def generate_votes(self, input_data, by_name=True,
missing_strategy=LAST_PREDICTION,
add_median=False, add_min=False, add_max=False,
add_unused_fields=False):
""" Generates a MultiVote object that contains the predictions
made by each of the models.
"""
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
prediction_info = model.predict( \
input_data, by_name=by_name,
add_confidence=True,
add_distribution=True,
add_count=True,
add_median=add_median,
add_min=add_min,
add_max=add_max,
add_unused_fields=add_unused_fields,
missing_strategy=missing_strategy)
if model.boosting is not None:
votes.boosting = True
prediction_info.update( \
{"weight": model.boosting.get("weight")})
if model.boosting.get("objective_class") is not None:
prediction_info.update( \
{"class": model.boosting.get("objective_class")})
votes.append(prediction_info)
return votes
def _generate_votes(self,
input_data,
missing_strategy=LAST_PREDICTION,
unused_fields=None):
""" Generates a MultiVote object that contains the predictions
made by each of the models. Please note that this function
calls a _predict method which assumes input data has been
properly checked against the model fields. Only casting
to the correct type will be applied.
"""
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
prediction_info = model._predict( \
input_data,
missing_strategy=missing_strategy, unused_fields=unused_fields)
if model.boosting is not None:
votes.boosting = True
prediction_info.update( \
{"weight": model.boosting.get("weight")})
if model.boosting.get("objective_class") is not None:
prediction_info.update( \
{"class": model.boosting.get("objective_class")})
votes.append(prediction_info)
return votes
def generate_votes(self, input_data, by_name=True,
missing_strategy=LAST_PREDICTION,
add_median=False, add_min=False, add_max=False,
add_unused_fields=False):
""" Generates a MultiVote object that contains the predictions
made by each of the models.
"""
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
prediction_info = model.predict( \
input_data, by_name=by_name,
add_confidence=True,
add_distribution=True,
add_count=True,
add_median=add_median,
add_min=add_min,
add_max=add_max,
add_unused_fields=add_unused_fields,
missing_strategy=missing_strategy)
if model.boosting is not None:
votes.boosting = True
prediction_info.update( \
{"weight": model.boosting.get("weight")})
if model.boosting.get("objective_class") is not None:
prediction_info.update( \
{"class": model.boosting.get("objective_class")})
votes.append(prediction_info)
return votes
def generate_probability_votes(self,
input_data,
by_name=True,
missing_strategy=LAST_PREDICTION,
method=PROBABILITY_CODE):
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
model.class_names = self.class_names
votes.probabilities = True
try:
if method == PROBABILITY_CODE:
prediction_info = model.predict_probability(
input_data,
by_name=by_name,
compact=True,
missing_strategy=missing_strategy)
elif method == CONFIDENCE_CODE:
prediction_info = model.predict_confidence(
input_data,
by_name=by_name,
compact=True,
missing_strategy=missing_strategy)
elif method == PLURALITY_CODE:
prediction_info = [0.0] * len(self.class_names)
prediction = model.predict(
input_data,
by_name=by_name,
missing_strategy=missing_strategy)
prediction_info[self.class_names.index(prediction)] = 1.0
else:
raise ValueError('%d is not a valid "method"' % method)
except (AttributeError, TypeError):
if method == PLURALITY_CODE:
prediction_info = [0.0] * len(self.class_names)
prediction = model.predict(input_data, by_name=by_name)
prediction_info[self.class_names.index(prediction)] = 1.0
else:
prediction_info = model.predict_probability(
input_data,
by_name=by_name,
compact=True)
votes.append(prediction_info)
return votes
def generate_votes(self, input_data, by_name=True,
missing_strategy=LAST_PREDICTION,
add_median=False):
""" Generates a MultiVote object that contains the predictions
made by each of the models.
"""
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
prediction_info = model.predict(input_data, by_name=by_name,
add_confidence=True,
add_distribution=True,
add_count=True,
add_median=add_median,
missing_strategy=missing_strategy)
votes.append(prediction_info)
return votes
def generate_votes(self,
input_data,
by_name=True,
missing_strategy=LAST_PREDICTION,
add_median=False):
""" Generates a MultiVote object that contains the predictions
made by each of the models.
"""
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
prediction_info = model.predict(input_data,
by_name=by_name,
add_confidence=True,
add_distribution=True,
add_count=True,
add_median=add_median,
missing_strategy=missing_strategy)
votes.append(prediction_info)
return votes
def generate_votes(self, input_data, missing_strategy=LAST_PREDICTION):
""" Generates a MultiVote object that contains the predictions
made by each of the models.
"""
votes = MultiVote([])
for order in range(0, len(self.models)):
model = self.models[order]
prediction_info = model.predict( \
input_data, missing_strategy=missing_strategy, full=True)
if model.boosting is not None:
votes.boosting = True
prediction_info.update( \
{"weight": model.boosting.get("weight")})
if model.boosting.get("objective_class") is not None:
prediction_info.update( \
{"class": model.boosting.get("objective_class")})
votes.append(prediction_info)
return votes
