def predict(self, input_data, by_name=True, method=PLURALITY_CODE,
with_confidence=False, options=None):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
"""
if len(self.models_splits) > 1:
# If there's more than one chunck of models, they must be
# sequentially used to generate the votes for the prediction
votes = MultiVote([])
for models_split in self.models_splits:
models = [retrieve_resource(self.api, model_id,
query_string=ONLY_MODEL)
for model_id in models_split]
multi_model = MultiModel(models, api=self.api)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
else:
# When only one group of models is found you use the
# corresponding multimodel to predict
votes_split = self.multi_model.generate_votes(input_data,
by_name=by_name)
votes = MultiVote(votes_split.predictions)
return votes.combine(method=method, with_confidence=with_confidence,
options=options)
def predict(self, input_data, by_name=True, method=PLURALITY_CODE,
with_confidence=False, options=None):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
"""
if len(self.models_splits) > 1:
# If there's more than one chunck of models, they must be
# sequentially used to generate the votes for the prediction
votes = MultiVote([])
for models_split in self.models_splits:
models = [retrieve_resource(self.api, model_id,
query_string=ONLY_MODEL)
for model_id in models_split]
multi_model = MultiModel(models, api=self.api)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
else:
# When only one group of models is found you use the
# corresponding multimodel to predict
votes_split = self.multi_model.generate_votes(input_data,
by_name=by_name)
votes = MultiVote(votes_split.predictions)
return votes.combine(method=method, with_confidence=with_confidence,
options=options)
def predict(self, input_data, by_name=True, method=PLURALITY_CODE,
with_confidence=False):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
"""
votes = MultiVote([])
for models_split in self.models_splits:
models = [retrieve_model(self.api, model_id)
for model_id in models_split]
multi_model = MultiModel(models)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
return votes.combine(method=method, with_confidence=with_confidence)
class Ensemble(object):
"""A local predictive Ensemble.
Uses a number of BigML remote models to build an ensemble local version
that can be used to generate predictions locally.
"""
def __init__(self, ensemble, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.ensemble_id = None
if isinstance(ensemble, list):
try:
models = [get_model_id(model) for model in ensemble]
except ValueError:
raise ValueError('Failed to verify the list of models. Check '
'your model id values.')
self.distributions = None
else:
self.ensemble_id = get_ensemble_id(ensemble)
ensemble = check_resource(ensemble, self.api.get_ensemble)
models = ensemble['object']['models']
self.distributions = ensemble['object'].get('distributions', None)
self.model_ids = models
self.fields = self.all_model_fields()
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [
models[index:(index + max_models)]
for index in range(0, number_of_models, max_models)
]
if len(self.models_splits) == 1:
models = [
retrieve_resource(self.api, model_id, query_string=ONLY_MODEL)
for model_id in self.models_splits[0]
]
self.multi_model = MultiModel(models, self.api)
def list_models(self):
"""Lists all the model/ids that compound the ensemble.
"""
return self.model_ids
def predict(self,
input_data,
by_name=True,
method=PLURALITY_CODE,
with_confidence=False,
options=None):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
"""
if len(self.models_splits) > 1:
# If there's more than one chunck of models, they must be
# sequentially used to generate the votes for the prediction
votes = MultiVote([])
for models_split in self.models_splits:
models = [
retrieve_resource(self.api,
model_id,
query_string=ONLY_MODEL)
for model_id in models_split
]
multi_model = MultiModel(models, api=self.api)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
else:
# When only one group of models is found you use the
# corresponding multimodel to predict
votes_split = self.multi_model.generate_votes(input_data,
by_name=by_name)
votes = MultiVote(votes_split.predictions)
return votes.combine(method=method,
with_confidence=with_confidence,
options=options)
def field_importance_data(self):
"""Computes field importance based on the field importance information
of the individual models in the ensemble.
"""
field_importance = {}
field_names = {}
if (self.distributions is not None
and isinstance(self.distributions, list)
and all('importance' in item for item in self.distributions)):
# Extracts importance from ensemble information
importances = [
model_info['importance'] for model_info in self.distributions
]
for index in range(0, len(importances)):
model_info = importances[index]
for field_info in model_info:
field_id = field_info[0]
if not field_id in field_importance:
field_importance[field_id] = 0.0
name = self.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
else:
# Old ensembles, extracts importance from model information
for model_id in self.model_ids:
local_model = BaseModel(model_id, api=self.api)
for field_info in local_model.field_importance:
field_id = field_info[0]
if not field_info[0] in field_importance:
field_importance[field_id] = 0.0
name = self.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
number_of_models = len(self.model_ids)
for field_id in field_importance.keys():
field_importance[field_id] /= number_of_models
return map(
list,
sorted(field_importance.items(), key=lambda x: x[1],
reverse=True)), field_names
def print_importance(self, out=sys.stdout):
"""Prints ensemble field importance
"""
print_importance(self, out=out)
def get_data_distribution(self, distribution_type="training"):
"""Returns the required data distribution by adding the distributions
in the models
"""
ensemble_distribution = []
categories = []
for model_distribution in self.distributions:
summary = model_distribution[distribution_type]
if 'bins' in summary:
distribution = summary['bins']
elif 'counts' in summary:
distribution = summary['counts']
elif 'categories' in summary:
distribution = summary['categories']
for point, instances in distribution:
if point in categories:
ensemble_distribution[categories.index(
point)][1] += instances
else:
categories.append(point)
ensemble_distribution.append([point, instances])
return sorted(ensemble_distribution, key=lambda x: x[0])
def summarize(self, out=sys.stdout):
"""Prints ensemble summary. Only field importance at present.
