def get_resource_dict(resource, resource_type, api=None):
"""Extracting the resource JSON info as a dict from the first argument of
the local object constructors, that can be:
- the path to a file that contains the JSON
- the ID of the resource
- the resource dict itself
"""
if api is None:
api = BigML(storage=STORAGE)
get_id = ID_GETTERS[resource_type]
resource_id = None
# the string can be a path to a JSON file
if isinstance(resource, basestring):
try:
with open(resource) as resource_file:
resource = json.load(resource_file)
resource_id = get_id(resource)
if resource_id is None:
raise ValueError("The JSON file does not seem"
" to contain a valid BigML %s"
" representation." % resource_type)
except IOError:
# if it is not a path, it can be a model id
resource_id = get_id(resource)
if resource_id is None:
if resource.find("%s/" % resource_type) > -1:
raise Exception(
api.error_message(resource,
resource_type=resource_type,
method="get"))
else:
raise IOError("Failed to open the expected JSON file"
" at %s." % resource)
except ValueError:
raise ValueError("Failed to interpret %s."
" JSON file expected." % resource)
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(resource, dict) and \
not check_model_fields(resource):
# if the fields used by the model are not
# available, use only ID to retrieve it again
resource = get_id(resource)
resource_id = resource
if not (isinstance(resource, dict) and 'resource' in resource and
resource['resource'] is not None):
query_string = ONLY_MODEL
resource = retrieve_resource(api, resource_id,
query_string=query_string)
else:
resource_id = get_id(resource)
return resource_id, resource
def check_local_info(model):
"""Whether the information in `model` is enough to use it locally
"""
try:
return check_local_but_fields(model) and \
check_model_fields(model)
except Exception:
return False
def retrieve_resource(api, resource_id, query_string=ONLY_MODEL,
no_check_fields=False):
""" Retrieves resource info either from a local repo or
from the remote server
"""
if api.storage is not None:
try:
stored_resource = "%s%s%s" % (api.storage, os.sep,
resource_id.replace("/", "_"))
with open(stored_resource) as resource_file:
resource = json.loads(resource_file.read())
# we check that the stored resource has enough fields information
# for local predictions to work. Otherwise we should retrieve it.
if no_check_fields or check_model_fields(resource):
return resource
except ValueError:
raise ValueError("The file %s contains no JSON")
except IOError:
pass
api_getter = api.getters[get_resource_type(resource_id)]
resource = check_resource(resource_id, api_getter, query_string)
return resource
def __init__(self, time_series, api=None):
self.resource_id = None
self.input_fields = []
self.objective_fields = []
self.all_numeric_objectives = False
self.period = 1
self.ets_models = {}
self.error = None
self.damped_trend = None
self.seasonality = None
self.trend = None
self.time_range = {}
self.field_parameters = {}
self._forecast = []
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(time_series, dict) and \
not check_model_fields(time_series):
# if the fields used by the logistic regression are not
# available, use only ID to retrieve it again
time_series = get_time_series_id( \
time_series)
self.resource_id = time_series
if not (isinstance(time_series, dict)
and 'resource' in time_series and
time_series['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_time_series_id(time_series)
if self.resource_id is None:
raise Exception(
api.error_message(time_series,
resource_type='time_series',
method='get'))
query_string = ONLY_MODEL
time_series = retrieve_resource(
api, self.resource_id, query_string=query_string)
else:
self.resource_id = get_time_series_id(time_series)
if 'object' in time_series and \
isinstance(time_series['object'], dict):
time_series = time_series['object']
try:
self.input_fields = time_series.get("input_fields", [])
self._forecast = time_series.get("forecast")
self.objective_fields = time_series.get(
"objective_fields", [])
objective_field = time_series['objective_field'] if \
time_series.get('objective_field') else \
time_series['objective_fields']
except KeyError:
raise ValueError("Failed to find the time series expected "
"JSON structure. Check your arguments.")
