run_id=None,
models_directory=None,
included_analyses=None,
analysis_level=None,
decomposition_methods=None,
highlight_feature_indices=None,
export_options=None,
analyses_directory=None,
evaluation_set_kind=None,
sample_size=None,
prediction_method=None,
prediction_training_set_kind=None,
model_versions=None,
**keyword_arguments):
"""Evaluate model on data set."""
if split_data_set is None:
split_data_set = defaults["data"]["split_data_set"]
if splitting_method is None:
splitting_method = defaults["data"]["splitting_method"]
if splitting_fraction is None:
splitting_fraction = defaults["data"]["splitting_fraction"]
if models_directory is None:
models_directory = defaults["models"]["directory"]
if evaluation_set_kind is None:
evaluation_set_kind = defaults["evaluation"]["data_set_name"]
if sample_size is None:
sample_size = defaults["models"]["sample_size"]
if prediction_method is None:
prediction_method = defaults["evaluation"]["prediction_method"]
if prediction_training_set_kind is None:
prediction_training_set_kind = defaults["evaluation"][
"prediction_training_set_kind"]
if model_versions is None:
model_versions = defaults["evaluation"]["model_versions"]
if analyses_directory is None:
analyses_directory = defaults["analyses"]["directory"]
evaluation_set_kind = normalise_string(evaluation_set_kind)
prediction_training_set_kind = normalise_string(
prediction_training_set_kind)
model_versions = parse_model_versions(model_versions)
print(title("Data"))
binarise_values = False
if reconstruction_distribution == "bernoulli":
if noisy_preprocessing_methods:
if noisy_preprocessing_methods[-1] != "binarise":
noisy_preprocessing_methods.append("binarise")
else:
binarise_values = True
data_set = DataSet(data_set_file_or_name,
data_format=data_format,
directory=data_directory,
map_features=map_features,
feature_selection=feature_selection,
example_filter=example_filter,
preprocessing_methods=preprocessing_methods,
binarise_values=binarise_values,
noisy_preprocessing_methods=noisy_preprocessing_methods)
if not split_data_set or evaluation_set_kind == "full":
data_set.load()
if split_data_set:
training_set, validation_set, test_set = data_set.split(
method=splitting_method, fraction=splitting_fraction)
data_subsets = [data_set, training_set, validation_set, test_set]
for data_subset in data_subsets:
clear_data_subset = True
if data_subset.kind == evaluation_set_kind:
evaluation_set = data_subset
clear_data_subset = False
if data_subset.kind == prediction_training_set_kind:
prediction_training_set = data_subset
clear_data_subset = False
if clear_data_subset:
data_subset.clear()
else:
splitting_method = None
splitting_fraction = None
evaluation_set = data_set
prediction_training_set = data_set
evaluation_subset_indices = indices_for_evaluation_subset(evaluation_set)
models_directory = build_directory_path(
models_directory,
data_set=evaluation_set,
splitting_method=splitting_method,
splitting_fraction=splitting_fraction)
analyses_directory = build_directory_path(
analyses_directory,
data_set=evaluation_set,
splitting_method=splitting_method,
splitting_fraction=splitting_fraction)
print(title("Model"))
if number_of_classes is None:
if evaluation_set.has_labels:
number_of_classes = (evaluation_set.number_of_classes -
evaluation_set.number_of_excluded_classes)
model = _setup_model(
data_set=evaluation_set,
model_type=model_type,
latent_size=latent_size,
hidden_sizes=hidden_sizes,
number_of_importance_samples=number_of_importance_samples,
number_of_monte_carlo_samples=number_of_monte_carlo_samples,
inference_architecture=inference_architecture,
latent_distribution=latent_distribution,
number_of_classes=number_of_classes,
parameterise_latent_posterior=parameterise_latent_posterior,
prior_probabilities_method=prior_probabilities_method,
generative_architecture=generative_architecture,
reconstruction_distribution=reconstruction_distribution,
number_of_reconstruction_classes=number_of_reconstruction_classes,
count_sum=count_sum,
proportion_of_free_nats_for_y_kl_divergence=(
proportion_of_free_nats_for_y_kl_divergence),
minibatch_normalisation=minibatch_normalisation,
batch_correction=batch_correction,
dropout_keep_probabilities=dropout_keep_probabilities,
number_of_warm_up_epochs=number_of_warm_up_epochs,
kl_weight=kl_weight,
models_directory=models_directory)
if ("best_model" in model_versions
