Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def print_config_values(self, prefix='- '):
print('Configuration values from ' + self.config_dir)
self.print_config_value(self.CONFIG_NAME_USER, prefix=prefix)
self.print_config_value(self.CONFIG_NAME_PATH, prefix=prefix)
self.print_c... | [
"a wrapper to print_config_value to print all configuration values\n Parameters\n ==========\n prefix: the character prefix to put before the printed config value\n defaults to \"- \"\n "
] |
Please provide a description of the function:def competitions_list(self,
group=None,
category=None,
sort_by=None,
page=1,
search=None):
valid_groups = ['general', 'entered',... | [
" make call to list competitions, format the response, and return\n a list of Competition instances\n\n Parameters\n ==========\n\n page: the page to return (default is 1)\n search: a search term to use (default is empty string)\n sort_by: how to sor... |
Please provide a description of the function:def competitions_list_cli(self,
group=None,
category=None,
sort_by=None,
page=1,
search=None,
c... | [
" a wrapper for competitions_list for the client.\n\n Parameters\n ==========\n group: group to filter result to\n category: category to filter result to\n sort_by: how to sort the result, see valid_sort_by for options\n page: the page to return (def... |
Please provide a description of the function:def competition_submit(self, file_name, message, competition, quiet=False):
if competition is None:
competition = self.get_config_value(self.CONFIG_NAME_COMPETITION)
if competition is not None and not quiet:
print('Usi... | [
" submit a competition!\n\n Parameters\n ==========\n file_name: the competition metadata file\n message: the submission description\n competition: the competition name\n quiet: suppress verbose output (default is False)\n "
] |
Please provide a description of the function:def competition_submit_cli(self,
file_name,
message,
competition,
competition_opt=None,
quiet=False):
c... | [
" submit a competition using the client. Arguments are same as for\n competition_submit, except for extra arguments provided here.\n Parameters\n ==========\n competition_opt: an alternative competition option provided by cli\n "
] |
Please provide a description of the function:def competition_submissions(self, competition):
submissions_result = self.process_response(
self.competitions_submissions_list_with_http_info(id=competition))
return [Submission(s) for s in submissions_result] | [
" get the list of Submission for a particular competition\n\n Parameters\n ==========\n competition: the name of the competition\n "
] |
Please provide a description of the function:def competition_submissions_cli(self,
competition=None,
competition_opt=None,
csv_display=False,
quiet=False):
com... | [
" wrapper to competition_submission, will return either json or csv\n to the user. Additional parameters are listed below, see\n competition_submissions for rest.\n\n Parameters\n ==========\n competition: the name of the competition. If None, look to config\n ... |
Please provide a description of the function:def competition_list_files(self, competition):
competition_list_files_result = self.process_response(
self.competitions_data_list_files_with_http_info(id=competition))
return [File(f) for f in competition_list_files_result] | [
" list files for competition\n Parameters\n ==========\n competition: the name of the competition\n "
] |
Please provide a description of the function:def competition_list_files_cli(self,
competition,
competition_opt=None,
csv_display=False,
quiet=False):
competition =... | [
" List files for a competition, if it exists\n\n Parameters\n ==========\n competition: the name of the competition. If None, look to config\n competition_opt: an alternative competition option provided by cli\n csv_display: if True, print comma separated value... |
Please provide a description of the function:def competition_download_file(self,
competition,
file_name,
path=None,
force=False,
quiet=False):
... | [
" download a competition file to a designated location, or use\n a default location\n\n Paramters\n =========\n competition: the name of the competition\n file_name: the configuration file name\n path: a path to download the file to\n forc... |
Please provide a description of the function:def competition_download_files(self,
competition,
path=None,
force=False,
quiet=True):
files = self.competition_list_f... | [
" a wrapper to competition_download_file to download all competition\n files.\n\n Parameters\n =========\n competition: the name of the competition\n path: a path to download the file to\n force: force the download if the file already exists (default... |
Please provide a description of the function:def competition_download_cli(self,
competition,
competition_opt=None,
file_name=None,
path=None,
force=False,
... | [
" a wrapper to competition_download_files, but first will parse input\n from API client. Additional parameters are listed here, see\n competition_download for remaining.\n\n Parameters\n =========\n competition: the name of the competition\n competit... |
Please provide a description of the function:def competition_leaderboard_download(self, competition, path, quiet=True):
response = self.process_response(
self.competition_download_leaderboard_with_http_info(
competition, _preload_content=False))
if path is None:
... | [
" Download competition leaderboards\n\n Parameters\n =========\n competition: the name of the competition\n path: a path to download the file to\n quiet: suppress verbose output (default is True)\n "
] |
Please provide a description of the function:def competition_leaderboard_view(self, competition):
result = self.process_response(
