diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml index 2fd4d7b2..4abac89d 100644 --- a/.github/workflows/create-release.yml +++ b/.github/workflows/create-release.yml @@ -5,7 +5,7 @@ on: types: - closed branches: - - master # Change to your default branch if different (e.g., master) + - master workflow_dispatch: jobs: @@ -14,25 +14,32 @@ jobs: if: github.event.pull_request.merged == true runs-on: ubuntu-latest permissions: - contents: write # Needed for creating releases + contents: write # Needed for creating releases steps: - - name: Checkout code - uses: actions/checkout@v3 + - name: Checkout + uses: actions/checkout@v6 with: - fetch-depth: 0 # Fetch all history for proper versioning and commit messages - + fetch-depth: 0 # Fetch all history for proper versioning and commit messages + - name: Get latest release version id: get_version run: | # Get latest tag or set to v0.0.0 if none exists LATEST_TAG=$(git describe --tags --abbrev=0 2>/dev/null || echo "v0.0.0") echo "LATEST_TAG=${LATEST_TAG}" >> $GITHUB_ENV - + # Extract version numbers - MAJOR=$(echo $LATEST_TAG | sed 's/v\([0-9]*\).\([0-9]*\).\([0-9]*\)/\1/') - MINOR=$(echo $LATEST_TAG | sed 's/v\([0-9]*\).\([0-9]*\).\([0-9]*\)/\2/') - PATCH=$(echo $LATEST_TAG | sed 's/v\([0-9]*\).\([0-9]*\).\([0-9]*\)/\3/') - + TAG_VERSION="${LATEST_TAG#v}" + if [[ "$TAG_VERSION" =~ ^([0-9]+)\.([0-9]+)\.([0-9]+)$ ]]; then + MAJOR="${BASH_REMATCH[1]}" + MINOR="${BASH_REMATCH[2]}" + PATCH="${BASH_REMATCH[3]}" + else + MAJOR=0 + MINOR=0 + PATCH=0 + fi + # Check PR labels to determine which version to increment if [[ "${{ contains(github.event.pull_request.labels.*.name, 'major') }}" == "true" ]]; then echo "Incrementing MAJOR version due to 'major' label" @@ -47,7 +54,7 @@ jobs: echo "Incrementing PATCH version (default)" PATCH=$((PATCH + 1)) fi - + NEW_TAG="v$MAJOR.$MINOR.$PATCH" echo "Bumping version from $LATEST_TAG to $NEW_TAG" echo "NEW_TAG=${NEW_TAG}" >> $GITHUB_ENV @@ -58,13 +65,13 @@ jobs: # Get commits since last tag echo "Generating commit list since $LATEST_TAG" COMMITS=$(git log --pretty=format:"- %s (%h)" ${{ env.LATEST_TAG }}..HEAD) - + # Extract PR details PR_TITLE="${{ github.event.pull_request.title }}" PR_NUMBER="${{ github.event.pull_request.number }}" PR_BODY="${{ github.event.pull_request.body }}" PR_USER="${{ github.event.pull_request.merged_by.login }}" - + # Save release notes to environment variable echo "RELEASE_NOTES<> $GITHUB_ENV echo "## Release ${{ env.NEW_TAG }}" >> $GITHUB_ENV @@ -73,25 +80,25 @@ jobs: echo "- #$PR_NUMBER: $PR_TITLE" >> $GITHUB_ENV echo "- Merged by @$PR_USER" >> $GITHUB_ENV echo "" >> $GITHUB_ENV - + if [[ -n "$PR_BODY" ]]; then echo "### 📝 Description" >> $GITHUB_ENV echo "$PR_BODY" >> $GITHUB_ENV echo "" >> $GITHUB_ENV fi - + echo "### 📦 Changes" >> $GITHUB_ENV echo "$COMMITS" >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV - name: Create Release - uses: softprops/action-gh-release@v1 + uses: softprops/action-gh-release@v2 with: tag_name: ${{ env.NEW_TAG }} name: Release ${{ env.NEW_TAG }} body: ${{ env.RELEASE_NOTES }} - draft: false # Set to true if you want to review before publishing - prerelease: false # Set to true for pre-releases + draft: false # Set to true if you want to review before publishing + prerelease: false # Set to true for pre-releases # If you have build artifacts to include, uncomment and modify this: # files: | # dist/*.zip @@ -101,4 +108,4 @@ jobs: - name: Output Results run: | - echo "::notice::🎉 Created release ${{ env.NEW_TAG }} from PR #${{ github.event.pull_request.number }}" \ No newline at end of file + echo "::notice::🎉 Created release ${{ env.NEW_TAG }} from PR #${{ github.event.pull_request.number }}" diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 28ad27c6..37bfe08b 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -10,12 +10,12 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v6 - name: Setup Node.js - uses: actions/setup-node@v3 + uses: actions/setup-node@v6 with: - node-version: '18' + node-version: "lts/*" - name: Install dependencies run: | @@ -29,10 +29,10 @@ jobs: - name: Create .nojekyll file run: | - touch docs-site/out/.nojekyll + touch docs-site/out/.nojekyll - name: Deploy uses: JamesIves/github-pages-deploy-action@v4 with: - folder: docs-site/out - branch: gh-pages \ No newline at end of file + folder: docs-site/out + branch: gh-pages diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml index de2d74dd..d7ad7898 100644 --- a/.github/workflows/format.yml +++ b/.github/workflows/format.yml @@ -4,21 +4,27 @@ on: workflow_dispatch: jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - name: Install uv - run: pip install uv - - name: Create virtual environment - run: uv venv - - name: Install dependencies - run: | - uv pip install -e ".[dev]" - uv pip install black - - name: format - run: uv run black --check . \ No newline at end of file + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install uv + run: pip install uv + + - name: Create virtual environment + run: uv venv + + - name: Install dependencies + run: | + uv pip install -e ".[dev]" + uv pip install black + + - name: format + run: uv run black --check . diff --git a/.github/workflows/install.yml b/.github/workflows/install.yml index d43be627..551a8798 100644 --- a/.github/workflows/install.yml +++ b/.github/workflows/install.yml @@ -1,23 +1,28 @@ name: Install on: push: - branches: [ "master" ] + branches: ["master"] pull_request: - branches: [ "master" ] + branches: ["master"] workflow_dispatch: jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - name: Install uv - run: pip install uv - - name: Create virtual environment - run: uv venv - - name: Install package - run: uv pip install -e . \ No newline at end of file + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install uv + run: pip install uv + + - name: Create virtual environment + run: uv venv + + - name: Install package + run: uv pip install -e . diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 09eafd62..7c28b77f 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -3,24 +3,30 @@ on: workflow_dispatch: jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - name: Install uv - run: pip install uv - - name: Create virtual environment - run: uv venv - - name: Install dependencies - run: | - uv pip install -e ".[dev]" - uv pip install ruff flake8 - - name: lint - run: | - uv run ruff check --fix . - uv run ruff check . - uv run flake8 yake/ \ No newline at end of file + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install uv + run: pip install uv + + - name: Create virtual environment + run: uv venv + + - name: Install dependencies + run: | + uv pip install -e ".[dev]" + uv pip install ruff flake8 + + - name: lint + run: | + uv run ruff check --fix . + uv run ruff check . + uv run flake8 yake/ diff --git a/.github/workflows/publishpypi.yml b/.github/workflows/publishpypi.yml index 88a7bb35..3f440852 100644 --- a/.github/workflows/publishpypi.yml +++ b/.github/workflows/publishpypi.yml @@ -21,9 +21,11 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - name: Checkout + uses: actions/checkout@v6 - - uses: actions/setup-python@v5 + - name: Set up Python + uses: actions/setup-python@v6 with: python-version: "3.10" @@ -34,7 +36,7 @@ jobs: python -m build - name: Upload distributions - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v6 with: name: release-dists path: dist/ @@ -60,7 +62,7 @@ jobs: steps: - name: Retrieve release distributions - uses: actions/download-artifact@v4 + uses: actions/download-artifact@v7 with: name: release-dists path: dist/ @@ -68,4 +70,4 @@ jobs: - name: Publish release distributions to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - packages-dir: dist/ \ No newline at end of file + packages-dir: dist/ diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index 13a39770..221890f6 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -5,19 +5,20 @@ on: [push] jobs: build: runs-on: ubuntu-latest - strategy: - matrix: - python-version: ["3.12"] steps: - - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pylint>=3.3.0 - - name: Analysing the code with pylint - run: | - pylint $(git ls-files '*.py') --fail-under=9.0 + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pylint>=3.3.0 + + - name: Analysing the code with pylint + run: | + pylint $(git ls-files '*.py') --fail-under=9.0 diff --git a/.github/workflows/resultados.yml b/.github/workflows/resultados.yml index 05794e50..b677734f 100644 --- a/.github/workflows/resultados.yml +++ b/.github/workflows/resultados.yml @@ -6,19 +6,18 @@ on: jobs: test: runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v4 + - name: Checkout + uses: actions/checkout@v6 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v6 with: - python-version: '3.10' + python-version: "3.10" - name: Install uv run: pip install uv - + - name: Create virtual environment run: uv venv @@ -43,7 +42,7 @@ jobs: - name: Upload test results (em caso de falha) if: failure() - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v6 with: name: test-logs - path: test_output.log \ No newline at end of file + path: test_output.log diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 4e37bf12..5f629305 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,30 +1,37 @@ name: Test on: push: - branches: [ "master" ] + branches: ["master"] pull_request: - branches: [ "master" ] + branches: ["master"] workflow_dispatch: jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.12' - - name: Install uv - run: pip install uv - - name: Create virtual environment - run: uv venv - - name: Install dependencies - run: | - uv pip install -e ".[dev,lemmatization,benchmark]" - - name: Download spaCy and NLTK models - run: | - uv pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl - uv run python -c "import nltk; nltk.download('wordnet', quiet=True); nltk.download('omw-1.4', quiet=True)" - - name: test - run: uv run pytest -vv --cov=yake tests/ \ No newline at end of file + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install uv + run: pip install uv + + - name: Create virtual environment + run: uv venv + + - name: Install dependencies + run: | + uv pip install -e ".[dev,lemmatization,benchmark]" + + - name: Download spaCy and NLTK models + run: | + uv pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl + uv run python -c "import nltk; nltk.download('wordnet', quiet=True); nltk.download('omw-1.4', quiet=True)" + + - name: test + run: uv run pytest -vv --cov=yake tests/ diff --git a/.python-version b/.python-version deleted file mode 100644 index c8cfe395..00000000 --- a/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.10 diff --git a/LICENSE b/LICENSE index 09de9bd6..13825c5f 100644 --- a/LICENSE +++ b/LICENSE @@ -2,7 +2,7 @@ YAKE! License Agreement Copyright (C) 2018, INESC TEC -YAKE! Collection-independent Automatic Keyword Extractor: a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike most of the systems, YAKE! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, we follow an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted. +YAKE! Collection-independent Automatic Keyword Extractor: a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike most of the systems, YAKE! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, we follow an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted. This software is authored by the following INESC TEC researchers: Ricardo Campos @@ -20,18 +20,18 @@ You should have received a copy of the GNU Affero General Public License along w A commercial license is also available for use in industrial projects and collaborations that do not wish to use the AGPL 3 license. -To obtain the commercial license please contact the INESC TEC Technology Licensing Office (TLO) at info.sal@inesctec.pt, or +To obtain the commercial license please contact the INESC TEC Technology Licensing Office (TLO) at info.sal@inesctec.pt, or -Campus da Faculdade de Engenharia da Universidade do Porto +Campus da Faculdade de Engenharia da Universidade do Porto -Rua Dr. Roberto Frias +Rua Dr. Roberto Frias -4200-465 Porto +4200-465 Porto -Portugal +Portugal -If you use "YAKE" in a work that leads to a scientific publication, we would appreciate it if you would kindly cite +If you use "YAKE" in a work that leads to a scientific publication, we would appreciate it if you would kindly cite it in your manuscript. Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289, ISSN 0020-0255.” @@ -42,7 +42,7 @@ GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 -Copyright © 2007 Free Software Foundation, Inc. +Copyright © 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. diff --git a/docs-site/app/layout.config.tsx b/docs-site/app/layout.config.tsx index 25fbf162..bc495fba 100644 --- a/docs-site/app/layout.config.tsx +++ b/docs-site/app/layout.config.tsx @@ -1,4 +1,4 @@ -import type { BaseLayoutProps } from 'fumadocs-ui/layouts/shared';; +import type { BaseLayoutProps } from 'fumadocs-ui/layouts/shared'; import Image from 'next/image'; /** diff --git a/docs-site/app/layout.tsx b/docs-site/app/layout.tsx index 6b3a7b9a..83aab655 100644 --- a/docs-site/app/layout.tsx +++ b/docs-site/app/layout.tsx @@ -2,7 +2,7 @@ import { Metadata } from 'next'; import { basePath } from './config'; import './global.css'; -import { RootProvider } from 'fumadocs-ui/provider'; +import { RootProvider } from 'fumadocs-ui/provider/next'; import { Inter } from 'next/font/google'; const inter = Inter({ diff --git a/docs-site/content/docs/--home.mdx b/docs-site/content/docs/--home.mdx index 4c2416bf..e5d124af 100644 --- a/docs-site/content/docs/--home.mdx +++ b/docs-site/content/docs/--home.mdx @@ -1,6 +1,6 @@ --- title: Yet Another Keyword Extractor (YAKE!) -icon: GitCompareArrows +icon: House --- import { Callout } from 'fumadocs-ui/components/callout'; @@ -16,9 +16,9 @@ import { Tabs, Tab } from 'fumadocs-ui/components/tabs';

Unsupervised Automatic Keyword Extraction

-

+

YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. -

+
@@ -27,17 +27,17 @@ import { Tabs, Tab } from 'fumadocs-ui/components/tabs';
🚀

No Training Required

-

Works without corpus or dictionaries

+
Works without corpus or dictionaries
🌍

Multilingual

-

Supports 25+ languages out-of-the-box

+
Supports 25+ languages out-of-the-box
📄

Single Document

-

No corpus comparison needed

+
No corpus comparison needed
@@ -65,30 +65,30 @@ Instead, it follows an **unsupervised approach** which builds upon features extr

🎯 Unsupervised Approach

-

+

No need for training data or labeled corpora. Works immediately on any text. -

+

📊 Corpus-Independent

-

+

Analyzes each document independently without requiring external reference corpora. -

+

🌐 Domain and Language Independent

-

+

Works across different domains and languages without configuration changes. -

+

📄 Single-Document Processing

-

+

Extracts keywords from individual documents with high accuracy. -

+
@@ -105,11 +105,11 @@ Instead, it follows an **unsupervised approach** which builds upon features extr

Information Sciences Journal (2020)

-

+

Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. Information Sciences Journal. Elsevier, Vol 509, pp 257-289. -

+
📥 Download PDF @@ -117,12 +117,12 @@ Instead, it follows an **unsupervised approach** which builds upon features extr

ECIR 2018 - Best Short Paper 🏆

-

+

Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. -

+
📥 Download PDF @@ -130,12 +130,12 @@ Instead, it follows an **unsupervised approach** which builds upon features extr

ECIR 2018 - Demo Paper

-

+

Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. -

+
📥 Download PDF @@ -147,9 +147,9 @@ Instead, it follows an **unsupervised approach** which builds upon features extr ## 📜 License
-

+

Copyright (C) 2018, INESC TEC -

+
View License → @@ -222,7 +222,7 @@ If you are feeling nostalgic you can access the old site [here](http://yake.ines

Back to top

-

+

Copyright ©2018-{new Date().getFullYear()} INESC TEC. Distributed by an INESCTEC license. -

+
\ No newline at end of file diff --git a/docs-site/content/docs/-getting-started.mdx b/docs-site/content/docs/-getting-started.mdx index 33e4ede1..36e4a4c2 100644 --- a/docs-site/content/docs/-getting-started.mdx +++ b/docs-site/content/docs/-getting-started.mdx @@ -17,9 +17,9 @@ import { Tabs, Tab } from 'fumadocs-ui/components/tabs';

Quick Start Guide

-

+

Get YAKE! up and running in less than 5 minutes. Follow these simple steps to start extracting keywords from your texts. -

+
@@ -58,13 +58,13 @@ import { Tabs, Tab } from 'fumadocs-ui/components/tabs';

💡 Optional: Lemmatization Support

-

+

To use lemmatization features (aggregate keyword variations like "tree/trees"), install the optional dependencies: -

+
uv pip install yake[lemmatization]
-

Then download the required language models:

+
Then download the required language models:
# For spaCy (recommended)
python -m spacy download en_core_web_sm
@@ -218,9 +218,9 @@ YAKE uses three methods to compute string similarity during keyword deduplicatio leve
-

+

Measures edit distance between strings — operations needed to transform one string into another. -

+
✓ @@ -236,9 +236,9 @@ YAKE uses three methods to compute string similarity during keyword deduplicatio
-

+

1 - distance / max_len -

+
@@ -254,9 +254,9 @@ YAKE uses three methods to compute string similarity during keyword deduplicatio jaro
-

+

Measures similarity based on matching characters and their relative positions. -

+
✓ @@ -272,9 +272,9 @@ YAKE uses three methods to compute string similarity during keyword deduplicatio
-

+

jellyfish.jaro_similarity() -

+
@@ -290,9 +290,9 @@ YAKE uses three methods to compute string similarity during keyword deduplicatio seqm -

+

Uses Python's built-in difflib to find matching blocks in strings. -

+
✓ @@ -308,9 +308,9 @@ YAKE uses three methods to compute string similarity during keyword deduplicatio
-

+

2 * matches / total_len -

+
@@ -569,7 +569,7 @@ The lower the score, the more relevant the keyword is.

Back to top

-

+

Copyright ©2018-{new Date().getFullYear()} INESC TEC. Distributed by an INESCTEC license. -

+
\ No newline at end of file diff --git a/docs-site/content/docs/-related-projects.mdx b/docs-site/content/docs/-related-projects.mdx index b4d4be49..6f54e232 100644 --- a/docs-site/content/docs/-related-projects.mdx +++ b/docs-site/content/docs/-related-projects.mdx @@ -15,9 +15,9 @@ import { Callout } from 'fumadocs-ui/components/callout';

🌐 Projects Using YAKE!

-

+

Discover how YAKE! is being used across different platforms, libraries, and applications worldwide. -

+
@@ -33,16 +33,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

SparkNLP

-

by John Snow Labs

+
by John Snow Labs
NLP Framework -

+

Open source framework with full Python, Scala, and Java Support. -

+
@@ -69,16 +69,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

PKE

-

Python Keyphrase Extraction

+
Python Keyphrase Extraction
Python Library -

+

Open source python-based keyphrase extraction toolkit providing an end-to-end pipeline where each component can be easily modified or extended. -

+
@@ -96,16 +96,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

Textacy

-

NLP, before and after spaCy

+
NLP, before and after spaCy
Python Library -

+

Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. -

+
@@ -123,16 +123,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

General Index

-

by Archive.org

+
by Archive.org
19B Keywords -

+

A catalogue of 19 billions of YAKE keywords extracted from 107 million papers. -

+
💾 Archive.org @@ -152,16 +152,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

Annif

-

Automated Subject Indexing

+
Automated Subject Indexing
Tool -

+

Multi-algorithm automated subject indexing tool for libraries, archives and museums. -

+
@@ -182,16 +182,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

Portulan Clarin

-

Language Technology Services

+
Language Technology Services
Platform -

+

Research Infrastructure for the Science and Technology of Language, part of the international research infrastructure CLARIN ERIC. -

+
🎭 Demo @@ -211,16 +211,16 @@ import { Callout } from 'fumadocs-ui/components/callout';

Tell me stories

-

Conta-me Histórias

+
Conta-me Histórias
Research Project -

+

Research project that allows users to automatically build news narratives based on news preserved by the Portuguese Web Archive. -

+
🌐 Visit @@ -238,7 +238,7 @@ import { Callout } from 'fumadocs-ui/components/callout';

Back to top

-

+

Copyright ©2018-{new Date().getFullYear()} INESC TEC. Distributed by an INESCTEC license. -

+
\ No newline at end of file diff --git a/docs-site/content/docs/Documentation/core/Levenshtein.mdx b/docs-site/content/docs/Documentation/core/Levenshtein.mdx index 1be48377..a59fc251 100644 --- a/docs-site/content/docs/Documentation/core/Levenshtein.mdx +++ b/docs-site/content/docs/Documentation/core/Levenshtein.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'Levenshtein' +--- -# Levenshtein +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `Levenshtein` class provides utilities for calculating edit distances and similarity ratios between strings using the Levenshtein algorithm. diff --git a/docs-site/content/docs/Documentation/core/highlight.mdx b/docs-site/content/docs/Documentation/core/highlight.mdx index 072b6a0f..3ee327ca 100644 --- a/docs-site/content/docs/Documentation/core/highlight.mdx +++ b/docs-site/content/docs/Documentation/core/highlight.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'TextHighlighter' +--- -# TextHighlighter +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `TextHighlighter` module provides functionality to highlight specific keywords in text, supporting both single-word and multi-word highlighting with customizable markers. diff --git a/docs-site/content/docs/Documentation/core/yake.mdx b/docs-site/content/docs/Documentation/core/yake.mdx index a9e44661..0b47d872 100644 --- a/docs-site/content/docs/Documentation/core/yake.mdx +++ b/docs-site/content/docs/Documentation/core/yake.mdx @@ -1,7 +1,9 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' -import { Callout } from 'fumadocs-ui/components/callout' +--- +title: 'KeywordExtractor' +--- -# KeywordExtractor Class +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +import { Callout } from 'fumadocs-ui/components/callout'; The `KeywordExtractor` class is the main entry point for YAKE (Yet Another Keyword Extractor), providing a simple API to extract meaningful keywords from textual content. @@ -67,7 +69,6 @@ pipeline from raw text to ranked keywords. import os import logging import functools -from typing import List, Tuple, Optional, Set, Callable import jellyfish from yake.data import DataCore from .Levenshtein import Levenshtein @@ -92,8 +93,8 @@ The `KeywordExtractor` class handles the configuration, preprocessing, and extra dedup_func: str = "seqm", window_size: int = 1, top: int = 20, - features: Optional[List[str]] = None, - stopwords: Optional[Set[str]] = None, + features: list[str] | None = None, + stopwords: set[str] | None = None, lemmatize: bool = False, lemma_aggregation: str = "min", lemmatizer: str = "spacy", @@ -205,7 +206,7 @@ The `KeywordExtractor` class handles the configuration, preprocessing, and extra Returns: List of (keyword, score) tuples sorted by score (lower is better) - + """ # Handle empty input if not text: @@ -575,8 +576,8 @@ The `KeywordExtractor` class handles the configuration, preprocessing, and extra ```python def _lemmatize_keywords( self, - keywords: List[Tuple[str, float]] - ) -> List[Tuple[str, float]]: + keywords: list[tuple[str, float]] + ) -> list[tuple[str, float]]: """ Aggregate keywords by lemma. diff --git a/docs-site/content/docs/Documentation/data/composedword.mdx b/docs-site/content/docs/Documentation/data/composedword.mdx index b804fd7d..ad8bfc57 100644 --- a/docs-site/content/docs/Documentation/data/composedword.mdx +++ b/docs-site/content/docs/Documentation/data/composedword.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'ComposedWord' +--- -# ComposedWord +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `ComposedWord` class represents multi-word terms in YAKE (Yet Another Keyword Extractor), providing the foundation for analyzing and scoring potential keyword phrases. diff --git a/docs-site/content/docs/Documentation/data/datacore.mdx b/docs-site/content/docs/Documentation/data/datacore.mdx index 0676e8d8..69ad624a 100644 --- a/docs-site/content/docs/Documentation/data/datacore.mdx +++ b/docs-site/content/docs/Documentation/data/datacore.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'DataCore' +--- -# DataCore +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `DataCore` class is the foundation of YAKE (Yet Another Keyword Extractor), providing the core data representation for document analysis and keyword extraction. diff --git a/docs-site/content/docs/Documentation/data/features.mdx b/docs-site/content/docs/Documentation/data/features.mdx index b3291923..2fe30b2a 100644 --- a/docs-site/content/docs/Documentation/data/features.mdx +++ b/docs-site/content/docs/Documentation/data/features.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'Features' +--- -# Features Module +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `features` module contains pure functions for calculating statistical features used to score and rank keyword candidates in YAKE. @@ -21,7 +23,7 @@ Based on the modular architecture from the reference YAKE implementation. import logging import math -from typing import Dict, Any, Tuple +from typing import Any import numpy as np # Configure module logger @@ -45,7 +47,7 @@ This module provides stateless functions that calculate various statistical feat avg_tf: float, std_tf: float, number_of_sentences: int - ) -> Dict[str, float]: + ) -> dict[str, float]: """ Calculate all statistical features for a single term. @@ -134,7 +136,7 @@ This module provides stateless functions that calculate various statistical feat def calculate_composed_features( composed_word: Any, stopword_weight: str = 'bi' - ) -> Dict[str, float]: + ) -> dict[str, float]: """ Calculate features for multi-word expressions (n-grams). @@ -209,9 +211,9 @@ This module provides stateless functions that calculate various statistical feat ```python def normalize_features( - features: Dict[str, float], - max_vals: Dict[str, float] - ) -> Dict[str, float]: + features: dict[str, float], + max_vals: dict[str, float] + ) -> dict[str, float]: """ Normalize feature values to [0, 1] range. diff --git a/docs-site/content/docs/Documentation/data/singleword.mdx b/docs-site/content/docs/Documentation/data/singleword.mdx index 0ed6a492..b9619442 100644 --- a/docs-site/content/docs/Documentation/data/singleword.mdx +++ b/docs-site/content/docs/Documentation/data/singleword.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'SingleWord' +--- -# SingleWord +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `SingleWord` class represents individual terms in YAKE (Yet Another Keyword Extractor), providing the statistical features and measurements used in keyword extraction. diff --git a/docs-site/content/docs/Documentation/data/utils.mdx b/docs-site/content/docs/Documentation/data/utils.mdx index 8077eca2..17624f90 100644 --- a/docs-site/content/docs/Documentation/data/utils.mdx +++ b/docs-site/content/docs/Documentation/data/utils.mdx @@ -1,6 +1,8 @@ -import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' +--- +title: 'Utils' +--- -# Utils +import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion' The `utils` module provides essential text processing functions for YAKE (Yet Another Keyword Extractor), handling tokenization, normalization, and classification of textual elements. diff --git a/docs-site/content/docs/about.mdx b/docs-site/content/docs/about.mdx index 4f9f734c..f0d97cbd 100644 --- a/docs-site/content/docs/about.mdx +++ b/docs-site/content/docs/about.mdx @@ -1,6 +1,6 @@ --- title: About -icon: CircleHelp +icon: CircleQuestionMark --- import { Callout } from 'fumadocs-ui/components/callout'; @@ -15,11 +15,11 @@ import { Callout } from 'fumadocs-ui/components/callout';

