def analyze_dispatcher(args=sys.argv[1:]):
"""Main processing of the parsed options for BigMLer analyze
"""
# 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 = command.parser.parse_args(command.args)
resume = command_args.resume
if 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 = command.parser.parse_args(command.args)
command_args.debug = debug
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
session_file = os.path.join(command_args.output_dir,
SESSIONS_LOG)
# If logging is required, open the file for logging
log = None
if command_args.log_file:
u.check_dir(command_args.log_file)
log = command_args.log_file
# If --clear_logs the log files are cleared
if command_args.clear_logs:
clear_log_files([log])
if command_args.model_fields:
model_fields = command_args.model_fields.split(',')
command_args.model_fields_ = [model_field.strip()
for model_field in model_fields]
else:
command_args.model_fields_ = {}
u.sys_log_message(u"%s\n" % os.path.abspath(command_args.output_dir),
log_file=DIRS_LOG)
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# create api instance form args
api = a.get_api_instance(command_args,
u.check_dir(session_file))
# --maximize flag will be deprecated. Use --optimize flag.
if command_args.maximize is not None and command_args.optimize is None:
command_args.optimize = command_args.maximize
incompatible_flags = [command_args.cv, command_args.features,
command_args.nodes]
if sum([int(bool(flag)) for flag in incompatible_flags]) > 1:
sys.exit("The following flags cannot be used together:\n --features"
"\n --cross-validation\n --nodes")
# k-fold cross-validation
if command_args.cv and command_args.dataset is not None:
create_kfold_cv(command_args, api, command.common_options,
resume=resume)
# features analysis
if command_args.features:
create_features_analysis(command_args, api, command.common_options,
resume=resume)
# node threshold analysis
if command_args.nodes:
create_nodes_analysis(command_args, api, command.common_options,
resume=resume)
def analyze_dispatcher(args=sys.argv[1:]):
"""Main processing of the parsed options for BigMLer analyze
"""
# 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 = command.parser.parse_args(command.args)
resume = command_args.resume
if resume:
command_args, session_file, _ = get_stored_command(
args,
command_args.debug,
command_log=COMMAND_LOG,
dirs_log=DIRS_LOG,
sessions_log=SESSIONS_LOG)
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# If logging is required, open the file for logging
log = None
if command_args.log_file:
u.check_dir(command_args.log_file)
log = command_args.log_file
# If --clear_logs the log files are cleared
if command_args.clear_logs:
clear_log_files([log])
if command_args.model_fields:
model_fields = command_args.model_fields.split(',')
command_args.model_fields_ = [
model_field.strip() for model_field in model_fields
]
else:
command_args.model_fields_ = {}
u.sys_log_message(u"%s\n" % os.path.abspath(command_args.output_dir),
log_file=DIRS_LOG)
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# create api instance form args
api = a.get_api_instance(command_args, u.check_dir(session_file))
# Creates the corresponding api instance
api = a.get_api_instance(command_args, u.check_dir(session_file))
a.transform_dataset_options(command_args, api)
# --maximize flag will be deprecated. Use --optimize flag.
if command_args.maximize is not None and command_args.optimize is None:
command_args.optimize = command_args.maximize
incompatible_flags = [
command_args.cv, command_args.features, command_args.nodes,
command_args.random_fields
]
if sum([int(bool(flag)) for flag in incompatible_flags]) > 1:
sys.exit("The following flags cannot be used together:\n --features"
"\n --cross-validation\n --nodes\n --random-fields")
if (command_args.dataset is None and command_args.datasets is None
and command_args.dataset_file is None):
sys.exit("The analyze command needs an existing dataset ID. Please, "
"use the --dataset flag.")
if not any(incompatible_flags):
sys.exit("You need to specify the type of analysis: features, node "
"threshold, cross validation or random fields.")
# k-fold cross-validation
if command_args.cv and command_args.dataset is not None:
create_kfold_cv(command_args,
api,
command.common_options,
resume=resume)
# features analysis
elif command_args.features:
create_features_analysis(command_args,
api,
command.common_options,
resume=resume)
# node threshold analysis
elif command_args.nodes:
create_nodes_analysis(command_args,
api,
command.common_options,
resume=resume)
# random fields analysis
elif command_args.random_fields:
create_candidates_analysis(command_args,
api,
command.common_options,
resume=resume)
else:
sys.exit("You must choose one of the available analysis: --features,"
" --nodes, --random-fields or --cross-validation. Add"
" your prefered option to"
" the command line or type\n bigmler analyze --help\n"
" to see all the available options.")