self.competition_view_leaderboard_with_http_info(competition))
return [LeaderboardEntry(e) for e in result['submissions']] | [
" view a leaderboard based on a competition name\n\n Parameters\n ==========\n competition: the competition name to view leadboard for\n "
] |
Please provide a description of the function:def competition_leaderboard_cli(self,
competition,
competition_opt=None,
path=None,
view=False,
... | [
" a wrapper for competition_leaderbord_view that will print the\n results as a table or comma separated values\n\n Parameters\n ==========\n competition: the competition name to view leadboard for\n competition_opt: an alternative competition option provided by... |
Please provide a description of the function:def dataset_list(self,
sort_by=None,
size=None,
file_type=None,
license_name=None,
tag_ids=None,
search=None,
user=None,
... | [
" return a list of datasets!\n\n Parameters\n ==========\n sort_by: how to sort the result, see valid_sort_bys for options\n size: the size of the dataset, see valid_sizes for string options\n file_type: the format, see valid_file_types for string options\n ... |
Please provide a description of the function:def dataset_list_cli(self,
sort_by=None,
size=None,
file_type=None,
license_name=None,
tag_ids=None,
search=None,
... | [
" a wrapper to datasets_list for the client. Additional parameters\n are described here, see dataset_list for others.\n\n Parameters\n ==========\n sort_by: how to sort the result, see valid_sort_bys for options\n size: the size of the dataset, see valid_sizes ... |
Please provide a description of the function:def dataset_view(self, dataset):
if '/' in dataset:
self.validate_dataset_string(dataset)
dataset_urls = dataset.split('/')
owner_slug = dataset_urls[0]
dataset_slug = dataset_urls[1]
else:
... | [
" view metadata for a dataset.\n\n Parameters\n ==========\n dataset: the string identified of the dataset\n should be in format [owner]/[dataset-name]\n "
] |
Please provide a description of the function:def dataset_list_files(self, dataset):
if dataset is None:
raise ValueError('A dataset must be specified')
if '/' in dataset:
self.validate_dataset_string(dataset)
dataset_urls = dataset.split('/')
owne... | [
" list files for a dataset\n Parameters\n ==========\n dataset: the string identified of the dataset\n should be in format [owner]/[dataset-name]\n "
] |
Please provide a description of the function:def dataset_list_files_cli(self,
dataset,
dataset_opt=None,
csv_display=False):
dataset = dataset or dataset_opt
result = self.dataset_list_files(dataset)
... | [
" a wrapper to dataset_list_files for the client\n (list files for a dataset)\n Parameters\n ==========\n dataset: the string identified of the dataset\n should be in format [owner]/[dataset-name]\n dataset_opt: an alternative option to pro... |
Please provide a description of the function:def dataset_status(self, dataset):
if dataset is None:
raise ValueError('A dataset must be specified')
if '/' in dataset:
self.validate_dataset_string(dataset)
dataset_urls = dataset.split('/')
owner_sl... | [
" call to get the status of a dataset from the API\n Parameters\n ==========\n dataset: the string identified of the dataset\n should be in format [owner]/[dataset-name]\n "
] |
Please provide a description of the function:def dataset_status_cli(self, dataset, dataset_opt=None):
dataset = dataset or dataset_opt
return self.dataset_status(dataset) | [
" wrapper for client for dataset_status, with additional\n dataset_opt to get the status of a dataset from the API\n Parameters\n ==========\n dataset_opt: an alternative to dataset\n "
] |
Please provide a description of the function:def dataset_download_file(self,
dataset,
file_name,
path=None,
force=False,
quiet=True):
if '/' in dataset:
... | [
" download a single file for a dataset\n\n Parameters\n ==========\n dataset: the string identified of the dataset\n should be in format [owner]/[dataset-name]\n file_name: the dataset configuration file\n path: if defined, download to this ... |
Please provide a description of the function:def dataset_download_files(self,
dataset,
path=None,
force=False,
quiet=True,
unzip=False):
if dataset ... | [
" download all files for a dataset\n\n Parameters\n ==========\n dataset: the string identified of the dataset\n should be in format [owner]/[dataset-name]\n path: the path to download the dataset to\n force: force the download if the file a... |
Please provide a description of the function:def dataset_download_cli(self,
dataset,
dataset_opt=None,
file_name=None,
path=None,
unzip=False,
for... | [
" client wrapper for dataset_download_files and download dataset file,\n either for a specific file (when file_name is provided),\n or all files for a dataset (plural)\n\n Parameters\n ==========\n dataset: the string identified of the dataset\n ... |
Please provide a description of the function:def dataset_upload_file(self, path, quiet):
file_name = os.path.basename(path)
content_length = os.path.getsize(path)
last_modified_date_utc = int(os.path.getmtime(path))
result = FileUploadInfo(
self.process_response(
... | [
" upload a dataset file\n\n Parameters\n ==========\n path: the complete path to upload\n quiet: suppress verbose output (default is False)\n "
] |
Please provide a description of the function:def dataset_create_version(self,
folder,
version_notes,
quiet=False,
convert_to_csv=True,
delete_old_versions=False,
... | [
" create a version of a dataset\n\n Parameters\n ==========\n folder: the folder with the dataset configuration / data files\n version_notes: notes to add for the version\n quiet: suppress verbose output (default is False)\n convert_to_csv: on upload... |
Please provide a description of the function:def dataset_create_version_cli(self,
