def __init__(self, model, api=None):
if not (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(error_message(model,
resource_type='model',
method='get'))
query_string = ONLY_MODEL
model = retrieve_model(api, self.resource_id,
query_string=query_string)
BaseModel.__init__(self, model, api=api)
if ('object' in model and isinstance(model['object'], dict)):
model = model['object']
if ('model' in model and isinstance(model['model'], dict)):
status = get_status(model)
if ('code' in status and status['code'] == FINISHED):
self.tree = Tree(
model['model']['root'],
self.fields,
self.objective_field)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, ensemble, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.ensemble_id = None
if isinstance(ensemble, list):
try:
models = [get_model_id(model) for model in ensemble]
except ValueError:
raise ValueError('Failed to verify the list of models. Check '
'your model id values.')
self.distributions = None
else:
self.ensemble_id = get_ensemble_id(ensemble)
ensemble = check_resource(ensemble, self.api.get_ensemble)
models = ensemble['object']['models']
self.distributions = ensemble['object'].get('distributions', None)
self.model_ids = models
self.fields = self.all_model_fields()
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [models[index:(index + max_models)] for index
in range(0, number_of_models, max_models)]
if len(self.models_splits) == 1:
models = [retrieve_resource(self.api, model_id,
query_string=ONLY_MODEL)
for model_id in self.models_splits[0]]
self.multi_model = MultiModel(models, self.api)
def __init__(self, ensemble, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.ensemble_id = None
if isinstance(ensemble, list):
try:
models = [get_model_id(model) for model in ensemble]
except ValueError:
raise ValueError('Failed to verify the list of models. Check '
'your model id values.')
self.distributions = None
else:
self.ensemble_id = get_ensemble_id(ensemble)
ensemble = check_resource(ensemble, self.api.get_ensemble)
models = ensemble['object']['models']
self.distributions = ensemble['object'].get('distributions', None)
self.model_ids = models
self.fields = self.all_model_fields()
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [models[index:(index + max_models)] for index
in range(0, number_of_models, max_models)]
if len(self.models_splits) == 1:
models = [retrieve_resource(self.api, model_id,
query_string=ONLY_MODEL)
for model_id in self.models_splits[0]]
self.multi_model = MultiModel(models, self.api)
def __init__(self, model, api=None):
if (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
self.resource_id = model['resource']
else:
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(error_message(model,
resource_type='model',
method='get'))
model = retrieve_model(api, self.resource_id)
if ('object' in model and isinstance(model['object'], dict)):
model = model['object']
if ('model' in model and isinstance(model['model'], dict)):
status = get_status(model)
if ('code' in status and status['code'] == FINISHED):
if 'model_fields' in model['model']:
fields = model['model']['model_fields']
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model['model']['fields']
for key in fields.keys()):
raise Exception("Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields.")
for field in fields:
field_info = model['model']['fields'][field]
fields[field]['summary'] = field_info['summary']
fields[field]['name'] = field_info['name']
else:
fields = model['model']['fields']
self.inverted_fields = invert_dictionary(fields)
self.all_inverted_fields = invert_dictionary(model['model']
['fields'])
self.tree = Tree(
model['model']['root'],
fields,
model['objective_fields'])
self.description = model['description']
self.field_importance = model['model'].get('importance',
None)
if self.field_importance:
self.field_importance = [element for element
in self.field_importance
if element[0] in fields]
self.locale = model.get('locale', DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None):
if (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
self.resource_id = model['resource']
else:
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(error_message(model,
resource_type='model',
method='get'))
model = retrieve_model(api, self.resource_id)
if ('object' in model and isinstance(model['object'], dict)):
model = model['object']
if ('model' in model and isinstance(model['model'], dict)):
status = get_status(model)
if ('code' in status and status['code'] == FINISHED):
if 'model_fields' in model['model']:
fields = model['model']['model_fields']
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model['model']['fields']
for key in fields.keys()):
raise Exception("Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields.")
