def whizzml_script(args, api):
"""Returns the ID of the script to be used to generate the output
"""
# each language has its own script, so first check:
# - whether the script exists in the account
# - whether it has the same version
# else, we act as if we wanted to upgrade the script
script_dir = os.path.join(REIFY_PACKAGE_PATH,
SCRIPT_FILE.get(args.language, args.language))
if not args.upgrade:
# the script is retrieved by name
# Reading the name of the script
with open(os.path.join(script_dir, "metadata.json")) as meta_file:
meta = json.load(meta_file)
# check for the last script used to retrain the model
query_string = "name=%s" % meta["name"]
reify_script = get_last_resource( \
"script",
api=api,
query_string=query_string)
else:
reify_script = None
# create or retrieve the script to generate the output
# if --upgrade, we force rebuilding the scriptified script
if reify_script is None:
try:
shutil.rmtree(
os.path.join(BIGMLER_SCRIPTS_DIRECTORY,
SCRIPT_FILE.get(args.language, args.language)))
except Exception, exc:
pass
def whizzml_script(args, api):
"""Returns the ID of the script to be used to generate the output
"""
# each language has its own script, so first check:
# - whether the script exists in the account
# - whether it has the same version
# else, we act as if we wanted to upgrade the script
script_dir = os.path.join(REIFY_PACKAGE_PATH,
SCRIPT_FILE.get(args.language, args.language))
if not args.upgrade:
# the script is retrieved by name
# Reading the name of the script
with open(os.path.join(script_dir, "metadata.json")) as meta_file:
meta = json.load(meta_file)
# check for the last script used to retrain the model
query_string = "name=%s" % meta["name"]
reify_script = get_last_resource( \
"script",
api=api,
query_string=query_string)
else:
reify_script = None
# create or retrieve the script to generate the output
# if --upgrade, we force rebuilding the scriptified script
if reify_script is None :
try:
shutil.rmtree(os.path.join(BIGMLER_SCRIPTS_DIRECTORY,
SCRIPT_FILE.get(args.language,
args.language)))
except Exception, exc:
pass
def library_processing(api, args, session_file=None, path=None, log=None):
"""Creating or retrieving a library
"""
library = None
resume = args.resume
if args.code_file or args.code:
# If resuming, try to extract args.library form log files
if resume:
message = u.dated("Library not found. Resuming.\n")
resume, library = c.checkpoint(c.is_library_created,
path,
debug=args.debug,
message=message,
log_file=session_file,
console=args.verbosity)
if not resume:
args.resume = resume
if args.code_file:
try:
with open(args.code_file) as code_file:
source_code = code_file.read()
except IOError:
sys.exit("Failed to find the source code file: %s" %
args.code_file)
else:
source_code = args.code
# Check if there's a created project for it
args.project_id = pp.project_processing(api,
args,
resume,
session_file=session_file,
path=path,
log=log)
# Check if we are upgrading
if args.upgrade:
library = u.get_last_resource("library", api,
build_query_string(args))
log_created_resources("library", path, library, mode='a')
message = u.dated("Library found: %s \n"
" (library ID: %s)\n" %
(args.name, library))
u.log_message(message,
log_file=session_file,
console=args.verbosity)
if library is None:
library_args = rl.set_library_args(args)
add_version_tag(library_args, args.name)
library = rl.create_library(source_code, library_args, args,
api, path, session_file, log)
return library
def library_processing(api, args,
session_file=None, path=None, log=None):
"""Creating or retrieving a library
"""
library = None
resume = args.resume
if args.code_file or args.code:
# If resuming, try to extract args.library form log files
if resume:
message = u.dated("Library not found. Resuming.\n")
resume, library = c.checkpoint(
c.is_library_created, path, debug=args.debug, message=message,
log_file=session_file, console=args.verbosity)
if not resume:
args.resume = resume
if args.code_file:
try:
with open(args.code_file) as code_file:
source_code = code_file.read()
except IOError:
sys.exit("Failed to find the source code file: %s" %
args.code_file)
else:
source_code = args.code
# Check if there's a created project for it
args.project_id = pp.project_processing(
api, args, resume, session_file=session_file,
path=path, log=log)
