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, association, api=None):
self.resource_id = None
self.complement = None
self.discretization = {}
self.field_discretizations = {}
self.items = []
self.max_k = None
self.max_lhs = None
self.min_confidence = None
self.min_leverage = None
self.min_support = None
self.min_lift = None
self.search_strategy = DEFAULT_SEARCH_STRATEGY
self.rules = []
self.significance_level = None
self.resource_id, association = get_resource_dict( \
association, "association", api=api)
if 'object' in association and isinstance(association['object'], dict):
association = association['object']
if 'associations' in association and \
isinstance(association['associations'], dict):
status = get_status(association)
if 'code' in status and status['code'] == FINISHED:
associations = association['associations']
fields = associations['fields']
ModelFields.__init__(self, fields)
self.complement = associations.get('complement', False)
self.discretization = associations.get('discretization', {})
self.field_discretizations = associations.get(
'field_discretizations', {})
self.items = [
Item(index, item, fields)
for index, item in enumerate(associations.get('items', []))
]
self.max_k = associations.get('max_k', 100)
self.max_lhs = associations.get('max_lhs', 4)
self.min_confidence = associations.get('min_confidence', 0)
self.min_leverage = associations.get('min_leverage', -1)
self.min_support = associations.get('min_support', 0)
self.min_lift = associations.get('min_lift', 0)
self.search_strategy = associations.get('search_strategy', \
DEFAULT_SEARCH_STRATEGY)
self.rules = [
AssociationRule(rule)
for rule in associations.get('rules', [])
]
self.significance_level = associations.get(
'significance_level', 0.05)
else:
raise Exception("The association isn't finished yet")
else:
raise Exception("Cannot create the Association instance. Could not"
" find the 'associations' key in the "
"resource:\n\n%s" % association)
def __init__(self, association, api=None):
self.resource_id = None
self.complement = None
self.discretization = {}
self.field_discretizations = {}
self.items = []
self.max_k = None
self.max_lhs = None
self.min_confidence = None
self.min_leverage = None
self.min_support = None
self.min_lift = None
self.search_strategy = DEFAULT_SEARCH_STRATEGY
self.rules = []
self.significance_level = None
self.resource_id, association = get_resource_dict( \
association, "association", api=api)
if 'object' in association and isinstance(association['object'], dict):
association = association['object']
if 'associations' in association and \
isinstance(association['associations'], dict):
status = get_status(association)
if 'code' in status and status['code'] == FINISHED:
associations = association['associations']
fields = associations['fields']
ModelFields.__init__(self, fields)
self.complement = associations.get('complement', False)
self.discretization = associations.get('discretization', {})
self.field_discretizations = associations.get(
'field_discretizations', {})
self.items = [Item(index, item, fields) for index, item in
enumerate(associations.get('items', []))]
self.max_k = associations.get('max_k', 100)
self.max_lhs = associations.get('max_lhs', 4)
self.min_confidence = associations.get('min_confidence', 0)
self.min_leverage = associations.get('min_leverage', -1)
self.min_support = associations.get('min_support', 0)
self.min_lift = associations.get('min_lift', 0)
self.search_strategy = associations.get('search_strategy', \
DEFAULT_SEARCH_STRATEGY)
self.rules = [AssociationRule(rule) for rule in
associations.get('rules', [])]
self.significance_level = associations.get(
'significance_level', 0.05)
else:
raise Exception("The association isn't finished yet")
else:
raise Exception("Cannot create the Association instance. Could not"
" find the 'associations' key in the "
"resource:\n\n%s" %
association)
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 = []
