diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 597e4cc0a8..747e250891 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -4,7 +4,10 @@ Contributing to ProgLearn (adopted from scikit-learn) The latest contributing guide is available in the repository at -`docs/contributing.rst` +`docs/contributing.rst`, or online at: + +[https://proglearn.neurodata.io/contributing.html](https://proglearn.neurodata.io/contributing.html) + There are many ways to contribute to ProgLearn, with the most common ones being contribution of code or documentation to the project. Improving the @@ -23,6 +26,13 @@ up" on issues that others reported and that are relevant to you. It also helps us if you spread the word: reference the project from your blog and articles, link to it from your website, or simply star it in GitHub to say "I use it". +Quick links +----------- + +* [Submitting a bug report or feature request](http://proglearn.neurodata.io/contributing.html#submitting-a-bug-report-or-a-feature-request) +* [Contributing code](http://proglearn.neurodata.io/contributing.html#contributing-code) +* [Coding guidelines](http://proglearn.neurodata.io/contributing.html#coding-guidelines) + Code of Conduct --------------- diff --git a/README.md b/README.md index 5e8780ded6..9ada60a9c9 100644 --- a/README.md +++ b/README.md @@ -1,80 +1,24 @@ # ProgLearn [![Build Status](https://travis-ci.org/neurodata/ProgLearn.svg?branch=main)](https://travis-ci.org/neurodata/ProgLearn) -[![codecov](https://codecov.io/gh/neurodata/ProgLearn/branch/main/graph/badge.svg)](https://codecov.io/gh/neurodata/ProgLearn) +[![codecov](https://codecov.io/gh/neurodata/ProgLearn/branches/main/graph/badge.svg)](https://codecov.io/gh/neurodata/ProgLearn) [![PyPI version](https://img.shields.io/pypi/v/proglearn.svg)](https://pypi.org/project/proglearn/) -[![arXiv shield](https://img.shields.io/badge/arXiv-2004.12908-red.svg?style=flat)](https://arxiv.org/abs/2004.12908) +[![arXiv](https://img.shields.io/badge/arXiv-2004.12908-red.svg?style=flat)](https://arxiv.org/abs/2004.12908) [![License](https://img.shields.io/badge/License-MIT-blue)](https://opensource.org/licenses/MIT) +[![Netlify Status](https://api.netlify.com/api/v1/badges/97f86f49-81ed-4292-a100-f7031b54ecc7/deploy-status)](https://app.netlify.com/sites/neuro-data-proglearn/deploys) +![Downloads](https://img.shields.io/pypi/dm/proglearn.svg) -`proglearn` (**Prog**ressive **Learn**ing) is a package for exploring and using progressive learning algorithms developed by the [neurodata group](https://neurodata.io). -- [Overview](#overview) -- [Documentation](#documentation) -- [System Requirements](#system-requirements) -- [Installation Guide](#installation-guide) -- [Contributing](#contributing) -- [License](#license) -- [Issues](#issues) +`ProgLearn` (**Prog**ressive **Learn**ing) is a package for exploring and using progressive learning algorithms developed by the [neurodata group](https://neurodata.io). -# Overview -The natural process of biological learning involves progressive acquisition of new information developing on past knowledge and experiences, which often leads to a performance improvement on a given task. Learning a second language, for instance, is associated with higher performance in an individual’s native language compared to that of monolinguals. In classical machine learning, the process usually begins from the state of tabula rasa, zero knowledge, and is optimized for a single task. The issues arise when the system is sequentially optimized for multiple tasks exhibiting “catastrophic forgetting,” diminishing performance of previously learned tasks. One of the current limitations of artificial intelligence revolves around this inability to transfer knowledge.

-The progressive learning package utilizes representation ensembling algorithms to sequentially learn a representation for each task and ensemble both old and new representations for all future decisions. Here, two complementary representation ensembling algorithms based on decision forests (Lifelong Forest) and deep networks (Lifelong Network) demonstrate forward and backward knowledge transfer of tasks on multiple real datasets, including both vision and language applications. +- **Installation Guide:** [http://proglearn.neurodata.io/install.html](http://proglearn.neurodata.io/install.html) +- **Documentation:** [http://proglearn.neurodata.io](http://proglearn.neurodata.io) +- **Tutorials:** [http://proglearn.neurodata.io/tutorials.html](http://proglearn.neurodata.io/tutorials.html) +- **Source Code:** [http://proglearn.neurodata.io/reference/index.html](http://proglearn.neurodata.io/reference/index.html) +- **Issues:** [https://github.com/neurodata/proglearn/issues](https://github.com/neurodata/proglearn/issues) +- **Contribution Guide:** [http://proglearn.neurodata.io/contributing.html](http://proglearn.neurodata.io/contributing.html) -# Documentation - - -# System Requirements -## Hardware requirements -`proglearn` package requires only a standard computer with enough RAM to support the in-memory operations. - -## Software requirements -### OS Requirements -This package is supported for *Linux* and *macOS*. The package has been tested on the following systems: -+ Linux: Ubuntu 16.04 -+ macOS: Mojave (10.14.1) -+ Windows: 10 - -### Python Requirements -This package is written for Python3. Currently, it is supported for Python 3.6 and 3.7. - -### Python Dependencies -`proglearn` mainly depends on the Python scientific stack. -``` -keras>=2.3.1 -tensorflow>=1.19.0 -scikit-learn>=0.22.0 -scipy==1.4.1 -numpy<1.19 -joblib>=0.14.1 -``` - -# Installation Guide -## Install from pip -``` -pip install proglearn -``` - -## Install from Github -``` -git clone https://github.com/neurodata/ProgLearn.git -cd ProgLearn -python3 setup.py install -``` - -# Contributing -We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/neurodata/ProgLearn/blob/main/CONTRIBUTING.md) before making a pull request. Our -[issues](https://github.com/neurodata/ProgLearn/issues) page is full of places we could use help! -If you have an idea for an improvement not listed there, please -[make an issue](https://github.com/neurodata/ProgLearn/issues/new) first so you can discuss with the -developers. - -# License -This project is covered under the [MIT License](https://github.com/neurodata/ProgLearn/blob/main/LICENSE). - -# Issues -We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/neurodata/ProgLearn/issues) page if you have questions or ideas. - -# Citing ProgLearn -If you find ProgLearn useful in your work, please cite the package via the [progressive-learning paper](https://arxiv.org/pdf/2004.12908.pdf) - -> Vogelstein JT, Helm HS, Mehta RD, Dey J, Yang W, Tower B, LeVine W, Larson J, White C, Priebe CE. A general approach to progressive learning. arXiv preprint arXiv:2004.12908. 2020 Apr 27. +Some system/package requirements: +- **Python**: 3.6+ +- **OS**: All major platforms (Linux, macOS, Windows) +- **Dependencies**: keras, scikit-learn, scipy, numpy, joblib diff --git a/experiments/cifar_exp/appendix_tables.ipynb b/benchmarks/cifar_exp/appendix_tables.ipynb similarity index 100% rename from experiments/cifar_exp/appendix_tables.ipynb rename to benchmarks/cifar_exp/appendix_tables.ipynb diff --git a/experiments/cifar_exp/benchmarking_pickle_conversion.py b/benchmarks/cifar_exp/benchmarking_pickle_conversion.py similarity index 100% rename from experiments/cifar_exp/benchmarking_pickle_conversion.py rename to benchmarks/cifar_exp/benchmarking_pickle_conversion.py diff --git a/experiments/cifar_exp/experiment_varying_task_sample.py b/benchmarks/cifar_exp/experiment_varying_task_sample.py similarity index 100% rename from experiments/cifar_exp/experiment_varying_task_sample.py rename 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experiments/xor_rxor_spiral_exp/result/figs/spiral5.pdf rename to benchmarks/xor_rxor_spiral_exp/result/figs/spiral5.pdf diff --git a/experiments/xor_rxor_spiral_exp/result/figs/xor_nxor_rxor_exp.pdf b/benchmarks/xor_rxor_spiral_exp/result/figs/xor_nxor_rxor_exp.pdf similarity index 100% rename from experiments/xor_rxor_spiral_exp/result/figs/xor_nxor_rxor_exp.pdf rename to benchmarks/xor_rxor_spiral_exp/result/figs/xor_nxor_rxor_exp.pdf diff --git a/experiments/xor_rxor_spiral_exp/result/figs/xor_rxor_spiral.pdf b/benchmarks/xor_rxor_spiral_exp/result/figs/xor_rxor_spiral.pdf similarity index 100% rename from experiments/xor_rxor_spiral_exp/result/figs/xor_rxor_spiral.pdf rename to benchmarks/xor_rxor_spiral_exp/result/figs/xor_rxor_spiral.pdf diff --git a/experiments/xor_rxor_spiral_exp/spiral_exp.py b/benchmarks/xor_rxor_spiral_exp/spiral_exp.py similarity index 100% rename from experiments/xor_rxor_spiral_exp/spiral_exp.py rename to benchmarks/xor_rxor_spiral_exp/spiral_exp.py diff --git a/docs/CNAME b/docs/CNAME new file mode 100644 index 0000000000..560999bb60 --- /dev/null +++ b/docs/CNAME @@ -0,0 +1 @@ +proglearn.neurodata.io diff --git a/docs/Makefile b/docs/Makefile index 298ea9e213..c53dae0706 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -5,15 +5,20 @@ SPHINXOPTS = SPHINXBUILD = sphinx-build SOURCEDIR = . -BUILDDIR = _build +BUILDDIR = _build/html + +.PHONY: help clean html + # Put it first so that "make" without argument is like "make help". help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" -.PHONY: help Makefile +clean: + -rm -rf _build/* -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file +html: + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) "$(SOURCEDIR)" "$(BUILDDIR)" + @echo + @echo "Build finished. The HTML pages are in build/html." diff --git a/docs/README.md b/docs/README.md index 75302039c0..a98c56fa74 100644 --- a/docs/README.md +++ b/docs/README.md @@ -30,4 +30,4 @@ To build the HTML documentation, enter: make html -in the `docs/` directory. If all goes well, this will generate a `build/html/` subdirectory containing the built documentation. \ No newline at end of file +in the `docs/` directory. If all goes well, this will generate a `_build/html/` subdirectory containing the built documentation. diff --git a/docs/_templates/footer.html b/docs/_templates/footer.html new file mode 100644 index 0000000000..140c2eda8b --- /dev/null +++ b/docs/_templates/footer.html @@ -0,0 +1,10 @@ +{% extends "!footer.html" %} +{% block extrafooter %} +

+ + + +

+{{ super() }} +{% endblock %} +0 comments on commit 90d7854 diff --git a/docs/_templates/numpy_docstring.rst b/docs/_templates/numpy_docstring.rst new file mode 100644 index 0000000000..fd6a35f766 --- /dev/null +++ b/docs/_templates/numpy_docstring.rst @@ -0,0 +1,16 @@ +{{index}} +{{summary}} +{{extended_summary}} +{{parameters}} +{{returns}} +{{yields}} +{{other_parameters}} +{{attributes}} +{{raises}} +{{warns}} +{{warnings}} +{{see_also}} +{{notes}} +{{references}} +{{examples}} +{{methods}} diff --git a/docs/conf.py b/docs/conf.py index 00315a5db3..f4401c5e4b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -29,76 +29,72 @@ release = '0.01' -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. +# -- Extension configuration ------------------------------------------------- extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.githubpages', + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.todo", + "sphinx.ext.viewcode", + "sphinx.ext.mathjax", + "numpydoc", + "sphinx.ext.ifconfig", + "sphinx.ext.githubpages", + "sphinxcontrib.rawfiles", + "nbsphinx", + "sphinx.ext.intersphinx", ] -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The master toctree document. -master_doc = 'index' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = None +# -- sphinxcontrib.rawfiles +# rawfiles = ["CNAME"] + +# -- numpydoc +# Below is needed to prevent errors +numpydoc_show_class_members = False +numpydoc_attributes_as_param_list = True +numpydoc_use_blockquotes = True + +# -- sphinx.ext.autosummary +autosummary_generate = True + +# -- sphinx.ext.autodoc +autoclass_content = "both" +autodoc_default_flags = ["members", "inherited-members"] +autodoc_member_order = "bysource" # default is alphabetical + +# -- sphinx.ext.intersphinx +intersphinx_mapping = { + "numpy": ("https://docs.scipy.org/doc/numpy", None), + "python": ("https://docs.python.org/3", None), + "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), + "sklearn": ("http://scikit-learn.org/dev", None), + "matplotlib": ("https://matplotlib.org", None), +} +# -- sphinx options ---------------------------------------------------------- +source_suffix = ".rst" +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] +master_doc = "index" +source_encoding = "utf-8" # -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'alabaster' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] +html_static_path = [] +modindex_common_prefix = ["proglearn."] + +pygments_style = "sphinx" +smartquotes = False + +# Use RTD Theme +import sphinx_rtd_theme + +html_theme = "sphinx_rtd_theme" +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] +html_theme_options = { + #'includehidden': False, + "navigation_depth": 3, + "collapse_navigation": False, +} # -- Options for HTMLHelp output --------------------------------------------- diff --git a/docs/contributing.rst b/docs/contributing.rst index f65613e333..804392b682 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -105,7 +105,8 @@ repository on GitHub, clone, and develop on a branch. Steps: Pull Request Checklist ---------------------- -We recommended that your contribution complies with the following rules +We recommended that your contribution complies with the following rules +(which are a brief summary of `The Bits and Brains PR Checklist `__) before you submit a pull request: - Follow the `coding-guidelines <#coding-guidelines>`__. diff --git a/docs/index.rst b/docs/index.rst index 26f4fc8b3f..434b6de388 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,30 +1,82 @@ -.. ProgL documentation master file, created by - sphinx-quickstart on Fri Sep 11 15:20:09 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. +.. -*- coding: utf-8 -*- -Welcome to ProgLearn's documentation! -================================= +.. _contents: -.. toctree:: - :maxdepth: 2 - :caption: Contents: +Overview of ProgLearn_ +====================== +.. _proglearn: https://proglearn.neurodata.io/ +.. image:: https://travis-ci.org/neurodata/ProgLearn.svg?branch=master + :target: https://travis-ci.org/neurodata/ProgLearn +.. image:: https://codecov.io/gh/neurodata/ProgLearn/branch/master/graph/badge.svg + :target: https://codecov.io/gh/neurodata/ProgLearn +.. image:: https://img.shields.io/pypi/v/proglearn.svg + :target: https://pypi.org/project/proglearn/ +.. image:: https://img.shields.io/badge/arXiv-2004.12908-red.svg?style=flat + :target: https://arxiv.org/abs/2004.12908 +.. image:: https://img.shields.io/badge/License-MIT-blue + :target: https://opensource.org/licenses/MIT +.. image:: https://api.netlify.com/api/v1/badges/97f86f49-81ed-4292-a100-f7031b54ecc7/deploy-status + :target: https://app.netlify.com/sites/neuro-data-proglearn/deploys +.. image:: https://img.shields.io/pypi/dm/proglearn.svg + :target: https://github.com/neurodata/ProgLearn -Indices and tables -================== +``ProgLearn`` (**Prog**\ ressive **Learn**\ ing) is a package for exploring and using progressive learning algorithms. -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` +Motivation +---------- + +In biological learning, data are used to improve performance simultaneously on the current task, as well as previously encountered and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called "catastrophic forgetting"). Many recent approaches have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of ``ProgLearn`` is to improve performance on all tasks (including past and future) with any new data. + +Python +------ + +Python is a powerful programming language that allows concise expressions of +network algorithms. Python has a vibrant and growing ecosystem of packages +that ProgLearn uses to provide more features such as numerical linear algebra and +plotting. In order to make the most out of ``ProgLearn`` you will want to know how +to write basic programs in Python. Among the many guides to Python, we +recommend the `Python documentation `_. + +Free software +------------- + +``ProgLearn`` is free software; you can redistribute it and/or modify it under the +terms of the :doc:`MIT `. We welcome contributions. Join us on +`GitHub `_. + +History +------- + +``ProgLearn`` was founded in February 2020. The source code was designed and written by +Will LeVine and Hayden Helm. The experiment code was designed and written by Jayanta Dey +and Will LeVine. The repository is maintained by Will LeVine and Jayanta Dey. Documentation ============= .. toctree:: - :maxdepth: 6 + :maxdepth: 1 + :caption: Using ProgLearn + install + tutorials/ reference/index contributing license + +.. toctree:: + :maxdepth: 1 + :caption: Useful Links + + proglearn @ GitHub + proglearn @ PyPi + Issue Tracker + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/install.rst b/docs/install.rst new file mode 100644 index 0000000000..bca2a08d90 --- /dev/null +++ b/docs/install.rst @@ -0,0 +1,78 @@ +Install +======= + +Below we assume you have the default Python environment already configured on +your computer and you intend to install ``ProgLearn`` inside of it. If you want to +create and work with Python virtual environments, please follow instructions +on `venv `_ and `virtual +environments `_. We +also highly recommend conda. For instructions to install this, please look +at +`conda `_. + +First, make sure you have the latest version of ``pip`` (the Python package +manager) installed. If you do not, refer to the `Pip documentation +`_ and install ``pip`` first. + +Install from PyPi +----------------- +Install the current release of ``ProgLearn`` from the Terminal with ``pip``:: + + $ pip install proglearn + +To upgrade to a newer release use the ``--upgrade`` flag:: + + $ pip install --upgrade proglearn + +If you do not have permission to install software systemwide, you can install +into your user directory using the ``--user`` flag:: + + $ pip install --user proglearn + +Install from Github +------------------- +You can manually download ``ProgLearn`` by cloning the git repo master version and +running the ``setup.py`` file. That is, unzip the compressed package folder +and run the following from the top-level source directory using the Terminal:: + + $ git clone https://github.com/neurodata/proglearn + $ cd proglearn + $ python3 setup.py install + +Or, alternatively, you can use ``pip``:: + + $ git clone https://github.com/neurodata/proglearn + $ cd proglearn + $ pip install . + +Python package dependencies +--------------------------- +``proglearn`` requires the following packages: + +- keras>=2.3.1 +- tensorflow>=1.19.0 +- scikit-learn>=0.22.0 +- scipy==1.4.1 +- numpy<1.19 +- joblib>=0.14.1 + +Hardware requirements +--------------------- +``ProgLearn`` package requires only a standard computer with enough RAM to support +the in-memory operations. GPU's can speed up the networks which are powered by +tensorflow's backend. + +OS Requirements +--------------- +This package is supported for all major operating systems. The following +versions of operating systems was tested on Travis CI: + +- **Linux**: Ubuntu 16.04 +- **macOS**: Mojave (10.14.1) +- **Windows**: 10 + +Testing +------- +``ProgLearn`` uses the Python ``pytest`` testing package. If you don't already have +that package installed, follow the directions on the `pytest homepage +`_. diff --git a/docs/reference/decider.rst b/docs/reference/decider.rst index 29ee87ae5b..570334ffdb 100644 --- a/docs/reference/decider.rst +++ b/docs/reference/decider.rst @@ -3,6 +3,9 @@ Deciders .. currentmodule:: proglearn.deciders +Simple Argmax Average +--------------------- + .. autoclass:: SimpleArgmaxAverage diff --git a/docs/reference/forest.rst b/docs/reference/forest.rst index 5973fbe9c8..02b94ac125 100644 --- a/docs/reference/forest.rst +++ b/docs/reference/forest.rst @@ -1,8 +1,11 @@ Lifelong Learning Forest -************************** +************************ .. currentmodule:: proglearn.forest -.. autoclass:: LifelongClassificationForest +Lifelong Classification Forest +------------------------------ +.. autoclass:: LifelongClassificationForest +.. autoclass:: UncertaintyForest diff --git a/docs/reference/index.rst b/docs/reference/index.rst index eef7a1860d..3530fbbfe7 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -1,3 +1,8 @@ +.. _reference: + +Reference +********* + .. toctree:: :maxdepth: 2 @@ -5,5 +10,4 @@ voter decider network - forest - progressive_learner \ No newline at end of file + forest \ No newline at end of file diff --git a/docs/reference/network.rst