diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..490d5ec --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,30 @@ +name: NiaARM Tests + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + python-version: ['3.11', '3.12', '3.13', '3.14'] + defaults: + run: + shell: bash + + steps: + - uses: actions/checkout@v6 + - name: Install uv and set up Python + uses: astral-sh/setup-uv@v7 + with: + python-version: ${{ matrix.python-version }} + enable-cache: true + - name: Install the project + run: uv sync --locked --all-extras --group test + - name: Run tests + run: uv run pytest diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..7d0ebd6 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,25 @@ +name: Publish NiaARM to PyPI +on: + release: + types: + - published + +jobs: + run: + runs-on: ubuntu-latest + environment: + name: pypi + permissions: + id-token: write + contents: read + steps: + - name: Checkout + uses: actions/checkout@v6 + - name: Install uv and set up Python + uses: astral-sh/setup-uv@v7 + with: + python-version: "3.13" + - name: Build + run: uv build + - name: Publish + run: uv publish diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml deleted file mode 100644 index c2f4c6a..0000000 --- a/.github/workflows/test.yml +++ /dev/null @@ -1,53 +0,0 @@ -name: NiaARM - -on: - push: - branches: [ main ] - pull_request: - branches: [ main ] - -jobs: - build: - - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest, windows-latest, macos-latest] - python-version: ['3.11', '3.12', '3.13'] - defaults: - run: - shell: bash - - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - - name: Get full Python version - id: full-python-version - run: echo ::set-output name=version::$(python -c "import sys; print('-'.join(str(v) for v in sys.version_info))") - - name: Install poetry - run: | - curl -sL https://install.python-poetry.org | python - -y - - name: Update path - if: ${{ matrix.os != 'windows-latest' }} - run: echo "$HOME/.local/bin" >> $GITHUB_PATH - - name: Update Windows path - if: ${{ matrix.os == 'windows-latest' }} - run: echo "$APPDATA\Python\Scripts" >> $GITHUB_PATH - - name: Configure poetry - run: poetry config virtualenvs.in-project true - - name: Set up cache - uses: actions/cache@v3 - id: cache - with: - path: .venv - key: venv-${{ runner.os }}-${{ steps.full-python-version.outputs.version }}-${{ hashFiles('**/poetry.lock') }} - - name: Ensure cache is healthy - if: steps.cache.outputs.cache-hit == 'true' - run: timeout 10s poetry run pip --version || rm -rf .venv - - name: Install dependencies - run: poetry install - - name: Run tests - run: poetry run pytest -k 'not test_text_mining' diff --git a/.gitignore b/.gitignore index 0fae19b..e924d59 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,6 @@ # Byte-compiled / optimized / DLL files __pycache__/ -*.py[cod] +*.py[codz] *$py.class # C extensions @@ -27,8 +27,8 @@ share/python-wheels/ MANIFEST # PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec @@ -46,7 +46,7 @@ htmlcov/ nosetests.xml coverage.xml *.cover -*.py,cover +*.py.cover .hypothesis/ .pytest_cache/ cover/ @@ -92,20 +92,64 @@ ipython_config.py # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow +# Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +# poetry.lock +# poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +# pdm.lock +# pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +# pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid +# Redis +*.rdb +*.aof +*.pid + +# RabbitMQ +mnesia/ +rabbitmq/ +rabbitmq-data/ + +# ActiveMQ +activemq-data/ + # SageMath parsed files *.sage.py # Environments .env +.envrc .venv env/ venv/ @@ -137,9 +181,36 @@ dmypy.json # Cython debug symbols cython_debug/ -# JOSS artifacts -paper.pdf -paper.jats - -# Ruff +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +.vscode/ + +# Ruff stuff: .ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 916ac0a..1552d01 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 + rev: v6.0.0 hooks: - id: end-of-file-fixer - id: trailing-whitespace @@ -15,12 +15,11 @@ repos: - id: check-ast - id: check-docstring-first - - repo: https://github.com/psf/black-pre-commit-mirror - rev: 23.10.1 - hooks: - - id: black - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.14.8 hooks: - - id: ruff - args: [--fix, --exit-non-zero-on-fix] + - id: ruff-check + types_or: [ python, pyi ] + args: [ --fix ] + - id: ruff-format + types_or: [ python, pyi ] diff --git a/.readthedocs.yml b/.readthedocs.yml index c292ee8..9547b52 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -1,9 +1,9 @@ version: 2 build: - os: ubuntu-20.04 + os: ubuntu-24.04 tools: - python: '3.9' + python: '3.13' python: install: @@ -12,3 +12,6 @@ python: sphinx: configuration: 'docs/conf.py' fail_on_warning: true + +formats: + - pdf diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d025da1..6a127d5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -7,7 +7,7 @@ This project and everyone participating in it is governed by the [NiaARM Code of ## How Can I Contribute? ### Reporting Bugs -Before creating bug reports, please check existing issues list as you might find out that you don't need to create one. When you are creating a bug report, please include as many details as possible in the [issue template](.github/templates/ISSUE_TEMPLATE.md). +Before creating bug reports, please check existing issues list as you might find out that you don't need to create one. When you are creating a bug report, please include as mAll details as possible in the [issue template](.github/templates/ISSUE_TEMPLATE.md). ### Suggesting Enhancements @@ -21,56 +21,59 @@ Fill in the [pull request template](.github/templates/PULL_REQUEST.md) and make ### Requirements -* Poetry: [https://python-poetry.org/docs/](https://python-poetry.org/docs/) +* uv: [https://docs.astral.sh/uv/](https://docs.astral.sh/uv/) -After installing Poetry and cloning the project from GitHub, you should run the following command from the root of the cloned project: +After installing uv and cloning the project from GitHub, you should +run the following command from the root of the cloned project: ```sh -poetry install +uv sync --dev ``` -All the project's dependencies should be installed and the project ready for further development. **Note that Poetry creates a separate virtual environment for your project.** +All the project's development dependencies should be installed and the project ready for further development. ### Dependencies -| Package | Version | Platform | -|---------|:-------:|:--------:| -| niapy | ^2.0.5 | All | -| numpy | ^1.26.1 | All | -| pandas | ^2.1.1 | All | -| nltk | ^3.8.1 | All | -| tomli | ^2.0.1 | All | +| Package | Version | Platform | +|--------------|:--------:|:--------:| +| niapy | \>=2.6.1 | All | +| numpy | \>=2.3.5 | All | +| pandas | \>=2.3.3 | All | +| nltk | \>=3.9.2 | All | +| scikit-learn | \>=1.8.0 | All | +| plotly | \>=6.5.0 | All | #### Development dependencies -| Package | Version | Platform | -|------------------|:-------:|:--------:| -| pytest | ^7.4.2 | Any | -| pytest-cov | ^4.1.0 | Any | -| pytest.randomly | ^3.15.0 | Any | +| Package | Version | Platform | +|-----------------|:---------:|:--------:| +| pytest | \>=9.0.0 | All | +| pytest-cov | \>=7.0.0 | All | +| pytest.randomly | \>=4.0.1 | All | +| pre-commit | \>=4.5.0 | All | +| ruff | \>=0.14.9 | All | -#### Extras +#### Documentation Dependencies -| Package | Version | Platform | -|----------------------|:-------:|:--------:| -| sphinx | ^7.2.6 | Any | -| sphinx-rtd-theme | ^1.0.0 | Any | -| sphinxcontrib-bibtex | ^2.4.1 | Any | +| Package | Version | Platform | +|----------------------|:--------:|:--------:| +| sphinx | \>=8.2.3 | All | +| sphinx-rtd-theme | \>=3.0.2 | All | +| sphinxcontrib-bibtex | \>=2.6.5 | All | -## Development Tasks - -### Testing +## Testing Manually run the tests: ```sh -$ poetry run pytest +uv run pytest ``` -### Documentation +## Documentation Build the documentation: ```sh -$ poetry run sphinx-build ./docs ./docs/_build +uv sync --group docs +uv run sphinx-build ./docs ./docs/_build ``` diff --git a/docs/conf.py b/docs/conf.py index 6dbffe9..011a09d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -68,5 +68,4 @@ html_logo = "_static/logo.png" html_theme_options = { "logo_only": True, - "display_version": False, } diff --git a/docs/documentation.rst b/docs/documentation.rst index 2b37aa4..38c1d49 100644 --- a/docs/documentation.rst +++ b/docs/documentation.rst @@ -5,7 +5,7 @@ To locally generate and preview documentation run the following commands in the .. code:: sh - poetry install --extras docs - poetry run sphinx-build ./docs ./docs/_build + uv sync --group docs + uv run sphinx-build ./docs ./docs/_build If the build of the documentation is successful, you can preview the documentation in the docs/_build folder by clicking the ``index.html`` file. diff --git a/docs/index.rst b/docs/index.rst index 408a47b..51ec41d 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,7 +7,7 @@ NiaARM is a minimalistic framework for numerical association rule mining. * **Free software:** MIT license * **Github repository:** https://github.com/firefly-cpp/NiaARM -* **Python versions:** 3.9.x, 3.10.x, 3.11.x, 3.12.x +* **Python versions:** 3.11.x, 3.12.x, 3.13.x, 3.14.x General outline of the framework --------------------------------- diff --git a/docs/installation.rst b/docs/installation.rst index 17712ca..47ff2e1 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -7,41 +7,44 @@ Setup development environment Requirements ~~~~~~~~~~~~ -- Poetry: https://python-poetry.org/docs/ +- uv: https://docs.astral.sh/uv -After installing Poetry and cloning the project from GitHub, you should +After installing uv and cloning the project from GitHub, you should run the following command from the root of the cloned project: .. code:: sh - $ poetry install + uv sync To also install the documentation dependencies run: .. code:: sh - $ poetry install --with docs + uv sync --group docs All of the project's dependencies should be installed and the project -ready for further development. **Note that Poetry creates a separate -virtual environment for your project.** +ready for further development. Dependencies ~~~~~~~~~~~~ -+----------------+--------------+------------+ -| Package | Version | Platform | -+================+==============+============+ -| niapy | ^2.0.5 | All | -+----------------+--------------+------------+ -| numpy | ^1.26.1 | All | -+----------------+--------------+------------+ -| pandas | ^2.1.1 | All | -+----------------+--------------+------------+ -| nltk | ^3.8.1 | All | -+----------------+--------------+------------+ -| tomli | ^2.0.1 | All | -+----------------+--------------+------------+ + ++----------------+--------------+-------------+ +| Package | Version | Platform | ++================+==============+=============+ +| niapy | >=2.6.1 | All | ++----------------+--------------+-------------+ +| numpy | >=2.3.5 | All | ++----------------+--------------+-------------+ +| pandas | >=2.3.3 | All | ++----------------+--------------+-------------+ +| nltk | >=3.9.2 | All | ++----------------+--------------+-------------+ +| scikit-learn | >=1.8.0 | All | ++----------------+--------------+-------------+ +| plotly | >=6.5.0 | All | ++----------------+--------------+-------------+ + Development Dependencies ~~~~~~~~~~~~~~~~~~~~~~~~ @@ -49,20 +52,15 @@ Development Dependencies +--------------------+-----------+------------+ | Package | Version | Platform | +====================+===========+============+ -| pre-commit | ^3.5.0 | Any | +| pytest | >=9.0.0 | All | +--------------------+-----------+------------+ - -Test Dependencies -~~~~~~~~~~~~~~~~~ - +| pytest-cov | >=7.0.0 | All | +--------------------+-----------+------------+ -| Package | Version | Platform | -+====================+===========+============+ -| pytest | ^7.4.2 | Any | +| pytest-randomly | >=4.0.1 | All | +--------------------+-----------+------------+ -| pytest-cov | ^4.1.0 | Any | +| pre-commit | >=4.5.0 | All | +--------------------+-----------+------------+ -| pytest-randomly | ^3.15.0 | Any | +| ruff | >=0.14.9 | All | +--------------------+-----------+------------+ Documentation dependencies @@ -71,11 +69,11 @@ Documentation dependencies +------------------------------+--------------+------------+ | Package | Version | Platform | +==============================+==============+============+ -| sphinx | ^7.2.6 | Any | +| sphinx | >=8.2.3 | All | +------------------------------+--------------+------------+ -| sphinx-rtd-theme | ^1.0.0 | Any | +| sphinx-rtd-theme | >=3.0.2 | All | +------------------------------+--------------+------------+ -| sphinxcontrib-bibtex | ^2.4.1 | Any | +| sphinxcontrib-bibtex | >=2.6.5 | All | +------------------------------+--------------+------------+ @@ -86,7 +84,7 @@ We use pre-commit hooks for formatting and linting. You can install the pre-comm .. code:: sh - $ pre-commit install + pre-commit install Once the pre-commit hooks are installed and configured, they will automatically run before each git commit. If any hook fails, the commit will be