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deep-forest-0.1.7
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Deep Forest
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deep-forest-0.1.7 |
| نام | deep-forest |
| نسخه کتابخانه | 0.1.7 |
| نگهدارنده | ['Yi-Xuan Xu'] |
| ایمیل نگهدارنده | ['xuyx@lamda.nju.edu.cn'] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/LAMDA-NJU/Deep-Forest |
| آدرس اینترنتی | https://pypi.org/project/deep-forest/ |
| مجوز | - |
Deep Forest (DF) 21
===================
|github|_ |readthedocs|_ |codecov|_ |python|_ |pypi|_ |style|_
.. |github| image:: https://github.com/LAMDA-NJU/Deep-Forest/workflows/DeepForest-CI/badge.svg
.. _github: https://github.com/LAMDA-NJU/Deep-Forest/actions
.. |readthedocs| image:: https://readthedocs.org/projects/deep-forest/badge/?version=latest
.. _readthedocs: https://deep-forest.readthedocs.io
.. |codecov| image:: https://codecov.io/gh/LAMDA-NJU/Deep-Forest/branch/master/graph/badge.svg?token=5BVXOT8RPO
.. _codecov: https://codecov.io/gh/LAMDA-NJU/Deep-Forest
.. |python| image:: https://img.shields.io/pypi/pyversions/deep-forest
.. _python: https://pypi.org/project/deep-forest/
.. |pypi| image:: https://img.shields.io/pypi/v/deep-forest?color=blue
.. _pypi: https://pypi.org/project/deep-forest/
.. |style| image:: https://img.shields.io/badge/code%20style-black-000000.svg
.. _style: https://github.com/psf/black
**DF21** is an implementation of `Deep Forest <https://arxiv.org/pdf/1702.08835.pdf>`__ 2021.2.1. It is designed to have the following advantages:
- **Powerful**: Better accuracy than existing tree-based ensemble methods.
- **Easy to Use**: Less efforts on tunning parameters.
- **Efficient**: Fast training speed and high efficiency.
- **Scalable**: Capable of handling large-scale data.
DF21 offers an effective & powerful option to the tree-based machine learning algorithms such as Random Forest or GBDT.
For a quick start, please refer to `How to Get Started <https://deep-forest.readthedocs.io/en/latest/how_to_get_started.html>`__. For a detailed guidance on parameter tunning, please refer to `Parameters Tunning <https://deep-forest.readthedocs.io/en/latest/parameters_tunning.html>`__.
DF21 is optimized for what a tree-based ensemble excels at (i.e., tabular data), if you want to use the multi-grained scanning part to better handle structured data like images, please refer to the `origin implementation <https://github.com/kingfengji/gcForest>`__ for details.
Installation
------------
DF21 can be installed using pip via `PyPI <https://pypi.org/project/deep-forest/>`__ which is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. Refer `this <https://pypi.org/project/pip/>`__ for the documentation of pip. Use this command to download DF21 :
.. code-block:: bash
pip install deep-forest
Quickstart
----------
Classification
**************
.. code-block:: python
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from deepforest import CascadeForestClassifier
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
model = CascadeForestClassifier(random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred) * 100
print("\nTesting Accuracy: {:.3f} %".format(acc))
>>> Testing Accuracy: 98.667 %
Regression
**********
.. code-block:: python
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from deepforest import CascadeForestRegressor
X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
model = CascadeForestRegressor(random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("\nTesting MSE: {:.3f}".format(mse))
>>> Testing MSE: 8.068
Resources
---------
* `Documentation <https://deep-forest.readthedocs.io/>`__
* Deep Forest: `[Conference] <https://www.ijcai.org/proceedings/2017/0497.pdf>`__ | `[Journal] <https://academic.oup.com/nsr/article-pdf/6/1/74/30336169/nwy108.pdf>`__
* Keynote at AISTATS 2019: `[Slides] <https://aistats.org/aistats2019/0-AISTATS2019-slides-zhi-hua_zhou.pdf>`__
Reference
---------
.. code-block:: latex
@article{zhou2019deep,
title={Deep forest},
author={Zhi-Hua Zhou and Ji Feng},
journal={National Science Review},
volume={6},
number={1},
pages={74--86},
year={2019}}
@inproceedings{zhou2017deep,
title = {{Deep Forest:} Towards an alternative to deep neural networks},
author = {Zhi-Hua Zhou and Ji Feng},
booktitle = {IJCAI},
pages = {3553--3559},
year = {2017}}
Thanks to all our contributors
------------------------------
|contributors|
.. |contributors| image:: https://contributors-img.web.app/image?repo=LAMDA-NJU/Deep-Forest
.. _contributors: https://github.com/LAMDA-NJU/Deep-Forest/graphs/contributors
نیازمندی
| مقدار | نام |
|---|---|
| >=1.14.6 | numpy |
| >=1.1.0 | scipy |
| >=0.11 | joblib |
| >=1.0 | scikit-learn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl deep-forest-0.1.7:
pip install deep-forest-0.1.7.whl
نصب پکیج tar.gz deep-forest-0.1.7:
pip install deep-forest-0.1.7.tar.gz