def cluster_dispatcher(args=sys.argv[1:]):
"""Parses command line and calls the different processing functions
"""
# If --clear-logs the log files are cleared
if "--clear-logs" in args:
clear_log_files(LOG_FILES)
command = command_handling(args, COMMAND_LOG)
# Parses command line arguments.
command_args = a.parse_and_check(command)
resume = command_args.resume
if command_args.resume:
# Keep the debug option if set
debug = command_args.debug
# Restore the args of the call to resume from the command log file
stored_command = StoredCommand(args, COMMAND_LOG, DIRS_LOG)
command = Command(None, stored_command=stored_command)
# Logs the issued command and the resumed command
session_file = os.path.join(stored_command.output_dir, SESSIONS_LOG)
stored_command.log_command(session_file=session_file)
# Parses resumed arguments.
command_args = a.parse_and_check(command)
if command_args.predictions is None:
command_args.predictions = os.path.join(stored_command.output_dir, DEFAULT_OUTPUT)
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
if command_args.predictions is None:
command_args.predictions = os.path.join(command_args.output_dir, DEFAULT_OUTPUT)
if len(os.path.dirname(command_args.predictions).strip()) == 0:
command_args.predictions = os.path.join(command_args.output_dir, command_args.predictions)
directory = u.check_dir(command_args.predictions)
session_file = os.path.join(directory, SESSIONS_LOG)
u.log_message(command.command + "\n", log_file=session_file)
try:
defaults_file = open(DEFAULTS_FILE, "r")
contents = defaults_file.read()
defaults_file.close()
defaults_copy = open(os.path.join(directory, DEFAULTS_FILE), "w", 0)
defaults_copy.write(contents)
defaults_copy.close()
except IOError:
pass
u.sys_log_message(u"%s\n" % os.path.abspath(directory), log_file=DIRS_LOG)
# Creates the corresponding api instance
if resume and debug:
command_args.debug = True
api = a.get_api_instance(command_args, u.check_dir(session_file))
# Selects the action to perform
if has_train(command_args) or has_test(command_args) or command_args.cluster_datasets is not None:
output_args = a.get_output_args(api, command_args, resume)
a.transform_args(command_args, command.flags, api, command.user_defaults)
compute_output(**output_args)
u.log_message("_" * 80 + "\n", log_file=session_file)
def cluster_dispatcher(args=sys.argv[1:]):
"""Parses command line and calls the different processing functions
"""
# If --clear-logs the log files are cleared
if "--clear-logs" in args:
clear_log_files(LOG_FILES)
command = command_handling(args, COMMAND_LOG)
# Parses command line arguments.
command_args = a.parse_and_check(command)
resume = command_args.resume
if command_args.resume:
# Keep the debug option if set
debug = command_args.debug
# Restore the args of the call to resume from the command log file
stored_command = StoredCommand(args, COMMAND_LOG, DIRS_LOG)
command = Command(None, stored_command=stored_command)
# Logs the issued command and the resumed command
session_file = os.path.join(stored_command.output_dir, SESSIONS_LOG)
stored_command.log_command(session_file=session_file)
# Parses resumed arguments.
command_args = a.parse_and_check(command)
if command_args.predictions is None:
command_args.predictions = os.path.join(stored_command.output_dir,
DEFAULT_OUTPUT)
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
if command_args.predictions is None:
command_args.predictions = os.path.join(command_args.output_dir,
DEFAULT_OUTPUT)
if len(os.path.dirname(command_args.predictions).strip()) == 0:
command_args.predictions = os.path.join(command_args.output_dir,
command_args.predictions)
directory = u.check_dir(command_args.predictions)
session_file = os.path.join(directory, SESSIONS_LOG)
u.log_message(command.command + "\n", log_file=session_file)
try:
defaults_file = open(DEFAULTS_FILE, 'r')
contents = defaults_file.read()
defaults_file.close()
defaults_copy = open(os.path.join(directory, DEFAULTS_FILE), 'w',
0)
defaults_copy.write(contents)
defaults_copy.close()
except IOError:
pass
u.sys_log_message(u"%s\n" % os.path.abspath(directory),
log_file=DIRS_LOG)
# Creates the corresponding api instance
if resume and debug:
command_args.debug = True
api = a.get_api_instance(command_args, u.check_dir(session_file))
# Selects the action to perform
if (has_train(command_args) or has_test(command_args)
or command_args.cluster_datasets is not None):
output_args = a.get_output_args(api, command_args, resume)
a.transform_args(command_args, command.flags, api,
command.user_defaults)
compute_output(**output_args)
u.log_message("_" * 80 + "\n", log_file=session_file)