aborted, and you'll need to address the issues raised by the hooks. @@ -95,4 +93,4 @@ To manually run the pre-commit hooks on all files, use the following command: .. code:: sh - $ pre-commit run --all-files + pre-commit run --all-files diff --git a/docs/requirements.txt b/docs/requirements.txt index fb2931c..26338a5 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,10 +1,138 @@ -niapy>=2.5.2,<3.0.0 -numpy>=1.22.4 -pandas>=2.2.2,<3.0.0 -nltk>=3.8.1,<4.0.0 -plotly>=5.22.0,<6.0.0 -scikit-learn>=1.5.1,<2.0.0 -sphinx-rtd-theme>=1.0.0,<2.0.0 -sphinxcontrib-bibtex>=2.4.1,<3.0.0 -sphinx>=7.2.6,<8.0.0 -tomli>=2.0.1,<3.0.0; python_version < "3.11" \ No newline at end of file +# This file was autogenerated by uv via the following command: +# uv pip compile pyproject.toml --group docs -o docs/requirements.txt +alabaster==1.0.0 + # via sphinx +babel==2.17.0 + # via sphinx +certifi==2025.11.12 + # via requests +charset-normalizer==3.4.4 + # via requests +click==8.3.1 + # via nltk +contourpy==1.3.3 + # via matplotlib +cycler==0.12.1 + # via matplotlib +docutils==0.21.2 + # via + # pybtex-docutils + # sphinx + # sphinx-rtd-theme + # sphinxcontrib-bibtex +et-xmlfile==2.0.0 + # via openpyxl +fonttools==4.61.0 + # via matplotlib +idna==3.11 + # via requests +imagesize==1.4.1 + # via sphinx +jinja2==3.1.6 + # via sphinx +joblib==1.5.2 + # via + # nltk + # scikit-learn +kiwisolver==1.4.9 + # via matplotlib +latexcodec==3.0.1 + # via pybtex +markupsafe==3.0.3 + # via jinja2 +matplotlib==3.10.8 + # via niapy +narwhals==2.13.0 + # via plotly +niapy==2.6.1 + # via niaarm (pyproject.toml) +nltk==3.9.2 + # via niaarm (pyproject.toml) +numpy==2.3.5 + # via + # niaarm (pyproject.toml) + # contourpy + # matplotlib + # niapy + # pandas + # scikit-learn + # scipy +openpyxl==3.1.5 + # via niapy +packaging==25.0 + # via + # matplotlib + # plotly + # sphinx +pandas==2.3.3 + # via + # niaarm (pyproject.toml) + # niapy +pillow==12.0.0 + # via matplotlib +plotly==6.5.0 + # via niaarm (pyproject.toml) +pybtex==0.25.1 + # via + # pybtex-docutils + # sphinxcontrib-bibtex +pybtex-docutils==1.0.3 + # via sphinxcontrib-bibtex +pygments==2.19.2 + # via sphinx +pyparsing==3.2.5 + # via matplotlib +python-dateutil==2.9.0.post0 + # via + # matplotlib + # pandas +pytz==2025.2 + # via pandas +pyyaml==6.0.3 + # via pybtex +regex==2025.11.3 + # via nltk +requests==2.32.5 + # via sphinx +roman-numerals-py==3.1.0 + # via sphinx +scikit-learn==1.8.0 + # via niaarm (pyproject.toml) +scipy==1.16.3 + # via scikit-learn +six==1.17.0 + # via python-dateutil +snowballstemmer==3.0.1 + # via sphinx +sphinx==8.2.3 + # via + # niaarm (pyproject.toml:docs) + # sphinx-rtd-theme + # sphinxcontrib-bibtex + # sphinxcontrib-jquery +sphinx-rtd-theme==3.0.2 + # via niaarm (pyproject.toml:docs) +sphinxcontrib-applehelp==2.0.0 + # via sphinx +sphinxcontrib-bibtex==2.6.5 + # via niaarm (pyproject.toml:docs) +sphinxcontrib-devhelp==2.0.0 + # via sphinx +sphinxcontrib-htmlhelp==2.1.0 + # via sphinx +sphinxcontrib-jquery==4.1 + # via sphinx-rtd-theme +sphinxcontrib-jsmath==1.0.1 + # via sphinx +sphinxcontrib-qthelp==2.0.0 + # via sphinx +sphinxcontrib-serializinghtml==2.0.0 + # via sphinx +threadpoolctl==3.6.0 + # via scikit-learn +tqdm==4.67.1 + # via nltk +tzdata==2025.2 + # via pandas +urllib3==2.6.2 + # via requests diff --git a/docs/testing.rst b/docs/testing.rst index efd9eef..9c9a33a 100644 --- a/docs/testing.rst +++ b/docs/testing.rst @@ -9,4 +9,4 @@ For the purpose of checking if all test are passing locally you can run followin .. code:: sh - $ poetry run pytest + uv run pytest diff --git a/examples/basic_run.py b/examples/basic_run.py index 487958b..12524b0 100644 --- a/examples/basic_run.py +++ b/examples/basic_run.py @@ -1,8 +1,7 @@ -from niaarm import NiaARM -from niaarm.dataset import Dataset from niapy.algorithms.basic import DifferentialEvolution -from niapy.task import Task, OptimizationType +from niapy.task import OptimizationType, Task +from niaarm import Dataset, NiaARM if __name__ == "__main__": # load and preprocess the dataset from csv @@ -10,9 +9,12 @@ # Create a problem # dimension represents the dimension of the problem; - # features represent the list of features, while transactions depicts the list of transactions - # the following 4 elements represent weights (support, confidence, coverage, shrinkage) - # A weight of 0.0 means that criteria are omitted and are, therefore, excluded from the fitness function + # features represent the list of features, while transactions depicts the list of + # transactions + # the following 4 elements represent weights (support, confidence, coverage, + # shrinkage) + # A weight of 0.0 means that criteria are omitted and are, therefore, excluded + # from the fitness function problem = NiaARM( data.dimension, data.features, @@ -27,7 +29,8 @@ ) # use Differential Evolution (DE) algorithm from the NiaPy library - # see full list of available algorithms: https://github.com/NiaOrg/NiaPy/blob/master/Algorithms.md + # see full list of available algorithms: + # https://github.com/NiaOrg/NiaPy/blob/master/Algorithms.md algo = DifferentialEvolution( population_size=50, differential_weight=0.5, crossover_probability=0.9 ) diff --git a/examples/basic_run_with_get_rules.py b/examples/basic_run_with_get_rules.py index fff31ce..50afa90 100644 --- a/examples/basic_run_with_get_rules.py +++ b/examples/basic_run_with_get_rules.py @@ -1,6 +1,7 @@ -from niaarm import Dataset, get_rules from niapy.algorithms.basic import DifferentialEvolution +from niaarm import Dataset, get_rules + # load dataset data = Dataset("datasets/Abalone.csv") diff --git a/examples/data_squashing.py b/examples/data_squashing.py index 959da5c..5ed8006 100644 --- a/examples/data_squashing.py +++ b/examples/data_squashing.py @@ -1,10 +1,10 @@ -from niaarm.dataset import Dataset -from niaarm.preprocessing import squash +from niaarm import Dataset, squash # load dataset dataset = Dataset("datasets/Abalone.csv") -# squash the dataset with a threshold of 0.9, using Euclidean distance as a similarity measure +# squash the dataset with a threshold of 0.9 +# using Euclidean distance as a similarity measure squashed = squash(dataset, threshold=0.9, similarity="euclidean") # print the squashed dataset diff --git a/examples/dataset_report.py b/examples/dataset_report.py index dcd5d4e..26e7bbf 100644 --- a/examples/dataset_report.py +++ b/examples/dataset_report.py @@ -1,8 +1,9 @@ -from niaarm.dataset import Dataset +from niaarm import Dataset -# this example prints details of the dataset including number of transactions, number of features -# and a feature report, listing feature names, their data types, lower and upper bounds for numeric features, -# categories for categorical features +# this example prints details of the dataset +# including number of transactions, number of features +# and a feature report, listing feature names, their data types, +# lower and upper bounds for numeric features, categories for categorical features if __name__ == "__main__": # load and preprocess the dataset from csv data = Dataset("datasets/Abalone.csv") diff --git a/examples/text_mining.py b/examples/text_mining.py index c4d6d10..08a744e 100644 --- a/examples/text_mining.py +++ b/examples/text_mining.py @@ -1,13 +1,15 @@ import pandas as pd -from niaarm.text import Corpus -from niaarm.mine import get_text_rules from niapy.algorithms.basic import ParticleSwarmOptimization +from niaarm.mine import get_text_rules +from niaarm.text import Corpus + # load corpus and extract the documents as a list of strings df = pd.read_json("datasets/text/artm_test_dataset.json", orient="records") documents = df["text"].tolist() -# create a Corpus object from the documents (requires nltk's punkt_tab tokenizer and the stopwords list) +# create a Corpus object from the documents +# requires nltk's punkt_tab tokenizer and the stopwords list try: corpus = Corpus.from_list(documents) except LookupError: diff --git a/examples/visualization.py b/examples/visualization.py index 541f2bb..93acd09 100644 --- a/examples/visualization.py +++ b/examples/visualization.py @@ -1,4 +1,5 @@ from matplotlib import pyplot as plt + from niaarm import Dataset, get_rules from niaarm.visualize import hill_slopes diff --git a/examples/visualization_examples/grouped_matrix_plot/data_developer_salary_grouped_matrix_plot.py b/examples/visualization_examples/grouped_matrix_plot/data_developer_salary_grouped_matrix_plot.py index 0e1d15d..d9d5873 100644 --- a/examples/visualization_examples/grouped_matrix_plot/data_developer_salary_grouped_matrix_plot.py +++ b/examples/visualization_examples/grouped_matrix_plot/data_developer_salary_grouped_matrix_plot.py @@ -1,4 +1,6 @@ -from examples.visualization_examples.prepare_datasets import get_data_developer_salary_data +from examples.visualization_examples.prepare_datasets import ( + get_data_developer_salary_data, +) from niaarm import Dataset, get_rules from niaarm.visualize import grouped_matrix_plot @@ -6,22 +8,17 @@ arm_df = get_data_developer_salary_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -31,7 +28,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/grouped_matrix_plot/football_players_grouped_matrix_plot.py b/examples/visualization_examples/grouped_matrix_plot/football_players_grouped_matrix_plot.py index 8d55d04..f24078f 100644 --- a/examples/visualization_examples/grouped_matrix_plot/football_players_grouped_matrix_plot.py +++ b/examples/visualization_examples/grouped_matrix_plot/football_players_grouped_matrix_plot.py @@ -6,22 +6,17 @@ arm_df = get_football_player_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -31,7 +26,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/grouped_matrix_plot/weather_data_grouped_matrix_plot.py b/examples/visualization_examples/grouped_matrix_plot/weather_data_grouped_matrix_plot.py index 1c91bd3..5623388 100644 --- a/examples/visualization_examples/grouped_matrix_plot/weather_data_grouped_matrix_plot.py +++ b/examples/visualization_examples/grouped_matrix_plot/weather_data_grouped_matrix_plot.py @@ -6,22 +6,17 @@ arm_df = get_weather_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -32,7 +27,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/prepare_datasets.py b/examples/visualization_examples/prepare_datasets.py index 564035a..176f27b 100644 --- a/examples/visualization_examples/prepare_datasets.py +++ b/examples/visualization_examples/prepare_datasets.py @@ -1,6 +1,7 @@ -import pandas as pd from datetime import datetime +import pandas as pd + def get_weather_data(): # Read csv and create DataFrame @@ -12,7 +13,6 @@ def get_weather_data(): # Extract month from Date_Time df["Month"] = df["Date_Time"].dt.month - # Function to map months to seasons def month_to_season(month): if month in [12, 1, 2]: @@ -24,7 +24,6 @@ def month_to_season(month): elif month in [9, 10, 11]: return "Autumn" - # Apply to months in DataFrame and create new Season column df["Season"] = df["Month"].apply(month_to_season) @@ -34,7 +33,6 @@ def month_to_season(month): # Remove duplicate rows df.drop_duplicates(inplace=True) - ######### DISCRETIZATION ######### def get_descriptive_stats(data_frame, column, bins_num): stats = data_frame[column].describe() @@ -46,19 +44,18 @@ def get_descriptive_stats(data_frame, column, bins_num): stats["50%"], stats["75%"], stats["max"], - stats["max"] + 0.01 + stats["max"] + 0.01, ] elif bins_num == 3: bins_values = [ stats["min"], (stats["min"] + (stats["max"] - stats["min"]) / 3), (stats["min"] + 2 * (stats["max"] - stats["min"]) / 3), - stats["max"] + 0.01 + stats["max"] + 0.01, ] return bins_values - # TEMPERATURE temperature_stats = get_descriptive_stats(df, "Temperature_C", 5) temperature_labels = ["Very cold", "Cold", "Mild", "Warm", "Hot"] @@ -66,7 +63,7 @@ def get_descriptive_stats(data_frame, column, bins_num): df["Temperature_C"], bins=temperature_stats, labels=temperature_labels, - include_lowest=True + include_lowest=True, ) # HUMIDITY @@ -76,38 +73,41 @@ def get_descriptive_stats(data_frame, column, bins_num): df["Humidity_pct"], bins=humidity_stats, labels=humidity_labels, - include_lowest=True + include_lowest=True, ) # PRECIPITATION precipitation_stats = get_descriptive_stats(df, "Precipitation_mm", 3) - precipitation_labels = ["Low precipitation", "Medium precipitation", "High precipitation"] + precipitation_labels = [ + "Low precipitation", + "Medium precipitation", + "High precipitation", + ] df["Precipitation_mm"] = pd.cut( df["Precipitation_mm"], bins=precipitation_stats, labels=precipitation_labels, - include_lowest=True + include_lowest=True, ) # WIND SPEED wind_stats = get_descriptive_stats(df, "Wind_Speed_kmh", 3) wind_labels = ["Calm", "Breeze", "Stormy"] df["Wind_Speed_kmh"] = pd.cut( - df["Wind_Speed_kmh"], - bins=wind_stats, - labels=wind_labels, - include_lowest=True + df["Wind_Speed_kmh"], bins=wind_stats, labels=wind_labels, include_lowest=True ) # Select relevant columns for ARM - arm_df = df[[ - "Location", - "Season", - "Temperature_C", - "Humidity_pct", - "Precipitation_mm", - "Wind_Speed_kmh" - ]] + arm_df = df[ + [ + "Location", + "Season", + "Temperature_C", + "Humidity_pct", + "Precipitation_mm", + "Wind_Speed_kmh", + ] + ] return