for field in fields:
field_info = model['model']['fields'][field]
fields[field]['summary'] = field_info['summary']
fields[field]['name'] = field_info['name']
else:
fields = model['model']['fields']
self.inverted_fields = invert_dictionary(fields)
self.all_inverted_fields = invert_dictionary(model['model']
['fields'])
self.tree = Tree(
model['model']['root'],
fields,
model['objective_fields'])
self.description = model['description']
self.field_importance = model['model'].get('importance',
None)
if self.field_importance:
self.field_importance = [element for element
in self.field_importance
if element[0] in fields]
self.locale = model.get('locale', DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None):
if check_model_structure(model):
self.resource_id = model["resource"]
else:
# If only the model id is provided, the short version of the model
# resource is used to build a basic summary of the model
if api is None:
api = BigML()
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(api.error_message(model, resource_type="model", method="get"))
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id, query_string=query_string)
# Stored copies of the model structure might lack some necessary
# keys
if not check_model_structure(model):
model = api.get_model(self.resource_id, query_string=query_string)
if "object" in model and isinstance(model["object"], dict):
model = model["object"]
if "model" in model and isinstance(model["model"], dict):
status = get_status(model)
if "code" in status and status["code"] == FINISHED:
if "model_fields" in model["model"] or "fields" in model["model"]:
fields = model["model"].get("model_fields", model["model"].get("fields", []))
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model["model"]["fields"] for key in fields.keys()):
raise Exception(
"Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields."
)
for field in fields:
field_info = model["model"]["fields"][field]
if "summary" in field_info:
fields[field]["summary"] = field_info["summary"]
fields[field]["name"] = field_info["name"]
objective_field = model["objective_fields"]
ModelFields.__init__(self, fields, objective_id=extract_objective(objective_field))
self.description = model["description"]
self.field_importance = model["model"].get("importance", None)
if self.field_importance:
self.field_importance = [element for element in self.field_importance if element[0] in fields]
self.locale = model.get("locale", DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception(
"Cannot create the BaseModel instance. Could not" " find the 'model' key in the resource:\n\n%s" % model
)
def __init__(self, model, api=None):
if not (isinstance(model, dict) and 'resource' in model
and model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(
api.error_message(model,
resource_type='model',
method='get'))
query_string = ONLY_MODEL
model = retrieve_model(api,
self.resource_id,
query_string=query_string)
BaseModel.__init__(self, model, api=api)
if ('object' in model and isinstance(model['object'], dict)):
model = model['object']
if ('model' in model and isinstance(model['model'], dict)):
status = get_status(model)
if ('code' in status and status['code'] == FINISHED):
distribution = model['model']['distribution']['training']
self.ids_map = {}
self.tree = Tree(model['model']['root'],
self.fields,
objective_field=self.objective_field,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map)
self.terms = {}
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
if self.tree.regression:
try:
import numpy
import scipy
self.regression_ready = True
except ImportError:
self.regression_ready = False
def __init__(self, model, api=None):
if not (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(api.error_message(model,
resource_type='model',
method='get'))
query_string = ONLY_MODEL
model = retrieve_model(api, self.resource_id,
query_string=query_string)
BaseModel.__init__(self, model, api=api)
if ('object' in model and isinstance(model['object'], dict)):
model = model['object']
if ('model' in model and isinstance(model['model'], dict)):
status = get_status(model)
if ('code' in status and status['code'] == FINISHED):
distribution = model['model']['distribution']['training']
self.ids_map = {}
self.tree = Tree(
model['model']['root'],
self.fields,
objective_field=self.objective_field,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map)
self.terms = {}
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
if self.tree.regression:
try:
import numpy
import scipy
self.regression_ready = True
except ImportError:
self.regression_ready = False
def are_models_created(path, number_of_models, api):
"""Reads the model ids from the models file in the path directory
"""
model_ids = []
try:
with open("%s%smodels" % (path, os.sep)) as models_file:
for line in models_file:
model = line.strip()
try:
model_id = api.get_model_id(model)
model_ids.append(model_id)
except ValueError:
return False, model_ids
if len(model_ids) == number_of_models:
return True, model_ids
else:
return False, model_ids
except IOError:
return False, model_ids
def __init__(self, model, api=None):
if check_model_structure(model):
self.resource_id = model['resource']
else:
# If only the model id is provided, the short version of the model
# resource is used to build a basic summary of the model
if api is None:
api = BigML()
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(api.error_message(model,
resource_type='model',
method='get'))
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id,
query_string=query_string)
# Stored copies of the model structure might lack some necessary
# keys
if not check_model_structure(model):
model = api.get_model(self.resource_id,
query_string=query_string)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
if ('model_fields' in model['model'] or
'fields' in model['model']):
fields = model['model'].get('model_fields',
model['model'].get('fields',
[]))
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model['model']['fields']
for key in fields.keys()):
raise Exception("Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields.")
