def __init__(self, deepnet, api=None):
"""The Deepnet constructor can be given as first argument:
- a deepnet structure
- a deepnet id
- a path to a JSON file containing a deepnet structure
"""
self.resource_id = None
self.regression = False
self.network = None
self.networks = None
self.input_fields = []
self.class_names = []
self.preprocess = []
self.optimizer = None
self.missing_numerics = False
self.api = get_api_connection(api)
self.resource_id, deepnet = get_resource_dict( \
deepnet, "deepnet", api=self.api)
if 'object' in deepnet and isinstance(deepnet['object'], dict):
deepnet = deepnet['object']
self.input_fields = deepnet['input_fields']
if 'deepnet' in deepnet and isinstance(deepnet['deepnet'], dict):
status = get_status(deepnet)
objective_field = deepnet['objective_fields']
deepnet = deepnet['deepnet']
if 'code' in status and status['code'] == FINISHED:
self.fields = deepnet['fields']
missing_tokens = deepnet.get('missing_tokens')
ModelFields.__init__(
self, self.fields,
objective_id=extract_objective(objective_field),
terms=True, categories=True, missing_tokens=missing_tokens)
self.regression = \
self.fields[self.objective_id]['optype'] == NUMERIC
if not self.regression:
self.class_names = [category for category, _ in \
self.fields[self.objective_id][ \
'summary']['categories']]
self.class_names.sort()
# order matters
self.objective_categories = [category for \
category, _ in self.fields[self.objective_id][ \
"summary"]["categories"]]
self.missing_numerics = deepnet.get('missing_numerics', False)
if 'network' in deepnet:
network = deepnet['network']
self.network = network
self.networks = network.get('networks', [])
self.preprocess = network.get('preprocess')
self.optimizer = network.get('optimizer', {})
else:
raise Exception("The deepnet isn't finished yet")
else:
raise Exception("Cannot create the Deepnet instance. Could not"
" find the 'deepnet' key in the resource:\n\n%s" %
deepnet)
def __init__(self, linear_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.bias = None
self.xtx_inverse = []
self.mean_squared_error = None
self.number_of_parameters = None
self.number_of_samples = None
self.resource_id, linear_regression = get_resource_dict( \
linear_regression, "linearregression", api=api)
if 'object' in linear_regression and \
isinstance(linear_regression['object'], dict):
linear_regression = linear_regression['object']
try:
self.input_fields = linear_regression.get("input_fields", [])
self.dataset_field_types = linear_regression.get(
"dataset_field_types", {})
self.weight_field = linear_regression.get("weight_field")
objective_field = linear_regression['objective_fields'] if \
linear_regression['objective_fields'] else \
linear_regression['objective_field']
except KeyError:
raise ValueError("Failed to find the linear regression expected "
"JSON structure. Check your arguments.")
if 'linear_regression' in linear_regression and \
isinstance(linear_regression['linear_regression'], dict):
status = get_status(linear_regression)
if 'code' in status and status['code'] == FINISHED:
linear_regression_info = linear_regression[ \
'linear_regression']
fields = linear_regression_info.get('fields', {})
if not self.input_fields:
self.input_fields = [ \
field_id for field_id, _ in
sorted(fields.items(),
key=lambda x: x[1].get("column_number"))]
self.coeff_ids = self.input_fields[:]
self.coefficients = linear_regression_info.get( \
'coefficients', [])
self.bias = linear_regression_info.get('bias', True)
self.field_codings = linear_regression_info.get( \
'field_codings', {})
self.number_of_parameters = linear_regression_info.get( \
"number_of_parameters")
missing_tokens = linear_regression_info.get("missing_tokens")
objective_id = extract_objective(objective_field)
ModelFields.__init__(self,
fields,
objective_id=objective_id,
terms=True,
categories=True,
numerics=True,
missing_tokens=missing_tokens)
self.field_codings = linear_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]
stats = linear_regression_info["stats"]
if STATS and stats is not None and \
stats.get("xtx_inverse") is not None:
self.xtx_inverse = stats["xtx_inverse"][:]
self.mean_squared_error = stats["mean_squared_error"]
self.number_of_samples = stats["number_of_samples"]
# to be used in predictions
self.t_crit = student_t.interval( \
CONFIDENCE,
self.number_of_samples - self.number_of_parameters)[1]
self.xtx_inverse = list( \
np.linalg.inv(np.array(self.xtx_inverse)))
else:
raise Exception("The linear regression isn't finished yet")
else:
raise Exception("Cannot create the LinearRegression instance."
