def __init__(self,
training_set,
training_set_header,
objective_field,
multi_label=False,
labels=None,
label_separator=None,
training_separator=None,
multi_label_fields=None,
label_aggregates=None,
objective=True):
"""Builds a generator from a csv file
`training_set`: path to the training data file
`training_set_header`: boolean, True means that headers are first
row in the file
`objective_field`: objective field column or field name
`labels`: Fields object with the expected fields structure.
"""
self.training_set = training_set
self.training_set_header = training_set_header
self.training_set_handler = None
self.training_reader = None
self.multi_label = multi_label
self.objective = objective
if label_aggregates is None:
label_aggregates = []
self.label_aggregates = label_aggregates
self.training_separator = (training_separator.decode("string_escape")
if training_separator is not None else
get_csv_delimiter())
if len(self.training_separator) > 1:
sys.exit("Only one character can be used as test data separator.")
# opening csv reader
self.reset()
self.label_separator = (label_separator.decode("string_escape")
if label_separator is not None else
get_csv_delimiter())
first_row = self.next(reset=not training_set_header)
self.row_length = len(first_row)
if training_set_header:
self.headers = [unicode(header, "utf-8") for header in first_row]
else:
self.headers = [("field_%s" % index)
for index in range(0, self.row_length)]
self.multi_label_fields = sorted(self._get_columns(multi_label_fields))
if objective:
self.objective_column = self._get_columns([objective_field])[0]
if not self.objective_column in self.multi_label_fields:
self.multi_label_fields.append(self.objective_column)
self.labels = labels
self.fields_labels = self._get_labels()
if objective:
if labels is None:
self.labels = self.fields_labels[self.objective_column]
self.objective_name = self.headers[self.objective_column]
def __init__(self, training_set, training_set_header, objective_field,
multi_label=False, labels=None, label_separator=None,
training_separator=None, multi_label_fields=None,
label_aggregates=None, objective=True):
"""Builds a generator from a csv file
`training_set`: path to the training data file
`training_set_header`: boolean, True means that headers are first
row in the file
`objective_field`: objective field column or field name
`labels`: Fields object with the expected fields structure.
"""
self.training_set = training_set
if training_set.__class__.__name__ == "StringIO":
self.encode = None
self.training_set = UTF8Recoder(training_set, SYSTEM_ENCODING)
else:
self.encode = None if PYTHON3 else FILE_ENCODING
self.training_set_header = training_set_header
self.training_reader = None
self.multi_label = multi_label
self.objective = objective
if label_aggregates is None:
label_aggregates = []
self.label_aggregates = label_aggregates
self.training_separator = (decode2(training_separator,
encoding="string_escape")
if training_separator is not None
else get_csv_delimiter())
if len(self.training_separator) > 1:
sys.exit("Only one character can be used as test data separator.")
# opening csv reader
self.reset()
self.label_separator = (decode2(label_separator,
encoding="string_escape")
if label_separator is not None
else get_csv_delimiter())
first_row = self.get_next(reset=not training_set_header)
self.row_length = len(first_row)
if training_set_header:
self.headers = first_row
else:
self.headers = [("field_%s" % index) for index in
range(0, self.row_length)]
self.multi_label_fields = sorted(self._get_columns(multi_label_fields))
if objective:
self.objective_column = self._get_columns([objective_field])[0]
if not self.objective_column in self.multi_label_fields:
self.multi_label_fields.append(self.objective_column)
self.labels = labels
self.fields_labels = self._get_labels()
if objective:
if labels is None:
self.labels = self.fields_labels[self.objective_column]
self.objective_name = self.headers[self.objective_column]
def __init__(self, training_set, training_set_header, objective_field,
multi_label=False, labels=None, label_separator=None,
training_separator=None):
"""Builds a generator from a csv file
`training_set`: path to the training data file
`training_set_header`: boolean, True means that headers are first
row in the file
`objective_field`: objective field column or field name
`labels`: Fields object with the expected fields structure.
"""
self.training_set = training_set
self.training_set_header = training_set_header
self.training_set_handler = None
self.training_reader = None
self.multi_label = multi_label
self.training_separator = (training_separator.decode("string_escape")
if training_separator is not None
else get_csv_delimiter())
if len(self.training_separator) > 1:
sys.exit("Only one character can be used as test data separator.")
