Python utilities used by Deep Procedural Intelligence

Project description

DPU Utilities

This contains a set of utilities used across projects of the DPU team.

Installation

pip install dpu-utils

Overview of Utilities:

Generic Utilities:

dpu_utils.utils.RichPath a convenient way of using both paths and Azure paths in your code.
dpu_utils.utils.*Iterator iterator wrappers that can parallelize their iteration in other threads/processes.
dpu_utils.utils.{load,save}_json[l]_gz convenience methods for loading .json[l].gz from the filesystem.
dpu_utils.utils.git_tag_run that tags the current working directory git the state of the code.
dpu_utils.utils.run_and_debug when an exception happens, start a debug session. Usually a wrapper of __main__.
dpu_utils.utils.ChunkWriter that helps writing chunks to the output.

TensorFlow Utilities:

dpu_utils.tfutils.GradRatioLoggingOptimizer a wrapper around optimizers that logs the ratios of grad norms to parameter norms.
dpu_utils.tfutils.unsorted_segment_logsumexp
dpu_utils.tfutils.unsorted_segment_log_softmax
dpu_utils.tfutils.TFVariableSaver save TF variables in an object that can be pickled.

General Machine Learning Utilities:

dpu_utils.mlutils.CharTensorizer for character-level tensorization.
dpu_utils.mlutils.Vocabulary a str to int vocabulary for machine learning models

TensorFlow Models:

dpu_utils.tfmodels.SparseGGNN a sparse GGNN implementation.
dpu_utils.tfmodels.AsyncGGNN an asynchronous GGNN implementation.

Code-related Utilities

dpu_utils.codeutils.split_identifier_into_parts() split identifiers into subtokens on CamelCase and snake_case.
dpu_utils.codeutils.{Lattice, CSharpLattice} represent lattices and some useful operations in Python.
dpu_utils.codeutils.get_language_keywords() that retrieves the keyword tokens for many programming languages.
dpu_utils.codeutils.deduplication.DuplicateDetector that detects duplicates in codebases.

Command-line tools

Approximate Duplicate Code Detection

You can use the deduplicationcli command to detect duplicates in pre-processed source code, by invoking

 $ deduplicationcli DATA_PATH OUT_JSON

where DATA_PATH is a file containing tokenized .jsonl.gz files and OUT_JSON is the target output file. For more options look at --help.

An exact (but usually slower) version of this can be found here along with code to tokenize Java, C#, Python and JavaScript into the relevant formats.

Tests

Run the unit tests

python setup.py test

Generate code coverage reports

# pip install coverage
coverage run --source dpu_utils/ setup.py test && \
  coverage html

The resulting HTML file will be in htmlcov/index.html.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Project details

Release history Release notifications | RSS feed

0.6.1

Sep 26, 2021

0.6.0

Sep 24, 2021

0.5.2

Sep 23, 2021

0.5.1

Sep 20, 2021

0.5.0

Jul 12, 2021

0.4.3

Jun 27, 2021

0.4.2

Jun 24, 2021

0.4.1

Apr 9, 2021

0.4.0

Mar 4, 2021

0.3.2

Feb 25, 2021

0.3.1

Feb 16, 2021

0.2.22

Jan 28, 2021

0.2.21

Jan 25, 2021

0.2.20

Jan 25, 2021

0.2.19

Oct 16, 2020

0.2.18

Jul 20, 2020

0.2.17

May 12, 2020

0.2.16

May 11, 2020

0.2.15

Apr 25, 2020

0.2.14

Apr 24, 2020

0.2.13

Apr 14, 2020

0.2.12

Apr 8, 2020

0.2.11

Mar 20, 2020

0.2.10

Mar 20, 2020

0.2.9

Mar 5, 2020

0.2.8

Jan 29, 2020

0.2.7

Jan 23, 2020

0.2.6

Jan 23, 2020

0.2.5

Jan 16, 2020

0.2.4

Jan 15, 2020

0.2.3

Jan 9, 2020

0.2.2

Jan 8, 2020

0.2.1

Jan 6, 2020

0.2.0

Dec 19, 2019

0.1.40

Dec 16, 2019

0.1.39

Dec 11, 2019

0.1.38

Dec 10, 2019

0.1.37

Dec 3, 2019

0.1.36

Nov 21, 2019

0.1.35

Oct 31, 2019

0.1.34

Jul 29, 2019

0.1.33

Jul 5, 2019

0.1.32

Jul 2, 2019

0.1.31

Jul 2, 2019

0.1.30

Jun 20, 2019

0.1.28

May 6, 2019

0.1.27

May 1, 2019

0.1.26

Feb 28, 2019

This version

0.1.25

Jan 22, 2019

0.1.24

Dec 10, 2018

0.1.23

Nov 20, 2018

0.1.22

Nov 20, 2018

0.1.21

Nov 20, 2018

0.1.20

Nov 20, 2018

0.1.19

Nov 1, 2018

0.1.18

Nov 1, 2018

0.1.17

Nov 1, 2018

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dpu_utils-0.1.25.tar.gz (28.2 kB view details)

Uploaded Jan 22, 2019 Source

Built Distribution

dpu_utils-0.1.25-py2.py3-none-any.whl (39.1 kB view details)

Uploaded Jan 22, 2019 Python 2 Python 3

File details

Details for the file dpu_utils-0.1.25.tar.gz.

File metadata

Download URL: dpu_utils-0.1.25.tar.gz
Upload date: Jan 22, 2019
Size: 28.2 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.29.1 CPython/3.6.6

File hashes

Hashes for dpu_utils-0.1.25.tar.gz
Algorithm	Hash digest
SHA256	`e6b4a73eea9abc2c71fe88cd0a6e21d0b6b933cffc1f75158f30220b77748a32`
MD5	`45d5716608009784edaf61db10136717`
BLAKE2b-256	`73741de65ae00c80e6cb23deb6eb2e057f3d3635b99b0f1c7a470204db078e83`

See more details on using hashes here.

File details

Details for the file dpu_utils-0.1.25-py2.py3-none-any.whl.

File metadata

Download URL: dpu_utils-0.1.25-py2.py3-none-any.whl
Upload date: Jan 22, 2019
Size: 39.1 kB
Tags: Python 2, Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/39.0.1 requests-toolbelt/0.8.0 tqdm/4.29.1 CPython/3.6.6

File hashes

Hashes for dpu_utils-0.1.25-py2.py3-none-any.whl
Algorithm	Hash digest
SHA256	`8b974f5e923ae51f5f86abce453b7544ccd201a29e412917bef10b643b120942`
MD5	`efa990eba992ccf26549e7263e1630cf`
BLAKE2b-256	`78dddf74acff6f49b6b1ec89e551239b6088197d2f0c64541d59220695991ff9`