def predict_probability(self, input_data,
missing_strategy=LAST_PREDICTION,
compact=False):
"""For classification models, Predicts a probability for
each possible output class, based on input values. The input
fields must be a dictionary keyed by field name or field ID.
For regressions, the output is a single element list
containing the prediction.
:param input_data: Input data to be predicted
:param missing_strategy: LAST_PREDICTION|PROPORTIONAL missing strategy
for missing fields
:param compact: If False, prediction is returned as a list of maps, one
per class, with the keys "prediction" and "probability"
mapped to the name of the class and it's probability,
respectively. If True, returns a list of probabilities
ordered by the sorted order of the class names.
"""
votes = MultiVoteList([])
if not self.missing_numerics:
check_no_missing_numerics(input_data, self.fields)
for models_split in self.models_splits:
models = []
for model in models_split:
if get_resource_type(model) == "fusion":
models.append(Fusion(model, api=self.api))
else:
models.append(SupervisedModel(model, api=self.api))
votes_split = []
for model in models:
try:
prediction = model.predict_probability( \
input_data,
missing_strategy=missing_strategy,
compact=True)
except ValueError:
# logistic regressions can raise this error if they
# have missing_numerics=False and some numeric missings
# are found
continue
if self.regression:
prediction = prediction[0]
if self.weights is not None:
prediction = self.weigh(prediction, model.resource_id)
else:
if self.weights is not None:
prediction = self.weigh( \
prediction, model.resource_id)
# we need to check that all classes in the fusion
# are also in the composing model
if not self.regression and \
self.class_names != model.class_names:
try:
prediction = rearrange_prediction( \
model.class_names,
self.class_names,
prediction)
except AttributeError:
# class_names should be defined, but just in case
pass
votes_split.append(prediction)
votes.extend(votes_split)
if self.regression:
total_weight = len(votes.predictions) if self.weights is None \
else sum(self.weights)
prediction = sum([prediction for prediction in \
votes.predictions]) / float(total_weight)
if compact:
output = [prediction]
else:
output = {"prediction": prediction}
else:
output = votes.combine_to_distribution(normalize=True)
if not compact:
output = [{'category': class_name,
'probability': probability}
for class_name, probability in
zip(self.class_names, output)]
return output
def predict_probability(self,
input_data,
missing_strategy=LAST_PREDICTION,
compact=False):
"""For classification models, Predicts a probability for
each possible output class, based on input values. The input
fields must be a dictionary keyed by field name or field ID.
For regressions, the output is a single element list
containing the prediction.
:param input_data: Input data to be predicted
:param missing_strategy: LAST_PREDICTION|PROPORTIONAL missing strategy
for missing fields
:param compact: If False, prediction is returned as a list of maps, one
per class, with the keys "prediction" and "probability"
mapped to the name of the class and it's probability,
respectively. If True, returns a list of probabilities
ordered by the sorted order of the class names.
"""
votes = MultiVoteList([])
for models_split in self.models_splits:
models = []
for model in models_split:
if get_resource_type(model) == "fusion":
models.append(Fusion(model, api=self.api))
else:
models.append(SupervisedModel(model, api=self.api))
votes_split = []
for model in models:
prediction = model.predict_probability( \
input_data,
missing_strategy=missing_strategy,
compact=True)
if self.regression:
prediction = prediction[0]
else:
# we need to check that all classes in the fusion
# are also in the composing model
if not self.regression and \
self.class_names != model.class_names:
try:
prediction = rearrange_prediction( \
model.class_names,
self.class_names,
prediction)
except AttributeError:
# class_names should be defined, but just in case
pass
votes_split.append(prediction)
votes.extend(votes_split)
if self.regression:
prediction = sum([prediction for prediction in \
votes.predictions]) / float(len(votes.predictions))
if compact:
output = [prediction]
else:
output = {"prediction": prediction}
else:
output = votes.combine_to_distribution(normalize=False)
if not compact:
output = [{
'category': class_name,
'probability': probability
} for class_name, probability in zip(self.class_names, output)]
return output