folder,
version_notes,
quiet=False,
convert_to_csv=True,
delete... | [
" client wrapper for creating a version of a dataset\n Parameters\n ==========\n folder: the folder with the dataset configuration / data files\n version_notes: notes to add for the version\n quiet: suppress verbose output (default is False)\n conve... |
Please provide a description of the function:def dataset_initialize(self, folder):
if not os.path.isdir(folder):
raise ValueError('Invalid folder: ' + folder)
ref = self.config_values[self.CONFIG_NAME_USER] + '/INSERT_SLUG_HERE'
licenses = []
default_license = {'nam... | [
" initialize a folder with a a dataset configuration (metadata) file\n\n Parameters\n ==========\n folder: the folder to initialize the metadata file in\n "
] |
Please provide a description of the function:def dataset_create_new(self,
folder,
public=False,
quiet=False,
convert_to_csv=True,
dir_mode='skip'):
if not os.path.isdir... | [
" create a new dataset, meaning the same as creating a version but\n with extra metadata like license and user/owner.\n Parameters\n ==========\n folder: the folder to initialize the metadata file in\n public: should the dataset be public?\n quiet: ... |
Please provide a description of the function:def dataset_create_new_cli(self,
folder=None,
public=False,
quiet=False,
convert_to_csv=True,
dir_mode='skip'):
... | [
" client wrapper for creating a new dataset\n Parameters\n ==========\n folder: the folder to initialize the metadata file in\n public: should the dataset be public?\n quiet: suppress verbose output (default is False)\n convert_to_csv: if True, conv... |
Please provide a description of the function:def download_file(self, response, outfile, quiet=True, chunk_size=1048576):
outpath = os.path.dirname(outfile)
if not os.path.exists(outpath):
os.makedirs(outpath)
size = int(response.headers['Content-Length'])
size_read ... | [
" download a file to an output file based on a chunk size\n\n Parameters\n ==========\n response: the response to download\n outfile: the output file to download to\n quiet: suppress verbose output (default is True)\n chunk_size: the size of the chun... |
Please provide a description of the function:def kernels_list(self,
page=1,
page_size=20,
dataset=None,
competition=None,
parent_kernel=None,
search=None,
mine=False,
... | [
" list kernels based on a set of search criteria\n\n Parameters\n ==========\n page: the page of results to return (default is 1)\n page_size: results per page (default is 20)\n dataset: if defined, filter to this dataset (default None)\n competition... |
Please provide a description of the function:def kernels_list_cli(self,
mine=False,
page=1,
page_size=20,
search=None,
csv_display=False,
parent=None,
... | [
" client wrapper for kernels_list, see this function for arguments.\n Additional arguments are provided here.\n Parameters\n ==========\n csv_display: if True, print comma separated values instead of table\n "
] |
Please provide a description of the function:def kernels_initialize(self, folder):
if not os.path.isdir(folder):
raise ValueError('Invalid folder: ' + folder)
resources = []
resource = {'path': 'INSERT_SCRIPT_PATH_HERE'}
resources.append(resource)
username ... | [
" create a new kernel in a specified folder from template, including\n json metadata that grabs values from the configuration.\n Parameters\n ==========\n folder: the path of the folder\n "
] |
Please provide a description of the function:def kernels_initialize_cli(self, folder=None):
folder = folder or os.getcwd()
meta_file = self.kernels_initialize(folder)
print('Kernel metadata template written to: ' + meta_file) | [
" client wrapper for kernels_initialize, takes same arguments but\n sets default folder to be None. If None, defaults to present\n working directory.\n Parameters\n ==========\n folder: the path of the folder (None defaults to ${PWD})\n "
] |
Please provide a description of the function:def kernels_push(self, folder):
if not os.path.isdir(folder):
raise ValueError('Invalid folder: ' + folder)
meta_file = os.path.join(folder, self.KERNEL_METADATA_FILE)
if not os.path.isfile(meta_file):
raise ValueErro... | [
" read the metadata file and kernel files from a notebook, validate\n both, and use Kernel API to push to Kaggle if all is valid.\n Parameters\n ==========\n folder: the path of the folder\n "
] |
Please provide a description of the function:def kernels_push_cli(self, folder):
folder = folder or os.getcwd()
result = self.kernels_push(folder)
if result is None:
print('Kernel push error: see previous output')
elif not result.error:
if result.invalid... | [
" client wrapper for kernels_push, with same arguments.\n "
] |
Please provide a description of the function:def kernels_pull(self, kernel, path, metadata=False, quiet=True):
existing_metadata = None
if kernel is None:
if path is None:
existing_metadata_path = os.path.join(
os.getcwd(), self.KERNEL_METADATA_FI... | [
" pull a kernel, including a metadata file (if metadata is True)\n and associated files to a specified path.\n Parameters\n ==========\n kernel: the kernel to pull\n path: the path to pull files to on the filesystem\n metadata: if True, also pull me... |
Please provide a description of the function:def kernels_pull_cli(self,
kernel,
kernel_opt=None,
path=None,
metadata=False):
kernel = kernel or kernel_opt
effective_path = self.kernels_pull(
... | [
" client wrapper for kernels_pull\n "
] |
Please provide a description of the function:def kernels_output(self, kernel, path, force=False, quiet=True):
if kernel is None:
raise ValueError('A kernel must be specified')
if '/' in kernel:
self.validate_kernel_string(kernel)
kernel_url_list = kernel.spli... | [