def analyze_dispatcher(args=sys.argv[1:]):
"""Main processing of the parsed options for BigMLer analyze
"""
# 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 = command.parser.parse_args(command.args)
resume = command_args.resume
if resume:
command_args, session_file, _ = get_stored_command(
args, command_args.debug, command_log=COMMAND_LOG,
dirs_log=DIRS_LOG, sessions_log=SESSIONS_LOG)
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
session_file = os.path.join(command_args.output_dir,
SESSIONS_LOG)
# If logging is required, open the file for logging
log = None
if command_args.log_file:
u.check_dir(command_args.log_file)
log = command_args.log_file
# If --clear_logs the log files are cleared
if command_args.clear_logs:
clear_log_files([log])
if command_args.model_fields:
model_fields = command_args.model_fields.split(',')
command_args.model_fields_ = [model_field.strip()
for model_field in model_fields]
else:
command_args.model_fields_ = {}
u.sys_log_message(u"%s\n" % os.path.abspath(command_args.output_dir),
log_file=DIRS_LOG)
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# create api instance form args
api = a.get_api_instance(command_args,
u.check_dir(session_file))
# --maximize flag will be deprecated. Use --optimize flag.
if command_args.maximize is not None and command_args.optimize is None:
command_args.optimize = command_args.maximize
incompatible_flags = [command_args.cv, command_args.features,
command_args.nodes, command_args.random_fields]
if sum([int(bool(flag)) for flag in incompatible_flags]) > 1:
sys.exit("The following flags cannot be used together:\n --features"
"\n --cross-validation\n --nodes\n --random-fields")
if (command_args.dataset is None and command_args.datasets is None and
command_args.dataset_file is None):
sys.exit("The analyze command needs an existing dataset ID. Please, "
"use the --dataset flag.")
if not any(incompatible_flags):
sys.exit("You need to specify the type of analysis: features, node "
"threshold, cross validation or random fields.")
# k-fold cross-validation
if command_args.cv and command_args.dataset is not None:
create_kfold_cv(command_args, api, command.common_options,
resume=resume)
# features analysis
elif command_args.features:
create_features_analysis(command_args, api, command.common_options,
resume=resume)
# node threshold analysis
elif command_args.nodes:
create_nodes_analysis(command_args, api, command.common_options,
resume=resume)
# random fields analysis
elif command_args.random_fields:
create_candidates_analysis(command_args, api, command.common_options,
resume=resume)
else:
sys.exit("You must choose one of the available analysis: --features,"
" --nodes, --random-fields or --cross-validation. Add"
" your prefered option to"
" the command line or type\n bigmler analyze --help\n"
" to see all the available options.")
def analyze_dispatcher(args=sys.argv[1:]):
"""Main processing of the parsed options for BigMLer analyze
"""
# 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 = command.parser.parse_args(command.args)
resume = command_args.resume
if 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 = command.parser.parse_args(command.args)
command_args.debug = debug
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# If logging is required, open the file for logging
log = None
if command_args.log_file:
u.check_dir(command_args.log_file)
log = command_args.log_file
# If --clear_logs the log files are cleared
if command_args.clear_logs:
clear_log_files([log])
if command_args.model_fields:
model_fields = command_args.model_fields.split(',')
command_args.model_fields_ = [
model_field.strip() for model_field in model_fields
]
else:
command_args.model_fields_ = {}
u.sys_log_message(u"%s\n" % os.path.abspath(command_args.output_dir),
log_file=DIRS_LOG)
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# create api instance form args
api = a.get_api_instance(command_args, u.check_dir(session_file))
# --maximize flag will be deprecated. Use --optimize flag.
if command_args.maximize is not None and command_args.optimize is None:
command_args.optimize = command_args.maximize
incompatible_flags = [
command_args.cv, command_args.features, command_args.nodes
]
if sum([int(bool(flag)) for flag in incompatible_flags]) > 1:
sys.exit("The following flags cannot be used together:\n --features"
"\n --cross-validation\n --nodes")
# k-fold cross-validation
if command_args.cv and command_args.dataset is not None:
create_kfold_cv(command_args,
api,
command.common_options,
resume=resume)
# features analysis
if command_args.features:
create_features_analysis(command_args,
api,
command.common_options,
resume=resume)
# node threshold analysis
if command_args.nodes:
create_nodes_analysis(command_args,
api,
command.common_options,
resume=resume)
def analyze_dispatcher(args=sys.argv[1:]):
"""Main processing of the parsed options for BigMLer analyze
"""
# 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 = command.parser.parse_args(command.args)
resume = command_args.resume
if 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 = command.parser.parse_args(command.args)
else:
if command_args.output_dir is None:
command_args.output_dir = a.NOW
session_file = os.path.join(command_args.output_dir,
SESSIONS_LOG)
csv_properties = {}
# If logging is required, open the file for logging
log = None
if command_args.log_file:
u.check_dir(command_args.log_file)
log = command_args.log_file
# If --clear_logs the log files are cleared
if command_args.clear_logs:
clear_log_files([log])
if command_args.model_fields:
model_fields = command_args.model_fields.split(',')
command_args.model_fields_ = map(str.strip, model_fields)
else:
command_args.model_fields_ = {}
with open(DIRS_LOG, "a", 0) as directory_log:
directory_log.write("%s\n" %
os.path.abspath(command_args.output_dir))
session_file = os.path.join(command_args.output_dir, SESSIONS_LOG)
# create api instance form args
api = a.get_api_instance(command_args,
u.check_dir(session_file))
# k-fold cross-validation
if (command_args.cv and command_args.dataset is not None):
create_kfold_cv(command_args, api, command.common_options,
resume=resume)
# features analysis
if command_args.features:
create_features_analysis(command_args, api, command.common_options,
resume=resume)
# node threshold analysis
if command_args.nodes:
create_nodes_analysis(command_args, api, command.common_options,
resume=resume)