b/docs/reference/network.rst index 49e8d03e7c..9ab0e9218b 100644 --- a/docs/reference/network.rst +++ b/docs/reference/network.rst @@ -1,8 +1,11 @@ -Lifelong Learning Networks +Lifelong Learning Network ************************** .. currentmodule:: proglearn.network +Lifelong Classification Network +------------------------------- + .. autoclass:: LifelongClassificationNetwork diff --git a/docs/reference/progressive_learner.rst b/docs/reference/progressive_learner.rst deleted file mode 100644 index 6e508711e8..0000000000 --- a/docs/reference/progressive_learner.rst +++ /dev/null @@ -1,8 +0,0 @@ -Lifelong Learner -**************** - -.. currentmodule:: proglearn.progressive_learner - -.. autoclass:: ProgressiveLearner -.. autoclass:: ClassificationProgressiveLearner - diff --git a/docs/reference/transformer.rst b/docs/reference/transformer.rst index d00b4a8b9e..fc1e1ec606 100644 --- a/docs/reference/transformer.rst +++ b/docs/reference/transformer.rst @@ -1,13 +1,15 @@ Transformers -**************** +************ .. currentmodule:: proglearn.transformers -Deepnet transformer -------------------- +DeepNetwork transformer +----------------------- + .. autoclass:: NeuralClassificationTransformer Tree transformer ------------------ +---------------- + .. autoclass:: TreeClassificationTransformer diff --git a/docs/reference/voter.rst b/docs/reference/voter.rst index c477a42cba..421d871caa 100644 --- a/docs/reference/voter.rst +++ b/docs/reference/voter.rst @@ -1,9 +1,15 @@ Voters -**************** +****** .. currentmodule:: proglearn.voters +Tree Classification Voter +------------------------- .. autoclass:: TreeClassificationVoter + +KNN Classification Voter +------------------------ + .. autoclass:: KNNClassificationVoter diff --git a/docs/requirements.txt b/docs/requirements.txt index 0e6ab5f9cf..eb32c0c227 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,7 +1,8 @@ sphinx==1.8.5 sphinx_rtd_theme==0.4.2 -nbsphinx==0.4.2 +sphinxcontrib-rawfiles +nbsphinx==0.7.1 ipython==7.4.0 ipykernel==5.1.0 numpydoc==0.7 -recommonmark==0.5.0 \ No newline at end of file +recommonmark==0.5.0 diff --git a/docs/tutorials.rst b/docs/tutorials.rst new file mode 100644 index 0000000000..63788b5f0d --- /dev/null +++ b/docs/tutorials.rst @@ -0,0 +1,16 @@ +********* +Tutorials +********* + +The following tutorials highlight what one can do with the ``ProgLearn`` package. + +.. toctree:: + :maxdepth: 1 + + tutorials/installation_guide + tutorials/xor_nxor_exp + tutorials/label_shuffle_exp + tutorials/random_class_exp + tutorials/uncertaintyforest_fig1 + tutorials/uncertaintyforest_running_example + tutorials/installation_guide diff --git a/tutorials/functions/label_shuffle_functions.py b/docs/tutorials/functions/label_shuffle_functions.py similarity index 100% rename from tutorials/functions/label_shuffle_functions.py rename to docs/tutorials/functions/label_shuffle_functions.py diff --git a/tutorials/functions/random_class_functions.py b/docs/tutorials/functions/random_class_functions.py similarity index 100% rename from tutorials/functions/random_class_functions.py rename to docs/tutorials/functions/random_class_functions.py diff --git a/docs/tutorials/functions/rotation_cifar_functions.py b/docs/tutorials/functions/rotation_cifar_functions.py new file mode 100644 index 0000000000..f819efcaa1 --- /dev/null +++ b/docs/tutorials/functions/rotation_cifar_functions.py @@ -0,0 +1,176 @@ +# Import the packages for experiment +import warnings + +warnings.simplefilter("ignore") + +import matplotlib.pyplot as plt +import random +from skimage.transform import rotate +from scipy import ndimage +from skimage.util import img_as_ubyte +import numpy as np +import seaborn as sns + +# Import the progressive learning packages +from proglearn.forest import LifelongClassificationForest + +# Randomized selection of training and testing subsets +def cross_val_data(data_x, data_y, total_cls=10): + # Creates copies of both data_x and data_y so that they can be modified without affecting the original sets + x = data_x.copy() + y = data_y.copy() + # Creates a sorted array of arrays that each contain the indices at which each unique element of data_y can be found + idx = [np.where(data_y == u)[0] for u in np.unique(data_y)] + + for i in range(total_cls): + # Chooses the i'th array within the larger idx array + indx = idx[i] + # The elements of indx are randomly shuffled + random.shuffle(indx) + + if i == 0: + # 250 training data points per task + train_x1 = x[indx[0:250], :] + train_x2 = x[indx[250:500], :] + train_y1 = y[indx[0:250]] + train_y2 = y[indx[250:500]] + + # 100 testing data points per task + test_x = x[indx[500:600], :] + test_y = y[indx[500:600]] + else: + # 250 training data points per task + train_x1 = np.concatenate((train_x1, x[indx[0:250], :]), axis=0) + train_x2 = np.concatenate((train_x2, x[indx[250:500], :]), axis=0) + train_y1 = np.concatenate((train_y1, y[indx[0:250]]), axis=0) + train_y2 = np.concatenate((train_y2, y[indx[250:500]]), axis=0) + + # 100 testing data points per task + test_x = np.concatenate((test_x, x[indx[500:600], :]), axis=0) + test_y = np.concatenate((test_y, y[indx[500:600]]), axis=0) + + return train_x1, train_y1, train_x2, train_y2, test_x, test_y + + +# Runs the experiments +def LF_experiment( + angle, data_x, data_y, granularity, max_depth, reps=1, ntrees=29, acorn=None +): + + # Set random seed to acorn if acorn is specified + if acorn is not None: + np.random.seed(acorn) + + errors = np.zeros( + 2 + ) # initializes array of errors that will be generated during each rep + + for rep in range(reps): + # training and testing subsets are randomly selected by calling the cross_val_data function + train_x1, train_y1, train_x2, train_y2, test_x, test_y = cross_val_data( + data_x, data_y, total_cls=10 + ) + + # Change data angle for second task + tmp_data = train_x2.copy() + _tmp_ = np.zeros((32, 32, 3), dtype=int) + total_data = tmp_data.shape[0] + + for i in range(total_data): + tmp_ = image_aug(tmp_data[i], angle) + # 2D image is flattened into a 1D array as random forests can only take in flattened images as inputs + tmp_data[i] = tmp_ + + # .shape gives the dimensions of each numpy array + # .reshape gives a new shape to the numpy array without changing its data + train_x1 = train_x1.reshape( + ( + train_x1.shape[0], + train_x1.shape[1] * train_x1.shape[2] * train_x1.shape[3], + ) + ) + tmp_data = tmp_data.reshape( + ( + tmp_data.shape[0], + tmp_data.shape[1] * tmp_data.shape[2] * tmp_data.shape[3], + ) + ) + test_x = test_x.reshape( + (test_x.shape[0], test_x.shape[1] * test_x.shape[2] * test_x.shape[3]) + ) + # number of trees (estimators) to use is passed as an argument because the default is 100 estimators + progressive_learner = LifelongClassificationForest( + n_estimators=ntrees, default_max_depth=max_depth + ) + + # Add the original task + progressive_learner.add_task(X=train_x1, y=train_y1) + + # Predict and get errors for original task + llf_single_task = progressive_learner.predict(test_x, task_id=0) + + # Add the new transformer + progressive_learner.add_transformer(X=tmp_data, y=train_y2) + + # Predict and get errors with the new transformer + llf_task1 = progressive_learner.predict(test_x, task_id=0) + + errors[1] = errors[1] + ( + 1 - np.mean(llf_task1 == test_y) + ) # errors from transfer learning + errors[0] = errors[0] + ( + 1 - np.mean(llf_single_task == test_y) + ) # errors from original task + + errors = ( + errors / reps + ) # errors are averaged across all reps ==> more reps means more accurate errors + + # Average errors for original task and transfer learning are returned for the angle tested + return errors + + +# Rotates the image by the given angle and zooms in to remove unnecessary white space at the corners +# Some image data is lost during rotation because of the zoom +def image_aug(pic, angle, centroid_x=23, centroid_y=23, win=16, scale=1.45): + # Calculates scaled dimensions of image + im_sz = int(np.floor(pic.shape[0] * scale)) + pic_ = np.uint8(np.zeros((im_sz, im_sz, 3), dtype=int)) + + # Uses zoom function from scipy.ndimage to zoom into the image + pic_[:, :, 0] = ndimage.zoom(pic[:, :, 0], scale) + pic_[:, :, 1] = ndimage.zoom(pic[:, :, 1], scale) + pic_[:, :, 2] = ndimage.zoom(pic[:, :, 2], scale) + + # Rotates image using rotate function from skimage.transform + image_aug = rotate(pic_, angle, resize=False) + image_aug_ = image_aug[ + centroid_x - win : centroid_x + win, centroid_y - win : centroid_y + win, : + ] + + # Converts the image to unsigned byte format with values in [0, 255] and then returns it + return img_as_ubyte(image_aug_) + + +def plot_bte(bte, angles): + # Choose which color to make the results + clr = ["#00008B"] + c = sns.color_palette(clr, n_colors=1) + fig, ax = plt.subplots(1, 1, figsize=(8, 8)) + + # Plot the data + ax.plot(angles, bte, c=c[0], label="L2F", linewidth=3) + + # Format and label the plot + ax.set_xticks([0, 30, 60, 90, 120, 150, 180]) + ax.tick_params(labelsize=20) + ax.set_xlabel("Angle of Rotation (Degrees)", fontsize=24) + ax.set_ylabel("Backward Transfer Efficiency", fontsize=24) + ax.set_title("Rotation Experiment", fontsize=24) + right_side = ax.spines["right"] + right_side.set_visible(False) + top_side = ax.spines["top"] + top_side.set_visible(False) + plt.tight_layout() + # x.legend(fontsize = 24) + plt.show() diff --git a/docs/tutorials/functions/unc_forest_tutorials_functions.py b/docs/tutorials/functions/unc_forest_tutorials_functions.py new file mode 100644 index 0000000000..d3c3ba91d1 --- /dev/null +++ b/docs/tutorials/functions/unc_forest_tutorials_functions.py @@ -0,0 +1,160 @@ +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +import pickle + +from sklearn.ensemble import RandomForestClassifier +from sklearn.calibration import CalibratedClassifierCV +from sklearn.model_selection import train_test_split +from sklearn.ensemble import BaggingClassifier +from sklearn.tree import DecisionTreeClassifier + +from tqdm.notebook import tqdm +from joblib import Parallel, delayed + +from proglearn.forest import UncertaintyForest +from proglearn.sims import generate_gaussian_parity + +def generate_data(n, mean, var): + ''' + Parameters + --- + n : int + The number of data to be generated + mean : double + The mean of the data to be generated + var : double + The variance in the data to be generated + ''' + y = 2 * np.random.binomial(1, .5, n) - 1 # classes are -1 and 1. + X = np.random.multivariate_normal(mean * y, var * np.eye(n), 1).T # creating the X values using + # the randomly distributed y that were generated in the line above + + return X, y + +def estimate_posterior(algo, n, mean, var, num_trials, X_eval, parallel = False): + ''' + Estimate posteriors for many trials and evaluate in the given X_eval range + + Parameters + --- + algo : dict + A dictionary of the learner to be used containing a key "instance" of the learner + n : int + The number of data to be generated + mean : double + The mean of the data used + var : double + The variance of the data used + num_trials : int + The number of trials to run over + X_eval : list + The range over which to evaluate X values for + ''' + obj = algo['instance'] # grabbing the instance of the learner + def worker(t): + X, y = generate_data(n, mean, var) # generating data with the function above + obj.fit(X, y) # using the fit function of the learner to fit the data + return obj.predict_proba(X_eval)[:,1] # using the predict_proba function on the range of desired X + + if parallel: + predicted_posterior = np.array(Parallel(n_jobs=-2)(delayed(worker)(t) for t in range(num_trials))) + else: + predicted_posterior = np.zeros((num_trials, X_eval.shape[0])) + for t in tqdm(range(num_trials)): + predicted_posterior[t, :] = worker(t) + + return predicted_posterior + +def plot_posterior(ax, algo, num_plotted_trials, X_eval): + """ + Will be used for CART, Honest, or Uncertainty Forest to plot P(Y = 1 | X = x). + This is the left three plots in figure 1. + Plots each of num_plotted_trials iterations, highlighting a single line + + Parameters + --- + ax : list + Holds the axes of the subplots + algo : dict + A dictionary of the learner to be used containing a key "instance" of the learner + num_plotted_trials : int + The number of trials that will be overlayed. This is shown as the lighter lines figure 1. + X_eval : list + The range over which to evaluate X values for + """ + for i in range(num_plotted_trials): + linewidth = 1 + opacity = .3 + if i == num_plotted_trials - 1: + opacity = 1 + linewidth = 8 + ax.set_title(algo['title']) + ax.plot(X_eval.flatten().ravel(), algo['predicted_posterior'][i, :].ravel(), + label = algo['label'], + linewidth = linewidth, + color = algo['color'], + alpha = opacity) + + +def plot_variance(ax, algos, X_eval): + """ + Will be used for the rightmost plot in figure 1. + Plots the variance over the number of trials. + + Parameters + --- + ax : list + Holds the axes of the subplots + algos : list + A list of dictionaries of the learners to be used + X_eval : list + The range over which to evaluate X values for + """ + ax.set_title('Posterior Variance') # adding a title to the plot + for algo in algos: # looping over the algorithms used + variance = np.var(algo['predicted_posterior'], axis = 0) # determining the variance + ax.plot(X_eval.flatten().ravel(), variance.ravel(), + label = algo['label'], + linewidth = 8, + color = algo['color']) # plotting + +def plot_fig1(algos, num_plotted_trials, X_eval): + """ + Sets the communal plotting parameters and creates figure 1 + + Parameters + --- + algos : list + A list of dictionaries of the learners to be used + num_plotted_trials : int + The number of trials that will be overlayed. This is shown as the lighter lines figure 1. + X_eval : list + The range over which to evaluate X values for + """ + sns.set(font_scale = 6) # setting font size + sns.set_style("ticks") # setting plot style + plt.rcParams['figure.figsize'] = [55, 14] # setting figure size + fig, axes = plt.subplots(1, 4) # creating the axes (that will be passed to the subsequent functions) + for ax in axes[0:3]: + ax.set_xlim(-2.1, 2.1) # setting x limits + ax.set_ylim(-0.05, 1.05) # setting y limits + + # Create the 3 posterior plots. (Left three plots in figure 1) + for i in range(len(algos)): + plot_posterior(axes[i], + algos[i], + num_plotted_trials, + X_eval) + + # Create the 1 variance plot. (Rightmost plot in figure 1) + plot_variance(axes[3], algos, X_eval) + + fig.text(0.5, .08, 'x', ha='center') # defining the style of the figure text + axes[0].set_ylabel(r"$\hat P(Y = 1|X = x)$") # labeling the axes + axes[0].set_xlabel(" ") + axes[3].set_ylabel(r"Var($\hat P(Y = 1|X = x)$)") + + fig.tight_layout() + plt.savefig("fig1.pdf") + plt.show() \ No newline at end of file diff --git a/docs/tutorials/functions/xor_nxor_functions.py b/docs/tutorials/functions/xor_nxor_functions.py new file mode 100644 index 0000000000..70c5c308f9 --- /dev/null +++ b/docs/tutorials/functions/xor_nxor_functions.py @@ -0,0 +1,167 @@ +import numpy as np +import random + +import seaborn as sns +import matplotlib.pyplot as plt + +from proglearn.forest import LifelongClassificationForest, UncertaintyForest +from proglearn.sims import * + + +def get_colors(colors, inds): + c = [colors[i] for i in inds] + return c + + +def plot_xor_nxor(data, labels, title): + colors = sns.color_palette("Dark2", n_colors=2) + fig, ax = plt.subplots(1, 1, figsize=(8, 8)) + ax.scatter(data[:, 0], data[:, 1], c=get_colors(colors, labels), s=50) + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_title(title, fontsize=30) + plt.tight_layout() + ax.axis("off") + plt.show() + + +def experiment(n_xor, n_nxor, n_test, reps, n_trees, max_depth, acorn=None): + """ + Runs the Gaussian XOR N-XOR experiment. + Returns the mean error. + """ + + # initialize experiment + if n_xor == 0 and n_nxor == 0: + raise ValueError("Wake up and provide samples to train!!!") + + # if acorn is specified, set random seed to it + if acorn != None: + np.random.seed(acorn) + + # initialize array for storing errors + errors = np.zeros((reps, 4), dtype=float) + + # run the progressive learning algorithm for a number of repetitions + for i in range(reps): + + # initialize learners + progressive_learner = LifelongClassificationForest(n_estimators=n_trees) + uf = UncertaintyForest(n_estimators=2 * n_trees) + + # source data + xor, label_xor = generate_gaussian_parity(n_xor, angle_params=0) + test_xor, test_label_xor = generate_gaussian_parity(n_test, angle_params=0) + + # target data + nxor, label_nxor = generate_gaussian_parity(n_nxor, angle_params=np.pi / 2) + test_nxor, test_label_nxor = generate_gaussian_parity( + n_test, angle_params=np.pi / 2 + ) + + if n_xor == 0: + # fit learners and predict + progressive_learner.add_task(nxor, label_nxor) + l2f_task2 = progressive_learner.predict(test_nxor, task_id=0) + uf.fit(nxor, label_nxor) + uf_task2 = uf.predict(test_nxor) + # record errors + errors[ + i, 0 + ] = 0.5 # no data, so random chance of guessing correctly (err = 0.5) + errors[ + i, 1 + ] = 0.5 # no data, so random chance of guessing correctly (err = 0.5) + errors[i, 2] = 1 - np.sum(uf_task2 == test_label_nxor) / n_test + errors[i, 3] = 1 - np.sum(l2f_task2 == test_label_nxor) / n_test + elif n_nxor == 0: + # fit learners and predict + progressive_learner.add_task(xor, label_xor) + l2f_task1 = progressive_learner.predict(test_xor, task_id=0) + uf.fit(xor, label_xor) + uf_task1 = uf.predict(test_xor) + # record errors + errors[i, 0] = 1 - np.sum(uf_task1 == test_label_xor) / n_test + errors[i, 1] = 1 - np.sum(l2f_task1 == test_label_xor) / n_test + errors[ + i, 2 + ] = 0.5 # no data, so random chance of guessing correctly (err = 0.5) + errors[ + i, 3 + ] = 0.5 # no data, so random chance of guessing correctly (err = 0.5) + else: + # fit learners and predict + progressive_learner.add_task(xor, label_xor) + progressive_learner.add_task(nxor, label_nxor) + l2f_task1 = progressive_learner.predict(test_xor, task_id=0) + l2f_task2 = progressive_learner.predict(test_nxor, task_id=1) + uf.fit(xor, label_xor) + uf_task1 = uf.predict(test_xor) + uf.fit(nxor, label_nxor) + uf_task2 = uf.predict(test_nxor) + # record errors + errors[i, 0] = 1 - np.sum(uf_task1 == test_label_xor) / n_test + errors[i, 1] = 1 - np.sum(l2f_task1 == test_label_xor) / n_test + errors[i, 2] = 1 - np.sum(uf_task2 == test_label_nxor) / n_test + errors[i, 3] = 1 - np.sum(l2f_task2 == test_label_nxor) / n_test + + return np.mean(errors, axis=0) + + +def plot_error(x, y1, y2, ls, task): + # define labels + algorithms = ["Uncertainty Forest", "Lifelong Forest"] + TASK1 = "XOR" + TASK2 = "N-XOR" + colors = sns.color_palette("Set1", n_colors=2) + # plot and format + fig1 = plt.figure(figsize=(8, 8)) + ax1 = fig1.add_subplot(1, 1, 1) + ax1.plot(x, y1, label=algorithms[0], c=colors[1], ls=ls, lw=3) + ax1.plot(x, y2, label=algorithms[1], c=colors[0], ls=ls, lw=3) + ax1.legend(loc="upper right", fontsize=24, frameon=False) + ax1.set_xlabel("Total Sample Size", fontsize=35) + ax1.set_ylabel("Generalization Error (%s)" % (task), fontsize=35) + ax1.tick_params(labelsize=27.5) + ax1.set_xticks([250, 750, 1500]) + ax1.set_yticks([0.05, 0.10]) + ax1.set_xlim(-10) + ax1.set_ylim(0.03, 0.15) + ax1.axvline(x=750, c="gray", linewidth=1.5, linestyle="dashed") + right_side = ax1.spines["right"] + right_side.set_visible(False) + top_side = ax1.spines["top"] + top_side.set_visible(False) + ax1.text(400, np.mean(ax1.get_ylim()), "%s" % (TASK1), fontsize=30) + ax1.text(900, np.mean(ax1.get_ylim()), "%s" % (TASK2), fontsize=30) + plt.tight_layout() + plt.show() + + +def plot_eff(x1, x2, y1, y2): + # define labels + algorithms = ["Forward Transfer", "Backward Transfer"] + TASK1 = "XOR" + TASK2 = "N-XOR" + colors = sns.color_palette("Set1", n_colors=2) + # plot and format + fig1 = plt.figure(figsize=(8, 8)) + ax1 = fig1.add_subplot(1, 1, 1) + ax1.plot(x1, y1, label=algorithms[0], c=colors[0], ls="-", lw=3) + ax1.plot(x2, y2, label=algorithms[1], c=colors[0], ls="--", lw=3) + ax1.set_ylabel("Transfer Efficiency", fontsize=35) + ax1.legend(loc="upper right", fontsize=24, frameon=False) + ax1.set_ylim(0.95, 1.42) + ax1.set_xlabel("Total Sample Size", fontsize=35) + ax1.tick_params(labelsize=27.5) + ax1.set_yticks([1, 1.4]) + ax1.set_xticks([250, 750, 1500]) + ax1.axvline(x=750, c="gray", linewidth=1.5, linestyle="dashed") + right_side = ax1.spines["right"] + right_side.set_visible(False) + top_side = ax1.spines["top"] + top_side.set_visible(False) + ax1.hlines(1, 50, 1500, colors="gray", linestyles="dashed", linewidth=1.5) + ax1.text(400, np.mean(ax1.get_ylim()), "%s" % (TASK1), fontsize=30) + ax1.text(900, np.mean(ax1.get_ylim()), "%s" % (TASK2), fontsize=30) + plt.tight_layout() diff --git a/docs/tutorials/installation_guide.ipynb b/docs/tutorials/installation_guide.ipynb new file mode 100644 index 0000000000..0d06a41be9 --- /dev/null +++ b/docs/tutorials/installation_guide.