self.resource_id, anomaly = get_resource_dict( \
anomaly, "anomaly", api=api)
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'), \
missing_tokens=anomaly['model'].get('missing_tokens'))
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, anomaly, api=None, cache_get=None):
if use_cache(cache_get):
# using a cache to store the Minomaly attributes
self.__dict__ = load(get_anomaly_id(anomaly), cache_get)
return
self.resource_id = None
self.sample_size = None
self.input_fields = None
self.default_numeric_value = None
self.mean_depth = None
self.expected_mean_depth = None
self.iforest = None
self.id_fields = []
api = get_api_connection(api)
self.resource_id, anomaly = get_resource_dict(
anomaly, "anomaly", api=api)
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.default_numeric_value = anomaly.get('default_numeric_value')
self.id_fields = anomaly.get('id_fields', [])
if 'model' in anomaly and isinstance(anomaly['model'], dict):
ModelFields.__init__(
self, anomaly['model'].get('fields'),
missing_tokens=anomaly['model'].get('missing_tokens'))
self.mean_depth = anomaly['model'].get('mean_depth')
self.normalization_factor = anomaly['model'].get(
'normalization_factor')
self.nodes_mean_depth = anomaly['model'].get(
'nodes_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")
self.norm = self.normalization_factor if \
self.normalization_factor is not None else \
self.norm_factor()
iforest = anomaly['model'].get('trees', [])
if iforest:
self.iforest = [
build_tree([anomaly_tree['root']])
for anomaly_tree in iforest]
self.top_anomalies = anomaly['model']['top_anomalies']
else:
raise Exception("The anomaly isn't finished yet")
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
if self.api is None:
self.api = BigML(storage=STORAGE)
self.resource_id, cluster = get_resource_dict( \
cluster, "cluster", api=api)
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, 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, fusion, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.resource_id = None
self.models_ids = None
self.objective_id = None
self.distribution = None
self.models_splits = []
self.cache_get = None
self.regression = False
self.fields = None
self.class_names = None
self.importance = {}
self.resource_id, fusion = get_resource_dict( \
fusion, "fusion", api=self.api)
if 'object' in fusion:
fusion = fusion.get('object', {})
self.model_ids, self.weights = get_models_weight( \
fusion['models'])
model_types = [get_resource_type(model) for model in self.model_ids]
for model_type in model_types:
if model_type not in LOCAL_SUPERVISED:
raise ValueError("The resource %s has not an allowed"
" supervised model type.")
self.importance = fusion.get('importance', [])
self.missing_numerics = fusion.get('missing_numerics', True)
if fusion.get('fusion'):
self.fields = fusion.get( \
'fusion', {}).get("fields")
self.objective_id = fusion.get("objective_field")
number_of_models = len(self.model_ids)
# Downloading the model information to cache it
if self.api.storage is not None:
for model_id in self.model_ids:
if get_resource_type(model_id) == "fusion":
Fusion(model_id, api=self.api)
else:
SupervisedModel(model_id, api=self.api)
if max_models is None:
self.models_splits = [self.model_ids]
else:
self.models_splits = [self.model_ids[index:(index + max_models)]
for index
in range(0, number_of_models, max_models)]
if self.fields:
summary = self.fields[self.objective_id]['summary']
if 'bins' in summary:
distribution = summary['bins']
elif 'counts' in summary:
distribution = summary['counts']
elif 'categories' in summary:
distribution = summary['categories']
else:
distribution = []
self.distribution = distribution
self.regression = \
self.fields[self.objective_id].get('optype') == 'numeric'
if not self.regression:
objective_field = self.fields[self.objective_id]
categories = objective_field['summary']['categories']
classes = [category[0] for category in categories]
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)
