An on going implementation of the gcForest algorithm

leopiney, updated 🕥 2022-06-21 21:12:22

WIP

deep-forest

Introduction

Inspired by https://arxiv.org/abs/1702.08835 and https://github.com/STO-OTZ/my_gcForest/

This paper introduces gcForest as an alternative to Deep Learning techniques. Here's an initial implementation of what I concluded the gcForest algorithm is.

To do

  1. Scikit-learn wrapper

Running the example

  1. Create virtual environment: python3.x -m venv env && source env/bin/activate
  2. Install dependencies: pip install -r requirements.txt
  3. Run Jupyter: jupyter-notebook
  4. Open the deep-forest-example notebook

Issues

Bump numpy from 1.12.0 to 1.22.0

opened on 2022-06-21 21:12:17 by dependabot[bot]

Bumps numpy from 1.12.0 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


Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) - `@dependabot use these labels` will set the current labels as the default for future PRs for this repo and language - `@dependabot use these reviewers` will set the current reviewers as the default for future PRs for this repo and language - `@dependabot use these assignees` will set the current assignees as the default for future PRs for this repo and language - `@dependabot use this milestone` will set the current milestone as the default for future PRs for this repo and language You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/leopiney/deep-forest/network/alerts).

Bump ipython from 5.3.0 to 7.16.3

opened on 2022-01-21 19:22:31 by dependabot[bot]

Bumps ipython from 5.3.0 to 7.16.3.

Commits


Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) - `@dependabot use these labels` will set the current labels as the default for future PRs for this repo and language - `@dependabot use these reviewers` will set the current reviewers as the default for future PRs for this repo and language - `@dependabot use these assignees` will set the current assignees as the default for future PRs for this repo and language - `@dependabot use this milestone` will set the current milestone as the default for future PRs for this repo and language You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/leopiney/deep-forest/network/alerts).

When I use all the data, the program will report an error.

opened on 2019-11-21 06:12:39 by nishiwen1214

Thank you for your completion！ err： A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker. The exit codes of the workers are {SIGKILL(-9)} My computer's memory is 32g. Have you ever encountered this situation? Looking forward to your reply！

Would you like to share code of theSDF in github? I'm very interested in the new forest structure

opened on 2018-02-06 14:26:58 by zhaopanpan73

I am confused to the training and testing architectures of SDF and the implementation details of the experiments. I am long to figure the detail out in order to do further research in this field with your team's method. looking forward your reply

data format

opened on 2017-10-04 05:21:10 by JenniferDai10

Hi, currently, I am trying use this deep forest model to test the EEG signal for the sleep stage classification. However, the dataset is 1-d, not the 2-d like image. So, could you lend me a hand, how can I change the code and make it possible for 1-d datasets training. Great thanks!!!

can't save model

opened on 2017-07-06 12:34:19 by aminobest

Hi,

following the tutorial in deep-forests-example.ipynb, at the end I tried to save the model using something like with open('out.pkl', 'wb') as pickle_file: pickle.dump(mgc_forest, pickle_file, protocol=2) , but then I get TypeError: can't pickle _thread.RLock objects.

Is there a way to save the model for later use?

Leonardo Piñeyro
GitHub Repository