# opening csv reader
self.reset()
self.label_separator = (label_separator.decode("string_escape")
if label_separator is not None
else get_csv_delimiter())
first_row = self.next(reset=not training_set_header)
row_length = len(first_row)
if training_set_header:
self.headers = [unicode(header, "utf-8") for header in first_row]
else:
self.headers = [("field_%s" % index) for index in
range(0, row_length)]
if isinstance(objective_field, int):
self.objective_column = objective_field
elif objective_field is None:
self.objective_column = row_length - 1
else:
try:
self.objective_column = self.headers.index(objective_field)
except ValueError:
sys.exit("The %s has been set as objective field but"
" it cannot be found in the headers row: \n %s" %
(objective_field,
", ".join([header.encode("utf-8")
for header in self.headers])))
self.labels = labels
self.labels = self.get_labels()
self.objective_name = self.headers[self.objective_column]
def __init__(self, test_set, test_set_header, fields, objective_field):
"""Builds a generator from a csv file and the fields' model structure
"""
self.test_set = test_set
self.test_set_header = test_set_header
self.fields = fields
self.objective_field = objective_field
try:
self.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)
self.headers = None
self.exclude = []
if test_set_header:
self.headers = self.test_reader.next()
# validate headers against model fields excluding objective_field,
# that may be present or not
objective_field = fields.field_column_number(objective_field)
fields_names = [
fields.fields[fields.field_id(i)]['name']
for i in sorted(fields.fields_by_column_number.keys())
if i != objective_field
]
self.headers = [
unicode(header, "utf-8") for header in self.headers
]
self.exclude = [
i for i in range(len(self.headers))
if not self.headers[i] in fields_names
]
self.exclude.reverse()
if self.exclude:
if len(self.headers) > len(self.exclude):
print(u"WARNING: predictions will be processed but some "
u"data might not be used. The used fields will be:"
u"\n\n%s"
u"\n\nwhile the headers found in the test file are:"
u"\n\n%s" % (",".join(fields_names), ",".join(
self.headers))).encode("utf-8")
for index in self.exclude:
del self.headers[index]
else:
raise Exception(
(u"No test field matches the model fields."
u"\nThe expected fields are:\n\n%s\n\n"
u"while "
u"the headers found in the test file are:"
u"\n\n%s\n\n"
u"Use --no-test-header flag if first li"
u"ne should not be interpreted as"
u" headers." % (",".join(fields_names), ",".join(
self.headers))).encode("utf-8"))
def __init__(self, test_set, test_set_header, fields, objective_field):
"""Builds a generator from a csv file and the fields' model structure
"""
self.test_set = test_set
self.test_set_header = test_set_header
self.fields = fields
self.objective_field = objective_field
try:
self.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)
self.headers = None
self.exclude = []
if test_set_header:
self.headers = self.test_reader.next()
# validate headers against model fields excluding objective_field,
# that may be present or not
objective_field = fields.field_column_number(objective_field)
fields_names = [fields.fields[fields.field_id(i)]
['name'] for i in
sorted(fields.fields_by_column_number.keys())
if i != objective_field]
self.headers = [unicode(header, "utf-8")
for header in self.headers]
self.exclude = [i for i in range(len(self.headers))
if not self.headers[i] in fields_names]
self.exclude.reverse()
if self.exclude:
if len(self.headers) > len(self.exclude):
print (u"WARNING: predictions will be processed but some "
u"data might not be used. The used fields will be:"
u"\n\n%s"
u"\n\nwhile the headers found in the test file are:"
u"\n\n%s" %
(",".join(fields_names),
",".join(self.headers))).encode("utf-8")
for index in self.exclude:
del self.headers[index]
else:
raise Exception((u"No test field matches the model fields."
u"\nThe expected fields are:\n\n%s\n\n"
u"while "
u"the headers found in the test file are:"
u"\n\n%s\n\n"
u"Use --no-test-header flag if first li"
u"ne should not be interpreted as"
u" headers." %
(",".join(fields_names),
",".join(self.headers))).encode("utf-8"))
class TstReader(object):
"""Retrieves csv info and builds a input data dict
"""
def __init__(self,
test_set,
test_set_header,
fields,
objective_field,
test_separator=None):
"""Builds a generator from a csv file and the fields' model structure
`test_set`: path to the test data file
`test_set_header`: boolean, True means that headers are first row
in the file
`fields`: Fields object with the expected fields structure.
`objective_field`: field_id of the objective field
"""
self.test_set = test_set
if test_set.__class__.__name__ == "StringIO":
self.encode = None
self.test_set = UTF8Recoder(test_set, SYSTEM_ENCODING)
else:
self.encode = None if PYTHON3 else FILE_ENCODING
self.test_set_header = test_set_header
self.fields = fields
if (objective_field is not None
and not objective_field in fields.fields):
try:
objective_field = fields.field_id(objective_field)
except ValueError, exc:
sys.exit(exc)
self.objective_field = objective_field
if test_separator and not PYTHON3:
test_separator = decode2(test_separator, encoding="string_escape")
self.test_separator = (test_separator if test_separator is not None
else get_csv_delimiter())
if len(self.test_separator) > 1:
sys.exit("Only one character can be used as test data separator.")
try:
self.test_reader = UnicodeReader(
self.test_set,
delimiter=self.test_separator,
lineterminator="\n").open_reader()
except IOError:
sys.exit("Error: cannot read test %s" % test_set)
self.headers = None
self.raw_headers = None
self.exclude = []
if test_set_header:
self.headers = self.test_reader.next()
# validate headers against model fields excluding objective_field,
# that may be present or not
if objective_field is not None:
objective_field = fields.field_column_number(objective_field)
try:
fields_names = [
fields.fields[fields.field_id(i)]['name']
for i in sorted(fields.fields_by_column_number.keys())
if objective_field is None or i != objective_field
]
except ValueError, exc:
sys.exit(exc)
self.raw_headers = self.headers[:]
self.exclude = [
i for i in range(len(self.headers))
if not self.headers[i] in fields_names
]
self.exclude.reverse()
if self.exclude:
if len(self.headers) > len(self.exclude):
for index in self.exclude:
del self.headers[index]
else:
raise Exception(
(u"No test field matches the model fields."
