id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
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32,400 | paramiko/paramiko | paramiko/client.py | SSHClient.close | def close(self):
"""
Close this SSHClient and its underlying `.Transport`.
.. warning::
Failure to do this may, in some situations, cause your Python
interpreter to hang at shutdown (often due to race conditions).
It's good practice to `close` your client obj... | python | def close(self):
"""
Close this SSHClient and its underlying `.Transport`.
.. warning::
Failure to do this may, in some situations, cause your Python
interpreter to hang at shutdown (often due to race conditions).
It's good practice to `close` your client obj... | [
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32,401 | paramiko/paramiko | paramiko/client.py | SSHClient.exec_command | def exec_command(
self,
command,
bufsize=-1,
timeout=None,
get_pty=False,
environment=None,
):
"""
Execute a command on the SSH server. A new `.Channel` is opened and
the requested command is executed. The command's input and output
s... | python | def exec_command(
self,
command,
bufsize=-1,
timeout=None,
get_pty=False,
environment=None,
):
"""
Execute a command on the SSH server. A new `.Channel` is opened and
the requested command is executed. The command's input and output
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32,402 | paramiko/paramiko | paramiko/client.py | SSHClient.invoke_shell | def invoke_shell(
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width_pixels=0,
height_pixels=0,
environment=None,
):
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Start an interactive shell session on the SSH server. A new `.Channel`
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height_pixels=0,
environment=None,
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Start an interactive shell session on the SSH server. A new `.Channel`
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32,403 | Kaggle/kaggle-api | kaggle/models/kernel_push_request.py | KernelPushRequest.language | def language(self, language):
"""Sets the language of this KernelPushRequest.
The language that the kernel is written in # noqa: E501
:param language: The language of this KernelPushRequest. # noqa: E501
:type: str
"""
if language is None:
raise ValueError... | python | def language(self, language):
"""Sets the language of this KernelPushRequest.
The language that the kernel is written in # noqa: E501
:param language: The language of this KernelPushRequest. # noqa: E501
:type: str
"""
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32,404 | Kaggle/kaggle-api | kaggle/models/kernel_push_request.py | KernelPushRequest.kernel_type | def kernel_type(self, kernel_type):
"""Sets the kernel_type of this KernelPushRequest.
The type of kernel. Cannot be changed once the kernel has been created # noqa: E501
:param kernel_type: The kernel_type of this KernelPushRequest. # noqa: E501
:type: str
"""
if ker... | python | def kernel_type(self, kernel_type):
"""Sets the kernel_type of this KernelPushRequest.
The type of kernel. Cannot be changed once the kernel has been created # noqa: E501
:param kernel_type: The kernel_type of this KernelPushRequest. # noqa: E501
:type: str
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32,405 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.read_config_environment | def read_config_environment(self, config_data=None, quiet=False):
"""read_config_environment is the second effort to get a username
and key to authenticate to the Kaggle API. The environment keys
are equivalent to the kaggle.json file, but with "KAGGLE_" prefix
to define a uniqu... | python | def read_config_environment(self, config_data=None, quiet=False):
"""read_config_environment is the second effort to get a username
and key to authenticate to the Kaggle API. The environment keys
are equivalent to the kaggle.json file, but with "KAGGLE_" prefix
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32,406 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi._load_config | def _load_config(self, config_data):
"""the final step of the authenticate steps, where we load the values
from config_data into the Configuration object.
Parameters
==========
config_data: a dictionary with configuration values (keys) to read
... | python | def _load_config(self, config_data):
"""the final step of the authenticate steps, where we load the values
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Parameters
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config_data: a dictionary with configuration values (keys) to read
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32,407 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.read_config_file | def read_config_file(self, config_data=None, quiet=False):
"""read_config_file is the first effort to get a username
and key to authenticate to the Kaggle API. Since we can get the
username and password from the environment, it's not required.
Parameters
==========
... | python | def read_config_file(self, config_data=None, quiet=False):
"""read_config_file is the first effort to get a username
and key to authenticate to the Kaggle API. Since we can get the
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Parameters
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32,408 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi._read_config_file | def _read_config_file(self):
"""read in the configuration file, a json file defined at self.config"""
try:
with open(self.config, 'r') as f:
config_data = json.load(f)
except FileNotFoundError:
config_data = {}
return config_data | python | def _read_config_file(self):
"""read in the configuration file, a json file defined at self.config"""
try:
with open(self.config, 'r') as f:
config_data = json.load(f)
except FileNotFoundError:
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32,409 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi._write_config_file | def _write_config_file(self, config_data, indent=2):
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Parameters
==========
config_data: the Configuration object to save a username and
password, if defined
indent: number of tab indentations to use when writing j... | python | def _write_config_file(self, config_data, indent=2):
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32,410 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.get_default_download_dir | def get_default_download_dir(self, *subdirs):
""" Get the download path for a file. If not defined, return default
from config.
Parameters
==========
subdirs: a single (or list of) subfolders under the basepath
"""
# Look up value for key "path" i... | python | def get_default_download_dir(self, *subdirs):
""" Get the download path for a file. If not defined, return default
from config.
Parameters
==========
subdirs: a single (or list of) subfolders under the basepath
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32,411 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.print_config_value | def print_config_value(self, name, prefix='- ', separator=': '):
"""print a single configuration value, based on a prefix and separator
Parameters
==========
name: the key of the config valur in self.config_values to print
prefix: the prefix to print
separ... | python | def print_config_value(self, name, prefix='- ', separator=': '):
"""print a single configuration value, based on a prefix and separator
Parameters
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name: the key of the config valur in self.config_values to print
prefix: the prefix to print
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32,412 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competitions_list | def competitions_list(self,
group=None,
category=None,
sort_by=None,
page=1,
search=None):
""" make call to list competitions, format the response, and return
a list of C... | python | def competitions_list(self,
group=None,
category=None,
sort_by=None,
page=1,
search=None):
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32,413 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competitions_list_cli | def competitions_list_cli(self,
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search=None,
csv_display=False):
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group=None,
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group: group to filter result to
category: category to filter result to
sort_by: how to sort the result, see valid_sort_by for options
page: the page to return (default is 1)
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32,414 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_submit | def competition_submit(self, file_name, message, competition, quiet=False):
""" submit a competition!