for field in fields:
field_info = model['model']['fields'][field]
if 'summary' in field_info:
fields[field]['summary'] = field_info['summary']
fields[field]['name'] = field_info['name']
objective_field = model['objective_fields']
ModelFields.__init__(
self, fields,
objective_id=extract_objective(objective_field))
self.description = model['description']
self.field_importance = model['model'].get('importance',
None)
if self.field_importance:
self.field_importance = [element for element
in self.field_importance
if element[0] in fields]
self.locale = model.get('locale', DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the BaseModel instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None):
"""The Model constructor can be given as first argument:
- a model structure
- a model id
- a path to a JSON file containing a model structure
"""
# the string can be a path to a JSON file
if isinstance(model, basestring):
try:
with open(model) as model_file:
model = json.load(model_file)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise ValueError("The JSON file does not seem"
" to contain a valid BigML model"
" representation.")
except IOError:
# if it is not a path, it can be a model id
self.resource_id = get_model_id(model)
if self.resource_id is None:
if model.find('model/') > -1:
raise Exception(
api.error_message(model,
resource_type='model',
method='get'))
else:
raise IOError("Failed to open the expected JSON file"
" at %s" % model)
except ValueError:
raise ValueError("Failed to interpret %s."
" JSON file expected.")
if not (isinstance(model, dict) and 'resource' in model
and model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
query_string = ONLY_MODEL
model = retrieve_resource(api,
self.resource_id,
query_string=query_string)
BaseModel.__init__(self, model, api=api)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
distribution = model['model']['distribution']['training']
self.ids_map = {}
self.tree = Tree(model['model']['root'],
self.fields,
objective_field=self.objective_id,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map)
self.terms = {}
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
if self.tree.regression:
try:
import numpy
import scipy
self.regression_ready = True
except ImportError:
self.regression_ready = False
def __init__(self, model, api=None):
if (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
self.resource_id = model['resource']
else:
# If only the model id is provided, the short version of the model
# resource is used to build a basic summary of the model
if api is None:
api = BigML()
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(error_message(model,
resource_type='model',
method='get'))
query_string = '%s;%s' % (ONLY_MODEL, EXCLUDE_ROOT)
model = retrieve_model(api, self.resource_id,
query_string=query_string)
if ('object' in model and isinstance(model['object'], dict)):
model = model['object']
if ('model' in model and isinstance(model['model'], dict)):
status = get_status(model)
if ('code' in status and status['code'] == FINISHED):
if 'model_fields' in model['model']:
fields = model['model']['model_fields']
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model['model']['fields']
for key in fields.keys()):
raise Exception("Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields.")