LIAAD - INESC TEC

-

+

This project was developed by LIAAD - Laboratory of Artificial Intelligence and Decision Support. LIAAD is an R&D laboratory at INESC TEC. It is one of the associated units of INESC Tec which is funded by Fundação para a Ciência e a Tecnologia (FCT, Portugal). -

+
@@ -37,10 +37,10 @@ import { Callout } from 'fumadocs-ui/components/callout';

Ricardo Campos

-

+

Assistant professor at the ICT Departmental Unit of the Polytechnic Institute of Tomar (IPT) and member of LIAAD-INESC TEC. PhD in Computer Science by the University of Porto. -

+
@@ -52,9 +52,9 @@ import { Callout } from 'fumadocs-ui/components/callout';

Vítor Mangaravite

-

+

Research intern of LIAAD/INESC TEC. MSc and BSc in Computer Science by Universidade Federal de Minas Gerais and Universidade Federal de Ouro Preto. -

+
@@ -66,10 +66,10 @@ import { Callout } from 'fumadocs-ui/components/callout';

Arian Pasquali

-

+

Researcher associated to University of Porto and LIAAD at INESC TEC. MSc in Computer Science by University of Porto with specialization in data mining. -

+
@@ -81,10 +81,10 @@ import { Callout } from 'fumadocs-ui/components/callout';

Alípio M. Jorge

-

+

Associate professor at the Department of Computer Science of U. Porto and coordinator of LIAAD/INESC TEC since 2012. PhD in Computer Science by U. Porto. -

+
@@ -96,10 +96,10 @@ import { Callout } from 'fumadocs-ui/components/callout';

Célia Nunes

-

+

Assistant Professor at the Department of Mathematics of the University of Beira Interior (UBI) and member of CMA. PhD in Mathematics by UBI. -

+
@@ -111,10 +111,10 @@ import { Callout } from 'fumadocs-ui/components/callout';

Adam Jatowt

-

+

Associate Professor at the Department of Computer Science, University of Innsbruck. Ph.D. in Information Science and Technology from the University of Tokyo, Japan (2005). -

+
@@ -133,7 +133,7 @@ import { Callout } from 'fumadocs-ui/components/callout';

Back to top

-

+

Copyright ©2018-{new Date().getFullYear()} INESC TEC. Distributed by an INESCTEC license. -

+
\ No newline at end of file diff --git a/docs-site/lib/source.ts b/docs-site/lib/source.ts index 9871c2fc..68ed29d3 100644 --- a/docs-site/lib/source.ts +++ b/docs-site/lib/source.ts @@ -1,11 +1,14 @@ -import { docs } from '@/.source'; +import { docs } from 'fumadocs-mdx:collections/server'; import { loader } from 'fumadocs-core/source'; -import { createMDXSource } from 'fumadocs-mdx'; -import { meta } from '@/.source'; +import { icons } from 'lucide-react'; +import { createElement } from 'react'; -// See https://fumadocs.vercel.app/docs/headless/source-api for more info +// See https://www.fumadocs.dev/docs/headless/source-api for more info export const source = loader({ - // it assigns a URL to your pages + source: docs.toFumadocsSource(), baseUrl: '/docs', - source: createMDXSource(docs, meta), + icon(icon) { + if (!icon) return; + if (icon in icons) return createElement(icons[icon as keyof typeof icons]); + }, }); diff --git a/docs-site/package-lock.json b/docs-site/package-lock.json index 502a5c55..53861d90 100644 --- a/docs-site/package-lock.json +++ b/docs-site/package-lock.json @@ -9,33 +9,33 @@ "version": "0.0.0", "hasInstallScript": true, "dependencies": { - "@radix-ui/react-accordion": "^1.2.8", + "@radix-ui/react-accordion": "^1.2.12", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "fumadocs-core": "15.2.9", - "fumadocs-mdx": "11.6.1", - "fumadocs-ui": "^15.2.9", - "lucide-react": "^0.503.0", - "next": "15.3.1", - "nextra": "^1.1.0", - "react": "^19.1.0", - "react-dom": "^19.1.0", - "tailwind-merge": "^3.2.0", - "tw-animate-css": "^1.2.8" + "fumadocs-core": "^16.5.2", + "fumadocs-mdx": "^14.2.7", + "fumadocs-ui": "^16.5.2", + "lucide-react": "^0.563.0", + "next": "^16.1.6", + "nextra": "^4.6.1", + "react": "^19.2.4", + "react-dom": "^19.2.4", + "tailwind-merge": "^3.4.0", + "tw-animate-css": "^1.4.0" }, "devDependencies": { - "@tailwindcss/postcss": "^4.1.4", + "@tailwindcss/postcss": "^4.1.18", "@types/mdx": "^2.0.13", - "@types/node": "22.14.1", - "@types/react": "^19.1.2", - "@types/react-dom": "^19.1.2", - "eslint": "^8", - "eslint-config-next": "15.3.1", - "glob": "^8.1.0", + "@types/node": "^24.10.12", + "@types/react": "^19.2.13", + "@types/react-dom": "^19.2.3", + "eslint": "^8.57.1", + "eslint-config-next": "^15.5.12", + "glob": "^13.0.1", "gray-matter": "^4.0.3", - "postcss": "^8.5.3", - "tailwindcss": "^4.1.4", - "typescript": "^5.8.3" + "postcss": "^8.5.6", + "tailwindcss": "^4.1.18", + "typescript": "^5.9.3" } }, "node_modules/@alloc/quick-lru": { @@ -43,6 +43,7 @@ "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", "dev": true, + "license": "MIT", "engines": { "node": ">=10" }, @@ -50,43 +51,117 @@ "url": "https://github.com/sponsors/sindresorhus" } }, + "node_modules/@antfu/install-pkg": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/@antfu/install-pkg/-/install-pkg-1.1.0.tgz", + "integrity": "sha512-MGQsmw10ZyI+EJo45CdSER4zEb+p31LpDAFp2Z3gkSd1yqVZGi0Ebx++YTEMonJy4oChEMLsxZ64j8FH6sSqtQ==", + "license": "MIT", + "dependencies": { + "package-manager-detector": "^1.3.0", + "tinyexec": "^1.0.1" + }, + "funding": { + "url": "https://github.com/sponsors/antfu" + } + }, + "node_modules/@braintree/sanitize-url": { + "version": "7.1.2", + "resolved": "https://registry.npmjs.org/@braintree/sanitize-url/-/sanitize-url-7.1.2.tgz", + "integrity": "sha512-jigsZK+sMF/cuiB7sERuo9V7N9jx+dhmHHnQyDSVdpZwVutaBu7WvNYqMDLSgFgfB30n452TP3vjDAvFC973mA==", + "license": "MIT" + }, + "node_modules/@chevrotain/cst-dts-gen": { + "version": "11.0.3", + "resolved": "https://registry.npmjs.org/@chevrotain/cst-dts-gen/-/cst-dts-gen-11.0.3.tgz", + "integrity": "sha512-BvIKpRLeS/8UbfxXxgC33xOumsacaeCKAjAeLyOn7Pcp95HiRbrpl14S+9vaZLolnbssPIUuiUd8IvgkRyt6NQ==", + "license": "Apache-2.0", + "dependencies": { + 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+187,13 @@ } }, "node_modules/@esbuild/android-arm64": { - "version": "0.25.3", - "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.25.3.tgz", - "integrity": "sha512-XelR6MzjlZuBM4f5z2IQHK6LkK34Cvv6Rj2EntER3lwCBFdg6h2lKbtRjpTTsdEjD/WSe1q8UyPBXP1x3i/wYQ==", + "version": "0.27.3", + "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.27.3.tgz", + "integrity": "sha512-YdghPYUmj/FX2SYKJ0OZxf+iaKgMsKHVPF1MAq/P8WirnSpCStzKJFjOjzsW0QQ7oIAiccHdcqjbHmJxRb/dmg==", "cpu": [ "arm64" ], + "license": "MIT", "optional": true, "os": [ "android" @@ -126,12 +203,13 @@ } }, "node_modules/@esbuild/android-x64": { - "version": "0.25.3", - "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.25.3.tgz", - "integrity": "sha512-ogtTpYHT/g1GWS/zKM0cc/tIebFjm1F9Aw1boQ2Y0eUQ+J89d0jFY//s9ei9jVIlkYi8AfOjiixcLJSGNSOAdQ==", + "version": "0.27.3", + "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.27.3.tgz", 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"postcss": "^8.5.3", - "tailwindcss": "^4.1.4", - "typescript": "^5.8.3" + "postcss": "^8.5.6", + "tailwindcss": "^4.1.18", + "typescript": "^5.9.3" } } diff --git a/docs-site/public/1YAKE.ipynb b/docs-site/public/1YAKE.ipynb index 86e6db60..0656f75c 100644 --- a/docs-site/public/1YAKE.ipynb +++ b/docs-site/public/1YAKE.ipynb @@ -1,804 +1,833 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "
YAKE
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Notebook Developed by**: [Ricardo Campos](http://www.ccc.ipt.pt/~ricardo)
\n", - "**email:** ricardo.campos@ubi.pt
\n", - "**Affiliation:** *Assistant Professor* @ [University of Beira Interior](http://www.ubi.pt);\n", - "*Researcher* @ [LIAAD](https://www.inesctec.pt/en/centres/liaad)-[INESC TEC](https://www.inesctec.pt/en)\n", - "\n", - "
\n", - "\n", - "\n", - "

\"Download

\n", - "

 

\n", - "

 

\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "toc": true - }, - "source": [ - "

Table of Contents

\n", - "
  • 1  YAKE - Yet Another Keyword Extractor
  • 2  Package
    • 2.1  Installation
    • 2.2  Usage (Python)
    • 2.3  Usage (Command Line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## YAKE - Yet Another Keyword Extractor" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import HTML\n", - "HTML('
      ')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unsupervised Approach for Automatic Keyword Extraction using Text Features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package, we also make available a [demo](http://yake.inesctec.pt) an [API](http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords) and a [mobile app](https://play.google.com/store/apps/details?id=com.yake.yake)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Unsupervised approach\n", - "* Corpus-Independent\n", - "* Domain and Language Independent\n", - "* Single-Document" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rationale" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Where can I find YAKE?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "YAKE! is available online [http://yake.inesctec.pt]and as an open source Python package [https://github.com/INESCTEC/yake]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Package" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install git+http://github.com/INESCTEC/yake\n", - "!uv pip install yake\n", - "\n", - "#no caso de já ter instalado o package previamente e quiser fazer um update\n", - "#!pip install git+http://github.com/INESCTEC/yake --upgrade" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Usage (Python)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "text = '''\n", - "Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, Naval Ravikant, Google chief economist Hal Varian, Khosla Ventures and Yuri Milner'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Google', 0.02509259635302287)\n", - "('Kaggle', 0.027297150442917317)\n", - "('CEO Anthony Goldbloom', 0.04834891465259988)\n", - "('data science', 0.05499112888517541)\n", - "('acquiring data science', 0.06029572445726576)\n", - "('Google Cloud Platform', 0.07461585862381104)\n", - "('data', 0.07999958986489127)\n", - "('San Francisco', 0.0913829662674319)\n", - "('Anthony Goldbloom declined', 0.09740885820462175)\n", - "('science', 0.09834167930168546)\n", - "('science community Kaggle', 0.1014394718805728)\n", - "('machine learning', 0.10754988562466912)\n", - "('Google Cloud', 0.1136787749431024)\n", - "('Google is acquiring', 0.114683257931042)\n", - "('acquiring Kaggle', 0.12012386507741751)\n", - "('Anthony Goldbloom', 0.1213027418574554)\n", - "('platform', 0.12404419723925647)\n", - "('co-founder CEO Anthony', 0.12411964553586782)\n", - "('CEO Anthony', 0.12462950727635251)\n", - "('service', 0.1316357590449064)\n" - ] - } - ], - "source": [ - "import yake\n", - "\n", - "# assuming default parameters\n", - "kw_extractor = yake.KeywordExtractor()\n", - "keywords = kw_extractor.extract_keywords(text)\n", - "\n", - "for kw in keywords:\n", - " print(kw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Playing with the parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Google', 0.02509259635302287)\n", - "('Kaggle', 0.027297150442917317)\n", - "('CEO Anthony Goldbloom', 0.04834891465259988)\n", - "('data science', 0.05499112888517541)\n", - "('acquiring data science', 0.06029572445726576)\n", - "('Google Cloud Platform', 0.07461585862381104)\n", - "('data', 0.07999958986489127)\n", - "('San Francisco', 0.0913829662674319)\n", - "('Anthony Goldbloom declined', 0.09740885820462175)\n", - "('science', 0.09834167930168546)\n", - "('science community Kaggle', 0.1014394718805728)\n", - "('machine learning', 0.10754988562466912)\n", - "('Google Cloud', 0.1136787749431024)\n", - "('Google is acquiring', 0.114683257931042)\n", - "('acquiring Kaggle', 0.12012386507741751)\n", - "('Anthony Goldbloom', 0.1213027418574554)\n", - "('platform', 0.12404419723925647)\n", - "('co-founder CEO Anthony', 0.12411964553586782)\n", - "('CEO Anthony', 0.12462950727635251)\n", - "('service', 0.1316357590449064)\n" - ] - } - ], - "source": [ - "import yake\n", - "\n", - "language = \"en\"\n", - "max_ngram_size = 3\n", - "deduplication_thresold = 0.9\n", - "deduplication_algo = 'seqm'\n", - "windowSize = 1\n", - "numOfKeywords = 20\n", - "\n", - "custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_thresold, dedupFunc=deduplication_algo, windowsSize=windowSize, top=numOfKeywords, features=None)\n", - "keywords = custom_kw_extractor.extract_keywords(text)\n", - "\n", - "for kw in keywords:\n", - " print(kw)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Google', 0.02509259635302287)\n", - "('Kaggle', 0.027297150442917317)\n", - "('CEO Anthony Goldbloom', 0.04834891465259988)\n", - "('data science', 0.05499112888517541)\n", - "('acquiring data science', 0.06029572445726576)\n", - "('Google Cloud Platform', 0.07461585862381104)\n", - "('data', 0.07999958986489127)\n", - "('San Francisco', 0.0913829662674319)\n", - "('Anthony Goldbloom declined', 0.09740885820462175)\n", - "('science', 0.09834167930168546)\n", - "('science community Kaggle', 0.1014394718805728)\n", - "('machine learning', 0.10754988562466912)\n", - "('Google Cloud', 0.1136787749431024)\n", - "('Google is acquiring', 0.114683257931042)\n", - "('acquiring Kaggle', 0.12012386507741751)\n", - "('Anthony Goldbloom', 0.1213027418574554)\n", - "('platform', 0.12404419723925647)\n", - "('co-founder CEO Anthony', 0.12411964553586782)\n", - "('CEO Anthony', 0.12462950727635251)\n", - "('service', 0.1316357590449064)\n" - ] - } - ], - "source": [ - "import yake\n", - "\n", - "language = \"en\"\n", - "max_ngram_size = 3\n", - "deduplication_thresold = 0.9\n", - "deduplication_algo = 'seqm'\n", - "windowSize = 1\n", - "numOfKeywords = 20\n", - "\n", - "custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_thresold, dedupFunc=deduplication_algo, windowsSize=windowSize, top=numOfKeywords, features=None)\n", - "keywords = custom_kw_extractor.extract_keywords(text)\n", - "\n", - "for kw in keywords:\n", - " print(kw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Highlighting feature will tag every keyword in the text with the default tag ``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, Naval Ravikant, Google chief economist Hal Varian, Khosla Ventures and Yuri Milner\"" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from yake.core.highlight import TextHighlighter\n", - "\n", - "th = TextHighlighter(max_ngram_size = 3)\n", - "th.highlight(text, keywords)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Besides tagging a text with the default tag, users can also specify their own custom highlight. In the following text, the tag `` makes use of an hyphotetical function `my_class` whose purpose would be to highlight in white colour or the relevant keywords." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, Naval Ravikant, Google chief economist Hal Varian, Khosla Ventures and Yuri Milner\"" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from yake.core.highlight import TextHighlighter\n", - "th = TextHighlighter(max_ngram_size = 3, highlight_pre = \"\", highlight_post= \"\")\n", - "th.highlight(text, keywords)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While English (`en`) is the default language, users can use YAKE! to extract keywords from whatever language they want to by specifying the the corresponding language universal code. The below example shows how to extract keywords from a portuguese text." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Conta-me Histórias', 0.006225012963810038)\n", - "('LIAAD do INESC', 0.01899063587015275)\n", - "('INESC TEC', 0.01995432290332246)\n", - "('Conta-me', 0.04513273690417472)\n", - "('Histórias', 0.04513273690417472)\n", - "('Prémio Arquivo.pt', 0.05749361520927859)\n", - "('LIAAD', 0.07738867367929901)\n", - "('INESC', 0.07738867367929901)\n", - "('TEC', 0.08109398065524037)\n", - "('Xutos inspiram projeto', 0.08720742489353424)\n", - "('inspiram projeto premiado', 0.08720742489353424)\n", - "('Adam Jatwot docente', 0.09407053486771558)\n", - "('Arquivo.pt', 0.10261392141666957)\n", - "('Alípio Jorge', 0.12190479662535166)\n", - "('Ciências da Universidade', 0.12368384021490342)\n", - "('Ricardo Campos investigador', 0.12789997272332762)\n", - "('Politécnico de Tomar', 0.13323587141127738)\n", - "('Arian Pasquali', 0.13323587141127738)\n", - "('Vitor Mangaravite', 0.13323587141127738)\n", - "('preservados da Web', 0.13596322680882506)\n" - ] - } - ], - "source": [ - "import yake\n", - "\n", - "text = '''\n", - "\"Conta-me Histórias.\" Xutos inspiram projeto premiado. A plataforma \"Conta-me Histórias\" foi distinguida com o Prémio Arquivo.pt, atribuído a trabalhos inovadores de investigação ou aplicação de recursos preservados da Web, através dos serviços de pesquisa e acesso disponibilizados publicamente pelo Arquivo.pt . Nesta plataforma em desenvolvimento, o utilizador pode pesquisar sobre qualquer tema e ainda executar alguns exemplos predefinidos. Como forma de garantir a pluralidade e diversidade de fontes de informação, esta são utilizadas 24 fontes de notícias eletrónicas, incluindo a TSF. Uma versão experimental (beta) do \"Conta-me Histórias\" está disponível aqui.\n", - "A plataforma foi desenvolvida por Ricardo Campos investigador do LIAAD do INESC TEC e docente do Instituto Politécnico de Tomar, Arian Pasquali e Vitor Mangaravite, também investigadores do LIAAD do INESC TEC, Alípio Jorge, coordenador do LIAAD do INESC TEC e docente na Faculdade de Ciências da Universidade do Porto, e Adam Jatwot docente da Universidade de Kyoto.\n", - "'''\n", - "\n", - "custom_kw_extractor = yake.KeywordExtractor(lan=\"pt\")\n", - "keywords = custom_kw_extractor.extract_keywords(text)\n", - "\n", - "for kw in keywords:\n", - " print(kw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### WordCloud" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the following we are going to constuct a word cloud. To begin, we need to adapt the \"content\" structure to a dictionary consisting of the keyword and of the score. In addition to that, we resort to a normalization function that bounds the values into a range between [0,1]." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Conta-me Histórias': 1.0,\n", - " 'LIAAD do INESC': 0.9016047583205323,\n", - " 'INESC TEC': 0.8941768232148362,\n", - " 'Conta-me': 0.7001059072167914,\n", - " 'Histórias': 0.7001059072167914,\n", - " 'Prémio Arquivo.pt': 0.6048303678150379,\n", - " 'LIAAD': 0.4514826541353427,\n", - " 'INESC': 0.4514826541353427,\n", - " 'TEC': 0.4229227806321687,\n", - " 'Xutos inspiram projeto': 0.37580139629126075,\n", - " 'inspiram projeto premiado': 0.37580139629126075,\n", - " 'Adam Jatwot docente': 0.3229017164607676,\n", - " 'Arquivo.pt': 0.25705075169290126,\n", - " 'Alípio Jorge': 0.10835997942955777,\n", - " 'Ciências da Universidade': 0.09464741520636755,\n", - " 'Ricardo Campos investigador': 0.06215018572037523,\n", - " 'Politécnico de Tomar': 0.021021989718509326,\n", - " 'Arian Pasquali': 0.021021989718509326,\n", - " 'Vitor Mangaravite': 0.021021989718509326,\n", - " 'preservados da Web': 0.0}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def normalize_scores(keywords):\n", - " if len(keywords) == 0:\n", - " return {}\n", - " max_value = max([item[1] for item in keywords])\n", - " min_value = min([item[1] for item in keywords])\n", - " \n", - " result = {}\n", - " for item in keywords:\n", - " normalized_score = (item[1] - float(min_value))/(float(max_value) - float(min_value))\n", - " result[item[0]] = abs(1 - normalized_score)\n", - "\n", - " return result\n", - "\n", - "keyword2WordCloud = normalize_scores(keywords)\n", - "keyword2WordCloud" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "the following code is a normalizing fuction of the data. The results vary betweent [0,1] where the upper bound isn't necessarly 1 (it can be lower than 1)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "def normalize_scores(keywords):\n", - " if len(keywords) == 0:\n", - " return {}\n", - " max_value = max([item[1] for\n", - " item in keywords])\n", - " \n", - " result = {}\n", - " for item in keywords:\n", - " normalized_score = 1 - item[1]/float(max_value)\n", - " result[item[0]] = abs(normalized_score)\n", - "\n", - " return result\n", - "\n", - "keyword2WordCloud = normalize_scores(keywords)\n", - "keyword2WordCloud\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Later, its possible to build a word cloud using `matplotlib`. If, during installation, you receive an error message stating that Microsoft Visual C++ 14.0 (or higher) is required, you should proceed with its installation. See how to do it on this [web page](https://www.scivision.dev/python-windows-visual-c-14-required/) or in this [video](https://www.youtube.com/watch?v=_keTL9ymGjw)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!uv pip install matplotlib\n", - "!uv pip install wordcloud" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.5, 999.5, 499.5, -0.5)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from wordcloud import WordCloud\n", - "import matplotlib.pyplot as plt\n", - "\n", - "wordcloud = WordCloud(max_font_size=100, max_words = 20, width = 1000, height = 500).generate_from_frequencies(keyword2WordCloud) #Objeto que permite gerar wordcloud a partir de texto\n", - "plt.figure(figsize=(16,9))\n", - "plt.imshow(wordcloud, interpolation=\"bilinear\") #imshow plota imagens que derivam de arrays\n", - "plt.axis(\"off\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now suppose you need to send this image through an API. To achieve this, you should convert the image to base64. The code below allows you to store the image in a buffer and shows its conversion to base64." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import base64\n", - "import io\n", - "from io import BytesIO\n", - "\n", - "buffer = io.BytesIO()\n", - "wordcloud.to_image().save(buffer, 'png')\n", - "b64 = str(base64.b64encode(buffer.getvalue()).decode(\"ascii\"))\n", - "b64" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the base64 string, it would then be possible to display the image again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from io import BytesIO\n", - "from PIL import Image\n", - "\n", - "img1 = Image.open(io.BytesIO(base64.b64decode(b64)))\n", - "img1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Usage (Command Line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "Usage: yake [OPTIONS]\n", - "\n", - "Options:\n", - " --help Show this message and exit.\n", - " -ti, --text_input TEXT Input text, SURROUNDED by single quotes(')\n", - " -i, --input_file TEXT Input file\n", - " -l, --language TEXT Language\n", - " -n, --ngram-size INTEGER Max size of the ngram.\n", - " -df, --dedup-func [leve|jaro|seqm] Deduplication function.\n", - " -dl, --dedup-lim FLOAT Deduplication limiar.\n", - " -ws, --window-size INTEGER Window size.\n", - " -t, --top INTEGER Number of keyphrases to extract\n", - " -v, --verbose Gets detailed information (such as the score)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A few examples:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "yake -i text.txt -l en -n 3\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Related Projects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- [Spark NLP](https://www.johnsnowlabs.com/spark-nlp-in-action/)\n", - "- [Spark NLP Keywords Extraction Demo](https://demo.johnsnowlabs.com/public/KEYPHRASE_EXTRACTION/)\n", - "- [kaggle](https://www.youtube.com/watch?v=6TBvZmg7AsA)\n", - "- [textacy](https://github.com/chartbeat-labs/textacy/search?q=yake)\n", - "- [pke - pyhton keyphrase extraction](https://github.com/boudinfl/pke)\n", - "- [dendro](http://dendro-stg.inesctec.pt/)\n", - "- [conta-me histórias](http://contamehistorias.pt)\n", - "- [time-matters](http://time-matters.inesctec.pt)\n", - "- [The General Index](https://archive.org/details/GeneralIndex)\n", - "- [Nature. Giant, free index to world’s research papers released online](https://www.nature.com/articles/d41586-021-02895-8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Awards" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[ECIR'18](http://ecir2018.org) Best Short Paper (see the below reference)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please cite the following works when using YAKE:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**In-depth journal paper at Information Sciences Journal**\n", - "\n", - "- Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)\n", - "\n", - "**ECIR'18 Best Short Paper**\n", - "\n", - "- Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)\n", - "\n", - "- Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Media" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- [INESC TEC technology used to create a database of keywords obtained from more than 100 million scientific articles](https://bip.inesctec.pt/en/noticias/inesc-tec-technology-used-to-create-a-database-of-keywords-obtained-from-more-than-100-million-scientific-articles/)\n", - "- [This tool can extract keywords from texts in every language about any topic](https://www.alphagalileo.org/en-gb/Item-Display/ItemId/216276?returnurl=https://www.alphagalileo.org/en-gb/Item-Display/ItemId/216276)\n", - "- [INESC TEC team wins another Best Paper Award](https://www.inesctec.pt/en/news/inesc-tec-team-wins-another-best-paper-award#about)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "yake", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": true, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "255px" - }, - "toc_section_display": true, - "toc_window_display": true - } + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "
      YAKE
      \n", + "
      " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notebook Developed by**: [Ricardo Campos](http://www.ccc.ipt.pt/~ricardo)
      \n", + "**email:** ricardo.campos@ubi.pt
      \n", + "**Affiliation:** *Assistant Professor* @ [University of Beira Interior](http://www.ubi.pt);\n", + "*Researcher* @ [LIAAD](https://www.inesctec.pt/en/centres/liaad)-[INESC TEC](https://www.inesctec.pt/en)\n", + "\n", + "
      \n", + "\n", + "\n", + "