Parameters
==========
file_name: the competition metadata file
message: the submission description
competition: the competition name
quie... | python | def competition_submit(self, file_name, message, competition, quiet=False):
""" submit a competition!
Parameters
==========
file_name: the competition metadata file
message: the submission description
competition: the competition name
quie... | [
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32,415 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_submissions | def competition_submissions(self, competition):
""" get the list of Submission for a particular competition
Parameters
==========
competition: the name of the competition
"""
submissions_result = self.process_response(
self.competitions_submission... | python | def competition_submissions(self, competition):
""" get the list of Submission for a particular competition
Parameters
==========
competition: the name of the competition
"""
submissions_result = self.process_response(
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32,416 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_submissions_cli | def competition_submissions_cli(self,
competition=None,
competition_opt=None,
csv_display=False,
quiet=False):
""" wrapper to competition_submission, will return either... | python | def competition_submissions_cli(self,
competition=None,
competition_opt=None,
csv_display=False,
quiet=False):
""" wrapper to competition_submission, will return either... | [
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32,417 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_list_files_cli | def competition_list_files_cli(self,
competition,
competition_opt=None,
csv_display=False,
quiet=False):
""" List files for a competition, if it exists
Paramet... | python | def competition_list_files_cli(self,
competition,
competition_opt=None,
csv_display=False,
quiet=False):
""" List files for a competition, if it exists
Paramet... | [
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competition_opt: an alternative competition option provided by cli
csv_display: if True, print comma separated values
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32,418 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_download_file | def competition_download_file(self,
competition,
file_name,
path=None,
force=False,
quiet=False):
""" download a competition file to a designa... | python | def competition_download_file(self,
competition,
file_name,
path=None,
force=False,
quiet=False):
""" download a competition file to a designa... | [
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32,419 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_download_files | def competition_download_files(self,
competition,
path=None,
force=False,
quiet=True):
""" a wrapper to competition_download_file to download all competition
fi... | python | def competition_download_files(self,
competition,
path=None,
force=False,
quiet=True):
""" a wrapper to competition_download_file to download all competition
fi... | [
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32,420 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_download_cli | def competition_download_cli(self,
competition,
competition_opt=None,
file_name=None,
path=None,
force=False,
quiet=False)... | python | def competition_download_cli(self,
competition,
competition_opt=None,
file_name=None,
path=None,
force=False,
quiet=False)... | [
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32,421 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_leaderboard_download | def competition_leaderboard_download(self, competition, path, quiet=True):
""" Download competition leaderboards
Parameters
=========
competition: the name of the competition
path: a path to download the file to
quiet: suppress verbose output (default... | python | def competition_leaderboard_download(self, competition, path, quiet=True):
""" Download competition leaderboards
Parameters
=========
competition: the name of the competition
path: a path to download the file to
quiet: suppress verbose output (default... | [
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competition: the name of the competition
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32,422 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_leaderboard_view | def competition_leaderboard_view(self, competition):
""" view a leaderboard based on a competition name
Parameters
==========
competition: the competition name to view leadboard for
"""
result = self.process_response(
self.competition_view_leaderb... | python | def competition_leaderboard_view(self, competition):
""" view a leaderboard based on a competition name
Parameters
==========
competition: the competition name to view leadboard for
"""
result = self.process_response(
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32,423 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.competition_leaderboard_cli | def competition_leaderboard_cli(self,
competition,
competition_opt=None,
path=None,
view=False,
download=False,
... | python | def competition_leaderboard_cli(self,
competition,
competition_opt=None,
path=None,
view=False,
download=False,
... | [
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32,424 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_list | def dataset_list(self,
sort_by=None,
size=None,
file_type=None,
license_name=None,
tag_ids=None,
search=None,
user=None,
mine=False,
... | python | def dataset_list(self,
sort_by=None,
size=None,
file_type=None,
license_name=None,
tag_ids=None,
search=None,
user=None,
mine=False,
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file_type: the format, see valid_file_types for string options
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32,425 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_list_cli | def dataset_list_cli(self,
sort_by=None,
size=None,
file_type=None,
license_name=None,
tag_ids=None,
search=None,
user=None,
... | python | def dataset_list_cli(self,
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search=None,
user=None,
... | [
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32,426 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_view | def dataset_view(self, dataset):
""" view metadata for a dataset.
Parameters
==========
dataset: the string identified of the dataset
should be in format [owner]/[dataset-name]
"""
if '/' in dataset:
self.validate_dataset_stri... | python | def dataset_view(self, dataset):
""" view metadata for a dataset.
Parameters
==========
dataset: the string identified of the dataset
should be in format [owner]/[dataset-name]
"""
if '/' in dataset:
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32,427 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_download_file | def dataset_download_file(self,
dataset,
file_name,
path=None,
force=False,
quiet=True):
""" download a single file for a dataset
Parameters
... | python | def dataset_download_file(self,
dataset,
file_name,
path=None,
force=False,
quiet=True):
""" download a single file for a dataset
Parameters
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32,428 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_download_files | def dataset_download_files(self,
dataset,
path=None,
force=False,
quiet=True,
unzip=False):
""" download all files for a dataset
Parameters
... | python | def dataset_download_files(self,
dataset,
path=None,
force=False,
quiet=True,
unzip=False):
""" download all files for a dataset
Parameters
... | [
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32,429 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_upload_file | def dataset_upload_file(self, path, quiet):
""" upload a dataset file
Parameters
==========
path: the complete path to upload
quiet: suppress verbose output (default is False)
"""
file_name = os.path.basename(path)
content_length = os.path... | python | def dataset_upload_file(self, path, quiet):
""" upload a dataset file
Parameters
==========
path: the complete path to upload
quiet: suppress verbose output (default is False)
"""
file_name = os.path.basename(path)
content_length = os.path... | [
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32,430 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.dataset_create_version | def dataset_create_version(self,
folder,
version_notes,
quiet=False,
convert_to_csv=True,
delete_old_versions=False,
dir_mode='skip'):... | python | def dataset_create_version(self,
folder,
version_notes,
quiet=False,
convert_to_csv=True,
delete_old_versions=False,
dir_mode='skip'):... | [
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version_notes: notes to add for the version
quiet: suppress verbose output (default is False)
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32,431 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.download_file | def download_file(self, response, outfile, quiet=True, chunk_size=1048576):
""" download a file to an output file based on a chunk size
Parameters
==========
response: the response to download
outfile: the output file to download to
quiet: suppress ve... | python | def download_file(self, response, outfile, quiet=True, chunk_size=1048576):
""" download a file to an output file based on a chunk size
Parameters
==========
response: the response to download
outfile: the output file to download to
quiet: suppress ve... | [
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response: the response to download
outfile: the output file to download to
quiet: suppress verbose output (default is True)
chunk_size: the size of the chunk to stream | [
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32,432 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.kernels_list | def kernels_list(self,
page=1,
page_size=20,
dataset=None,
competition=None,
parent_kernel=None,
search=None,
mine=False,
user=None,
... | python | def kernels_list(self,
page=1,
page_size=20,
dataset=None,
competition=None,
parent_kernel=None,
search=None,
mine=False,
user=None,
... | [
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page_size: results per page (default is 20)
dataset: if defined, filter to this dataset (default None)
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32,433 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.kernels_push_cli | def kernels_push_cli(self, folder):
""" client wrapper for kernels_push, with same arguments.