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial Overview\n", + "This tutorial includes instruction on installation and package setup for the progressive learning repository. After following the steps below, you should have the progressive learning and necessary packages installed on your own machine.\n", + "\n", + "## 1. Installation\n", + "### *Goal: Clone the repository on your local machine and understand what it includes*\n", + "\n", + "### Let's clone the repository\n", + "Steps:\n", + "1. Open the command line on your local machine (called \"Terminal\" on Mac)\n", + "2. Navigate to the location where you'd like to put the repository.\n", + " 1. Find a location in a file explorer (\"Finder\" on Mac)\n", + " 2. Type \"cd \" in the command prompt\n", + " 3. Drag and drop the folder where you'd like to place the repository from the file explorer to the command line\n", + " The command prompt should show something like:\n", + " `bstraus@BS-Mac ~ % cd /Users/bstraus/Desktop `\n", + "3. Type `git clone REPOSITORY_URL` where `REPOSITORY_URL` is replaced by the URL of the neurodata/progressive-learning repository (as of 2020-09-21, it is https://github.com/neurodata/progressive-learning)\n", + "4. Wait for the process to finish. You'll know it's done because you'll see the first part of the command prompt pop up. For me, that looks like: `bstraus@BS-Mac ~ %`\n", + "\n", + "Congrats! You've now cloned the progressive-learning repository.\n", + "\n", + "Last step here, install the package with:\n", + "`python3 setup.py install`\n", + "\n", + "### Let's take a tour\n", + "Currently, you're looking at this tutorial, which lives in progressive-learning/tutorials/.\n", + "This folder also currently houses a notebook running one of the experiments.\n", + "\n", + "In the root directory, we have:\n", + "* `progressive-learning/docs` : contains files that will tell you requirements (we'll use this later), contributing guidelines, and some other administrative files\n", + "\n", + "* `progressive-learning/experiments` : contains notebooks and results for many of the experiments that utilize the functions/classes in the repository\n", + "\n", + "* `progressive-learning/proglearn` : the heart of the repository containing the python files for the progressive learning classes. We'll focus on the UncertaintyForest class which lives in the `forest.py` file in this directory.\n", + "\n", + "* `progressive-learning/tests` : contains python files for various tests\n", + "\n", + "* `progressive-learning/tutorials` : contains python notebooks (like this one) that will guide you through using the classes in the repository and running the experiments\n", + "\n", + "In future notebooks of this tutorial, we'll discuss how to prepare to run the code for the UncertaintyForest class. That code lives in the `progressive-learning/proglearn/forest.py` file. \n", + "\n", + "But, for now, we'll prepare to do that by making a virtual environment and installing the required packages to run that code.\n", + "\n", + "### You're done with part 1 of the tutorial!\n", + "\n", + "### Move on to part 2 \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2: Package Setup\n", + "### *Goal: Create a virtual environment and install requirements per requirements.txt in order to run the UncertaintyForest class*\n", + "\n", + "### First, let's create the virtual environment \n", + "**Note:** that the following instructions were designed for Mac operating systems. If you're running another OS, look for the equivalent steps tailored to that OS.\n", + "\n", + "1. Open the command line on your local machine (called \"Terminal\" on Mac)\n", + "2. Navigate to the location where you'd like to put the virtual environment.\n", + " 1. Find a location in a file explorer (\"Finder\" on Mac)\n", + " 2. Type \"cd \" in the command prompt\n", + " 3. Drag and drop the folder where you'd like to place the virtual environment from the file explorer to the command line\n", + " The command prompt should show something like:\n", + " `bstraus@BS-Mac ~ % cd /Users/bstraus/Desktop `\n", + "3. Create the virtual environment by typing `python3 -m venv UncertaintyForestEnv`\n", + "\n", + "### Next, let's install the requirements for running the UncertaintyForest class\n", + "4. Activate the virtual environment by typing `source UncertaintyForestEnv/bin/activate`\n", + "5. Navigate to the folder `progressive-learning/docs/`. You can do this with the same process as in step 2 above.\n", + "5. Install necessary packages by typing `pip install -r requirements.txt`\n", + "6. You'll also want to install the following packages by typing the code below:\n", + " 1.`pip install jupyterlab`\n", + " 2.`pip install notebook`\n", + " 3.`pip install numpy scipy pandas scikit-learn matplotlib seaborn joblib keras tensorflow tqdm ipywidgets`\n", + "\n", + "You now have set up your virtual environment and installed necessary packages. Note that you'll need to activate your virtual environment each time you want to run things for this class. You can do this easily by repeating steps 1, 2, and 4.\n", + "\n", + "### You're done with part 2 of the tutorial!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The next steps depends on what you want to do. One possibility is to go through the tutorial for the UncertaintyForest class in the UncertaintyForestTutorials Folder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/label_shuffle_exp.ipynb b/docs/tutorials/label_shuffle_exp.ipynb similarity index 100% rename from tutorials/label_shuffle_exp.ipynb rename to docs/tutorials/label_shuffle_exp.ipynb diff --git a/tutorials/random_class_exp.ipynb b/docs/tutorials/random_class_exp.ipynb similarity index 100% rename from tutorials/random_class_exp.ipynb rename to docs/tutorials/random_class_exp.ipynb diff --git a/docs/tutorials/rotation_cifar.ipynb b/docs/tutorials/rotation_cifar.ipynb new file mode 100644 index 0000000000..b1cdc6004e --- /dev/null +++ b/docs/tutorials/rotation_cifar.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rotation CIFAR Experiment\n", + "\n", + "This experiment will use images from the **CIFAR-100** database (https://www.cs.toronto.edu/~kriz/cifar.html) and showcase the backward transfer efficiency of algorithms in the **Progressive Learning** project (https://github.com/neurodata/progressive-learning) as the images are rotated." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages\n", + "import numpy as np\n", + "import keras\n", + "from multiprocessing import Pool\n", + "from functools import partial" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Create array to store errors\n", + "errors_array = []" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Loads and reshapes data sets\n", + "(X_train, y_train), (X_test, y_test) = keras.datasets.cifar100.load_data()\n", + "\n", + "# Joins the training and testing arrays into one\n", + "data_x = np.concatenate([X_train, X_test]) \n", + "data_y = np.concatenate([y_train, y_test]) \n", + "data_y = data_y[:, 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameters\n", + "\n", + "Hyperparameters determine how the model will run. \n", + "\n", + "`granularity` refers to the amount by which the angle will be increased each time. Setting this value at 1 will cause the algorithm to test every whole number rotation angle between 0 and 180 degrees.\n", + "\n", + "`reps` refers to the number of repetitions tested for each angle of rotation. For each repetition, the data is randomly resampled.\n", + "\n", + "`max_depth` refers to the maximum depth of each tree in the Lifelong Classification Forest. If this value is not specified, LifelongClassificationForest defaults to a max tree depth of 30." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "### MAIN HYPERPARAMS ###\n", + "granularity = 2\n", + "reps = 4\n", + "max_depth = 30\n", + "########################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Algorithms\n", + "\n", + "The progressive-learning repo contains two main algorithms, **Lifelong Learning Forests** (L2F) and **Lifelong Learning Network** (L2N), within `forest.py` and `network.py`, respectively. The main difference is that L2F uses random forests while L2N uses deep neural networks. Both algorithms, unlike LwF, EWC, Online_EWC, and SI, have been shown to achieve both forward and backward knowledge transfer. \n", + "\n", + "For the purposes of this experiment, the L2F algorithm will be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiment\n", + "\n", + "If the chosen algorithm is trained on both straight up-and-down CIFAR images and rotated CIFAR images, rather than just straight up-and-down CIFAR images, will it perform better (achieve a higher backward transfer efficiency) when tested on straight up-and-down CIFAR images? How does the angle at which training images are rotated affect these results?\n", + "\n", + "At a rotation angle of 0 degrees, the rotated images simply provide additional straight up-and-down CIFAR training data, so the backward transfer efficiency at this angle show whether or not the chosen algorithm can even achieve backward knowledge transfer. As the angle of rotation increases, the rotated images become less and less similar to the original dataset, so the backward transfer efficiency should logically decrease, while still being above 1." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# L2F\n", + "from functions.rotation_cifar_functions import LF_experiment\n", + "\n", + "# Generate set of angles to test for BTE\n", + "angles = np.arange(0, 181, granularity)\n", + "\n", + "# Parallel processing\n", + "with Pool(8) as p:\n", + " # Multiple sets of errors for each set of angles are appended to a larger array containing errors for all angles\n", + " # Calling LF_experiment will run the experiment at a new angle of rotation\n", + " errors_array.append(\n", + " p.map(partial(LF_experiment, data_x=data_x, data_y=data_y, granularity=granularity, max_depth=max_depth, reps=reps, ntrees=16, acorn=1), angles)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rotation CIFAR Plot\n", + "\n", + "This section takes the results of the experiment and plots the backward transfer efficiency against the angle of rotation for the images in **CIFAR-100**.\n", + "\n", + "## Expected Results\n", + "\n", + "If done correctly, the plot should show that Backward Transfer Efficiency (BTE) is greater than 1 regardless of rotation, but the BTE should decrease as the angle of rotation is increased. The more the number of reps and the finer the granularity, the smoother this downward sloping curve should look." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate BTE for each angle of rotation\n", + "bte = []\n", + "for angle in angles:\n", + " orig_error, transfer_error = errors_array[0][int(angle/granularity)] # (angle/granularity) gives the index of the errors for that angle\n", + " bte.append(orig_error / transfer_error) # (original error/transfer error) gives the BTE" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot angle of rotation vs. BTE\n", + "from functions.rotation_cifar_functions import plot_bte\n", + "plot_bte(bte, angles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FAQs\n", + "\n", + "### Why am I getting an \"out of memory\" error?\n", + "`Pool(8)` in the previous cell allows for parallel processing, so the number within the parenthesis should be, at max, the number of cores in the device on which this notebook is being run. Even if a warning is produced, the results of the experimented should not be affected.\n", + "\n", + "### Why is this taking so long to run? How can I speed it up to see if I am getting the expected outputs?\n", + "Decreasing the value of `reps`, decreasing the value of `max_depth`, or increasing the value of `granularity` will all decrease runtime at the cost of noisier results." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/uncertaintyforest_fig1.ipynb b/docs/tutorials/uncertaintyforest_fig1.ipynb new file mode 100644 index 0000000000..8c80e94a52 --- /dev/null +++ b/docs/tutorials/uncertaintyforest_fig1.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial Overview\n", + "This set of two tutorials (`uncertaintyforest_running_example.ipynb` and `uncertaintyforest_fig1.ipynb`) will explain the UncertaintyForest class. After following both tutorials, you should have the ability to run UncertaintyForest code on your own machine and generate Figure 1 from [this paper](https://arxiv.org/pdf/1907.00325.pdf). \n", + "\n", + "If you haven't seen it already, take a look at other tutorials to setup and install the progressive learning package `Installation-and-Package-Setup-Tutorial.ipynb`\n", + "\n", + "# Analyzing the UncertaintyForest Class by Reproducing Figure 1\n", + "## *Goal: Run the UncertaintyForest class to produce the results from Figure 1*\n", + "*Note: Figure 1 refers to Figure 1 from [this paper](https://arxiv.org/pdf/1907.00325.pdf)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First, we'll import the necessary packages that will be required" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.calibration import CalibratedClassifierCV\n", + "\n", + "from proglearn.forest import UncertaintyForest\n", + "from functions.unc_forest_tutorials_functions import generate_data, estimate_posterior, plot_posterior, plot_variance, plot_fig1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, we'll specify some parameters " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# The following are two sets of parameters.\n", + "# The first are those that were actually used to produce figure 1.\n", + "# These take a long time to actually run since there are 6000 data points.\n", + "# Below those, you'll find some testing parameters so that you can see the results quicker.\n", + "\n", + "# Here are the \"Real Parameters\"\n", + "#n = 6000\n", + "#mean = 1\n", + "#var = 1\n", + "#num_trials = 100 \n", + "#X_eval = np.linspace(-2, 2, num = 30).reshape(-1, 1)\n", + "#n_estimators = 300\n", + "#num_plotted_trials = 10\n", + "\n", + "# Here are the \"Test Parameters\"\n", + "n = 300 # number of data points\n", + "mean = 1 # mean of the data\n", + "var = 1 # variance of the data\n", + "num_trials = 3 # number of trials to run\n", + "X_eval = np.linspace(-2, 2, num = 10).reshape(-1, 1) # the evaluation span (over X) for the plot\n", + "n_estimators = 200 # the number of estimators\n", + "num_plotted_trials = 2 # the number of \"fainter\" lines to be displayed on the figure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, we'll specify which learners we'll compare. Figure 1 uses three different learners specified below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Algorithms used to produce figure 1\n", + "algos = [\n", + " {\n", + " 'instance': RandomForestClassifier(n_estimators = n_estimators),\n", + " 'label': 'CART',\n", + " 'title': 'CART Forest',\n", + " 'color': \"#1b9e77\",\n", + " },\n", + " {\n", + " 'instance': CalibratedClassifierCV(base_estimator=RandomForestClassifier(n_estimators = n_estimators // 5), \n", + " method='isotonic', \n", + " cv = 5),\n", + " 'label': 'IRF',\n", + " 'title': 'Isotonic Reg. Forest',\n", + " 'color': \"#fdae61\",\n", + " },\n", + " {\n", + " 'instance': UncertaintyForest(n_estimators = n_estimators),\n", + " 'label': 'UF',\n", + " 'title': 'Uncertainty Forest',\n", + " 'color': \"#F41711\",\n", + " },\n", + "]\n", + "\n", + "# Plotting parameters\n", + "parallel = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, we'll run the code to obtain the results that will be displayed in Figure1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# This is the code that actually generates data and predictions.\n", + "for algo in algos:\n", + " algo['predicted_posterior'] = estimate_posterior(algo, n, mean, var, num_trials, X_eval, parallel = parallel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finally, create figure 1." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_fig1(algos, num_plotted_trials, X_eval)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/uncertaintyforest_running_example.ipynb b/docs/tutorials/uncertaintyforest_running_example.ipynb new file mode 100644 index 0000000000..85ae49f97c --- /dev/null +++ b/docs/tutorials/uncertaintyforest_running_example.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial Overview\n", + "This set of two tutorials (`uncertaintyforest_running_example.ipynb` and `uncertaintyforest_fig1.ipynb`) will explain the UncertaintyForest class. After following both tutorials, you should have the ability to run UncertaintyForest code on your own machine and generate Figure 1 from [this paper](https://arxiv.org/pdf/1907.00325.pdf). \n", + "\n", + "If you haven't seen it already, take a look at other tutorials to setup and install the progressive learning package `Installation-and-Package-Setup-Tutorial.ipynb`\n", + "\n", + "# Simply Running the Uncertainty Forest class\n", + "## *Goal: Train the UncertaintyForest classifier on some training data and produce a metric of accuracy on some test data*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1: First, we'll import required packages and set some parameters for the forest. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from proglearn.forest import UncertaintyForest\n", + "from proglearn.sims import generate_gaussian_parity" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Real Params.\n", + "n_train = 10000 # number of training data points\n", + "n_test = 1000 # number of testing data points\n", + "num_trials = 10 # number of trials\n", + "n_estimators = 100 # number of estimators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We've done a lot. Can we just run it now? Yes!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2: Creating & Training our UncertaintyForest \n", + "First, generate our data:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = generate_gaussian_parity(n_train+n_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, split that data into training and testing data. We don't want to accidently train on our test data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = X[0:n_train] # Takes the first n_train number of data points and saves as X_train\n", + "y_train = y[0:n_train] # same as above for the labels\n", + "X_test = X[n_train:] # Takes the remainder of the data (n_test data points) and saves as X_test\n", + "y_test = y[n_train:] # same as above for the labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, create our forest:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "UF = UncertaintyForest(n_estimators = n_estimators)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then fit our learner:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "UF.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, we're done. Exciting right?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3: Producing a Metric of Accuracy for Our Learner\n", + "We've now created our learner and trained it. But to actually show if what we did is effective at predicting the class labels of the data, we'll create some test data (with the same distribution as the train data) and see if we classify it correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X_test, y_test = generate_gaussian_parity(n_test) # creates the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = UF.predict(X_test) # predict the class labels of the test data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see the learner's accuracy, we'll now compare the predictions with the actual test data labels. We'll find the number correct and divide by the number of data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "accuracy = sum(predictions == y_test)/n_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And, let's take a look at our accuracy:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.933\n" + ] + } + ], + "source": [ + "print(accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ta-da. That's an uncertainty forest at work. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's next? --> See a metric on the power of uncertainty forest by generating Figure 1 from [this paper](https://arxiv.org/pdf/1907.00325.pdf)\n", + "### To do this, check out `uncertaintyforest_fig1`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/xor_nxor_exp.ipynb b/docs/tutorials/xor_nxor_exp.ipynb new file mode 100644 index 0000000000..d23d3a5360 --- /dev/null +++ b/docs/tutorials/xor_nxor_exp.ipynb @@ -0,0 +1,1275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Progressive Learning in a Simple Environment\n", + "## Gaussian XOR and Gaussian N-XOR Experiment\n", + "\n", + "One key goal of progressive learning is to be able to continually improve upon past performance with the introduction of new data, without forgetting too much of the past tasks. This transfer of information can be evaluated using a variety of metrics; however, here, we use a generalization of Pearl's transfer-benefit ratio (TBR) in both the forward and backward directions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As described in [Vogelstein, et al. (2020)](https://arxiv.org/pdf/2004.12908.pdf), the forward transfer efficiency of task $f_n$ for task $t$ given $n$ samples is:\n", + "$$FTE^t(f_n) := \\mathbb{E}[R^t(f^{t}_n)/R^t(f^{1$, the algorithm demonstrates positive forward transfer, i.e. past task data has been used to improve performance on the current task.\n", + "\n", + "Similarly, the backward transfer efficiency of task $f_n$ for task $t$ given $n$ samples is:\n", + "$$BTE^t(f_n) := \\mathbb{E}[R^{1$, the algorithm demonstrates positive backward transfer, i.e. data from the current task has been used to improve performance on past tasks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Progressive learning in a simple environment can therefore be demonstrated using two simple tasks: Gaussian XOR and Gaussian Not-XOR (N-XOR). Here, forward transfer efficiency is the ratio of generalization errors for N-XOR, whereas backward transfer efficiency is the ratio of generalization errors for XOR. These two tasks share the same discriminant boundaries, so learning can be easily transferred between them.\n", + "\n", + "This experiment compares the performance of lifelong forests to uncertainty forests in undergoing these tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/pandas/compat/__init__.py:120: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from math import log2, ceil\n", + "\n", + "from joblib import Parallel, delayed\n", + "\n", + "from proglearn.sims import *\n", + "import functions.xor_nxor_functions as fn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** This notebook tutorial uses functions stored externally within `functions/xor_nxor_functions.py`, to simplify presentation of code. These functions are imported above, along with other libraries." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification Problem\n", + "\n", + "First, let's visualize Gaussian XOR and N-XOR.\n", + "\n", + "Gaussian XOR is a two-class classification problem, where...\n", + "- Class 0 is drawn from two Gaussians with $\\mu = \\pm [0.5, 0.5]^T$ and $\\sigma^2 = I$.\n", + "- Class 1 is drawn from two Gaussians with $\\mu = \\pm [0.5, -0.5]^T$ and $\\sigma^2 = I$.\n", + "\n", + "Gaussian N-XOR has the same distribution as Gaussian XOR, but with the class labels flipped, i.e...\n", + "- Class 0 is drawn from two Gaussians with $\\mu = \\pm [0.5, -0.5]^T$ and $\\sigma^2 = I$.\n", + "- Class 1 is drawn from two Gaussians with $\\mu = \\pm [0.5, 0.5]^T$ and $\\sigma^2 = I$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within the proglearn package, we can make use of the simulations within the `sims` folder to generate simulated data. The `generate_gaussian_parity` function within `gaussian_sim.py` can be used to create the Gaussian XOR and N-XOR problems. Let's generate data and plot it to see what these problems look like!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# call function to return gaussian xor and n-xor data:\n", + "X, Y = generate_gaussian_parity(750, angle_params=0)\n", + "Z, W = generate_gaussian_parity(750, angle_params=np.pi/2)\n", + "\n", + "# plot and format:\n", + "fn.plot_xor_nxor(X, Y, 'Gaussian XOR')\n", + "fn.plot_xor_nxor(Z, W, 'Gaussian N-XOR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Experiment\n", + "\n", + "Now that we have generated the data, we can prepare to run the experiment. The function for running the experiment, `experiment`, can be found within `functions/xor_nxor_functions.py`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first declare the hyperparameters to be used for the experiment, which are as follows:\n", + "- `mc_rep`: number of repetitions to run the progressive learning algorithm for\n", + "- `n_test`: number of xor/nxor data points in the test set\n", + "- `n_trees`: number of trees\n", + "- `n_xor`: array containing number of xor data points fed to learner, ranges from 50 to 725 in increments of 25\n", + "- `n_nxor`: array containing number of nxor data points fed to learner, ranges from 50 to 750 in increments of 25" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# define hyperparameters:\n", + "mc_rep = 100\n", + "n_test = 1000\n", + "n_trees = 10\n", + "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n", + "n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once those are determined, the experiment can be initialized and performed. We iterate over the values in `n_xor` and `n_nxor` sequentially, running each experiment for the number of iterations declared in `mc_rep`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 50 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.5s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 75 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 100 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.0s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 125 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 150 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.7s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 175 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.9s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 200 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.1s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 225 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 250 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 275 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 15.4s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 300 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 15.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 325 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 15.4s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 350 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 15.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 375 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 19.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 400 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 20.1s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 425 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 20.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 450 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 21.0s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 475 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 21.0s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 500 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 23.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 525 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 21.7s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 550 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 22.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 575 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 22.9s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 600 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 22.7s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 625 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 23.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 650 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 23.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 675 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 23.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 700 xor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 24.5s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 50 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 48.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 75 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 49.2s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 100 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 50.4s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 125 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 50.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 150 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 52.7s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 175 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 51.1s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 200 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 53.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 225 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 53.0s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 250 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 53.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 275 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 54.3s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 300 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 54.4s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 325 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 55.2s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 350 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 56.0s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 375 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 56.7s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 400 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 56.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 425 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 57.1s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 450 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 57.5s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 475 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 58.5s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 500 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 58.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 525 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 550 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 59.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 575 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 600 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 625 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 650 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 675 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 700 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting to compute 725 nxor\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.3min finished\n" + ] + } + ], + "source": [ + "# running the experiment:\n", + "\n", + "# create empty arrays for storing results\n", + "mean_error = np.zeros((4, len(n_xor)+len(n_nxor)))\n", + "std_error = np.zeros((4, len(n_xor)+len(n_nxor)))\n", + "mean_te = np.zeros((2, len(n_xor)+len(n_nxor)))\n", + "std_te = np.zeros((2, len(n_xor)+len(n_nxor)))\n", + "\n", + "# initialize learning on xor data\n", + "for i,n1 in enumerate(n_xor):\n", + " print('starting to compute %s xor\\n'%n1)\n", + " # run experiment in parallel\n", + " error = np.array(\n", + " Parallel(n_jobs=-1,verbose=1)(\n", + " delayed(fn.experiment)(n1,0,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)\n", + " )\n", + " )\n", + " # extract relevant data and store in arrays\n", + " mean_error[:,i] = np.mean(error,axis=0)\n", + " std_error[:,i] = np.std(error,ddof=1,axis=0)\n", + " mean_te[0,i] = np.mean(error[:,0]/error[:,1])\n", + " mean_te[1,i] = np.mean(error[:,2]/error[:,3])\n", + " std_te[0,i] = np.std(error[:,0]/error[:,1],ddof=1)\n", + " std_te[1,i] = np.std(error[:,2]/error[:,3],ddof=1)\n", + " \n", + " # initialize learning on n-xor data\n", + " if n1==n_xor[-1]:\n", + " for j,n2 in enumerate(n_nxor):\n", + " print('starting to compute %s nxor\\n'%n2)\n", + " # run experiment in parallel\n", + " error = np.array(\n", + " Parallel(n_jobs=-1,verbose=1)(\n", + " delayed(fn.experiment)(n1,n2,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)\n", + " )\n", + " )\n", + " # extract relevant data and store in arrays\n", + " mean_error[:,i+j+1] = np.mean(error,axis=0)\n", + " std_error[:,i+j+1] = np.std(error,ddof=1,axis=0)\n", + " mean_te[0,i+j+1] = np.mean(error[:,0]/error[:,1])\n", + " mean_te[1,i+j+1] = np.mean(error[:,2]/error[:,3])\n", + " std_te[0,i+j+1] = np.std(error[:,0]/error[:,1],ddof=1)\n", + " std_te[1,i+j+1] = np.std(error[:,2]/error[:,3],ddof=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! The experiment should now be complete, with the results stored in four arrays: `mean_error`, `std_error`, `mean_te`, and `std_te`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the Results\n", + "\n", + "Now that the experiment is complete, the results can be visualized by extracting the data from these arrays and plotting it. \n", + "\n", + "Here, we again utilize functions from `functions/xor_nxor_functions.py` to help in plotting:\n", + "- `plot_error`: plots generalization error for uncertainty forest and lifelong forests \n", + "- `plot_eff`: plots transfer efficiency (ratio of errors for lifelong forest to uncertainty forest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generalization Error for XOR Data\n", + "\n", + "By plotting the generalization error for XOR data, we can see how the introduction of N-XOR data influenced the performance of both the uncertainty forest and lifelong forest algorithms. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# define x and y labels\n", + "xor_x_vals = np.concatenate((n_xor, n_nxor + n_xor[-1]))\n", + "xor_y1_vals = mean_error[0]\n", + "xor_y2_vals = mean_error[1]\n", + "\n", + "# plot data\n", + "fn.plot_error(xor_x_vals, xor_y1_vals, xor_y2_vals, '-', 'XOR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When N-XOR data is available, lifelong forest outperforms uncertainty forest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generalization Error for N-XOR Data\n", + "\n", + "Similarly, by plotting the generalization error for N-XOR data, we can also see how the presence of XOR data influenced the performance of both algorithms. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# define x and y labels\n", + "nxor_x_vals = xor_x_vals[len(n_xor):]\n", + "nxor_y1_vals = mean_error[2, len(n_xor):]\n", + "nxor_y2_vals = mean_error[3, len(n_xor):]\n", + "\n", + "# plot data\n", + "fn.plot_error(nxor_x_vals, nxor_y1_vals, nxor_y2_vals, '--', 'N-XOR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given XOR data, lifelong forest outperforms uncertainty forests on classifying N-XOR data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transfer Efficiency for XOR Data\n", + "\n", + "Given the generalization errors plotted above, we can find the transfer efficiency as a ratio of the generalization error for lifelong forest to uncertainty forest. The forward and backward transfer efficiencies can then be plotted as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# define labels\n", + "x1_vals = xor_x_vals\n", + "x2_vals = nxor_x_vals\n", + "y1_vals = mean_te[0]\n", + "y2_vals = mean_te[1, len(n_xor):]\n", + "\n", + "\n", + "# plot data\n", + "fn.plot_eff(x1_vals, x2_vals, y1_vals, y2_vals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lifelong forests demonstrate both positive forward and backward transfer in this environment." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/proglearn/__init__.py b/proglearn/__init__.py index a97a483d48..74c02b13b5 100755 --- a/proglearn/__init__.py +++ b/proglearn/__init__.py @@ -1,4 +1,7 @@ -from .progressive_learner import * -from .transformers import * -from .voters import * -from .deciders import * +from .transformers import * +from .voters import * +from .deciders import * +from .forest import * +from .network import * + +__version__ = "0.0.2" diff --git a/proglearn/base.py b/proglearn/base.py index 60f6f989e1..99c2e06f7b 100644 --- a/proglearn/base.py +++ b/proglearn/base.py @@ -15,14 +15,9 @@ class and TransformerMixin mixin. ---------- None - Methods + Attributes ---------- - fit(X, y) - fits the transformer to data X with labels y - transform(X) - transformers the given data, X - is_fitted() - indicates whether the transformer is fitted + None """ @abstractmethod @@ -51,17 +46,6 @@ def transform(self, X): """ pass - @abstractmethod - def is_fitted(self): - """ - Indicates whether the transformer is fitted. - - Parameters - ---------- - None - """ - pass - class BaseVoter(ABC, BaseEstimator): """ @@ -71,14 +55,9 @@ class BaseVoter(ABC, BaseEstimator): ---------- None - Methods + Attributes ---------- - fit(X, y) - fits the voter to data X with labels y - predict(X) - decides on the given input data X - is_fitted() - indicates whether the voter is fitted + None """ @abstractmethod @@ -90,6 +69,7 @@ def fit(self, X, y): ---------- X : ndarray Transformed data matrix. + y : ndarray Output (i.e. response) data matrix. """ @@ -107,17 +87,6 @@ def predict(self, X): """ pass - @abstractmethod - def is_fitted(self): - """ - Indicates whether the voter is fitted. - - Parameters - ---------- - None - """ - pass - class BaseClassificationVoter(BaseVoter, ClassifierMixin): """ @@ -129,10 +98,9 @@ class BaseClassificationVoter(BaseVoter, ClassifierMixin): ---------- None - Methods + Attributes ---------- - predict_proba(X) - provides inference votes on the given transformed data, X + None """ @abstractmethod @@ -156,14 +124,9 @@ class BaseDecider(ABC, BaseEstimator): ---------- None - Methods + Attributes ---------- - fit(X, y, transformer_id_to_transformers, voter_id_to_voters) - fits transformer to data X with labels y - predict(X) - decides on the given input data X - is_fitted() - indicates whether the decider is fitted + None """ @abstractmethod @@ -175,10 +138,13 @@ def fit(self, X, y, transformer_id_to_transformers, voter_id_to_voters): ---------- X : ndarray Input data matrix. + y : ndarray Output (i.e. response) data matrix. + transformer_id_to_transformers : dict A dictionary with keys of transformer ids and values of the corresponding transformers. + voter_id_to_voters : dict A dictionary with keys of voter ids and values of the corresponding voter. """ @@ -196,17 +162,6 @@ def predict(self, X): """ pass - @abstractmethod - def is_fitted(self): - """ - Indicates whether the decider is fitted. - - Parameters - ---------- - None - """ - pass - class BaseClassificationDecider(BaseDecider, ClassifierMixin): """ @@ -217,10 +172,9 @@ class BaseClassificationDecider(BaseDecider, ClassifierMixin): ---------- None - Methods + Attributes ---------- - predict_proba(X) - returns class-posteriors for input data X + None """ @abstractmethod @@ -244,16 +198,9 @@ class BaseProgressiveLearner(ABC): ---------- None - Methods + Attributes ---------- - add_task(X, y) - adds a new unseen task to the progressive learner - add_transformer(X, y) - adds a new transformer (but no voters or transformers corresponding - to the task from which the transformer data was collected. - predict(X, task_id): - performs inference corresponding to the input task_id using the - progressive learner. + None """ @abstractmethod @@ -265,6 +212,7 @@ def add_task(self, X, y): ---------- X : ndarray Input data matrix. + y : ndarray Output (i.e. response) data matrix. """ @@ -280,6 +228,7 @@ def add_transformer(self, X, y): ---------- X : ndarray Input data matrix. + y : ndarray Output (i.e. response) data matrix. """ @@ -295,6 +244,7 @@ def predict(self, X, task_id): ---------- X : ndarray Input data matrix. + task_id : obj The task on which you are interested in performing inference. """ @@ -310,11 +260,9 @@ class BaseClassificationProgressiveLearner(BaseProgressiveLearner): ---------- None - Methods + Attributes ---------- - predict_proba(X, task_id): - provides class-posteriors corresponding to the input task_id on input data X using - the progressive learner. + None """ @abstractmethod @@ -326,6 +274,7 @@ def predict_proba(self, X, task_id): ---------- X : ndarray Input data matrix. + task_id : obj The task on which you are interested in estimating posteriors. """ diff --git a/proglearn/deciders.py b/proglearn/deciders.py index 7e7820974f..3d5c3412bb 100755 --- a/proglearn/deciders.py +++ b/proglearn/deciders.py @@ -9,60 +9,34 @@ from sklearn.utils.validation import ( check_X_y, check_array, - NotFittedError, + check_is_fitted, ) -from sklearn.utils.multiclass import type_of_target - class SimpleArgmaxAverage(BaseClassificationDecider): """ A class for a decider that uses the average vote for classification. - Uses BaseClassificationDecider as a base class. - Parameters: - ----------- + Parameters + ---------- classes : list, default=[] List of final