" retrieve output for a specified kernel\n Parameters\n ==========\n kernel: the kernel to output\n path: the path to pull files to on the filesystem\n force: if output already exists, force overwrite (default False)\n quiet: suppress verbosity (def... |
Please provide a description of the function:def kernels_output_cli(self,
kernel,
kernel_opt=None,
path=None,
force=False,
quiet=False):
kernel = kernel or kernel_opt
... | [
" client wrapper for kernels_output, with same arguments. Extra\n arguments are described below, and see kernels_output for others.\n Parameters\n ==========\n kernel_opt: option from client instead of kernel, if not defined\n "
] |
Please provide a description of the function:def kernels_status(self, kernel):
if kernel is None:
raise ValueError('A kernel must be specified')
if '/' in kernel:
self.validate_kernel_string(kernel)
kernel_url_list = kernel.split('/')
owner_slug =... | [
" call to the api to get the status of a kernel.\n Parameters\n ==========\n kernel: the kernel to get the status for\n "
] |
Please provide a description of the function:def kernels_status_cli(self, kernel, kernel_opt=None):
kernel = kernel or kernel_opt
response = self.kernels_status(kernel)
status = response['status']
message = response['failureMessage']
if message:
print('%s has... | [
" client wrapper for kernel_status\n Parameters\n ==========\n kernel_opt: additional option from the client, if kernel not defined\n "
] |
Please provide a description of the function:def download_needed(self, response, outfile, quiet=True):
try:
remote_date = datetime.strptime(response.headers['Last-Modified'],
'%a, %d %b %Y %X %Z')
if isfile(outfile):
lo... | [
" determine if a download is needed based on timestamp. Return True\n if needed (remote is newer) or False if local is newest.\n Parameters\n ==========\n response: the response from the API\n outfile: the output file to write to\n quiet: suppress v... |
Please provide a description of the function:def print_table(self, items, fields):
formats = []
borders = []
for f in fields:
length = max(
len(f), max([len(self.string(getattr(i, f))) for i in items]))
justify = '>' if isinstance(getattr(
... | [
" print a table of items, for a set of fields defined\n\n Parameters\n ==========\n items: a list of items to print\n fields: a list of fields to select from items\n "
] |
Please provide a description of the function:def print_csv(self, items, fields):
writer = csv.writer(sys.stdout)
writer.writerow(fields)
for i in items:
i_fields = [self.string(getattr(i, f)) for f in fields]
writer.writerow(i_fields) | [
" print a set of fields in a set of items using a csv.writer\n\n Parameters\n ==========\n items: a list of items to print\n fields: a list of fields to select from items\n "
] |
Please provide a description of the function:def process_response(self, result):
if len(result) == 3:
data = result[0]
headers = result[2]
if self.HEADER_API_VERSION in headers:
api_version = headers[self.HEADER_API_VERSION]
if (not se... | [
" process a response from the API. We check the API version against\n the client's to see if it's old, and give them a warning (once)\n\n Parameters\n ==========\n result: the result from the API\n "
] |
Please provide a description of the function:def is_up_to_date(self, server_version):
client_split = self.__version__.split('.')
client_len = len(client_split)
server_split = server_version.split('.')
server_len = len(server_split)
# Make both lists the same length
... | [
" determine if a client (on the local user's machine) is up to date\n with the version provided on the server. Return a boolean with True\n or False\n Parameters\n ==========\n server_version: the server version string to compare to the host\n "
] |
Please provide a description of the function:def upload_files(self,
request,
resources,
folder,
quiet=False,
dir_mode='skip'):
for file_name in os.listdir(folder):
if (file_name == self.... | [
" upload files in a folder\n Parameters\n ==========\n request: the prepared request\n resources: the files to upload\n folder: the folder to upload from\n quiet: suppress verbose output (default is False)\n "
] |
Please provide a description of the function:def _upload_file(self, file_name, full_path, quiet, request, resources):
if not quiet:
print('Starting upload for file ' + file_name)
content_length = os.path.getsize(full_path)
token = self.dataset_upload_file(full_path, quiet)... | [
" Helper function to upload a single file\n Parameters\n ==========\n file_name: name of the file to upload\n full_path: path to the file to upload\n request: the prepared request\n resources: optional file metadata\n quiet: suppress verbo... |
Please provide a description of the function:def process_column(self, column):
processed_column = DatasetColumn(
name=self.get_or_fail(column, 'name'),
description=self.get_or_default(column, 'description', ''))
if 'type' in column:
original_type = column['ty... | [
" process a column, check for the type, and return the processed\n column\n Parameters\n ==========\n column: a list of values in a column to be processed\n "
] |
Please provide a description of the function:def upload_complete(self, path, url, quiet):
file_size = os.path.getsize(path)
try:
with tqdm(
total=file_size,
unit='B',
unit_scale=True,
unit_divisor=1024,
... | [
" function to complete an upload to retrieve a path from a url\n Parameters\n ==========\n path: the path for the upload that is read in\n url: the url to send the POST to\n quiet: suppress verbose output (default is False)\n "
] |
Please provide a description of the function:def validate_dataset_string(self, dataset):
if dataset:
if '/' not in dataset:
raise ValueError('Dataset must be specified in the form of '
'\'{username}/{dataset-slug}\'')
split = dat... | [