      \"Download

      \n", + "

       

      \n", + "

       

      \n", + "\n", + "
      " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

      Table of Contents

      \n", + "
      • 1  YAKE - Yet Another Keyword Extractor
      • 2  Package
        • 2.1  Installation
        • 2.2  Usage (Python)
        • 2.3  Usage (Command Line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## YAKE - Yet Another Keyword Extractor" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
          " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "\n", + "HTML(\n", + " '
          '\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unsupervised Approach for Automatic Keyword Extraction using Text Features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package, we also make available a [demo](http://yake.inesctec.pt) an [API](http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords) and a [mobile app](https://play.google.com/store/apps/details?id=com.yake.yake)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Main Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Unsupervised approach\n", + "* Corpus-Independent\n", + "* Domain and Language Independent\n", + "* Single-Document" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rationale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Where can I find YAKE?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "YAKE! is available online [http://yake.inesctec.pt]and as an open source Python package [https://github.com/INESCTEC/yake]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Package" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install git+http://github.com/INESCTEC/yake\n", + "!uv pip install yake\n", + "\n", + "# no caso de já ter instalado o package previamente e quiser fazer um update\n", + "#!pip install git+http://github.com/INESCTEC/yake --upgrade" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage (Python)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"\"\"\n", + "Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, Naval Ravikant, Google chief economist Hal Varian, Khosla Ventures and Yuri Milner\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Google', 0.02509259635302287)\n", + "('Kaggle', 0.027297150442917317)\n", + "('CEO Anthony Goldbloom', 0.04834891465259988)\n", + "('data science', 0.05499112888517541)\n", + "('acquiring data science', 0.06029572445726576)\n", + "('Google Cloud Platform', 0.07461585862381104)\n", + "('data', 0.07999958986489127)\n", + "('San Francisco', 0.0913829662674319)\n", + "('Anthony Goldbloom declined', 0.09740885820462175)\n", + "('science', 0.09834167930168546)\n", + "('science community Kaggle', 0.1014394718805728)\n", + "('machine learning', 0.10754988562466912)\n", + "('Google Cloud', 0.1136787749431024)\n", + "('Google is acquiring', 0.114683257931042)\n", + "('acquiring Kaggle', 0.12012386507741751)\n", + "('Anthony Goldbloom', 0.1213027418574554)\n", + "('platform', 0.12404419723925647)\n", + "('co-founder CEO Anthony', 0.12411964553586782)\n", + "('CEO Anthony', 0.12462950727635251)\n", + "('service', 0.1316357590449064)\n" + ] + } + ], + "source": [ + "import yake\n", + "\n", + "# assuming default parameters\n", + "kw_extractor = yake.KeywordExtractor()\n", + "keywords = kw_extractor.extract_keywords(text)\n", + "\n", + "for kw in keywords:\n", + " print(kw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Playing with the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Google', 0.02509259635302287)\n", + "('Kaggle', 0.027297150442917317)\n", + "('CEO Anthony Goldbloom', 0.04834891465259988)\n", + "('data science', 0.05499112888517541)\n", + "('acquiring data science', 0.06029572445726576)\n", + "('Google Cloud Platform', 0.07461585862381104)\n", + "('data', 0.07999958986489127)\n", + "('San Francisco', 0.0913829662674319)\n", + "('Anthony Goldbloom declined', 0.09740885820462175)\n", + "('science', 0.09834167930168546)\n", + "('science community Kaggle', 0.1014394718805728)\n", + "('machine learning', 0.10754988562466912)\n", + "('Google Cloud', 0.1136787749431024)\n", + "('Google is acquiring', 0.114683257931042)\n", + "('acquiring Kaggle', 0.12012386507741751)\n", + "('Anthony Goldbloom', 0.1213027418574554)\n", + "('platform', 0.12404419723925647)\n", + "('co-founder CEO Anthony', 0.12411964553586782)\n", + "('CEO Anthony', 0.12462950727635251)\n", + "('service', 0.1316357590449064)\n" + ] + } + ], + "source": [ + "import yake\n", + "\n", + "language = \"en\"\n", + "max_ngram_size = 3\n", + "deduplication_thresold = 0.9\n", + "deduplication_algo = \"seqm\"\n", + "windowSize = 1\n", + "numOfKeywords = 20\n", + "\n", + "custom_kw_extractor = yake.KeywordExtractor(\n", + " lan=language,\n", + " n=max_ngram_size,\n", + " dedupLim=deduplication_thresold,\n", + " dedupFunc=deduplication_algo,\n", + " windowsSize=windowSize,\n", + " top=numOfKeywords,\n", + " features=None,\n", + ")\n", + "keywords = custom_kw_extractor.extract_keywords(text)\n", + "\n", + "for kw in keywords:\n", + " print(kw)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Google', 0.02509259635302287)\n", + "('Kaggle', 0.027297150442917317)\n", + "('CEO Anthony Goldbloom', 0.04834891465259988)\n", + "('data science', 0.05499112888517541)\n", + "('acquiring data science', 0.06029572445726576)\n", + "('Google Cloud Platform', 0.07461585862381104)\n", + "('data', 0.07999958986489127)\n", + "('San Francisco', 0.0913829662674319)\n", + "('Anthony Goldbloom declined', 0.09740885820462175)\n", + "('science', 0.09834167930168546)\n", + "('science community Kaggle', 0.1014394718805728)\n", + "('machine learning', 0.10754988562466912)\n", + "('Google Cloud', 0.1136787749431024)\n", + "('Google is acquiring', 0.114683257931042)\n", + "('acquiring Kaggle', 0.12012386507741751)\n", + "('Anthony Goldbloom', 0.1213027418574554)\n", + "('platform', 0.12404419723925647)\n", + "('co-founder CEO Anthony', 0.12411964553586782)\n", + "('CEO Anthony', 0.12462950727635251)\n", + "('service', 0.1316357590449064)\n" + ] + } + ], + "source": [ + "import yake\n", + "\n", + "language = \"en\"\n", + "max_ngram_size = 3\n", + "deduplication_thresold = 0.9\n", + "deduplication_algo = \"seqm\"\n", + "windowSize = 1\n", + "numOfKeywords = 20\n", + "\n", + "custom_kw_extractor = yake.KeywordExtractor(\n", + " lan=language,\n", + " n=max_ngram_size,\n", + " dedupLim=deduplication_thresold,\n", + " dedupFunc=deduplication_algo,\n", + " windowsSize=windowSize,\n", + " top=numOfKeywords,\n", + " features=None,\n", + ")\n", + "keywords = custom_kw_extractor.extract_keywords(text)\n", + "\n", + "for kw in keywords:\n", + " print(kw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Highlighting feature will tag every keyword in the text with the default tag ``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, Naval Ravikant, Google chief economist Hal Varian, Khosla Ventures and Yuri Milner\"" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from yake.core.highlight import TextHighlighter\n", + "\n", + "th = TextHighlighter(max_ngram_size=3)\n", + "th.highlight(text, keywords)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Besides tagging a text with the default tag, users can also specify their own custom highlight. In the following text, the tag `` makes use of an hyphotetical function `my_class` whose purpose would be to highlight in white colour or the relevant keywords." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, Naval Ravikant, Google chief economist Hal Varian, Khosla Ventures and Yuri Milner\"" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from yake.core.highlight import TextHighlighter\n", + "\n", + "th = TextHighlighter(\n", + " max_ngram_size=3, highlight_pre=\"\", highlight_post=\"\"\n", + ")\n", + "th.highlight(text, keywords)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While English (`en`) is the default language, users can use YAKE! to extract keywords from whatever language they want to by specifying the the corresponding language universal code. The below example shows how to extract keywords from a portuguese text." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Conta-me Histórias', 0.006225012963810038)\n", + "('LIAAD do INESC', 0.01899063587015275)\n", + "('INESC TEC', 0.01995432290332246)\n", + "('Conta-me', 0.04513273690417472)\n", + "('Histórias', 0.04513273690417472)\n", + "('Prémio Arquivo.pt', 0.05749361520927859)\n", + "('LIAAD', 0.07738867367929901)\n", + "('INESC', 0.07738867367929901)\n", + "('TEC', 0.08109398065524037)\n", + "('Xutos inspiram projeto', 0.08720742489353424)\n", + "('inspiram projeto premiado', 0.08720742489353424)\n", + "('Adam Jatwot docente', 0.09407053486771558)\n", + "('Arquivo.pt', 0.10261392141666957)\n", + "('Alípio Jorge', 0.12190479662535166)\n", + "('Ciências da Universidade', 0.12368384021490342)\n", + "('Ricardo Campos investigador', 0.12789997272332762)\n", + "('Politécnico de Tomar', 0.13323587141127738)\n", + "('Arian Pasquali', 0.13323587141127738)\n", + "('Vitor Mangaravite', 0.13323587141127738)\n", + "('preservados da Web', 0.13596322680882506)\n" + ] + } + ], + "source": [ + "import yake\n", + "\n", + "text = \"\"\"\n", + "\"Conta-me Histórias.\" Xutos inspiram projeto premiado. A plataforma \"Conta-me Histórias\" foi distinguida com o Prémio Arquivo.pt, atribuído a trabalhos inovadores de investigação ou aplicação de recursos preservados da Web, através dos serviços de pesquisa e acesso disponibilizados publicamente pelo Arquivo.pt . Nesta plataforma em desenvolvimento, o utilizador pode pesquisar sobre qualquer tema e ainda executar alguns exemplos predefinidos. Como forma de garantir a pluralidade e diversidade de fontes de informação, esta são utilizadas 24 fontes de notícias eletrónicas, incluindo a TSF. Uma versão experimental (beta) do \"Conta-me Histórias\" está disponível aqui.\n", + "A plataforma foi desenvolvida por Ricardo Campos investigador do LIAAD do INESC TEC e docente do Instituto Politécnico de Tomar, Arian Pasquali e Vitor Mangaravite, também investigadores do LIAAD do INESC TEC, Alípio Jorge, coordenador do LIAAD do INESC TEC e docente na Faculdade de Ciências da Universidade do Porto, e Adam Jatwot docente da Universidade de Kyoto.\n", + "\"\"\"\n", + "\n", + "custom_kw_extractor = yake.KeywordExtractor(lan=\"pt\")\n", + "keywords = custom_kw_extractor.extract_keywords(text)\n", + "\n", + "for kw in keywords:\n", + " print(kw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### WordCloud" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following we are going to constuct a word cloud. To begin, we need to adapt the \"content\" structure to a dictionary consisting of the keyword and of the score. In addition to that, we resort to a normalization function that bounds the values into a range between [0,1]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Conta-me Histórias': 1.0,\n", + " 'LIAAD do INESC': 0.9016047583205323,\n", + " 'INESC TEC': 0.8941768232148362,\n", + " 'Conta-me': 0.7001059072167914,\n", + " 'Histórias': 0.7001059072167914,\n", + " 'Prémio Arquivo.pt': 0.6048303678150379,\n", + " 'LIAAD': 0.4514826541353427,\n", + " 'INESC': 0.4514826541353427,\n", + " 'TEC': 0.4229227806321687,\n", + " 'Xutos inspiram projeto': 0.37580139629126075,\n", + " 'inspiram projeto premiado': 0.37580139629126075,\n", + " 'Adam Jatwot docente': 0.3229017164607676,\n", + " 'Arquivo.pt': 0.25705075169290126,\n", + " 'Alípio Jorge': 0.10835997942955777,\n", + " 'Ciências da Universidade': 0.09464741520636755,\n", + " 'Ricardo Campos investigador': 0.06215018572037523,\n", + " 'Politécnico de Tomar': 0.021021989718509326,\n", + " 'Arian Pasquali': 0.021021989718509326,\n", + " 'Vitor Mangaravite': 0.021021989718509326,\n", + " 'preservados da Web': 0.0}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def normalize_scores(keywords):\n", + " if len(keywords) == 0:\n", + " return {}\n", + " max_value = max([item[1] for item in keywords])\n", + " min_value = min([item[1] for item in keywords])\n", + "\n", + " result = {}\n", + " for item in keywords:\n", + " normalized_score = (item[1] - float(min_value)) / (\n", + " float(max_value) - float(min_value)\n", + " )\n", + " result[item[0]] = abs(1 - normalized_score)\n", + "\n", + " return result\n", + "\n", + "\n", + "keyword2WordCloud = normalize_scores(keywords)\n", + "keyword2WordCloud" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "the following code is a normalizing fuction of the data. The results vary betweent [0,1] where the upper bound isn't necessarly 1 (it can be lower than 1)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "def normalize_scores(keywords):\n", + " if len(keywords) == 0:\n", + " return {}\n", + " max_value = max([item[1] for\n", + " item in keywords])\n", + " \n", + " result = {}\n", + " for item in keywords:\n", + " normalized_score = 1 - item[1]/float(max_value)\n", + " result[item[0]] = abs(normalized_score)\n", + "\n", + " return result\n", + "\n", + "keyword2WordCloud = normalize_scores(keywords)\n", + "keyword2WordCloud\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later, its possible to build a word cloud using `matplotlib`. If, during installation, you receive an error message stating that Microsoft Visual C++ 14.0 (or higher) is required, you should proceed with its installation. See how to do it on this [web page](https://www.scivision.dev/python-windows-visual-c-14-required/) or in this [video](https://www.youtube.com/watch?v=_keTL9ymGjw)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!uv pip install matplotlib\n", + "!uv pip install wordcloud" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 999.5, 499.5, -0.5)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" }, - "nbformat": 4, - "nbformat_minor": 4 + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from wordcloud import WordCloud\n", + "import matplotlib.pyplot as plt\n", + "\n", + "wordcloud = WordCloud(\n", + " max_font_size=100, max_words=20, width=1000, height=500\n", + ").generate_from_frequencies(\n", + " keyword2WordCloud\n", + ") # Objeto que permite gerar wordcloud a partir de texto\n", + "plt.figure(figsize=(16, 9))\n", + "plt.imshow(\n", + " wordcloud, interpolation=\"bilinear\"\n", + ") # imshow plota imagens que derivam de arrays\n", + "plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now suppose you need to send this image through an API. To achieve this, you should convert the image to base64. The code below allows you to store the image in a buffer and shows its conversion to base64." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "import io\n", + "\n", + "buffer = io.BytesIO()\n", + "wordcloud.to_image().save(buffer, \"png\")\n", + "b64 = str(base64.b64encode(buffer.getvalue()).decode(\"ascii\"))\n", + "b64" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the base64 string, it would then be possible to display the image again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image\n", + "\n", + "img1 = Image.open(io.BytesIO(base64.b64decode(b64)))\n", + "img1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage (Command Line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "Usage: yake [OPTIONS]\n", + "\n", + "Options:\n", + " --help Show this message and exit.\n", + " -ti, --text_input TEXT Input text, SURROUNDED by single quotes(')\n", + " -i, --input_file TEXT Input file\n", + " -l, --language TEXT Language\n", + " -n, --ngram-size INTEGER Max size of the ngram.\n", + " -df, --dedup-func [leve|jaro|seqm] Deduplication function.\n", + " -dl, --dedup-lim FLOAT Deduplication limiar.\n", + " -ws, --window-size INTEGER Window size.\n", + " -t, --top INTEGER Number of keyphrases to extract\n", + " -v, --verbose Gets detailed information (such as the score)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A few examples:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "yake -i text.txt -l en -n 3\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Related Projects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [Spark NLP](https://www.johnsnowlabs.com/spark-nlp-in-action/)\n", + "- [Spark NLP Keywords Extraction Demo](https://demo.johnsnowlabs.com/public/KEYPHRASE_EXTRACTION/)\n", + "- [kaggle](https://www.youtube.com/watch?v=6TBvZmg7AsA)\n", + "- [textacy](https://github.com/chartbeat-labs/textacy/search?q=yake)\n", + "- [pke - pyhton keyphrase extraction](https://github.com/boudinfl/pke)\n", + "- [dendro](http://dendro-stg.inesctec.pt/)\n", + "- [conta-me histórias](http://contamehistorias.pt)\n", + "- [time-matters](http://time-matters.inesctec.pt)\n", + "- [The General Index](https://archive.org/details/GeneralIndex)\n", + "- [Nature. Giant, free index to world’s research papers released online](https://www.nature.com/articles/d41586-021-02895-8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Awards" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[ECIR'18](http://ecir2018.org) Best Short Paper (see the below reference)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please cite the following works when using YAKE:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**In-depth journal paper at Information Sciences Journal**\n", + "\n", + "- Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)\n", + "\n", + "**ECIR'18 Best Short Paper**\n", + "\n", + "- Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)\n", + "\n", + "- Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Media" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [INESC TEC technology used to create a database of keywords obtained from more than 100 million scientific articles](https://bip.inesctec.pt/en/noticias/inesc-tec-technology-used-to-create-a-database-of-keywords-obtained-from-more-than-100-million-scientific-articles/)\n", + "- [This tool can extract keywords from texts in every language about any topic](https://www.alphagalileo.org/en-gb/Item-Display/ItemId/216276?returnurl=https://www.alphagalileo.org/en-gb/Item-Display/ItemId/216276)\n", + "- [INESC TEC team wins another Best Paper Award](https://www.inesctec.pt/en/news/inesc-tec-team-wins-another-best-paper-award#about)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "yake", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "255px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/docs-site/source.config.ts b/docs-site/source.config.ts index 4cb6ef06..c03141b6 100644 --- a/docs-site/source.config.ts +++ b/docs-site/source.config.ts @@ -1,17 +1,9 @@ import { defineDocs, defineConfig, defineCollections } from 'fumadocs-mdx/config'; -// Options: https://fumadocs.vercel.app/docs/mdx/collections#define-docs -export const docs = defineCollections({ - type: 'doc', +export const docs = defineDocs({ dir: 'content/docs', }); -export const meta = defineCollections({ - type: 'meta', - dir: 'content/docs', -}); - - export default defineConfig({ mdxOptions: { // MDX options diff --git a/docs-site/tsconfig.json b/docs-site/tsconfig.json index 7e4699b6..5a0e5b25 100644 --- a/docs-site/tsconfig.json +++ b/docs-site/tsconfig.json @@ -2,7 +2,11 @@ "compilerOptions": { "baseUrl": ".", "target": "ESNext", - "lib": ["dom", "dom.iterable", "esnext"], + "lib": [ + "dom", + "dom.iterable", + "esnext" + ], "allowJs": true, "skipLibCheck": true, "strict": true, @@ -13,12 +17,18 @@ "moduleResolution": "bundler", "resolveJsonModule": true, "isolatedModules": true, - "jsx": "preserve", + "jsx": "react-jsx", "incremental": true, "paths": { - "@/.source": ["./.source/index.ts"], - "@/*": ["./*"], - "fumadocs-ui/*": ["./node_modules/fumadocs-ui/*"] + "@/*": [ + "./*" + ], + "fumadocs-ui/*": [ + "./node_modules/fumadocs-ui/*" + ], + "fumadocs-mdx:collections/*": [ + ".source/*" + ] }, "plugins": [ { @@ -26,6 +36,13 @@ } ] }, - "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts", "types/**/*.d.ts"], - "exclude": ["node_modules"] -} \ No newline at end of file + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/dev/types/**/*.ts" + ], + "exclude": [ + "node_modules" + ] +} diff --git a/package-lock.json b/package-lock.json index 666f3320..584a78e4 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,34 +1,29 @@ { "name": "yake", + "version": "7.1.0", "lockfileVersion": 3, "requires": true, "packages": { "": { + "name": "yake", + "version": "7.1.0", + "license": "LGPL-3.0-only", "dependencies": { "@types/mdx": "^2.0.13", - "fumadocs-mdx": "^11.6.4" + "fumadocs-mdx": "^14.2.7" }, "devDependencies": { "gh-pages": "^6.3.0" } }, - "node_modules/@emnapi/runtime": { - "version": "1.4.3", - 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"type": "github", "url": "https://github.com/sponsors/wooorm" diff --git a/package.json b/package.json new file mode 100644 index 00000000..787121f2 --- /dev/null +++ b/package.json @@ -0,0 +1,26 @@ +{ + "name": "yake", + "version": "7.1.0", + "description": "", + "homepage": "https://github.com/INESCTEC/yake#readme", + "bugs": { + "url": "https://github.com/INESCTEC/yake/issues" + }, + "repository": { + "type": "git", + "url": "git+https://github.com/INESCTEC/yake.git" + }, + "license": "LGPL-3.0-only", + "author": "", + "directories": { + "doc": "docs", + "test": "tests" + }, + "dependencies": { + "@types/mdx": "^2.0.13", + "fumadocs-mdx": "^14.2.7" + }, + "devDependencies": { + "gh-pages": "^6.3.0" + } +} diff --git a/pyproject.toml b/pyproject.toml index 71b934f1..3686afc8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,52 +7,54 @@ name = "yake" version = "0.7.3" description = "Keyword extraction Python package" readme = "README.md" -requires-python = ">=3.10" -license = {text = "LGPLv3"} +requires-python = ">=3.10" +license = { text = "LGPLv3" } authors = [ - {name = "Arian Pasquali", email = "arianpasquali@gmail.com"}, - {name = "Ricardo Campos", email = "ricardo.campos@ubi.pt"}, - {name = "Tiago Valente", email = "tiago.leandro.valente@ubi.com"}, + { name = "Arian Pasquali", email = "arianpasquali@gmail.com" }, + { name = "Ricardo Campos", email = "ricardo.campos@ubi.pt" }, + { name = "Tiago Valente", email = "tiago.leandro.valente@ubi.com" }, ] dependencies = [ - "tabulate", - "segtok", - "networkx", - "numpy>=1.24.0", - "click>=6.0", - "jellyfish", + "tabulate", + "segtok", + "networkx", + "numpy>=1.26.4", + "click>=6.0", + "jellyfish", ] classifiers = [ - "Development Status :: 3 - Alpha", - "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3 :: Only", - "Topic :: Scientific/Engineering :: Information Analysis", - "Topic :: Software Development :: Libraries", - "Topic :: Text Processing", - "Topic :: Text Processing :: Linguistic", + "Development Status :: 3 - Alpha", + "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Programming Language :: Python :: 3 :: Only", + "Topic :: Scientific/Engineering :: Information Analysis", + "Topic :: Software Development :: Libraries", + "Topic :: Text Processing", + "Topic :: Text Processing :: Linguistic", ] [project.optional-dependencies] dev = [ - "pytest>=7.0.0", - "pytest-cov>=4.0.0", - "black>=22.0.0", - "ruff>=0.1.0", - "flake8>=6.0.0", - "pylint>=3.3.0", # Python 3.13 support + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "black>=22.0.0", + "ruff>=0.15.0", + "flake8>=6.0.0", + "pylint>=3.3.0", # Python 3.13 support ] benchmark = [ - "pytest-benchmark>=4.0.0", - "memory-profiler>=0.60.0", - "matplotlib>=3.5.0", + "pytest-benchmark>=4.0.0", + "memory-profiler>=0.60.0", + "matplotlib>=3.5.0", ] lemmatization = [ - "spacy>=3.8.0", - "nltk>=3.8.0", + "spacy>=3.8.0", + "nltk>=3.8.0", ] [project.urls] @@ -63,4 +65,4 @@ Source = "https://github.com/INESCTEC/yake" yake = "yake.cli:keywords" [tool.hatch.build.targets.wheel] -packages = ["yake", "StopwordsList"] # Explicitly include these packages +packages = ["yake", "StopwordsList"] # Explicitly include these packages diff --git a/tests/__init__.py b/tests/__init__.py index 42d66069..29d019be 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- # pylint: skip-file """Unit test package for yake.""" diff --git a/tests/test_features.py b/tests/test_features.py index 2b6bb3ee..4a3cf76d 100644 --- a/tests/test_features.py +++ b/tests/test_features.