"""
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:
... | python | def kernels_push_cli(self, folder):
""" client wrapper for kernels_push, with same arguments.
"""
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:
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32,434 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.kernels_pull_cli | def kernels_pull_cli(self,
kernel,
kernel_opt=None,
path=None,
metadata=False):
""" client wrapper for kernels_pull
"""
kernel = kernel or kernel_opt
effective_path = self.kernels_pull(
... | python | def kernels_pull_cli(self,
kernel,
kernel_opt=None,
path=None,
metadata=False):
""" client wrapper for kernels_pull
"""
kernel = kernel or kernel_opt
effective_path = self.kernels_pull(
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32,435 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.print_table | def print_table(self, items, fields):
""" print a table of items, for a set of fields defined
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items
"""
formats = []
borders = []
for f i... | python | def print_table(self, items, fields):
""" print a table of items, for a set of fields defined
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items
"""
formats = []
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32,436 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.print_csv | def print_csv(self, items, fields):
""" print a set of fields in a set of items using a csv.writer
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items
"""
writer = csv.writer(sys.stdout)
writ... | python | def print_csv(self, items, fields):
""" print a set of fields in a set of items using a csv.writer
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items
"""
writer = csv.writer(sys.stdout)
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32,437 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.validate_resources | def validate_resources(self, folder, resources):
""" validate resources is a wrapper to validate the existence of files
and that there are no duplicates for a folder and set of resources.
Parameters
==========
folder: the folder to validate
resources:... | python | def validate_resources(self, folder, resources):
""" validate resources is a wrapper to validate the existence of files
and that there are no duplicates for a folder and set of resources.
Parameters
==========
folder: the folder to validate
resources:... | [
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32,438 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.validate_files_exist | def validate_files_exist(self, folder, resources):
""" ensure that one or more resource files exist in a folder
Parameters
==========
folder: the folder to validate
resources: one or more resources to validate within the folder
"""
for item in res... | python | def validate_files_exist(self, folder, resources):
""" ensure that one or more resource files exist in a folder
Parameters
==========
folder: the folder to validate
resources: one or more resources to validate within the folder
"""
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32,439 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.validate_no_duplicate_paths | def validate_no_duplicate_paths(self, resources):
""" ensure that the user has not provided duplicate paths in
a list of resources.
Parameters
==========
resources: one or more resources to validate not duplicated
"""
paths = set()
for ite... | python | def validate_no_duplicate_paths(self, resources):
""" ensure that the user has not provided duplicate paths in
a list of resources.
Parameters
==========
resources: one or more resources to validate not duplicated
"""
paths = set()
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32,440 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | KaggleApi.convert_to_dataset_file_metadata | def convert_to_dataset_file_metadata(self, file_data, path):
""" convert a set of file_data to a metadata file at path
Parameters
==========
file_data: a dictionary of file data to write to file
path: the path to write the metadata to
"""
as_metad... | python | def convert_to_dataset_file_metadata(self, file_data, path):
""" convert a set of file_data to a metadata file at path
Parameters
==========
file_data: a dictionary of file data to write to file
path: the path to write the metadata to
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32,441 | Kaggle/kaggle-api | kaggle/api/kaggle_api_extended.py | TqdmBufferedReader.read | def read(self, *args, **kwargs):
""" read the buffer, passing named and non named arguments to the
io.BufferedReader function.
"""
buf = io.BufferedReader.read(self, *args, **kwargs)
self.increment(len(buf))
return buf | python | def read(self, *args, **kwargs):
""" read the buffer, passing named and non named arguments to the
io.BufferedReader function.
"""
buf = io.BufferedReader.read(self, *args, **kwargs)
self.increment(len(buf))
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32,442 | Kaggle/kaggle-api | kaggle/api_client.py | ApiClient.parameters_to_tuples | def parameters_to_tuples(self, params, collection_formats):
"""Get parameters as list of tuples, formatting collections.
:param params: Parameters as dict or list of two-tuples
:param dict collection_formats: Parameter collection formats
:return: Parameters as list of tuples, collection... | python | def parameters_to_tuples(self, params, collection_formats):
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:param params: Parameters as dict or list of two-tuples
:param dict collection_formats: Parameter collection formats
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32,443 | Kaggle/kaggle-api | kaggle/api_client.py | ApiClient.__deserialize_file | def __deserialize_file(self, response):
"""Deserializes body to file
Saves response body into a file in a temporary folder,
using the filename from the `Content-Disposition` header if provided.
:param response: RESTResponse.
:return: file path.
"""
fd, path = t... | python | def __deserialize_file(self, response):
"""Deserializes body to file
Saves response body into a file in a temporary folder,
using the filename from the `Content-Disposition` header if provided.
:param response: RESTResponse.
:return: file path.
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32,444 | Kaggle/kaggle-api | kaggle/models/dataset_new_request.py | DatasetNewRequest.license_name | def license_name(self, license_name):
"""Sets the license_name of this DatasetNewRequest.