output classification labels of type obj. - Defaults to an empty list of classes. - - Attributes (objects): - ----------- - classes : list, default=[] - List of final output classification labels of type obj. - Defaults to an empty list of classes. - - _is_fitted : boolean, default=False - Boolean variable to see if the decider is fitted, defaults to False - transformer_id_to_transformers : dict + Attributes + ---------- + transformer_id_to_transformers_ : dict A dictionary with keys of type obj corresponding to transformer ids and values of type obj corresponding to a transformer. This dictionary maps transformers to a particular transformer id. - transformer_id_to_voters : dict + transformer_id_to_voters_ : dict A dictionary with keys of type obj corresponding to transformer ids and values of type obj corresponding to a voter class. This dictionary maps voter classes to a particular transformer id. - - Methods - ----------- - fit(X, y, transformer_id_to_transformers, transformer_id_to_voters, classes=None) - Fits the decider to inputs X and final classification outputs y. - - predict_proba(X, transformers_id=None) - Predicts posterior probabilities given input data, X, for each class. - - predict(X, transformer, transformer_ids=None) - Predicts the most likely class given input data X. - - is_fitted() - Returns if the decider has been fitted. """ def __init__(self, classes=[]): self.classes = classes - self._is_fitted = False def fit( self, @@ -70,7 +44,6 @@ def fit( y, transformer_id_to_transformers, transformer_id_to_voters, - classes=None, ): """ Function for fitting. @@ -95,18 +68,15 @@ def fit( and values of type obj corresponding to a voter class. This dictionary thus maps voter classes to a particular transformer id. - classes : list, default=None - List of final output classification labels of type obj. + Returns + ------- + self : SimpleArgmaxAverage + The object itself. - Raises: - ----------- - ValueError : + Raises + ------- + ValueError When the labels have not been provided and the classes are empty. - - Returns: - ---------- - SimpleArgmaxAverage : obj - The ClassificationDecider object of class SimpleArgmaxAverage is returned. """ if not isinstance(self.classes, (list, np.ndarray)): if len(y) == 0: @@ -117,10 +87,8 @@ def fit( self.classes = np.unique(y) else: self.classes = np.array(self.classes) - self.transformer_id_to_transformers = transformer_id_to_transformers - self.transformer_id_to_voters = transformer_id_to_voters - - self._is_fitted = True + self.transformer_id_to_transformers_ = transformer_id_to_transformers + self.transformer_id_to_voters_ = transformer_id_to_voters return self def predict_proba(self, X, transformer_ids=None): @@ -130,11 +98,11 @@ def predict_proba(self, X, transformer_ids=None): Loops through each transformer and bag of transformers. Performs a transformation of the input data with the transformer. Gets a voter to map the transformed input data into a posterior distribution. - Gets the mean vote per bag and append it to a vote per transformer id. - Returns the average vote per transformer id. + Gets the mean vote per bagging component and append it to a vote per transformer id. + Returns the aggregate average vote. - Parameters: - ----------- + Parameters + ---------- X : ndarray Input data matrix. @@ -142,37 +110,34 @@ def predict_proba(self, X, transformer_ids=None): A list with specific transformer ids that will be used for inference. Defaults to using all transformers if no transformer ids are given. - Raises: - ----------- - NotFittedError : - When the model is not fitted. + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example - Returns: - ----------- - Returns mean vote across transformer ids as an ndarray. + + Raises + ------ + NotFittedError + When the model is not fitted. """ + check_is_fitted(self) vote_per_transformer_id = [] for transformer_id in ( transformer_ids if transformer_ids is not None - else self.transformer_id_to_voters.keys() + else self.transformer_id_to_voters_.keys() ): - if not self.is_fitted(): - msg = ( - "This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this decider." - ) - raise NotFittedError(msg % {"name": type(self).__name__}) - + check_is_fitted(self) vote_per_bag_id = [] for bag_id in range( - len(self.transformer_id_to_transformers[transformer_id]) + len(self.transformer_id_to_transformers_[transformer_id]) ): - transformer = self.transformer_id_to_transformers[transformer_id][ + transformer = self.transformer_id_to_transformers_[transformer_id][ bag_id ] X_transformed = transformer.transform(X) - voter = self.transformer_id_to_voters[transformer_id][bag_id] + voter = self.transformer_id_to_voters_[transformer_id][bag_id] vote = voter.predict_proba(X_transformed) vote_per_bag_id.append(vote) vote_per_transformer_id.append(np.mean(vote_per_bag_id, axis=0)) @@ -185,8 +150,8 @@ def predict(self, X, transformer_ids=None): Uses the predict_proba method to get the mean vote per id. Returns the class with the highest vote. - Parameters: - ----------- + Parameters + ---------- X : ndarray Input data matrix. @@ -194,26 +159,15 @@ def predict(self, X, transformer_ids=None): A list with all transformer ids. Defaults to None if no transformer ids are given. - Returns: - ----------- - The class with the highest vote based on the argmax of the votes as an int. - """ - if not self.is_fitted(): - msg = ( - "This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this decider." - ) - raise NotFittedError(msg % {"name": type(self).__name__}) + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example + Raises + ------ + NotFittedError + When the model is not fitted. + """ vote_overall = self.predict_proba(X, transformer_ids=transformer_ids) return self.classes[np.argmax(vote_overall, axis=1)] - - def is_fitted(self): - """ - Getter function to check if the decider is fitted. - - Returns: - ----------- - Boolean class attribute _is_fitted. - """ - return self._is_fitted diff --git a/proglearn/forest.py b/proglearn/forest.py index 0821cbe058..92db2e31b0 100644 --- a/proglearn/forest.py +++ b/proglearn/forest.py @@ -13,38 +13,29 @@ class LifelongClassificationForest(ClassificationProgressiveLearner): """ A class used to represent a lifelong classification forest. - Parameters: - --- + Parameters + ---------- n_estimators : int, default=100 The number of estimators used in the Lifelong Classification Forest + default_tree_construction_proportion : int, default=0.67 The proportions of the input data set aside to train each decision tree. The remainder of the data is used to fill in voting posteriors. This is used if 'tree_construction_proportion' is not fed to add_task. + default_finite_sample_correction : bool, default=False Boolean indicating whether this learner will have finite sample correction. This is used if 'finite_sample_correction' is not fed to add_task. + default_max_depth : int, default=30 The maximum depth of a tree in the Lifelong Classification Forest. This is used if 'max_depth' is not fed to add_task. - Methods - --- - add_task(X, y, task_id, tree_construction_proportion, finite_sample_correction, max_depth) - adds a task with id task_id, max tree depth max_depth, given input data matrix X - and output data matrix y, to the Lifelong Classification Forest. Also splits - data for training and voting based on tree_construction_proportion and uses the - value of finite_sample_correction to determine whether the learner will have - finite sample correction. - add_transformer(X, y, transformer_id, max_depth) - adds a transformer with id transformer_id and max tree depth max_depth, trained on - given input data matrix, X, and output data matrix, y, to the Lifelong Classification Forest. - Also trains the voters and deciders from new transformer to previous tasks, and will - train voters and deciders from this transformer to all new tasks. - predict(X, task_id) - predicts class labels under task_id for each example in input data X. - predict_proba(X, task_id) - estimates class posteriors under task_id for each example in input data X. + Attributes + ---------- + pl_ : ClassificationProgressiveLearner + Internal ClassificationProgressiveLearner used to train and make + inference. """ def __init__( @@ -58,7 +49,7 @@ def __init__( self.default_tree_construction_proportion = default_tree_construction_proportion self.default_finite_sample_correction = default_finite_sample_correction self.default_max_depth = default_max_depth - self.pl = ClassificationProgressiveLearner( + self.pl_ = ClassificationProgressiveLearner( default_transformer_class=TreeClassificationTransformer, default_transformer_kwargs={}, default_voter_class=TreeClassificationVoter, @@ -86,23 +77,33 @@ def add_task( finite sample correction. Parameters - --- + ---------- X : ndarray The input data matrix. + y : ndarray The output (response) data matrix. + task_id : obj, default=None The id corresponding to the task being added. + tree_construction_proportion : int, default=None The proportions of the input data set aside to train each decision tree. The remainder of the data is used to fill in voting posteriors. The default is used if 'None' is provided. + finite_sample_correction : bool, default=False Boolean indicating whether this learner will have finite sample correction. The default is used if 'None' is provided. + max_depth : int, default=30 The maximum depth of a tree in the Lifelong Classification Forest. The default is used if 'None' is provided. + + Returns + ------- + self : LifelongClassificationForest + The object itself. """ if tree_construction_proportion is None: tree_construction_proportion = self.default_tree_construction_proportion @@ -111,7 +112,7 @@ def add_task( if max_depth is None: max_depth = self.default_max_depth - self.pl.add_task( + self.pl_.add_task( X, y, task_id=task_id, @@ -138,21 +139,29 @@ def add_transformer(self, X, y, transformer_id=None, max_depth=None): train voters and deciders from this transformer to all new tasks. Parameters - --- + ---------- X : ndarray The input data matrix. + y : ndarray The output (response) data matrix. + transformer_id : obj, default=None The id corresponding to the transformer being added. + max_depth : int, default=30 The maximum depth of a tree in the UncertaintyForest. The default is used if 'None' is provided. + + Returns + ------- + self : LifelongClassificationForest + The object itself. """ if max_depth is None: max_depth = self.default_max_depth - self.pl.add_transformer( + self.pl_.add_transformer( X, y, transformer_kwargs={"kwargs": {"max_depth": max_depth}}, @@ -162,57 +171,66 @@ def add_transformer(self, X, y, transformer_id=None, max_depth=None): return self - def predict(self, X, task_id): + def predict_proba(self, X, task_id): """ - predicts class labels under task_id for each example in input data X. + estimates class posteriors under task_id for each example in input data X. Parameters - --- + ---------- X : ndarray The input data matrix. - task_id : obj + + task_id: The id corresponding to the task being mapped to. + + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example """ - return self.pl.predict(X, task_id) + return self.pl_.predict_proba(X, task_id) - def predict_proba(self, X, task_id): + def predict(self, X, task_id): """ - estimates class posteriors under task_id for each example in input data X. + predicts class labels under task_id for each example in input data X. Parameters - --- + ---------- X : ndarray The input data matrix. - task_id: + + task_id : obj The id corresponding to the task being mapped to. + + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example """ - return self.pl.predict_proba(X, task_id) + return self.pl_.predict(X, task_id) class UncertaintyForest: """ A class used to represent an uncertainty forest. - Attributes - --- - lf : LifelongClassificationForest - A lifelong classification forest object + Parameters + ---------- n_estimators : int, default=100 The number of trees in the UncertaintyForest + finite_sample_correction : bool, default=False Boolean indicating whether this learner will use finite sample correction + max_depth : int, default=30 The maximum depth of a tree in the UncertaintyForest - Methods - --- - fit(X, y) - fits forest to data X with labels y - predict(X) - predicts class labels for each example in input data X. - predict_proba(X) - estimates class posteriors for each example in input data X. + Attributes + ---------- + lf_ : LifelongClassificationForest + Internal LifelongClassificationForest used to train and make + inference. """ def __init__(self, n_estimators=100, finite_sample_correction=False, max_depth=30): @@ -225,38 +243,54 @@ def fit(self, X, y): fits forest to data X with labels y Parameters - --- + ---------- X : array of shape [n_samples, n_features] The data that will be trained on + y : array of shape [n_samples] The label for cluster membership of the given data + + Returns + ------- + self : UncertaintyForest + The object itself. """ - self.lf = LifelongClassificationForest( + self.lf_ = LifelongClassificationForest( n_estimators=self.n_estimators, default_finite_sample_correction=self.finite_sample_correction, - default_max_depth=max_depth, + default_max_depth=self.max_depth, ) - self.lf.add_task(X, y, task_id=0) + self.lf_.add_task(X, y, task_id=0) return self - def predict(self, X): + def predict_proba(self, X): """ - predicts class labels for each example in input data X. + estimates class posteriors for each example in input data X. Parameters - --- + ---------- X : array of shape [n_samples, n_features] - The data on which we are performing inference. + The data whose posteriors we are estimating. + + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example """ - return self.lf.predict(X, 0) + return self.lf_.predict_proba(X, 0) - def predict_proba(self, X): + def predict(self, X): """ - estimates class posteriors for each example in input data X. + predicts class labels for each example in input data X. Parameters - --- + ---------- X : array of shape [n_samples, n_features] - The data whose posteriors we are estimating. + The data on which we are performing inference. + + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example """ - return self.lf.predict_proba(X, 0) + return self.lf_.predict(X, 0) diff --git a/proglearn/network.py b/proglearn/network.py index 2655fcda5a..9ed97570d1 100644 --- a/proglearn/network.py +++ b/proglearn/network.py @@ -17,41 +17,38 @@ class LifelongClassificationNetwork(ClassificationProgressiveLearner): """ A class for progressive learning using Lifelong Learning Networks in a classification setting. - Attributes + Parameters ---------- network: Keras model Transformer network used to map input to output. + loss: string String name of the function used to calculate the loss between labels and predictions. - optimizer: Keras optimizer + + optimizer: str or instance of keras.optimizers Algorithm used as the optimizer. + epochs: int Number of times the entire training set is iterated over. + batch_size: int Batch size used in the training of the network. + verbose: bool Boolean indicating the production of detailed logging information during training of the network. + default_transformer_voter_decider_split: ndarray 1D array of length 3 corresponding to the proportions of data used to train the transformer(s) corresponding to the task_id, to train the voter(s) from the transformer(s) to the task_id, and to train the decider for task_id, respectively. This will be used if it isn't provided in add_task. - Methods - --- - add_task(X, y, task_id) - adds a task with id task_id, given input data matrix X - and output data matrix y, to the Lifelong Classification Network - add_transformer(X, y, transformer_id) - adds a transformer with id transformer_id, trained on given input data matrix, X - and output data matrix, y, to the Lifelong Classification Network. Also - trains the voters and deciders from new transformer to previous tasks, and will - train voters and deciders from this transformer to all new tasks. - predict(X, task_id) - predicts class labels under task_id for each example in input data X. - predict_proba(X, task_id) - estimates class posteriors under task_id for each example in input data X. + Attributes + ---------- + pl_ : ClassificationProgressiveLearner + Internal ClassificationProgressiveLearner used to train and make + inference. """ def __init__( @@ -89,7 +86,7 @@ def __init__( }, } - self.pl = ClassificationProgressiveLearner( + self.pl_ = ClassificationProgressiveLearner( default_transformer_class=NeuralClassificationTransformer, default_transformer_kwargs=default_transformer_kwargs, default_voter_class=KNNClassificationVoter, @@ -107,22 +104,30 @@ def add_task(self, X, y, task_id=None, transformer_voter_decider_split=None): ---------- X: ndarray Input data matrix. + y: ndarray Output (response) data matrix. + task_id: obj The id corresponding to the task being added. + transformer_voter_decider_split: ndarray, default=None 1D array of length 3 corresponding to the proportions of data used to train the transformer(s) corresponding to the task_id, to train the voter(s) from the transformer(s) to the task_id, and to train the decider for task_id, respectively. The default is used if 'None' is provided. + + Returns + ------- + self : LifelongClassificationNetwork + The object itself. """ if transformer_voter_decider_split is None: transformer_voter_decider_split = ( self.default_transformer_voter_decider_split ) - self.pl.add_task( + return self.pl_.add_task( X, y, task_id=task_id, @@ -131,8 +136,6 @@ def add_task(self, X, y, task_id=None, transformer_voter_decider_split=None): voter_kwargs={"classes": np.unique(y)}, ) - return self - def add_transformer(self, X, y, transformer_id=None): """ adds a transformer with id transformer_id, trained on given input data matrix, X @@ -144,15 +147,19 @@ def add_transformer(self, X, y, transformer_id=None): ---------- X: ndarray Input data matrix. + y: ndarray Output (response) data matrix. + transformer_id: obj The id corresponding to the transformer being added. - """ - - self.pl.add_transformer(X, y, transformer_id=transformer_id) - return self + Returns + ------- + self : LifelongClassificationNetwork + The object itself. + """ + return self.pl_.add_transformer(X, y, transformer_id=transformer_id) def predict(self, X, task_id): """ @@ -162,11 +169,16 @@ def predict(self, X, task_id): ---------- X: ndarray Input data matrix. + task_id: obj The task on which you are interested in performing inference. - """ - return self.pl.predict(X, task_id) + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example + """ + return self.pl_.predict(X, task_id) def predict_proba(self, X, task_id): """ @@ -176,8 +188,13 @@ def predict_proba(self, X, task_id): ---------- X: ndarray Input data matrix. + task_id: obj The task on which you are interested in estimating posteriors. - """ - return self.pl.predict_proba(X, task_id) + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example + """ + return self.pl_.predict_proba(X, task_id) diff --git a/proglearn/progressive_learner.py b/proglearn/progressive_learner.py index 7ea2d8d128..c3c64001e3 100755 --- a/proglearn/progressive_learner.py +++ b/proglearn/progressive_learner.py @@ -135,29 +135,6 @@ class ProgressiveLearner(BaseProgressiveLearner): default_decider_kwargs : dict Stores the default decider kwargs as specified by the parameter default_decider_kwargs. - - Methods - --- - add_transformer(X, y, transformer_data_proportion=1.0, transformer_voter_data_idx=None, - transformer_id=None, num_transformers=1, transformer_class=None, - transformer_kwargs=None, voter_class=None, voter_kwargs=None, - backward_task_ids=None) - Adds a transformer to the progressive learner and trains the voters and - deciders from this new transformer to the specified backward_task_ids. - add_task(X, y, task_id=None, transformer_voter_decider_split=[0.67, 0.33, 0], - num_transformers=1, transformer_class=None, transformer_kwargs=None, voter_class=None, - voter_kwargs=None, decider_class=None, decider_kwargs=None,backward_task_ids=None, - forward_transformer_ids=None) - Adds a task to the progressive learner. Optionally trains one or more - transformer from the input data (if num_transformers > 0), adds voters - and deciders from this/these new transformer(s) to the tasks specified - in backward_task_ids, and adds voters and deciders from the transformers - specified in forward_transformer_ids (and from the newly added transformer(s) - corresponding to the input task_id if num_transformers > 0) to the - new task_id. - predict(X, task_id, transformer_ids=None) - predicts labels under task_id for each example in input data X - using the given transformer_ids. """ def __init__( @@ -458,14 +435,17 @@ def