and not better_model_exists(model, run_id=run_id)):
model_versions.remove("best_model")
if ("early_stopping" in model_versions
and not model_stopped_early(model, run_id=run_id)):
model_versions.remove("early_stopping")
print(subtitle("Analysis"))
analyses.analyse_model(model=model,
run_id=run_id,
included_analyses=included_analyses,
analysis_level=analysis_level,
export_options=export_options,
analyses_directory=analyses_directory)
print(title("Results"))
print("Evaluation set: {} set.".format(evaluation_set.kind))
print("Model version{}: {}.".format(
"" if len(model_versions) == 1 else "s",
enumerate_strings([v.replace("_", " ") for v in model_versions],
conjunction="and")))
if prediction_method:
prediction_specifications = PredictionSpecifications(
method=prediction_method,
number_of_clusters=number_of_classes,
training_set_kind=prediction_training_set.kind)
print("Prediction method: {}.".format(
prediction_specifications.method))
print("Number of clusters: {}.".format(
prediction_specifications.number_of_clusters))
print("Prediction training set: {} set.".format(
prediction_specifications.training_set_kind))
print()
for model_version in model_versions:
use_best_model = False
use_early_stopping_model = False
if model_version == "best_model":
use_best_model = True
elif model_version == "early_stopping":
use_early_stopping_model = True
print(subtitle(model_version.replace("_", " ").capitalize()))
print(
heading("{} evaluation".format(
model_version.replace("_", "-").capitalize())))
(transformed_evaluation_set, reconstructed_evaluation_set,
latent_evaluation_sets) = model.evaluate(
evaluation_set=evaluation_set,
evaluation_subset_indices=evaluation_subset_indices,
minibatch_size=minibatch_size,
run_id=run_id,
use_best_model=use_best_model,
use_early_stopping_model=use_early_stopping_model,
output_versions="all")
print()
if sample_size:
print(
heading("{} sampling".format(
model_version.replace("_", "-").capitalize())))
sample_reconstruction_set, __ = model.sample(
sample_size=sample_size,
minibatch_size=minibatch_size,
run_id=run_id,
use_best_model=use_best_model,
use_early_stopping_model=use_early_stopping_model)
print()
else:
sample_reconstruction_set = None
if prediction_method:
print(
heading("{} prediction".format(
model_version.replace("_", "-").capitalize())))
latent_prediction_training_sets = model.evaluate(
evaluation_set=prediction_training_set,
minibatch_size=minibatch_size,
run_id=run_id,
use_best_model=use_best_model,
use_early_stopping_model=use_early_stopping_model,
output_versions="latent",
log_results=False)
print()
cluster_ids, predicted_labels, predicted_superset_labels = (
predict_labels(
training_set=latent_prediction_training_sets["z"],
evaluation_set=latent_evaluation_sets["z"],
specifications=prediction_specifications))
evaluation_set_versions = [
transformed_evaluation_set, reconstructed_evaluation_set
] + list(latent_evaluation_sets.values())
for evaluation_set_version in evaluation_set_versions:
evaluation_set_version.update_predictions(
prediction_specifications=prediction_specifications,
predicted_cluster_ids=cluster_ids,
predicted_labels=predicted_labels,
predicted_superset_labels=predicted_superset_labels)
print()
print(
heading("{} analysis".format(
model_version.replace("_", "-").capitalize())))
analyses.analyse_results(
evaluation_set=transformed_evaluation_set,
reconstructed_evaluation_set=reconstructed_evaluation_set,
latent_evaluation_sets=latent_evaluation_sets,
model=model,
run_id=run_id,
sample_reconstruction_set=sample_reconstruction_set,
decomposition_methods=decomposition_methods,
evaluation_subset_indices=evaluation_subset_indices,
highlight_feature_indices=highlight_feature_indices,
best_model=use_best_model,
early_stopping=use_early_stopping_model,
included_analyses=included_analyses,
analysis_level=analysis_level,
export_options=export_options,
analyses_directory=analyses_directory)
return 0
def analyse(data_set_file_or_name,
data_format=None,
data_directory=None,
map_features=None,
feature_selection=None,
example_filter=None,
preprocessing_methods=None,
split_data_set=None,
splitting_method=None,
splitting_fraction=None,
included_analyses=None,
analysis_level=None,
decomposition_methods=None,
highlight_feature_indices=None,
export_options=None,
analyses_directory=None,
**keyword_arguments):
"""Analyse data set."""