for field in fields:
field_info = model['model']['fields'][field]
if 'summary' in field_info:
fields[field]['summary'] = field_info['summary']
fields[field]['name'] = field_info['name']
else:
fields = model['model']['fields']
objective_field = model['objective_fields']
self.objective_field = extract_objective(objective_field)
self.uniquify_varnames(fields)
self.inverted_fields = invert_dictionary(fields)
self.all_inverted_fields = invert_dictionary(model['model']
['fields'])
self.fields = fields
self.description = model['description']
self.field_importance = model['model'].get('importance',
None)
if self.field_importance:
self.field_importance = [element for element
in self.field_importance
if element[0] in fields]
self.locale = model.get('locale', DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the BaseModel instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None):
"""The Model constructor can be given as first argument:
- a model structure
- a model id
- a path to a JSON file containing a model structure
"""
self.resource_id = None
self.ids_map = {}
self.terms = {}
# the string can be a path to a JSON file
if isinstance(model, basestring):
try:
with open(model) as model_file:
model = json.load(model_file)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise ValueError("The JSON file does not seem"
" to contain a valid BigML model"
" representation.")
except IOError:
# if it is not a path, it can be a model id
self.resource_id = get_model_id(model)
if self.resource_id is None:
if model.find('model/') > -1:
raise Exception(
api.error_message(model,
resource_type='model',
method='get'))
else:
raise IOError("Failed to open the expected JSON file"
" at %s" % model)
except ValueError:
raise ValueError("Failed to interpret %s."
" JSON file expected.")
if not (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id,
query_string=query_string)
else:
self.resource_id = get_model_id(model)
BaseModel.__init__(self, model, api=api)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
distribution = model['model']['distribution']['training']
self.tree = Tree(
model['model']['root'],
self.fields,
objective_field=self.objective_id,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
if self.tree.regression:
try:
import numpy
import scipy
self.regression_ready = True
except ImportError:
self.regression_ready = False
def __init__(self, model, api=None):
"""The Model constructor can be given as first argument:
- a model structure
- a model id
- a path to a JSON file containing a model structure
"""
self.resource_id = None
self.ids_map = {}
self.terms = {}
# the string can be a path to a JSON file
if isinstance(model, basestring):
try:
with open(model) as model_file:
model = json.load(model_file)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise ValueError(
"The JSON file does not seem" " to contain a valid BigML model" " representation."
)
except IOError:
# if it is not a path, it can be a model id
self.resource_id = get_model_id(model)
if self.resource_id is None:
if model.find("model/") > -1:
raise Exception(api.error_message(model, resource_type="model", method="get"))
else:
raise IOError("Failed to open the expected JSON file" " at %s" % model)
except ValueError:
raise ValueError("Failed to interpret %s." " JSON file expected.")
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(model, dict) and not check_model_fields(model):
# if the fields used by the model are not
# available, use only ID to retrieve it again
model = get_model_id(model)
self.resource_id = model
if not (isinstance(model, dict) and "resource" in model and model["resource"] is not None):
if api is None:
api = BigML(storage=STORAGE)
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id, query_string=query_string)
else:
self.resource_id = get_model_id(model)
BaseModel.__init__(self, model, api=api)
if "object" in model and isinstance(model["object"], dict):
model = model["object"]
if "model" in model and isinstance(model["model"], dict):
status = get_status(model)
if "code" in status and status["code"] == FINISHED:
distribution = model["model"]["distribution"]["training"]
# will store global information in the tree: regression and
# max_bins number
tree_info = {"max_bins": 0}
self.tree = Tree(
model["model"]["root"],
self.fields,
objective_field=self.objective_id,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map,
tree_info=tree_info,
)
self.tree.regression = tree_info["regression"]
if self.tree.regression:
self._max_bins = tree_info["max_bins"]
else:
raise Exception("The model isn't finished yet")
else:
raise Exception(
"Cannot create the Model instance. Could not" " find the 'model' key in the resource:\n\n%s" % model
)
if self.tree.regression:
try:
import numpy
import scipy
self.regression_ready = True
except ImportError:
self.regression_ready = False
def __init__(self, model, api=None, fields=None):
if check_model_structure(model):
self.resource_id = model['resource']
else:
# If only the model id is provided, the short version of the model
# resource is used to build a basic summary of the model
if api is None:
api = BigML()
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(api.error_message(model,
resource_type='model',
method='get'))
if fields is not None and isinstance(fields, dict):
query_string = EXCLUDE_FIELDS
else:
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id,
query_string=query_string)
# Stored copies of the model structure might lack some necessary
# keys
if not check_model_structure(model):
model = api.get_model(self.resource_id,
query_string=query_string)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
if (fields is None and ('model_fields' in model['model'] or
'fields' in model['model'])):
fields = model['model'].get('model_fields',
model['model'].get('fields',
[]))
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model['model']['fields']
for key in fields.keys()):
raise Exception("Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields.")