add_transformer( ---------- X : ndarray Input data matrix. + y : ndarray Output (response) data matrix. + transformer_data_proportion : float, default=1.0 The proportion of the data set aside to train the transformer. The remainder of the data is used to train voters. This is used in the case that you are using a bagging algorithm and want the various components in that bagging ensemble to train on disjoint subsets of the data. This parameter is mostly for internal use. + transformer_voter_data_idx : ndarray, default=None A 1d array of type int used to specify the aggregate indices of the input data used to train the transformers and voters. This is used in the @@ -473,25 +453,37 @@ def add_transformer( transformers or voters (e.g. X and/or y contains decider training data disjoint from the transformer/voter data). This parameter is mostly for internal use. + transformer_id : obj, default=None The id corresponding to the transformer being added. + num_transformers : int, default=1 The number of transformers to add corresponding to the given inputs. + transformer_class : BaseTransformer, default=None The class of the transformer(s) being added. + transformer_kwargs : dict, default=None A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines the kwargs of the transformer(s) being added. + voter_class : BaseVoter, default=None The class of the voter(s) being added. + voter_kwargs : dict, default=None A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines the kwargs of the voter(s) being added. + backward_task_ids : ndarray, default=None A 1d array of type obj used to specify to which existing task voters and deciders will be trained from the transformer(s) being added. + + Returns + ------- + self : ProgressiveLearner + The object itself. """ if transformer_id is None: transformer_id = len(self.get_transformer_ids()) @@ -549,6 +541,8 @@ def add_transformer( ), ) + return self + def add_task( self, X, @@ -578,10 +572,13 @@ def add_task( ---------- X : ndarray Input data matrix. + y : ndarray Output (response) data matrix. + task_id : obj, default=None The id corresponding to the task being added. + transformer_voter_decider_split : ndarray, default=[0.67, 0.33, 0] A 1d array of length 3. The 0th index indicates the proportions of the input data used to train the (optional) newly added transformer(s) corresponding to @@ -596,33 +593,47 @@ def add_task( proportion of the data set aside to train the decider - these indices are saved internally and will be used to train all further deciders corresponding to this task for all function calls. + num_transformers : int, default=1 The number of transformers to add corresponding to the given inputs. + transformer_class : BaseTransformer, default=None The class of the transformer(s) being added. + transformer_kwargs : dict, default=None A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines the kwargs of the transformer(s) being added. + voter_class : BaseVoter, default=None The class of the voter(s) being added. + voter_kwargs : dict, default=None A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines the kwargs of the voter(s) being added. + decider_class : BaseDecider, default=None The class of the decider(s) being added. + decider_kwargs : dict, default=None A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines the kwargs of the decider(s) being added. + backward_task_ids : ndarray, default=None A 1d array of type obj used to specify to which existing task voters and deciders will be trained from the transformer(s) being added. + foward_transformer_ids : ndarray, default=None A 1d array of type obj used to specify from which existing transformer(s) voters and deciders will be trained to the new task. If num_transformers > 0, the input task_id corresponding to the task being added is automatically appended to this 1d array. + + Returns + ------- + self : ProgressiveLearner + The object itself. """ if task_id is None: task_id = max( @@ -680,21 +691,30 @@ def add_task( decider_kwargs=decider_kwargs, ) + return self + def predict(self, X, task_id, transformer_ids=None): """ predicts labels under task_id for each example in input data X using the given transformer_ids. Parameters - --- + ---------- X : ndarray The input data matrix. + task_id : obj The id corresponding to the task being mapped to. + transformer_ids : list, default=None The list of transformer_ids through which a user would like to send X (which will be pipelined with their corresponding voters) to make an inference prediction. + + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example """ return self.task_id_to_decider[task_id].predict( X, transformer_ids=transformer_ids @@ -706,12 +726,6 @@ class ClassificationProgressiveLearner( ): """ A class for progressive learning in the classification setting. - - Methods - --- - predict_proba(X, task_id, transformer_ids=None) - predicts posteriors under task_id for each example in input data X - using the given transformer_ids. """ def predict_proba(self, X, task_id, transformer_ids=None): @@ -720,15 +734,22 @@ def predict_proba(self, X, task_id, transformer_ids=None): using the given transformer_ids. Parameters - --- + ---------- X : ndarray The input data matrix. + task_id : obj The id corresponding to the task being mapped to. + transformer_ids : list, default=None The list of transformer_ids through which a user would like to send X (which will be pipelined with their corresponding voters) to estimate posteriors. + + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example """ decider = self.task_id_to_decider[task_id] return self.task_id_to_decider[task_id].predict_proba( diff --git a/proglearn/tests/test_forest.py b/proglearn/tests/test_forest.py index d530fcf1e3..c967c66ac2 100644 --- a/proglearn/tests/test_forest.py +++ b/proglearn/tests/test_forest.py @@ -16,29 +16,29 @@ def test_initialize(self): def test_correct_default_transformer(self): l2f = LifelongClassificationForest() - assert l2f.pl.default_transformer_class == TreeClassificationTransformer + assert l2f.pl_.default_transformer_class == TreeClassificationTransformer def test_correct_default_voter(self): l2f = LifelongClassificationForest() - assert l2f.pl.default_voter_class == TreeClassificationVoter + assert l2f.pl_.default_voter_class == TreeClassificationVoter def test_correct_default_decider(self): l2f = LifelongClassificationForest() - assert l2f.pl.default_decider_class == SimpleArgmaxAverage + assert l2f.pl_.default_decider_class == SimpleArgmaxAverage def test_correct_default_kwargs(self): l2f = LifelongClassificationForest() # transformer - assert l2f.pl.default_transformer_kwargs == {} + assert l2f.pl_.default_transformer_kwargs == {} # voter - assert len(l2f.pl.default_voter_kwargs) == 1 - assert "finite_sample_correction" in list(l2f.pl.default_voter_kwargs.keys()) - assert l2f.pl.default_voter_kwargs["finite_sample_correction"] == False + assert len(l2f.pl_.default_voter_kwargs) == 1 + assert "finite_sample_correction" in list(l2f.pl_.default_voter_kwargs.keys()) + assert l2f.pl_.default_voter_kwargs["finite_sample_correction"] == False # decider - assert l2f.pl.default_decider_kwargs == {} + assert l2f.pl_.default_decider_kwargs == {} def test_correct_default_n_estimators(self): l2f = LifelongClassificationForest() @@ -46,4 +46,4 @@ def test_correct_default_n_estimators(self): def test_correct_true_initilization_finite_sample_correction(self): l2f = LifelongClassificationForest(default_finite_sample_correction=True) - assert l2f.pl.default_voter_kwargs == {"finite_sample_correction": True} + assert l2f.pl_.default_voter_kwargs == {"finite_sample_correction": True} diff --git a/proglearn/tests/test_network.py b/proglearn/tests/test_network.py index 2146dacb97..9e5c4f819c 100644 --- a/proglearn/tests/test_network.py +++ b/proglearn/tests/test_network.py @@ -18,27 +18,27 @@ def test_initialize(self): def test_correct_default_transformer(self): l2n = LifelongClassificationNetwork(keras.Sequential()) - assert l2n.pl.default_transformer_class == NeuralClassificationTransformer + assert l2n.pl_.default_transformer_class == NeuralClassificationTransformer def test_correct_default_voter(self): l2n = LifelongClassificationNetwork(keras.Sequential()) - assert l2n.pl.default_voter_class == KNNClassificationVoter + assert l2n.pl_.default_voter_class == KNNClassificationVoter def test_correct_default_decider(self): l2n = LifelongClassificationNetwork(keras.Sequential()) - assert l2n.pl.default_decider_class == SimpleArgmaxAverage + assert l2n.pl_.default_decider_class == SimpleArgmaxAverage def test_correct_default_kwargs_length(self): l2n = LifelongClassificationNetwork(keras.Sequential()) # transformer - assert len(l2n.pl.default_transformer_kwargs) == 5 + assert len(l2n.pl_.default_transformer_kwargs) == 5 # voter - assert len(l2n.pl.default_voter_kwargs) == 0 + assert len(l2n.pl_.default_voter_kwargs) == 0 # decider - assert len(l2n.pl.default_decider_kwargs) == 0 + assert len(l2n.pl_.default_decider_kwargs) == 0 def test_correct_default_transformer_voter_decider_split(self): l2n = LifelongClassificationNetwork(keras.Sequential()) diff --git a/proglearn/tests/test_voter.py b/proglearn/tests/test_voter.py index aec19f99e7..82c2e2cbaa 100644 --- a/proglearn/tests/test_voter.py +++ b/proglearn/tests/test_voter.py @@ -46,15 +46,6 @@ def test_vote_without_fit(self): X = np.random.randn(100, 3) testing.assert_raises(NotFittedError, KNNClassificationVoter().predict_proba, X) - def test_correct_k(self): - # generate training data and classes - X = np.concatenate((np.zeros(100), np.ones(100))).reshape(-1, 1) - Y = np.concatenate((np.zeros(100), np.ones(100))) - - # train model - assert KNNClassificationVoter(3).fit(X, Y).k == 3 - assert KNNClassificationVoter().fit(X, Y).k == int(np.log2(len(X))) - def test_correct_vote(self): # set random seed np.random.seed(0) diff --git a/proglearn/transformers.py b/proglearn/transformers.py index 895f86b74f..3ce1f63e17 100755 --- a/proglearn/transformers.py +++ b/proglearn/transformers.py @@ -9,11 +9,9 @@ from sklearn.utils.validation import ( check_X_y, check_array, - NotFittedError, + check_is_fitted, ) -from sklearn.utils.multiclass import check_classification_targets - import keras as keras from .base import BaseTransformer @@ -27,16 +25,22 @@ class NeuralClassificationTransformer(BaseTransformer): ---------- network : object A neural network used in the classification transformer. + euclidean_layer_idx : int An integer to represent the final layer of the transformer. - optimizer : str + + optimizer : str or keras.optimizers instance An optimizer used when compiling the neural network. + loss : str, default="categorical_crossentropy" A loss function used when compiling the neural network. + pretrained : bool, default=False A boolean used to identify if the network is pretrained. + compile_kwargs : dict, default={"metrics": ["acc"]} A dictionary containing metrics for judging network performance. + fit_kwargs : dict, default={ "epochs": 100, "callbacks": [keras.callbacks.EarlyStopping(patience=5, monitor="val_acc")], @@ -45,36 +49,11 @@ class NeuralClassificationTransformer(BaseTransformer): }, A dictionary to hold epochs, callbacks, verbose, and validation split for the network. - Attributes (class) - ---------- - None - - Attributes (object) + Attributes ---------- - network : object - A Keras model cloned from the network parameter. - encoder : object - A Keras model with inputs and outputs based on the network attribute. Output layers - are determined by the euclidean_layer_idx parameter. - _is_fitted : bool - A boolean to identify if the network has already been fitted. - optimizer : str - A string to identify the optimizer used in the network. - loss : str - A string to identify the loss function used in the network. - compile_kwargs : dict - A dictionary containing metrics for judging network performance. - fit_kwargs : dict - A dictionary to hold epochs, callbacks, verbose, and validation split for the network. - - Methods - ---------- - fit(X, y) - Fits the transformer to data X with labels y. - transform(X) - Performs inference using the transformer. - is_fitted() - Indicates whether the transformer is fitted. + encoder_ : object + A Keras model with inputs and outputs based on the network attribute. + Output layers are determined by the euclidean_layer_idx parameter. """ def __init__( @@ -93,11 +72,11 @@ def __init__( }, ): self.network = keras.models.clone_model(network) - self.encoder = keras.models.Model( + self.encoder_ = keras.models.Model( inputs=self.network.inputs, outputs=self.network.layers[euclidean_layer_idx].output, ) - self._is_fitted = pretrained + self.pretrained = pretrained self.optimizer = optimizer self.loss = loss self.compile_kwargs = compile_kwargs @@ -113,22 +92,21 @@ def fit(self, X, y): Input data matrix. y : ndarray Output (i.e. response data matrix). - """ - check_classification_targets(y) + Returns + ------- + self : NeuralClassificationTransformer + The object itself. + """ + check_X_y(X, y) _, y = np.unique(y, return_inverse=True) - self.num_classes = len(np.unique(y)) # more typechecking self.network.compile( loss=self.loss, optimizer=self.optimizer, **self.compile_kwargs ) - self.network.fit( - X, - keras.utils.to_categorical(y, num_classes=self.num_classes), - **self.fit_kwargs - ) - self._is_fitted = True + + self.network.fit(X, keras.utils.to_categorical(y), **self.fit_kwargs) return self @@ -140,57 +118,40 @@ def transform(self, X): ---------- X : ndarray Input data matrix. - """ - - if not self.is_fitted(): - msg = ( - "This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this transformer." - ) - raise NotFittedError(msg % {"name": type(self).__name__}) - - # type check X - return self.encoder.predict(X) - def is_fitted(self): - """ - Indicates whether the transformer is fitted. + Returns + ------- + X_transformed : ndarray + The transformed input. - Parameters - ---------- - None + Raises + ------ + NotFittedError + When the model is not fitted. """ - - return self._is_fitted + check_is_fitted(self) + check_array(X) + return self.encoder_.predict(X) class TreeClassificationTransformer(BaseTransformer): """ A class used to transform data from a category to a specialized representation. - Attributes (object) + Parameters ---------- - kwargs : dict + kwargs : dict, default={} A dictionary to contain parameters of the tree. - _is_fitted_ : bool - A boolean to identify if the model is currently fitted. - Methods + Attributes ---------- - fit(X, y) - Fits the transformer to data X with labels y. - transform(X) - Performs inference using the transformer. - is_fitted() - Indicates whether the transformer is fitted. + transformer : sklearn.tree.DecisionTreeClassifier + an internal sklearn DecisionTreeClassifier """ def __init__(self, kwargs={}): - self.kwargs = kwargs - self._is_fitted = False - def fit(self, X, y): """ Fits the transformer to data X with labels y. @@ -201,15 +162,14 @@ def fit(self, X, y): Input data matrix. y : ndarray Output (i.e. response data matrix). - """ + Returns + ------- + self : TreeClassificationTransformer + The object itself. + """ X, y = check_X_y(X, y) - - # define the ensemble - self.transformer = DecisionTreeClassifier(**self.kwargs).fit(X, y) - - self._is_fitted = True - + self.transformer_ = DecisionTreeClassifier(**self.kwargs).fit(X, y) return self def transform(self, X): @@ -220,25 +180,17 @@ def transform(self, X): ---------- X : ndarray Input data matrix. - """ - if not self.is_fitted(): - msg = ( - "This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this transformer." - ) - raise NotFittedError(msg % {"name": type(self).__name__}) + Returns + ------- + X_transformed : ndarray + The transformed input. - X = check_array(X) - return self.transformer.apply(X) - - def is_fitted(self): + Raises + ------ + NotFittedError + When the model is not fitted. """ - Indicates whether the transformer is fitted. - - Parameters - ---------- - None - """ - - return self._is_fitted + check_is_fitted(self) + X = check_array(X) + return self.transformer_.apply(X) diff --git a/proglearn/voters.py b/proglearn/voters.py index a47c3fe4af..5ba1a8662e 100755 --- a/proglearn/voters.py +++ b/proglearn/voters.py @@ -3,17 +3,13 @@ Corresponding Email: levinewill@icloud.com """ import numpy as np - from sklearn.neighbors import KNeighborsClassifier - from sklearn.utils.validation import ( check_X_y, check_array, - NotFittedError, + check_is_fitted, ) - from sklearn.utils.multiclass import check_classification_targets - from .base import BaseClassificationVoter @@ -22,55 +18,62 @@ class TreeClassificationVoter(BaseClassificationVoter): A class used to vote on data transformed under a tree, which inherits from the BaseClassificationVoter class in base.py. - Attributes - --- + Parameters + ---------- finite_sample_correction : bool boolean indicating whether this voter will have finite sample correction - Methods - --- - fit(X, y) - fits tree classification to transformed data X with labels y - predict_proba(X) - predicts posterior probabilities given transformed data, X, for each class - predict(X) - predicts class labels given input data X - is_fitted() - returns if the classifier has been fitted for this transformation yet - _finite_sample_correction(posteriors, num_points_in_partition, num_classes) - performs finite sample correction on input data + classes : list, default=[] + list of all possible output label values + + Attributes + ---------- + missing_label_indices_ : list + a (potentially empty) list of label values + that exist in the ``classes`` parameter but + are missing in the latest ``fit`` function + call + + uniform_posterior_ : ndarray of shape (n_classes,) + the uniform posterior associated with the """ def __init__(self, finite_sample_correction=False, classes=[]): self.finite_sample_correction = finite_sample_correction - self._is_fitted = False self.classes = classes def fit(self, X, y): """ Fits transformed data X given corresponding class labels y. - Attributes - --- + Parameters + ---------- X : array of shape [n_samples, n_features] the transformed input data y : array of shape [n_samples] the class labels + + Returns + ------- + self : TreeClassificationVoter + The object itself. """ check_classification_targets(y) - num_classes = len(np.unique(y)) - self.missing_label_indices = [] + num_fit_classes = len(np.unique(y)) + self.missing_label_indices_ = [] - if np.asarray(self.classes).size != 0 and num_classes < len(self.classes): + if np.asarray(self.classes).size != 0 and num_fit_classes < len(self.classes): for label in self.classes: if label not in np.unique(y): - self.missing_label_indices.append(label) + self.missing_label_indices_.append(label) - self.uniform_posterior = np.ones(num_classes) / num_classes + num_classes = num_fit_classes + len(self.missing_label_indices_) - self.leaf_to_posterior = {} + self.uniform_posterior_ = np.ones(num_classes) / num_classes + + self.leaf_to_posterior_ = {} for leaf_id in np.unique(X): idxs_in_leaf = np.where(X == leaf_id)[0] @@ -81,12 +84,10 @@ def fit(self, X, y): if self.finite_sample_correction: posteriors = self._finite_sample_correction( - posteriors, len(idxs_in_leaf), len(np.unique(y)) + posteriors, len(idxs_in_leaf), num_classes ) - self.leaf_to_posterior[leaf_id] = posteriors - - self._is_fitted = True + self.leaf_to_posterior_[leaf_id] = posteriors return self @@ -94,34 +95,33 @@ def predict_proba(self, X): """ Returns the posterior probabilities of each class for data X. - Attributes - --- + Parameters + ---------- X : array of shape [n_samples, n_features] the transformed input data + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example + Raises - --- - NotFittedError : - when the model has not yet been fit for this transformation + ------ + NotFittedError + When the model is not fitted. """ - if not self.is_fitted(): - msg = ( - "This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this voter." - ) - raise NotFittedError(msg % {"name": type(self).