if 'time_series' in time_series and \
isinstance(time_series['time_series'], dict):
status = get_status(time_series)
if 'code' in status and status['code'] == FINISHED:
time_series_info = time_series['time_series']
fields = time_series_info.get('fields', {})
self.fields = fields
if not self.input_fields:
self.input_fields = [ \
field_id for field_id, _ in
sorted(self.fields.items(),
key=lambda x: x[1].get("column_number"))]
self.all_numeric_objectives = time_series_info.get( \
'all_numeric_objectives')
self.period = time_series_info.get('period', 1)
self.ets_models = time_series_info.get('ets_models', {})
self.error = time_series_info.get('error')
self.damped_trend = time_series_info.get('damped_trend')
self.seasonality = time_series_info.get('seasonality')
self.trend = time_series_info.get('trend')
self.time_range = time_series_info.get('time_range')
self.field_parameters = time_series_info.get( \
'field_parameters', {})
objective_id = extract_objective(objective_field)
ModelFields.__init__(
self, fields,
objective_id=objective_id)
else:
raise Exception("The time series isn't finished yet")
else:
raise Exception("Cannot create the TimeSeries instance."
" Could not find the 'time_series' key"
" in the resource:\n\n%s" %
time_series)
def __init__(self, logistic_regression, api=None):
self.resource_id = None
self.input_fields = []
self.term_forms = {}
self.tag_clouds = {}
self.term_analysis = {}
self.items = {}
self.item_analysis = {}
self.categories = {}
self.coefficients = {}
self.data_field_types = {}
self.field_codings = {}
self.numeric_fields = {}
self.bias = None
self.missing_numerics = None
self.c = None
self.eps = None
self.lr_normalize = None
self.balance_fields = None
self.regularization = None
old_coefficients = False
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(logistic_regression, dict) and \
not check_model_fields(logistic_regression):
# if the fields used by the logistic regression are not
# available, use only ID to retrieve it again
logistic_regression = get_logistic_regression_id( \
logistic_regression)
self.resource_id = logistic_regression
if not (isinstance(logistic_regression, dict)
and 'resource' in logistic_regression and
logistic_regression['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_logistic_regression_id(logistic_regression)
if self.resource_id is None:
raise Exception(
api.error_message(logistic_regression,
resource_type='logistic_regression',
method='get'))
query_string = ONLY_MODEL
logistic_regression = retrieve_resource(
api, self.resource_id, query_string=query_string)
else:
self.resource_id = get_logistic_regression_id(logistic_regression)
if 'object' in logistic_regression and \
isinstance(logistic_regression['object'], dict):
logistic_regression = logistic_regression['object']
try:
self.input_fields = logistic_regression.get("input_fields", [])
self.dataset_field_types = logistic_regression.get(
"dataset_field_types", {})
objective_field = logistic_regression['objective_fields'] if \
logistic_regression['objective_fields'] else \
logistic_regression['objective_field']
except KeyError:
raise ValueError("Failed to find the logistic regression expected "
"JSON structure. Check your arguments.")