for field in fields:
field_info = model['model']['fields'][field]
if 'summary' in field_info:
fields[field]['summary'] = field_info['summary']
fields[field]['name'] = field_info['name']
objective_field = model['objective_fields']
ModelFields.__init__(
self, fields,
objective_id=extract_objective(objective_field))
self.description = model['description']
self.field_importance = model['model'].get('importance',
None)
if self.field_importance:
self.field_importance = [element for element
in self.field_importance
if element[0] in fields]
self.locale = model.get('locale', DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the BaseModel instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None, fields=None):
"""The Model constructor can be given as first argument:
- a model structure
- a model id
- a path to a JSON file containing a model structure
"""
self.resource_id = None
self.ids_map = {}
self.terms = {}
self.regression = False
self.boosting = None
self.class_names = None
if not hasattr(self, 'tree_class'):
self.tree_class = Tree
# the string can be a path to a JSON file
if isinstance(model, basestring):
try:
with open(model) as model_file:
model = json.load(model_file)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise ValueError("The JSON file does not seem"
" to contain a valid BigML model"
" representation.")
except IOError:
# if it is not a path, it can be a model id
self.resource_id = get_model_id(model)
if self.resource_id is None:
if model.find('model/') > -1:
raise Exception(
api.error_message(model,
resource_type='model',
method='get'))
else:
raise IOError("Failed to open the expected JSON file"
" at %s" % model)
except ValueError:
raise ValueError("Failed to interpret %s."
" JSON file expected.")
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(model, dict) and \
not fields and \
not check_model_fields(model):
# if the fields used by the model are not
# available, use only ID to retrieve it again
model = get_model_id(model)
self.resource_id = model
if not (isinstance(model, dict) and 'resource' in model and
model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
if fields is not None and isinstance(fields, dict):
query_string = EXCLUDE_FIELDS
else:
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id,
query_string=query_string)
else:
self.resource_id = get_model_id(model)
BaseModel.__init__(self, model, api=api, fields=fields)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
# boosting models are to be handled using the BoostedTree
# class
if model.get("boosted_ensemble"):
self.boosting = model.get('boosting', False)
if self.boosting == {}:
self.boosting = False
self.regression = \
not self.boosting and \
self.fields[self.objective_id]['optype'] == 'numeric' \
or (self.boosting and \
self.boosting.get("objective_class") is None)
if self.boosting:
self.tree = BoostedTree(
model['model']['root'],
self.fields,
objective_field=self.objective_id)
else:
distribution = model['model']['distribution']['training']
# will store global information in the tree: regression and
# max_bins number
tree_info = {'max_bins': 0}
self.tree = self.tree_class(
model['model']['root'],
self.fields,
objective_field=self.objective_id,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map,
tree_info=tree_info)
self.tree.regression = tree_info['regression']
if self.tree.regression:
try:
import numpy
import scipy
self._max_bins = tree_info['max_bins']
self.regression_ready = True
except ImportError:
self.regression_ready = False
else:
root_dist = self.tree.distribution
self.class_names = sorted([category[0]
for category in root_dist])
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None, fields=None, checked=True):
check_fn = check_local_but_fields if fields is not None else \
check_local_info
if isinstance(model, dict) and (checked or check_fn(model)):
self.resource_id = model['resource']
else:
# If only the model id is provided, the short version of the model
# resource is used to build a basic summary of the model
self.api = get_api_connection(api)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise Exception(self.api.error_message(model,
resource_type='model',
method='get'))
if fields is not None and isinstance(fields, dict):
query_string = EXCLUDE_FIELDS
else:
query_string = ONLY_MODEL
model = retrieve_resource(api, self.resource_id,
query_string=query_string,
no_check_fields=fields is not None)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
if (fields is None and ('model_fields' in model['model'] or
'fields' in model['model'])):
fields = model['model'].get('model_fields',
model['model'].get('fields',
[]))
# model_fields doesn't contain the datetime fields
fields.update(datetime_fields(model['model'].get('fields',
{})))
# pagination or exclusion might cause a field not to
# be in available fields dict
if not all(key in model['model']['fields']
for key in list(fields.keys())):
raise Exception("Some fields are missing"
" to generate a local model."
