def local_batch_predict(models, headers, test_reader, exclude, fields, resume,
output_path, max_models, number_of_tests, api, output,
verbosity, method, objective_field, session_file,
debug):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" % (
localize(current), localize(total), pct))
models_total = len(models)
models_splits = [models[index:(index + max_models)] for index
in range(0, models_total, max_models)]
input_data_list = []
for row in test_reader:
for index in exclude:
del row[index]
input_data_list.append(fields.pair(row, headers,
objective_field))
total_votes = []
models_count = 0
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model,
output_path)
u.checkpoint(u.are_predictions_created,
pred_file,
number_of_tests, debug=debug)
complete_models = []
for index in range(len(models_split)):
complete_models.append(api.check_resource(
models_split[index], api.get_model))
local_model = MultiModel(complete_models)
local_model.batch_predict(input_data_list,
output_path, reuse=True)
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index].predictions
predictions.extend(votes[index].predictions)
else:
total_votes = votes
message = u.dated("Combining predictions.\n")
u.log_message(message, log_file=session_file, console=verbosity)
for multivote in total_votes:
u.write_prediction(multivote.combine(method), output)
def local_batch_predict(models, test_reader, prediction_file, api,
max_models=MAX_MODELS,
resume=False, output_path=None, output=None,
verbosity=True, method=PLURALITY_CODE,
session_file=None, debug=False, prediction_info=None):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" % (
localize(current), localize(total), pct))
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if output is None:
try:
output = open(prediction_file, 'w', 0)
except IOError:
raise IOError("Failed to write in %s" % prediction_file)
models_total = len(models)
models_splits = [models[index:(index + max_models)] for index
in range(0, models_total, max_models)]
input_data_list = []
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
input_data_list.append(test_reader.dict(input_data))
total_votes = []
models_count = 0
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model,
output_path)
c.checkpoint(c.are_predictions_created,
pred_file,
test_reader.number_of_tests(), debug=debug)
complete_models = []
for index in range(len(models_split)):
model = models_split[index]
if (isinstance(model, basestring) or
bigml.api.get_status(model)['code'] != bigml.api.FINISHED):
try:
model = u.check_resource(model, api.get_model, FIELDS_QS)
except ValueError, exception:
sys.exit("Failed to get model: %s" % (model,
str(exception)))
complete_models.append(model)
local_model = MultiModel(complete_models)
local_model.batch_predict(input_data_list,
output_path,
by_name=test_set_header,
reuse=True)
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index].predictions
predictions.extend(votes[index].predictions)
else:
total_votes = votes
def local_batch_predict(models,
test_reader,
prediction_file,
api,
args,
resume=False,
output_path=None,
output=None,
method=PLURALITY_CODE,
options=None,
session_file=None,
labels=None,
ordered=True,
exclude=None,
models_per_label=1,
other_label=OTHER,
multi_label_data=None):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" %
(localize(current), localize(total), pct),
reset=True)
max_models = args.max_batch_models
if labels is None:
labels = []
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if output is None:
try:
output = open(prediction_file, 'w', 0)
except IOError:
raise IOError("Failed to write in %s" % prediction_file)
models_total = len(models)
models_splits = [
models[index:(index + max_models)]
for index in range(0, models_total, max_models)
]
# Input data is stored as a list and predictions are made for all rows
# with each model
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
total_votes = []
models_order = []
models_count = 0
single_model = models_total == 1
query_string = FIELDS_QS if single_model else ALL_FIELDS_QS
# processing the models in slots
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model, output_path)
c.checkpoint(c.are_predictions_created,
pred_file,
test_reader.number_of_tests(),
debug=args.debug)
# retrieving the full models allowed by --max-batch-models to be used
# in a multimodel slot
complete_models, models_order = retrieve_models_split(
models_split,
api,
query_string=query_string,
labels=labels,
multi_label_data=multi_label_data,
ordered=ordered,
models_order=models_order)
# predicting with the multimodel slot
if complete_models:
local_model = MultiModel(complete_models, api=api)
# added to ensure garbage collection at each step of the loop
gc.collect()
try:
votes = local_model.batch_predict(
raw_input_data_list,
output_path,
by_name=test_set_header,
reuse=True,
missing_strategy=args.missing_strategy,
headers=test_reader.raw_headers,
to_file=(not args.fast),
use_median=args.median)
except ImportError:
sys.exit("Failed to find the numpy and scipy libraries needed"