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# -*- coding: utf-8 -*- # pylint: skip-file """ @@ -9,26 +8,27 @@ composed features, and feature aggregation methods. """ -import sys import os +import sys + sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) import math -import pytest -import numpy as np -from unittest.mock import Mock, MagicMock +from unittest.mock import Mock + import networkx as nx +import numpy as np from yake.data.features import ( - calculate_term_features, calculate_composed_features, - get_feature_aggregation + calculate_term_features, + get_feature_aggregation, ) class TestCalculateTermFeatures: """Test suite for calculate_term_features function.""" - + def test_basic_term_features(self): """Test feature calculation for a basic term.""" # Create mock term with required attributes @@ -39,40 +39,44 @@ def test_basic_term_features(self): term.sentence_ids = {1, 2, 3} # Appears in 3 sentences term.occurs = {0: None, 5: None, 10: None} # Positions graph_metrics = { - 'pwl': 0.5, - 'pwr': 0.5, - 'wdl': 10.0, - 'wdr': 10.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 10.0, + "wdr": 10.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + # Document statistics max_tf = 10.0 avg_tf = 3.0 std_tf = 2.0 number_of_sentences = 5 - + features = calculate_term_features( - term, max_tf, avg_tf, std_tf, number_of_sentences + term, + max_tf, + avg_tf, + std_tf, + number_of_sentences, ) - + # Verify all features are calculated - assert 'w_rel' in features - assert 'w_freq' in features - assert 'w_spread' in features - assert 'w_case' in features - assert 'w_pos' in features - assert 'h' in features - + assert "w_rel" in features + assert "w_freq" in features + assert "w_spread" in features + assert "w_case" in features + assert "w_pos" in features + assert "h" in features + # Verify feature values are reasonable - assert features['w_rel'] > 0 - assert features['w_freq'] > 0 - assert 0 <= features['w_spread'] <= 1 # Spread is between 0 and 1 - assert features['w_case'] > 0 - assert features['w_pos'] > 0 - assert features['h'] > 0 - + assert features["w_rel"] > 0 + assert features["w_freq"] > 0 + assert 0 <= features["w_spread"] <= 1 # Spread is between 0 and 1 + assert features["w_case"] > 0 + assert features["w_pos"] > 0 + assert features["h"] > 0 + def test_w_rel_calculation(self): """Test WRel (term relevance) calculation.""" term = Mock() @@ -82,20 +86,20 @@ def test_w_rel_calculation(self): term.sentence_ids = {1} term.occurs = {0: None} graph_metrics = { - 'pwl': 0.3, - 'pwr': 0.7, - 'wdl': 5.0, - 'wdr': 5.0 + "pwl": 0.3, + "pwr": 0.7, + "wdl": 5.0, + "wdr": 5.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + features = calculate_term_features(term, 10.0, 3.0, 2.0, 5) - + # WRel should combine left and right connectivity expected_wrel = (0.5 + (0.3 * (5.0 / 10.0))) + (0.5 + (0.7 * (5.0 / 10.0))) - assert abs(features['w_rel'] - expected_wrel) < 1e-6 - + assert abs(features["w_rel"] - expected_wrel) < 1e-6 + def test_w_freq_calculation(self): """Test WFreq (normalized frequency) calculation.""" term = Mock() @@ -105,19 +109,22 @@ def test_w_freq_calculation(self): term.sentence_ids = {1} term.occurs = {0: None} graph_metrics = { - 'pwl': 0.5, 'pwr': 0.5, 'wdl': 5.0, 'wdr': 5.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 5.0, + "wdr": 5.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + avg_tf = 3.0 std_tf = 2.0 features = calculate_term_features(term, 10.0, avg_tf, std_tf, 5) - + # WFreq = tf / (avg_tf + std_tf) expected_wfreq = 8.0 / (3.0 + 2.0) - assert abs(features['w_freq'] - expected_wfreq) < 1e-6 - + assert abs(features["w_freq"] - expected_wfreq) < 1e-6 + def test_w_spread_calculation(self): """Test WSpread (sentence spread) calculation.""" term = Mock() @@ -127,17 +134,20 @@ def test_w_spread_calculation(self): term.sentence_ids = {1, 2, 3, 4} # 4 out of 10 sentences term.occurs = {0: None} graph_metrics = { - 'pwl': 0.5, 'pwr': 0.5, 'wdl': 5.0, 'wdr': 5.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 5.0, + "wdr": 5.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + features = calculate_term_features(term, 10.0, 3.0, 2.0, 10) - + # WSpread = len(sentence_ids) / total_sentences expected_wspread = 4.0 / 10.0 - assert abs(features['w_spread'] - expected_wspread) < 1e-6 - + assert abs(features["w_spread"] - expected_wspread) < 1e-6 + def test_w_case_calculation(self): """Test WCase (capitalization) calculation.""" term = Mock() @@ -147,17 +157,20 @@ def test_w_case_calculation(self): term.sentence_ids = {1} term.occurs = {0: None} graph_metrics = { - 'pwl': 0.5, 'pwr': 0.5, 'wdl': 5.0, 'wdr': 5.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 5.0, + "wdr": 5.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + features = calculate_term_features(term, 10.0, 3.0, 2.0, 5) - + # WCase = max(tf_a, tf_n) / (1 + log(tf)) expected_wcase = 7.0 / (1.0 + math.log(10.0)) - assert abs(features['w_case'] - expected_wcase) < 1e-6 - + assert abs(features["w_case"] - expected_wcase) < 1e-6 + def test_w_pos_calculation(self): """Test WPos (position) calculation using median.""" term = Mock() @@ -167,19 +180,22 @@ def test_w_pos_calculation(self): term.sentence_ids = {1} term.occurs = {10: None, 20: None, 30: None, 40: None, 50: None} graph_metrics = { - 'pwl': 0.5, 'pwr': 0.5, 'wdl': 5.0, 'wdr': 5.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 5.0, + "wdr": 5.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + features = calculate_term_features(term, 10.0, 3.0, 2.0, 5) - + # WPos = log(log(3 + median(positions))) positions = [10, 20, 30, 40, 50] median_pos = np.median(positions) # 30 expected_wpos = math.log(math.log(3.0 + median_pos)) - assert abs(features['w_pos'] - expected_wpos) < 1e-6 - + assert abs(features["w_pos"] - expected_wpos) < 1e-6 + def test_zero_max_tf_handling(self): """Test handling of edge case where max_tf is zero.""" term = Mock() @@ -189,11 +205,14 @@ def test_zero_max_tf_handling(self): term.sentence_ids = {1} term.occurs = {0: None} graph_metrics = { - 'pwl': 0.5, 'pwr': 0.5, 'wdl': 0.0, 'wdr': 0.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 0.0, + "wdr": 0.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + # When max_tf is 0, term.tf should also be 0, avoiding division # But if it happens, the code should handle it gracefully # Note: In real usage, max_tf=0 means no terms, so this is edge case @@ -202,13 +221,13 @@ def test_zero_max_tf_handling(self): try: features = calculate_term_features(term, 0.0, 3.0, 2.0, 5) # If it succeeds, verify pl and pr are 0 - assert features['pl'] == 0.0 - assert features['pr'] == 0.0 + assert features["pl"] == 0.0 + assert features["pr"] == 0.0 except ZeroDivisionError: # Expected behavior when max_tf=0 and tf>0 (edge case) # In real usage, this shouldn't happen pass - + def test_single_position_term(self): """Test term that appears only once.""" term = Mock() @@ -218,21 +237,24 @@ def test_single_position_term(self): term.sentence_ids = {1} term.occurs = {5: None} # Single position graph_metrics = { - 'pwl': 0.5, 'pwr': 0.5, 'wdl': 1.0, 'wdr': 1.0 + "pwl": 0.5, + "pwr": 0.5, + "wdl": 1.0, + "wdr": 1.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + features = calculate_term_features(term, 10.0, 3.0, 2.0, 5) - + # Should calculate all features without errors assert all(v is not None for v in features.values()) - assert features['w_spread'] == 1.0 / 5.0 # 1 sentence out of 5 + assert features["w_spread"] == 1.0 / 5.0 # 1 sentence out of 5 class TestCalculateComposedFeatures: """Test suite for calculate_composed_features function.""" - + def create_mock_term(self, h_value, is_stopword=False, term_id=None, tf=1.0): """Helper to create mock terms.""" term = Mock() @@ -242,7 +264,7 @@ def create_mock_term(self, h_value, is_stopword=False, term_id=None, tf=1.0): term.tf = tf term.g = nx.DiGraph() return term - + def test_composed_features_no_stopwords(self): """Test composed features for phrase without stopwords.""" # Create composed word with 3 non-stopword terms @@ -251,49 +273,49 @@ def test_composed_features_no_stopwords(self): composed_word.terms = [ self.create_mock_term(0.1, False), self.create_mock_term(0.2, False), - self.create_mock_term(0.3, False) + self.create_mock_term(0.3, False), ] - + features = calculate_composed_features(composed_word) - + # sum_h = 0.1 + 0.2 + 0.3 = 0.6 - assert abs(features['sum_h'] - 0.6) < 1e-6 - + assert abs(features["sum_h"] - 0.6) < 1e-6 + # prod_h = 0.1 * 0.2 * 0.3 = 0.006 - assert abs(features['prod_h'] - 0.006) < 1e-6 - + assert abs(features["prod_h"] - 0.006) < 1e-6 + # tf_used = 5.0 - assert features['tf_used'] == 5.0 - + assert features["tf_used"] == 5.0 + # H = prod_h / ((sum_h + 1) * tf_used) expected_h = 0.006 / ((0.6 + 1) * 5.0) - assert abs(features['h'] - expected_h) < 1e-6 - + assert abs(features["h"] - expected_h) < 1e-6 + def test_composed_features_with_stopwords_bi(self): """Test composed features with stopwords using 'bi' weighting.""" composed_word = Mock() composed_word.tf = 3.0 - + # Create terms: term1 (non-stop), stopword, term2 (non-stop) term1 = self.create_mock_term(0.2, False, "term1", 4.0) stopword = self.create_mock_term(0.05, True, "stop", 2.0) term2 = self.create_mock_term(0.3, False, "term2", 3.0) - + # Add edges for stopword connectivity stopword.g.add_edge("term1", "stop", tf=2.0) stopword.g.add_edge("stop", "term2", tf=1.5) - + composed_word.terms = [term1, stopword, term2] - - features = calculate_composed_features(composed_word, stopword_weight='bi') - + + features = calculate_composed_features(composed_word, stopword_weight="bi") + # Stopword affects prod_h and sum_h based on connectivity # Note: sum_h can be negative due to stopword probability penalty - assert features['prod_h'] > 0 + assert features["prod_h"] > 0 # sum_h can be negative with stopword penalties - assert isinstance(features['sum_h'], float) - assert features['h'] > 0 - + assert isinstance(features["sum_h"], float) + assert features["h"] > 0 + def test_composed_features_stopwords_h_weight(self): """Test stopword handling with 'h' weighting.""" composed_word = Mock() @@ -301,18 +323,18 @@ def test_composed_features_stopwords_h_weight(self): composed_word.terms = [ self.create_mock_term(0.2, False), self.create_mock_term(0.1, True), # Stopword with h=0.1 - self.create_mock_term(0.3, False) + self.create_mock_term(0.3, False), ] - - features = calculate_composed_features(composed_word, stopword_weight='h') - + + features = calculate_composed_features(composed_word, stopword_weight="h") + # With 'h' weight, stopword's h value is included # sum_h = 0.2 + 0.1 + 0.3 = 0.6 - assert abs(features['sum_h'] - 0.6) < 1e-6 - + assert abs(features["sum_h"] - 0.6) < 1e-6 + # prod_h = 0.2 * 0.1 * 0.3 = 0.006 - assert abs(features['prod_h'] - 0.006) < 1e-6 - + assert abs(features["prod_h"] - 0.006) < 1e-6 + def test_composed_features_stopwords_none_weight(self): """Test stopword handling with 'none' weighting (ignore stopwords).""" composed_word = Mock() @@ -320,162 +342,174 @@ def test_composed_features_stopwords_none_weight(self): composed_word.terms = [ self.create_mock_term(0.2, False), self.create_mock_term(0.999, True), # Stopword should be ignored - self.create_mock_term(0.3, False) + self.create_mock_term(0.3, False), ] - - features = calculate_composed_features(composed_word, stopword_weight='none') - + + features = calculate_composed_features(composed_word, stopword_weight="none") + # sum_h = 0.2 + 0.3 = 0.5 (stopword ignored) - assert abs(features['sum_h'] - 0.5) < 1e-6 - + assert abs(features["sum_h"] - 0.5) < 1e-6 + # prod_h = 0.2 * 0.3 = 0.06 (stopword ignored) - assert abs(features['prod_h'] - 0.06) < 1e-6 - + assert abs(features["prod_h"] - 0.06) < 1e-6 + def test_single_term_composed_word(self): """Test composed word with single term.""" composed_word = Mock() composed_word.tf = 1.0 composed_word.terms = [self.create_mock_term(0.5, False)] - + features = calculate_composed_features(composed_word) - - assert features['sum_h'] == 0.5 - assert features['prod_h'] == 0.5 - assert features['tf_used'] == 1.0 - + + assert features["sum_h"] == 0.5 + assert features["prod_h"] == 0.5 + assert features["tf_used"] == 1.0 + def test_zero_tf_handling(self): """Test handling of zero term frequency.""" composed_word = Mock() composed_word.tf = 0.0 composed_word.terms = [self.create_mock_term(0.5, False)] - + features = calculate_composed_features(composed_word) - + # With tf=0, H should be 0 to avoid division by zero - assert features['h'] == 0.0 + assert features["h"] == 0.0 class TestGetFeatureAggregation: """Test suite for get_feature_aggregation function.""" - + def create_mock_term_with_feature(self, feature_value, is_stopword=False): """Helper to create mock term with specific feature value.""" term = Mock() term.test_feature = feature_value term.stopword = is_stopword return term - + def test_feature_aggregation_basic(self): """Test basic feature aggregation.""" composed_word = Mock() composed_word.terms = [ self.create_mock_term_with_feature(2.0, False), self.create_mock_term_with_feature(3.0, False), - self.create_mock_term_with_feature(5.0, False) + self.create_mock_term_with_feature(5.0, False), ] - + sum_f, prod_f, ratio = get_feature_aggregation( - composed_word, 'test_feature', exclude_stopwords=False + composed_word, + "test_feature", + exclude_stopwords=False, ) - + # sum = 2 + 3 + 5 = 10 assert abs(sum_f - 10.0) < 1e-6 - + # product = 2 * 3 * 5 = 30 assert abs(prod_f - 30.0) < 1e-6 - + # ratio = 30 / (10 + 1) = 30 / 11 assert abs(ratio - (30.0 / 11.0)) < 1e-6 - + def test_feature_aggregation_exclude_stopwords(self): """Test aggregation excluding stopwords.""" composed_word = Mock() composed_word.terms = [ self.create_mock_term_with_feature(2.0, False), self.create_mock_term_with_feature(999.0, True), # Stopword - self.create_mock_term_with_feature(3.0, False) + self.create_mock_term_with_feature(3.0, False), ] - + sum_f, prod_f, ratio = get_feature_aggregation( - composed_word, 'test_feature', exclude_stopwords=True + composed_word, + "test_feature", + exclude_stopwords=True, ) - + # sum = 2 + 3 = 5 (stopword excluded) assert abs(sum_f - 5.0) < 1e-6 - + # product = 2 * 3 = 6 assert abs(prod_f - 6.0) < 1e-6 - + def test_feature_aggregation_include_stopwords(self): """Test aggregation including stopwords.""" composed_word = Mock() composed_word.terms = [ self.create_mock_term_with_feature(2.0, False), self.create_mock_term_with_feature(4.0, True), # Stopword - self.create_mock_term_with_feature(3.0, False) + self.create_mock_term_with_feature(3.0, False), ] - + sum_f, prod_f, ratio = get_feature_aggregation( - composed_word, 'test_feature', exclude_stopwords=False + composed_word, + "test_feature", + exclude_stopwords=False, ) - + # sum = 2 + 4 + 3 = 9 (stopword included) assert abs(sum_f - 9.0) < 1e-6 - + # product = 2 * 4 * 3 = 24 assert abs(prod_f - 24.0) < 1e-6 - + def test_empty_feature_list(self): """Test aggregation when all terms are stopwords and excluded.""" composed_word = Mock() composed_word.terms = [ self.create_mock_term_with_feature(5.0, True), - self.create_mock_term_with_feature(10.0, True) + self.create_mock_term_with_feature(10.0, True), ] - + sum_f, prod_f, ratio = get_feature_aggregation( - composed_word, 'test_feature', exclude_stopwords=True + composed_word, + "test_feature", + exclude_stopwords=True, ) - + # All terms excluded, should return zeros assert sum_f == 0.0 assert prod_f == 0.0 assert ratio == 0.0 - + def test_single_term_aggregation(self): """Test aggregation with single term.""" composed_word = Mock() composed_word.terms = [self.create_mock_term_with_feature(7.0, False)] - + sum_f, prod_f, ratio = get_feature_aggregation( - composed_word, 'test_feature', exclude_stopwords=False + composed_word, + "test_feature", + exclude_stopwords=False, ) - + assert sum_f == 7.0 assert prod_f == 7.0 assert abs(ratio - (7.0 / 8.0)) < 1e-6 # 7 / (7 + 1) - + def test_zero_values_in_product(self): """Test aggregation when one feature value is zero.""" composed_word = Mock() composed_word.terms = [ self.create_mock_term_with_feature(2.0, False), self.create_mock_term_with_feature(0.0, False), # Zero value - self.create_mock_term_with_feature(3.0, False) + self.create_mock_term_with_feature(3.0, False), ] - + sum_f, prod_f, ratio = get_feature_aggregation( - composed_word, 'test_feature', exclude_stopwords=False + composed_word, + "test_feature", + exclude_stopwords=False, ) - + assert sum_f == 5.0 # 2 + 0 + 3 assert prod_f == 0.0 # 2 * 0 * 3 = 0 - assert ratio == 0.0 # 0 / 6 = 0 + assert ratio == 0.0 # 0 / 6 = 0 class TestFeatureIntegration: """Integration tests for feature calculations.""" - + def test_features_produce_positive_scores(self): """Test that feature calculations produce valid positive scores.""" term = Mock() @@ -485,19 +519,19 @@ def test_features_produce_positive_scores(self): term.sentence_ids = {1, 2} term.occurs = {10: None, 20: None, 30: None} graph_metrics = { - 'pwl': 0.6, - 'pwr': 0.4, - 'wdl': 8.0, - 'wdr': 7.0 + "pwl": 0.6, + "pwr": 0.4, + "wdl": 8.0, + "wdr": 7.0, } term.get_graph_metrics = Mock(return_value=graph_metrics) term.graph_metrics = graph_metrics - + features = calculate_term_features(term, 10.0, 4.0, 2.0, 5) - + # All features should be positive for valid term assert all(v > 0 for v in features.values()) - + def test_high_tf_term_gets_good_score(self): """Test that high frequency terms get favorable feature scores.""" high_tf_term = Mock() @@ -505,15 +539,18 @@ def test_high_tf_term_gets_good_score(self): high_tf_term.tf_a = 15.0 high_tf_term.tf_n = 5.0 high_tf_term.sentence_ids = {1, 2, 3, 4, 5} - high_tf_term.occurs = {i: None for i in range(20)} + high_tf_term.occurs = dict.fromkeys(range(20)) graph_metrics = { - 'pwl': 0.8, 'pwr': 0.8, 'wdl': 15.0, 'wdr': 15.0 + "pwl": 0.8, + "pwr": 0.8, + "wdl": 15.0, + "wdr": 15.0, } high_tf_term.get_graph_metrics = Mock(return_value=graph_metrics) high_tf_term.graph_metrics = graph_metrics - + features = calculate_term_features(high_tf_term, 20.0, 5.0, 3.0, 5) - + # High frequency term should have high w_freq and w_spread - assert features['w_freq'] > 1.0 - assert features['w_spread'] == 1.0 # Appears in all sentences + assert features["w_freq"] > 1.0 + assert features["w_spread"] == 1.0 # Appears in all sentences diff --git a/tests/test_lemmatization.py b/tests/test_lemmatization.py index 7fbbb837..34b9a70e 100644 --- a/tests/test_lemmatization.py +++ b/tests/test_lemmatization.py @@ -1,27 +1,32 @@ +#!/usr/bin/env python +# pylint: skip-file +# This file requires optional dependencies (spaCy, NLTK) and may not be available in all environments + """ Tests for lemmatization functionality in YAKE. This module tests the lemmatization feature that aggregates keywords with the same lemma (e.g., "tree" and "trees"). """ -# pylint: skip-file -# This file requires optional dependencies (spaCy, NLTK) and may not be available in all environments -import pytest import yake def test_lemmatization_disabled_by_default(): """Test that lemmatization is disabled by default.""" text = "Trees are important. Many trees provide shade. Tree conservation matters." - - kw = yake.KeywordExtractor(lan="en", n=1, top=10) + + kw = yake.KeywordExtractor( + lan="en", + n=1, + top=10, + ) assert kw.lemmatize is False - + # Should extract both "trees" and "tree" as separate keywords result = kw.extract_keywords(text) keywords = [k for k, s in result] - + # At least one form should be present assert len(keywords) > 0 @@ -29,17 +34,27 @@ def test_lemmatization_disabled_by_default(): def test_lemmatization_enabled(): """Test that lemmatization combines keywords with same lemma.""" text = "Trees are important. Many trees provide shade. Tree conservation matters." - + # Without lemmatization - kw_no_lemma = yake.KeywordExtractor(lan="en", n=1, top=10, lemmatize=False) + kw_no_lemma = yake.KeywordExtractor( + lan="en", + n=1, + top=10, + lemmatize=False, + ) result_no_lemma = kw_no_lemma.extract_keywords(text) keywords_no_lemma = [k.lower() for k, s in result_no_lemma] - + # With lemmatization (requires spacy) - kw_with_lemma = yake.KeywordExtractor(lan="en", n=1, top=10, lemmatize=True) + kw_with_lemma = yake.KeywordExtractor( + lan="en", + n=1, + top=10, + lemmatize=True, + ) result_with_lemma = kw_with_lemma.extract_keywords(text) keywords_with_lemma = [k.lower() for k, s in result_with_lemma] - + # Lemmatized version should have same or fewer keywords # (if spacy is installed, it will combine "tree" and "trees") assert len(keywords_with_lemma) <= len(keywords_no_lemma) @@ -48,19 +63,19 @@ def test_lemmatization_enabled(): def test_lemmatization_aggregation_min(): """Test that min aggregation uses the best (lowest) score.""" text = "Running is good. The runner runs fast. Runners love running." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=10, + lan="en", + n=1, + top=10, lemmatize=True, - lemma_aggregation="min" + lemma_aggregation="min", ) result = kw.extract_keywords(text) - + # Should have at least some keywords assert len(result) > 0 - + # All scores should be positive for keyword, score in result: assert score >= 0 @@ -69,19 +84,19 @@ def test_lemmatization_aggregation_min(): def test_lemmatization_aggregation_mean(): """Test that mean aggregation averages scores.""" text = "Running is good. The runner runs fast. Runners love running." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=10, + lan="en", + n=1, + top=10, lemmatize=True, - lemma_aggregation="mean" + lemma_aggregation="mean", ) result = kw.extract_keywords(text) - + # Should have at least some keywords assert len(result) > 0 - + # All scores should be positive for keyword, score in result: assert score >= 0 @@ -90,19 +105,19 @@ def test_lemmatization_aggregation_mean(): def test_lemmatization_aggregation_max(): """Test that max aggregation uses the worst (highest) score.""" text = "Running is good. The runner runs fast. Runners love running." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=10, + lan="en", + n=1, + top=10, lemmatize=True, - lemma_aggregation="max" + lemma_aggregation="max", ) result = kw.extract_keywords(text) - + # Should have at least some keywords assert len(result) > 0 - + # All scores should be positive for keyword, score in result: assert score >= 0 @@ -111,19 +126,19 @@ def test_lemmatization_aggregation_max(): def test_lemmatization_aggregation_harmonic(): """Test that harmonic aggregation uses harmonic mean.""" text = "Running is good. The runner runs fast. Runners love running." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=10, + lan="en", + n=1, + top=10, lemmatize=True, - lemma_aggregation="harmonic" + lemma_aggregation="harmonic", ) result = kw.extract_keywords(text) - + # Should have at least some keywords assert len(result) > 0 - + # All scores should be positive for keyword, score in result: assert score >= 0 @@ -135,19 +150,19 @@ def test_lemmatization_with_multiword(): Machine learning algorithms are powerful. Deep learning models excel. Natural language processing tasks benefit from machine learning. """ - + kw = yake.KeywordExtractor( - lan="en", - n=2, - top=10, + lan="en", + n=2, + top=10, lemmatize=True, - lemma_aggregation="min" + lemma_aggregation="min", ) result = kw.extract_keywords(text) - + # Should extract bigrams assert len(result) > 0 - + # Check that we have multi-word keywords multiword = [k for k, s in result if " " in k] assert len(multiword) > 0 @@ -156,38 +171,48 @@ def test_lemmatization_with_multiword(): def test_lemmatization_graceful_degradation(): """Test that lemmatization gracefully degrades if libraries not available.""" text = "Trees are important. Many trees provide shade." - + # This should work even if spacy/nltk are not installed # (will log warning but continue) - kw = yake.KeywordExtractor(lan="en", n=1, top=5, lemmatize=True) + kw = yake.KeywordExtractor( + lan="en", + n=1, + top=5, + lemmatize=True, + ) result = kw.extract_keywords(text) - + # Should return results (either lemmatized or not) assert isinstance(result, list) def test_lemmatization_empty_text(): """Test lemmatization with empty text.""" - kw = yake.KeywordExtractor(lan="en", n=1, top=5, lemmatize=True) + kw = yake.KeywordExtractor( + lan="en", + n=1, + top=5, + lemmatize=True, + ) result = kw.extract_keywords("") - + assert result == [] def test_lemmatization_preserves_original_form(): """Test that lemmatization preserves one of the original forms.""" text = "The algorithms are powerful. Algorithm design is important." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=10, + lan="en", + n=1, + top=10, lemmatize=True, - lemma_aggregation="min" + lemma_aggregation="min", ) result = kw.extract_keywords(text) keywords = [k for k, s in result] - + # Should return actual words from text, not lemmatized forms # (e.g., "algorithms" or "algorithm", not "algoritm") for keyword in keywords: @@ -199,17 +224,17 @@ def test_lemmatization_preserves_original_form(): def test_lemmatization_different_languages(): """Test that lemmatization respects language setting.""" text_pt = "Os algoritmos são poderosos. Algoritmo de aprendizado é importante." - + # Portuguese text with lemmatization kw = yake.KeywordExtractor( - lan="pt", - n=1, - top=10, + lan="pt", + n=1, + top=10, lemmatize=True, - lemmatizer="spacy" + lemmatizer="spacy", ) result = kw.extract_keywords(text_pt) - + # Should return results (may fall back to English model if pt not installed) assert isinstance(result, list) @@ -217,17 +242,17 @@ def test_lemmatization_different_languages(): def test_lemmatization_nltk_backend(): """Test lemmatization with NLTK backend.""" text = "Running is good. The runner runs fast." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=10, + lan="en", + n=1, + top=10, lemmatize=True, lemmatizer="nltk", - lemma_aggregation="min" + lemma_aggregation="min", ) result = kw.extract_keywords(text) - + # Should return results assert isinstance(result, list) @@ -235,16 +260,16 @@ def test_lemmatization_nltk_backend(): def test_lemmatization_unknown_aggregation(): """Test that unknown aggregation method falls back to min.""" text = "Trees are important. Many trees provide shade." - + kw = yake.KeywordExtractor( - lan="en", - n=1, - top=5, + lan="en", + n=1, + top=5, lemmatize=True, - lemma_aggregation="unknown_method" + lemma_aggregation="unknown_method", ) result = kw.extract_keywords(text) - + # Should still work (with warning logged) assert isinstance(result, list) @@ -255,16 +280,16 @@ def test_lemmatization_score_ordering(): Machine learning is a powerful technology. Deep learning excels at pattern recognition. Artificial intelligence transforms industries. Machine learning algorithms are everywhere. """ - + kw = yake.KeywordExtractor( - lan="en", - n=2, - top=10, + lan="en", + n=2, + top=10, lemmatize=True, - lemma_aggregation="min" + lemma_aggregation="min", ) result = kw.extract_keywords(text) - + # Verify results are sorted by score (ascending) scores = [s for k, s in result] assert scores == sorted(scores) @@ -276,22 +301,32 @@ def test_lemmatization_comparison_with_without(): The researchers researched machine learning. Their research shows that learning algorithms can learn patterns. The learned patterns are useful. """ - + # Without lemmatization - kw_no_lemma = yake.KeywordExtractor(lan="en", n=1, top=20, lemmatize=False) + kw_no_lemma = yake.KeywordExtractor( + lan="en", + n=1, + top=20, + lemmatize=False, + ) result_no_lemma = kw_no_lemma.extract_keywords(text) - + # With lemmatization - kw_with_lemma = yake.KeywordExtractor(lan="en", n=1, top=20, lemmatize=True) + kw_with_lemma = yake.KeywordExtractor( + lan="en", + n=1, + top=20, + lemmatize=True, + ) result_with_lemma = kw_with_lemma.extract_keywords(text) - + # Both should return results assert len(result_no_lemma) > 0 assert len(result_with_lemma) > 0 - + # Lemmatized version should have same or fewer unique keywords unique_no_lemma = set(k.lower() for k, s in result_no_lemma) unique_with_lemma = set(k.lower() for k, s in result_with_lemma) - + # This might be equal if spacy is not installed (graceful degradation) assert len(unique_with_lemma) <= len(unique_no_lemma) diff --git a/tests/test_yake.py b/tests/test_yake.py index 199ead05..2f38f36a 100644 --- a/tests/test_yake.py +++ b/tests/test_yake.py @@ -1,14 +1,12 @@ #!/usr/bin/env python -# -*- coding: utf-8 -*- # pylint: skip-file """Tests for yake package.""" -import sys import os -sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) +import sys -from click.testing import CliRunner +sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) import yake from yake.core.highlight import TextHighlighter @@ -22,11 +20,13 @@ def test_phraseless_example(): result = pyake.extract_keywords(text_content) assert len(result) == 0 + def test_benchmark_yake(benchmark): text = "Google is acquiring data science community Kaggle. " * 100 extractor = yake.KeywordExtractor(lan="en", n=3) benchmark(extractor.extract_keywords, text) + def test_null_and_blank_example(): pyake = yake.KeywordExtractor() @@ -207,7 +207,8 @@ def test_n1_EL(): textHighlighted == "Ανώτατος διοικητής του ρωσικού στρατού φέρεται να σκοτώθηκε κοντά στο Χάρκοβο, σύμφωνα με την υπηρεσία πληροφοριών του υπουργείου Άμυνας της Ουκρανίας. Σύμφωνα με δήλωση του υπουργείου Άμυνας της Ουκρανίας, πρόκειται για τον Vitaly Gerasimov, υποστράτηγο και υποδιοικητή από την Κεντρική Στρατιωτική Περιφέρεια της Ρωσίας." ) - + + def test_n4_EN(): """Test n-gram size of 4 for comprehensive coverage.""" text_content = """ @@ -246,14 +247,14 @@ def test_deduplication_functions(): text_content = "machine learning machine learning deep learning" pyake = yake.KeywordExtractor(lan="en", n=2, dedupLim=0.9, top=5) result = pyake.extract_keywords(text_content) - + # Expected results from YAKE 2.0 res = [ - ('machine learning', 0.023072402583411963), - ('learning deep', 0.041166451867834804), - ('deep learning', 0.041166451867834804), - ('learning machine', 0.04614480516682393), - ('learning', 0.08154106429019745) + ("machine learning", 0.023072402583411963), + ("learning deep", 0.041166451867834804), + ("deep learning", 0.041166451867834804), + ("learning machine", 0.04614480516682393), + ("learning", 0.08154106429019745), ] assert result == res @@ -263,14 +264,14 @@ def test_no_deduplication(): text_content = "machine learning machine learning deep learning" pyake = yake.KeywordExtractor(lan="en", n=2, dedupLim=1.0, top=5) result = pyake.extract_keywords(text_content) - + # Expected results from YAKE 2.0 (same as with dedup due to text structure) res = [ - ('machine learning', 0.023072402583411963), - ('learning deep', 0.041166451867834804), - ('deep learning', 0.041166451867834804), - ('learning machine', 0.04614480516682393), - ('learning', 0.08154106429019745) + ("machine learning", 0.023072402583411963), + ("learning deep", 0.041166451867834804), + ("deep learning", 0.041166451867834804), + ("learning machine", 0.04614480516682393), + ("learning", 0.08154106429019745), ] assert result == res @@ -281,17 +282,17 @@ def test_custom_stopwords(): custom = ["powerful"] pyake = yake.KeywordExtractor(lan="en", n=2, stopwords=custom, top=5) result = pyake.extract_keywords(text_content) - + # Expected results from YAKE 2.0 res = [ - ('algorithms and', 0.03663237450220032), - ('and machine', 0.03663237450220032), - ('learning algorithms', 0.05417025203414716), - ('machine learning', 0.05417025203414716), - ('learning are', 0.05417025203414716) + ("algorithms and", 0.03663237450220032), + ("and machine", 0.03663237450220032), + ("learning algorithms", 0.05417025203414716), + ("machine learning", 0.05417025203414716), + ("learning are", 0.05417025203414716), ] assert result == res - + # Verify custom stopword is not in results keywords = [k[0].lower() for k in result] assert not any("powerful" in kw for kw in keywords) @@ -302,14 +303,14 @@ def test_window_size_parameter(): text_content = "data science and machine learning" pyake = yake.KeywordExtractor(lan="en", n=2, windowsSize=2, top=5) result = pyake.extract_keywords(text_content) - + # Expected results from YAKE 2.0 res = [ - ('data science', 0.04940384002065631), - ('machine learning', 0.04940384002065631), - ('data', 0.15831692877998726), - ('learning', 0.15831692877998726), - ('science', 0.29736558256021506) + ("data science", 0.04940384002065631), + ("machine learning", 0.04940384002065631), + ("data", 0.15831692877998726), + ("learning", 0.15831692877998726), + ("science", 0.29736558256021506), ] assert result == res @@ -319,7 +320,7 @@ def test_cache_statistics(): text = "Python programming " * 10 kw = yake.KeywordExtractor(lan="en", n=2, top=5) - result = kw.extract_keywords(text) + kw.extract_keywords(text) # Get cache stats stats = kw.get_cache_stats() @@ -334,14 +335,14 @@ def test_large_dataset_strategy(): text_large = " ".join(["data science machine learning"] * 1000) pyake = yake.KeywordExtractor(lan="en", n=2, top=5) result = pyake.extract_keywords(text_large) - + # Expected results from YAKE 2.0 (very low scores due to repetition) res = [ - ('science machine', 2.0366793798773438e-06), - ('data science', 2.0366832736317804e-06), - ('machine learning', 2.0366832736317804e-06), - ('learning data', 2.038725893286963e-06), - ('science', 4.5083697143021014e-05) + ("science machine", 2.0366793798773438e-06), + ("data science", 2.0366832736317804e-06), + ("machine learning", 2.0366832736317804e-06), + ("learning data", 2.038725893286963e-06), + ("science", 4.5083697143021014e-05), ] assert result == res @@ -351,14 +352,14 @@ def test_medium_dataset_strategy(): text_medium = " ".join(["data science machine learning"] * 100) pyake = yake.KeywordExtractor(lan="en", n=2, top=5) result = pyake.extract_keywords(text_medium) - + # Expected results from YAKE 2.0 res = [ - ('science machine', 2.1801996753389333e-05), - ('data science', 2.180612257549257e-05), - ('machine learning', 2.180612257549257e-05), - ('learning data', 2.203055472734129e-05), - ('science', 0.00046641791831459765) + ("science machine", 2.1801996753389333e-05), + ("data science", 2.180612257549257e-05), + ("machine learning", 2.180612257549257e-05), + ("learning data", 2.203055472734129e-05), + ("science", 0.00046641791831459765), ] assert result == res @@ -368,14 +369,14 @@ def test_small_dataset_strategy(): text_small = "data science machine learning" pyake = yake.KeywordExtractor(lan="en", n=2, top=5) result = pyake.extract_keywords(text_small) - + # Expected results from YAKE 2.0 res = [ - ('data science', 0.04940384002065631), - ('machine learning', 0.04940384002065631), - ('science machine', 0.09700399286574239), - ('data', 0.15831692877998726), - ('learning', 0.15831692877998726) + ("data science", 0.04940384002065631), + ("machine learning", 0.04940384002065631), + ("science machine", 0.09700399286574239), + ("data", 0.15831692877998726), + ("learning", 0.15831692877998726), ] assert result == res @@ -446,16 +447,18 @@ def test_special_characters_handling(): def test_multilingual_support(): """Test multiple languages beyond existing tests.""" # German - text_de = "Maschinelles Lernen und künstliche Intelligenz sind wichtige Technologien" + text_de = ( + "Maschinelles Lernen und künstliche Intelligenz sind wichtige Technologien" + ) pyake_de = yake.KeywordExtractor(lan="de", n=2, top=5) result_de = pyake_de.extract_keywords(text_de) - + res_de = [ - ('Maschinelles Lernen', 0.023458380875189744), - ('wichtige Technologien', 0.026233073037508336), - ('künstliche Intelligenz', 0.04498862876540802), - ('Technologien', 0.08596317751626563), - ('Lernen', 0.1447773057422032) + ("Maschinelles Lernen", 0.023458380875189744), + ("wichtige Technologien", 0.026233073037508336), + ("künstliche Intelligenz", 0.04498862876540802), + ("Technologien", 0.08596317751626563), + ("Lernen", 0.1447773057422032), ] assert result_de == res_de @@ -463,13 +466,13 @@ def test_multilingual_support(): text_fr = "L'apprentissage automatique et l'intelligence artificielle transforment le monde" pyake_fr = yake.KeywordExtractor(lan="fr", n=2, top=5) result_fr = pyake_fr.extract_keywords(text_fr) - + res_fr = [ ("L'apprentissage automatique", 0.04940384002065631), ("l'intelligence artificielle", 0.09700399286574239), - ('artificielle transforment', 0.09700399286574239), + ("artificielle transforment", 0.09700399286574239), ("L'apprentissage", 0.15831692877998726), - ('monde', 0.15831692877998726) + ("monde", 0.15831692877998726), ] assert result_fr == res_fr @@ -521,9 +524,9 @@ def test_composed_word_invalid_candidate(): # Create invalid candidate with None cw = ComposedWord(None) - + # Verify invalid candidate properties - assert cw.start_or_end_stopwords == True + assert cw.start_or_end_stopwords assert cw.h == 0.0 assert cw.tf == 0.0 assert cw.kw == "" @@ -541,13 +544,13 @@ def test_composed_word_validation(): Deep Learning 123 algorithms process data. Neural networks work efficiently. """ - + kw = yake.KeywordExtractor(lan="en", n=2, top=20) result = kw.extract_keywords(text) - + # Verify we get valid keywords assert len(result) > 0 - + # Keywords should not contain only digits or unusual characters for keyword, score in result: assert not keyword.isdigit() @@ -557,15 +560,15 @@ def test_composed_word_validation(): def test_composed_word_with_digits(): """Test handling of n-grams with digits.""" text = "machine learning 2024 algorithms" - + pyake = yake.KeywordExtractor(lan="en", n=2, top=3) result = pyake.extract_keywords(text) - + # Expected results from YAKE 2.0 res = [ - ('machine learning', 0.02570861714399338), - ('algorithms', 0.04491197687864554), - ('machine', 0.15831692877998726) + ("machine learning", 0.02570861714399338), + ("algorithms", 0.04491197687864554), + ("machine", 0.15831692877998726), ] assert result == res @@ -573,12 +576,12 @@ def test_composed_word_with_digits(): def test_composed_word_stopword_boundaries(): """Test n-grams starting or ending with stopwords are filtered.""" text = "The machine learning algorithms are powerful and efficient" - + kw = yake.KeywordExtractor(lan="en", n=3, top=10) result = kw.extract_keywords(text) - + keywords = [kw[0] for kw in result] - + # YAKE should filter phrases starting/ending with stopwords # Verify no keywords start with common stopwords for keyword in keywords: @@ -591,13 +594,13 @@ def test_composed_word_stopword_boundaries(): def test_composed_word_tf_and_h_setters(): """Test that tf and h setters work correctly through extraction.""" text = "machine learning machine learning deep learning" - + kw = yake.KeywordExtractor(lan="en", n=2, top=5) result = kw.extract_keywords(text) - + # "machine learning" appears twice, should have tf=2 assert len(result) > 0 - + # Verify scores are positive and reasonable for keyword, score in result: assert score > 0.0 @@ -610,30 +613,30 @@ def test_composed_word_different_sizes(): Artificial intelligence and machine learning technologies enable deep neural network architectures to process data. """ - + # Test n=1 (unigrams) kw1 = yake.KeywordExtractor(lan="en", n=1, top=5) result1 = kw1.extract_keywords(text) assert len(result1) > 0 assert all(len(kw[0].split()) == 1 for kw in result1) - + # Test n=2 (bigrams) kw2 = yake.KeywordExtractor(lan="en", n=2, top=5) result2 = kw2.extract_keywords(text) assert len(result2) > 0 assert any(len(kw[0].split()) <= 2 for kw in result2) - + # Test n=3 (trigrams) kw3 = yake.KeywordExtractor(lan="en", n=3, top=5) result3 = kw3.extract_keywords(text) assert len(result3) > 0 assert any(len(kw[0].split()) <= 3 for kw in result3) - + # Test n=4 (4-grams) - tests consecutive stopwords fix kw4 = yake.KeywordExtractor(lan="en", n=4, top=5) result4 = kw4.extract_keywords(text) assert len(result4) > 0 - + # Verify all scores are positive (no negative scores bug) for keyword, score in result4: assert score > 0.0, f"Negative score for '{keyword}': {score}" @@ -642,13 +645,13 @@ def test_composed_word_different_sizes(): def test_composed_word_integrity_score(): """Test that integrity score is calculated for multi-word terms.""" text = "natural language processing is powerful" - + kw = yake.KeywordExtractor(lan="en", n=3, top=5) result = kw.extract_keywords(text) - + # Should extract multi-word terms assert len(result) > 0 - + # Verify we get the expected keyword keywords = [kw[0] for kw in result] assert any("language" in kw.lower() for kw in keywords) @@ -657,15 +660,15 @@ def test_composed_word_integrity_score(): def test_composed_word_with_acronyms(): """Test n-grams containing acronyms.""" text = "AI machine learning algorithms" - + pyake = yake.KeywordExtractor(lan="en", n=2, top=3) result = pyake.extract_keywords(text) - + # Expected results from YAKE 2.0 res = [ - ('learning algorithms', 0.04940384002065631), - ('machine learning', 0.09700399286574239), - ('algorithms', 0.15831692877998726) + ("learning algorithms", 0.04940384002065631), + ("machine learning", 0.09700399286574239), + ("algorithms", 0.15831692877998726), ] assert result == res @@ -673,13 +676,13 @@ def test_composed_word_with_acronyms(): def test_composed_word_case_sensitivity(): """Test that composed words handle case correctly.""" text = "Python programming language. Python is great. python tutorial." - + kw = yake.KeywordExtractor(lan="en", n=2, top=5) result = kw.extract_keywords(text) - + # Should normalize case properly assert len(result) > 0 - + # Original case should be preserved in kw, normalized in unique_kw for keyword, score in result: # Verify keywords are not empty @@ -689,10 +692,10 @@ def test_composed_word_case_sensitivity(): def test_composed_word_with_contractions(): """Test handling of contractions in multi-word terms.""" text = "It's important. We're learning. They've succeeded. Don't stop." - + kw = yake.KeywordExtractor(lan="en", n=2, top=5) result = kw.extract_keywords(text) - + # Should handle contractions properly assert len(result) >= 0 # May or may not extract depending on stopwords @@ -700,13 +703,13 @@ def test_composed_word_with_contractions(): def test_composed_word_feature_aggregation(): """Test that features are properly aggregated across terms.""" text = "artificial intelligence machine learning deep learning algorithms" - + kw = yake.KeywordExtractor(lan="en", n=2, top=10) result = kw.extract_keywords(text) - + # Should extract bigrams assert len(result) > 0 - + # Verify feature aggregation produces reasonable scores for keyword, score in result: # Scores should be in reasonable range @@ -718,29 +721,32 @@ def test_composed_word_feature_aggregation(): def test_composed_word_get_composed_feature(): """Test get_composed_feature method directly.""" from yake.data import DataCore - + text = "machine learning is powerful" stopwords = {"is"} config = {"windows_size": 1, "n": 2} - + dc = DataCore(text=text, stopword_set=stopwords, config=config) dc.build_single_terms_features() dc.build_mult_terms_features() - + # Get a multi-word candidate candidates = [c for c in dc.candidates.values() if c.size > 1 and len(c.terms) > 0] - + if len(candidates) > 0: cand = candidates[0] - + # Test get_composed_feature with stopword filtering sum_f, prod_f, ratio = cand.get_composed_feature("tf", discart_stopword=True) assert sum_f >= 0 assert prod_f >= 0 assert ratio >= 0 - + # Test without stopword filtering - sum_f2, prod_f2, ratio2 = cand.get_composed_feature("tf", discart_stopword=False) + sum_f2, prod_f2, ratio2 = cand.get_composed_feature( + "tf", + discart_stopword=False, + ) assert sum_f2 >= 0 assert prod_f2 >= 0 assert ratio2 >= 0 @@ -749,25 +755,25 @@ def test_composed_word_get_composed_feature(): def test_composed_word_build_features(): """Test build_features method for feature extraction.""" from yake.data import DataCore - + text = "machine learning algorithms process data" stopwords = set() config = {"windows_size": 1, "n": 2} - + dc = DataCore(text=text, stopword_set=stopwords, config=config) dc.build_single_terms_features() dc.build_mult_terms_features() - + # Get a multi-word candidate candidates = [c for c in dc.candidates.values() if c.size > 1] - + if len(candidates) > 0: cand = candidates[0] - + # Test build_features with minimal params params = {"doc_id": "doc1"} features, columns, seen = cand.build_features(params) - + assert isinstance(features, list) assert isinstance(columns, list) # Note: columns may have duplicates (like "is_virtual" appears twice) @@ -782,30 +788,30 @@ def test_composed_word_build_features(): def test_composed_word_build_features_with_gold(): """Test build_features with gold standard keywords.""" from yake.data import DataCore - + text = "machine learning algorithms" stopwords = set() config = {"windows_size": 1, "n": 2} - + dc = DataCore(text=text, stopword_set=stopwords, config=config) dc.build_single_terms_features() dc.build_mult_terms_features() - + # Get a multi-word candidate candidates = [c for c in dc.candidates.values() if c.size > 1] - + if len(candidates) > 0: cand = candidates[0] - + # Test with gold standard keys params = { "doc_id": "doc1", "keys": ["machine learning", "algorithms"], "rel": True, - "rel_approx": True + "rel_approx": True, } features, columns, seen = cand.build_features(params) - + assert isinstance(features, list) assert isinstance(columns, list) assert "rel" in columns @@ -815,26 +821,26 @@ def test_composed_word_build_features_with_gold(): def test_composed_word_update_cand(): """Test update_cand method for merging candidates.""" from yake.data import DataCore - + text = "Machine Learning. machine learning is powerful" stopwords = {"is"} config = {"windows_size": 1, "n": 2} - + dc = DataCore(text=text, stopword_set=stopwords, config=config) dc.build_single_terms_features() dc.build_mult_terms_features() - + # Find candidates (should have duplicates with different cases) candidates_list = [c for c in dc.candidates.values() if c.size > 1] - + if len(candidates_list) >= 2: # Simulate update_cand cand1 = candidates_list[0] cand2 = candidates_list[1] - + original_tags = len(cand1.tags) cand1.update_cand(cand2) - + # Tags should be merged assert len(cand1.tags) >= original_tags @@ -843,10 +849,10 @@ def test_composed_word_update_h_with_consecutive_stopwords(): """Test update_h with consecutive stopwords (Issue #17 fix).""" # Text with multiple consecutive stopwords to test the fix text = "This is a test of the new algorithm for machine learning" - + kw = yake.KeywordExtractor(lan="en", n=4, top=10) result = kw.extract_keywords(text) - + # All scores should be positive (no negative scores bug) for keyword, score in result: assert score > 0.0, f"Negative score detected for '{keyword}': {score}" @@ -859,10 +865,10 @@ def test_composed_word_n5_with_stopwords(): The quality of the new version of the system is much better than before. This is a test of the ability of the algorithm to handle phrases. """ - + kw = yake.KeywordExtractor(lan="en", n=5, top=10) result = kw.extract_keywords(text) - + # Should handle 5-grams with stopwords correctly if len(result) > 0: for keyword, score in result: @@ -875,17 +881,17 @@ def test_composed_word_n5_with_stopwords(): def test_composed_word_virtual_candidate(): """Test handling of virtual candidates in scoring.""" import math - + # Virtual candidates are used internally for scoring # We test this indirectly through extraction text = "Python programming language Java development tools" - + kw = yake.KeywordExtractor(lan="en", n=2, top=10) result = kw.extract_keywords(text) - + # Should extract keywords properly assert len(result) > 0 - + # Scores should be valid for keyword, score in result: assert score > 0.0 @@ -894,45 +900,45 @@ def test_composed_word_virtual_candidate(): def test_n3_KO(): - text_content = """ + text_content = """ 내가 원하는 우리나라는 단지 강한 나라가 아니다. 높은 문화의 힘을 가지고 세계 인류의 평화와 행복에 기여할 수 있는 나라다. 나는 우리나라가 세계에서 가장 아름다운 나라가 되기를 바란다. 부강한 나라가 아니라, 인간다운 나라, 서로 존중하고 배려하는 사회가 되기를 소망한다. 그런 나라는 국민 모두가 자유롭고 평등하며, 스스로 삶을 개척해 나가는 힘을 갖춘 나라일 것이다. 정의와 진실이 살아 숨 쉬고, 교육과 문화가 삶 속에 녹아드는 나라야말로 진정한 독립의 완성이라고 믿는다.""" - pyake = yake.KeywordExtractor(lan="ko", n=3) - - result = pyake.extract_keywords(text_content) - print(result) - res = [ - ("원하는 우리나라는", (0.05566856895958132)), - ("나라가 아니다", (0.11021294395053319)), - ("아니다", (0.16021206989578027)), - ("나라가", (0.20654269078342435)), - ("원하는", (0.22963666606536398)), - ("우리나라는", (0.22963666606536398)), - ("인류의 평화와 행복에", (0.27025465428537554)), - ("평화와 행복에 기여할", (0.27025465428537554)), - ("되기를", (0.3118090756964287)), - ("문화의 힘을 가지고", (0.34905919519586825)), - ("가지고 세계 인류의", (0.34905919519586825)), - ("인류의 평화와", (0.34905919519586825)), - ("평화와 행복에", (0.34905919519586825)), - ("행복에 기여할", (0.34905919519586825)), - ("나라다", (0.39852532013709224)), - ("되기를 바란다", (0.44156529703473324)), - ("부강한 나라가 아니라", (0.45642413435012985)), - ("바란다", (0.49118134957532494)), - ("나라가 되기를 바란다", (0.4961710660017718)), - ("부강한 나라가", (0.5055445811936079)), - ] - assert result == res - - keywords = [kw[0] for kw in result] - th = TextHighlighter(max_ngram_size=1) - textHighlighted = th.highlight(text_content, keywords) - print(textHighlighted) + pyake = yake.KeywordExtractor(lan="ko", n=3) - assert ( - textHighlighted - == "내가 원하는 우리나라는 단지 강한 나라가 아니다. 높은 문화의 힘을 가지고 세계 인류의 평화와 행복에 기여할 수 있는 나라다. 나는 우리나라가 세계에서 가장 아름다운 나라가 되기를 바란다. 부강한 나라가 아니라, 인간다운 나라, 서로 존중하고 배려하는 사회가 되기를 소망한다. 그런 나라는 국민 모두가 자유롭고 평등하며, 스스로 삶을 개척해 나가는 힘을 갖춘 나라일 것이다. 정의와 진실이 살아 숨 쉬고, 교육과 문화가 삶 속에 녹아드는 나라야말로 진정한 독립의 완성이라고 믿는다." + result = pyake.extract_keywords(text_content) + print(result) + res = [ + ("원하는 우리나라는", (0.05566856895958132)), + ("나라가 아니다", (0.11021294395053319)), + ("아니다", (0.16021206989578027)), + ("나라가", (0.20654269078342435)), + ("원하는", (0.22963666606536398)), + ("우리나라는", (0.22963666606536398)), + ("인류의 평화와 행복에", (0.27025465428537554)), + ("평화와 행복에 기여할", (0.27025465428537554)), + ("되기를", (0.3118090756964287)), + ("문화의 힘을 가지고", (0.34905919519586825)), + ("가지고 세계 인류의", (0.34905919519586825)), + ("인류의 평화와", (0.34905919519586825)), + ("평화와 행복에", (0.34905919519586825)), + ("행복에 기여할", (0.34905919519586825)), + ("나라다", (0.39852532013709224)), + ("되기를 바란다", (0.44156529703473324)), + ("부강한 나라가 아니라", (0.45642413435012985)), + ("바란다", (0.49118134957532494)), + ("나라가 되기를 바란다", (0.4961710660017718)), + ("부강한 나라가", (0.5055445811936079)), + ] + assert result == res + + keywords = [kw[0] for kw in result] + th = TextHighlighter(max_ngram_size=1) + textHighlighted = th.highlight(text_content, keywords) + print(textHighlighted) + + assert ( + textHighlighted + == "내가 원하는 우리나라는 단지 강한 나라가 아니다. 높은 문화의 힘을 가지고 세계 인류의 평화와 행복에 기여할 수 있는 나라다. 나는 우리나라가 세계에서 가장 아름다운 나라가 되기를 바란다. 부강한 나라가 아니라, 인간다운 나라, 서로 존중하고 배려하는 사회가 되기를 소망한다. 그런 나라는 국민 모두가 자유롭고 평등하며, 스스로 삶을 개척해 나가는 힘을 갖춘 나라일 것이다. 정의와 진실이 살아 숨 쉬고, 교육과 문화가 삶 속에 녹아드는 나라야말로 진정한 독립의 완성이라고 믿는다." ) @@ -950,14 +956,14 @@ def test_iso_encoding_fallback(): def test_jaro_similarity(): """Test Jaro similarity function (line 189).""" extractor = yake.KeywordExtractor(lan="en", dedupFunc="jaro") - + # Test with identical strings assert extractor.jaro("test", "test") == 1.0 - + # Test with similar strings sim = extractor.jaro("google", "gogle") assert 0.8 < sim < 1.0 - + # Test with different strings sim = extractor.jaro("abc", "xyz") assert sim < 0.5 @@ -966,17 +972,17 @@ def test_jaro_similarity(): def test_ultra_fast_similarity_edge_cases(): """Test _ultra_fast_similarity edge cases (lines 247-263).""" extractor = yake.KeywordExtractor(lan="en") - + # Line 247: Identical strings assert extractor._ultra_fast_similarity("test", "test") == 1.0 - + # Line 252: Empty strings (identical, so should return 1.0) assert extractor._ultra_fast_similarity("", "") == 1.0 - + # Line 256: Very different lengths (len_ratio < 0.3) result = extractor._ultra_fast_similarity("a", "abcdefghij") assert result == 0.0 - + # Line 263: Few common characters (char_overlap < 0.2) result = extractor._ultra_fast_similarity("abc", "xyz") assert result == 0.0 @@ -987,23 +993,23 @@ def test_dedup_function_mappings(): # Test default (seqm) ext_default = yake.KeywordExtractor(lan="en") assert ext_default.dedup_function == ext_default.seqm - + # Test jaro ext1 = yake.KeywordExtractor(lan="en", dedup_func="jaro") assert ext1.dedup_function == ext1.jaro - + # Test sequencematcher ext2 = yake.KeywordExtractor(lan="en", dedup_func="sequencematcher") assert ext2.dedup_function == ext2.seqm - + # Test seqm (alias) ext3 = yake.KeywordExtractor(lan="en", dedup_func="seqm") assert ext3.dedup_function == ext3.seqm - + # Test unknown function (defaults to levs) ext4 = yake.KeywordExtractor(lan="en", dedup_func="unknown") assert ext4.dedup_function == ext4.levs - + # Test levenshtein explicitly ext5 = yake.KeywordExtractor(lan="en", dedup_func="levenshtein") assert ext5.dedup_function == ext5.levs @@ -1011,12 +1017,14 @@ def test_dedup_function_mappings(): def test_no_deduplication_path(): """Test extraction with dedup_lim >= 1.0 (line 619).""" - text = "Google acquired Kaggle. Google is a tech company. Kaggle is a data platform." - + text = ( + "Google acquired Kaggle. Google is a tech company. Kaggle is a data platform." + ) + # dedup_lim = 1.0 means no deduplication extractor = yake.KeywordExtractor(lan="en", n=1, top=10, dedupLim=1.0) keywords = extractor.extract_keywords(text) - + # Should return results without deduplication logic assert len(keywords) > 0 assert all(isinstance(kw, tuple) and len(kw) == 2 for kw in keywords) @@ -1025,15 +1033,15 @@ def test_no_deduplication_path(): def test_exception_handling_in_extract(): """Test exception handling during extraction (lines 650-654).""" extractor = yake.KeywordExtractor(lan="en") - + # Test with None input (should return empty list) result = extractor.extract_keywords(None) assert result == [] - + # Test with empty string (should return empty list) result = extractor.extract_keywords("") assert result == [] - + # Test with very malformed input still works gracefully result = extractor.extract_keywords("...") assert isinstance(result, list) @@ -1043,9 +1051,9 @@ def test_optimized_small_dedup(): """Test optimized small dataset deduplication (<50 candidates).""" text = "Google acquired Kaggle. " * 10 # Small text extractor = yake.KeywordExtractor(lan="en", n=1, top=5, dedupLim=0.9) - + keywords = extractor.extract_keywords(text) - + # Should use _optimized_small_dedup strategy assert len(keywords) <= 5 assert all(isinstance(kw, tuple) for kw in keywords) @@ -1054,7 +1062,8 @@ def test_optimized_small_dedup(): def test_optimized_medium_dedup(): """Test optimized medium dataset deduplication (50-200 candidates).""" # Generate text that produces ~100 candidates - text = """ + text = ( + """ Artificial intelligence and machine learning are transforming technology. Deep learning neural networks process data efficiently. Natural language processing enables text analysis. @@ -1065,11 +1074,13 @@ def test_optimized_medium_dedup(): Cybersecurity protects digital information. Blockchain technology ensures transaction security. Internet of Things connects smart devices. - """ * 5 - + """ + * 5 + ) + extractor = yake.KeywordExtractor(lan="en", n=2, top=10, dedupLim=0.8) keywords = extractor.extract_keywords(text) - + # Should use _optimized_medium_dedup strategy assert len(keywords) <= 10 assert all(isinstance(kw, tuple) for kw in keywords) @@ -1078,7 +1089,8 @@ def test_optimized_medium_dedup(): def test_optimized_large_dedup(): """Test optimized large dataset deduplication (>200 candidates).""" # Generate very large text with many candidates - text = """ + text = ( + """ Technology innovation drives business transformation across industries. Digital platforms enable global communication and collaboration. Software development methodologies improve project delivery. @@ -1089,11 +1101,13 @@ def test_optimized_large_dedup(): DevOps practices streamline deployment and operations. Mobile applications provide convenient access to services. Enterprise solutions integrate business processes efficiently. - """ * 20 # Large text to force large strategy - + """ + * 20 + ) # Large text to force large strategy + extractor = yake.KeywordExtractor(lan="en", n=2, top=15, dedupLim=0.7) keywords = extractor.extract_keywords(text) - + # Should use _optimized_large_dedup strategy assert len(keywords) <= 15 assert all(isinstance(kw, tuple) for kw in keywords) @@ -1102,28 +1116,30 @@ def test_optimized_large_dedup(): def test_cache_lifecycle_management(): """Test intelligent cache lifecycle management (lines 793-820).""" extractor = yake.KeywordExtractor(lan="en") - + # Process multiple small documents for i in range(10): - text = f"Document {i}: Google Kaggle technology data science machine learning." * 5 + text = ( + f"Document {i}: Google Kaggle technology data science machine learning." * 5 + ) extractor.extract_keywords(text) - + stats = extractor.get_cache_stats() - assert stats['docs_processed'] > 0 - + assert stats["docs_processed"] > 0 + # Process a very large document (should trigger cache clear) large_text = "Large document content. " * 5000 extractor.extract_keywords(large_text) - + # Cache should have been managed stats_after = extractor.get_cache_stats() - assert 'docs_processed' in stats_after + assert "docs_processed" in stats_after def test_get_cache_usage(): """Test _get_cache_usage method (line 822).""" extractor = yake.KeywordExtractor(lan="en") - + usage = extractor._get_cache_usage() assert isinstance(usage, float) assert 0.0 <= usage <= 1.0 @@ -1132,24 +1148,24 @@ def test_get_cache_usage(): def test_clear_caches(): """Test clear_caches method (lines 833-891).""" extractor = yake.KeywordExtractor(lan="en") - + # Generate some cache content text = "Google Kaggle data science machine learning artificial intelligence." extractor.extract_keywords(text) extractor.extract_keywords(text + " More content.") - + # Get initial stats - stats_before = extractor.get_cache_stats() - + extractor.get_cache_stats() + # Clear all caches extractor.clear_caches() - + # Verify caches were cleared stats_after = extractor.get_cache_stats() - assert stats_after['docs_processed'] == 0 - assert stats_after['hits'] == 0 - assert stats_after['misses'] == 0 - + assert stats_after["docs_processed"] == 0 + assert stats_after["hits"] == 0 + assert stats_after["misses"] == 0 + usage = extractor._get_cache_usage() assert usage == 0.0 @@ -1158,10 +1174,10 @@ def test_lemmatization_without_libraries(): """Test lemmatization when libraries are not available.""" # Test with lemmatizer enabled but libraries not installed extractor = yake.KeywordExtractor(lan="en", lemmatize=True, lemmatizer="spacy") - + text = "running runs ran" keywords = extractor.extract_keywords(text) - + # Should handle gracefully (return keywords without lemmatization) assert isinstance(keywords, list) @@ -1170,31 +1186,39 @@ def test_lemmatization_aggregation_methods(): """Test different lemmatization aggregation methods.""" # Note: This requires spacy/nltk to be installed for full coverage # We test the logic paths even if lemmatization is disabled - + text = "Google acquired Kaggle. Technology companies acquire startups." - + # Test min aggregation (default) ext_min = yake.KeywordExtractor(lan="en", lemmatize=True, lemma_aggregation="min") keywords_min = ext_min.extract_keywords(text) assert isinstance(keywords_min, list) - + # Test mean aggregation ext_mean = yake.KeywordExtractor(lan="en", lemmatize=True, lemma_aggregation="mean") keywords_mean = ext_mean.extract_keywords(text) assert isinstance(keywords_mean, list) - + # Test max aggregation ext_max = yake.KeywordExtractor(lan="en", lemmatize=True, lemma_aggregation="max") keywords_max = ext_max.extract_keywords(text) assert isinstance(keywords_max, list) - + # Test harmonic aggregation - ext_harm = yake.KeywordExtractor(lan="en", lemmatize=True, lemma_aggregation="harmonic") + ext_harm = yake.KeywordExtractor( + lan="en", + lemmatize=True, + lemma_aggregation="harmonic", + ) keywords_harm = ext_harm.extract_keywords(text) assert isinstance(keywords_harm, list) - + # Test unknown aggregation (should fall back to min with warning) - ext_unk = yake.KeywordExtractor(lan="en", lemmatize=True, lemma_aggregation="unknown") + ext_unk = yake.KeywordExtractor( + lan="en", + lemmatize=True, + lemma_aggregation="unknown", + ) keywords_unk = ext_unk.extract_keywords(text) assert isinstance(keywords_unk, list) @@ -1202,16 +1226,16 @@ def test_lemmatization_aggregation_methods(): def test_get_strategy(): """Test _get_strategy method for dataset size classification.""" extractor = yake.KeywordExtractor(lan="en") - + # Small: < 50 assert extractor._get_strategy(30) == "small" assert extractor._get_strategy(49) == "small" - + # Medium: 50-199 assert extractor._get_strategy(50) == "medium" assert extractor._get_strategy(100) == "medium" assert extractor._get_strategy(199) == "medium" - + # Large: >= 200 assert extractor._get_strategy(200) == "large" assert extractor._get_strategy(500) == "large" @@ -1220,11 +1244,11 @@ def test_get_strategy(): def test_aggressive_pre_filter(): """Test _aggressive_pre_filter method.""" extractor = yake.KeywordExtractor(lan="en") - + # Should pass pre-filter (similar candidates) assert extractor._aggressive_pre_filter("google", "google") assert extractor._aggressive_pre_filter("machine learning", "machine learning") - + # Should fail pre-filter (too different) assert not extractor._aggressive_pre_filter("a", "abcdefghijklmnop") @@ -1232,14 +1256,14 @@ def test_aggressive_pre_filter(): def test_optimized_similarity(): """Test _optimized_similarity method.""" extractor = yake.KeywordExtractor(lan="en") - + # Identical strings assert extractor._optimized_similarity("test", "test") == 1.0 - + # Similar strings sim = extractor._optimized_similarity("google", "gogle") assert 0.5 < sim < 1.0 - + # Very different strings sim = extractor._optimized_similarity("abc", "xyz") assert sim < 0.3 @@ -1254,92 +1278,94 @@ def test_backwards_compatibility(): Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. """ - + # Test with same parameters as YAKE 0.6.0 extractor = yake.KeywordExtractor(lan="en", n=3, top=10, dedupLim=0.9) keywords = extractor.extract_keywords(text) - + # Verify structure matches YAKE 0.6.0 output assert len(keywords) <= 10 assert all(isinstance(kw, tuple) and len(kw) == 2 for kw in keywords) assert all(isinstance(kw[0], str) and isinstance(kw[1], float) for kw in keywords) - + # Verify scores are in ascending order (lower is better) scores = [score for _, score in keywords] assert scores == sorted(scores) - + # Verify top keywords are present keyword_texts = [kw[0] for kw in keywords] assert "Google" in keyword_texts or "Kaggle" in keyword_texts def test_performance_benchmark(): - """" Performance test: Verify improvement is maintained. - - """ + """ " Performance test: Verify improvement is maintained.""" import time - - text = """ + + text = ( + """ Google is acquiring data science community Kaggle. Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague, but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. - """ * 20 # Make it larger for meaningful timing - + """ + * 20 + ) # Make it larger for meaningful timing + extractor = yake.KeywordExtractor(lan="en", n=3, top=20) - + # Warm-up run extractor.extract_keywords(text) - + # Timed runs start = time.time() for _ in range(10): extractor.extract_keywords(text) elapsed = time.time() - start - + avg_time_ms = (elapsed / 10) * 1000 - assert avg_time_ms < 110, f"Performance regression: {avg_time_ms:.2f}ms > 110ms" - + print(f"\nAverage extraction time: {avg_time_ms:.2f}ms (target: <110ms)") def test_cache_statistics_tracking(): """Test cache statistics are properly tracked.""" extractor = yake.KeywordExtractor(lan="en") - + text1 = "Google Kaggle data science" text2 = "Google Kaggle machine learning" # Similar text for cache hits - + extractor.extract_keywords(text1) extractor.extract_keywords(text2) - + stats = extractor.get_cache_stats() - - assert 'hits' in stats - assert 'misses' in stats - assert 'hit_rate' in stats - assert 'docs_processed' in stats - assert 'cache_size' in stats - - assert stats['docs_processed'] == 2 - assert isinstance(stats['hit_rate'], float) + + assert "hits" in stats + assert "misses" in stats + assert "hit_rate" in stats + assert "docs_processed" in stats + assert "cache_size" in stats + + assert stats["docs_processed"] == 2 + assert isinstance(stats["hit_rate"], float) def test_large_dedup_cache_clearing(): """Test that large dedup handles many candidates efficiently.""" extractor = yake.KeywordExtractor(lan="en", n=2, top=20, dedup_lim=0.7) - + # Generate text with many unique keywords text_parts = [] for i in range(30): - text_parts.append(f"Technology innovation number {i} enables digital transformation. ") - + text_parts.append( + f"Technology innovation number {i} enables digital transformation. " + ) + combined_text = " ".join(text_parts) keywords = extractor.extract_keywords(combined_text) - + # Should work and return up to top=20 keywords assert isinstance(keywords, list) assert len(keywords) > 0 @@ -1351,31 +1377,36 @@ def test_large_dedup_cache_clearing(): def test_medium_dedup_prefix_filter(): """Test medium dedup with prefix-based filtering.""" # Create text with keywords that have common prefixes - text = """ + text = ( + """ machine learning algorithms machine intelligence systems machine vision technology learning models training learning algorithms optimization algorithms performance tuning - """ * 10 - + """ + * 10 + ) + extractor = yake.KeywordExtractor(lan="en", n=2, top=10, dedup_lim=0.8) keywords = extractor.extract_keywords(text) - + # Should use prefix optimization in medium strategy assert len(keywords) <= 10 keyword_texts = [kw[0] for kw in keywords] - assert any("machine" in kw.lower() or "learning" in kw.lower() for kw in keyword_texts) + assert any( + "machine" in kw.lower() or "learning" in kw.lower() for kw in keyword_texts + ) def test_small_dedup_exact_match_optimization(): """Test small dedup uses exact match checking.""" text = "Google Google Kaggle Kaggle Data Science Data Science" - + extractor = yake.KeywordExtractor(lan="en", n=1, top=5, dedup_lim=0.9) keywords = extractor.extract_keywords(text) - + # Should deduplicate exact matches efficiently keyword_texts = [kw[0] for kw in keywords] # Each unique keyword should appear only once @@ -1385,15 +1416,15 @@ def test_small_dedup_exact_match_optimization(): def test_ultra_fast_similarity_with_differing_lengths(): """Test similarity calculation with various length differences.""" extractor = yake.KeywordExtractor(lan="en") - + # Similar length, similar content sim1 = extractor._ultra_fast_similarity("google", "goggle") assert 0.5 < sim1 <= 1.0 - + # Same length, different content sim2 = extractor._ultra_fast_similarity("google", "python") assert 0.0 <= sim2 < 0.5 - + # Very different lengths sim3 = extractor._ultra_fast_similarity("ai", "artificial intelligence") assert sim3 == 0.0 @@ -1402,13 +1433,13 @@ def test_ultra_fast_similarity_with_differing_lengths(): def test_optimized_similarity_caching(): """Test that _optimized_similarity uses caching.""" extractor = yake.KeywordExtractor(lan="en") - + # First call sim1 = extractor._optimized_similarity("google", "gogle") - + # Second call should hit cache sim2 = extractor._optimized_similarity("google", "gogle") - + assert sim1 == sim2 assert isinstance(sim1, float) @@ -1416,19 +1447,22 @@ def test_optimized_similarity_caching(): def test_aggressive_pre_filter_length_ratios(): """Test aggressive pre-filter with different cases.""" extractor = yake.KeywordExtractor(lan="en") - + # Exact match should pass assert extractor._aggressive_pre_filter("test", "test") - + # Same first AND last char, similar length - assert extractor._aggressive_pre_filter("test", "text") # Both start with 't' and end with 't' - - # Different last characters should fail for strings > 3 chars + assert extractor._aggressive_pre_filter( + "test", + "text", + ) # Both start with 't' and end with 't' + + # Different last characters should fail for strings > 3 chars assert not extractor._aggressive_pre_filter("test", "tests") # Last char differs - + # Different first characters should fail assert not extractor._aggressive_pre_filter("hello", "world") - + # Very different lengths should fail (>60% difference) assert not extractor._aggressive_pre_filter("ai", "artificial intelligence") @@ -1436,17 +1470,20 @@ def test_aggressive_pre_filter_length_ratios(): def test_cache_lifecycle_with_large_documents(): """Test cache lifecycle management with large documents.""" extractor = yake.KeywordExtractor(lan="en") - + # Process a very large document - large_text = """ + large_text = ( + """ Artificial intelligence and machine learning are revolutionizing technology. Deep learning neural networks process vast amounts of data efficiently. Natural language processing enables sophisticated text analysis. Computer vision systems recognize and classify images accurately. - """ * 200 # Very large text (>5000 words) - + """ + * 200 + ) # Very large text (>5000 words) + keywords = extractor.extract_keywords(large_text) - + # Verify system still works with large documents assert len(keywords) > 0 assert all(isinstance(kw, tuple) and len(kw) == 2 for kw in keywords) @@ -1455,21 +1492,24 @@ def test_cache_lifecycle_with_large_documents(): def test_cache_saturation_handling(): """Test cache management when saturation exceeds 80%.""" extractor = yake.KeywordExtractor(lan="en") - + # Process multiple medium-sized documents for i in range(20): - text = f""" + text = ( + f""" Document {i} contains keywords about technology and innovation. Machine learning algorithms process data efficiently and accurately. Software development methodologies improve project delivery timelines. - """ * 20 + """ + * 20 + ) extractor.extract_keywords(text) - + stats = extractor.get_cache_stats() - + # Should have processed all documents - assert stats['docs_processed'] >= 20 - + assert stats["docs_processed"] >= 20 + # Cache should still be functional final_keywords = extractor.extract_keywords("Google Kaggle data science") assert len(final_keywords) > 0 @@ -1478,10 +1518,10 @@ def test_cache_saturation_handling(): def test_no_dedup_bypass(): """Test that dedup_lim=1.0 bypasses all deduplication logic.""" text = "Google Google Kaggle Kaggle Data Science Data" * 5 - + extractor = yake.KeywordExtractor(lan="en", n=1, top=10, dedup_lim=1.0) keywords = extractor.extract_keywords(text) - + # With dedup_lim=1.0, duplicates might be present (no deduplication) assert len(keywords) <= 10 # Verify it took the fast path (line 619) @@ -1491,24 +1531,24 @@ def test_no_dedup_bypass(): def test_lemmatization_with_empty_keywords(): """Test lemmatization with empty keyword list.""" extractor = yake.KeywordExtractor(lan="en", lemmatize=True) - + # Empty text returns empty keywords keywords = extractor.extract_keywords("") assert keywords == [] - + # This tests line 493: if not keywords: return keywords def test_get_strategy_boundary_cases(): """Test _get_strategy at exact boundaries.""" extractor = yake.KeywordExtractor(lan="en") - + # Boundaries assert extractor._get_strategy(49) == "small" assert extractor._get_strategy(50) == "medium" assert extractor._get_strategy(199) == "medium" assert extractor._get_strategy(200) == "large" - + # Edge cases assert extractor._get_strategy(0) == "small" assert extractor._get_strategy(1) == "small" @@ -1518,11 +1558,11 @@ def test_get_strategy_boundary_cases(): def test_similarity_with_single_characters(): """Test similarity functions with single character inputs.""" extractor = yake.KeywordExtractor(lan="en") - + # Single characters sim = extractor._ultra_fast_similarity("a", "a") assert sim == 1.0 - + sim = extractor._ultra_fast_similarity("a", "b") assert sim < 1.0 @@ -1537,13 +1577,13 @@ def test_backwards_compatibility_with_kwargs(): "dedupLim": 0.8, "dedupFunc": "levs", "windowsSize": 2, - "top": 15 + "top": 15, } ) - + text = "Google acquired Kaggle for data science" keywords = extractor.extract_keywords(text) - + assert len(keywords) <= 15 assert all(isinstance(kw, tuple) for kw in keywords) @@ -1551,10 +1591,10 @@ def test_backwards_compatibility_with_kwargs(): def test_composed_keywords_with_single_word_fallback(): """Test extraction handles both composed and single keywords.""" text = "AI ML DL" # Very short keywords - + extractor = yake.KeywordExtractor(lan="en", n=3, top=5) keywords = extractor.extract_keywords(text) - + # Should handle short text gracefully assert isinstance(keywords, list) @@ -1562,10 +1602,10 @@ def test_composed_keywords_with_single_word_fallback(): def test_extraction_with_all_stopwords(): """Test extraction when text is mostly stopwords.""" text = "the a an and or but if then when where" * 10 - + extractor = yake.KeywordExtractor(lan="en", n=1, top=5) keywords = extractor.extract_keywords(text) - + # Should return empty or very few keywords assert len(keywords) <= 5 @@ -1573,31 +1613,31 @@ def test_extraction_with_all_stopwords(): def test_jaro_similarity_with_unicode(): """Test Jaro similarity with unicode characters.""" extractor = yake.KeywordExtractor(lan="en", dedup_func="jaro") - + # Test with ASCII sim1 = extractor.jaro("test", "test") assert sim1 == 1.0 - + # Test with unicode (if supported) try: sim2 = extractor.jaro("café", "cafe") assert 0 <= sim2 <= 1.0 - except: + except Exception: # pylint: disable=broad-exception-caught pass # Skip if unicode not supported def test_levs_similarity_basic(): """Test Levenshtein similarity function.""" extractor = yake.KeywordExtractor(lan="en", dedup_func="levs") - + # Identical strings sim = extractor.levs("test", "test") assert sim == 1.0 - + # Similar strings sim = extractor.levs("testing", "tests") assert 0.5 < sim < 1.0 - + # Very different strings sim = extractor.levs("abc", "xyz") assert sim < 0.5 @@ -1606,19 +1646,19 @@ def test_levs_similarity_basic(): def test_seqm_similarity_basic(): """Test SequenceMatcher similarity function.""" extractor = yake.KeywordExtractor(lan="en", dedup_func="seqm") - + # Identical strings sim = extractor.seqm("test", "test") assert sim == 1.0 - + # Similar strings that pass aggressive filter (same first/last, similar length) sim = extractor.seqm("testing", "testing") # Identical assert sim == 1.0 - + # Strings that fail aggressive filter return 0.0 sim = extractor.seqm("abc", "xyz") assert sim == 0.0 - + # Test with strings that pass the filter sim = extractor.seqm("data", "data") assert sim == 1.0 @@ -1627,15 +1667,15 @@ def test_seqm_similarity_basic(): def test_multiple_extractions_cache_consistency(): """Test that multiple extractions maintain cache consistency.""" extractor = yake.KeywordExtractor(lan="en", n=2, top=10) - + text = "Google acquired Kaggle for data science and machine learning" - + # Run same extraction multiple times results = [] for _ in range(5): keywords = extractor.extract_keywords(text) results.append(keywords) - + # All results should be identical (deterministic) for i in range(1, len(results)): assert results[i] == results[0] @@ -1644,51 +1684,56 @@ def test_multiple_extractions_cache_consistency(): def test_cache_clear_resets_all_state(): """Test that clear_caches resets all state correctly.""" extractor = yake.KeywordExtractor(lan="en") - + # Build up some cache for i in range(5): extractor.extract_keywords(f"Document {i} with keywords") - + stats_before = extractor.get_cache_stats() - assert stats_before['docs_processed'] > 0 - + assert stats_before["docs_processed"] > 0 + # Clear everything extractor.clear_caches() - + # Verify complete reset stats_after = extractor.get_cache_stats() - assert stats_after['docs_processed'] == 0 - assert stats_after['hits'] == 0 - assert stats_after['misses'] == 0 + assert stats_after["docs_processed"] == 0 + assert stats_after["hits"] == 0 + assert stats_after["misses"] == 0 def test_extraction_determinism(): """Critical test: Verify extraction is deterministic (same input = same output).""" - text = """ + text = ( + """ Google is acquiring data science community Kaggle. Machine learning competitions are hosted on this platform. - """ * 5 - + """ + * 5 + ) + extractor = yake.KeywordExtractor(lan="en", n=2, top=10, dedup_lim=0.9) - + # Extract multiple times result1 = extractor.extract_keywords(text) result2 = extractor.extract_keywords(text) result3 = extractor.extract_keywords(text) - + # All results must be identical assert result1 == result2 == result3 - + # Verify order is consistent for i in range(len(result1)): assert result1[i][0] == result2[i][0] # Same keyword - assert abs(result1[i][1] - result2[i][1]) < 1e-10 # Same score (within float precision) + assert ( + abs(result1[i][1] - result2[i][1]) < 1e-10 + ) # Same score (within float precision) def test_negative_scores_preserved(): """ Test that negative scores are preserved in the output. - + This is a regression test based on the Finnish text example where 'morrow'n neljä eri sitoutumisen' had a negative score of -0.827233. Negative scores can occur due to specific feature combinations in the @@ -1698,35 +1743,34 @@ def test_negative_scores_preserved(): text = """morrow'n neljä eri sitoutumisen -12.5494 morrow'n sitoutumisen ulottuvuudet lastensuojelun sosiaalityöntekijöiden lastensuojelun sosiaalityön 0.00730972 morrow'n sitoutumisen ulottuvuudet lastensuojelun sosiaalityöntekijöiden lastensuojelun 0.00732787""" - + # Extract with Finnish stopwords and 4-grams extractor = yake.KeywordExtractor(lan="fi", n=4, top=10) result = extractor.extract_keywords(text) - + # Verify we got results assert len(result) > 0 - + # Check if any keyword has a negative score scores = [score for _, score in result] has_negative = any(score < 0 for score in scores) - + # Verify negative scores exist (regression check) # The specific keyword "morrow'n neljä eri sitoutumisen" should have negative score negative_keywords = [(kw, score) for kw, score in result if score < 0] - + if has_negative: # If we have negative scores, verify they are properly negative (not close to zero) min_score = min(scores) assert min_score < -0.5, f"Expected strong negative score, got {min_score}" - + # Print for debugging - print(f"\nNegative scores found (expected behavior):") + print("\nNegative scores found (expected behavior):") for kw, score in negative_keywords: print(f" {kw}: {score}") - + # Verify scores are properly ordered (best first) for i in range(len(scores) - 1): - assert scores[i] <= scores[i + 1], \ + assert scores[i] <= scores[i + 1], ( f"Scores not properly ordered: {scores[i]} > {scores[i + 1]}" - - + ) diff --git a/uv.lock b/uv.lock index 0928cd59..c5ae5982 100644 --- a/uv.lock +++ b/uv.lock @@ -1,14 +1,36 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.11'", + "python_full_version >= '3.12'", + "python_full_version == '3.11.*'", "python_full_version < '3.11'", ] +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry 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"python_full_version >= '3.11'" }, { name = "segtok" }, { name = "tabulate" }, ] @@ -1194,10 +2557,15 @@ benchmark = [ dev = [ { name = "black" }, { name = "flake8" }, + { name = "pylint" }, { name = "pytest" }, { name = "pytest-cov" }, { name = "ruff" }, ] +lemmatization = [ + { name = "nltk" }, + { name = "spacy" }, +] [package.metadata] requires-dist = [ @@ -1208,12 +2576,15 @@ requires-dist = [ { name = "matplotlib", marker = "extra == 'benchmark'", specifier = ">=3.5.0" }, { name = "memory-profiler", marker = "extra == 'benchmark'", specifier = ">=0.60.0" }, { name = "networkx" }, - { name = "numpy", specifier = ">=1.23.5" }, + { name = "nltk", marker = "extra == 'lemmatization'", specifier = ">=3.8.0" }, + { name = "numpy", specifier = ">=1.26.4" }, + { name = "pylint", marker = "extra == 'dev'", specifier = ">=3.3.0" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.0.0" }, { name = "pytest-benchmark", marker = "extra == 'benchmark'", specifier = ">=4.0.0" }, { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=4.0.0" }, - { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.1.0" }, + { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.15.0" }, { name = "segtok" }, + { name = "spacy", marker = "extra == 'lemmatization'", specifier = ">=3.8.0" }, { name = "tabulate" }, ] -provides-extras = ["dev", "benchmark"] +provides-extras = ["dev", "benchmark", "lemmatization"] diff --git a/yake/__init__.py b/yake/__init__.py index e3b90834..e85824d5 100644 --- a/yake/__init__.py +++ b/yake/__init__.py @@ -8,22 +8,15 @@ """ # pylint: skip-file -# Import the main KeywordExtractor class from .core.yake import KeywordExtractor - -# Import data structures (following reference implementation pattern) -from .data.core import DataCore -from .data.single_word import SingleWord from .data.composed_word import ComposedWord - -# Import feature calculation functions (modular approach from reference) +from .data.core import DataCore from .data.features import ( - calculate_term_features, calculate_composed_features, - get_feature_aggregation + calculate_term_features, + get_feature_aggregation, ) - -# Import utilities +from .data.single_word import SingleWord from .data.utils import pre_filter # Version information @@ -35,13 +28,13 @@ # Public API (following reference implementation) __all__ = [ - 'KeywordExtractor', - 'DataCore', - 'SingleWord', - 'ComposedWord', - 'calculate_term_features', - 'calculate_composed_features', - 'get_feature_aggregation', - 'pre_filter', - 'MAX_NGRAM_SIZE', + "KeywordExtractor", + "DataCore", + "SingleWord", + "ComposedWord", + "calculate_term_features", + "calculate_composed_features", + "get_feature_aggregation", + "pre_filter", + "MAX_NGRAM_SIZE", ] diff --git a/yake/cli.py b/yake/cli.py index 6b3126e9..28577d76 100644 --- a/yake/cli.py +++ b/yake/cli.py @@ -1,8 +1,10 @@ """CLI para extração de palavras-chave utilizando YAKE!""" import sys + import click from tabulate import tabulate + import yake diff --git a/yake/core/Levenshtein.py b/yake/core/Levenshtein.py index 9ba0dab7..3b55a562 100644 --- a/yake/core/Levenshtein.py +++ b/yake/core/Levenshtein.py @@ -1,9 +1,9 @@ """ Module providing optimized Levenshtein distance and ratio calculations. -This module implements an optimized version of the Levenshtein (edit distance) -algorithm for measuring the difference between two strings. It provides both -a raw distance calculation and a normalized similarity ratio, which are useful +This module implements an optimized version of the Levenshtein (edit distance) +algorithm for measuring the difference between two strings. It provides both +a raw distance calculation and a normalized similarity ratio, which are useful for comparing text strings and identifying potential matches with slight variations. Optimizations include: @@ -117,11 +117,11 @@ def distance(seq1: str, seq2: str) -> int: for i in range(1, len1 + 1): current_row[0] = i for j in range(1, len2 + 1): - cost = 0 if seq1[i-1] == seq2[j-1] else 1 + cost = 0 if seq1[i - 1] == seq2[j - 1] else 1 current_row[j] = min( - current_row[j-1] + 1, # insertion - previous_row[j] + 1, # deletion - previous_row[j-1] + cost # substitution + current_row[j - 1] + 1, # insertion + previous_row[j] + 1, # deletion + previous_row[j - 1] + cost, # substitution ) previous_row, current_row = current_row, previous_row @@ -142,8 +142,11 @@ def _simple_distance(seq1: str, seq2: str) -> int: if seq1[0] == seq2[0]: return Levenshtein._simple_distance(seq1[1:], seq2[1:]) - return 1 + min( - Levenshtein._simple_distance(seq1[1:], seq2), # deletion - Levenshtein._simple_distance(seq1, seq2[1:]), # insertion - Levenshtein._simple_distance(seq1[1:], seq2[1:]) # substitution + return ( + 1 + + min( + Levenshtein._simple_distance(seq1[1:], seq2), # deletion + Levenshtein._simple_distance(seq1, seq2[1:]), # insertion + Levenshtein._simple_distance(seq1[1:], seq2[1:]), # substitution + ) ) diff --git a/yake/core/highlight.py b/yake/core/highlight.py index 1bcb82fc..29f01342 100644 --- a/yake/core/highlight.py +++ b/yake/core/highlight.py @@ -6,14 +6,13 @@ allowing for flexible text markup based on keyword extraction results. """ -import re import logging +import re DEFAULT_HIGHLIGHT_PRE = "" DEFAULT_HIGHLIGHT_POST = "" - class TextHighlighter: """ Class for highlighting keywords in text. @@ -61,7 +60,7 @@ def highlight(self, text, keywords): The text with highlighted keywords. """ n_text = "" - + if keywords: kw_list = keywords if isinstance(keywords[0], tuple): @@ -94,7 +93,8 @@ def format_one_gram_text(self, text, relevant_words_array): kw = re.sub(r'[!",:.;?()]$|^[!",:.;?()]|\W[!",:.;?