" Please, provide a model with"
" the complete list of fields.")
for field in fields:
field_info = model['model']['fields'][field]
if 'summary' in field_info:
fields[field]['summary'] = field_info['summary']
fields[field]['name'] = field_info['name']
objective_field = model['objective_fields']
missing_tokens = model['model'].get('missing_tokens')
ModelFields.__init__(
self, fields, objective_id=extract_objective(objective_field),
missing_tokens=missing_tokens)
self.description = model['description']
self.field_importance = model['model'].get('importance',
None)
if self.field_importance:
self.field_importance = [element for element
in self.field_importance
if element[0] in fields]
self.locale = model.get('locale', DEFAULT_LOCALE)
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the BaseModel instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, model, api=None, fields=None):
"""The Model constructor can be given as first argument:
- a model structure
- a model id
- a path to a JSON file containing a model structure
"""
self.resource_id = None
self.ids_map = {}
self.terms = {}
self.regression = False
self.boosting = None
self.class_names = None
if not hasattr(self, 'tree_class'):
self.tree_class = Tree
# the string can be a path to a JSON file
if isinstance(model, basestring):
try:
with open(model) as model_file:
model = json.load(model_file)
self.resource_id = get_model_id(model)
if self.resource_id is None:
raise ValueError("The JSON file does not seem"
" to contain a valid BigML model"
" representation.")
except IOError:
# if it is not a path, it can be a model id
self.resource_id = get_model_id(model)
if self.resource_id is None:
if model.find('model/') > -1:
raise Exception(
api.error_message(model,
resource_type='model',
method='get'))
else:
raise IOError("Failed to open the expected JSON file"
" at %s" % model)
except ValueError:
raise ValueError("Failed to interpret %s."
" JSON file expected.")
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(model, dict) and \
not fields and \
not check_model_fields(model):
# if the fields used by the model are not
# available, use only ID to retrieve it again
model = get_model_id(model)
self.resource_id = model
if not (isinstance(model, dict) and 'resource' in model
and model['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
if fields is not None and isinstance(fields, dict):
query_string = EXCLUDE_FIELDS
else:
query_string = ONLY_MODEL
model = retrieve_resource(api,
self.resource_id,
query_string=query_string)
else:
self.resource_id = get_model_id(model)
BaseModel.__init__(self, model, api=api, fields=fields)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
# boosting models are to be handled using the BoostedTree
# class
if model.get("boosted_ensemble"):
self.boosting = model.get('boosting', False)
if self.boosting == {}:
self.boosting = False
self.regression = \
not self.boosting and \
self.fields[self.objective_id]['optype'] == 'numeric' \
or (self.boosting and \
self.boosting.get("objective_class") is None)
if self.boosting:
self.tree = BoostedTree(model['model']['root'],
self.fields,
objective_field=self.objective_id)
else:
distribution = model['model']['distribution']['training']
# will store global information in the tree: regression and
# max_bins number
tree_info = {'max_bins': 0}
self.tree = self.tree_class(
model['model']['root'],
self.fields,
objective_field=self.objective_id,
root_distribution=distribution,
parent_id=None,
ids_map=self.ids_map,
tree_info=tree_info)
self.tree.regression = tree_info['regression']
if self.tree.regression:
try:
import numpy
import scipy
self._max_bins = tree_info['max_bins']
self.regression_ready = True
except ImportError:
self.regression_ready = False
else:
root_dist = self.tree.distribution
self.class_names = sorted(
[category[0] for category in root_dist])
else:
raise Exception("The model isn't finished yet")