The license that should be associated with the dataset # noqa: E501
:param license_name: The license_name of this DatasetNewRequest. # noqa: E501
:type: str
"""
allowed_values = ... | python | def license_name(self, license_name):
"""Sets the license_name of this DatasetNewRequest.
The license that should be associated with the dataset # noqa: E501
:param license_name: The license_name of this DatasetNewRequest. # noqa: E501
:type: str
"""
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32,445 | dmlc/gluon-nlp | scripts/sentiment_analysis/sentiment_analysis_cnn.py | train | def train(net, train_data, test_data):
"""Train textCNN model for sentiment analysis."""
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=tr... | python | def train(net, train_data, test_data):
"""Train textCNN model for sentiment analysis."""
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=tr... | [
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32,446 | dmlc/gluon-nlp | scripts/bert/embedding.py | BertEmbedding.embedding | def embedding(self, sentences, oov_way='avg'):
"""
Get tokens, tokens embedding
Parameters
----------
sentences : List[str]
sentences for encoding.
oov_way : str, default avg.
use **avg**, **sum** or **last** to get token embedding for those out o... | python | def embedding(self, sentences, oov_way='avg'):
"""
Get tokens, tokens embedding
Parameters
----------
sentences : List[str]
sentences for encoding.
oov_way : str, default avg.
use **avg**, **sum** or **last** to get token embedding for those out o... | [
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32,447 | dmlc/gluon-nlp | scripts/bert/embedding.py | BertEmbedding.data_loader | def data_loader(self, sentences, shuffle=False):
"""Load, tokenize and prepare the input sentences."""
dataset = BertEmbeddingDataset(sentences, self.transform)
return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle) | python | def data_loader(self, sentences, shuffle=False):
"""Load, tokenize and prepare the input sentences."""
dataset = BertEmbeddingDataset(sentences, self.transform)
return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle) | [
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32,448 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | get_bert_model | 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,
root=os.path.join(get_home_dir(), 'mod... | python | 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,
root=os.path.join(get_home_dir(), 'mod... | [
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model_name : str or None, default None
Options include 'bert_24_1024_16' and 'bert_12_768_12'.
dataset_name : str or None, default None
Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased'
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32,449 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTLayerNorm.hybrid_forward | def hybrid_forward(self, F, data, gamma, beta):
"""forward computation."""
# 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('float32')
g... | python | def hybrid_forward(self, F, data, gamma, beta):
"""forward computation."""
# 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('float32')
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32,450 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTModel._get_classifier | def _get_classifier(self, prefix):
""" Construct a decoder for the next sentence prediction task """
with self.name_scope():
classifier = nn.Dense(2, prefix=prefix)
return classifier | python | def _get_classifier(self, prefix):
""" Construct a decoder for the next sentence prediction task """
with self.name_scope():
classifier = nn.Dense(2, prefix=prefix)
return classifier | [
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32,451 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTModel._get_decoder | def _get_decoder(self, units, vocab_size, embed, prefix):
""" Construct a decoder for the masked language model task """
with self.name_scope():
decoder = nn.HybridSequential(prefix=prefix)
decoder.add(nn.Dense(units, flatten=False))
decoder.add(GELU())
de... | python | def _get_decoder(self, units, vocab_size, embed, prefix):
""" Construct a decoder for the masked language model task """
with self.name_scope():
decoder = nn.HybridSequential(prefix=prefix)
decoder.add(nn.Dense(units, flatten=False))
decoder.add(GELU())
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32,452 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTModel._get_embed | def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
""" Construct an embedding block. """
if embed is None:
assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
'token_type_embed is not gi... | python | def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
""" Construct an embedding block. """
if embed is None:
assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
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32,453 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTModel._get_pooler | def _get_pooler(self, units, prefix):
""" Construct pooler.
The pooler slices and projects the hidden output of first token
in the sequence for segment level classification.
"""
with self.name_scope():
pooler = nn.Dense(units=units, flatten=False, activation='tanh',... | python | def _get_pooler(self, units, prefix):
""" Construct pooler.
The pooler slices and projects the hidden output of first token
in the sequence for segment level classification.
"""
with self.name_scope():
pooler = nn.Dense(units=units, flatten=False, activation='tanh',... | [
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32,454 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTModel._encode_sequence | def _encode_sequence(self, inputs, token_types, valid_length=None):
"""Generate the representation given the input sequences.
This is used for pre-training or fine-tuning a BERT model.
"""
# embedding
word_embedding = self.word_embed(inputs)
type_embedding = self.token_t... | python | def _encode_sequence(self, inputs, token_types, valid_length=None):
"""Generate the representation given the input sequences.
This is used for pre-training or fine-tuning a BERT model.
"""
# embedding
word_embedding = self.word_embed(inputs)
type_embedding = self.token_t... | [
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32,455 | dmlc/gluon-nlp | src/gluonnlp/model/bert.py | BERTModel._decode | def _decode(self, sequence, masked_positions):
"""Generate unnormalized prediction for the masked language model task.
This is only used for pre-training the BERT model.
Inputs:
- **sequence**: input tensor of sequence encodings.
Shape (batch_size, seq_length, units).... | python | def _decode(self, sequence, masked_positions):
"""Generate unnormalized prediction for the masked language model task.
This is only used for pre-training the BERT model.
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- **sequence**: input tensor of sequence encodings.
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32,456 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | _ngrams | def _ngrams(segment, n):
"""Extracts n-grams from an input segment.
Parameters
----------
segment: list
Text segment from which n-grams will be extracted.
n: int
Order of n-gram.
Returns
-------
ngram_counts: Counter
Contain all the nth n-grams in segment with a... | python | def _ngrams(segment, n):
"""Extracts n-grams from an input segment.
Parameters
----------
segment: list
Text segment from which n-grams will be extracted.
n: int
Order of n-gram.