if 'logistic_regression' in logistic_regression and \
isinstance(logistic_regression['logistic_regression'], dict):
status = get_status(logistic_regression)
if 'code' in status and status['code'] == FINISHED:
logistic_regression_info = logistic_regression[ \
'logistic_regression']
fields = logistic_regression_info.get('fields', {})
if not self.input_fields:
self.input_fields = [ \
field_id for field_id, _ in
sorted(self.fields.items(),
key=lambda x: x[1].get("column_number"))]
self.coefficients.update(logistic_regression_info.get( \
'coefficients', []))
if not isinstance(self.coefficients.values()[0][0], list):
old_coefficients = True
self.bias = logistic_regression_info.get('bias', True)
self.c = logistic_regression_info.get('c')
self.eps = logistic_regression_info.get('eps')
self.lr_normalize = logistic_regression_info.get('normalize')
self.balance_fields = logistic_regression_info.get( \
'balance_fields')
self.regularization = logistic_regression_info.get( \
'regularization')
self.field_codings = logistic_regression_info.get( \
'field_codings', {})
# old models have no such attribute, so we set it to False in
# this case
self.missing_numerics = logistic_regression_info.get( \
'missing_numerics', False)
objective_id = extract_objective(objective_field)
for field_id, field in fields.items():
if field['optype'] == 'text':
self.term_forms[field_id] = {}
self.term_forms[field_id].update(
field['summary']['term_forms'])
self.tag_clouds[field_id] = []
self.tag_clouds[field_id] = [tag for [tag, _] in field[
'summary']['tag_cloud']]
self.term_analysis[field_id] = {}
self.term_analysis[field_id].update(
field['term_analysis'])
if field['optype'] == 'items':
self.items[field_id] = []
self.items[field_id] = [item for item, _ in \
field['summary']['items']]
self.item_analysis[field_id] = {}
self.item_analysis[field_id].update(
field['item_analysis'])
if field['optype'] == 'categorical':
self.categories[field_id] = [category for \
[category, _] in field['summary']['categories']]
if self.missing_numerics and field['optype'] == 'numeric':
self.numeric_fields[field_id] = True
ModelFields.__init__(
self, fields,
objective_id=objective_id)
self.field_codings = logistic_regression_info.get( \
'field_codings', {})
self.format_field_codings()
for field_id in self.field_codings:
if field_id not in fields and \
field_id in self.inverted_fields:
self.field_codings.update( \
{self.inverted_fields[field_id]: \
self.field_codings[field_id]})
del self.field_codings[field_id]
if old_coefficients:
self.map_coefficients()
else:
raise Exception("The logistic regression isn't finished yet")
else:
raise Exception("Cannot create the LogisticRegression instance."
" Could not find the 'logistic_regression' key"
" in the resource:\n\n%s" %
logistic_regression)
def __init__(self, anomaly, api=None):
self.resource_id = None
self.sample_size = None
self.input_fields = None
self.mean_depth = None
self.expected_mean_depth = None
self.iforest = None
self.top_anomalies = None
self.id_fields = []
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(anomaly, dict) and \
not check_model_fields(anomaly):
# if the fields used by the anomaly detector are not
# available, use only ID to retrieve it again
anomaly = get_anomaly_id(anomaly)
self.resource_id = anomaly
if not (isinstance(anomaly, dict) and 'resource' in anomaly and
anomaly['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_anomaly_id(anomaly)
if self.resource_id is None:
raise Exception(api.error_message(anomaly,
resource_type='anomaly',
method='get'))
query_string = ONLY_MODEL
anomaly = retrieve_resource(api, self.resource_id,
query_string=query_string)
else:
self.resource_id = get_anomaly_id(anomaly)
if 'object' in anomaly and isinstance(anomaly['object'], dict):
anomaly = anomaly['object']
self.sample_size = anomaly.get('sample_size')
self.input_fields = anomaly.get('input_fields')
self.id_fields = anomaly.get('id_fields', [])
if 'model' in anomaly and isinstance(anomaly['model'], dict):
ModelFields.__init__(self, anomaly['model'].get('fields'))
if ('top_anomalies' in anomaly['model'] and
isinstance(anomaly['model']['top_anomalies'], list)):
self.mean_depth = anomaly['model'].get('mean_depth')
status = get_status(anomaly)
if 'code' in status and status['code'] == FINISHED:
self.expected_mean_depth = None
if self.mean_depth is None or self.sample_size is None:
raise Exception("The anomaly data is not complete. "
"Score will"
" not be available")
else:
default_depth = (
2 * (DEPTH_FACTOR + \
math.log(self.sample_size - 1) - \
(float(self.sample_size - 1) / self.sample_size)))
self.expected_mean_depth = min(self.mean_depth,
default_depth)
iforest = anomaly['model'].get('trees', [])
if iforest:
self.iforest = [
AnomalyTree(anomaly_tree['root'], self.fields)
for anomaly_tree in iforest]
self.top_anomalies = anomaly['model']['top_anomalies']
else:
raise Exception("The anomaly isn't finished yet")
else:
raise Exception("Cannot create the Anomaly instance. Could not"
" find the 'top_anomalies' key in the"
" resource:\n\n%s" % anomaly['model'].keys())
def __init__(self, cluster, api=None):
self.resource_id = None
self.centroids = None
self.cluster_global = None
self.total_ss = None
self.within_ss = None
self.between_ss = None
self.ratio_ss = None
self.critical_value = None
self.default_numeric_value = None
self.k = None
self.summary_fields = []
self.scales = {}
self.term_forms = {}
self.tag_clouds = {}
self.term_analysis = {}
self.item_analysis = {}
self.items = {}
self.datasets = {}
self.api = api
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(cluster, dict) and \
not check_model_fields(cluster):
# if the fields used by the cluster are not
# available, use only ID to retrieve it again
cluster = get_cluster_id(cluster)
self.resource_id = cluster
if not (isinstance(cluster, dict) and 'resource' in cluster
and cluster['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.api = api
self.resource_id = get_cluster_id(cluster)
if self.resource_id is None:
raise Exception(
api.error_message(cluster,
resource_type='cluster',
method='get'))
query_string = ONLY_MODEL
cluster = retrieve_resource(api,
self.resource_id,
query_string=query_string)
else:
self.resource_id = get_cluster_id(cluster)
if 'object' in cluster and isinstance(cluster['object'], dict):
cluster = cluster['object']
if 'clusters' in cluster and isinstance(cluster['clusters'], dict):
status = get_status(cluster)
if 'code' in status and status['code'] == FINISHED:
self.default_numeric_value = cluster.get( \
"default_numeric_value")
self.summary_fields = cluster.get("summary_fields", [])
self.datasets = cluster.get("cluster_datasets", {})
the_clusters = cluster['clusters']
cluster_global = the_clusters.get('global')
clusters = the_clusters['clusters']
self.centroids = [Centroid(centroid) for centroid in clusters]
self.cluster_global = cluster_global
if cluster_global:
self.cluster_global = Centroid(cluster_global)
# "global" has no "name" and "count" then we set them
self.cluster_global.name = GLOBAL_CLUSTER_LABEL
self.cluster_global.count = \
self.cluster_global.distance['population']
self.total_ss = the_clusters.get('total_ss')
self.within_ss = the_clusters.get('within_ss')
if not self.within_ss:
self.within_ss = sum(centroid.distance['sum_squares']
for centroid in self.centroids)
self.between_ss = the_clusters.get('between_ss')
self.ratio_ss = the_clusters.get('ratio_ss')
self.critical_value = cluster.get('critical_value', None)
self.k = cluster.get('k')
self.scales.update(cluster['scales'])
self.term_forms = {}
self.tag_clouds = {}
self.term_analysis = {}
fields = cluster['clusters']['fields']
summary_fields = cluster['summary_fields']
for field_id in summary_fields:
try:
del fields[field_id]
except KeyError:
# clusters retrieved from API will only contain
# model fields
pass
for field_id, field in fields.items():
if field['optype'] == 'text':
self.term_forms[field_id] = {}
self.term_forms[field_id].update(
field['summary']['term_forms'])
self.tag_clouds[field_id] = {}
self.tag_clouds[field_id].update(
field['summary']['tag_cloud'])
self.term_analysis[field_id] = {}
self.term_analysis[field_id].update(
field['term_analysis'])
if field['optype'] == 'items':
self.items[field_id] = {}
self.items[field_id].update(
dict(field['summary']['items']))
self.item_analysis[field_id] = {}
self.item_analysis[field_id].update(
field['item_analysis'])
ModelFields.__init__(self, fields)
if not all(
[field_id in self.fields for field_id in self.scales]):
raise Exception("Some fields are missing"
" to generate a local cluster."
