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Bump the pip group across 3 directories with 6 updates #22

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Bumps the pip group with 2 updates in the /Exploration - Document Image Quality Assessment directory: numpy and scikit-learn.
Bumps the pip group with 6 updates in the /Exploration - Document Segmentation directory:

Package From To
tensorflow-gpu 1.13.1 2.12.0
opencv-python 4.0.1.23 4.2.0.32
numpy 1.16.2 1.22.0
scikit-learn 0.20.3 1.5.0
tqdm 4.31.1 4.66.3
requests 2.21.0 2.32.2

Bumps the pip group with 2 updates in the /Exploration - Document Type Classification directory: numpy and scikit-learn.

Updates numpy from 1.16.2 to 1.22.0

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

Updates scikit-learn from 0.20.3 to 1.5.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Scikit-learn 1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

Scikit-learn 1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

... (truncated)

Commits

Updates tensorflow-gpu from 1.13.1 to 2.12.0

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

  • Build, Compilation and Packaging

    • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

  • tf.function:

    • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
      • Using functools.wraps on a function with different signature
      • Using functools.partial with an invalid tf.function input
    • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
    • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
    • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
    • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

  • Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

  • Support for Python 3.11 has been added.

  • Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

  • tf.lite:

    • Add 16-bit float type support for built-in op fill.
    • Transpose now supports 6D tensors.
    • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

  • tf.experimental.dtensor:

    • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
    • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

  • tf.data:

    • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

  • tf.test:

    • Added tf.test.experimental.sync_devices, which is useful for accurately measuring performance in benchmarks.

  • tf.experimental.dtensor:

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.12.0

Breaking Changes

  • Build, Compilation and Packaging

    • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

  • tf.function:

    • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
      • Using functools.wraps on a function with different signature
      • Using functools.partial with an invalid tf.function input
    • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
    • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
    • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
    • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

  • Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

  • Support for Python 3.11 has been added.

  • Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

  • tf.lite:

    • Add 16-bit float type support for built-in op fill.
    • Transpose now supports 6D tensors.
    • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

  • tf.experimental.dtensor:

    • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
    • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

  • tf.data:

    • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

  • tf.test:

    • Added tf.test.experimental.sync_devices, which is useful for accurately measuring performance in benchmarks.

  • tf.experimental.dtensor:

    • Added experimental support to ReduceScatter fuse on GPU (NCCL).

... (truncated)

Commits
  • 0db597d Merge pull request #60051 from tensorflow/venkat2469-patch-1
  • 1a12f59 Update RELEASE.md
  • aa4d558 Merge pull request #60050 from tensorflow/venkat-patch-6
  • bd1ab8a Update the security section in RELEASE.md
  • 4905be0 Merge pull request #60049 from tensorflow/venkat-patch-5
  • 9f96caa Update setup.py on TF release branch with released version of Estimator and k...
  • e719b6b Update Relese.md (#60033)
  • 64a9d54 Merge pull request #60017 from tensorflow/joefernandez-patch-2.12-release-notes
  • 7a4ebfd Update RELEASE.md
  • e0e10a9 Merge pull request #59988 from tensorflow-jenkins/version-numbers-2.12.0-8756
  • Additional commits viewable in compare view

Updates opencv-python from 4.0.1.23 to 4.2.0.32

Release notes

Sourced from opencv-python's releases.

4.2.0.32

OpenCV version 4.2.0.

Changes:

  • macOS environment updated from xcode8.3 to xcode 9.4
  • macOS uses now Qt 5 instead of Qt 4
  • Nasm version updated to Docker containers
  • multibuild updated

Fixes:

  • don't use deprecated brew tap-pin, instead refer to the full package name when installing #267
  • replace get_config_var() with get_config_vars() in setup.py #274
  • add workaround for DLL errors in Windows Server #264

4.1.2.30

OpenCV version 4.1.2.

Changes:

  • Python 3.8 builds added to the build matrix
  • Support for Python 3.4 builds dropped (Python 3.4 is in EOL)
  • multibuild updated
  • minor build logic changes
  • Docker images rebuilt

Notes:

Please note that Python 2.7 enters into EOL phase in January 2020. opencv-python Python 2.7 wheels won't be provided after that.

4.1.1.26

OpenCV version 4.1.1.