" determine if a dataset string is valid, meaning it is in the format\n of {username}/{dataset-slug}.\n Parameters\n ==========\n dataset: the dataset name to validate\n "
] |
Please provide a description of the function:def validate_kernel_string(self, kernel):
if kernel:
if '/' not in kernel:
raise ValueError('Kernel must be specified in the form of '
'\'{username}/{kernel-slug}\'')
split = kernel.sp... | [
" determine if a kernel string is valid, meaning it is in the format\n of {username}/{kernel-slug}.\n Parameters\n ==========\n kernel: the kernel name to validate\n "
] |
Please provide a description of the function:def validate_resources(self, folder, resources):
self.validate_files_exist(folder, resources)
self.validate_no_duplicate_paths(resources) | [
" validate resources is a wrapper to validate the existence of files\n and that there are no duplicates for a folder and set of resources.\n\n Parameters\n ==========\n folder: the folder to validate\n resources: one or more resources to validate within the fol... |
Please provide a description of the function:def validate_files_exist(self, folder, resources):
for item in resources:
file_name = item.get('path')
full_path = os.path.join(folder, file_name)
if not os.path.isfile(full_path):
raise ValueError('%s does... | [
" ensure that one or more resource files exist in a folder\n\n Parameters\n ==========\n folder: the folder to validate\n resources: one or more resources to validate within the folder\n "
] |
Please provide a description of the function:def validate_no_duplicate_paths(self, resources):
paths = set()
for item in resources:
file_name = item.get('path')
if file_name in paths:
raise ValueError(
'%s path was specified more than ... | [
" ensure that the user has not provided duplicate paths in\n a list of resources.\n\n Parameters\n ==========\n resources: one or more resources to validate not duplicated\n "
] |
Please provide a description of the function:def convert_to_dataset_file_metadata(self, file_data, path):
as_metadata = {
'path': os.path.join(path, file_data['name']),
'description': file_data['description']
}
schema = {}
fields = []
for column ... | [
" convert a set of file_data to a metadata file at path\n\n Parameters\n ==========\n file_data: a dictionary of file data to write to file\n path: the path to write the metadata to\n "
] |
Please provide a description of the function:def read(self, *args, **kwargs):
buf = io.BufferedReader.read(self, *args, **kwargs)
self.increment(len(buf))
return buf | [
" read the buffer, passing named and non named arguments to the\n io.BufferedReader function.\n "
] |
Please provide a description of the function:def parameters_to_tuples(self, params, collection_formats):
new_params = []
if collection_formats is None:
collection_formats = {}
for k, v in six.iteritems(params) if isinstance(params, dict) else params: # noqa: E501
... | [
"Get parameters as list of tuples, formatting collections.\n\n :param params: Parameters as dict or list of two-tuples\n :param dict collection_formats: Parameter collection formats\n :return: Parameters as list of tuples, collections formatted\n "
] |
Please provide a description of the function:def prepare_post_parameters(self, post_params=None, files=None):
params = []
if post_params:
params = post_params
if files:
for k, v in six.iteritems(files):
if not v:
continue
... | [
"Builds form parameters.\n\n :param post_params: Normal form parameters.\n :param files: File parameters.\n :return: Form parameters with files.\n "
] |
Please provide a description of the function:def __deserialize_file(self, response):
fd, path = tempfile.mkstemp(dir=self.configuration.temp_folder_path)
os.close(fd)
os.remove(path)
content_disposition = response.getheader("Content-Disposition")
if content_disposition:... | [
"Deserializes body to file\n\n Saves response body into a file in a temporary folder,\n using the filename from the `Content-Disposition` header if provided.\n\n :param response: RESTResponse.\n :return: file path.\n "
] |
Please provide a description of the function:def __deserialize_primitive(self, data, klass):
try:
return klass(data)
except UnicodeEncodeError:
return six.text_type(data)
except TypeError:
return data | [
"Deserializes string to primitive type.\n\n :param data: str.\n :param klass: class literal.\n\n :return: int, long, float, str, bool.\n "
] |
Please provide a description of the function:def logger_file(self, value):
self.__logger_file = value
if self.__logger_file:
# If set logging file,
# then add file handler and remove stream handler.
self.logger_file_handler = logging.FileHandler(self.__logger... | [
"The logger file.\n\n If the logger_file is None, then add stream handler and remove file\n handler. Otherwise, add file handler and remove stream handler.\n\n :param value: The logger_file path.\n :type: str\n "
] |
Please provide a description of the function:def license_name(self, license_name):
allowed_values = ["CC0-1.0", "CC-BY-SA-4.0", "GPL-2.0", "ODbL-1.0", "CC-BY-NC-SA-4.0", "unknown", "DbCL-1.0", "CC-BY-SA-3.0", "copyright-authors", "other", "reddit-api", "world-bank"] # noqa: E501
if license_nam... | [
"Sets the license_name of this DatasetNewRequest.\n\n The license that should be associated with the dataset # noqa: E501\n\n :param license_name: The license_name of this DatasetNewRequest. # noqa: E501\n :type: str\n "
] |
Please provide a description of the function:def train(net, train_data, test_data):
start_pipeline_time = time.time()
net, trainer = text_cnn.init(net, vocab, args.model_mode, context, args.lr)
random.shuffle(train_data)
sp = int(len(train_data)*0.9)
train_dataloader = DataLoader(dataset=train_... | [
"Train textCNN model for sentiment analysis."