" to use proportional missing strategy for"
" regressions. Please, install them manually")
# extending the votes for each input data with the new model-slot
# predictions
if not args.fast:
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if args.verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index]
predictions.extend(votes[index].predictions)
else:
total_votes = votes
if not single_model:
message = u.dated("Combining predictions.\n")
u.log_message(message, log_file=session_file, console=args.verbosity)
# combining the votes to issue the final prediction for each input data
for index in range(0, len(total_votes)):
multivote = total_votes[index]
input_data = raw_input_data_list[index]
if single_model:
# single model predictions need no combination
prediction = [
multivote.predictions[0]['prediction'],
multivote.predictions[0]['confidence']
]
elif method == AGGREGATION:
# multi-labeled fields: predictions are concatenated
prediction = aggregate_multivote(
multivote,
options,
labels,
models_per_label,
ordered,
models_order,
label_separator=args.label_separator)
elif method == COMBINATION:
# used in --max-categories flag: each model slot contains a
# subset of categories and the predictions for all of them
# are combined in a global distribution to obtain the final
# prediction
prediction = combine_multivote(multivote, other_label=other_label)
else:
prediction = multivote.combine(method=method,
with_confidence=True,
options=options)
write_prediction(prediction, output, args.prediction_info, input_data,
exclude)
def local_batch_predict(models, test_reader, prediction_file, api,
max_models=MAX_MODELS,
resume=False, output_path=None, output=None,
verbosity=True, method=PLURALITY_CODE, options=None,
session_file=None, debug=False,
prediction_info=NORMAL_FORMAT,
labels=None, label_separator=None, ordered=True,
exclude=None, models_per_label=1, other_label=OTHER,
multi_label_data=None):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" % (
localize(current), localize(total), pct))
if labels is None:
labels = []
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if output is None:
try:
output = open(prediction_file, 'w', 0)
except IOError:
raise IOError("Failed to write in %s" % prediction_file)
models_total = len(models)
models_splits = [models[index:(index + max_models)] for index
in range(0, models_total, max_models)]
input_data_list = []
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
input_data_list.append(test_reader.dict(input_data))
total_votes = []
models_count = 0
if not ordered:
models_order = []
single_model = models_total == 1
query_string = FIELDS_QS if single_model else ALL_FIELDS_QS
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model,
output_path)
c.checkpoint(c.are_predictions_created,
pred_file,
test_reader.number_of_tests(), debug=debug)
complete_models = []
for index in range(len(models_split)):
model = models_split[index]
if (isinstance(model, basestring) or
bigml.api.get_status(model)['code'] != bigml.api.FINISHED):
try:
model = u.check_resource(model, api.get_model,
query_string)
except ValueError, exception:
sys.exit("Failed to get model: %s. %s" % (model,
str(exception)))
# When user selects the labels in multi-label predictions, we must
# filter the models that will be used to predict
if labels:
objective_column = str(multi_label_data['objective_column'])
labels_info = multi_label_data[
'generated_fields'][objective_column]
labels_columns = [label_info[1] for label_info in labels_info
if label_info[0] in labels]
model_objective_id = model['object']['objective_fields'][0]
model_fields = model['object']['model']['fields']
model_objective = model_fields[model_objective_id]
model_column = model_objective['column_number']
if (model_column in labels_columns):
# When the list of models comes from a --model-tag
# selection, the models are not retrieved in the same
# order they were created. We must keep track of the
# label they are associated with to label their
# predictions properly
if not ordered:
models_order.append(model_column)
complete_models.append(model)
else:
complete_models.append(model)
if complete_models:
local_model = MultiModel(complete_models)
local_model.batch_predict(input_data_list,
output_path,
by_name=test_set_header,
reuse=True)
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index]
predictions.extend(votes[index].predictions)
else:
total_votes = votes
def local_batch_predict(models, test_reader, prediction_file, api, args,
resume=False, output_path=None, output=None,
method=PLURALITY_CODE, options=None,
session_file=None, labels=None, ordered=True,
exclude=None, models_per_label=1, other_label=OTHER,
multi_label_data=None):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" % (
localize(current), localize(total), pct), reset=True)
max_models = args.max_batch_models
if labels is None:
labels = []
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if output is None:
try:
output = open(prediction_file, 'w', 0)
except IOError:
raise IOError("Failed to write in %s" % prediction_file)