def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
cluster = None
clusters = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
cluster_ids = args.cluster_ids_
output = args.predictions
# there's only one cluster to be generated at present
args.max_parallel_clusters = 1
# clusters cannot be published yet.
args.public_cluster = False
# It is compulsory to have a description to publish either datasets or
# clusters
if (not args.description_
and (args.public_cluster or args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
(dataset, datasets, test_dataset, resume, csv_properties,
fields) = pms.get_dataset_info(api, args, resume, source, csv_properties,
fields, session_file, path, log)
if args.cluster_file:
# cluster is retrieved from the contents of the given local JSON file
cluster, csv_properties, fields = u.read_local_resource(
args.cluster_file, csv_properties=csv_properties)
clusters = [cluster]
cluster_ids = [cluster['resource']]
else:
# cluster is retrieved from the remote object
clusters, cluster_ids, resume = pc.clusters_processing(
datasets,
clusters,
cluster_ids,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
if clusters:
cluster = clusters[0]
# We update the cluster's public state if needed
if cluster:
if isinstance(cluster, basestring):
if args.cluster_datasets is None and not has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
cluster = u.check_resource(cluster,
api.get_cluster,
query_string=query_string)
clusters[0] = cluster
if (args.public_cluster or
(args.shared_flag and r.shared_changed(args.shared, cluster))):
cluster_args = {}
if args.shared_flag and r.shared_changed(args.shared, cluster):
cluster_args.update(shared=args.shared)
if args.public_cluster:
cluster_args.update(r.set_publish_cluster_args(args))
if cluster_args:
cluster = r.update_cluster(cluster,
cluster_args,
args,
api=api,
path=path,
session_file=session_file)
clusters[0] = cluster
# We get the fields of the cluster if we haven't got
# them yet and need them
if cluster and args.test_set:
fields = pc.get_cluster_fields(cluster, csv_properties, args)
# If predicting
if clusters and (has_test(args) or (test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote centroids: centroids are computed as batch centroids
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# create test source from file
test_name = "%s - test" % args.name
if args.test_source is None:
(test_source, resume, csv_properties,
test_fields) = ps.test_source_processing(
api,
args,
resume,
name=test_name,
session_file=session_file,
path=path,
log=log)
else:
test_source_id = bigml.api.get_source_id(args.test_source)
test_source = api.check_resource(test_source_id)
if test_dataset is None:
# create test dataset from test source
dataset_args = r.set_basic_dataset_args(args, name=test_name)
test_dataset, resume = pd.alternative_dataset_processing(
test_source,
"test",
dataset_args,
api,
args,
resume,
session_file=session_file,
path=path,
log=log)
else:
test_dataset_id = bigml.api.get_dataset_id(test_dataset)
test_dataset = api.check_resource(test_dataset_id)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
batch_centroid_args = r.set_batch_centroid_args(
args, fields=fields, dataset_fields=test_fields)
remote_centroid(cluster,
test_dataset,
batch_centroid_args,
args,
api,
resume,
prediction_file=output,
session_file=session_file,
path=path,
log=log)
else:
centroid(clusters, fields, args, session_file=session_file)
if cluster and args.cluster_datasets is not None:
centroids_info = cluster['object']['clusters']['clusters']
centroids = {
centroid['name']: centroid['id']
for centroid in centroids_info
}
datasets = cluster['object']['cluster_datasets']
if args.cluster_datasets == '':
centroid_ids = centroids.values()
else:
centroid_ids = [
centroids[cluster_name]
for cluster_name in args.cluster_datasets_
if datasets[centroids[cluster_name]] == ''
]
for centroid_id in centroid_ids:
dataset_args = {'centroid': centroid_id}
r.create_dataset(cluster,
dataset_args,
args,
api=api,
path=path,
session_file=session_file,
log=log,
dataset_type='cluster')