Returns
-------
ngram_counts: Counter
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32,457 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | _bpe_to_words | def _bpe_to_words(sentence, delimiter='@@'):
"""Convert a sequence of bpe words into sentence."""
words = []
word = ''
delimiter_len = len(delimiter)
for subwords in sentence:
if len(subwords) >= delimiter_len and subwords[-delimiter_len:] == delimiter:
word += subwords[:-delimit... | python | def _bpe_to_words(sentence, delimiter='@@'):
"""Convert a sequence of bpe words into sentence."""
words = []
word = ''
delimiter_len = len(delimiter)
for subwords in sentence:
if len(subwords) >= delimiter_len and subwords[-delimiter_len:] == delimiter:
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32,458 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | compute_bleu | 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"""Compute bleu score of translation against references.
Parameters
----------
reference_corpus... | python | 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"""Compute bleu score of translation against references.
Parameters
----------
reference_corpus... | [
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reference_corpus_list: list of list(list(str)) or list of list(str)
list of list(list(str)): tokenized references
list of list(str): plain text
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32,459 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | _compute_precision | def _compute_precision(references, translation, n):
"""Compute ngram precision.
Parameters
----------
references: list(list(str))
A list of references.
translation: list(str)
A translation.
n: int
Order of n-gram.
Returns
-------
matches: int
Number ... | python | def _compute_precision(references, translation, n):
"""Compute ngram precision.
Parameters
----------
references: list(list(str))
A list of references.
translation: list(str)
A translation.
n: int
Order of n-gram.
Returns
-------
matches: int
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32,460 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | _brevity_penalty | def _brevity_penalty(ref_length, trans_length):
"""Calculate brevity penalty.
Parameters
----------
ref_length: int
Sum of all closest references'lengths for every translations in a corpus
trans_length: int
Sum of all translations's lengths in a corpus.
Returns
-------
... | python | def _brevity_penalty(ref_length, trans_length):
"""Calculate brevity penalty.
Parameters
----------
ref_length: int
Sum of all closest references'lengths for every translations in a corpus
trans_length: int
Sum of all translations's lengths in a corpus.
Returns
-------
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32,461 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | _closest_ref_length | def _closest_ref_length(references, trans_length):
"""Find the reference that has the closest length to the translation.
Parameters
----------
references: list(list(str))
A list of references.
trans_length: int
Length of the translation.
Returns
-------
closest_ref_len:... | python | def _closest_ref_length(references, trans_length):
"""Find the reference that has the closest length to the translation.
Parameters
----------
references: list(list(str))
A list of references.
trans_length: int
Length of the translation.
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32,462 | dmlc/gluon-nlp | scripts/machine_translation/bleu.py | _smoothing | def _smoothing(precision_fractions, c=1):
"""Compute the smoothed precision for all the orders.
Parameters
----------
precision_fractions: list(tuple)
Contain a list of (precision_numerator, precision_denominator) pairs
c: int, default 1
Smoothing constant to use
Returns
--... | python | def _smoothing(precision_fractions, c=1):
"""Compute the smoothed precision for all the orders.
Parameters
----------
precision_fractions: list(tuple)
Contain a list of (precision_numerator, precision_denominator) pairs
c: int, default 1
Smoothing constant to use
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32,463 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | preprocess_dataset | def preprocess_dataset(data, min_freq=5, max_vocab_size=None):
"""Dataset preprocessing helper.
Parameters
----------
data : mx.data.Dataset
Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9
min_freq : int, default 5
Minimum token frequency for a token to be inclu... | python | def preprocess_dataset(data, min_freq=5, max_vocab_size=None):
"""Dataset preprocessing helper.
Parameters
----------
data : mx.data.Dataset
Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9
min_freq : int, default 5
Minimum token frequency for a token to be inclu... | [
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32,464 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | wiki | def wiki(wiki_root, wiki_date, wiki_language, max_vocab_size=None):
"""Wikipedia dump helper.
Parameters
----------
wiki_root : str
Parameter for WikiDumpStream
wiki_date : str
Parameter for WikiDumpStream
wiki_language : str
Parameter for WikiDumpStream
max_vocab_si... | python | def wiki(wiki_root, wiki_date, wiki_language, max_vocab_size=None):
"""Wikipedia dump helper.
Parameters
----------
wiki_root : str
Parameter for WikiDumpStream
wiki_date : str
Parameter for WikiDumpStream
wiki_language : str
Parameter for WikiDumpStream
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32,465 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | cbow_fasttext_batch | def cbow_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype,
index_dtype):
"""Create a batch for CBOW training objective with subwords."""
_, contexts_row, contexts_col = contexts
data, row, col = subword_lookup(contexts_row, contexts_col)
centers = mx.nd.array(c... | python | def cbow_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype,
index_dtype):
"""Create a batch for CBOW training objective with subwords."""
_, contexts_row, contexts_col = contexts
data, row, col = subword_lookup(contexts_row, contexts_col)
centers = mx.nd.array(c... | [
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32,466 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | skipgram_fasttext_batch | def skipgram_fasttext_batch(centers, contexts, num_tokens, subword_lookup,
dtype, index_dtype):
"""Create a batch for SG training objective with subwords."""
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
data, row, col = subword_lookup(centers)
centers = mx.nd.array(... | python | def skipgram_fasttext_batch(centers, contexts, num_tokens, subword_lookup,
dtype, index_dtype):
"""Create a batch for SG training objective with subwords."""
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
data, row, col = subword_lookup(centers)
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32,467 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | cbow_batch | def cbow_batch(centers, contexts, num_tokens, dtype, index_dtype):
"""Create a batch for CBOW training objective."""
contexts_data, contexts_row, contexts_col = contexts
centers = mx.nd.array(centers, dtype=index_dtype)
contexts = mx.nd.sparse.csr_matrix(
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"""Create a batch for CBOW training objective."""
contexts_data, contexts_row, contexts_col = contexts
centers = mx.nd.array(centers, dtype=index_dtype)
contexts = mx.nd.sparse.csr_matrix(
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32,468 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | skipgram_batch | def skipgram_batch(centers, contexts, num_tokens, dtype, index_dtype):
"""Create a batch for SG training objective."""
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_matri... | python | def skipgram_batch(centers, contexts, num_tokens, dtype, index_dtype):
"""Create a batch for SG training objective."""
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
indptr = mx.nd.arange(len(centers) + 1)
centers = mx.nd.array(centers, dtype=index_dtype)
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32,469 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | skipgram_lookup | def skipgram_lookup(indices, subwordidxs, subwordidxsptr, offset=0):
"""Get a sparse COO array of words and subwords for SkipGram.