" Could not find the 'linear_regression' key"
" in the resource:\n\n%s" % linear_regression)
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.regularization = None
old_coefficients = False
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', 0)
self.c = logistic_regression_info.get('c')
self.eps = logistic_regression_info.get('eps')
self.lr_normalize = logistic_regression_info.get('normalize')
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, 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 = {}
self.resource_id, time_series = get_resource_dict( \
time_series, "timeseries", api=api)
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.numeric_fields = {}
self.bias = None
self.missing_numerics = None
self.c = None
self.eps = None
self.lr_normalize = None
self.regularization = None
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', []))
self.bias = logistic_regression_info.get('bias', 0)
self.c = logistic_regression_info.get('c')
self.eps = logistic_regression_info.get('eps')
self.lr_normalize = logistic_regression_info.get('normalize')
self.regularization = logistic_regression_info.get( \
'regularization')
# 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.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, logistic_regression, api=None, cache_get=None):
if use_cache(cache_get):
# using a cache to store the model attributes
self.__dict__ = load(get_logistic_regression_id( \
logistic_regression), cache_get)
return
self.resource_id = None
self.class_names = 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
api = get_api_connection(api)
old_coefficients = False
self.resource_id, logistic_regression = get_resource_dict( \
logistic_regression, "logisticregression", api=api)
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", {})
self.weight_field = logistic_regression.get("weight_field")
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(list(fields.items()),
key=lambda x: x[1].get("column_number"))]
self.coefficients.update(logistic_regression_info.get( \
'coefficients', []))
if not isinstance(
list(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)
missing_tokens = logistic_regression_info.get("missing_tokens")
ModelFields.__init__(self,
fields,
objective_id=objective_id,
terms=True,
categories=True,
numerics=True,
missing_tokens=missing_tokens)
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 self.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()
categories = self.fields[self.objective_id].get( \
"summary", {}).get('categories')
if len(list(self.coefficients.keys())) > len(categories):
self.class_names = [""]
else:
self.class_names = []
self.class_names.extend(
sorted([category[0] for category in categories]))
# order matters
self.objective_categories = [
category[0] for category in categories
]
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, 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, 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 = {}
self.api = get_api_connection(api)
self.resource_id, time_series = get_resource_dict( \
time_series, "timeseries", api=self.api)
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, deepnet, api=None):
"""The Deepnet constructor can be given as first argument:
- a deepnet structure
- a deepnet id
- a path to a JSON file containing a deepnet structure
"""
self.resource_id = None
self.regression = False
self.network = None
self.networks = None
self.input_fields = []
self.class_names = []
self.preprocess = []
self.optimizer = None
self.missing_numerics = False
# the string can be a path to a JSON file
if isinstance(deepnet, basestring):
try:
with open(deepnet) as deepnet_file:
deepnet = json.load(deepnet_file)
self.resource_id = get_deepnet_id(deepnet)
if self.resource_id is None:
raise ValueError("The JSON file does not seem"
" to contain a valid BigML deepnet"
" representation.")