] |
Please provide a description of the function:def embedding(self, sentences, oov_way='avg'):
data_iter = self.data_loader(sentences=sentences)
batches = []
for token_ids, valid_length, token_types in data_iter:
token_ids = token_ids.as_in_context(self.ctx)
valid_l... | [
"\n Get tokens, tokens embedding\n\n Parameters\n ----------\n sentences : List[str]\n sentences for encoding.\n oov_way : str, default avg.\n use **avg**, **sum** or **last** to get token embedding for those out of\n vocabulary words\n\n Re... |
Please provide a description of the function:def data_loader(self, sentences, shuffle=False):
dataset = BertEmbeddingDataset(sentences, self.transform)
return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle) | [
"Load, tokenize and prepare the input sentences."
] |
Please provide a description of the function:def oov(self, batches, oov_way='avg'):
sentences = []
for token_ids, sequence_outputs in batches:
tokens = []
tensors = []
oov_len = 1
for token_id, sequence_output in zip(token_ids, sequence_outputs):
... | [
"\n How to handle oov. Also filter out [CLS], [SEP] tokens.\n\n Parameters\n ----------\n batches : List[(tokens_id,\n sequence_outputs,\n pooled_output].\n batch token_ids (max_seq_length, ),\n sequence_output... |
Please provide a description of the function:def get_bert_model(model_name=None, dataset_name=None, vocab=None,
pretrained=True, ctx=mx.cpu(),
use_pooler=True, use_decoder=True, use_classifier=True,
output_attention=False, output_all_encodings=False,
... | [
"Any BERT pretrained model.\n\n Parameters\n ----------\n model_name : str or None, default None\n Options include 'bert_24_1024_16' and 'bert_12_768_12'.\n dataset_name : str or None, default None\n Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased'\n for bo... |
Please provide a description of the function:def hybrid_forward(self, F, data, gamma, beta):
# TODO(haibin): LayerNorm does not support fp16 safe reduction. Issue is tracked at:
# https://github.com/apache/incubator-mxnet/issues/14073
if self._dtype:
data = data.astype('floa... | [
"forward computation."
] |
Please provide a description of the function:def _get_classifier(self, prefix):
with self.name_scope():
classifier = nn.Dense(2, prefix=prefix)
return classifier | [
" Construct a decoder for the next sentence prediction task "
] |
Please provide a description of the function:def _get_decoder(self, units, vocab_size, embed, prefix):
with self.name_scope():
decoder = nn.HybridSequential(prefix=prefix)
decoder.add(nn.Dense(units, flatten=False))
decoder.add(GELU())
decoder.add(BERTLay... | [
" Construct a decoder for the masked language model task "
] |
Please provide a description of the function:def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
if embed is None:
assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
'token_type_embed i... | [
" Construct an embedding block. "
] |
Please provide a description of the function:def _get_pooler(self, units, prefix):
with self.name_scope():
pooler = nn.Dense(units=units, flatten=False, activation='tanh',
prefix=prefix)
return pooler | [
" Construct pooler.\n\n The pooler slices and projects the hidden output of first token\n in the sequence for segment level classification.\n\n "
] |
Please provide a description of the function:def _encode_sequence(self, inputs, token_types, valid_length=None):
# embedding
word_embedding = self.word_embed(inputs)
type_embedding = self.token_type_embed(token_types)
embedding = word_embedding + type_embedding
# encodin... | [
"Generate the representation given the input sequences.\n\n This is used for pre-training or fine-tuning a BERT model.\n "
] |
Please provide a description of the function:def _decode(self, sequence, masked_positions):
batch_size = sequence.shape[0]
num_masked_positions = masked_positions.shape[1]
ctx = masked_positions.context
dtype = masked_positions.dtype
# batch_idx = [0,0,0,1,1,1,2,2,2...]
... | [
"Generate unnormalized prediction for the masked language model task.\n\n This is only used for pre-training the BERT model.\n\n Inputs:\n - **sequence**: input tensor of sequence encodings.\n Shape (batch_size, seq_length, units).\n - **masked_positions**: input ten... |
Please provide a description of the function:def _ngrams(segment, n):
ngram_counts = Counter()
for i in range(0, len(segment) - n + 1):
ngram = tuple(segment[i:i + n])
ngram_counts[ngram] += 1
return ngram_counts | [
"Extracts n-grams from an input segment.\n\n Parameters\n ----------\n segment: list\n Text segment from which n-grams will be extracted.\n n: int\n Order of n-gram.\n\n Returns\n -------\n ngram_counts: Counter\n Contain all the nth n-grams in segment with a count of how m... |
Please provide a description of the function:def _bpe_to_words(sentence, delimiter='@@'):
words = []
word = ''
delimiter_len = len(delimiter)
for subwords in sentence:
if len(subwords) >= delimiter_len and subwords[-delimiter_len:] == delimiter:
word += subwords[:-delimiter_len]... | [
"Convert a sequence of bpe words into sentence."