Parameters
----------
indices : numpy.ndarray
Array containing numbers in [0, vocabulary_size). The element at
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indices : numpy.ndarray
Array containing numbers in [0, vocabulary_size). The element at
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32,470 | dmlc/gluon-nlp | scripts/word_embeddings/data.py | cbow_lookup | def cbow_lookup(context_row, context_col, subwordidxs, subwordidxsptr,
offset=0):
"""Get a sparse COO array of words and subwords for CBOW.
Parameters
----------
context_row : numpy.ndarray of dtype int64
Array of same length as context_col containing numbers in [0,
batc... | python | def cbow_lookup(context_row, context_col, subwordidxs, subwordidxsptr,
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Parameters
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context_row : numpy.ndarray of dtype int64
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32,471 | dmlc/gluon-nlp | src/gluonnlp/data/translation.py | _TranslationDataset.src_vocab | def src_vocab(self):
"""Source Vocabulary of the Dataset.
Returns
-------
src_vocab : Vocab
Source vocabulary.
"""
if self._src_vocab is None:
src_vocab_file_name, src_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + ... | python | def src_vocab(self):
"""Source Vocabulary of the Dataset.
Returns
-------
src_vocab : Vocab
Source vocabulary.
"""
if self._src_vocab is None:
src_vocab_file_name, src_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + ... | [
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32,472 | dmlc/gluon-nlp | src/gluonnlp/data/translation.py | _TranslationDataset.tgt_vocab | def tgt_vocab(self):
"""Target Vocabulary of the Dataset.
Returns
-------
tgt_vocab : Vocab
Target vocabulary.
"""
if self._tgt_vocab is None:
tgt_vocab_file_name, tgt_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + ... | python | def tgt_vocab(self):
"""Target Vocabulary of the Dataset.
Returns
-------
tgt_vocab : Vocab
Target vocabulary.
"""
if self._tgt_vocab is None:
tgt_vocab_file_name, tgt_vocab_hash = \
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32,473 | dmlc/gluon-nlp | scripts/machine_translation/train_gnmt.py | evaluate | def evaluate(data_loader):
"""Evaluate given the data loader
Parameters
----------
data_loader : DataLoader
Returns
-------
avg_loss : float
Average loss
real_translation_out : list of list of str
The translation output
"""
translation_out = []
all_inst_ids ... | python | def evaluate(data_loader):
"""Evaluate given the data loader
Parameters
----------
data_loader : DataLoader
Returns
-------
avg_loss : float
Average loss
real_translation_out : list of list of str
The translation output
"""
translation_out = []
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32,474 | dmlc/gluon-nlp | src/gluonnlp/model/train/__init__.py | get_cache_model | def get_cache_model(name, dataset_name='wikitext-2', window=2000,
theta=0.6, lambdas=0.2, ctx=mx.cpu(), **kwargs):
r"""Returns a cache model using a pre-trained language model.
We implement the neural cache language model proposed in the following work::
@article{grave2016improving... | python | def get_cache_model(name, dataset_name='wikitext-2', window=2000,
theta=0.6, lambdas=0.2, ctx=mx.cpu(), **kwargs):
r"""Returns a cache model using a pre-trained language model.
We implement the neural cache language model proposed in the following work::
@article{grave2016improving... | [
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32,475 | dmlc/gluon-nlp | src/gluonnlp/data/dataset.py | NumpyDataset.get_field | def get_field(self, field):
"""Return the dataset corresponds to the provided key.
Example::
a = np.ones((2,2))
b = np.zeros((2,2))
np.savez('data.npz', a=a, b=b)
dataset = NumpyDataset('data.npz')
data_a = dataset.get_field('a')
d... | python | def get_field(self, field):
"""Return the dataset corresponds to the provided key.
Example::
a = np.ones((2,2))
b = np.zeros((2,2))
np.savez('data.npz', a=a, b=b)
dataset = NumpyDataset('data.npz')
data_a = dataset.get_field('a')
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dataset = NumpyDataset('data.npz')
data_a = dataset.get_field('a')
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32,476 | dmlc/gluon-nlp | scripts/bert/bert_qa_evaluate.py | get_F1_EM | def get_F1_EM(dataset, predict_data):
"""Calculate the F1 and EM scores of the predicted results.
Use only with the SQuAD1.1 dataset.
Parameters
----------
dataset_file: string
Path to the data file.
predict_data: dict
All final predictions.
Returns
-------
scores: ... | python | def get_F1_EM(dataset, predict_data):
"""Calculate the F1 and EM scores of the predicted results.
Use only with the SQuAD1.1 dataset.
Parameters
----------
dataset_file: string
Path to the data file.
predict_data: dict
All final predictions.
Returns
-------
scores: ... | [
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dataset_file: string
Path to the data file.
predict_data: dict
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32,477 | dmlc/gluon-nlp | scripts/bert/finetune_classifier.py | preprocess_data | def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len, pad=False):
"""Data preparation function."""
# transformation
trans = BERTDatasetTransform(
tokenizer,
max_len,
labels=task.get_labels(),
pad=pad,
pair=task.is_pair,
label_dtype='float32... | python | def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len, pad=False):
"""Data preparation function."""
# transformation
trans = BERTDatasetTransform(
tokenizer,
max_len,
labels=task.get_labels(),
pad=pad,
pair=task.is_pair,
label_dtype='float32... | [
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32,478 | dmlc/gluon-nlp | scripts/bert/finetune_classifier.py | log_train | def log_train(batch_id, batch_num, metric, step_loss, log_interval, epoch_id, learning_rate):
"""Generate and print out the log message for training.
"""
metric_nm, metric_val = metric.get()
if not isinstance(metric_nm, list):
metric_nm = [metric_nm]
metric_val = [metric_val]
train_... | python | def log_train(batch_id, batch_num, metric, step_loss, log_interval, epoch_id, learning_rate):
"""Generate and print out the log message for training.
"""
metric_nm, metric_val = metric.get()
if not isinstance(metric_nm, list):
metric_nm = [metric_nm]
metric_val = [metric_val]
train_... | [
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32,479 | dmlc/gluon-nlp | scripts/bert/finetune_classifier.py | log_inference | def log_inference(batch_id, batch_num, metric, step_loss, log_interval):
"""Generate and print out the log message for inference.
