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
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,
query_string=ONLY_MODEL)
for model_id in self.models_splits[0]]
self.multi_model = MultiModel(models)
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 __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 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, 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)