] |
Please provide a description of the function:def _tokenize_mteval_13a(segment):
r
norm = segment.rstrip()
norm = norm.replace('<skipped>', '')
norm = norm.replace('-\n', '')
norm = norm.replace('\n', ' ')
norm = norm.replace('"', '"')
norm = norm.replace('&', '&')
norm = norm.... | [
"\n Tokenizes a string following the tokenizer in mteval-v13a.pl.\n See https://github.com/moses-smt/mosesdecoder/\"\n \"blob/master/scripts/generic/mteval-v14.pl#L917-L942\n Parameters\n ----------\n segment: str\n A string to be tokenized\n\n Returns\n -------\n The tokeni... |
Please provide a description of the function:def _tokenize_mteval_v14_intl(segment):
r
segment = segment.rstrip()
segment = unicodeRegex.nondigit_punct_re.sub(r'\1 \2 ', segment)
segment = unicodeRegex.punct_nondigit_re.sub(r' \1 \2', segment)
segment = unicodeRegex.symbol_re.sub(r' \1 ', segment)
... | [
"Tokenize a string following following the international tokenizer in mteval-v14a.pl.\n See https://github.com/moses-smt/mosesdecoder/\"\n \"blob/master/scripts/generic/mteval-v14.pl#L954-L983\n\n Parameters\n ----------\n segment: str\n A string to be tokenized\n\n Returns\n ----... |
Please provide a description of the function:def compute_bleu(reference_corpus_list, translation_corpus, tokenized=True,
tokenizer='13a', max_n=4, smooth=False, lower_case=False,
bpe=False, split_compound_word=False):
r
precision_numerators = [0 for _ in range(max_n)]
preci... | [
"Compute bleu score of translation against references.\n\n Parameters\n ----------\n reference_corpus_list: list of list(list(str)) or list of list(str)\n list of list(list(str)): tokenized references\n list of list(str): plain text\n List of references for each translation.\n trans... |
Please provide a description of the function:def _compute_precision(references, translation, n):
matches = 0
candidates = 0
ref_ngram_counts = Counter()
for reference in references:
ref_ngram_counts |= _ngrams(reference, n)
trans_ngram_counts = _ngrams(translation, n)
overlap_ngram... | [
"Compute ngram precision.\n\n Parameters\n ----------\n references: list(list(str))\n A list of references.\n translation: list(str)\n A translation.\n n: int\n Order of n-gram.\n\n Returns\n -------\n matches: int\n Number of matched nth order n-grams\n candid... |
Please provide a description of the function:def _brevity_penalty(ref_length, trans_length):
if trans_length > ref_length:
return 1
# If translation is empty, brevity penalty = 0 should result in BLEU = 0.0
elif trans_length == 0:
return 0
else:
return math.exp(1 - float(ref... | [
"Calculate brevity penalty.\n\n Parameters\n ----------\n ref_length: int\n Sum of all closest references'lengths for every translations in a corpus\n trans_length: int\n Sum of all translations's lengths in a corpus.\n\n Returns\n -------\n bleu's brevity penalty: float\n "
] |
Please provide a description of the function:def _closest_ref_length(references, trans_length):
ref_lengths = (len(reference) for reference in references)
closest_ref_len = min(ref_lengths,
key=lambda ref_length: (abs(ref_length - trans_length), ref_length))
return closest_re... | [
"Find the reference that has the closest length to the translation.\n\n Parameters\n ----------\n references: list(list(str))\n A list of references.\n trans_length: int\n Length of the translation.\n\n Returns\n -------\n closest_ref_len: int\n Length of the reference that... |
Please provide a description of the function:def _smoothing(precision_fractions, c=1):
ratios = [0] * len(precision_fractions)
for i, precision_fraction in enumerate(precision_fractions):
if precision_fraction[1] > 0:
ratios[i] = float(precision_fraction[0] + c) / (precision_fraction[1]... | [
"Compute the smoothed precision for all the orders.\n\n Parameters\n ----------\n precision_fractions: list(tuple)\n Contain a list of (precision_numerator, precision_denominator) pairs\n c: int, default 1\n Smoothing constant to use\n\n Returns\n -------\n ratios: list of floats\... |
Please provide a description of the function:def forward(self, true_classes):
num_sampled = self._num_sampled
ctx = true_classes.context
num_tries = 0
log_range = math.log(self._range_max + 1)
# sample candidates
f = ndarray._internal._sample_unique_zipfian
... | [
"Draw samples from log uniform distribution and returns sampled candidates,\n expected count for true classes and sampled classes.\n\n Parameters\n ----------\n true_classes: NDArray\n The true classes.\n\n Returns\n -------\n samples: NDArray\n ... |
Please provide a description of the function:def preprocess_dataset(data, min_freq=5, max_vocab_size=None):
with print_time('count and construct vocabulary'):
counter = nlp.data.count_tokens(itertools.chain.from_iterable(data))
vocab = nlp.Vocab(counter, unknown_token=None, padding_token=None,
... | [
"Dataset preprocessing helper.\n\n Parameters\n ----------\n data : mx.data.Dataset\n Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9\n min_freq : int, default 5\n Minimum token frequency for a token to be included in the vocabulary\n and returned DataStream.\n ... |