# Check if we are upgrading
if args.upgrade:
library = u.get_last_resource("library",
api,
build_query_string(args))
r.log_created_resources("library", path,
library, mode='a')
message = u.dated("Library found: %s \n"
" (library ID: %s)\n" %
(args.name, library))
u.log_message(message, log_file=session_file,
console=args.verbosity)
if library is None:
library_args = r.set_library_args(args)
add_version_tag(library_args, args.name)
library = r.create_library(source_code, library_args, args, api,
path, session_file, log)
return library
def retrain_model(args, api, common_options, session_file=None):
"""Retrieve or create the retrain script for a model and
execute it with the new provided data
"""
# retrieve the modeling resource to be retrained by tag or id
if args.resource_id:
resource_id = args.resource_id
reference_tag = "retrain:%s" % resource_id
else:
for model_type in MODEL_TYPES:
if hasattr(args, "%s_tag" % model_type) and \
getattr(args, "%s_tag" % model_type) is not None:
tag = getattr(args, "%s_tag" % model_type)
query_string = "tags=%s" % tag
resource_id = get_last_resource( \
model_type.replace("_", ""),
api=api,
query_string=query_string)
if resource_id is None:
sys.exit("Failed to find the %s with tag %s. "
"Please, check the tag and"
" the connection info (domain and credentials)." %
(model_type.replace("_", " "), tag))
reference_tag = tag
break
if args.upgrade:
shutil.rmtree(BIGMLER_SCRIPTS_DIRECTORY)
script_id = None
else:
# check for the last script used to retrain the model
query_string = "tags=retrain:%s" % resource_id
script_id = get_last_resource( \
"script",
api=api,
query_string=query_string)
if script_id is None:
# if the script to retrain does not exist:
# check whether the model exists
try:
bigml.api.check_resource(resource_id, raise_on_error=True, api=api)
except Exception, exc:
sys.exit("Failed to find the model %s. Please, check its ID and"
" the connection info (domain and credentials)." %
resource_id)
# look for the script that creates the rebuild script.
retrain_file = os.path.join(BIGMLER_SCRIPTS_DIRECTORY,
"retrain",
"scripts")
reify_script = get_script_id(retrain_file)
if reify_script is None:
# new bigmler command: creating the scriptify scripts
whizzml_command = ['whizzml',
'--package-dir', INCREMENTAL_PACKAGE_PATH,
'--output-dir', BIGMLER_SCRIPTS_DIRECTORY]
whizzml_dispatcher(args=whizzml_command)
reify_script = get_script_id(retrain_file)
# new bigmler command: creating the retrain script
execute_command = ['execute',
'--script', reify_script,
'--output-dir', args.output_dir]
command_args, _, _, exe_session_file, _ = get_context(execute_command,
EXE_SETTINGS)
command_args.arguments_ = [["model-resource", resource_id]]
command_args.inputs = json.dumps(command_args.arguments_)
# process the command
execute_whizzml(command_args, api, session_file)
script_id = extract_retrain_id(command_args, api, session_file)
def retrain_model(args, api, common_options, session_file=None):
"""Retrieve or create the retrain script for a model and
execute it with the new provided data
"""
# retrieve the modeling resource to be retrained by tag or id
if args.resource_id:
resource_id = args.resource_id
reference_tag = "retrain:%s" % resource_id
else:
for model_type in MODEL_TYPES:
if hasattr(args, "%s_tag" % model_type) and \
getattr(args, "%s_tag" % model_type) is not None:
tag = getattr(args, "%s_tag" % model_type)
query_string = "tags=%s" % tag
resource_id = get_last_resource( \
model_type.replace("_", ""),
api=api,
query_string=query_string)
if resource_id is None:
sys.exit("Failed to find the %s with tag %s. "
"Please, check the tag and"
" the connection info (domain and credentials)." %
(model_type.replace("_", " "), tag))
reference_tag = tag
break
if args.upgrade:
shutil.rmtree(BIGMLER_SCRIPTS_DIRECTORY)
script_id = None
else:
# check for the last script used to retrain the model
query_string = "tags=retrain:%s" % resource_id
script_id = get_last_resource( \
"script",
api=api,
query_string=query_string)
if script_id is None:
# if the script to retrain does not exist:
# check whether the model exists
try:
bigml.api.check_resource(resource_id, raise_on_error=True, api=api)
except Exception, exc:
sys.exit("Failed to find the model %s. Please, check its ID and"
" the connection info (domain and credentials)." %
resource_id)