arm_df @@ -129,16 +129,18 @@ def get_football_player_data(): df["Age"] = current_year - df["Born"] # Select relevant columns - arm_df = df[[ - "Origin", - "From(Country)", - "From(Club)", - "To(Country)", - "To(Club)", - "Position", - "Fee(€ mln)", - "Age" - ]] + arm_df = df[ + [ + "Origin", + "From(Country)", + "From(Club)", + "To(Country)", + "To(Club)", + "Position", + "Fee(€ mln)", + "Age", + ] + ] return arm_df @@ -155,23 +157,25 @@ def get_data_developer_salary_data(): df.drop_duplicates(inplace=True) # Select relevant columns - arm_df = df[[ - "experience_level", - "employment_type", - "job_title", - "salary_in_usd", - "company_location", - "company_size" - ]] + arm_df = df[ + [ + "experience_level", + "employment_type", + "job_title", + "salary_in_usd", + "company_location", + "company_size", + ] + ] return arm_df def get_abalone_data(): - # Read csv and create DataFrame + # Read csv and create DataFrame df = pd.read_csv("datasets/Abalone.csv") - - ######### DISCRETIZATION ######### + + ######### DISCRETIZATION ######### def get_descriptive_stats(data_frame, column, bins_num): stats = data_frame[column].describe() bins_values = [] @@ -182,110 +186,99 @@ def get_descriptive_stats(data_frame, column, bins_num): stats["50%"], stats["75%"], stats["max"], - stats["max"] + 0.01 + stats["max"] + 0.01, ] elif bins_num == 3: bins_values = [ stats["min"], (stats["min"] + (stats["max"] - stats["min"]) / 3), (stats["min"] + 2 * (stats["max"] - stats["min"]) / 3), - stats["max"] + 0.01 + stats["max"] + 0.01, ] return bins_values - - # LENGTH + + # LENGTH length_stats = get_descriptive_stats(df, "Length", 3) length_labels = ["Small", "Medium", "Large"] df["Length"] = pd.cut( - df["Length"], - bins=length_stats, - labels=length_labels, - include_lowest=True + df["Length"], bins=length_stats, labels=length_labels, include_lowest=True ) - - # DIAMETER + + # DIAMETER diameter_stats = get_descriptive_stats(df, "Diameter", 3) diameter_labels = ["Small", "Medium", "Large"] df["Diameter"] = pd.cut( - df["Diameter"], - bins=diameter_stats, - labels=diameter_labels, - include_lowest=True + df["Diameter"], bins=diameter_stats, labels=diameter_labels, include_lowest=True ) - - # HEIGHT + + # HEIGHT height_stats = get_descriptive_stats(df, "Height", 3) height_labels = ["Small", "Medium", "Large"] df["Height"] = pd.cut( - df["Height"], - bins=height_stats, - labels=height_labels, - include_lowest=True - ) - - # WHOLE WEIGHT + df["Height"], bins=height_stats, labels=height_labels, include_lowest=True + ) + + # WHOLE WEIGHT whole_weight_stats = get_descriptive_stats(df, "Whole weight", 3) whole_weight_labels = ["Light", "Medium", "Heavy"] df["Whole weight"] = pd.cut( df["Whole weight"], bins=whole_weight_stats, labels=whole_weight_labels, - include_lowest=True + include_lowest=True, ) - - # SHUCKED WEIGHT + + # SHUCKED WEIGHT shucked_weight_stats = get_descriptive_stats(df, "Shucked weight", 3) shucked_weight_labels = ["Light", "Medium", "Heavy"] df["Shucked weight"] = pd.cut( df["Shucked weight"], bins=shucked_weight_stats, labels=shucked_weight_labels, - include_lowest=True + include_lowest=True, ) - - # VISCERA WEIGHT + + # VISCERA WEIGHT viscera_weight_stats = get_descriptive_stats(df, "Viscera weight", 3) viscera_weight_labels = ["Light", "Medium", "Heavy"] df["Viscera weight"] = pd.cut( df["Viscera weight"], bins=viscera_weight_stats, labels=viscera_weight_labels, - include_lowest=True + include_lowest=True, ) - - # SHELL WEIGHT + + # SHELL WEIGHT shell_weight_stats = get_descriptive_stats(df, "Shell weight", 3) shell_weight_labels = ["Light", "Medium", "Heavy"] df["Shell weight"] = pd.cut( df["Shell weight"], bins=shell_weight_stats, labels=shell_weight_labels, - include_lowest=True + include_lowest=True, ) - - # AGE + + # AGE age_stats = get_descriptive_stats(df, "Rings", 3) age_labels = ["Young", "Adult", "Old"] df["Age"] = pd.cut( - df["Rings"], - bins=age_stats, - labels=age_labels, - include_lowest=True + df["Rings"], bins=age_stats, labels=age_labels, include_lowest=True ) - + # Select relevant columns for ARM - arm_df = df[[ - "Sex", - "Length", - "Diameter", - "Height", - "Whole weight", - "Shucked weight", - "Viscera weight", - "Shell weight", - "Age" - ]] + arm_df = df[ + [ + "Sex", + "Length", + "Diameter", + "Height", + "Whole weight", + "Shucked weight", + "Viscera weight", + "Shell weight", + "Age", + ] + ] return arm_df - \ No newline at end of file diff --git a/examples/visualization_examples/sankey_diagram/abalone_sankey_diagram.py b/examples/visualization_examples/sankey_diagram/abalone_sankey_diagram.py index 4bce801..92aa09d 100644 --- a/examples/visualization_examples/sankey_diagram/abalone_sankey_diagram.py +++ b/examples/visualization_examples/sankey_diagram/abalone_sankey_diagram.py @@ -6,18 +6,12 @@ arm_df = get_abalone_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Sort rules @@ -25,7 +19,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/sankey_diagram/weather_data_sankey_diagram.py b/examples/visualization_examples/sankey_diagram/weather_data_sankey_diagram.py index 5cca6f3..d87b11d 100644 --- a/examples/visualization_examples/sankey_diagram/weather_data_sankey_diagram.py +++ b/examples/visualization_examples/sankey_diagram/weather_data_sankey_diagram.py @@ -6,22 +6,17 @@ arm_df = get_weather_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -31,7 +26,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/scatter_plot/data_developer_salary_scatter_plot.py b/examples/visualization_examples/scatter_plot/data_developer_salary_scatter_plot.py index b5e1f38..1676f29 100644 --- a/examples/visualization_examples/scatter_plot/data_developer_salary_scatter_plot.py +++ b/examples/visualization_examples/scatter_plot/data_developer_salary_scatter_plot.py @@ -1,4 +1,6 @@ -from examples.visualization_examples.prepare_datasets import get_data_developer_salary_data +from examples.visualization_examples.prepare_datasets import ( + get_data_developer_salary_data, +) from niaarm import Dataset, get_rules from niaarm.visualize import scatter_plot @@ -6,22 +8,17 @@ arm_df = get_data_developer_salary_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -31,7 +28,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/scatter_plot/football_players_scatter_plot.py b/examples/visualization_examples/scatter_plot/football_players_scatter_plot.py index cbaf88d..016b307 100644 --- a/examples/visualization_examples/scatter_plot/football_players_scatter_plot.py +++ b/examples/visualization_examples/scatter_plot/football_players_scatter_plot.py @@ -6,22 +6,17 @@ arm_df = get_football_player_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -31,7 +26,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/scatter_plot/weather_data_scatter_plot.py b/examples/visualization_examples/scatter_plot/weather_data_scatter_plot.py index 0b6be94..f744817 100644 --- a/examples/visualization_examples/scatter_plot/weather_data_scatter_plot.py +++ b/examples/visualization_examples/scatter_plot/weather_data_scatter_plot.py @@ -6,22 +6,17 @@ arm_df = get_weather_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Add lift after the rules have been generated -# Cannot be in metrics before because get_rules metrics doesn't contain lift, therefore we need to add after +# Cannot be in metrics before because get_rules metrics doesn't contain lift, +# therefore we need to add after metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) @@ -31,7 +26,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/visualization_examples/two_key_plot/data_developer_salary_two_key_plot.py b/examples/visualization_examples/two_key_plot/data_developer_salary_two_key_plot.py index 67263e5..6041dc8 100644 --- a/examples/visualization_examples/two_key_plot/data_developer_salary_two_key_plot.py +++ b/examples/visualization_examples/two_key_plot/data_developer_salary_two_key_plot.py @@ -1,4 +1,6 @@ -from examples.visualization_examples.prepare_datasets import get_data_developer_salary_data +from examples.visualization_examples.prepare_datasets import ( + get_data_developer_salary_data, +) from niaarm import Dataset, get_rules from niaarm.visualize import two_key_plot @@ -6,18 +8,12 @@ arm_df = get_data_developer_salary_data() # Prepare Dataset -dataset = Dataset( - path_or_df=arm_df, - delimiter="," -) +dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules( - dataset=dataset, - algorithm="DifferentialEvolution", - metrics=metrics, - max_evals=500 + dataset=dataset, algorithm="DifferentialEvolution", metrics=metrics, max_evals=500 ) # Sort rules @@ -25,7 +21,7 @@ # Print rule information print("\nRules:") print(rules) -print(f'\nTime to generate rules: {f"{run_time:.3f}"} seconds') +print(f"\nTime to generate rules: {f'{run_time:.3f}'} seconds") print("\nRule information: ", rules[3]) print("Antecedent: ", rules[3].antecedent) print("Consequent: ", rules[3].consequent) diff --git a/examples/working_with_rule_list.py b/examples/working_with_rule_list.py index 7659897..71dc0d6 100644 --- a/examples/working_with_rule_list.py +++ b/examples/working_with_rule_list.py @@ -1,13 +1,13 @@ """ -Example usage of the RuleList class. The RuleList class is a wrapper around a python list, with some added features, mainly -getting statistical data of rule metrics and sorting by metric. +Example usage of the RuleList class. The RuleList class is a wrapper around +a python list, with some added features, mainly getting statistical data +of rule metrics and sorting by metric. 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-[package.extras] -docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] -test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\" or platform_python_implementation == \"GraalVM\" or platform_python_implementation == \"CPython\" and sys_platform == \"win32\" and python_version >= \"3.13\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""] - -[metadata] -lock-version = "2.1" -python-versions = ">=3.10,<=3.14" -content-hash = "fb791434fc1741e78cba87948a94b6b8f8606b46b9c242a4c8aee86c6350eea8" diff --git a/pyproject.toml b/pyproject.toml index 6aae76d..e46534e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,51 +1,80 @@ -[tool.poetry] +[project] name = "niaarm" version = "0.4.5" description = "A minimalistic framework for numerical association rule mining" -authors = ["Žiga Stupan ", "Iztok Fister Jr. "] -keywords = ['association rule mining', 'data science', 'numerical association rule mining', 'preprocessing', 'visualization'] -homepage = "https://github.com/firefly-cpp/NiaARM" -repository = "https://github.com/firefly-cpp/NiaARM" -documentation = "https://niaarm.readthedocs.io/en/latest/" -license = "MIT" +authors = [ + { name = "Žiga Stupan", email = "ziga.stupan1@student.um.si" }, + { name = "Iztok Fister Jr.", email = "iztok@iztok-jr-fister.eu" }, +] +requires-python = ">=3.11" readme = "README.md" - -include = [ - { path="LICENSE", format="sdist" }, - { path="CHANGELOG.md", format="sdist" }, - { path="CITATION.cff", format="sdist" }, - { path="niaarm.1", format="sdist" }, +license = "MIT" +keywords = [ + "association rule mining", + "data science", + "numerical association rule mining", + "preprocessing", + "visualization", +] +dependencies = [ + "niapy>=2.6.1", + "numpy>=2.3.5", + "pandas>=2.3.3", + "nltk>=3.9.2", + "plotly>=6.5.0", + "scikit-learn>=1.8.0", ] -[tool.poetry.dependencies] -python = ">=3.10,<=3.14" -niapy = "^2.6.1" -numpy = ">=1.22.4" -pandas = "^2.2.2" -nltk = "^3.8.1" -tomli = { version = "^2.0.1", python = "<3.11" } -plotly = "^5.22.0" -scikit-learn = "^1.5.1" - -[tool.poetry.group.dev.dependencies] -pre-commit = "^3.5.0" - -[tool.poetry.group.test.dependencies] -pytest = "^7.4.2" -pytest-cov = "^4.1.0" -pytest-randomly = "^3.15.0" +[project.urls] +Homepage = "https://github.com/firefly-cpp/NiaARM" +Repository = "https://github.com/firefly-cpp/NiaARM" +Documentation = "https://niaarm.readthedocs.io/en/latest/" -[tool.poetry.group.docs] -optional = true +[project.scripts] +niaarm = "niaarm.cli:main" -[tool.poetry.group.docs.dependencies] -sphinx-rtd-theme = "^1.0.0" -sphinxcontrib-bibtex = "^2.4.1" -sphinx = "^7.2.6" +[dependency-groups] +dev = [ + { include-group = "pre-commit" }, + { include-group = "test" }, + { include-group = "lint" }, +] +test = [ + "pytest>=9.0.0", + "pytest-cov>=7.0.0", + "pytest-randomly>=4.0.1", +] +lint = [ + "ruff>=0.14.9", +] +pre-commit = [ + "pre-commit>=4.5.0" +] +docs = [ + "sphinx-rtd-theme>=3.0.2", + "sphinxcontrib-bibtex>=2.6.5", + "sphinx>=8.2.3", +] -[tool.poetry.scripts] -niaarm = 'niaarm.cli:main' +[tool.uv.build-backend] +source-include = [ + "LICENSE", + "CHANGELOG.md", + "CITATION.cff", + "niaarm.1", +] [build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +requires = ["uv_build>=0.9.17,<0.10.0"] +build-backend = "uv_build" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade +] \ No newline at end of file diff --git a/niaarm/__init__.py b/src/niaarm/__init__.py similarity index 99% rename from niaarm/__init__.py rename to src/niaarm/__init__.py index 161e7b1..921d98a 100644 --- a/niaarm/__init__.py +++ b/src/niaarm/__init__.py @@ -1,11 +1,10 @@ -from niaarm.niaarm import NiaARM from niaarm.dataset import Dataset -from niaarm.rule import Rule from niaarm.feature import Feature from niaarm.mine import get_rules -from niaarm.rule_list import RuleList +from niaarm.niaarm import NiaARM from niaarm.preprocessing import squash - +from niaarm.rule import Rule +from niaarm.rule_list import RuleList __all__ = ["NiaARM", "Dataset", "Feature", "Rule", "RuleList", "get_rules", "squash"] diff --git a/niaarm/__main__.py b/src/niaarm/__main__.py similarity index 100% rename from niaarm/__main__.py rename to src/niaarm/__main__.py index 58b2d94..9c4917a 100644 --- a/niaarm/__main__.py +++ b/src/niaarm/__main__.py @@ -1,6 +1,6 @@ import sys -from niaarm import cli +from niaarm import cli if __name__ == "__main__": sys.exit(cli.main()) diff --git a/niaarm/cli.py b/src/niaarm/cli.py similarity index 97% rename from niaarm/cli.py rename to src/niaarm/cli.py index 97dc116..b47170c 100644 --- a/niaarm/cli.py +++ b/src/niaarm/cli.py @@ -1,23 +1,20 @@ import argparse -from inspect import getmodule, getmembers, isfunction import os -from pathlib import Path import platform import subprocess import sys - -try: - import tomllib -except ImportError: - import tomli as tomllib +import tomllib +from inspect import getmembers, getmodule, isfunction +from pathlib import Path import numpy as np -import niaarm -from niaarm import NiaARM, Dataset, get_rules, squash -from niapy.util.factory import get_algorithm -from niapy.util import distances, repair -from niapy.algorithms.other import mts from niapy.algorithms.basic import de +from niapy.algorithms.other import mts +from niapy.util import distances, repair +from niapy.util.factory import get_algorithm + +import niaarm +from niaarm import Dataset, NiaARM, get_rules, squash DEFAULT_CONFIG = { "input_file": None, @@ -42,7 +39,8 @@ def get_parser(): parser = argparse.ArgumentParser( prog="niaarm", - description="Perform ARM, output mined rules as csv, get mined rules' statistics", + description="Perform ARM, output mined rules as csv, get mined rules' " + "statistics", ) parser.add_argument( "-v", @@ -117,7 +115,7 @@ def get_parser(): def deep_update(dictionary, other): - """Same as `dict.update` but for nested dictionaries.""" + """Same as ``dict.update`` but for nested dictionaries.""" updated_dict = dictionary.copy() for k, v in other.items(): if ( @@ -289,7 +287,7 @@ def main(): return 1 weights = weights if weights else [1] * len(metrics) - metrics = dict(zip(metrics, weights)) + metrics = dict(zip(metrics, weights, strict=True)) try: dataset = Dataset(config["input_file"]) diff --git a/niaarm/dataset.py b/src/niaarm/dataset.py similarity index 90% rename from niaarm/dataset.py rename to src/niaarm/dataset.py index c089891..6c7032c 100644 --- a/niaarm/dataset.py +++ b/src/niaarm/dataset.py @@ -1,6 +1,7 @@ import numpy as np import pandas as pd -from pandas.api.types import is_float_dtype, is_integer_dtype, is_bool_dtype +from pandas.api.types import is_bool_dtype, is_float_dtype, is_integer_dtype + from niaarm.feature import Feature @@ -8,12 +9,14 @@ class Dataset: r"""Class for working with a dataset. Args: - path_or_df (Union[str, os.PathLike, pandas.DataFrame]): Path to the dataset (csv) file or a pandas DataFrame. + path_or_df (str | os.PathLike | pandas.DataFrame): Path to the dataset (csv) + file or a pandas DataFrame. delimiter (str): The delimiter in the csv file. - header (Optional[int]): Row to use as header (zero-based). Default: 0. + header (int | None): Row to use as header (zero-based). Default: 0. Pass ``header=None`` if the file doesn't contain a header. - names (Optional[list[str]]): List of feature names to use. - If the file already contains a header row, pass ``header=0`` to override the feature names. + names (list[str] | None): List of feature names to use. + If the file already contains a header row, pass ``header=0`` to override the + feature names. Attributes: transactions (pandas.DataFrame): Transactional data. diff --git a/niaarm/feature.py b/src/niaarm/feature.py similarity index 74% rename from niaarm/feature.py rename to src/niaarm/feature.py index 6cb01d2..8346fef 100644 --- a/niaarm/feature.py +++ b/src/niaarm/feature.py @@ -7,9 +7,9 @@ class Feature: Args: name (str): Name of the feature. dtype (str): Datatype of the feature. - min_val (Optional[float]): Minimum value of the feature in the transaction database. - max_val (Optional[float]): Maximum value of the feature in the transaction database. - categories (Optional[list[str]]): Possible categorical feature's values. + min_val (float | None): Minimum value of the feature in the transaction database. + max_val (float | None): Maximum value of the feature in the transaction database. + categories (list[str] | None): Possible categorical feature's values. """ @@ -36,7 +36,10 @@ def __eq__(self, other): def __repr__(self): string = f"{self.name}(" if self.dtype == "cat": - string += f"{self.categories if len(self.categories) != 1 else self.categories[0]})" + if len(self.categories) > 1: + string += f"{self.categories})" + else: + string += f"{self.categories[0]})" else: if math.isclose(self.min_val, self.max_val, rel_tol=1e-6, abs_tol=1e-6): string += f"{self.min_val})" diff --git a/niaarm/mine.py b/src/niaarm/mine.py similarity index 66% rename from niaarm/mine.py rename to src/niaarm/mine.py index 82ab7bd..5ca1149 100644 --- a/niaarm/mine.py +++ b/src/niaarm/mine.py @@ -1,9 +1,11 @@ -from collections import namedtuple import time +from collections import namedtuple + import numpy as np -from niaarm.niaarm import NiaARM from niapy.task import OptimizationType, Task from niapy.util.factory import get_algorithm + +from niaarm.niaarm import NiaARM from niaarm.text import NiaARTM @@ -32,19 +34,21 @@ def get_rules( Args: dataset (Dataset): Dataset to mine rules on. - algorithm (Union[niapy.algorithms.Algorithm, str]): Algorithm to use. + algorithm (niapy.algorithms.Algorithm | str): Algorithm to use. Can be either an Algorithm object or the class name as a string. In that case, algorithm parameters can be passed in as keyword arguments. - metrics (Union[Dict[str, float], Sequence[str]]): Metrics to take into account when computing the fitness. - Metrics can either be passed as a Dict of pairs {'metric_name': } or - a sequence of metrics as strings, in which case, the weights of the metrics will be set to 1. - max_evals (Optional[int]): Maximum number of iterations. Default: ``inf``. At least one of ``max_evals`` or - ``max_iters`` must be provided. - max_iters (Optional[int]): Maximum number of fitness evaluations. Default: ``inf``. + metrics (dict[str, float] | Sequence[str]): Metrics to take into account when + computing the fitness. Metrics can either be passed as a Dict of pairs + {'metric_name': } or a sequence of metrics as strings, in which case, + the weights of the metrics will be set to 1. + max_evals (int | None): Maximum number of iterations. Default: ``inf``. At + least one of ``max_evals`` or ``max_iters`` must be provided. + max_iters (int | None): Maximum number of fitness evaluations. Default: ``inf``. logging (bool): Enable logging of fitness improvements. Default: ``False``. Returns: - Result: A named tuple containing the list of mined rules and the algorithm's run time in seconds. + Result: A named tuple containing the list of mined rules and the algorithm's + run time in seconds. """ problem = NiaARM( @@ -87,23 +91,26 @@ def get_text_rules( Args: corpus (Corpus): Dataset to mine rules on. max_terms (int): Maximum number of terms in association rule. - algorithm (Union[niapy.algorithms.Algorithm, str]): Algorithm to use. + algorithm (niapy.algorithms.Algorithm | str): Algorithm to use. Can be either an Algorithm object or the class name as a string. In that case, algorithm parameters can be passed in as keyword arguments. - metrics (Union[Dict[str, float], Sequence[str]]): Metrics to take into account when computing the fitness. - Metrics can either be passed as a Dict of pairs {'metric_name': } or - a sequence of metrics as strings, in which case, the weights of the metrics will be set to 1. + metrics (dict[str, float] | Sequence[str]): Metrics to take into account when + computing the fitness. Metrics can either be passed as a dict of pairs + {'metric_name': } or a sequence of metrics as strings, + in which case, the weights of the metrics will be set to 1. smooth (bool): Smooth idf to prevent division by 0 error. Default: ``True``. norm (int): Order of norm for normalizing the tf-idf matrix. Default: 2. - threshold (Optional[float]): Threshold of tf-idf weights. If a weight is less than or equal to the - threshold, the term is not included in the transaction. Default: 0. - max_evals (Optional[int]): Maximum number of iterations. Default: ``inf``. At least one of ``max_evals`` or - ``max_iters`` must be provided. - max_iters (Optional[int]): Maximum number of fitness evaluations. Default: ``inf``. + threshold (float | None): Threshold of tf-idf weights. If a weight is less + than or equal to the threshold, the term is not included in the transaction. + Default: 0. + max_evals (int | None): Maximum number of iterations. Default: ``inf``. At + least one of ``max_evals`` or ``max_iters`` must be provided. + max_iters (int | None): Maximum number of fitness evaluations. Default: ``inf``. logging (bool): Enable logging of fitness improvements. Default: ``False``. Returns: - Result: A named tuple containing the list of mined rules and the algorithm's run time in seconds. + Result: A named tuple containing the list of mined rules and the algorithm's + run time in seconds. """ problem = NiaARTM( diff --git a/niaarm/niaarm.py b/src/niaarm/niaarm.py similarity index 94% rename from niaarm/niaarm.py rename to src/niaarm/niaarm.py index dc2b16d..e6d844c 100644 --- a/niaarm/niaarm.py +++ b/src/niaarm/niaarm.py @@ -1,8 +1,9 @@ -from niaarm.rule import Rule +import numpy as np +from niapy.problems import Problem + from niaarm.feature import Feature +from niaarm.rule import Rule from niaarm.rule_list import RuleList -from niapy.problems import Problem -import numpy as np class NiaARM(Problem): @@ -11,23 +12,27 @@ class NiaARM(Problem): The implementation is composed of ideas found in the following papers: * I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. - [Differential evolution for association rule mining using categorical and numerical attributes] + [Differential evolution for association rule mining using categorical and + numerical attributes] (http://www.iztok-jr-fister.eu/static/publications/231.pdf) - In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018. + In: Intelligent data engineering and automated learning - IDEAL 2018, + pp. 79-88, 2018. * I. Fister Jr., V. Podgorelec, I. Fister. [Improved Nature-Inspired Algorithms for Numeric Association Rule Mining] (https://link.springer.com/chapter/10.1007/978-3-030-68154-8_19) - In: Vasant P., Zelinka I., Weber GW. (eds.) Intelligent Computing and Optimization. ICO 2020. + In: Vasant P., Zelinka I., Weber GW. (eds.) Intelligent Computing and + Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. Args: dimension (int): Dimension of the optimization problem for the dataset. features (list[Feature]): List of the dataset's features. transactions (pandas.Dataframe): The dataset's transactions. - metrics (Union[Dict[str, float], Sequence[str]]): Metrics to take into account when computing the fitness. - Metrics can either be passed as a Dict of pairs {'metric_name': } or - a sequence of metrics as strings, in which case, the weights of the metrics will be set to 1. + metrics (dict[str, float] | Sequence[str]): Metrics to take into account when + computing the fitness. Metrics can either be passed as a Dict of pairs + {'metric_name': } or a sequence of metrics as strings, in which case, + the weights of the metrics will be set to 1. logging (bool): Enable logging of fitness improvements. Default: ``False``. Attributes: diff --git a/niaarm/preprocessing.py b/src/niaarm/preprocessing.py similarity index 95% rename from niaarm/preprocessing.py rename to src/niaarm/preprocessing.py index 0ae4efe..d491e6b 100644 --- a/niaarm/preprocessing.py +++ b/src/niaarm/preprocessing.py @@ -1,6 +1,7 @@ import numpy as np import pandas as pd from pandas.api.types import is_float_dtype, is_integer_dtype + from niaarm.dataset import Dataset @@ -12,8 +13,10 @@ def euclidean(reference, targets, *, cat_data, num_data, cat_weights, num_weight targets (np.ndarray): Indices of target transactions to compare cat_data (np.ndarray | None): Categorical feature data (keyword-only) num_data (np.ndarray | None): Numerical feature data (keyword-only) - cat_weights (np.ndarray | None): Squared weights for categorical features (keyword-only) - num_weights (np.ndarray | None): Squared weights for numerical features (keyword-only) + cat_weights (np.ndarray | None): Squared weights for categorical features ( + keyword-only) + num_weights (np.ndarray | None): Squared weights for numerical features ( + keyword-only) Returns: np.ndarray: Distances from input to each target @@ -50,7 +53,8 @@ def cosine_similarity(reference, targets, *, transactions): def