def number_of_tests(self):
"""Returns the number of tests in the test file
"""
tests = u.file_number_of_lines(self.test_set)
if self.test_set_header:
tests -= 1
return tests
def predict(test_set, test_set_header, models, fields, output,
objective_field, remote=False, api=None, log=None,
max_models=MAX_MODELS, method=0, resume=False,
tags=None, verbosity=1, session_file=None, debug=False):
"""Computes a prediction for each entry in the `test_set`.
Predictions can be computed remotely, locally using MultiModels built
on all the models or locally using MultiModels on subgroups of models.
Chosing a max_batch_models value not bigger than the number_of_models
flag will lead to the last case, where memory usage is bounded and each
model predictions are saved for further use.
"""
try:
test_reader = csv.reader(open(test_set, "U"),
delimiter=get_csv_delimiter(),
lineterminator="\n")
except IOError:
sys.exit("Error: cannot read test %s" % test_set)
headers = None
exclude = []
if test_set_header:
headers = test_reader.next()
# validate headers against model fields excluding objective_field,
# that may be present or not
fields_names = [fields.fields[fields.field_id(i)]
['name'] for i in
sorted(fields.fields_by_column_number.keys())
if i != fields.field_column_number(objective_field)]
headers = [unicode(header, "utf-8") for header in headers]
exclude = [i for i in range(len(headers)) if not headers[i]
in fields_names]
exclude.reverse()
if len(exclude):
if (len(headers) - len(exclude)):
print (u"WARNING: predictions will be processed but some data"
u" might not be used. The used fields will be:\n\n%s"
u"\n\nwhile the headers found in the test file are:"
u"\n\n%s" %
(",".join(fields_names),
",".join(headers))).encode("utf-8")
for index in exclude:
del headers[index]
else:
raise Exception((u"No test field matches the model fields.\n"
u"The expected fields are:\n\n%s\n\nwhile "
u"the headers found in the test file are:\n\n"
u"%s\n\nUse --no-test-header flag if first li"
u"ne should not be interpreted as headers." %
(",".join(fields_names),
",".join(headers))).encode("utf-8"))
prediction_file = output
output_path = u.check_dir(output)
output = open(output, 'w', 0)
number_of_tests = None
if resume:
number_of_tests = u.file_number_of_lines(test_set)
if test_set_header:
number_of_tests -= 1
# Remote predictions: predictions are computed in bigml.com and stored
# in a file named after the model in the following syntax:
# model_[id of the model]__predictions.csv
# For instance,
# model_50c0de043b563519830001c2_predictions.csv
if remote:
remote_predict(models, headers, output_path, number_of_tests, resume,
verbosity, test_reader, exclude, fields, api,
prediction_file, method, tags, objective_field,
session_file, test_set_header, log, debug)
# Local predictions: Predictions are computed locally using models' rules
# with MultiModel's predict method
else:
message = u.dated("Creating local predictions.\n")
u.log_message(message, log_file=session_file, console=verbosity)
# For a small number of models, we build a MultiModel using all of
# the given models and issue a combined prediction
if len(models) < max_models:
local_predict(models, headers, test_reader, exclude, fields,
method, objective_field, output, test_set_header)
# For large numbers of models, we split the list of models in chunks
# and build a MultiModel for each chunk, issue and store predictions
# for each model and combine all of them eventually.
else:
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)
output.close()