# look for the script that creates the rebuild script.
retrain_file = os.path.join(BIGMLER_SCRIPTS_DIRECTORY, "retrain",
"scripts")
reify_script = get_script_id(retrain_file)
if reify_script is None:
# new bigmler command: creating the scriptify scripts
whizzml_command = [
'whizzml', '--package-dir', INCREMENTAL_PACKAGE_PATH,
'--output-dir', BIGMLER_SCRIPTS_DIRECTORY
]
whizzml_dispatcher(args=whizzml_command)
reify_script = get_script_id(retrain_file)
# new bigmler command: creating the retrain script
execute_command = [
'execute', '--script', reify_script, '--output-dir',
args.output_dir
]
command_args, _, _, exe_session_file, _ = get_context(
execute_command, EXE_SETTINGS)
command_args.arguments_ = [["model-resource", resource_id]]
command_args.inputs = json.dumps(command_args.arguments_)
# process the command
execute_whizzml(command_args, api, session_file)
script_id = extract_retrain_id(command_args, api, session_file)
def retrain_model(args, api, command, session_file=None):
"""Retrieve or create the retrain script for a model and
execute it with the new provided data
"""
retrain_file = os.path.join(BIGMLER_SCRIPTS_DIRECTORY, "retrain",
"scripts")
try:
os.remove(UPGRADE_FILE)
reify_script = None
try:
shutil.rmtree(BIGMLER_SCRIPTS_DIRECTORY)
except OSError:
pass
except OSError:
# look for the script that creates the rebuild script.
reify_script = get_script_id(retrain_file)
if reify_script is None:
# new bigmler command: creating the scriptify scripts
whizzml_command = [
'whizzml', '--package-dir', INCREMENTAL_PACKAGE_PATH,
'--output-dir', BIGMLER_SCRIPTS_DIRECTORY
]
add_api_context(whizzml_command, args)
whizzml_dispatcher(args=whizzml_command)
reify_script = get_script_id(retrain_file)
# retrieve the modeling resource to be retrained by tag or id
if args.resource_id:
resource_id = args.resource_id
reference_tag = "retrain:%s" % resource_id
else:
for model_type in MODEL_TYPES:
if hasattr(args, "%s_tag" % model_type) and \
getattr(args, "%s_tag" % model_type) is not None:
tag = getattr(args, "%s_tag" % model_type)
query_string = "tags=%s" % tag
resource_id = get_first_resource( \
model_type.replace("_", ""),
api=api,
query_string=query_string)
if resource_id is None:
sys.exit("Failed to find the %s with tag %s. "
"Please, check the tag and"
" the connection info (domain and credentials)." %
(model_type.replace("_", " "), tag))
reference_tag = tag
break
# updating the dataset that generated the model with the reference tag
model = api.getters[get_resource_type(resource_id)](resource_id)
dataset_id = model["object"]["dataset"]
dataset = api.get_dataset(dataset_id)
tags = dataset["object"]["tags"]
if reference_tag not in tags:
tags.append(reference_tag)
api.update_dataset(dataset_id, {"tags": tags})
# if --upgrade, we force rebuilding the scriptified script
if args.upgrade:
script_id = None
else:
# check for the last script used to retrain the model
query_string = "tags=%s" % reference_tag
script_id = get_last_resource( \
"script",
api=api,
query_string=query_string)
if script_id is None:
# if the script to retrain does not exist:
# check whether the model exists
try:
bigml.api.check_resource(resource_id, raise_on_error=True, api=api)
except Exception:
sys.exit("Failed to find the model %s. Please, check its ID and"
" the connection info (domain and credentials)." %
resource_id)