Please provide a description of the function:def wiki(wiki_root, wiki_date, wiki_language, max_vocab_size=None):
data = WikiDumpStream(
root=os.path.expanduser(wiki_root), language=wiki_language,
date=wiki_date)
vocab = data.vocab
if max_vocab_size:
for token in vocab.idx_to_tok... | [
"Wikipedia dump helper.\n\n Parameters\n ----------\n wiki_root : str\n Parameter for WikiDumpStream\n wiki_date : str\n Parameter for WikiDumpStream\n wiki_language : str\n Parameter for WikiDumpStream\n max_vocab_size : int, optional\n Specifies a maximum size for the... |
Please provide a description of the function:def transform_data_fasttext(data, vocab, idx_to_counts, cbow, ngram_buckets,
ngrams, batch_size, window_size,
frequent_token_subsampling=1E-4, dtype='float32',
index_dtype='int64'):
... | [
"Transform a DataStream of coded DataSets to a DataStream of batches.\n\n Parameters\n ----------\n data : gluonnlp.data.DataStream\n DataStream where each sample is a valid input to\n gluonnlp.data.EmbeddingCenterContextBatchify.\n vocab : gluonnlp.Vocab\n Vocabulary containing all... |
Please provide a description of the function:def transform_data_word2vec(data, vocab, idx_to_counts, cbow, batch_size,
window_size, frequent_token_subsampling=1E-4,
dtype='float32', index_dtype='int64'):
sum_counts = float(sum(idx_to_counts))
idx_to_... | [
"Transform a DataStream of coded DataSets to a DataStream of batches.\n\n Parameters\n ----------\n data : gluonnlp.data.DataStream\n DataStream where each sample is a valid input to\n gluonnlp.data.EmbeddingCenterContextBatchify.\n vocab : gluonnlp.Vocab\n Vocabulary containing all... |
Please provide a description of the function:def cbow_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype,
index_dtype):
_, contexts_row, contexts_col = contexts
data, row, col = subword_lookup(contexts_row, contexts_col)
centers = mx.nd.array(centers, dtype=inde... | [
"Create a batch for CBOW training objective with subwords."
] |
Please provide a description of the function:def skipgram_fasttext_batch(centers, contexts, num_tokens, subword_lookup,
dtype, index_dtype):
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
data, row, col = subword_lookup(centers)
centers = mx.nd.array(centers, dtype=i... | [
"Create a batch for SG training objective with subwords."
] |
Please provide a description of the function:def cbow_batch(centers, contexts, num_tokens, dtype, index_dtype):
contexts_data, contexts_row, contexts_col = contexts
centers = mx.nd.array(centers, dtype=index_dtype)
contexts = mx.nd.sparse.csr_matrix(
(contexts_data, (contexts_row, contexts_col)... | [
"Create a batch for CBOW training objective."
] |
Please provide a description of the function:def skipgram_batch(centers, contexts, num_tokens, dtype, index_dtype):
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
indptr = mx.nd.arange(len(centers) + 1)
centers = mx.nd.array(centers, dtype=index_dtype)
centers_csr = mx.nd.sparse.csr_matrix(... | [
"Create a batch for SG training objective."
] |
Please provide a description of the function:def skipgram_lookup(indices, subwordidxs, subwordidxsptr, offset=0):
row = []
col = []
data = []
for i, idx in enumerate(indices):
start = subwordidxsptr[idx]
end = subwordidxsptr[idx + 1]
row.append(i)
col.append(idx)
... | [
"Get a sparse COO array of words and subwords for SkipGram.\n\n Parameters\n ----------\n indices : numpy.ndarray\n Array containing numbers in [0, vocabulary_size). The element at\n position idx is taken to be the word that occurs at row idx in the\n SkipGram batch.\n offset : int\... |
Please provide a description of the function:def cbow_lookup(context_row, context_col, subwordidxs, subwordidxsptr,
offset=0):
row = []
col = []
data = []
num_rows = np.max(context_row) + 1
row_to_numwords = np.zeros(num_rows)
for i, idx in enumerate(context_col):
... | [
"Get a sparse COO array of words and subwords for CBOW.\n\n Parameters\n ----------\n context_row : numpy.ndarray of dtype int64\n Array of same length as context_col containing numbers in [0,\n batch_size). For each idx, context_row[idx] specifies the row that\n context_col[idx] occur... |
Please provide a description of the function:def src_vocab(self):
if self._src_vocab is None:
src_vocab_file_name, src_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + self._src_lang]
[src_vocab_path] = self._fetch_data_path([(src_vocab_file_name, s... | [
"Source Vocabulary of the Dataset.\n\n Returns\n -------\n src_vocab : Vocab\n Source vocabulary.\n "
] |
Please provide a description of the function:def tgt_vocab(self):
if self._tgt_vocab is None:
tgt_vocab_file_name, tgt_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + self._tgt_lang]
[tgt_vocab_path] = self._fetch_data_path([(tgt_vocab_file_name, t... | [
"Target Vocabulary of the Dataset.\n\n Returns\n -------\n tgt_vocab : Vocab\n Target vocabulary.\n "
] |
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