__name__}) - + check_is_fitted(self) votes_per_example = [] for x in X: - if x in list(self.leaf_to_posterior.keys()): - votes_per_example.append(self.leaf_to_posterior[x]) + if x in list(self.leaf_to_posterior_.keys()): + votes_per_example.append(self.leaf_to_posterior_[x]) else: - votes_per_example.append(self.uniform_posterior) + votes_per_example.append(self.uniform_posterior_) votes_per_example = np.array(votes_per_example) - if len(self.missing_label_indices) > 0: - for i in self.missing_label_indices: + if len(self.missing_label_indices_) > 0: + for i in self.missing_label_indices_: new_col = np.zeros(votes_per_example.shape[0]) votes_per_example = np.insert(votes_per_example, i, new_col, axis=1) @@ -131,33 +131,41 @@ def predict(self, X): """ Returns the predicted class labels for data X. - Attributes - --- + Parameters + ---------- X : array of shape [n_samples, n_features] the transformed input data - """ - return np.argmax(self.predict_proba(X), axis=1) + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example - def is_fitted(self): - """ - Returns boolean indicating whether the voter has been fit. + Raises + ------ + NotFittedError + When the model is not fitted. """ - - return self._is_fitted + return np.argmax(self.predict_proba(X), axis=1) def _finite_sample_correction(posteriors, num_points_in_partition, num_classes): """ Encourage posteriors to approach uniform when there is low data through a finite sample correction. - Attributes - --- + + Parameters + ---------- posteriors : array of shape[n_samples, n_classes] posterior of each class for each sample num_points_in_partition : int number of samples in this particular transformation num_classes : int number of classes or labels + + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example """ correction_constant = 1 / (num_classes * num_points_in_partition) @@ -175,28 +183,32 @@ class KNNClassificationVoter(BaseClassificationVoter): in continuous Euclidean space, which inherits from the BaseClassificationVoter class in base.py. - Attributes - --- + Parameters + ---------- k : int integer indicating number of neighbors to use for each prediction during fitting and voting - kwargs : dictionary + + kwargs : dictionary, default={} contains all keyword arguments for the underlying KNN - Methods - --- - fit(X, y) - fits tree classification to transformed data X with labels y - predict_proba(X) - predicts posterior probabilities given transformed data, X, for each class label - predict(X) - predicts class labels given input data X - is_fitted() - returns if the classifier has been fitted for this transformation yet + classes : list, default=[] + list of all possible output label values + + Attributes + ---------- + missing_label_indices_ : list + a (potentially empty) list of label values + that exist in the ``classes`` parameter but + are missing in the latest ``fit`` function + call + + knn_ : sklearn.neighbors.KNeighborsClassifier + the internal sklearn instance of KNN + classifier """ def __init__(self, k=None, kwargs={}, classes=[]): - self._is_fitted = False self.k = k self.kwargs = kwargs self.classes = classes @@ -205,34 +217,30 @@ def fit(self, X, y): """ Fits data X given class labels y. - Attributes - --- + Parameters + ---------- X : array of shape [n_samples, n_features] the transformed data that will be trained on y : array of shape [n_samples] the label for class membership of the given data + + Returns + ------- + self : KNNClassificationVoter + The object itself. """ X, y = check_X_y(X, y) - self.k = int(np.log2(len(X))) if self.k == None else self.k - self.knn = KNeighborsClassifier(self.k, **self.kwargs) - self.knn.fit(X, y) - self._is_fitted = True - - num_classes = len(np.unique(y)) - self.missing_label_indices = [] - - if np.asarray(self.classes).size != 0 and num_classes < len(self.classes): - for label in self.classes: - if label not in np.unique(y): - self.missing_label_indices.append(label) + k = int(np.log2(len(X))) if self.k == None else self.k + self.knn_ = KNeighborsClassifier(k, **self.kwargs) + self.knn_.fit(X, y) num_classes = len(np.unique(y)) - self.missing_label_indices = [] + self.missing_label_indices_ = [] if np.asarray(self.classes).size != 0 and num_classes < len(self.classes): for label in self.classes: if label not in np.unique(y): - self.missing_label_indices.append(label) + self.missing_label_indices_.append(label) return self @@ -240,28 +248,27 @@ def predict_proba(self, X): """ Returns the posterior probabilities of each class for data X. - Attributes - --- + Parameters + ---------- X : array of shape [n_samples, n_features] the transformed input data + Returns + ------- + y_proba_hat : ndarray of shape [n_samples, n_classes] + posteriors per example + Raises - --- - NotFittedError : - when the model has not yet been fit for this transformation + ------ + NotFittedError + When the model is not fitted. """ - if not self.is_fitted(): - msg = ( - "This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this transformer." - ) - raise NotFittedError(msg % {"name": type(self).__name__}) - + check_is_fitted(self) X = check_array(X) - votes_per_example = self.knn.predict_proba(X) + votes_per_example = self.knn_.predict_proba(X) - if len(self.missing_label_indices) > 0: - for i in self.missing_label_indices: + if len(self.missing_label_indices_) > 0: + for i in self.missing_label_indices_: new_col = np.zeros(votes_per_example.shape[0]) votes_per_example = np.insert(votes_per_example, i, new_col, axis=1) @@ -271,16 +278,19 @@ def predict(self, X): """ Returns the predicted class labels for data X. - Attributes - --- + Parameters + ---------- X : array of shape [n_samples, n_features] the transformed input data - """ - return np.argmax(self.predict_proba(X), axis=1) + Returns + ------- + y_hat : ndarray of shape [n_samples] + predicted class label per example - def is_fitted(self): - """ - Returns boolean indicating whether the voter has been fit. + Raises + ------ + NotFittedError + When the model is not fitted. """ - return self._is_fitted + return np.argmax(self.predict_proba(X), axis=1) diff --git a/setup.py b/setup.py index 9aa056d51f..6adfffd4f3 100644 --- a/setup.py +++ b/setup.py @@ -1,19 +1,21 @@ from setuptools import setup, find_packages +import os -requirements = [ - "keras", - "tensorflow ", - "scikit-learn", - "numpy", - "joblib", -] +# Find mgc version. +PROJECT_PATH = os.path.dirname(os.path.abspath(__file__)) +for line in open(os.path.join(PROJECT_PATH, "proglearn", "__init__.py")): + if line.startswith("__version__ = "): + VERSION = line.strip().split()[2][1:-1] with open("README.md", mode="r", encoding = "utf8") as f: LONG_DESCRIPTION = f.read() +with open("requirements.txt", mode="r", encoding = "utf8") as f: + REQUIREMENTS = f.read() + setup( name="proglearn", - version="0.0.1", + version=VERSION, author="Will LeVine, Jayanta Dey, Hayden Helm", author_email="levinewill@icloud.com", maintainer="Will LeVine, Jayanta Dey", @@ -31,7 +33,7 @@ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7" ], - install_requires=requirements, + install_requires=REQUIREMENTS, packages=find_packages(exclude=["tests", "tests.*", "tests/*"]), include_package_data=True ) diff --git a/tutorials/README.md b/tutorials/README.md deleted file mode 100644 index 50a110ee0b..0000000000 --- a/tutorials/README.md +++ /dev/null @@ -1 +0,0 @@ -add tutorials here, we will move as needed in the future. \ No newline at end of file diff --git a/tutorials/rotation_cifar.ipynb b/tutorials/rotation_cifar.ipynb deleted file mode 100644 index beb4d4fbaa..0000000000 --- a/tutorials/rotation_cifar.ipynb +++ /dev/null @@ -1,844 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Rotation CIFAR Experiment\n", - "\n", - "This experiment will use images from the **CIFAR-100** database (https://www.cs.toronto.edu/~kriz/cifar.html) and showcase the backward transfer efficiency of algorithms in the **Progressive Learning** project (https://github.com/neurodata/progressive-learning) as the images are rotated." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the packages for experiment\n", - "import warnings\n", - "warnings.simplefilter(\"ignore\")\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from skimage.transform import rotate\n", - "from scipy import ndimage\n", - "from skimage.util import img_as_ubyte\n", - "from joblib import Parallel, delayed\n", - "from sklearn.ensemble.forest import _generate_unsampled_indices\n", - "from sklearn.ensemble.forest import _generate_sample_indices\n", - "import numpy as np\n", - "from sklearn.ensemble import BaggingClassifier\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "from itertools import product\n", - "import keras\n", - "from keras import layers\n", - "from joblib import Parallel, delayed\n", - "from multiprocessing import Pool\n", - "import tensorflow as tf\n", - "from numba import cuda\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the progressive learning packages\n", - "from proglearn.network import LifelongClassificationNetwork\n", - "from proglearn.forest import LifelongClassificationForest\n", - "\n", - "# Create array to store errors\n", - "errors_array = []" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Randomized selection of training and testing subsets\n", - "def cross_val_data(data_x, data_y, total_cls=10):\n", - " # Creates copies of both data_x and data_y so that they can be modified without affecting the original sets\n", - " x = data_x.copy()\n", - " y = data_y.copy()\n", - " # Creates a sorted array of arrays that each contain the indices at which each unique element of data_y can be found\n", - " idx = [np.where(data_y == u)[0] for u in np.unique(data_y)]\n", - " \n", - " for i in range(total_cls):\n", - " # Chooses the i'th array within the larger idx array\n", - " indx = idx[i]\n", - " # The elements of indx are randomly shuffled\n", - " random.shuffle(indx)\n", - " \n", - " if i==0:\n", - " # 250 training data points per task\n", - " train_x1 = x[indx[0:250],:]\n", - " train_x2 = x[indx[250:500],:]\n", - " train_y1 = y[indx[0:250]]\n", - " train_y2 = y[indx[250:500]]\n", - " \n", - " # 100 testing data points per task\n", - " test_x = x[indx[500:600],:]\n", - " test_y = y[indx[500:600]]\n", - " else:\n", - " # 250 training data points per task\n", - " train_x1 = np.concatenate((train_x1, x[indx[0:250],:]), axis=0)\n", - " train_x2 = np.concatenate((train_x2, x[indx[250:500],:]), axis=0)\n", - " train_y1 = np.concatenate((train_y1, y[indx[0:250]]), axis=0)\n", - " train_y2 = np.concatenate((train_y2, y[indx[250:500]]), axis=0)\n", - " \n", - " # 100 testing data points per task\n", - " test_x = np.concatenate((test_x, x[indx[500:600],:]), axis=0)\n", - " test_y = np.concatenate((test_y, y[indx[500:600]]), axis=0)\n", - " \n", - " return train_x1, train_y1, train_x2, train_y2, test_x, test_y " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Algorithms\n", - "\n", - "The progressive-learning repo contains two main algorithms, **Lifelong Learning Forests** (L2F) and **Lifelong Learning Network** (L2N), within `forest.py` and `network.py`, respectively. The main difference is that L2F uses random forests while L2N uses deep neural networks. Both algorithms, unlike LwF, EWC, Online_EWC, and SI, have been shown to achieve both forward and backward knowledge transfer. Either algorithm can be chosen for the purpose of this experiment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Experiment\n", - "\n", - "If the chosen algorithm is trained on both straight up-and-down CIFAR images and rotated CIFAR images, rather than just straight up-and-down CIFAR images, will it perform better (achieve a higher backward transfer efficiency) when tested on straight up-and-down CIFAR images? How does the angle at which training images are rotated affect these results?\n", - "\n", - "At a rotation angle of 0 degrees, the rotated images simply provide additional straight up-and-down CIFAR training data, so the backward transfer efficiency at this angle show whether or not the chosen algorithm can even achieve backward knowledge transfer. As the angle of rotation increases, the rotated images become less and less similar to the original dataset, so the backward transfer efficiency should logically decrease, while still being above 1." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Chooses model to use as transformer\n", - "def choose_transformer(train_x1, test_x, test_y, tmp_data):\n", - " \n", - " # Deep Neural Networks model is used as transformer\n", - " if model == \"dnn\":\n", - "\n", - " # Transformer network used to map input to output\n", - " network = keras.Sequential()\n", - " network.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu', input_shape=np.shape(train_x1)[1:]))\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Conv2D(filters=32, kernel_size=(3, 3), strides = 2, padding = \"same\", activation='relu'))\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides = 2, padding = \"same\", activation='relu'))\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Conv2D(filters=128, kernel_size=(3, 3), strides = 2, padding = \"same\", activation='relu'))\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Conv2D(filters=254, kernel_size=(3, 3), strides = 2, padding = \"same\", activation='relu'))\n", - "\n", - " network.add(layers.Flatten())\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Dense(2000, activation='relu'))\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Dense(2000, activation='relu'))\n", - " network.add(layers.BatchNormalization())\n", - " network.add(layers.Dense(units=10, activation = 'softmax'))\n", - " \n", - " return (train_x1, test_x, tmp_data, network)\n", - "\n", - " # Lifelong Classification Forest model is used as transformer\n", - " elif model == \"lf\":\n", - "\n", - " # .shape gives the dimensions of each numpy array\n", - " # .reshape gives a new shape to the numpy array without changing its data\n", - " train_x1 = train_x1.reshape((train_x1.shape[0], train_x1.shape[1] * train_x1.shape[2] * train_x1.shape[3]))\n", - " tmp_data = tmp_data.reshape((tmp_data.shape[0], tmp_data.shape[1] * tmp_data.shape[2] * tmp_data.shape[3]))\n", - " test_x = test_x.reshape((test_x.shape[0], test_x.shape[1] * test_x.shape[2] * test_x.shape[3]))\n", - " \n", - " return (train_x1, test_x, tmp_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Runs the experiments\n", - "def LF_experiment(data_x, data_y, angle, model, granularity, max_depth, reps=1, ntrees=29, acorn=None):\n", - " \n", - " # Set random seed to acorn if acorn is specified\n", - " if acorn is not None:\n", - " np.random.seed(acorn)\n", - " \n", - " errors = np.zeros(2) # initializes array of errors that will be generated during each rep\n", - " \n", - " with tf.device('/gpu:'+str(int(angle // granularity) % 4)):\n", - " for rep in range(reps):\n", - " print(\"rep:{}\".format(rep)) # Allows user to track the progress of the notebook while the experiment is running\n", - " \n", - " # training and testing subsets are randomly selected by calling the cross_val_data function\n", - " train_x1, train_y1, train_x2, train_y2, test_x, test_y = cross_val_data(data_x, data_y, total_cls=10)\n", - "\n", - " # Change data angle for second task\n", - " tmp_data = train_x2.copy()\n", - " _tmp_ = np.zeros((32,32,3), dtype=int)\n", - " total_data = tmp_data.shape[0]\n", - "\n", - " for i in range(total_data):\n", - " tmp_ = image_aug(tmp_data[i],angle)\n", - " # 2D image is flattened into a 1D array as random forests can only take in flattened images as inputs\n", - " tmp_data[i] = tmp_\n", - " \n", - " if model == \"lf\": # random forests\n", - " # Call function to choose model for transformer\n", - " (train_x1, test_x, tmp_data) = choose_transformer(train_x1, test_x, test_y, tmp_data)\n", - " # number of trees (estimators) to use is passed as an argument because the default is 100 estimators\n", - " progressive_learner = LifelongClassificationForest(n_estimators = ntrees, default_max_depth = max_depth)\n", - " elif model == \"dnn\": # deep net\n", - " # Call function to choose model for transformer\n", - " (train_x1, test_x, tmp_data, network) = choose_transformer(train_x1, test_x, test_y, tmp_data)\n", - " # network is passed as an argument so that LifelongClassificationNetwork knows which transformer network to use\n", - " progressive_learner = LifelongClassificationNetwork(network = network)\n", - "\n", - " # Add the original task\n", - " progressive_learner.add_task(X = train_x1, y = train_y1)\n", - "\n", - " # Predict and get errors for original task\n", - " llf_single_task=progressive_learner.predict(test_x, task_id=0)\n", - " \n", - " # Add the new transformer\n", - " progressive_learner.add_transformer(X = tmp_data, y = train_y2)\n", - "\n", - " # Predict and get errors with the new transformer\n", - " llf_task1=progressive_learner.predict(test_x, task_id=0)\n", - "\n", - " errors[1] = errors[1]+(1 - np.mean(llf_task1 == test_y)) # errors from transfer learning\n", - " errors[0] = errors[0]+(1 - np.mean(llf_single_task == test_y)) # errors from original task\n", - " \n", - " errors = errors/reps # errors are averaged across all reps ==> more reps means more accurate errors\n", - " # Prints errors for each angle\n", - " print(\"Errors For Angle {}: {}\".format(angle, errors))\n", - " \n", - " # Average errors for original task and transfer learning are returned for the angle tested\n", - " return(errors)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Rotates the image by the given angle and zooms in to remove unnecessary white space at the corners\n", - "# Some image data is lost during rotation because of the zoom\n", - "def image_aug(pic, angle, centroid_x=23, centroid_y=23, win=16, scale=1.45):\n", - " # Calculates scaled dimensions of image\n", - " im_sz = int(np.floor(pic.shape[0]*scale))\n", - " pic_ = np.uint8(np.zeros((im_sz,im_sz,3),dtype=int))\n", - " \n", - " # Uses zoom function from scipy.ndimage to zoom into the image\n", - " pic_[:,:,0] = ndimage.zoom(pic[:,:,0],scale)\n", - " pic_[:,:,1] = ndimage.zoom(pic[:,:,1],scale)\n", - " pic_[:,:,2] = ndimage.zoom(pic[:,:,2],scale)\n", - " \n", - " # Rotates image using rotate function from skimage.transform\n", - " image_aug = rotate(pic_, angle, resize=False)\n", - " image_aug_ = image_aug[centroid_x-win:centroid_x+win,centroid_y-win:centroid_y+win,:]\n", - " \n", - " # Converts the image to unsigned byte format with values in [0, 255] and then returns it\n", - " return img_as_ubyte(image_aug_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hyperparameters\n", - "\n", - "Hyperparameters determine how the model will run. Changing the value of `model` to `\"lf\"` will run the L2F algorithm, while `\"dnn\"` will run the L2N algorithm.\n", - "\n", - "`granularity` refers to the amount by which the angle will be increased each time. Setting this value at 1 will cause the algorithm to test every whole number rotation angle between 0 and 180 degrees.\n", - "\n", - "`reps` refers to the number of repetitions tested for each angle of rotation. For each repetition, the data is randomly resampled.\n", - "\n", - "`max_depth` refers to the maximum depth of each tree in the Lifelong Classification Forest. If this value is not specified, LifelongClassificationForest defaults to a max tree depth of 30." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "### MAIN HYPERPARAMS ###\n", - "model = \"lf\"\n", - "granularity = 45\n", - "reps = 75\n", - "max_depth = 5\n", - "########################" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Loads and reshapes data sets\n", - "(X_train, y_train), (X_test, y_test) = keras.datasets.cifar100.load_data()\n", - "# Joins the training and testing arrays into one\n", - "data_x = np.concatenate([X_train, X_test]) \n", - "data_y = np.concatenate([y_train, y_test]) \n", - "data_y = data_y[:, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Runs the experiment at a new angle of rotation\n", - "def perform_angle(angle):\n", - " error_list = LF_experiment(data_x, data_y, angle, model, granularity, max_depth, reps=reps, ntrees=16, acorn=1)\n", - 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" errors_array.append(p.map(perform_angle, angles))\n", - " \n", - "# Run L2F\n", - "elif model == \"lf\":\n", - " # Generate set of angles to test for BTE\n", - " angles = np.arange(0, 181, granularity)\n", - " # Parallel processing\n", - " with Pool(8) as p:\n", - " # Multiple sets of errors for each set of angles are appended to a larger array containing errors for all angles\n", - " errors_array.append(p.map(perform_angle, angles))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Rotation CIFAR Plot\n", - "\n", - "This section takes the results of the experiment and plots the backward transfer efficiency against the angle of rotation for the images in **CIFAR-100**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Expected Results\n", - "\n", - "If done correctly, the plot should show that Backward Transfer Efficiency (BTE) is greater than 1 regardless of rotation, but the BTE should decrease as the angle of rotation is increased. The more the number of reps and the finer the granularity, the smoother this downward sloping curve should look." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose which algorithms to plot\n", - "alg_name = ['L2F']#['L2N']\n", - "tes = [[] for _ in range(len(alg_name))]\n", - "\n", - "# Calculate BTE for each angle of rotation for each algorithm\n", - "for algo_no,alg in enumerate(alg_name):\n", - " for angle in angles:\n", - " orig_error, transfer_error = errors_array[0][int(angle/granularity)] # (angle/granularity) gives the index of the errors for that angle\n", - " tes[algo_no].append(orig_error / transfer_error) # (original error/transfer error) gives the BTE" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Choose which color to make each algorithm's results\n", - "clr = [\"#00008B\"]#[\"#e41a1c\"]\n", - "c = sns.color_palette(clr, n_colors=len(clr))\n", - "fig, ax = plt.subplots(1,1, figsize=(8,8))\n", - "\n", - "# Plot the data\n", - "for alg_no,alg in enumerate(alg_name):\n", - " if alg_no<2:\n", - " ax.plot(angles,tes[alg_no], c=c[alg_no], label=alg_name[alg_no], linewidth=3)\n", - " else:\n", - " ax.plot(angles,tes[alg_no], c=c[alg_no], label=alg_name[alg_no])\n", - "\n", - "# Format and label the plot\n", - "ax.set_xticks([0,30,60,90,120,150,180])\n", - "ax.tick_params(labelsize=20)\n", - "ax.set_xlabel('Angle of Rotation (Degrees)', fontsize=24)\n", - "ax.set_ylabel('Backward Transfer Efficiency', fontsize=24)\n", - "ax.set_title(\"Rotation Experiment\", fontsize = 24)\n", - "right_side = ax.spines[\"right\"]\n", - "right_side.set_visible(False)\n", - "top_side = ax.spines[\"top\"]\n", - "top_side.set_visible(False)\n", - "plt.tight_layout()\n", - "#x.legend(fontsize = 24)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# FAQs\n", - "\n", - "### Why am I getting an \"out of memory\" error?\n", - "`Pool(8)` in the previous cell allows for parallel processing, so the number within the parenthesis should be, at max, the number of cores in the device on which this notebook is being run. Even if a warning is produced, the results of the experimented should not be affected.\n", - "\n", - "### Why is this taking so long to run? How can I speed it up to see if I am getting the expected outputs?\n", - "Decreasing the value of `reps` or increasing the value of `granularity` will both decrease runtime at the cost of noisier results." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/xor_nxor_exp.ipynb b/tutorials/xor_nxor_exp.ipynb deleted file mode 100644 index b6a77a30d9..0000000000 --- a/tutorials/xor_nxor_exp.ipynb +++ /dev/null @@ -1,1515 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Progressive Learning in a Simple Environment\n", - "## Gaussian XOR and Gaussian N-XOR Experiment\n", - "\n", - "One key goal of progressive learning is to be able to continually improve upon past performance with the introduction of new data, without forgetting too much of the past tasks. This transfer of information can be evaluated using a variety of metrics; however, here, we use a generalization of Pearl's transfer-benefit ratio (TBR) in both the forward and backward directions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As described in [Vogelstein, et al. (2020)](https://arxiv.org/pdf/2004.12908.pdf), the forward transfer efficiency of task $f_n$ for task $t$ given $n$ samples is:\n", - "$$FTE^t(f_n) := \\mathbb{E}[R^t(f^{t}_n)/R^t(f^{1$, the algorithm demonstrates positive forward transfer, i.e. past task data has been used to improve performance on the current task.\n", - "\n", - "Similarly, the backward transfer efficiency of task $f_n$ for task $t$ given $n$ samples is:\n", - "$$BTE^t(f_n) := \\mathbb{E}[R^t(f^{1$, the algorithm demonstrates positive backward transfer, i.e. data from the current task has been used to improve performance on past tasks." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Progressive learning in a simple environment can therefore be demonstrated using two simple tasks: Gaussian XOR and Gaussian Not-XOR (N-XOR). Here, forward transfer efficiency is the ratio of generalization errors for N-XOR, whereas backward transfer efficiency is the ratio of generalization errors for XOR." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/site-packages/pandas/compat/__init__.py:120: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import random\n", - "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", - "import tensorflow.keras as keras\n", - "import seaborn as sns \n", - "\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from math import log2, ceil \n", - "\n", - "import sys\n", - "from joblib import Parallel, delayed\n", - "\n", - "from proglearn.forest import LifelongClassificationForest, UncertaintyForest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Classification Problem\n", - "\n", - "First, let's visualize Gaussian XOR and N-XOR.\n", - "\n", - "Gaussian XOR is a two-class classification problem, where...\n", - "- Class 0 is drawn from two Gaussians with $\\mu = \\pm [0.5, 0.5]^T$ and $\\sigma^2 = I$.\n", - "- Class 1 is drawn from two Gaussians with $\\mu = \\pm [0.5, -0.5]^T$ and $\\sigma^2 = I$.\n", - "\n", - "Gaussian N-XOR has the same distribution as Gaussian XOR, but with the class labels flipped, i.e...\n", - "- Class 0 is drawn from two Gaussians with $\\mu = \\pm [0.5, -0.5]^T$ and $\\sigma^2 = I$.\n", - "- Class 1 is drawn from two Gaussians with $\\mu = \\pm [0.5, 0.5]^T$ and $\\sigma^2 = I$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define a few functions to help in creating simulated XOR and N-XOR data:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# define functions for creating gaussian xor and n-xor data:\n", - "\n", - "def generate_2d_rotation(theta=0, acorn=None):\n", - " \"\"\"\n", - " Generates a rotation by angle theta.\n", - " Returns:\n", - " R - Array representing rotation by theta.\n", - " \"\"\"\n", - " \n", - " # if acorn is specified, set random seed to it\n", - " if acorn is not None:\n", - " np.random.seed(acorn)\n", - " \n", - " # create array to represent rotation of angle theta\n", - " R = np.array([\n", - " [np.cos(theta), np.sin(theta)],\n", - " [-np.sin(theta), np.cos(theta)]\n", - " ])\n", - " return R\n", - "\n", - "def generate_gaussian_parity(n, mean=np.array([-1, -1]), cov_scale=1, angle_params=None, k=1, acorn=None):\n", - " \"\"\"\n", - " Generates Gaussian XOR problems and its variants (N-XOR, R-XOR). \n", - " Returns:\n", - " X - Distributions of data points.\n", - " Y - Class labels for the X distributions.\n", - " \"\"\"\n", - " \n", - " # if acorn is specified, set random seed to it\n", - " if acorn is not None:\n", - " np.random.seed(acorn)\n", - " \n", - " # create distributions\n", - " d = len(mean)\n", - " if mean[0] == -1 and mean[1] == -1:\n", - " mean = mean + 1 / 2**k\n", - " mnt = np.random.multinomial(n, 1/(4**k) * np.ones(4**k))\n", - " cumsum = np.cumsum(mnt)\n", - " cumsum = np.concatenate(([0], cumsum))\n", - " Y = np.zeros(n)\n", - " X = np.zeros((n, d))\n", - " for i in range(2**k):\n", - " for j in range(2**k):\n", - " temp = np.random.multivariate_normal(mean, cov_scale * np.eye(d), \n", - " size=mnt[i*(2**k) + j])\n", - " temp[:, 0] += i*(1/2**(k-1))\n", - " temp[:, 1] += j*(1/2**(k-1))\n", - " X[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = temp\n", - " if i % 2 == j % 2:\n", - " Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 0\n", - " else:\n", - " Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 1\n", - " \n", - " # rotate the resulting matrix by angle_params\n", - " if d == 2:\n", - " if angle_params is None:\n", - " angle_params = np.random.uniform(0, 2*np.pi)\n", - " R = generate_2d_rotation(angle_params)\n", - " X = X @ R\n", - " else:\n", - " raise ValueError('d=%i not implemented!'%(d))\n", - " \n", - " return X, Y.astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've assembled the code to create the Gaussian XOR and N-XOR problems, let's generate the data and plot it to see what they look like!" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# call function to return gaussian xor and n-xor data:\n", - "X, Y = generate_gaussian_parity(750, cov_scale=0.1, angle_params=0)\n", - "Z, W = generate_gaussian_parity(750, cov_scale=0.1, angle_params=np.pi/2)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot and format plot:\n", - "\n", - "def get_colors(colors, inds):\n", - " c = [colors[i] for i in inds]\n", - " return c\n", - "\n", - "colors = sns.color_palette('Dark2', n_colors=2)\n", - "fig, ax = plt.subplots(1,1, figsize=(8,8))\n", - "ax.scatter(X[:, 0], X[:, 1], c=get_colors(colors, Y), s=50)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_title('Gaussian XOR', fontsize=30)\n", - "plt.tight_layout()\n", - "ax.axis('off')\n", - "\n", - "colors = sns.color_palette('Dark2', n_colors=2)\n", - "fig, ax = plt.subplots(1,1, figsize=(8,8))\n", - "ax.scatter(Z[:, 0], Z[:, 1], c=get_colors(colors, W), s=50)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])\n", - "ax.set_title('Gaussian N-XOR', fontsize=30)\n", - "ax.axis('off')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Experiment\n", - "\n", - "Since the functions for simulating the classification problem are now defined, the experiment can now be performed. We create another function to call the progressive learning algorithms, as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def experiment(n_xor, n_nxor, n_test, reps, n_trees, max_depth, acorn=None):\n", - " \"\"\"\n", - " Runs the Gaussian XOR N-XOR experiment.\n", - " Returns the mean error.\n", - " \"\"\"\n", - " \n", - " # initialize experiment\n", - " if n_xor==0 and n_nxor==0:\n", - " raise ValueError('Wake up and provide samples to train!!!')\n", - " \n", - " # if acorn is specified, set random seed to it\n", - " if acorn != None:\n", - " np.random.seed(acorn)\n", - " \n", - " errors = np.zeros((reps,4),dtype=float)\n", - " \n", - " # run the progressive learning algorithm for a number of repetitions\n", - " for i in range(reps):\n", - " \n", - " progressive_learner = LifelongClassificationForest(n_estimators=n_trees)\n", - " uf = UncertaintyForest(n_estimators=2*n_trees)\n", - " \n", - " #source data\n", - " xor, label_xor = generate_gaussian_parity(n_xor,cov_scale=0.1,angle_params=0)\n", - " test_xor, test_label_xor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=0)\n", - " \n", - " #target data\n", - " nxor, label_nxor = generate_gaussian_parity(n_nxor,cov_scale=0.1,angle_params=np.pi/2)\n", - " test_nxor, test_label_nxor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=np.pi/2)\n", - " \n", - " if n_xor == 0:\n", - " progressive_learner.add_task(nxor, label_nxor)\n", - " l2f_task2=progressive_learner.predict(test_nxor, task_id=0)\n", - " uf.fit(nxor, label_nxor)\n", - " uf_task2=uf.predict(test_nxor)\n", - " \n", - " errors[i,0] = 0.5\n", - " errors[i,1] = 0.5\n", - " errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test\n", - " errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test\n", - " elif n_nxor == 0:\n", - " progressive_learner.add_task(xor, label_xor)\n", - " l2f_task1=progressive_learner.predict(test_xor, task_id=0)\n", - " uf.fit(xor, label_xor)\n", - " uf_task1=uf.predict(test_xor)\n", - " \n", - " errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test\n", - " errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test\n", - " errors[i,2] = 0.5\n", - " errors[i,3] = 0.5\n", - " else:\n", - " progressive_learner.add_task(xor, label_xor)\n", - " progressive_learner.add_task(nxor, label_nxor)\n", - " l2f_task1=progressive_learner.predict(test_xor, task_id=0)\n", - " l2f_task2=progressive_learner.predict(test_nxor, task_id=1)\n", - " \n", - " uf.fit(xor, label_xor)\n", - " uf_task1=uf.predict(test_xor)\n", - " uf.fit(nxor, label_nxor)\n", - " uf_task2=uf.predict(test_nxor)\n", - " \n", - " errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test\n", - " errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test\n", - " errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test\n", - " errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test\n", - "\n", - " return np.mean(errors,axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can select the primary parameters with which we'd like to run the experiment using." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# define primary parameters:\n", - "mc_rep = 500\n", - "n_test = 1000\n", - "n_trees = 10\n", - "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n", - "n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once those are determined, the experiment can be initialized and performed" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 50 xor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 1.0min finished\n", - 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"[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 600 nxor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 5.7min finished\n", - "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 625 nxor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 5.8min finished\n", - "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 650 nxor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 5.8min finished\n", - "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 675 nxor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 6.0min finished\n", - "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 700 nxor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 6.0min finished\n", - "[Parallel(n_jobs=-1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting to compute 725 nxor\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 6.0min finished\n" - ] - } - ], - "source": [ - "# running the experiment:\n", - "\n", - "# create empty arrays for storing results\n", - "mean_error = np.zeros((4, len(n_xor)+len(n_nxor)))\n", - "std_error = np.zeros((4, len(n_xor)+len(n_nxor)))\n", - "mean_te = np.zeros((2, len(n_xor)+len(n_nxor)))\n", - "std_te = np.zeros((2, len(n_xor)+len(n_nxor)))\n", - "\n", - "# initialize learning on xor data\n", - "for i,n1 in enumerate(n_xor):\n", - " print('starting to compute %s xor\\n'%n1)\n", - " # run experiment in parallel\n", - " error = np.array(\n", - " Parallel(n_jobs=-1,verbose=1)(\n", - " delayed(experiment)(n1,0,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)\n", - " )\n", - " )\n", - " # extract relevant data and store in arrays\n", - " mean_error[:,i] = np.mean(error,axis=0)\n", - " std_error[:,i] = np.std(error,ddof=1,axis=0)\n", - " mean_te[0,i] = np.mean(error[:,0]/error[:,1])\n", - " mean_te[1,i] = np.mean(error[:,2]/error[:,3])\n", - " std_te[0,i] = np.std(error[:,0]/error[:,1],ddof=1)\n", - " std_te[1,i] = np.std(error[:,2]/error[:,3],ddof=1)\n", - " \n", - " # initialize learning on n-xor data\n", - " if n1==n_xor[-1]:\n", - " for j,n2 in enumerate(n_nxor):\n", - " print('starting to compute %s nxor\\n'%n2)\n", - " # run experiment in parallel\n", - " error = np.array(\n", - " Parallel(n_jobs=-1,verbose=1)(\n", - " delayed(experiment)(n1,n2,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)\n", - " )\n", - " )\n", - " # extract relevant data and store in arrays\n", - " mean_error[:,i+j+1] = np.mean(error,axis=0)\n", - " std_error[:,i+j+1] = np.std(error,ddof=1,axis=0)\n", - " mean_te[0,i+j+1] = np.mean(error[:,0]/error[:,1])\n", - " mean_te[1,i+j+1] = np.mean(error[:,2]/error[:,3])\n", - " std_te[0,i+j+1] = np.std(error[:,0]/error[:,1],ddof=1)\n", - " std_te[1,i+j+1] = np.std(error[:,2]/error[:,3],ddof=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! The experiment should now be complete, with the results stored in four arrays: `mean_error`, `std_error`, `mean_te`, and `std_te`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing the Results\n", - "\n", - "Now that the experiment is complete, the results can be visualized by extracting the data from these arrays and plotting it. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generalization Error for XOR Data\n", - "\n", - "By plotting the generalization error for XOR data, we can see how the introduction of N-XOR data influenced the performance of both the uncertainty forest and lifelong forest algorithms. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# define scale and labels\n", - "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n", - "n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)\n", - "n1s = n_xor\n", - "n2s = n_nxor\n", - "ns = np.concatenate((n1s, n2s + n1s[-1]))\n", - "ls=['-', '--']\n", - "algorithms = ['Uncertainty Forest', 'Lifelong Forest']\n", - "TASK1='XOR'\n", - "TASK2='N-XOR'\n", - "\n", - "# plot and format figure\n", - "fontsize=35\n", - "labelsize=27.5\n", - "colors = sns.color_palette(\"Set1\", n_colors = 2)\n", - "\n", - "fig1 = plt.figure(figsize=(8,8))\n", - "ax1 = fig1.add_subplot(1,1,1)\n", - "ax1.plot(ns, mean_error[0], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)\n", - "ax1.plot(ns, mean_error[1], label=algorithms[1], c=colors[0], ls=ls[np.sum(1 > 1).astype(int)], lw=3)\n", - "ax1.set_ylabel('Generalization Error (%s)'%(TASK1), fontname=\"Arial\", fontsize=fontsize)\n", - "ax1.legend(loc='upper right', fontsize=24, frameon=False)\n", - "ax1.set_ylim(0.1, 0.21)\n", - "ax1.set_xlabel('Total Sample Size', fontname=\"Arial\", fontsize=fontsize)\n", - "ax1.tick_params(labelsize=labelsize)\n", - "ax1.set_yticks([0.15, 0.2])\n", - "ax1.set_xticks([250,750,1500])\n", - "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n", - "right_side = ax1.spines[\"right\"]\n", - "right_side.set_visible(False)\n", - "top_side = ax1.spines[\"top\"]\n", - "top_side.set_visible(False)\n", - "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=30)\n", - "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=30)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When N-XOR data is available, lifelong forest outperforms uncertainty forest." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generalization Error for N-XOR Data\n", - "\n", - "Similarly, by plotting the generalization error for N-XOR data, we can also see how the presence of XOR data influenced the performance of both algorithms. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# define labels\n", - "algorithms = ['Uncertainty Forest', 'Lifelong Forest']\n", - "TASK1='XOR'\n", - "TASK2='N-XOR'\n", - "\n", - "# plot and format figure\n", - "fig1 = plt.figure(figsize=(8,8))\n", - "ax1 = fig1.add_subplot(1,1,1)\n", - "ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[0], c=colors[1], ls=ls[1], lw=3)\n", - "ax1.plot(ns[len(n1s):], mean_error[3, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)\n", - "ax1.set_ylabel('Generalization Error (%s)'%(TASK2), fontsize=fontsize)\n", - "ax1.legend(loc='upper right', fontsize=24, frameon=False)\n", - "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n", - "ax1.tick_params(labelsize=labelsize)\n", - "ax1.set_yticks([0.15, 0.2])\n", - "ax1.set_xticks([250,750,1500])\n", - "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n", - "ax1.set_ylim(0.11, 0.21)\n", - "ax1.set_xlim(-10)\n", - "right_side = ax1.spines[\"right\"]\n", - "right_side.set_visible(False)\n", - "top_side = ax1.spines[\"top\"]\n", - "top_side.set_visible(False)\n", - "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=30)\n", - "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=30)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Given XOR data, lifelong forest outperforms uncertainty forests on classifying N-XOR data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Transfer Efficiency for XOR Data\n", - "\n", - "Given the generalization errors plotted above, we can find the transfer efficiency as a ratio of the generalization error for lifelong forest to uncertainty forest. The forward and backward transfer efficiencies can then be plotted as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# define labels\n", - "algorithms = ['Forward Transfer', 'Backward Transfer']\n", - "TASK1='XOR'\n", - "TASK2='N-XOR'\n", - "\n", - "# plot and format figure\n", - "fig1 = plt.figure(figsize=(8,8))\n", - "ax1 = fig1.add_subplot(1,1,1)\n", - "ax1.plot(ns, mean_te[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)\n", - "ax1.plot(ns[len(n1s):], mean_te[1, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)\n", - "ax1.set_ylabel('Transfer Efficiency', fontsize=fontsize)\n", - "ax1.legend(loc='upper right', fontsize=24, frameon=False)\n", - "ax1.set_ylim(0.95, 1.42)\n", - "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n", - "ax1.tick_params(labelsize=labelsize)\n", - "ax1.set_yticks([1, 1.4])\n", - "ax1.set_xticks([250,750,1500])\n", - "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n", - "right_side = ax1.spines[\"right\"]\n", - "right_side.set_visible(False)\n", - "top_side = ax1.spines[\"top\"]\n", - "top_side.set_visible(False)\n", - "ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)\n", - "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=30)\n", - "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=30)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lifelong forests demonstrate both positive forward and backward transfer in this environment." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}