"""
distribution = self.get_data_distribution("training")
out.write(u"Data distribution:\n")
print_distribution(distribution, out=out)
out.write(u"\n\n")
predictions = self.get_data_distribution("predictions")
out.write(u"Predicted distribution:\n")
print_distribution(predictions, out=out)
out.write(u"\n\n")
out.write(u"Field importance:\n")
self.print_importance(out=out)
out.flush()
def all_model_fields(self):
"""Retrieves the fields used as predictors in all the ensemble
models
"""
fields = {}
for model_id in self.model_ids:
local_model = Model(model_id, self.api)
gc.collect()
fields.update(local_model.fields)
return fields
class Ensemble(object):
"""A local predictive Ensemble.
Uses a number of BigML remote models to build an ensemble local version
that can be used to generate predictions locally.
"""
def __init__(self, ensemble, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.ensemble_id = None
if isinstance(ensemble, list):
try:
models = [get_model_id(model) for model in ensemble]
except ValueError:
raise ValueError('Failed to verify the list of models. Check '
'your model id values.')
self.distributions = None
else:
self.ensemble_id = get_ensemble_id(ensemble)
ensemble = check_resource(ensemble, self.api.get_ensemble)
models = ensemble['object']['models']
self.distributions = ensemble['object'].get('distributions', None)
self.model_ids = models
self.fields = self.all_model_fields()
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [models[index:(index + max_models)] for index
in range(0, number_of_models, max_models)]
if len(self.models_splits) == 1:
models = [retrieve_resource(self.api, model_id,
query_string=ONLY_MODEL)
for model_id in self.models_splits[0]]
self.multi_model = MultiModel(models, self.api)
def list_models(self):
"""Lists all the model/ids that compound the ensemble.
"""
return self.model_ids
def predict(self, input_data, by_name=True, method=PLURALITY_CODE,
with_confidence=False, options=None):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
"""
if len(self.models_splits) > 1:
# If there's more than one chunck of models, they must be
# sequentially used to generate the votes for the prediction
votes = MultiVote([])
for models_split in self.models_splits:
models = [retrieve_resource(self.api, model_id,
query_string=ONLY_MODEL)
for model_id in models_split]
multi_model = MultiModel(models, api=self.api)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
else:
# When only one group of models is found you use the
# corresponding multimodel to predict
votes_split = self.multi_model.generate_votes(input_data,
by_name=by_name)
votes = MultiVote(votes_split.predictions)
return votes.combine(method=method, with_confidence=with_confidence,
options=options)
def field_importance_data(self):
"""Computes field importance based on the field importance information
of the individual models in the ensemble.
"""
field_importance = {}
field_names = {}
if (self.distributions is not None and
isinstance(self.distributions, list) and
all('importance' in item for item in self.distributions)):
# Extracts importance from ensemble information
importances = [model_info['importance'] for model_info in
self.distributions]
for index in range(0, len(importances)):
model_info = importances[index]
for field_info in model_info:
field_id = field_info[0]
if not field_id in field_importance:
field_importance[field_id] = 0.0
name = self.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
else:
# Old ensembles, extracts importance from model information
for model_id in self.model_ids:
local_model = BaseModel(model_id, api=self.api)
for field_info in local_model.field_importance:
field_id = field_info[0]
if not field_info[0] in field_importance:
field_importance[field_id] = 0.0
name = self.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
number_of_models = len(self.model_ids)
for field_id in field_importance.keys():
field_importance[field_id] /= number_of_models
return map(list, sorted(field_importance.items(), key=lambda x: x[1],
reverse=True)), field_names
def print_importance(self, out=sys.stdout):
"""Prints ensemble field importance
"""
print_importance(self, out=out)
def get_data_distribution(self, distribution_type="training"):
"""Returns the required data distribution by adding the distributions
in the models
"""
ensemble_distribution = []
categories = []
for model_distribution in self.distributions:
summary = model_distribution[distribution_type]
if 'bins' in summary:
distribution = summary['bins']
elif 'counts' in summary:
distribution = summary['counts']
elif 'categories' in summary:
distribution = summary['categories']
for point, instances in distribution:
if point in categories:
ensemble_distribution[
categories.index(point)][1] += instances
else:
categories.append(point)
ensemble_distribution.append([point, instances])
return sorted(ensemble_distribution, key=lambda x: x[0])
def summarize(self, out=sys.stdout):
"""Prints ensemble summary. Only field importance at present.