mean_or_mode(column): - """Aggregate function that returns the mode for categorical features, and the mean for numerical features.""" + """Aggregate function that returns the mode for categorical features, and the + mean for numerical features.""" if is_float_dtype(column): return column.mean() elif is_integer_dtype(column): @@ -65,7 +69,8 @@ def squash(dataset, threshold, similarity="euclidean"): Args: dataset (Dataset): Dataset to squash. threshold (float): Similarity threshold. Should be between 0 and 1. - similarity (Literal["euclidean", "cosine"]): Similarity measure for comparing transactions (euclidean or cosine). Default: 'euclidean'. + similarity (Literal["euclidean", "cosine"]): Similarity measure for comparing + transactions (euclidean or cosine). Default: 'euclidean'. Returns: Dataset: Squashed dataset. diff --git a/niaarm/rule.py b/src/niaarm/rule.py similarity index 72% rename from niaarm/rule.py rename to src/niaarm/rule.py index 15eb221..4fbfa16 100644 --- a/niaarm/rule.py +++ b/src/niaarm/rule.py @@ -1,6 +1,6 @@ import math + import numpy as np -import pandas as pd class Rule: @@ -9,53 +9,65 @@ class Rule: Args: antecedent (list[Feature]): A list of antecedents of the association rule. consequent (list[Feature]): A list of consequents of the association rule. - fitness (Optional[float]): Fitness value of the association rule. - transactions (Optional[pandas.DataFrame]): Transactional database. + fitness (float | None): Fitness value of the association rule. + transactions (pandas.DataFrame | None): Transactional database. Attributes: cls.metrics (tuple[str]): List of all available interest measures. - support: Support is defined on an itemset as the proportion of transactions that contain the attribute :math:`X`. + support: Support is defined on an itemset as the proportion of transactions + that contain the attribute :math:`X`. :math:`supp(X) = \frac{n_{X}}{|D|},` where :math:`|D|` is the number of records in the transactional database. - For an association rule, support is defined as the support of all the attributes in the rule. + For an association rule, support is defined as the support of all the + attributes in the rule. :math:`supp(X \implies Y) = \frac{n_{XY}}{|D|}` **Range:** :math:`[0, 1]` - **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, + **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used + Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures - confidence: Confidence of the rule, defined as the proportion of transactions that contain - the consequent in the set of transactions that contain the antecedent. This proportion is an estimate - of the probability of seeing the consequent, if the antecedent is present in the transaction. + confidence: Confidence of the rule, defined as the proportion of transactions + that contain + the consequent in the set of transactions that contain the antecedent. This + proportion is an estimate + of the probability of seeing the consequent, if the antecedent is present in + the transaction. :math:`conf(X \implies Y) = \frac{supp(X \implies Y)}{supp(X)}` **Range:** :math:`[0, 1]` - **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, + **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used + Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures - lift: Lift measures how many times more often the antecedent and the consequent Y + lift: Lift measures how many times more often the antecedent and the + consequent Y occur together than expected if they were statistically independent. :math:`lift(X \implies Y) = \frac{conf(X \implies Y)}{supp(Y)}` **Range:** :math:`[0, \infty]` (1 means independence) - **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, + **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used + Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures - coverage: Coverage, also known as antecedent support, is an estimate of the probability that - the rule applies to a randomly selected transaction. It is the proportion of transactions + coverage: Coverage, also known as antecedent support, is an estimate of the + probability that + the rule applies to a randomly selected transaction. It is the proportion of + transactions that contain the antecedent. :math:`cover(X \implies Y) = supp(X)` **Range:** :math:`[0, 1]` - **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, + **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used + Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures rhs_support: Support of the consequent. @@ -63,109 +75,149 @@ class Rule: **Range:** :math:`[0, 1]` - **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, + **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used + Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures - conviction: Conviction can be interpreted as the ratio of the expected frequency that the antecedent occurs without + conviction: Conviction can be interpreted as the ratio of the expected + frequency that the antecedent occurs without the consequent. :math:`conv(X \implies Y) = \frac{1 - supp(Y)}{1 - conf(X \implies Y)}` - **Range:** :math:`[0, \infty]` (1 means independence, :math:`\infty` means the rule always holds) + **Range:** :math:`[0, \infty]` (1 means independence, :math:`\infty` means + the rule always holds) - **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, + **Reference:** Michael Hahsler, A Probabilistic Comparison of Commonly Used + Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures - inclusion: Inclusion is defined as the ratio between the number of attributes of the rule + inclusion: Inclusion is defined as the ratio between the number of attributes + of the rule and all attributes in the database. :math:`inclusion(X \implies Y) = \frac{|X \cup Y|}{m},` - where :math:`m` is the total number of attributes in the transactional database. + where :math:`m` is the total number of attributes in the transactional + database. **Range:** :math:`[0, 1]` - **Reference:** I. Fister Jr., V. Podgorelec, I. Fister. Improved Nature-Inspired Algorithms for Numeric Association - Rule Mining. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in + **Reference:** I. Fister Jr., V. Podgorelec, I. Fister. Improved + Nature-Inspired Algorithms for Numeric Association + Rule Mining. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent + Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. - amplitude: Amplitude measures the quality of a rule, preferring attributes with smaller intervals. + amplitude: Amplitude measures the quality of a rule, preferring attributes + with smaller intervals. - :math:`ampl(X \implies Y) = 1 - \frac{1}{n}\sum_{k = 1}^{n}{\frac{Ub_k - Lb_k}{max(o_k) - min(o_k)}},` + :math:`ampl(X \implies Y) = 1 - \frac{1}{n}\sum_{k = 1}^{n}{\frac{Ub_k - + Lb_k}{max(o_k) - min(o_k)}},` - where :math:`n` is the total number of attributes in the rule, :math:`Ub_k` and :math:`Lb_k` are upper and lower - bounds of the selected attribute, and :math:`max(o_k)` and :math:`min(o_k)` are the maximum and minimum + where :math:`n` is the total number of attributes in the rule, :math:`Ub_k` + and :math:`Lb_k` are upper and lower + bounds of the selected attribute, and :math:`max(o_k)` and :math:`min(o_k)` + are the maximum and minimum feasible values of the attribute :math:`o_k` in the transactional database. **Range:** :math:`[0, 1]` - **Reference:** I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical + **Reference:** I. Fister Jr., I. Fister A brief overview of swarm + intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (2020). interestingness: Interestingness of the rule, defined as: - :math:`interest(X \implies Y) = \frac{supp(X \implies Y)}{supp(X)} \cdot \frac{supp(X \implies Y)}{supp(Y)} + :math:`interest(X \implies Y) = \frac{supp(X \implies Y)}{supp(X)} \cdot + \frac{supp(X \implies Y)}{supp(Y)} \cdot (1 - \frac{supp(X \implies Y)}{|D|})` - Here, the first part gives us the probability of generating the rule based on the antecedent, the second part - gives us the probability of generating the rule based on the consequent and the third part is the probability - that the rule won't be generated. Thus, rules with very high support will be deemed uninteresting. + Here, the first part gives us the probability of generating the rule based + on the antecedent, the second part + gives us the probability of generating the rule based on the consequent and + the third part is the probability + that the rule won't be generated. Thus, rules with very high support will be + deemed uninteresting. **Range:** :math:`[0, 1]` - **Reference:** I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical + **Reference:** I. Fister Jr., I. Fister A brief overview of swarm + intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (2020). - comprehensibility: Comprehensibility of the rule. Rules with fewer attributes in the consequent are more + comprehensibility: Comprehensibility of the rule. Rules with fewer attributes + in the consequent are more comprehensible. :math:`comp(X \implies Y) = \frac{log(1 + |Y|)}{log(1 + |X \cup Y|)}` **Range:** :math:`[0, 1]` - **Reference:** I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical + **Reference:** I. Fister Jr., I. Fister A brief overview of swarm + intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (2020). netconf: The netconf metric evaluates the interestingness of association rules depending on the support of the rule and the support of the antecedent and consequent of the rule. - :math:`netconf(X \implies Y) = \frac{supp(X \implies Y) - supp(X)supp(Y)}{supp(X)(1 - supp(X))}` + :math:`netconf(X \implies Y) = \frac{supp(X \implies Y) - supp(X)supp(Y)}{ + supp(X)(1 - supp(X))}` - **Range:** :math:`[-1, 1]` (Negative values represent negative dependence, positive values represent positive + **Range:** :math:`[-1, 1]` (Negative values represent negative dependence, + positive values represent positive dependence and 0 represents independence) - **Reference:** E. V. Altay and B. Alatas, "Sensitivity Analysis of MODENAR Method for Mining of Numeric Association - Rules," 2019 1st International Informatics and Software Engineering Conference (UBMYK), 2019, pp. 1-6, + **Reference:** E. V. Altay and B. Alatas, "Sensitivity Analysis of MODENAR + Method for Mining of Numeric Association + Rules," 2019 1st International Informatics and Software Engineering + Conference (UBMYK), 2019, pp. 1-6, doi: 10.1109/UBMYK48245.2019.8965539. - yulesq: The Yule's Q metric represents the correlation between two possibly related dichotomous events. + yulesq: The Yule's Q metric represents the correlation between two possibly + related dichotomous events. :math:`yulesq(X \implies Y) = - \frac{supp(X \implies Y)supp(\neg X \implies \neg Y) - supp(X \implies \neg Y)supp(\neg X \implies Y)} - {supp(X \implies Y)supp(\neg X \implies \neg Y) + supp(X \implies \neg Y)supp(\neg X \implies Y)}` + \frac{supp(X \implies Y)supp(\neg X \implies \neg Y) - supp(X \implies \neg + Y)supp(\neg X \implies Y)} + {supp(X \implies Y)supp(\neg X \implies \neg Y) + supp(X \implies \neg + Y)supp(\neg X \implies Y)}` - **Range:** :math:`[-1, 1]` (-1 reflects total negative association, 1 reflects perfect positive association + **Range:** :math:`[-1, 1]` (-1 reflects total negative association, + 1 reflects perfect positive association and 0 reflects independence) - **Reference:** E. V. Altay and B. Alatas, "Sensitivity Analysis of MODENAR Method for Mining of Numeric Association - Rules," 2019 1st International Informatics and Software Engineering Conference (UBMYK), 2019, pp. 1-6, + **Reference:** E. V. Altay and B. Alatas, "Sensitivity Analysis of MODENAR + Method for Mining of Numeric Association + Rules," 2019 1st International Informatics and Software Engineering + Conference (UBMYK), 2019, pp. 1-6, doi: 10.1109/UBMYK48245.2019.8965539. - zhang: Zheng's metric measures the strength of association (positive or negative) between the antecedent and consequent, taking into account both their co-occurrence and non-co-occurrence. + zhang: Zheng's metric measures the strength of association (positive or + negative) between the antecedent and consequent, taking into account both + their co-occurrence and non-co-occurrence. :math:`zhang(X \implies Y) = - \frac{conf(X \implies Y) - conf(\neg X \implies Y)}{max\{conf(X \implies Y), conf(\neg X \implies Y)\}}` + \frac{conf(X \implies Y) - conf(\neg X \implies Y)}{max\{conf(X \implies Y), + conf(\neg X \implies Y)\}}` - **Range:** :math:`[-1, 1]` (-1 reflects total negative association, 1 reflects perfect positive association + **Range:** :math:`[-1, 1]` (-1 reflects total negative association, + 1 reflects perfect positive association and 0 reflects independence) - **Reference:** T. Zhang, “Association Rules,” in Knowledge Discovery and Data Mining. Current Issues and New + **Reference:** T. Zhang, “Association Rules,” in Knowledge Discovery and + Data Mining. Current Issues and New Applications, 2000, pp. 245–256. doi: 10.1007/3-540-45571-X_31. - leverage: difference between the frequency of antecedent and the consequent appearing together and the expected + leverage: difference between the frequency of antecedent and the consequent + appearing together and the expected frequency of them appearing separately based on their individual support - :math:`leverage(X \implies Y) = support(X \implies Y) - (support(X) \times support(Y))` + :math:`leverage(X \implies Y) = support(X \implies Y) - (support(X) \times + support(Y))` - **Range:** :math:`[-1, 1]` (-1 reflects total negative association, 1 reflects perfect positive association + **Range:** :math:`[-1, 1]` (-1 reflects total negative association, + 1 reflects perfect positive association and 0 reflects independence) - **Reference:** Gregory Piatetsky-Shapiro. 