"""
metric_nm, metric_val = metric.get()
if not isinstance(metric_nm, list):
metric_nm = [metric_nm]
metric_val = [metric_val]
eval_str = '[Batch %d/%d] ... | python | def log_inference(batch_id, batch_num, metric, step_loss, log_interval):
"""Generate and print out the log message for inference.
"""
metric_nm, metric_val = metric.get()
if not isinstance(metric_nm, list):
metric_nm = [metric_nm]
metric_val = [metric_val]
eval_str = '[Batch %d/%d] ... | [
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32,480 | dmlc/gluon-nlp | scripts/bert/finetune_classifier.py | inference | def inference(metric):
"""Inference function."""
logging.info('Now we are doing BERT classification inference on %s!', ctx)
model = BERTClassifier(bert, dropout=0.1, num_classes=len(task.get_labels()))
model.hybridize(static_alloc=True)
model.load_parameters(model_parameters, ctx=ctx)
metric.r... | python | def inference(metric):
"""Inference function."""
logging.info('Now we are doing BERT classification inference on %s!', ctx)
model = BERTClassifier(bert, dropout=0.1, num_classes=len(task.get_labels()))
model.hybridize(static_alloc=True)
model.load_parameters(model_parameters, ctx=ctx)
metric.r... | [
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32,481 | dmlc/gluon-nlp | scripts/question_answering/data_processing.py | preprocess_dataset | def preprocess_dataset(dataset, question_max_length, context_max_length):
"""Process SQuAD dataset by creating NDArray version of data
:param Dataset dataset: SQuAD dataset
:param int question_max_length: Maximum length of question (padded or trimmed to that size)
:param int context_max_length: Maximum... | python | def preprocess_dataset(dataset, question_max_length, context_max_length):
"""Process SQuAD dataset by creating NDArray version of data
:param Dataset dataset: SQuAD dataset
:param int question_max_length: Maximum length of question (padded or trimmed to that size)
:param int context_max_length: Maximum... | [
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32,482 | dmlc/gluon-nlp | scripts/question_answering/data_processing.py | SQuADTransform._get_answer_spans | def _get_answer_spans(answer_list, answer_start_list):
"""Find all answer spans from the context, returning start_index and end_index
:param list[str] answer_list: List of all answers
:param list[int] answer_start_list: List of all answers' start indices
Returns
-------
... | python | def _get_answer_spans(answer_list, answer_start_list):
"""Find all answer spans from the context, returning start_index and end_index
:param list[str] answer_list: List of all answers
:param list[int] answer_start_list: List of all answers' start indices
Returns
-------
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32,483 | dmlc/gluon-nlp | scripts/question_answering/data_processing.py | VocabProvider.get_word_level_vocab | def get_word_level_vocab(self):
"""Provides word level vocabulary
Returns
-------
Vocab
Word level vocabulary
"""
def simple_tokenize(source_str, token_delim=' ', seq_delim='\n'):
return list(filter(None, re.split(token_delim + '|' + seq_delim, s... | python | def get_word_level_vocab(self):
"""Provides word level vocabulary
Returns
-------
Vocab
Word level vocabulary
"""
def simple_tokenize(source_str, token_delim=' ', seq_delim='\n'):
return list(filter(None, re.split(token_delim + '|' + seq_delim, s... | [
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Word level vocabulary | [
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32,484 | dmlc/gluon-nlp | src/gluonnlp/data/transforms.py | BERTBasicTokenizer._clean_text | def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp in (0, 0xfffd) or self._is_control(char):
continue
if self._is_whitespace(char):
... | python | def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp in (0, 0xfffd) or self._is_control(char):
continue
if self._is_whitespace(char):
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32,485 | dmlc/gluon-nlp | src/gluonnlp/data/transforms.py | BERTBasicTokenizer._is_control | def _is_control(self, char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char in ['\t', '\n', '\r']:
return False
cat = unicodedata.category(char)
if cat.start... | python | def _is_control(self, char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char in ['\t', '\n', '\r']:
return False
cat = unicodedata.category(char)
if cat.start... | [
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32,486 | dmlc/gluon-nlp | src/gluonnlp/data/transforms.py | BERTBasicTokenizer._run_split_on_punc | def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if self._is_punctuation(char):
output.append([char])
... | python | def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if self._is_punctuation(char):
output.append([char])
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32,487 | dmlc/gluon-nlp | src/gluonnlp/data/transforms.py | BERTBasicTokenizer._is_whitespace | def _is_whitespace(self, char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char in [' ', '\t', '\n', '\r']:
return True
c... | python | def _is_whitespace(self, char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
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32,488 | dmlc/gluon-nlp | src/gluonnlp/data/transforms.py | BERTSentenceTransform._truncate_seq_pair | def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tok... | python | def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tok... | [
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32,489 | dmlc/gluon-nlp | scripts/word_embeddings/evaluate_pretrained.py | get_args | def get_args():
"""Construct the argument parser."""
parser = argparse.ArgumentParser(
description='Word embedding evaluation with Gluon.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Embeddings arguments
group = parser.add_argument_group('Embedding arguments')
group.a... | python | def get_args():
"""Construct the argument parser."""
parser = argparse.ArgumentParser(
description='Word embedding evaluation with Gluon.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Embeddings arguments
group = parser.add_argument_group('Embedding arguments')
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32,490 | dmlc/gluon-nlp | scripts/word_embeddings/evaluate_pretrained.py | load_embedding_from_path | def load_embedding_from_path(args):
"""Load a TokenEmbedding."""
if args.embedding_path.endswith('.bin'):
with utils.print_time('load fastText model.'):
model = \
nlp.model.train.FasttextEmbeddingModel.load_fasttext_format(
args.embedding_path)
idx... | python | def load_embedding_from_path(args):
"""Load a TokenEmbedding."""
if args.embedding_path.endswith('.bin'):
with utils.print_time('load fastText model.'):
model = \
nlp.model.train.FasttextEmbeddingModel.load_fasttext_format(
args.embedding_path)
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32,491 | dmlc/gluon-nlp | scripts/bert/fp16_utils.py | grad_global_norm | def grad_global_norm(parameters, max_norm):
"""Calculate the 2-norm of gradients of parameters, and how much they should be scaled down
such that their 2-norm does not exceed `max_norm`.