" Please, provide a cluster with"
" the complete list of fields.")
else:
raise Exception("The cluster isn't finished yet")
else:
raise Exception("Cannot create the Cluster instance. Could not"
" find the 'clusters' key in the resource:\n\n%s" %
cluster)
def __init__(self, cluster, api=None):
self.resource_id = None
self.centroids = None
self.cluster_global = None
self.total_ss = None
self.within_ss = None
self.between_ss = None
self.ratio_ss = None
self.critical_value = None
self.default_numeric_value = None
self.k = None
self.summary_fields = []
self.scales = {}
self.term_forms = {}
self.tag_clouds = {}
self.term_analysis = {}
self.item_analysis = {}
self.items = {}
self.datasets = {}
self.api = api
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(cluster, dict) and \
not check_model_fields(cluster):
# if the fields used by the cluster are not
# available, use only ID to retrieve it again
cluster = get_cluster_id(cluster)
self.resource_id = cluster
if not (isinstance(cluster, dict) and 'resource' in cluster and
cluster['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.api = api
self.resource_id = get_cluster_id(cluster)
if self.resource_id is None:
raise Exception(api.error_message(cluster,
resource_type='cluster',
method='get'))
query_string = ONLY_MODEL
cluster = retrieve_resource(api, self.resource_id,
query_string=query_string)
else:
self.resource_id = get_cluster_id(cluster)
if 'object' in cluster and isinstance(cluster['object'], dict):
cluster = cluster['object']
if 'clusters' in cluster and isinstance(cluster['clusters'], dict):
status = get_status(cluster)
if 'code' in status and status['code'] == FINISHED:
self.default_numeric_value = cluster.get( \
"default_numeric_value")
self.summary_fields = cluster.get("summary_fields", [])
self.datasets = cluster.get("cluster_datasets", {})
the_clusters = cluster['clusters']
cluster_global = the_clusters.get('global')
clusters = the_clusters['clusters']
self.centroids = [Centroid(centroid) for centroid in clusters]
self.cluster_global = cluster_global
if cluster_global:
self.cluster_global = Centroid(cluster_global)
# "global" has no "name" and "count" then we set them
self.cluster_global.name = GLOBAL_CLUSTER_LABEL
self.cluster_global.count = \
self.cluster_global.distance['population']
self.total_ss = the_clusters.get('total_ss')
self.within_ss = the_clusters.get('within_ss')
if not self.within_ss:
self.within_ss = sum(centroid.distance['sum_squares'] for
centroid in self.centroids)
self.between_ss = the_clusters.get('between_ss')
self.ratio_ss = the_clusters.get('ratio_ss')
self.critical_value = cluster.get('critical_value', None)
self.k = cluster.get('k')
self.scales.update(cluster['scales'])
self.term_forms = {}
self.tag_clouds = {}
self.term_analysis = {}
fields = cluster['clusters']['fields']
summary_fields = cluster['summary_fields']
for field_id in summary_fields:
del fields[field_id]
for field_id, field in fields.items():
if field['optype'] == 'text':
self.term_forms[field_id] = {}
self.term_forms[field_id].update(field[
'summary']['term_forms'])
self.tag_clouds[field_id] = {}
self.tag_clouds[field_id].update(field[
'summary']['tag_cloud'])
self.term_analysis[field_id] = {}
self.term_analysis[field_id].update(
field['term_analysis'])
if field['optype'] == 'items':
self.items[field_id] = {}
self.items[field_id].update(
dict(field['summary']['items']))
self.item_analysis[field_id] = {}
self.item_analysis[field_id].update(
field['item_analysis'])
ModelFields.__init__(self, fields)
if not all([field_id in self.fields for
field_id in self.scales]):
raise Exception("Some fields are missing"
" to generate a local cluster."
" Please, provide a cluster with"
" the complete list of fields.")
else:
raise Exception("The cluster isn't finished yet")
else:
raise Exception("Cannot create the Cluster instance. Could not"
" find the 'clusters' key in the resource:\n\n%s" %
cluster)
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):
"""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):
"""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