u.print_generated_files(path,
log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
cluster = None
clusters = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
cluster_ids = args.cluster_ids_
output = args.predictions
# there's only one cluster to be generated at present
args.max_parallel_clusters = 1
# clusters cannot be published yet.
args.public_cluster = False
# It is compulsory to have a description to publish either datasets or
# clusters
if (not args.description_ and (args.public_cluster or
args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
(dataset, datasets, test_dataset, resume,
csv_properties, fields) = pms.get_dataset_info(
api, args, resume, source,
csv_properties, fields, session_file, path, log)
if args.cluster_file:
# cluster is retrieved from the contents of the given local JSON file
cluster, csv_properties, fields = u.read_local_resource(
args.cluster_file,
csv_properties=csv_properties)
clusters = [cluster]
cluster_ids = [cluster['resource']]
else:
# cluster is retrieved from the remote object
clusters, cluster_ids, resume = pc.clusters_processing(
datasets, clusters, cluster_ids, api, args, resume, fields=fields,
session_file=session_file, path=path, log=log)
if clusters:
cluster = clusters[0]
# We update the cluster's public state if needed
if cluster:
if isinstance(cluster, basestring):
if args.cluster_datasets is None and not has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
cluster = u.check_resource(cluster, api.get_cluster,
query_string=query_string)
clusters[0] = cluster
if (args.public_cluster or
(args.shared_flag and r.shared_changed(args.shared, cluster))):
cluster_args = {}
if args.shared_flag and r.shared_changed(args.shared, cluster):
cluster_args.update(shared=args.shared)
if args.public_cluster:
cluster_args.update(r.set_publish_cluster_args(args))
if cluster_args:
cluster = r.update_cluster(cluster, cluster_args, args,
api=api, path=path,
session_file=session_file)
clusters[0] = cluster
# We get the fields of the cluster if we haven't got
# them yet and need them
if cluster and args.test_set:
fields = pc.get_cluster_fields(cluster, csv_properties, args)
# If predicting
if clusters and (has_test(args) or (test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote centroids: centroids are computed as batch centroids
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# create test source from file
test_name = "%s - test" % args.name
if args.test_source is None:
(test_source,
resume,
csv_properties,
test_fields) = ps.test_source_processing(
api, args, resume, name=test_name,
session_file=session_file, path=path, log=log)
else:
test_source_id = bigml.api.get_source_id(args.test_source)
test_source = api.check_resource(test_source_id)
if test_dataset is None:
# create test dataset from test source
dataset_args = r.set_basic_dataset_args(args, name=test_name)
test_dataset, resume = pd.alternative_dataset_processing(
test_source, "test", dataset_args, api, args,
resume, session_file=session_file, path=path, log=log)
else:
test_dataset_id = bigml.api.get_dataset_id(test_dataset)
test_dataset = api.check_resource(test_dataset_id)
test_fields = pd.get_fields_structure(test_dataset,
csv_properties)
batch_centroid_args = r.set_batch_centroid_args(
args, fields=fields,
dataset_fields=test_fields)
remote_centroid(cluster, test_dataset, batch_centroid_args, args,
api, resume, prediction_file=output,
session_file=session_file, path=path, log=log)
else:
centroid(clusters, fields, args, session_file=session_file)
if cluster and args.cluster_datasets is not None:
centroids_info = cluster['object']['clusters']['clusters']
centroids = {centroid['name']: centroid['id']
for centroid in centroids_info}
datasets = cluster['object']['cluster_datasets']
if args.cluster_datasets == '':
centroid_ids = centroids.values()
else:
centroid_ids = [centroids[cluster_name] for cluster_name in
args.cluster_datasets_
if datasets[centroids[cluster_name]] == '']
for centroid_id in centroid_ids:
dataset_args = {'centroid': centroid_id}
r.create_dataset(cluster, dataset_args, args, api=api, path=path,
session_file=session_file, log=log,
dataset_type='cluster')
u.print_generated_files(path, log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
def compute_output(api, args):
""" Creates one or more anomaly detectors using the `training_set`
or uses the ids of previously created BigML models to make
predictions for the `test_set`.