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:\n\n%s" %
model)
def __init__(self, ensemble, api=None, max_models=None, cache_get=None):
self.model_splits = []
self.multi_model = None
self.api = get_api_connection(api)
self.fields = None
self.class_names = None
if use_cache(cache_get):
# using a cache to store the model attributes
self.__dict__ = load(get_ensemble_id(ensemble), cache_get)
self.api = get_api_connection(api)
if len(self.models_splits) == 1:
# retrieve the models from a cache get function
try:
models = [
Model(model_id, cache_get=cache_get)
for model_id in self.models_splits[0]
]
except Exception as exc:
raise Exception('Error while calling the user-given'
' function %s: %s' %
(cache_get.__name__, str(exc)))
self.multi_model = MultiModel(models,
self.api,
fields=self.fields,
class_names=self.class_names,
cache_get=cache_get)
return
self.resource_id = None
self.objective_id = None
self.distributions = None
self.distribution = None
self.boosting = None
self.boosting_offsets = None
self.cache_get = None
self.regression = False
self.importance = {}
query_string = ONLY_MODEL
no_check_fields = False
self.input_fields = []
if isinstance(ensemble, list):
if all([isinstance(model, Model) for model in ensemble]):
models = ensemble
self.model_ids = [
local_model.resource_id for local_model in models
]
else:
try:
models = [get_model_id(model) for model in ensemble]
self.model_ids = models
except ValueError as exc:
raise ValueError('Failed to verify the list of models.'
' Check your model id values: %s' %
str(exc))
else:
ensemble = self.get_ensemble_resource(ensemble)
self.resource_id = get_ensemble_id(ensemble)
if not check_local_but_fields(ensemble):
# avoid checking fields because of old ensembles
ensemble = retrieve_resource(self.api,
self.resource_id,
no_check_fields=True)
if ensemble['object'].get('type') == BOOSTING:
self.boosting = ensemble['object'].get('boosting')
models = ensemble['object']['models']
self.distributions = ensemble['object'].get('distributions', [])
self.importance = ensemble['object'].get('importance', [])
self.model_ids = models
# new ensembles have the fields structure
if ensemble['object'].get('ensemble'):
self.fields = ensemble['object'].get( \
'ensemble', {}).get("fields")
self.objective_id = ensemble['object'].get("objective_field")
query_string = EXCLUDE_FIELDS
no_check_fields = True
self.input_fields = ensemble['object'].get('input_fields')
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [
models[index:(index + max_models)]
for index in range(0, number_of_models, max_models)
]
if len(self.models_splits) == 1:
if not isinstance(models[0], Model):
if use_cache(cache_get):
# retrieve the models from a cache get function
try:
models = [
Model(model_id, cache_get=cache_get)
for model_id in self.models_splits[0]
]
self.cache_get = cache_get
except Exception as exc:
raise Exception('Error while calling the user-given'
' function %s: %s' %
(cache_get.__name__, str(exc)))
else:
models = [retrieve_resource( \
self.api,
model_id,
query_string=query_string,
no_check_fields=no_check_fields)
for model_id in self.models_splits[0]]
model = models[0]
else:
# only retrieving first model
self.cache_get = cache_get
if not isinstance(models[0], Model):
if use_cache(cache_get):
# retrieve the models from a cache get function
try:
model = Model(self.models_splits[0][0],
cache_get=cache_get)
self.cache_get = cache_get
except Exception as exc:
raise Exception('Error while calling the user-given'
' function %s: %s' %
(cache_get.__name__, str(exc)))
else:
model = retrieve_resource( \
self.api,
self.models_splits[0][0],
query_string=query_string,
no_check_fields=no_check_fields)
models = [model]
if self.distributions is None:
try:
self.distributions = []
for model in models:
self.distributions.append(
{'training': model.root_distribution})
except AttributeError:
self.distributions = [