1991. Discovery, Analysis, and Presentation of Strong Rules. In - Knowledge Discovery in Databases, Gregory Piatetsky-Shapiro and William J. Frawley (Eds.). AAAI/MIT Press, 229–248. + **Reference:** Gregory Piatetsky-Shapiro. 1991. Discovery, Analysis, + and Presentation of Strong Rules. In + Knowledge Discovery in Databases, Gregory Piatetsky-Shapiro and William J. + Frawley (Eds.). AAAI/MIT Press, 229–248. """ __slots__ = ( @@ -229,13 +281,19 @@ def __post_init__(self, transactions): if attribute.dtype != "cat": feature_min = min_[attribute.name] feature_max = max_[attribute.name] - acc += 1 if feature_max == feature_min \ - else (attribute.max_val - attribute.min_val) / (feature_max - feature_min) - contains_antecedent &= ((transactions[attribute.name] <= attribute.max_val) & - (transactions[attribute.name] >= attribute.min_val)).to_numpy() - else: + acc += ( + 1 + if feature_max == feature_min + else (attribute.max_val - attribute.min_val) + / (feature_max - feature_min) + ) contains_antecedent &= ( - np.isin(transactions[attribute.name], attribute.categories) + (transactions[attribute.name] <= attribute.max_val) + & (transactions[attribute.name] >= attribute.min_val) + ).to_numpy() + else: + contains_antecedent &= np.isin( + transactions[attribute.name], attribute.categories ) self.antecedent_count = contains_antecedent.sum() @@ -246,13 +304,19 @@ def __post_init__(self, transactions): if attribute.dtype != "cat": feature_min = min_[attribute.name] feature_max = max_[attribute.name] - acc += 1 if feature_max == feature_min \ - else (attribute.max_val - attribute.min_val) / (feature_max - feature_min) - contains_consequent &= ((transactions[attribute.name] <= attribute.max_val) & - (transactions[attribute.name] >= attribute.min_val)).to_numpy() - else: + acc += ( + 1 + if feature_max == feature_min + else (attribute.max_val - attribute.min_val) + / (feature_max - feature_min) + ) contains_consequent &= ( - np.isin(transactions[attribute.name], attribute.categories) + (transactions[attribute.name] <= attribute.max_val) + & (transactions[attribute.name] >= attribute.min_val) + ).to_numpy() + else: + contains_consequent &= np.isin( + transactions[attribute.name], attribute.categories ) self.__amplitude = 1 - (1 / (len(self.antecedent) + len(self.consequent))) * acc self.consequent_count = contains_consequent.sum() @@ -337,7 +401,9 @@ def zhang(self): @property def leverage(self): return self.support - ( - (self.antecedent_count / self.num_transactions) * (self.consequent_count / self.num_transactions)) + (self.antecedent_count / self.num_transactions) + * (self.consequent_count / self.num_transactions) + ) def __eq__(self, other): return ( diff --git a/niaarm/rule_list.py b/src/niaarm/rule_list.py similarity index 68% rename from niaarm/rule_list.py rename to src/niaarm/rule_list.py index 614b639..1765bb1 100644 --- a/niaarm/rule_list.py +++ b/src/niaarm/rule_list.py @@ -1,5 +1,6 @@ -from collections import UserList import csv +from collections import UserList + import numpy as np @@ -7,7 +8,7 @@ class RuleList(UserList): """A list of rules.""" def get(self, metric): - """Get values of `metric` for each rule as a numpy array. + """Get values of ``metric`` for each rule as a numpy array. Args: metric (str): Metric. @@ -22,7 +23,7 @@ def sort(self, by="fitness", reverse=True): """Sort rules by metric. Args: - by (str): Metric to sort rules by. Default: ``'fitness'``. + by (str): Metric to sort rules by. Default: `'fitness'`. reverse (bool): Sort in descending order. Default: ``True``. """ @@ -109,21 +110,23 @@ def __str__(self): string = ( f"STATS:\n" f"Total rules: {len(self)}\n" - f'Average fitness: {self.mean("fitness")}\n' - f'Average support: {self.mean("support")}\n' - f'Average confidence: {self.mean("confidence")}\n' - f'Average lift: {self.mean("lift")}\n' - f'Average coverage: {self.mean("coverage")}\n' - f'Average consequent support: {self.mean("rhs_support")}\n' - f'Average conviction: {self.mean("conviction")}\n' - f'Average amplitude: {self.mean("amplitude")}\n' - f'Average inclusion: {self.mean("inclusion")}\n' - f'Average interestingness: {self.mean("interestingness")}\n' - f'Average comprehensibility: {self.mean("comprehensibility")}\n' - f'Average netconf: {self.mean("netconf")}\n' - f'Average Yule\'s Q: {self.mean("yulesq")}\n' - f'Average Zhang\'s Metric: {self.mean("zhang")}\n' - f"Average antecedent length: {sum(len(rule.antecedent) for rule in self) / len(self)}\n" - f"Average consequent length: {sum(len(rule.consequent) for rule in self) / len(self)}\n" + f"Average fitness: {self.mean('fitness')}\n" + f"Average support: {self.mean('support')}\n" + f"Average confidence: {self.mean('confidence')}\n" + f"Average lift: {self.mean('lift')}\n" + f"Average coverage: {self.mean('coverage')}\n" + f"Average consequent support: {self.mean('rhs_support')}\n" + f"Average conviction: {self.mean('conviction')}\n" + f"Average amplitude: {self.mean('amplitude')}\n" + f"Average inclusion: {self.mean('inclusion')}\n" + f"Average interestingness: {self.mean('interestingness')}\n" + f"Average comprehensibility: {self.mean('comprehensibility')}\n" + f"Average netconf: {self.mean('netconf')}\n" + f"Average Yule's Q: {self.mean('yulesq')}\n" + f"Average Zhang's Metric: {self.mean('zhang')}\n" + f"Average antecedent length: " + f"{sum(len(rule.antecedent) for rule in self) / len(self)}\n" + f"Average consequent length: " + f"{sum(len(rule.consequent) for rule in self) / len(self)}\n" ) return string diff --git a/niaarm/text.py b/src/niaarm/text.py similarity index 82% rename from niaarm/text.py rename to src/niaarm/text.py index c405be6..a055aa8 100644 --- a/niaarm/text.py +++ b/src/niaarm/text.py @@ -1,13 +1,15 @@ import os import string -import numpy as np -import pandas as pd from collections import Counter from pathlib import Path + +import numpy as np +import pandas as pd from nltk.corpus import stopwords from nltk.tokenize import word_tokenize -from niaarm.rule import Rule + from niaarm.niaarm import NiaARM, _cut_point +from niaarm.rule import Rule def normalize(a, order=2, axis=-1): @@ -21,9 +23,9 @@ class Document: Args: text (str): Document text. - language (str): Document language. Used for tokenization and stopword removal. Default: 'english'. - remove_stopwords (bool): If ``True``, remove stopwords from text. Default: ``True``. - lowercase (bool): If ``True``, make text lowercase. Default: ``True``. + language (str): Document language. Default: 'english'. + remove_stopwords (bool): Remove stopwords from text. Default: ``True``. + lowercase (bool): Make text lowercase. Default: ``True``. """ @@ -76,8 +78,8 @@ def from_file( path (str): Path to file. encoding (str): Encoding of the file. Default: 'utf-8'. language (str): Language of the file. Default: 'english'. - remove_stopwords (bool): If ``True``, remove stopwords from text. Default: ``True``. - lowercase (bool): If ``True``, make text lowercase. Default: ``True``. + remove_stopwords (bool): Remove stopwords from text. Default: ``True``. + lowercase (bool): Make text lowercase. Default: ``True``. Returns: Document: The constructed document. @@ -91,7 +93,8 @@ class Corpus: """The text corpus class. Args: - documents (Optional[list[Document]]): List of documents. If ``None``, an empty list will be created. Default: ``None``. + documents (list[Document] | None): List of documents. + If ``None``, an empty list will be created. Default: ``None``. """ @@ -132,8 +135,8 @@ def from_list(cls, lst, language="english", remove_stopwords=True, lowercase=Tru Args: lst (list[str]): List of documents as strings. language (str): Language of the file. Default: 'english'. - remove_stopwords (bool): If ``True``, remove stopwords from text. Default: ``True``. - lowercase (bool): If ``True``, make text lowercase. Default: ``True``. + remove_stopwords (bool): Remove stopwords from text. Default: ``True``. + lowercase (bool): Make text lowercase. Default: ``True``. Returns: Corpus: The constructed corpus. @@ -158,8 +161,8 @@ def from_directory( path (str): Path to directory. encoding (str): Encoding of the files. Default: 'utf-8'. language (str): Language of the files. Default: 'english'. - remove_stopwords (bool): If ``True``, remove stopwords from text. Default: ``True``. - lowercase (bool): If ``True``, make text lowercase. Default: ``True``. + remove_stopwords (bool): Remove stopwords from text. Default: ``True``. + lowercase (bool): Make text lowercase. Default: ``True``. Returns: Corpus: The constructed corpus. @@ -179,8 +182,7 @@ def tf_idf_matrix(self, smooth=True, norm=2): """Get the tf-idf weights matrix as a pandas DataFrame. Args: - smooth (bool): Smooth idf by adding one to the numerator and the denominator to prevent division by 0 errors. - Default: ``True``. + smooth (bool): Apply smoothing. Default: ``True``. norm (int): Order of the norm to normalize the matrix with. Default: 2. Returns: @@ -210,15 +212,14 @@ def tf_idf_matrix(self, smooth=True, norm=2): class TextRule(Rule): r"""Class representing a text association rule. - The class contains all the metrics in :class:`~niaarm.rule.Rule`, except for amplitude, which returns nan. - Args: antecedent (list[str]): A list of antecedent terms of the text rule. consequent (list[str]): A list of consequent terms of the text rule. - fitness (Optional[float]): Fitness value of the text rule. - transactions (Optional[pandas.DataFrame]): The tf-idf matrix as a pandas DataFrame. - threshold (Optional[float]): Threshold of tf-idf weights. If a weight is less than or equal to the - threshold, the term is not included in the transaction. Default: 0. + fitness (float | None): Fitness value of the text rule. + transactions (pandas.DataFrame | None): The tf-idf matrix. + threshold (float | None): Threshold of tf-idf weights. + If a weight is less than or equal to the threshold, + the term is not included in the transaction. Default: 0. Attributes: aws: The sum of tf-idf values for all the terms in the rule. @@ -302,19 +303,23 @@ class NiaARTM(NiaARM): The implementation is composed of ideas found in the following paper: - * I. Fister, S. Deb, I. Fister, „Population-based metaheuristics for Association Rule Text Mining“, - in Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, + * I. Fister, S. Deb, I. Fister, „Population-based metaheuristics for Association + Rule Text Mining“, + in Proceedings of the 2020 4th International Conference on Intelligent Systems, + Metaheuristics & Swarm Intelligence, New York, NY, USA, mar. 2020, pp. 19–23. doi: 10.1145/3396474.3396493. Args: - max_terms (int): Maximum number of terms in association rule.. - features (list[str]): List of unique terms in the corpus. + max_terms (int): Maximum number of terms in association rule. + terms(list[str]): List of unique terms in the corpus. transactions (pandas.Dataframe): The tf-idf matrix. - metrics (Union[Dict[str, float], Sequence[str]]): Metrics to take into account when computing the fitness. - Metrics can either be passed as a Dict of pairs {'metric_name': } or - a sequence of metrics as strings, in which case, the weights of the metrics will be set to 1. - threshold (Optional[float]): Threshold of tf-idf weights. If a weight is less than or equal to the - threshold, the term is not included in the transaction. Default: 0. + metrics (dict[str, float] | Sequence[str]): Metrics to take into account when + computing the fitness. + Metrics can either be passed as a dict of {: } or + a sequence of metrics as strings, in which case, the weights will default to 1. + threshold (float | None): Threshold of tf-idf weights. + If a weight is less than or equal to the threshold, + the term is not included in the transaction. Default: 0. logging (bool): Enable logging of fitness improvements. Default: ``False``. Attributes: diff --git a/niaarm/visualize.py b/src/niaarm/visualize.py similarity index 81% rename from niaarm/visualize.py rename to src/niaarm/visualize.py index dbc1fd4..f8ad358 100644 --- a/niaarm/visualize.py +++ b/src/niaarm/visualize.py @@ -1,20 +1,24 @@ +from itertools import combinations + import matplotlib.pyplot as plt -from matplotlib.cm import ScalarMappable -from matplotlib.colors import Normalize import numpy as np +import pandas as pd import plotly.express as px import plotly.graph_objects as go -import pandas as pd +from matplotlib.cm import ScalarMappable +from matplotlib.colors import Normalize from sklearn.cluster import KMeans -from itertools import combinations def