Changes:

... (truncated)

Commits

Updates numpy from 1.16.2 to 1.22.0

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

Updates scikit-learn from 0.20.3 to 1.5.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Scikit-learn 1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

Scikit-learn 1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

... (truncated)

Commits

Updates tqdm from 4.31.1 to 4.66.3

Release notes

Sourced from tqdm's releases.

tqdm v4.66.3 stable

tqdm v4.66.2 stable

  • pandas: add DataFrame.progress_map (#1549)
  • notebook: fix HTML padding (#1506)
  • keras: fix resuming training when verbose>=2 (#1508)
  • fix format_num negative fractions missing leading zero (#1548)
  • fix Python 3.12 DeprecationWarning on import (#1519)
  • linting: use f-strings (#1549)
  • update tests (#1549)
  • CI: bump actions (#1549)

tqdm v4.66.1 stable

  • fix utils.envwrap types (#1493 <- #1491, #1320 <- #966, #1319)
    • e.g. cloudwatch & kubernetes workaround: export TQDM_POSITION=-1
  • drop mentions of unsupported Python versions

tqdm v4.66.0 stable

  • environment variables to override defaults (TQDM_*) (#1491 <- #1061, #950 <- #614, #1318, #619, #612, #370)
    • e.g. in CI jobs, export TQDM_MININTERVAL=5 to avoid log spam
    • add tests & docs for tqdm.utils.envwrap
  • fix & update CLI completion
  • fix & update API docs
  • minor code tidy: replace os.path => pathlib.Path
  • fix docs image hosting
  • release with CI bot account again (cli/cli#6680)

tqdm v4.65.2 stable

  • exclude examples from distributed wheel (#1492)

tqdm v4.65.1 stable

  • migrate setup.{cfg,py} => pyproject.toml (#1490)
    • fix asv benchmarks
    • update docs
  • fix snap build (#1490)
  • fix & update tests (#1490)
    • fix flaky notebook tests
    • bump pre-commit
    • bump workflow actions

tqdm v4.65.0 stable

  • add Python 3.11 and drop Python 3.6 support (#1439, #1419, #502 <- #720, #620)
  • misc code & docs tidy
  • fix & update CI workflows & tests

tqdm v4.64.1 stable

... (truncated)

Commits

Updates requests from 2.21.0 to 2.32.2

Release notes

Sourced from requests's releases.

v2.32.2

2.32.2 (2024-05-21)

Deprecations

  • To provide a more stable migration for custom HTTPAdapters impacted by the CVE changes in 2.32.0, we've renamed _get_connection to a new public API, get_connection_with_tls_context. Existing custom HTTPAdapters will need to migrate their code to use this new API. get_connection is considered deprecated in all versions of Requests>=2.32.0.

    A minimal (2-line) example has been provided in the linked PR to ease migration, but we strongly urge users to evaluate if their custom adapter is subject to the same issue described in CVE-2024-35195. (#6710)

v2.32.1

2.32.1 (2024-05-20)

Bugfixes

  • Add missing test certs to the sdist distributed on PyPI.

v2.32.0

2.32.0 (2024-05-20)

🐍 PYCON US 2024 EDITION 🐍

Security

  • Fixed an issue where setting verify=False on the first request from a Session will cause subsequent requests to the same origin to also ignore cert verification, regardless of the value of verify. (GHSA-9wx4-h78v-vm56)

Improvements

  • verify=True now reuses a global SSLContext which should improve request time variance between first and subsequent requests. It should also minimize certificate load time on Windows systems when using a Python version built with OpenSSL 3.x. (#6667)
  • Requests now supports optional use of character detection (chardet or charset_normalizer) when repackaged or vendored. This enables pip and other projects to minimize their vendoring surface area. The Response.text() and apparent_encoding APIs will default to utf-8 if neither library is present. (#6702)

Bugfixes

  • Fixed bug in length detection where emoji length was incorrectly calculated in the request content-length. (#6589)
  • Fixed deserialization bug in JSONDecodeError. (#6629)
  • Fixed bug where an extra leading / (path separator) could lead urllib3 to unnecessarily reparse the request URI. (#6644)

... (truncated)

Changelog

Sourced from requests's changelog.

2.32.2 (2024-05-21)

Deprecations

  • To provide a more stable migration for custom HTTPAdapters impacted by the CVE changes in 2.32.0, we've renamed _get_connection to a new public API, get_connection_with_tls_context. Existing custom HTTPAdapters will need to migrate their code to use this new API. get_connection is considered deprecated in all versions of Requests>=2.32.0.

    A minimal (2-line) example has been provided in the linked PR to ease migration, but we strongly urge users to evaluate if their custom adapter is subject to the same issue described in CVE-2024-35195. (#6710)

2.32.1 (2024-05-20)

Bugfixes

  • Add missing test certs to the sdist distributed on PyPI.