models_total = len(models)
models_splits = [models[index:(index + max_models)] for index
in range(0, models_total, max_models)]
# Input data is stored as a list and predictions are made for all rows
# with each model
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
total_votes = []
models_order = []
models_count = 0
single_model = models_total == 1
query_string = FIELDS_QS if single_model else ALL_FIELDS_QS
# processing the models in slots
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model,
output_path)
c.checkpoint(c.are_predictions_created,
pred_file,
test_reader.number_of_tests(), debug=args.debug)
# retrieving the full models allowed by --max-batch-models to be used
# in a multimodel slot
complete_models, models_order = retrieve_models_split(
models_split, api, query_string=query_string, labels=labels,
multi_label_data=multi_label_data, ordered=ordered,
models_order=models_order)
# predicting with the multimodel slot
if complete_models:
local_model = MultiModel(complete_models, api=api)
# added to ensure garbage collection at each step of the loop
gc.collect()
try:
votes = local_model.batch_predict(
raw_input_data_list, output_path, by_name=test_set_header,
reuse=True, missing_strategy=args.missing_strategy,
headers=test_reader.raw_headers, to_file=(not args.fast),
use_median=args.median)
except ImportError:
sys.exit("Failed to find the numpy and scipy libraries needed"
" to use proportional missing strategy for"
" regressions. Please, install them manually")
# extending the votes for each input data with the new model-slot
# predictions
if not args.fast:
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if args.verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index]
predictions.extend(votes[index].predictions)
else:
total_votes = votes
if not single_model:
message = u.dated("Combining predictions.\n")
u.log_message(message, log_file=session_file, console=args.verbosity)
# combining the votes to issue the final prediction for each input data
for index in range(0, len(total_votes)):
multivote = total_votes[index]
input_data = raw_input_data_list[index]
if single_model:
# single model predictions need no combination
prediction = [multivote.predictions[0]['prediction'],
multivote.predictions[0]['confidence']]
elif method == AGGREGATION:
# multi-labeled fields: predictions are concatenated
prediction = aggregate_multivote(
multivote, options, labels, models_per_label, ordered,
models_order, label_separator=args.label_separator)
elif method == COMBINATION:
# used in --max-categories flag: each model slot contains a
# subset of categories and the predictions for all of them
# are combined in a global distribution to obtain the final
# prediction
prediction = combine_multivote(multivote, other_label=other_label)
else:
prediction = multivote.combine(method=method, with_confidence=True,
options=options)
write_prediction(prediction, output, args.prediction_info, input_data,
exclude)
def local_batch_predict(models,
test_reader,
prediction_file,
api,
max_models=MAX_MODELS,
resume=False,
output_path=None,
output=None,
verbosity=True,
method=PLURALITY_CODE,
session_file=None,
debug=False,
prediction_info=None):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" %
(localize(current), localize(total), pct))
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if output is None:
try:
output = open(prediction_file, 'w', 0)
except IOError:
raise IOError("Failed to write in %s" % prediction_file)
models_total = len(models)
models_splits = [
models[index:(index + max_models)]
for index in range(0, models_total, max_models)
]
input_data_list = []
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
input_data_list.append(test_reader.dict(input_data))
total_votes = []
models_count = 0
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model, output_path)
c.checkpoint(c.are_predictions_created,
pred_file,
test_reader.number_of_tests(),
debug=debug)
complete_models = []
for index in range(len(models_split)):
model = models_split[index]
if (isinstance(model, basestring) or
bigml.api.get_status(model)['code'] != bigml.api.FINISHED):
try:
model = u.check_resource(model, api.get_model, FIELDS_QS)
except ValueError, exception:
sys.exit("Failed to get model: %s" %
(model, str(exception)))
complete_models.append(model)
local_model = MultiModel(complete_models)
local_model.batch_predict(input_data_list,
output_path,
by_name=test_set_header,
reuse=True)
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index].predictions
predictions.extend(votes[index].predictions)
else:
total_votes = votes
def local_batch_predict(models,
test_reader,
prediction_file,
api,
args,
resume=False,
output_path=None,
output=None,
method=PLURALITY_CODE,
options=None,
session_file=None,
labels=None,
ordered=True,
exclude=None,
models_per_label=1,
other_label=OTHER,
multi_label_data=None):
"""Get local predictions form partial Multimodel, combine and save to file
"""
def draw_progress_bar(current, total):
"""Draws a text based progress report.