def __init__(self, topic_model, api=None):
self.resource_id = None
self.stemmer = None
self.seed = None
self.case_sensitive = False
self.bigrams = False
self.ntopics = None
self.temp = None
self.phi = None
self.term_to_index = None
self.topics = []
self.resource_id, topic_model = get_resource_dict( \
topic_model, "topicmodel", api=api)
if 'object' in topic_model and isinstance(topic_model['object'], dict):
topic_model = topic_model['object']
if 'topic_model' in topic_model \
and isinstance(topic_model['topic_model'], dict):
status = get_status(topic_model)
if 'code' in status and status['code'] == FINISHED:
model = topic_model['topic_model']
self.topics = model['topics']
if 'language' in model and model['language'] is not None:
lang = model['language']
if lang in CODE_TO_NAME:
self.stemmer = Stemmer.Stemmer(CODE_TO_NAME[lang])
self.term_to_index = {self.stem(term): index for index, term
in enumerate(model['termset'])}
self.seed = abs(model['hashed_seed'])
self.case_sensitive = model['case_sensitive']
self.bigrams = model['bigrams']
self.ntopics = len(model['term_topic_assignments'][0])
self.alpha = model['alpha']
self.ktimesalpha = self.ntopics * self.alpha
self.temp = [0] * self.ntopics
assignments = model['term_topic_assignments']
beta = model['beta']
nterms = len(self.term_to_index)
sums = [sum(n[index] for n in assignments) for index
in range(self.ntopics)]
self.phi = [[0 for _ in range(nterms)]
for _ in range(self.ntopics)]
for k in range(self.ntopics):
norm = sums[k] + nterms * beta
for w in range(nterms):
self.phi[k][w] = (assignments[w][k] + beta) / norm
ModelFields.__init__(self, model['fields'])
else:
raise Exception("The topic model isn't finished yet")
else:
raise Exception("Cannot create the topic model instance. Could not"
" find the 'topic_model' key in the"
" resource:\n\n%s" % topic_model)
def __init__(self, pca, api=None):
self.resource_id = None
self.input_fields = []
self.term_forms = {}
self.tag_clouds = {}
self.dataset_field_types = {}
self.term_analysis = {}
self.categories = {}
self.categories_probabilities = {}
self.items = {}
self.fields = {}
self.item_analysis = {}
self.standardize = None
self.famd_j = 1
self.resource_id, pca = get_resource_dict( \
pca, "pca", api=api)
if 'object' in pca and \
isinstance(pca['object'], dict):
pca = pca['object']
try:
self.input_fields = pca.get("input_fields", [])
self.dataset_field_types = pca.get("dataset_field_types", {})
self.famd_j = 1 if (self.dataset_field_types['categorical'] != \
self.dataset_field_types['total']) else \
self.dataset_field_types['categorical']
except KeyError:
raise ValueError("Failed to find the pca expected "
"JSON structure. Check your arguments.")
if 'pca' in pca and \
isinstance(pca['pca'], dict):
status = get_status(pca)
if 'code' in status and status['code'] == FINISHED:
pca_info = pca[ \
'pca']
fields = pca_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"))]
missing_tokens = pca_info.get("missing_tokens")
ModelFields.__init__(self,
fields,
objective_id=None,
terms=True,
categories=True,
numerics=False,
missing_tokens=missing_tokens)
for field_id in self.categories:
field = self.fields[field_id]
probabilities = [probability for _, probability in \
field["summary"]["categories"]]
if field["summary"].get("missing_count", 0) > 0:
probabilities.append(field["summary"]["missing_count"])
total = float(sum(probabilities))
if total > 0:
probabilities = [probability / total for probability \
in probabilities]
self.categories_probabilities[field_id] = probabilities
self.components = pca_info.get('components')
self.eigenvectors = pca_info.get('eigenvectors')
self.cumulative_variance = pca_info.get('cumulative_variance')
self.text_stats = pca_info.get('text_stats')
self.standardized = pca_info.get('standardized')
self.variance = pca_info.get('variance')
else:
raise Exception("The pca isn't finished yet")
else:
raise Exception("Cannot create the PCA instance."