u"\nThe expected fields are:\n\n%s\n\n"
u"while "
u"the headers found in the test file are:"
u"\n\n%s\n\n"
u"Use --no-test-header flag if first li"
u"ne should not be interpreted as"
u" headers." % (",".join(fields_names), ",".join(
self.headers))).encode("utf-8"))
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()
def __init__(self, test_set, test_set_header, fields, objective_field,
test_separator=None):
"""Builds a generator from a csv file and the fields' model structure
`test_set`: path to the test data file
`test_set_header`: boolean, True means that headers are first row
in the file
`fields`: Fields object with the expected fields structure.
`objective_field`: field_id of the objective field
"""
self.test_set = test_set
if test_set.__class__.__name__ == "StringIO":
self.encode = "utf-8"
self.test_set_handler = UTF8Recoder(test_set, self.encode)
else:
self.encode = None
self.test_set_handler = open(test_set, "U")
self.test_set_header = test_set_header
self.fields = fields
if not objective_field in fields.fields:
objective_field = fields.field_id(objective_field)
self.objective_field = objective_field
self.test_separator = (test_separator.decode("string_escape")
if test_separator is not None
else get_csv_delimiter())
if len(self.test_separator) > 1:
sys.exit("Only one character can be used as test data separator.")
try:
self.test_reader = csv.reader(self.test_set_handler,
delimiter=self.test_separator,
lineterminator="\n")
except IOError:
sys.exit("Error: cannot read test %s" % test_set)
self.headers = None
self.raw_headers = None
self.exclude = []
if test_set_header:
self.headers = self.test_reader.next()
self.raw_headers = self.headers
# validate headers against model fields excluding objective_field,
# that may be present or not
objective_field = fields.field_column_number(objective_field)
fields_names = [fields.fields[fields.field_id(i)]
['name'] for i in
sorted(fields.fields_by_column_number.keys())
if i != objective_field]
self.headers = [unicode(header, "utf-8")
for header in self.headers]
self.exclude = [i for i in range(len(self.headers))
if not self.headers[i] in fields_names]
self.exclude.reverse()
if self.exclude:
if len(self.headers) > len(self.exclude):
print (u"WARNING: predictions will be processed but some "
u"data might not be used. The used fields will be:"
u"\n\n%s"
u"\n\nwhile the headers found in the test file are:"
u"\n\n%s" %
(",".join(fields_names),
",".join(self.headers))).encode("utf-8")
for index in self.exclude:
del self.headers[index]
else:
raise Exception((u"No test field matches the model fields."
u"\nThe expected fields are:\n\n%s\n\n"
u"while "
u"the headers found in the test file are:"
u"\n\n%s\n\n"
u"Use --no-test-header flag if first li"
u"ne should not be interpreted as"
u" headers." %
(",".join(fields_names),
",".join(self.headers))).encode("utf-8"))
def __init__(self,
training_set,
training_set_header,
objective_field,
multi_label=False,
labels=None,
label_separator=None,
training_separator=None,
multi_label_fields=None,
label_aggregates=None,
objective=True):
"""Builds a generator from a csv file
`training_set`: path to the training data file
`training_set_header`: boolean, True means that headers are first
row in the file
`objective_field`: objective field column or field name
`labels`: Fields object with the expected fields structure.
"""
self.training_set = training_set
if training_set.__class__.__name__ == "StringIO":
self.encode = None
self.training_set = UTF8Recoder(training_set, BIGML_SYS_ENCODING)
else:
self.encode = None if PYTHON3 else FILE_ENCODING
self.training_set_header = training_set_header
self.training_reader = None
self.multi_label = multi_label
self.objective = objective
if label_aggregates is None:
label_aggregates = []
self.label_aggregates = label_aggregates
self.training_separator = (decode2(
training_separator, encoding="string_escape") if training_separator
is not None else get_csv_delimiter())
if len(self.training_separator) > 1:
sys.exit("Only one character can be used as test data separator.")
# opening csv reader
self.reset()
self.label_separator = (decode2(
label_separator, encoding="string_escape") if label_separator
is not None else get_csv_delimiter())
first_row = self.get_next(reset=not training_set_header)
self.row_length = len(first_row)
if training_set_header:
self.headers = first_row
else:
self.headers = [("field_%s" % index)
for index in range(0, self.row_length)]
self.multi_label_fields = sorted(self._get_columns(multi_label_fields))
if objective:
self.objective_column = self._get_columns([objective_field])[0]
if not self.objective_column in self.multi_label_fields:
self.multi_label_fields.append(self.objective_column)
self.labels = labels
self.fields_labels = self._get_labels()
if objective:
if labels is None:
self.labels = self.fields_labels[self.objective_column]
self.objective_name = self.headers[self.objective_column]