"""
pct = 100 - ((total - current) * 100) / (total)
console_log("Predicted on %s out of %s models [%s%%]" %
(localize(current), localize(total), pct))
max_models = args.max_batch_models
label_separator = args.label_separator
if labels is None:
labels = []
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if output is None:
try:
output = open(prediction_file, 'w', 0)
except IOError:
raise IOError("Failed to write in %s" % prediction_file)
models_total = len(models)
models_splits = [
models[index:(index + max_models)]
for index in range(0, models_total, max_models)
]
input_data_list = []
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
input_data_list.append(test_reader.dict(input_data))
total_votes = []
models_count = 0
if not ordered:
models_order = []
single_model = models_total == 1
query_string = FIELDS_QS if single_model else ALL_FIELDS_QS
for models_split in models_splits:
if resume:
for model in models_split:
pred_file = get_predictions_file_name(model, output_path)
c.checkpoint(c.are_predictions_created,
pred_file,
test_reader.number_of_tests(),
debug=args.debug)
complete_models = []
for index in range(len(models_split)):
model = models_split[index]
if (isinstance(model, basestring) or
bigml.api.get_status(model)['code'] != bigml.api.FINISHED):
try:
model = u.check_resource(model, api.get_model,
query_string)
except ValueError, exception:
sys.exit("Failed to get model: %s. %s" %
(model, str(exception)))
# When user selects the labels in multi-label predictions, we must
# filter the models that will be used to predict
if labels:
objective_column = str(multi_label_data['objective_column'])
labels_info = multi_label_data['generated_fields'][
objective_column]
labels_columns = [
label_info[1] for label_info in labels_info
if label_info[0] in labels
]
model_objective_id = model['object']['objective_fields'][0]
model_fields = model['object']['model']['fields']
model_objective = model_fields[model_objective_id]
model_column = model_objective['column_number']
if (model_column in labels_columns):
# When the list of models comes from a --model-tag
# selection, the models are not retrieved in the same
# order they were created. We must keep track of the
# label they are associated with to label their
# predictions properly
if not ordered:
models_order.append(model_column)
complete_models.append(model)
else:
complete_models.append(model)
if complete_models:
local_model = MultiModel(complete_models)
try:
local_model.batch_predict(
input_data_list,
output_path,
by_name=test_set_header,
reuse=True,
missing_strategy=args.missing_strategy)
except ImportError:
sys.exit("Failed to find the numpy and scipy libraries needed"
" to use proportional missing strategy for"
" regressions. Please, install them manually")
votes = local_model.batch_votes(output_path)
models_count += max_models
if models_count > models_total:
models_count = models_total
if args.verbosity:
draw_progress_bar(models_count, models_total)
if total_votes:
for index in range(0, len(votes)):
predictions = total_votes[index]
predictions.extend(votes[index].predictions)
else:
total_votes = votes