If gradients exist for more than one context for a parameter, user needs to explicitly call
``trainer.allreduce_grad... | python | def grad_global_norm(parameters, max_norm):
"""Calculate the 2-norm of gradients of parameters, and how much they should be scaled down
such that their 2-norm does not exceed `max_norm`.
If gradients exist for more than one context for a parameter, user needs to explicitly call
``trainer.allreduce_grad... | [
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32,492 | dmlc/gluon-nlp | scripts/bert/fp16_utils.py | FP16Trainer.backward | def backward(self, loss):
"""backward propagation with loss"""
with mx.autograd.record():
if isinstance(loss, (tuple, list)):
ls = [l * self._scaler.loss_scale for l in loss]
else:
ls = loss * self._scaler.loss_scale
mx.autograd.backward(ls... | python | def backward(self, loss):
"""backward propagation with loss"""
with mx.autograd.record():
if isinstance(loss, (tuple, list)):
ls = [l * self._scaler.loss_scale for l in loss]
else:
ls = loss * self._scaler.loss_scale
mx.autograd.backward(ls... | [
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32,493 | dmlc/gluon-nlp | scripts/bert/fp16_utils.py | LossScaler.has_overflow | def has_overflow(self, params):
""" detect inf and nan """
is_not_finite = 0
for param in params:
if param.grad_req != 'null':
grad = param.list_grad()[0]
is_not_finite += mx.nd.contrib.isnan(grad).sum()
is_not_finite += mx.nd.contrib.i... | python | def has_overflow(self, params):
""" detect inf and nan """
is_not_finite = 0
for param in params:
if param.grad_req != 'null':
grad = param.list_grad()[0]
is_not_finite += mx.nd.contrib.isnan(grad).sum()
is_not_finite += mx.nd.contrib.i... | [
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32,494 | dmlc/gluon-nlp | scripts/bert/fp16_utils.py | DynamicLossScaler.update_scale | def update_scale(self, overflow):
"""dynamically update loss scale"""
iter_since_rescale = self._num_steps - self._last_rescale_iter
if overflow:
self._last_overflow_iter = self._num_steps
self._overflows_since_rescale += 1
percentage = self._overflows_since_r... | python | def update_scale(self, overflow):
"""dynamically update loss scale"""
iter_since_rescale = self._num_steps - self._last_rescale_iter
if overflow:
self._last_overflow_iter = self._num_steps
self._overflows_since_rescale += 1
percentage = self._overflows_since_r... | [
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32,495 | dmlc/gluon-nlp | src/gluonnlp/data/sampler.py | FixedBucketSampler.stats | def stats(self):
"""Return a string representing the statistics of the bucketing sampler.
Returns
-------
ret : str
String representing the statistics of the buckets.
"""
ret = '{name}:\n' \
' sample_num={sample_num}, batch_num={batch_num}\n' \
... | python | def stats(self):
"""Return a string representing the statistics of the bucketing sampler.
Returns
-------
ret : str
String representing the statistics of the buckets.
"""
ret = '{name}:\n' \
' sample_num={sample_num}, batch_num={batch_num}\n' \
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32,496 | dmlc/gluon-nlp | scripts/language_model/large_word_language_model.py | evaluate | def evaluate():
""" Evaluate loop for the trained model """
print(eval_model)
eval_model.initialize(mx.init.Xavier(), ctx=context[0])
eval_model.hybridize(static_alloc=True, static_shape=True)
epoch = args.from_epoch if args.from_epoch else 0
while epoch < args.epochs:
checkpoint_name = ... | python | def evaluate():
""" Evaluate loop for the trained model """
print(eval_model)
eval_model.initialize(mx.init.Xavier(), ctx=context[0])
eval_model.hybridize(static_alloc=True, static_shape=True)
epoch = args.from_epoch if args.from_epoch else 0
while epoch < args.epochs:
checkpoint_name = ... | [
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32,497 | dmlc/gluon-nlp | scripts/sentiment_analysis/process_data.py | load_dataset | def load_dataset(data_name):
"""Load sentiment dataset."""
if data_name == 'MR' or data_name == 'Subj':
train_dataset, output_size = _load_file(data_name)
vocab, max_len = _build_vocab(data_name, train_dataset, [])
train_dataset, train_data_lengths = _preprocess_dataset(train_dataset, vo... | python | def load_dataset(data_name):
"""Load sentiment dataset."""
if data_name == 'MR' or data_name == 'Subj':
train_dataset, output_size = _load_file(data_name)
vocab, max_len = _build_vocab(data_name, train_dataset, [])
train_dataset, train_data_lengths = _preprocess_dataset(train_dataset, vo... | [
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32,498 | dmlc/gluon-nlp | scripts/natural_language_inference/dataset.py | read_dataset | def read_dataset(args, dataset):
"""
Read dataset from tokenized files.
"""
path = os.path.join(vars(args)[dataset])
logger.info('reading data from {}'.format(path))
examples = [line.strip().split('\t') for line in open(path)]
if args.max_num_examples > 0:
examples = examples[:args.m... | python | def read_dataset(args, dataset):
"""
Read dataset from tokenized files.
"""
path = os.path.join(vars(args)[dataset])
logger.info('reading data from {}'.format(path))
examples = [line.strip().split('\t') for line in open(path)]
if args.max_num_examples > 0:
examples = examples[:args.m... | [
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32,499 | dmlc/gluon-nlp | scripts/natural_language_inference/dataset.py | build_vocab | def build_vocab(dataset):
"""
Build vocab given a dataset.
"""
counter = nlp.data.count_tokens([w for e in dataset for s in e[:2] for w in s],
to_lower=True)
vocab = nlp.Vocab(counter)
return vocab | python | def build_vocab(dataset):
"""
Build vocab given a dataset.
"""
counter = nlp.data.count_tokens([w for e in dataset for s in e[:2] for w in s],
to_lower=True)
vocab = nlp.Vocab(counter)
return vocab | [
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