model['object']['model']['distribution']
for model in models
]
if self.boosting is None:
self._add_models_attrs(model, max_models)
if self.fields is None:
self.fields, self.objective_id = self.all_model_fields(
max_models=max_models)
if self.fields:
add_distribution(self)
self.regression = \
self.fields[self.objective_id].get('optype') == NUMERIC
if self.boosting:
self.boosting_offsets = ensemble['object'].get('initial_offset',
0) \
if self.regression else dict(ensemble['object'].get( \
'initial_offsets', []))
if not self.regression:
try:
objective_field = self.fields[self.objective_id]
categories = objective_field['summary']['categories']
classes = [category[0] for category in categories]
except (AttributeError, KeyError):
classes = set()
for distribution in self.distributions:
for category in distribution['training']['categories']:
classes.add(category[0])
self.class_names = sorted(classes)
self.objective_categories = [category for \
category, _ in self.fields[self.objective_id][ \
"summary"]["categories"]]
ModelFields.__init__( \
self, self.fields,
objective_id=self.objective_id)
if len(self.models_splits) == 1:
self.multi_model = MultiModel(models,
self.api,
fields=self.fields,
class_names=self.class_names)
def __init__(self, model, api=None, fields=None, cache_get=None):
"""The Model constructor can be given as first argument:
- a model structure
- a model id
- a path to a JSON file containing a model structure
"""
if use_cache(cache_get):
# using a cache to store the model attributes
self.__dict__ = load(get_model_id(model), cache_get)
return
self.resource_id = None
self.ids_map = {}
self.terms = {}
self.regression = False
self.boosting = None
self.class_names = None
api = get_api_connection(api)
# retrieving model information from
self.resource_id, model = get_resource_dict( \
model, "model", api=api, no_check_fields=fields is not None)
if 'object' in model and isinstance(model['object'], dict):
model = model['object']
if 'model' in model and isinstance(model['model'], dict):
status = get_status(model)
if 'code' in status and status['code'] == FINISHED:
# fill boosting info before creating modelfields
if model.get("boosted_ensemble"):
self.boosting = model.get('boosting', False)
if self.boosting == {}:
self.boosting = False
self.input_fields = model["input_fields"]
BaseModel.__init__(self, model, api=api, fields=fields)
root = model['model']['root']
self.weighted = "weighted_objective_summary" in root
if self.boosting:
# build boosted tree
self.tree = b.build_boosting_tree( \
model['model']['root'])
elif self.regression:
self.root_distribution = model['model'][ \
'distribution']['training']
# build regression tree
self.tree = r.build_regression_tree(root, \
distribution=self.root_distribution, \
weighted=self.weighted)
else:
# build classification tree
self.root_distribution = model['model'][\
'distribution']['training']
self.laplacian_term = laplacian_term( \
extract_distribution(self.root_distribution)[1],
self.weighted)
self.tree = c.build_classification_tree( \
model['model']['root'], \
distribution=self.root_distribution, \
weighted=self.weighted)
self.class_names = sorted( \
[category[0] for category in \
self.root_distribution["categories"]])
self.objective_categories = [category for \
category, _ in self.fields[self.objective_id][ \
"summary"]["categories"]]
if self.boosting:
self.tree_type = BOOSTING
self.offsets = b.OFFSETS
elif self.regression:
self.tree_type = REGRESSION
self.offsets = r.OFFSETS[str(self.weighted)]
else:
self.tree_type = CLASSIFICATION
self.offsets = c.OFFSETS[str(self.weighted)]
else:
raise Exception("Cannot create the Model instance."
" Only correctly finished models can be"
" used. The model status is currently:"
" %s\n" % STATUSES[status['code']])
else:
raise Exception("Cannot create the Model instance. Could not"
" find the 'model' key in the resource:"
"\n\n%s" % model)