hill_slopes(rule, transactions): """Visualize rule as hill slopes. - **Reference:** Fister, I. et al. (2020). Visualization of Numerical Association Rules by Hill Slopes. - In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. - IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_10 + **Reference:** Fister, I. et al. (2020). Visualization of Numerical Association + Rules by Hill Slopes. + In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data + Engineering and Automated Learning – IDEAL 2020. + IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, + Cham. https://doi.org/10.1007/978-3-030-62362-3_10 Args: rule (Rule): Association rule to visualize. @@ -121,7 +125,8 @@ def scatter_plot(rules, metrics, interactive=False): Visualize rules/rule as scatter plot Args: rules (Rule): Association rule or rules to visualize. - metrics (tuple): Metrics to display in visualization_examples. Maximum of 2 metrics. + metrics (tuple): Metrics to display in visualization_examples. Maximum of 2 + metrics. interactive (bool): Make plot interactive. Default: ``False`` Returns: @@ -160,8 +165,11 @@ def prepare_data(arm_rules, arm_metrics): # Use plotly for interactive scatter plot visualization_examples if interactive: # Set title - title = f'Interactive scatter plot for {len(rules)} rules' \ - if len(rules) > 1 else "Interactive scatter plot for rule" + title = ( + f"Interactive scatter plot for {len(rules)} rules" + if len(rules) > 1 + else "Interactive scatter plot for rule" + ) # Create figure fig = px.scatter( data_frame=df, @@ -170,16 +178,16 @@ def prepare_data(arm_rules, arm_metrics): y=metrics[1], color="lift", size="lift", - hover_name="rule" + hover_name="rule", ) # Set titles and colorbar fig.update_layout( xaxis_title=metrics[0], yaxis_title=metrics[1], - coloraxis_colorbar=dict( - title="lift", - ) + coloraxis_colorbar={ + "title": "lift", + }, ) return fig @@ -187,8 +195,11 @@ def prepare_data(arm_rules, arm_metrics): # Use matplotlib for static scatter plot visualization_examples else: # Set title - title = f'Scatter plot for {len(rules)} rules' \ - if len(rules) > 1 else 'Scatter plot for rule' + title = ( + f"Scatter plot for {len(rules)} rules" + if len(rules) > 1 + else "Scatter plot for rule" + ) plt.title(title) # Set figure size plt.figure(figsize=(24, 14)) @@ -256,7 +267,8 @@ def prepare_data(arm_rules, arm_metrics): return data_frame def encode_antecedents(data_frame): - # Get unique antecedents from the dataframe (for mapping we need only unique antecedents) + # Get unique antecedents from the dataframe (for mapping we need only unique + # antecedents) antecedents = data_frame["antecedent"].unique() # Create dictionary that maps each antecedent to an integer @@ -264,7 +276,8 @@ def encode_antecedents(data_frame): for i, antecedent in enumerate(antecedents): ant_to_int[antecedent] = i - # Create new column in dataframe where each antecedent is replaced by its corresponding integer from the + # Create new column in dataframe where each antecedent is replaced by its + # corresponding integer from the # dictionary data_frame["antecedent_int"] = data_frame["antecedent"].map(ant_to_int) @@ -273,7 +286,9 @@ def encode_antecedents(data_frame): def perform_clustering(data_frame, num_clusters): # Create clusters - kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(data_frame[["antecedent_int"]]) + kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit( + data_frame[["antecedent_int"]] + ) # Assigns each antecedent into a cluster data_frame["cluster"] = kmeans.labels_ @@ -284,8 +299,9 @@ def perform_clustering(data_frame, num_clusters): def find_most_interesting_items(data_frame): cluster_to_ant = {} - # Iterate through each rule and append the antecedent and support to the corresponding cluster - for index, rule in data_frame.iterrows(): + # Iterate through each rule and append the antecedent and support to the + # corresponding cluster + for _index, rule in data_frame.iterrows(): cluster = rule["cluster"] if cluster not in cluster_to_ant: cluster_to_ant[cluster] = [] @@ -296,18 +312,22 @@ def find_most_interesting_items(data_frame): # Sort cluster for clarity cluster_to_ant = dict(sorted(cluster_to_ant.items())) - # Dictionary where keys are clusters and values are the most interesting antecedent + # Dictionary where keys are clusters and values are the most interesting + # antecedent most_interesting_antecedent_from_clusters = {} for cluster, ant in cluster_to_ant.items(): # Determine most interesting rule # Can add more metrics if needed most_interesting_rule = max(ant, key=lambda metric: metric[1]) - most_interesting_antecedent_from_clusters[cluster] = most_interesting_rule[0] + most_interesting_antecedent_from_clusters[cluster] = most_interesting_rule[ + 0 + ] return cluster_to_ant, most_interesting_antecedent_from_clusters def create_cluster_descriptions(cluster_to_ant, most_interesting, data_frame): - # Create a description for every cluster, where each cluster has the total number of rules in the cluster + # Create a description for every cluster, where each cluster has the total + # number of rules in the cluster cluster_desc = {} for cluster, ant in cluster_to_ant.items(): # Collect unique antecedents @@ -322,7 +342,10 @@ def create_cluster_descriptions(cluster_to_ant, most_interesting, data_frame): num_rules = len(ant) # Create description - cluster_desc[cluster] = "{} rules; {{{}}}, +{} items".format(num_rules, most_interesting[cluster], total_items) + cluster_desc[cluster] = ( + f"{num_rules} rules; {{{most_interesting[cluster]}}}, +{total_items} " + f"items" + ) # Creates a new column with these descriptions. data_frame["cluster_description"] = data_frame["cluster"].map(cluster_desc) @@ -341,14 +364,16 @@ def create_plot_data(data_frame): # Prepare data for plot plot_data = [] - for index, rule in data_frame.iterrows(): - plot_data.append({ - "cluster": rule["cluster_description"], - "consequent": rule["consequent"], - "support": rule["support"], - "lift": rule["lift"], - "size": rule["support"] * 1000 - }) + for _index, rule in data_frame.iterrows(): + plot_data.append( + { + "cluster": rule["cluster_description"], + "consequent": rule["consequent"], + "support": rule["support"], + "lift": rule["lift"], + "size": rule["support"] * 1000, + } + ) # Create DataFrame plot_data_frame = pd.DataFrame(plot_data) @@ -365,13 +390,17 @@ def create_plot_data(data_frame): df = perform_clustering(df, k) # Find the most interesting item in each cluster - cluster_to_antecedents, cluster_to_most_interesting = find_most_interesting_items(df) + cluster_to_antecedents, cluster_to_most_interesting = find_most_interesting_items( + df + ) # Maps each rules cluster with the most interesting antecedent in the cluster df["grouped_antecedent"] = df["cluster"].map(cluster_to_most_interesting) # Create descriptions for clusters - df, cluster_descriptions = create_cluster_descriptions(cluster_to_antecedents, cluster_to_most_interesting, df) + df, cluster_descriptions = create_cluster_descriptions( + cluster_to_antecedents, cluster_to_most_interesting, df + ) # Get data for plotting plot_df, consequent_to_int, consequents = create_plot_data(df) @@ -389,23 +418,23 @@ def create_plot_data(data_frame): labels={ "cluster": "Grouped Antecedents", "consequent": "Consequents", - "lift": "Lift" - } + "lift": "Lift", + }, ) fig.update_layout( xaxis_title="Items in LHS Groups", yaxis_title="RHS", - coloraxis_colorbar=dict( - title="Lift", - orientation="h", - x=0.5, - y=-0.3, - xanchor="center", - yanchor="top" - ), - xaxis=dict(side="top"), - yaxis=dict(side="right") + coloraxis_colorbar={ + "title": "Lift", + "orientation": "h", + "x": 0.5, + "y": -0.3, + "xanchor": "center", + "yanchor": "top", + }, + xaxis={"side": "top"}, + yaxis={"side": "right"}, ) return fig @@ -414,7 +443,7 @@ def create_plot_data(data_frame): else: fig, ax = plt.subplots(figsize=(24, 14)) scatter_plots = [] - for idx, row in plot_df.iterrows(): + for _idx, row in plot_df.iterrows(): x = list(cluster_descriptions.values()).index(row["cluster"]) y = consequent_to_int[row["consequent"]] size = row["size"] @@ -427,7 +456,7 @@ def create_plot_data(data_frame): cmap="plasma", alpha=0.6, edgecolors="w", - linewidth=0.5 + linewidth=0.5, ) scatter_plots.append(scatter) @@ -435,7 +464,12 @@ def create_plot_data(data_frame): ax.set_xlabel("Items in LHS Groups") ax.set_ylabel("RHS") ax.set_xticks(np.arange(k)) - ax.set_xticklabels([cluster_descriptions[i] for i in range(k)], rotation=-35, ha="right", rotation_mode="anchor") + ax.set_xticklabels( + [cluster_descriptions[i] for i in range(k)], + rotation=-35, + ha="right", + rotation_mode="anchor", + ) ax.set_yticks(np.arange(len(consequents))) ax.set_yticklabels(consequents) ax.xaxis.set_label_position("top") @@ -451,15 +485,18 @@ def create_plot_data(data_frame): return plt + def two_key_plot(rules, metrics, interactive=False): """ - Visualize rules as a two key plot with two primary metrics (support, confidence) and rule order. - + Visualize rules as a two key plot with two primary metrics (support, confidence) + and rule order. + Args: rules (Rule): Association rule or rules to visualize. - metrics (tuple): Two metrics to display on the x and y axes. 'order' will be used for point color. + metrics (tuple): Two metrics to display on the x and y axes. 'order' will be + used for point color. interactive (bool): Make plot interactive. Default: False. - + Returns: Figure or plot. """ @@ -474,20 +511,21 @@ def prepare_data(rules, metrics): "rule": [], metrics[0]: [], metrics[1]: [], - "order": [] # Store rule order (length) + "order": [], # Store rule order (length) } for rule in rules: data["rule"].append(rule.__repr__()) data[metrics[0]].append(getattr(rule, metrics[0])) data[metrics[1]].append(getattr(rule, metrics[1])) - - # Calculate order dynamically as the total number of items in antecedent and consequent - if hasattr(rule, 'antecedent') and hasattr(rule, 'consequent'): + + # Calculate order dynamically as the total number of items in antecedent + # and consequent + if hasattr(rule, "antecedent") and hasattr(rule, "consequent"): rule_order = len(rule.antecedent) + len(rule.consequent) else: rule_order = 0 # Fallback if structure is missing - + data["order"].append(rule_order) # Return as DataFrame @@ -503,9 +541,12 @@ def prepare_data(rules, metrics): # Interactive plot using Plotly if interactive: - title = f'Interactive two-key plot for {len(rules)} rules' \ - if len(rules) > 1 else "Interactive two-key plot for rule" - + title = ( + f"Interactive two-key plot for {len(rules)} rules" + if len(rules) > 1 + else "Interactive two-key plot for rule" + ) + # Create figure fig = px.scatter( data_frame=df, @@ -515,12 +556,10 @@ def prepare_data(rules, metrics): hover_name="rule", title=title, labels={"color": "order"}, - color_discrete_sequence=px.colors.qualitative.Plotly + color_discrete_sequence=px.colors.qualitative.Plotly, ) fig.update_layout( - xaxis_title=metrics[0], - yaxis_title=metrics[1], - legend_title = "Order" + xaxis_title=metrics[0], yaxis_title=metrics[1], legend_title="Order" ) return fig @@ -531,7 +570,7 @@ def prepare_data(rules, metrics): # Map each order to a unique color unique_orders = sorted(df["order"].unique()) color_map = plt.colormaps.get_cmap("Set1") - color_indices = np.linspace(0, 1, len(unique_orders)) + color_indices = np.linspace(0, 1, len(unique_orders)) colors = [color_map(i) for i in color_indices] color_mapping = {order: colors[i] for i, order in enumerate(unique_orders)} @@ -540,17 +579,13 @@ def prepare_data(rules, metrics): subset = df[df["order"] == order] x_data = np.array(subset[metrics[0]].tolist()) y_data = np.array(subset[metrics[1]].tolist()) - + plt.scatter( - x_data, - y_data, - label=order, - color=color_mapping[order], - alpha=0.7 + x_data, y_data, label=order, color=color_mapping[order], alpha=0.7 ) # Add legend and labels - plt.title(f'Two-key plot for {len(rules)} rules') + plt.title(f"Two-key plot for {len(rules)} rules") plt.xlabel(metrics[0]) plt.ylabel(metrics[1]) plt.legend(title="Order") @@ -561,17 +596,17 @@ def prepare_data(rules, metrics): def sankey_diagram(rules, interestingness_measure, M=4): """ Visualize rules as a sankey diagram. - + Args: rules (Rule): Association rule or rules to visualize. - interestingness_measures (str): Interestingness measure Z = {supp, cons, lift},reflecting the quality of a particular connection. + interestingness_measures (str): Interestingness measure Z = {supp, cons, + lift},reflecting the quality of a particular connection. m (int): Maximum number of rules to be selected for visualization. Default: 4 - + Returns: Figure or plot. """ - def compute_similarity(rule1, rule2): """Compute similarity between two rules.""" ant_inter = len(set(str(rule1.antecedent)) & set(str(rule2.antecedent))) @@ -590,33 +625,37 @@ def build_adjacency_matrix(rules): adjacency_matrix[j, i] = similarity return adjacency_matrix - + def knapsack_selection(adj_matrix, rules, M): fitness_scores = np.array([rule.fitness for rule in rules]) N = len(rules) # number of rules - weights = np.ones(N) # all rules have the same weight + weights = np.ones(N) # all rules have the