except IOError:
# if it is not a path, it can be a deepnet id
self.resource_id = get_deepnet_id(deepnet)
if self.resource_id is None:
if deepnet.find('deepnet/') > -1:
raise Exception(
api.error_message(deepnet,
resource_type='deepnet',
method='get'))
else:
raise IOError("Failed to open the expected JSON file"
" at %s" % deepnet)
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(deepnet, dict) and \
not check_model_fields(deepnet):
# if the fields used by the deepenet are not
# available, use only ID to retrieve it again
deepnet = get_deepnet_id(deepnet)
self.resource_id = deepnet
if not (isinstance(deepnet, dict) and 'resource' in deepnet
and deepnet['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
query_string = ONLY_MODEL
deepnet = retrieve_resource(api,
self.resource_id,
query_string=query_string)
else:
self.resource_id = get_deepnet_id(deepnet)
if 'object' in deepnet and isinstance(deepnet['object'], dict):
deepnet = deepnet['object']
self.input_fields = deepnet['input_fields']
if 'deepnet' in deepnet and isinstance(deepnet['deepnet'], dict):
status = get_status(deepnet)
objective_field = deepnet['objective_fields']
deepnet = deepnet['deepnet']
if 'code' in status and status['code'] == FINISHED:
self.fields = deepnet['fields']
ModelFields.__init__(
self,
self.fields,
objective_id=extract_objective(objective_field),
terms=True,
categories=True)
self.regression = \
self.fields[self.objective_id]['optype'] == NUMERIC
if not self.regression:
self.class_names = [category for category,_ in \
self.fields[self.objective_id][ \
'summary']['categories']]
self.class_names.sort()
self.missing_numerics = deepnet.get('missing_numerics', False)
if 'network' in deepnet:
network = deepnet['network']
self.network = network
self.networks = network.get('networks', [])
self.preprocess = network.get('preprocess')
self.optimizer = network.get('optimizer', {})
else:
raise Exception("The deepnet isn't finished yet")
else:
raise Exception("Cannot create the Deepnet instance. Could not"
" find the 'deepnet' key in the resource:\n\n%s" %
deepnet)
def __init__(self, linear_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.bias = None
self.xtx_inverse = []
self.mean_squared_error = None
self.number_of_parameters = None
self.number_of_samples = None
self.resource_id, linear_regression = get_resource_dict( \
linear_regression, "linearregression", api=api)
if 'object' in linear_regression and \
isinstance(linear_regression['object'], dict):
linear_regression = linear_regression['object']
try:
self.input_fields = linear_regression.get("input_fields", [])
self.dataset_field_types = linear_regression.get(
"dataset_field_types", {})
self.weight_field = linear_regression.get("weight_field")
objective_field = linear_regression['objective_fields'] if \
linear_regression['objective_fields'] else \
linear_regression['objective_field']
except KeyError:
raise ValueError("Failed to find the linear regression expected "
"JSON structure. Check your arguments.")
if 'linear_regression' in linear_regression and \
isinstance(linear_regression['linear_regression'], dict):
status = get_status(linear_regression)
if 'code' in status and status['code'] == FINISHED:
linear_regression_info = linear_regression[ \
'linear_regression']
fields = linear_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.coeff_ids = self.input_fields[:]
self.coefficients = linear_regression_info.get( \
'coefficients', [])
self.bias = linear_regression_info.get('bias', True)
self.field_codings = linear_regression_info.get( \
'field_codings', {})
self.number_of_parameters = linear_regression_info.get( \
"number_of_parameters")
objective_id = extract_objective(objective_field)
ModelFields.__init__(
self, fields,
objective_id=objective_id, terms=True, categories=True,
numerics=True)
self.field_codings = linear_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]
stats = linear_regression_info["stats"]
if stats is not None and stats.get("xtx_inverse") is not None:
self.xtx_inverse = stats["xtx_inverse"][:]
self.mean_squared_error = stats["mean_squared_error"]
self.number_of_samples = stats["number_of_samples"]
# to be used in predictions
self.t_crit = student_t.interval( \
CONFIDENCE,
self.number_of_samples - self.number_of_parameters)[1]
self.xtx_inverse = list( \
np.linalg.inv(np.array(self.xtx_inverse)))
else:
raise Exception("The linear regression isn't finished yet")
else:
raise Exception("Cannot create the LinearRegression instance."
" Could not find the 'linear_regression' key"
" in the resource:\n\n%s" %
linear_regression)
def __init__(self, logistic_regression, api=None):
self.resource_id = None
self.class_names = 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)
ModelFields.__init__(
self, fields,
objective_id=objective_id, terms=True, categories=True,
numerics=True)
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()
categories = self.fields[self.objective_id].get( \
"summary", {}).get('categories')
if len(self.coefficients.keys()) > len(categories):
self.class_names = [""]
else:
self.class_names = []
self.class_names.extend(sorted([category[0]
for category in categories]))
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)