"""
anomaly = None
anomalies = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
anomaly_ids = args.anomaly_ids_
output = args.predictions
# there's only one anomaly detector to be generated at present
args.max_parallel_anomalies = 1
# anomalies cannot be published yet.
args.public_anomaly = False
# It is compulsory to have a description to publish either datasets or
# anomalies
if (not args.description_ and (args.public_anomaly or
args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
(dataset, datasets, test_dataset, resume,
csv_properties, fields) = pms.get_dataset_info(
api, args, resume, source,
csv_properties, fields, session_file, path, log)
if args.anomaly_file:
# anomaly is retrieved from the contents of the given local JSON file
anomaly, csv_properties, fields = u.read_local_resource(
args.anomaly_file,
csv_properties=csv_properties)
anomalies = [anomaly]
anomaly_ids = [anomaly['resource']]
else:
# anomaly is retrieved from the remote object
anomalies, anomaly_ids, resume = pa.anomalies_processing(
datasets, anomalies, anomaly_ids, api, args, resume, fields=fields,
session_file=session_file, path=path, log=log)
if anomalies:
anomaly = anomalies[0]
# We update the anomaly's public state if needed
if anomaly:
if isinstance(anomaly, basestring):
if not has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
anomaly = u.check_resource(anomaly, query_string=query_string,
api=api)
anomalies[0] = anomaly
if (args.public_anomaly or
(args.shared_flag and r.shared_changed(args.shared, anomaly))):
anomaly_args = {}
if args.shared_flag and r.shared_changed(args.shared, anomaly):
anomaly_args.update(shared=args.shared)
if args.public_anomaly:
anomaly_args.update(r.set_publish_anomaly_args(args))
if anomaly_args:
anomaly = r.update_anomaly(anomaly, anomaly_args, args,
api=api, path=path,
session_file=session_file)
anomalies[0] = anomaly
# We get the fields of the anomaly detector if we haven't got
# them yet and need them
if anomaly and args.test_set:
fields = pa.get_anomaly_fields(anomaly, csv_properties, args)
# If predicting
if anomalies and (has_test(args) or (test_dataset and args.remote)):
# test dataset can be defined by --test-split or --test-dataset or
# --test-datasets
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote anomaly scores: scores are computed as batch anomaly scores
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# create test source from file
test_name = "%s - test" % args.name
if args.test_source is None:
(test_source,
resume,
csv_properties,
test_fields) = ps.test_source_processing(
api, args, resume, name=test_name,
session_file=session_file, path=path, log=log)
else:
test_source_id = bigml.api.get_source_id(args.test_source)
test_source = api.check_resource(test_source_id)
if test_dataset is None:
# create test dataset from test source
dataset_args = r.set_basic_dataset_args(args, name=test_name)
test_dataset, resume = pd.alternative_dataset_processing(
test_source, "test", dataset_args, api, args,
resume, session_file=session_file, path=path, log=log)
else:
test_dataset_id = bigml.api.get_dataset_id(test_dataset)
test_dataset = api.check_resource(test_dataset_id)
test_fields = pd.get_fields_structure(test_dataset,
csv_properties)
batch_anomaly_score_args = r.set_batch_anomaly_score_args(
args, fields=fields,
dataset_fields=test_fields)
remote_anomaly_score(anomaly, test_dataset,
batch_anomaly_score_args, args,
api, resume, prediction_file=output,
session_file=session_file, path=path, log=log)
else:
anomaly_score(anomalies, fields, args, session_file=session_file)
u.print_generated_files(path, log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