same weight similarity_weight = 1.0 fitness_weight = 0.5 - combined_profits = similarity_weight * np.sum(adj_matrix) + fitness_weight * fitness_scores # combined similarities with fitness for values - + combined_profits = ( + similarity_weight * np.sum(adj_matrix) + fitness_weight * fitness_scores + ) # combined similarities with fitness for values + selected = np.zeros(N, dtype=int) - + # Initialize DP table dp = np.zeros((N + 1, M + 1)) for i in range(1, N + 1): for w in range(1, M + 1): if weights[i - 1] <= w: - dp[i, w] = max(dp[i - 1, w], dp[i - 1, w - 1] + combined_profits[i - 1]) + dp[i, w] = max( + dp[i - 1, w], dp[i - 1, w - 1] + combined_profits[i - 1] + ) else: dp[i, w] = dp[i - 1, w] - + # Backtrack to find selected rules w = M for i in range(N, 0, -1): if dp[i, w] != dp[i - 1, w]: selected[i - 1] = 1 w -= 1 - + selected_rules = [rules[i] for i in range(N) if selected[i]] return selected_rules @@ -624,31 +663,31 @@ def knapsack_selection(adj_matrix, rules, M): def prepare_data(rules, M, interestingness_measure): if not rules: return [], [], [], [] - + adj_matrix = build_adjacency_matrix(rules) selected_rules = knapsack_selection(adj_matrix, rules, M) - sources=[] - targets=[] - values=[] - labels=[] + sources = [] + targets = [] + values = [] + labels = [] node_indices = {} for rule in selected_rules: - # Ensure all antecedents and consequents exist in the node list + # Ensure all antecedents and consequents exist in the node list for item in rule.antecedent + rule.consequent: item_str = str(item) if item_str not in node_indices: node_indices[item_str] = len(labels) labels.append(item_str) - # Connect each antecedent to each consequent + # Connect each antecedent to each consequent for antecedent in rule.antecedent: for consequent in rule.consequent: sources.append(node_indices[str(antecedent)]) targets.append(node_indices[str(consequent)]) - # Assign measure value for each connection + # Assign measure value for each connection if hasattr(rule, interestingness_measure): measure_value = getattr(rule, interestingness_measure) else: @@ -659,21 +698,21 @@ def prepare_data(rules, M, interestingness_measure): labels, sources, targets, values = prepare_data(rules, M, interestingness_measure) - # Visualization using Plotly - fig = go.Figure(go.Sankey( - node=dict( - pad=15, - thickness=20, - line=dict(color='black', width=0.5), - label=labels - ), - link=dict( - source=sources, - target=targets, - value=values + # Visualization using Plotly + fig = go.Figure( + go.Sankey( + node={ + "pad": 15, + "thickness": 20, + "line": {"color": "black", "width": 0.5}, + "label": labels, + }, + link={"source": sources, "target": targets, "value": values}, ) - )) - fig.update_layout(title_text=f'Sankey Diagram of Association Rules ({interestingness_measure})', font_size=10) - - return fig - \ No newline at end of file + ) + fig.update_layout( + title_text=f"Sankey Diagram of Association Rules ({interestingness_measure})", + font_size=10, + ) + + return fig diff --git a/tests/test_cut_point.py b/tests/test_cut_point.py index 1789b28..efe1c36 100644 --- a/tests/test_cut_point.py +++ b/tests/test_cut_point.py @@ -1,11 +1,11 @@ +import os from unittest import TestCase -from niaarm.niaarm import NiaARM, _cut_point -from niaarm.feature import Feature + from niaarm.dataset import Dataset -import os +from niaarm.feature import Feature +from niaarm.niaarm import NiaARM, _cut_point -# The basic test for checking the identification of the appropriate cut point of association rule. class TestCutPoint(TestCase): # let's borrow a test case from Wikipedia: # https://en.wikipedia.org/wiki/Lift_(data_mining) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 186eb49..12bebe8 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,6 +1,8 @@ import os -import pandas as pd from unittest import TestCase + +import pandas as pd + from niaarm import Dataset, Feature, Rule @@ -123,22 +125,29 @@ def test_comprehensibility(self): self.assertAlmostEqual(self.rule.comprehensibility, 0.630929753571) def test_netconf(self): - self.assertAlmostEqual(self.rule.netconf, ((2/3) - (2/3 * 2/3))/(2/3 * 1/3)) + self.assertAlmostEqual( + self.rule.netconf, ((2 / 3) - (2 / 3 * 2 / 3)) / (2 / 3 * 1 / 3) + ) def test_yulesq(self): - self.assertAlmostEqual(self.rule.yulesq,1) + self.assertAlmostEqual(self.rule.yulesq, 1) def test_zhang(self): self.assertAlmostEqual(self.rule.zhang, 1) def test_leverage(self): - self.assertAlmostEqual(self.rule.leverage, 2/3 - (2/3 * 2/3)) + self.assertAlmostEqual(self.rule.leverage, 2 / 3 - (2 / 3 * 2 / 3)) class TestMetricsShuffleDataframe(TestCase): def setUp(self): self.data = Dataset( - pd.DataFrame({"col1": [1.5, 2.5, 1.0], "col2": ["Green", "Blue", "Red"]}).sample(frac=1.0) + pd.DataFrame( + { + "col1": [1.5, 2.5, 1.0], + "col2": ["Green", "Blue", "Red"], + } + ).sample(frac=1.0) ) self.rule = Rule( [Feature("col1", dtype="float", min_val=1.0, max_val=1.5)], @@ -181,13 +190,15 @@ def test_comprehensibility(self): self.assertAlmostEqual(self.rule.comprehensibility, 0.630929753571) def test_netconf(self): - self.assertAlmostEqual(self.rule.netconf, ((2/3) - (2/3 * 2/3))/(2/3 * 1/3)) + self.assertAlmostEqual( + self.rule.netconf, ((2 / 3) - (2 / 3 * 2 / 3)) / (2 / 3 * 1 / 3) + ) def test_yulesq(self): - self.assertAlmostEqual(self.rule.yulesq,1) + self.assertAlmostEqual(self.rule.yulesq, 1) def test_zhang(self): self.assertAlmostEqual(self.rule.zhang, 1) def test_leverage(self): - self.assertAlmostEqual(self.rule.leverage, 2/3 - (2/3 * 2/3)) + self.assertAlmostEqual(self.rule.leverage, 2 / 3 - (2 / 3 * 2 / 3)) diff --git a/tests/test_read_csv.py b/tests/test_read_csv.py index e2b3036..ebd4d29 100644 --- a/tests/test_read_csv.py +++ b/tests/test_read_csv.py @@ -4,9 +4,10 @@ borders for numerical attributes. """ +import os from unittest import TestCase + from niaarm.dataset import Dataset -import os class TestReadCSVAbalone(TestCase): diff --git a/tests/test_rule_building.py b/tests/test_rule_building.py index 9f014c7..7688b61 100644 --- a/tests/test_rule_building.py +++ b/tests/test_rule_building.py @@ -3,11 +3,12 @@ an association rule from candidate solutions. """ +import os from unittest import TestCase -from niaarm.niaarm import NiaARM + from niaarm.dataset import Dataset from niaarm.feature import Feature -import os +from niaarm.niaarm import NiaARM class TestBuildRuleA(TestCase): diff --git a/tests/test_sankey_diagram.py b/tests/test_sankey_diagram.py index 1356a40..a3eebf1 100644 --- a/tests/test_sankey_diagram.py +++ b/tests/test_sankey_diagram.py @@ -1,47 +1,49 @@ import unittest -from niaarm.visualize import sankey_diagram + from niaarm import Rule +from niaarm.visualize import sankey_diagram + class TestSankeyDiagram(unittest.TestCase): - - @classmethod - def setUpClass(cls): - cls.rule1 = Rule(antecedent=["A", "B"], consequent=["C"]) - cls.rule1.fitness = 1.0 - cls.rule1.num_transactions = 10 - cls.rule2 = Rule(antecedent=["D"], consequent=["E", "F"]) - cls.rule2.fitness = 0.8 - cls.rule2.num_transactions = 15 - cls.rule3 = Rule(antecedent=["G", "H"], consequent=["I"]) - cls.rule3.fitness = 0.9 - cls.rule3.num_transactions = 12 - - cls.rules = [cls.rule1, cls.rule2, cls.rule3] - - def test_sankey_output_type(self): - fig = sankey_diagram(self.rules, "support", M=3) - self.assertEqual(fig.__class__.__name__, "Figure") - - def test_sankey_structure(self): - fig = sankey_diagram(self.rules, "support", M=3) - self.assertTrue("source" in fig.data[0].link) - - def test_sankey_values(self): - fig = sankey_diagram(self.rules, "support", M=3) - link_data = fig.data[0].link - flow_values = link_data['value'] - expected_links = sum(len(rule.antecedent) * len(rule.consequent) for rule in self.rules) - self.assertEqual(len(flow_values), expected_links) - - def test_sankey_with_custom_fitness(self): - fig = sankey_diagram(self.rules, "support", M=2) - link_data = fig.data[0].link - flow_values = link_data['value'] - self.assertGreater(len(flow_values), 0) - - def test_sankey_no_empty_rules(self): - fig = sankey_diagram([], "support", M=3) - self.assertEqual(len(fig.data[0].link['source']), 0) - self.assertEqual(len(fig.data[0].link['target']), 0) - self.assertEqual(len(fig.data[0].link['value']), 0) - \ No newline at end of file + @classmethod + def setUpClass(cls): + cls.rule1 = Rule(antecedent=["A", "B"], consequent=["C"]) + cls.rule1.fitness = 1.0 + cls.rule1.num_transactions = 10 + cls.rule2 = Rule(antecedent=["D"], consequent=["E", "F"]) + cls.rule2.fitness = 0.8 + cls.rule2.num_transactions = 15 + cls.rule3 = Rule(antecedent=["G", "H"], consequent=["I"]) + cls.rule3.fitness = 0.9 + cls.rule3.num_transactions = 12 + + cls.rules = [cls.rule1, cls.rule2, cls.rule3] + + def test_sankey_output_type(self): + fig = sankey_diagram(self.rules, "support", M=3) + self.assertEqual(fig.__class__.__name__, "Figure") + + def test_sankey_structure(self): + fig = sankey_diagram(self.rules, "support", M=3) + self.assertTrue("source" in fig.data[0].link) + + def test_sankey_values(self): + fig = sankey_diagram(self.rules, "support", M=3) + link_data = fig.data[0].link + flow_values = link_data["value"] + expected_links = sum( + len(rule.antecedent) * len(rule.consequent) for rule in self.rules + ) + self.assertEqual(len(flow_values), expected_links) + + def test_sankey_with_custom_fitness(self): + fig = sankey_diagram(self.rules, "support", M=2) + link_data = fig.data[0].link + flow_values = link_data["value"] + self.assertGreater(len(flow_values), 0) + + def test_sankey_no_empty_rules(self): + fig = sankey_diagram([], "support", M=3) + self.assertEqual(len(fig.data[0].link["source"]), 0) + self.assertEqual(len(fig.data[0].link["target"]), 0) + self.assertEqual(len(fig.data[0].link["value"]), 0) diff --git a/tests/test_text_mining.py b/tests/test_text_mining.py index 15e9087..9be0732 100644 --- a/tests/test_text_mining.py +++ b/tests/test_text_mining.py @@ -1,11 +1,12 @@ import os from unittest import TestCase + +import nltk import numpy as np import pandas as pd -import nltk from niaarm.niaarm import _cut_point -from niaarm.text import Corpus, TextRule, NiaARTM +from niaarm.text import Corpus, NiaARTM, TextRule class TestTextMining(TestCase): diff --git a/tests/test_two_key_plot.py b/tests/test_two_key_plot.py index 66bd3bf..040489e 100644 --- a/tests/test_two_key_plot.py +++ b/tests/test_two_key_plot.py @@ -1,8 +1,10 @@ from unittest import TestCase + import matplotlib.pyplot as plt -import pandas as pd + from niaarm.visualize import two_key_plot + class Rule: def __init__(self, antecedent, consequent, support, confidence): self.antecedent = antecedent @@ -13,15 +15,25 @@ def __init__(self, antecedent, consequent, support, confidence): def __repr__(self): return f"Rule({self.antecedent} -> {self.consequent})" + class TestTwoKeyPlot(TestCase): - @classmethod def setUpClass(cls): - cls.rule1 = Rule(antecedent=["A", "B"], consequent=["C"], support=0.3, confidence=0.8) - cls.rule2 = Rule(antecedent=["D"], consequent=["E", "F"], support=0.5, confidence=0.7) - cls.rule3 = Rule(antecedent=["G", "H"], consequent=["I"], support=0.2, confidence=0.9) - - cls.rules = [cls.rule1, cls.rule2, cls.rule3] # Ensure rules are available to all tests + cls.rule1 = Rule( + antecedent=["A", "B"], consequent=["C"], support=0.3, confidence=0.8 + ) + cls.rule2 = Rule( + antecedent=["D"], consequent=["E", "F"], support=0.5, confidence=0.7 + ) + cls.rule3 = Rule( + antecedent=["G", "H"], consequent=["I"], support=0.2, confidence=0.9 + ) + + cls.rules = [ + cls.rule1, + cls.rule2, + cls.rule3, + ] # Ensure rules are available to all tests def test_two_key_plot(self): metrics = ("support", "confidence") @@ -43,4 +55,8 @@ def test_interactive_plot(self): fig = two_key_plot(self.rules, metrics, interactive=True) # Verify that a Plotly figure is returned - self.assertEqual(fig.__class__.__name__, "Figure", "Expected a Plotly figure but got a different type.") + self.assertEqual( + fig.__class__.__name__, + "Figure", + "Expected a Plotly figure but got a different type.", + ) diff --git a/tests/test_visualization.py b/tests/test_visualization.py index 4cf984c..276b273 100644 --- a/tests/test_visualization.py +++ b/tests/test_visualization.py @@ -1,8 +1,10 @@ import os from unittest import TestCase + import numpy as np + from niaarm import Dataset, Feature, Rule -from niaarm.visualize import hill_slopes, _ribbon +from niaarm.visualize import _ribbon, hill_slopes class TestHillSlopes(TestCase): @@ -30,16 +32,48 @@ def setUp(self): def test_hill_slopes(self): support = np.array( - [0.934286, 0.847143, 0.74, 0.561429, 0.244286, 0.225714, 0.00714286] + [ + 0.934286, + 0.847143, + 0.74, + 0.561429, + 0.244286, + 0.225714, + 0.00714286, + ] ) confidence = np.array( - [0.934286, 0.847095, 0.753823, 0.570336, 0.261468, 0.224771, 0.00458716] + [ + 0.934286, + 0.847095, + 0.753823, + 0.570336, + 0.261468, + 0.224771, + 0.00458716, + ] ) length = np.array( - [1.32128, 1.19801, 1.05634, 0.800303, 0.357828, 0.318542, 0.00848896] + [ + 1.32128, + 1.19801, + 1.05634, + 0.800303, + 0.357828, + 0.318542, + 0.00848896, + ] ) position = np.array( - 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