if split_data_set is None:
split_data_set = defaults["data"]["split_data_set"]
if splitting_method is None:
splitting_method = defaults["data"]["splitting_method"]
if splitting_fraction is None:
splitting_fraction = defaults["data"]["splitting_fraction"]
if analyses_directory is None:
analyses_directory = defaults["analyses"]["directory"]
print(title("Data"))
data_set = DataSet(
data_set_file_or_name,
data_format=data_format,
directory=data_directory,
map_features=map_features,
feature_selection=feature_selection,
example_filter=example_filter,
preprocessing_methods=preprocessing_methods,
)
data_set.load()
if split_data_set:
training_set, validation_set, test_set = data_set.split(
method=splitting_method, fraction=splitting_fraction)
all_data_sets = [data_set, training_set, validation_set, test_set]
else:
all_data_sets = [data_set]
splitting_method = None
splitting_fraction = None
analyses_directory = build_directory_path(
analyses_directory,
data_set=data_set,
splitting_method=splitting_method,
splitting_fraction=splitting_fraction,
preprocessing=False)
print(subtitle("Analysing data"))
analyses.analyse_data(data_sets=all_data_sets,
decomposition_methods=decomposition_methods,
highlight_feature_indices=highlight_feature_indices,
included_analyses=included_analyses,
analysis_level=analysis_level,
export_options=export_options,
analyses_directory=analyses_directory)
return 0
def train(data_set_file_or_name,
data_format=None,
data_directory=None,
map_features=None,
feature_selection=None,
example_filter=None,
noisy_preprocessing_methods=None,
preprocessing_methods=None,
split_data_set=None,
splitting_method=None,
splitting_fraction=None,
model_type=None,
latent_size=None,
hidden_sizes=None,
number_of_importance_samples=None,
number_of_monte_carlo_samples=None,
inference_architecture=None,
latent_distribution=None,
number_of_classes=None,
parameterise_latent_posterior=False,
prior_probabilities_method=None,
generative_architecture=None,
reconstruction_distribution=None,
number_of_reconstruction_classes=None,
count_sum=None,
proportion_of_free_nats_for_y_kl_divergence=None,
minibatch_normalisation=None,
batch_correction=None,
dropout_keep_probabilities=None,
number_of_warm_up_epochs=None,
kl_weight=None,
number_of_epochs=None,
minibatch_size=None,
learning_rate=None,
run_id=None,
new_run=False,
reset_training=None,
models_directory=None,
caches_directory=None,
analyses_directory=None,
**keyword_arguments):
"""Train model on data set."""
if split_data_set is None:
split_data_set = defaults["data"]["split_data_set"]
if splitting_method is None:
splitting_method = defaults["data"]["splitting_method"]
if splitting_fraction is None:
splitting_fraction = defaults["data"]["splitting_fraction"]
if models_directory is None:
models_directory = defaults["models"]["directory"]
print(title("Data"))
binarise_values = False
if reconstruction_distribution == "bernoulli":
if noisy_preprocessing_methods:
if noisy_preprocessing_methods[-1] != "binarise":
noisy_preprocessing_methods.append("binarise")
else:
binarise_values = True
data_set = DataSet(data_set_file_or_name,
data_format=data_format,
directory=data_directory,
map_features=map_features,
feature_selection=feature_selection,
example_filter=example_filter,
preprocessing_methods=preprocessing_methods,
binarise_values=binarise_values,
noisy_preprocessing_methods=noisy_preprocessing_methods)
if split_data_set:
training_set, validation_set, __ = data_set.split(
method=splitting_method, fraction=splitting_fraction)
else:
data_set.load()
splitting_method = None
splitting_fraction = None
training_set = data_set
validation_set = None
models_directory = build_directory_path(
models_directory,
data_set=data_set,
splitting_method=splitting_method,
splitting_fraction=splitting_fraction)
if analyses_directory:
analyses_directory = build_directory_path(
analyses_directory,
data_set=data_set,
splitting_method=splitting_method,
splitting_fraction=splitting_fraction)
model_caches_directory = None
if caches_directory:
model_caches_directory = os.path.join(caches_directory, "log")
model_caches_directory = build_directory_path(
model_caches_directory,
data_set=data_set,
splitting_method=splitting_method,
splitting_fraction=splitting_fraction)
print(title("Model"))
if number_of_classes is None:
if training_set.has_labels:
number_of_classes = (training_set.number_of_classes -
training_set.number_of_excluded_classes)
model = _setup_model(
data_set=training_set,
model_type=model_type,
latent_size=latent_size,
hidden_sizes=hidden_sizes,
number_of_importance_samples=number_of_importance_samples,
number_of_monte_carlo_samples=number_of_monte_carlo_samples,
inference_architecture=inference_architecture,
latent_distribution=latent_distribution,
number_of_classes=number_of_classes,
parameterise_latent_posterior=parameterise_latent_posterior,
prior_probabilities_method=prior_probabilities_method,
generative_architecture=generative_architecture,
reconstruction_distribution=reconstruction_distribution,
number_of_reconstruction_classes=number_of_reconstruction_classes,
count_sum=count_sum,
proportion_of_free_nats_for_y_kl_divergence=(
proportion_of_free_nats_for_y_kl_divergence),
minibatch_normalisation=minibatch_normalisation,
batch_correction=batch_correction,
dropout_keep_probabilities=dropout_keep_probabilities,
number_of_warm_up_epochs=number_of_warm_up_epochs,
kl_weight=kl_weight,
models_directory=models_directory)
print(model.description)
print()
print(model.parameters)
print()
print(subtitle("Training"))
if analyses_directory:
intermediate_analyser = analyses.analyse_intermediate_results
else:
intermediate_analyser = None
model.train(training_set,
validation_set,
number_of_epochs=number_of_epochs,
minibatch_size=minibatch_size,
learning_rate=learning_rate,
intermediate_analyser=intermediate_analyser,
run_id=run_id,
new_run=new_run,
reset_training=reset_training,
analyses_directory=analyses_directory,
temporary_log_directory=model_caches_directory)
# Remove temporary directories created and emptied during training
if model_caches_directory and os.path.exists(caches_directory):
remove_empty_directories(caches_directory)
return 0