# new bigmler command: creating the retrain script
execute_command = [
'execute', '--script', reify_script, '--tag', reference_tag,
'--output-dir', args.output_dir
]
command.propagate(execute_command)
command_args, _, _, exe_session_file, _ = get_context(
execute_command, EXE_SETTINGS)
command_args.arguments_ = [["model-resource", resource_id]]
command_args.inputs = json.dumps(command_args.arguments_)
# process the command
execute_whizzml(command_args, api, session_file)
script_id = extract_retrain_id(command_args, api, session_file)
# apply the retrain script to the new data:
# add new data: depending on the script we will need to use
# a source-url, a source or a dataset
if args.add:
script_inputs = api.get_script(script_id)['object']['inputs']
input_type = script_inputs[0]['type']
command_args, api, exe_session_file = \
create_input(args, api, input_type, script_id, command)
# process the command
execute_whizzml(command_args, api, exe_session_file)
with open("%s.json" % command_args.output) as file_handler:
model_resource_id = json.load(file_handler)['result']
message = (u'The new retrained model is: %s.\n'
u'You can use the\n\n%s\n\nquery to retrieve the latest'
u' retrained model.\n\n') % \
(model_resource_id, last_resource_url( \
resource_id, api, \
"limit=1;full=yes;tags=%s" % reference_tag))
log_message(message, log_file=session_file, console=1)
def script_processing(api, args,
session_file=None, path=None, log=None):
"""Creating or retrieving a script
"""
script = None
resume = args.resume
if args.code_file or args.code:
# If resuming, try to extract args.script form log files
if resume:
message = u.dated("Script not found. Resuming.\n")
resume, args.script = c.checkpoint(
c.are_scripts_created, path, debug=args.debug, message=message,
log_file=session_file, console=args.verbosity)
script = args.script
if not resume:
args.resume = resume
imports_code = []
if args.embedded_imports is not None:
for import_file in args.embedded_imports_:
with open(import_file) as code_file:
imports_code.append(code_file.read())
if args.code_file:
try:
with open(args.code_file) as code_file:
source_code = code_file.read()
except IOError:
sys.exit("Failed to find the source code file: %s" %
args.code_file)
else:
source_code = args.code
if imports_code:
source_code = "%s\n%s" % ("\n".join(imports_code), source_code)
# Check if there's a created project for it
args.project_id = pp.project_processing(
api, args, resume, session_file=session_file,
path=path, log=log)
# Check if we are upgrading
if args.upgrade:
script = u.get_last_resource("script",
api,
build_query_string(args))
r.log_created_resources("script", path,
script, mode='a')
message = u.dated("Script found: %s"
"\n (script ID: %s)\n" %
(args.name, script))
u.log_message(message, log_file=session_file,
console=args.verbosity)
if script is None:
script_args = r.set_script_args(args)
add_version_tag(script_args, args.name)
script = r.create_script(source_code, script_args, args, api,
path, session_file, log)
args.script = script if isinstance(script, basestring) else \
script.get('resource')
scripts = [script]
# If a script is provided either through the command line or in resume
# steps, we use it.
elif args.script:
script = bigml.api.get_script_id(args.script)
scripts = [script]
elif args.scripts:
scripts = [script for script in args.script_ids]
script = scripts[0]
return script, scripts