"""
distribution = self.get_data_distribution("training")
out.write(u"Data distribution:\n")
print_distribution(distribution, out=out)
out.write(u"\n\n")
predictions = self.get_data_distribution("predictions")
out.write(u"Predicted distribution:\n")
print_distribution(predictions, out=out)
out.write(u"\n\n")
out.write(u"Field importance:\n")
self.print_importance(out=out)
out.flush()
def all_model_fields(self):
"""Retrieves the fields used as predictors in all the ensemble
models
"""
fields = {}
for model_id in self.model_ids:
local_model = Model(model_id, self.api)
gc.collect()
fields.update(local_model.fields)
return fields
class Ensemble(object):
"""A local predictive Ensemble.
Uses a number of BigML remote models to build an ensemble local version
that can be used to generate predictions locally.
"""
def __init__(self, ensemble, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.ensemble_id = None
if isinstance(ensemble, list):
try:
models = [get_model_id(model) for model in ensemble]
except ValueError:
raise ValueError('Failed to verify the list of models. Check '
'your model id values.')
else:
self.ensemble_id = get_ensemble_id(ensemble)
ensemble = check_resource(ensemble, self.api.get_ensemble)
models = ensemble['object']['models']
self.model_ids = models
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [
models[index:(index + max_models)]
for index in range(0, number_of_models, max_models)
]
if len(self.models_splits) == 1:
models = [
retrieve_model(self.api, model_id)
for model_id in self.models_splits[0]
]
self.multi_model = MultiModel(models)
def list_models(self):
"""Lists all the model/ids that compound the ensemble.
"""
return self.model_ids
def predict(self,
input_data,
by_name=True,
method=PLURALITY_CODE,
with_confidence=False):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
"""
if len(self.models_splits) > 1:
# If there's more than one chunck of models, they must be
# sequentially used to generate the votes for the prediction
votes = MultiVote([])
for models_split in self.models_splits:
models = [
retrieve_model(self.api, model_id)
for model_id in models_split
]
multi_model = MultiModel(models)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
else:
# When only one group of models is found you use the
# corresponding multimodel to predict
votes_split = self.multi_model.generate_votes(input_data,
by_name=by_name)
votes = MultiVote(votes_split.predictions)
return votes.combine(method=method, with_confidence=with_confidence)
class Ensemble(object):
"""A local predictive Ensemble.
Uses a number of BigML remote models to build an ensemble local version
that can be used to generate predictions locally.
"""
def __init__(self, ensemble, api=None, max_models=None):
if api is None:
self.api = BigML(storage=STORAGE)
else:
self.api = api
self.ensemble_id = None
if isinstance(ensemble, list):
try:
models = [get_model_id(model) for model in ensemble]
except ValueError:
raise ValueError('Failed to verify the list of models. Check '
'your model id values.')
else:
self.ensemble_id = get_ensemble_id(ensemble)
ensemble = check_resource(ensemble, self.api.get_ensemble)
models = ensemble['object']['models']
self.model_ids = models
number_of_models = len(models)
if max_models is None:
self.models_splits = [models]
else:
self.models_splits = [models[index:(index + max_models)] for index
in range(0, number_of_models, max_models)]
if len(self.models_splits) == 1:
models = [retrieve_model(self.api, model_id)
for model_id in self.models_splits[0]]
self.multi_model = MultiModel(models)
def list_models(self):
"""Lists all the model/ids that compound the ensemble.
"""
return self.model_ids
def predict(self, input_data, by_name=True, method=PLURALITY_CODE,
with_confidence=False, options=None):
"""Makes a prediction based on the prediction made by every model.
The method parameter is a numeric key to the following combination
methods in classifications/regressions:
0 - majority vote (plurality)/ average: PLURALITY_CODE
1 - confidence weighted majority vote / error weighted:
CONFIDENCE_CODE
2 - probability weighted majority vote / average:
PROBABILITY_CODE
3 - threshold filtered vote / doesn't apply:
THRESHOLD_CODE
"""
if len(self.models_splits) > 1:
# If there's more than one chunck of models, they must be
# sequentially used to generate the votes for the prediction
votes = MultiVote([])
for models_split in self.models_splits:
models = [retrieve_model(self.api, model_id)
for model_id in models_split]
multi_model = MultiModel(models)
votes_split = multi_model.generate_votes(input_data,
by_name=by_name)
votes.extend(votes_split.predictions)
else:
# When only one group of models is found you use the
# corresponding multimodel to predict
votes_split = self.multi_model.generate_votes(input_data,
by_name=by_name)
votes = MultiVote(votes_split.predictions)
return votes.combine(method=method, with_confidence=with_confidence,
options=options)
def field_importance_data(self):
"""Computes field importance based on the field importance information
of the individual models in the ensemble.
"""
field_importance = {}
field_names = {}
for model_id in self.model_ids:
local_model = BaseModel(model_id)
for field_info in local_model.field_importance:
field_id = field_info[0]
if not field_info[0] in field_importance:
field_importance[field_id] = 0.0
name = local_model.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
number_of_models = len(self.model_ids)
for field_id in field_importance.keys():
field_importance[field_id] /= number_of_models
return map(list, sorted(field_importance.items(), key=lambda x: x[1],
reverse=True)), field_names
def print_importance(self, out=sys.stdout):
"""Prints ensemble field importance
"""
print_importance(self, out=out)