2.32.0 (2024-05-20)

Security

  • Fixed an issue where setting verify=False on the first request from a Session will cause subsequent requests to the same origin to also ignore cert verification, regardless of the value of verify. (GHSA-9wx4-h78v-vm56)

Improvements

  • verify=True now reuses a global SSLContext which should improve request time variance between first and subsequent requests. It should also minimize certificate load time on Windows systems when using a Python version built with OpenSSL 3.x. (#6667)
  • Requests now supports optional use of character detection (chardet or charset_normalizer) when repackaged or vendored. This enables pip and other projects to minimize their vendoring surface area. The Response.text() and apparent_encoding APIs will default to utf-8 if neither library is present. (#6702)

Bugfixes

  • Fixed bug in length detection where emoji length was incorrectly calculated in the request content-length. (#6589)
  • Fixed deserialization bug in JSONDecodeError. (#6629)
  • Fixed bug where an extra leading / (path separator) could lead urllib3 to unnecessarily reparse the request URI. (#6644)

Deprecations

... (truncated)

Commits
  • 88dce9d v2.32.2
  • c98e4d1 Merge pull request #6710 from nateprewitt/api_rename
  • 92075b3 Add deprecation warning
  • aa1461b Move _get_connection to get_connection_with_tls_context
  • 970e8ce v2.32.1
  • d6ebc4a v2.32.0
  • 9a40d12 Avoid reloading root certificates to improve concurrent performance (#6667)
  • 0c030f7 Merge pull request #6702 from nateprewitt/no_char_detection
  • 555b870 Allow character detection dependencies to be optional in post-packaging steps
  • d6dded3 Merge pull request #6700 from franekmagiera/update-redirect-to-invalid-uri-test
  • Additional commits viewable in compare view

Updates numpy from 1.16.2 to 1.22.0

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

Updates scikit-learn from 0.20.3 to 1.5.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Scikit-learn 1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

Scikit-learn 1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

... (truncated)

Commits

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Bump the pip group across 3 directories with 6 updates
ff65841 
Bumps the pip group with 2 updates in the /Exploration - Document Image Quality Assessment directory: [numpy](https://github.com/numpy/numpy) and [scikit-learn](https://github.com/scikit-learn/scikit-learn).
Bumps the pip group with 6 updates in the /Exploration - Document Segmentation directory:

| Package | From | To |
| --- | --- | --- |
| [tensorflow-gpu](https://github.com/tensorflow/tensorflow) | `1.13.1` | `2.12.0` |
| [opencv-python](https://github.com/opencv/opencv-python) | `4.0.1.23` | `4.2.0.32` |
| [numpy](https://github.com/numpy/numpy) | `1.16.2` | `1.22.0` |
| [scikit-learn](https://github.com/scikit-learn/scikit-learn) | `0.20.3` | `1.5.0` |
| [tqdm](https://github.com/tqdm/tqdm) | `4.31.1` | `4.66.3` |
| [requests](https://github.com/psf/requests) | `2.21.0` | `2.32.2` |

Bumps the pip group with 2 updates in the /Exploration - Document Type Classification directory: [numpy](https://github.com/numpy/numpy) and [scikit-learn](https://github.com/scikit-learn/scikit-learn).


Updates `numpy` from 1.16.2 to 1.22.0
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v1.16.2...v1.22.0)

Updates `scikit-learn` from 0.20.3 to 1.5.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.20.3...1.5.0)

Updates `tensorflow-gpu` from 1.13.1 to 2.12.0
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v1.13.1...v2.12.0)

Updates `opencv-python` from 4.0.1.23 to 4.2.0.32
- [Release notes](https://github.com/opencv/opencv-python/releases)
- [Commits](https://github.com/opencv/opencv-python/commits)

Updates `numpy` from 1.16.2 to 1.22.0
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v1.16.2...v1.22.0)

Updates `scikit-learn` from 0.20.3 to 1.5.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.20.3...1.5.0)

Updates `tqdm` from 4.31.1 to 4.66.3
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](tqdm/tqdm@v4.31.1...v4.66.3)

Updates `requests` from 2.21.0 to 2.32.2
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.21.0...v2.32.2)

Updates `numpy` from 1.16.2 to 1.22.0
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v1.16.2...v1.22.0)

Updates `scikit-learn` from 0.20.3 to 1.5.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.20.3...1.5.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: scikit-learn
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tensorflow-gpu
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: opencv-python
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: numpy
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: scikit-learn
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tqdm
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: requests
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: numpy
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: scikit-learn
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Jun 17, 2024
@dependabot dependabot bot mentioned this pull request Jun 17, 2024
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