" Could not find the 'pca' key"
" in the resource:\n\n%s" % pca)
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, association, api=None, cache_get=None):
if use_cache(cache_get):
# using a cache to store the association attributes
self.__dict__ = load(get_association_id(association), cache_get)
for index, item in enumerate(self.items):
self.items[index] = Item(item["index"], item, self.fields)
for index, rule in enumerate(self.rules):
self.rules[index] = AssociationRule(rule)
return
self.resource_id = None
self.complement = None
self.discretization = {}
self.default_numeric_value = None
self.field_discretizations = {}
self.items = []
self.max_k = None
self.max_lhs = None
self.min_confidence = None
self.min_leverage = None
self.min_support = None
self.min_lift = None
self.search_strategy = DEFAULT_SEARCH_STRATEGY
self.rules = []
self.significance_level = None
api = get_api_connection(api)
self.resource_id, association = get_resource_dict( \
association, "association", api=api)
if 'object' in association and isinstance(association['object'], dict):
association = association['object']
if 'associations' in association and \
isinstance(association['associations'], dict):
status = get_status(association)
if 'code' in status and status['code'] == FINISHED:
self.input_fields = association['input_fields']
self.default_numeric_value = association.get( \
'default_numeric_value')
associations = association['associations']
fields = associations['fields']
ModelFields.__init__( \
self, fields, \
missing_tokens=associations.get('missing_tokens'))
self.complement = associations.get('complement', False)
self.discretization = associations.get('discretization', {})
self.field_discretizations = associations.get(
'field_discretizations', {})
self.items = [Item(index, item, fields) for index, item in
enumerate(associations.get('items', []))]
self.max_k = associations.get('max_k', 100)
self.max_lhs = associations.get('max_lhs', 4)
self.min_confidence = associations.get('min_confidence', 0)
self.min_leverage = associations.get('min_leverage', -1)
self.min_support = associations.get('min_support', 0)
self.min_lift = associations.get('min_lift', 0)
self.search_strategy = associations.get('search_strategy', \
DEFAULT_SEARCH_STRATEGY)
self.rules = [AssociationRule(rule) for rule in
associations.get('rules', [])]
self.significance_level = associations.get(
'significance_level', 0.05)
else:
raise Exception("The association isn't finished yet")
else:
raise Exception("Cannot create the Association instance. Could not"
" find the 'associations' key in the "
"resource:\n\n%s" %
association)
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)
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, 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, 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, pca, api=None):
self.resource_id = None
self.input_fields = []
self.term_forms = {}
self.tag_clouds = {}
self.dataset_field_types = {}
self.term_analysis = {}
self.categories = {}
self.categories_probabilities = {}
self.items = {}
self.fields = {}
self.item_analysis = {}
self.standardize = None
self.famd_j = 1
self.resource_id, pca = get_resource_dict( \
pca, "pca", api=api)
if 'object' in pca and \
isinstance(pca['object'], dict):
pca = pca['object']
try:
self.input_fields = pca.get("input_fields", [])
self.dataset_field_types = pca.get("dataset_field_types", {})
self.famd_j = 1 if (self.dataset_field_types['categorical'] != \
self.dataset_field_types['total']) else \
self.dataset_field_types['categorical']
except KeyError:
raise ValueError("Failed to find the pca expected "
"JSON structure. Check your arguments.")
if 'pca' in pca and \
isinstance(pca['pca'], dict):
status = get_status(pca)
if 'code' in status and status['code'] == FINISHED:
pca_info = pca[ \
'pca']
fields = pca_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"))]
ModelFields.__init__(
self, fields,
objective_id=None, terms=True, categories=True,
numerics=False)
for field_id in self.categories:
field = self.fields[field_id]
probabilities = [probability for _, probability in \
field["summary"]["categories"]]
if field["summary"].get("missing_count", 0) > 0:
probabilities.append(field["summary"]["missing_count"])
total = float(sum(probabilities))
if total > 0:
probabilities = [probability / total for probability \
in probabilities]
self.categories_probabilities[field_id] = probabilities
self.components = pca_info.get('components')
self.eigenvectors = pca_info.get('eigenvectors')
self.cumulative_variance = pca_info.get('cumulative_variance')
self.text_stats = pca_info.get('text_stats')
self.standardized = pca_info.get('standardized')
self.variance = pca_info.get('variance')
else:
raise Exception("The pca isn't finished yet")
else:
raise Exception("Cannot create the PCA instance."
" Could not find the 'pca' key"
" in the resource:\n\n%s" %
pca)