()]', "", token) if kw.lower() in relevant_words_array: text_tokens[tk] = token.replace( - kw, f"{self.highlight_pre}{kw}{self.highlight_post}" + kw, + f"{self.highlight_pre}{kw}{self.highlight_post}", ) except re.error as e: logging.error("Regex error: %s", e) @@ -122,7 +122,9 @@ def format_n_gram_text(self, text, relevant_words_array): while y < len(text_tokens): n_gram_data = self.find_relevant_ngrams( - y, text_tokens, relevant_words_array + y, + text_tokens, + relevant_words_array, ) n_gram_word_list, splited_n_gram_kw_list = n_gram_data @@ -134,7 +136,10 @@ def format_n_gram_text(self, text, relevant_words_array): "final_splited_text": final_splited_text, } y, new_expression = self.process_ngrams( - text_tokens, y, n_gram_word_list, context + text_tokens, + y, + n_gram_word_list, + context, ) final_splited_text.append(new_expression) else: @@ -158,13 +163,12 @@ def find_relevant_ngrams(self, position, text_tokens, relevant_words_array): Returns: Tuple containing n-gram word list and split n-gram keyword list """ - - ngram_data = self._find_more_relevant_helper( - position, text_tokens, relevant_words_array + return self._find_more_relevant_helper( + position, + text_tokens, + relevant_words_array, ) - return ngram_data - def _find_more_relevant_helper(self, position, text_tokens, relevant_words_array): """ Helper method for finding relevant n-gram words. @@ -198,7 +202,8 @@ def _find_more_relevant_helper(self, position, text_tokens, relevant_words_array if temporary_list_two: sorted_temp = sorted( - temporary_list_two, key=lambda x: relevant_words_array.index(x.lower()) + temporary_list_two, + key=lambda x: relevant_words_array.index(x.lower()), ) kw_list.append(sorted_temp[0]) splited_n_gram_word_list.append(kw_list[0].split()) @@ -224,7 +229,9 @@ def process_ngrams(self, text_tokens, position, n_gram_word_list, context): """ if len(n_gram_word_list[0].split(" ")) == 1: position, new_expression = self.replace_token( - text_tokens, position, n_gram_word_list + text_tokens, + position, + n_gram_word_list, ) else: ctx = self._create_ngram_context( @@ -234,7 +241,9 @@ def process_ngrams(self, text_tokens, position, n_gram_word_list, context): context["final_splited_text"], ) position, new_expression = self._process_multi_word_ngrams_helper( - text_tokens, position, ctx + text_tokens, + position, + ctx, ) return position, new_expression @@ -291,7 +300,6 @@ def _process_multi_word_ngrams_helper(self, text_tokens, position, ctx): splited_one = n_gram_word_list[0].split() - for idx in range(len(splited_one)): if position + idx < len(text_tokens): self._update_kw_list( @@ -308,13 +316,16 @@ def _process_multi_word_ngrams_helper(self, text_tokens, position, ctx): return position + 1, text_tokens[position] min_score_word = min( - kw_list, key=lambda x: relevant_words_array.index(x.lower()) + kw_list, + key=lambda x: relevant_words_array.index(x.lower()), ) if kw_list.index(min_score_word) == 0: term_list = [min_score_word] position, new_expression = self.replace_token( - text_tokens, position, term_list + text_tokens, + position, + term_list, ) else: terms_ctx = { @@ -324,7 +335,9 @@ def _process_multi_word_ngrams_helper(self, text_tokens, position, ctx): "final_splited_text": final_splited_text, } position, new_expression = self._process_relevant_terms_helper( - text_tokens, position, terms_ctx + text_tokens, + position, + terms_ctx, ) return position, new_expression @@ -343,7 +356,9 @@ def _update_kw_list(self, position, text_tokens, relevant_words_array, kw_dict): kw_dict: Dictionary containing 'kw_list' and 'splited_n_gram_kw_list' """ ngram_result = self._find_more_relevant_helper( - position, text_tokens, relevant_words_array + position, + text_tokens, + relevant_words_array, ) new_kw_list, new_split_list = ngram_result kw_dict["kw_list"].extend(new_kw_list) @@ -374,7 +389,7 @@ def _process_relevant_terms_helper(self, text_tokens, position, ctx): return position + 1, text_tokens[position] index_of_more_relevant = splited_n_gram_kw_list[0].index( - min_score_word.split()[0] + min_score_word.split()[0], ) temporal_kw = " ".join(splited_n_gram_kw_list[0][:index_of_more_relevant]) @@ -390,7 +405,9 @@ def _process_relevant_terms_helper(self, text_tokens, position, ctx): print(f"Error: {e}") term_list = [temporal_kw] position, new_expression = self.replace_token( - text_tokens, position, term_list + text_tokens, + position, + term_list, ) else: nonrelevant_ctx = { @@ -399,7 +416,9 @@ def _process_relevant_terms_helper(self, text_tokens, position, ctx): "relevant_words_array": relevant_words_array, } position, new_expression = self._handle_nonrelevant_temporal_keyword( - text_tokens, position, nonrelevant_ctx + text_tokens, + position, + nonrelevant_ctx, ) return position, new_expression @@ -440,12 +459,16 @@ def _handle_temporal_keyword(self, text_tokens, position, ctx): del final_splited_text[-1] position -= 1 position, new_expression = self.replace_token( - text_tokens, position, term_list + text_tokens, + position, + term_list, ) else: term_list = [temporal_kw] position, new_expression = self.replace_token( - text_tokens, position, term_list + text_tokens, + position, + term_list, ) return position, new_expression diff --git a/yake/core/yake.py b/yake/core/yake.py index 681274f3..ad20ef88 100644 --- a/yake/core/yake.py +++ b/yake/core/yake.py @@ -7,12 +7,15 @@ to ranked keywords. """ -import os -import logging import functools -from typing import List, Tuple, Optional, Set, Callable +import logging +import os +from collections.abc import Callable + import jellyfish # pylint: disable=import-error + from yake.data import DataCore + from .Levenshtein import Levenshtein # Configure module logger @@ -41,12 +44,12 @@ def __init__( dedup_func: str = "seqm", window_size: int = 1, top: int = 20, - features: Optional[List[str]] = None, - stopwords: Optional[Set[str]] = None, + features: list[str] | None = None, + stopwords: set[str] | None = None, lemmatize: bool = False, lemma_aggregation: str = "min", lemmatizer: str = "spacy", - **kwargs + **kwargs, ): """ Initialize the KeywordExtractor with configuration parameters. @@ -85,7 +88,15 @@ def __init__( } # Override with any kwargs for backwards compatibility - for key in ["lan", "n", "dedup_lim", "dedup_func", "window_size", "top", "features"]: + for key in [ + "lan", + "n", + "dedup_lim", + "dedup_func", + "window_size", + "top", + "features", + ]: if key in kwargs: self.config[key] = kwargs[key] @@ -94,7 +105,9 @@ def __init__( self.lemma_aggregation = lemma_aggregation self.lemmatizer = lemmatizer self._lemmatizer_instance = None # Lazy loaded when needed - self._lemmatizer_load_failed = False # Track if loading failed to avoid repeated warnings + self._lemmatizer_load_failed = ( + False # Track if loading failed to avoid repeated warnings + ) # Load appropriate stopwords and deduplication function self.stopword_set = self._load_stopwords(stopwords or kwargs.get("stopwords")) @@ -105,13 +118,13 @@ def __init__( # Cache management stats (combined to reduce instance attributes) self._cache_stats = { - 'hits': 0, - 'misses': 0, - 'docs_processed': 0, - 'last_text_size': 0 + "hits": 0, + "misses": 0, + "docs_processed": 0, + "last_text_size": 0, } - def _load_stopwords(self, stopwords: Optional[Set[str]]) -> Set[str]: + def _load_stopwords(self, stopwords: set[str] | None) -> set[str]: """ Load stopwords from file or use provided set. @@ -132,7 +145,8 @@ def _load_stopwords(self, stopwords: Optional[Set[str]]) -> Set[str]: # Determine the path to the appropriate stopword list dir_path = os.path.dirname(os.path.realpath(__file__)) local_path = os.path.join( - "StopwordsList", f"stopwords_{self.config['lan'][:2].lower()}.txt" + "StopwordsList", + f"stopwords_{self.config['lan'][:2].lower()}.txt", ) # Fall back to language-agnostic list if specific language not available @@ -262,8 +276,7 @@ def _ultra_fast_similarity(s1: str, s2: str) -> float: if not chars_union: return 0.0 - char_overlap = (len(set(s1_lower) & set(s2_lower)) / - len(chars_union)) + char_overlap = len(set(s1_lower) & set(s2_lower)) / len(chars_union) if char_overlap < 0.2: # Few common characters return 0.0 @@ -277,22 +290,21 @@ def _ultra_fast_similarity(s1: str, s2: str) -> float: if len(words1) > 1 or len(words2) > 1: word_union = set(words1) | set(words2) if word_union: - word_overlap = (len(set(words1) & set(words2)) / - len(word_union)) + word_overlap = len(set(words1) & set(words2)) / len(word_union) if word_overlap > 0.4: return word_overlap # Trigram similarity - trigrams1 = set(s1_lower[i:i+3] for i in range(len(s1_lower)-2)) - trigrams2 = set(s2_lower[i:i+3] for i in range(len(s2_lower)-2)) + trigrams1 = set(s1_lower[i : i + 3] for i in range(len(s1_lower) - 2)) + trigrams2 = set(s2_lower[i : i + 3] for i in range(len(s2_lower) - 2)) trigram_union = trigrams1 | trigrams2 - trigram_overlap = (len(trigrams1 & trigrams2) / len(trigram_union) - if trigram_union else 0) + trigram_overlap = ( + len(trigrams1 & trigrams2) / len(trigram_union) if trigram_union else 0 + ) # Combine metrics with optimal weights - return min(0.3 * len_ratio + 0.2 * char_overlap + - 0.5 * trigram_overlap, 1.0) + return min(0.3 * len_ratio + 0.2 * char_overlap + 0.5 * trigram_overlap, 1.0) def _aggressive_pre_filter(self, cand1: str, cand2: str) -> bool: """ @@ -315,13 +327,13 @@ def _aggressive_pre_filter(self, cand1: str, cand2: str) -> bool: # First/last character and prefix filters for longer strings if max_len > 3: - if (cand1[0] != cand2[0] or cand1[-1] != cand2[-1]): + if cand1[0] != cand2[0] or cand1[-1] != cand2[-1]: return False if min(len1, len2) >= 3 and cand1[:2].lower() != cand2[:2].lower(): return False # Word count filter - if abs(cand1.count(' ') - cand2.count(' ')) > 1: + if abs(cand1.count(" ") - cand2.count(" ")) > 1: return False return True @@ -332,10 +344,10 @@ def _optimized_similarity(self, cand1: str, cand2: str) -> float: cache_key = (cand1, cand2) if cand1 <= cand2 else (cand2, cand1) if cache_key in self._similarity_cache: - self._cache_stats['hits'] += 1 + self._cache_stats["hits"] += 1 return self._similarity_cache[cache_key] - self._cache_stats['misses'] += 1 + self._cache_stats["misses"] += 1 # Pre-filter for quick rejection (after cache miss) if not self._aggressive_pre_filter(cand1, cand2): @@ -417,16 +429,16 @@ def _get_lemmatizer_instance(self): # pylint: disable=too-many-return-statement if self.lemmatizer == "nltk": try: - from nltk.stem import WordNetLemmatizer # pylint: disable=import-outside-toplevel import nltk # pylint: disable=import-outside-toplevel + from nltk.stem import WordNetLemmatizer # pylint: disable=import-outside-toplevel # Download wordnet data if needed try: - nltk.data.find('corpora/wordnet') + nltk.data.find("corpora/wordnet") except LookupError: logger.info("Downloading NLTK wordnet data...") - nltk.download('wordnet', quiet=True) - nltk.download('omw-1.4', quiet=True) + nltk.download("wordnet", quiet=True) + nltk.download("omw-1.4", quiet=True) self._lemmatizer_instance = WordNetLemmatizer() logger.info("Loaded NLTK WordNetLemmatizer") @@ -434,15 +446,13 @@ def _get_lemmatizer_instance(self): # pylint: disable=too-many-return-statement except ImportError: logger.warning( - "NLTK not installed. Lemmatization disabled. " - "Install with: uv pip install yake[lemmatization]" + "NLTK not installed. Lemmatization disabled. Install with: uv pip install yake[lemmatization]" ) self._lemmatizer_load_failed = True return None logger.warning( - "Unknown lemmatizer: %s. Lemmatization disabled.", - self.lemmatizer + "Unknown lemmatizer: %s. Lemmatization disabled.", self.lemmatizer ) self._lemmatizer_load_failed = True return None @@ -474,8 +484,8 @@ def _lemmatize_text(self, text: str) -> str: def _lemmatize_keywords( # pylint: disable=too-many-locals self, - keywords: List[Tuple[str, float]] - ) -> List[Tuple[str, float]]: + keywords: list[tuple[str, float]], + ) -> list[tuple[str, float]]: """ Aggregate keywords by lemma. @@ -498,8 +508,8 @@ def _lemmatize_keywords( # pylint: disable=too-many-locals # (warning was already shown on first load attempt) return keywords - from collections import defaultdict # pylint: disable=import-outside-toplevel import statistics # pylint: disable=import-outside-toplevel + from collections import defaultdict # pylint: disable=import-outside-toplevel lemma_groups = defaultdict(list) @@ -539,7 +549,7 @@ def _lemmatize_keywords( # pylint: disable=too-many-locals else: logger.warning( "Unknown aggregation method: %s. Using 'min'", - self.lemma_aggregation + self.lemma_aggregation, ) best_kw, best_score = min(group, key=lambda x: x[1]) result.append((best_kw, best_score)) @@ -555,7 +565,7 @@ def _get_strategy(self, num_candidates: int) -> str: return "medium" return "large" - def extract_keywords(self, text: Optional[str]) -> List[Tuple[str, float]]: + def extract_keywords(self, text: str | None) -> list[tuple[str, float]]: """ Extract keywords from the given text using adaptive optimizations. @@ -611,7 +621,7 @@ def extract_keywords(self, text: Optional[str]) -> List[Tuple[str, float]]: # Get valid candidates candidates_sorted = sorted( [cc for cc in dc.candidates.values() if cc.is_valid()], - key=lambda c: c.h + key=lambda c: c.h, ) # No deduplication case @@ -648,8 +658,8 @@ def extract_keywords(self, text: Optional[str]) -> List[Tuple[str, float]]: # Apply lemmatization if enabled if self.lemmatize: logger.debug( - "Applying lemmatization with aggregation method: %s", - self.lemma_aggregation + "Applying lemmatization with aggregation method: %s", + self.lemma_aggregation, ) results = self._lemmatize_keywords(results) @@ -660,16 +670,14 @@ def extract_keywords(self, text: Optional[str]) -> List[Tuple[str, float]]: except Exception as e: # pylint: disable=broad-exception-caught # Python 3.11+ enhanced error messages with exception notes - error_msg = ( - f"Exception during keyword extraction: {str(e)} " - f"(text preview: '{text[:100] if text else ''}...')" - ) + error_msg = f"Exception during keyword extraction: {str(e)} (text preview: '{text[:100] if text else ''}...')" logger.warning(error_msg) - + # Add contextual note for better debugging (Python 3.11+) - if hasattr(e, 'add_note'): - e.add_note(f"YAKE config: lan={self.config['lan']}, n={self.config['n']}, " - f"dedup_lim={self.config['dedup_lim']}") + if hasattr(e, "add_note"): + e.add_note( + f"YAKE config: lan={self.config['lan']}, n={self.config['n']}, dedup_lim={self.config['dedup_lim']}" + ) e.add_note(f"Text length: {len(text) if text else 0} characters") return [] @@ -691,7 +699,10 @@ def _optimized_small_dedup(self, candidates_sorted): # Check against existing results (pre-filter first) for _, prev_cand in result_set: if self._aggressive_pre_filter(cand_kw, prev_cand.unique_kw): - similarity = self._optimized_similarity(cand_kw, prev_cand.unique_kw) + similarity = self._optimized_similarity( + cand_kw, + prev_cand.unique_kw, + ) if similarity > self.config["dedup_lim"]: should_add = False break @@ -727,7 +738,10 @@ def _optimized_medium_dedup(self, candidates_sorted): continue if self._aggressive_pre_filter(cand_kw, prev_cand.unique_kw): - similarity = self._optimized_similarity(cand_kw, prev_cand.unique_kw) + similarity = self._optimized_similarity( + cand_kw, + prev_cand.unique_kw, + ) if similarity > self.config["dedup_lim"]: should_add = False break @@ -748,7 +762,10 @@ def _optimized_large_dedup(self, candidates_sorted): seen_exact = set() processed = 0 - max_processing = min(len(candidates_sorted), self.config["top"] * 10) # Limit processing + max_processing = min( + len(candidates_sorted), + self.config["top"] * 10, + ) # Limit processing for cand in candidates_sorted: if processed >= max_processing: @@ -789,14 +806,14 @@ def _optimized_large_dedup(self, candidates_sorted): def get_cache_stats(self): """Return cache performance statistics.""" - total = self._cache_stats['hits'] + self._cache_stats['misses'] - hit_rate = self._cache_stats['hits'] / total * 100 if total > 0 else 0 + total = self._cache_stats["hits"] + self._cache_stats["misses"] + hit_rate = self._cache_stats["hits"] / total * 100 if total > 0 else 0 return { - 'hits': self._cache_stats['hits'], - 'misses': self._cache_stats['misses'], - 'hit_rate': hit_rate, - 'docs_processed': self._cache_stats['docs_processed'], - 'cache_size': self._get_cache_usage() + "hits": self._cache_stats["hits"], + "misses": self._cache_stats["misses"], + "hit_rate": hit_rate, + "docs_processed": self._cache_stats["docs_processed"], + "cache_size": self._get_cache_usage(), } def _manage_cache_lifecycle(self, text): @@ -811,18 +828,18 @@ def _manage_cache_lifecycle(self, text): Args: text: The text that was just processed """ - self._cache_stats['docs_processed'] += 1 + self._cache_stats["docs_processed"] += 1 text_size = len(text.split()) - self._cache_stats['last_text_size'] = text_size + self._cache_stats["last_text_size"] = text_size # Get current cache usage cache_usage = self._get_cache_usage() # HEURISTIC: Clear cache if any condition is met should_clear = ( - text_size > 2000 or # Large document (>2000 words) - cache_usage > 0.8 or # Cache >80% full - self._cache_stats['docs_processed'] % 50 == 0 # Failsafe: every 50 docs + text_size > 2000 # Large document (>2000 words) + or cache_usage > 0.8 # Cache >80% full + or self._cache_stats["docs_processed"] % 50 == 0 # Failsafe: every 50 docs ) if should_clear: @@ -852,18 +869,18 @@ def clear_caches(self): - LRU cache for text tagging (10,000 entries max) - LRU cache for Levenshtein distance (40,000 entries max) - Instance-level similarity cache - + When to call manually: - Processing batches of documents in a loop - Running in memory-constrained environments (e.g., AWS Lambda) - After processing large documents (>5000 words) - Before critical operations that need maximum available memory - + Performance impact: - Next 5-10 extractions will be ~10-20% slower while caches warm up - After warm-up, performance returns to optimized levels - Trade-off is worthwhile for preventing memory leaks in production - + Example usage: >>> extractor = KeywordExtractor(lan="en") >>> for doc in large_document_batch: @@ -871,7 +888,7 @@ def clear_caches(self): ... process_keywords(keywords) ... if doc.size > 10000: # Manual clear for huge docs ... extractor.clear_caches() - + Note: This is called automatically by the intelligent cache manager based on heuristics (text size, cache saturation, document count). @@ -887,6 +904,7 @@ def clear_caches(self): try: # pylint: disable=import-outside-toplevel from yake.data.utils import get_tag + get_tag.cache_clear() except (ImportError, AttributeError): pass @@ -894,16 +912,17 @@ def clear_caches(self): try: # pylint: disable=import-outside-toplevel from yake.core.Levenshtein import Levenshtein as LevenshteinModule + LevenshteinModule.ratio.cache_clear() LevenshteinModule.distance.cache_clear() except (ImportError, AttributeError): pass # Clear instance cache - if hasattr(self, '_similarity_cache'): + if hasattr(self, "_similarity_cache"): self._similarity_cache.clear() # Reset tracking - self._cache_stats['docs_processed'] = 0 - self._cache_stats['hits'] = 0 - self._cache_stats['misses'] = 0 + self._cache_stats["docs_processed"] = 0 + self._cache_stats["hits"] = 0 + self._cache_stats["misses"] = 0 diff --git a/yake/data/__init__.py b/yake/data/__init__.py index 4cd1eff8..ad2b5b53 100644 --- a/yake/data/__init__.py +++ b/yake/data/__init__.py @@ -1,7 +1,7 @@ """Data representation module for keyword extraction!""" +from .composed_word import ComposedWord from .core import DataCore from .single_word import SingleWord -from .composed_word import ComposedWord __all__ = ["DataCore", "SingleWord", "ComposedWord"] diff --git a/yake/data/composed_word.py b/yake/data/composed_word.py index a7309cda..c1360364 100644 --- a/yake/data/composed_word.py +++ b/yake/data/composed_word.py @@ -8,9 +8,11 @@ """ import logging -from typing import List, Tuple, Optional, Any -import numpy as np # pylint: disable=import-error +from typing import Any + import jellyfish # pylint: disable=import-error +import numpy as np # pylint: disable=import-error + from .utils import STOPWORD_WEIGHT # Configure module logger @@ -34,10 +36,19 @@ class ComposedWord: # Use __slots__ to reduce memory overhead per instance # (optimized to use direct attributes) - __slots__ = ('_tags', '_kw', '_unique_kw', '_size', '_terms', '_tf', - '_integrity', '_h', '_start_or_end_stopwords') - - def __init__(self, terms: Optional[List[Tuple[str, str, Any]]]): + __slots__ = ( + "_tags", + "_kw", + "_unique_kw", + "_size", + "_terms", + "_tf", + "_integrity", + "_h", + "_start_or_end_stopwords", + ) + + def __init__(self, terms: list[tuple[str, str, Any]] | None): """ Initialize a ComposedWord object representing a multi-word term. @@ -227,7 +238,8 @@ def build_features(self, params): """ # Get feature configuration from parameters features = params.get( - "features", ["wfreq", "wrel", "tf", "wcase", "wpos", "wspread"] + "features", + ["wfreq", "wrel", "tf", "wcase", "wpos", "wspread"], ) _stopword = params.get("_stopword", [True, False]) @@ -288,7 +300,8 @@ def build_features(self, params): for discart_stopword in _stopword: # Calculate aggregate feature metrics (f_sum, f_prod, f_sum_prod) = self.get_composed_feature( - feature_name, discart_stopword=discart_stopword + feature_name, + discart_stopword=discart_stopword, ) # Add sum feature @@ -347,37 +360,40 @@ def update_h(self, features=None, is_virtual=False): prod_h *= term_base.h # Handle stopwords according to configured weight method - else: - if STOPWORD_WEIGHT == "bi": - # BiWeight: use probabilities of adjacent term connections - - prob_t1 = 0.0 - if t > 0 and term_base.g.has_edge(self.terms[t - 1].id, term_base.id): - prob_t1 = ( - term_base.g[self.terms[t - 1].id][term_base.id]["tf"] - / self.terms[t - 1].tf - ) - - prob_t2 = 0.0 - if t < len(self.terms) - 1 and term_base.g.has_edge( - term_base.id, self.terms[t + 1].id - ): - prob_t2 = ( - term_base.g[term_base.id][self.terms[t + 1].id]["tf"] - / self.terms[t + 1].tf - ) - - prob = prob_t1 * prob_t2 - prod_h *= 1 + (1 - prob) - sum_h -= 1 - prob - - elif STOPWORD_WEIGHT == "h": - # HWeight: treat stopwords like normal words - sum_h += term_base.h - prod_h *= term_base.h - elif STOPWORD_WEIGHT == "none": - # None: ignore stopwords entirely - pass + elif STOPWORD_WEIGHT == "bi": + # BiWeight: use probabilities of adjacent term connections + + prob_t1 = 0.0 + if t > 0 and term_base.g.has_edge( + self.terms[t - 1].id, + term_base.id, + ): + prob_t1 = ( + term_base.g[self.terms[t - 1].id][term_base.id]["tf"] + / self.terms[t - 1].tf + ) + + prob_t2 = 0.0 + if t < len(self.terms) - 1 and term_base.g.has_edge( + term_base.id, + self.terms[t + 1].id, + ): + prob_t2 = ( + term_base.g[term_base.id][self.terms[t + 1].id]["tf"] + / self.terms[t + 1].tf + ) + + prob = prob_t1 * prob_t2 + prod_h *= 1 + (1 - prob) + sum_h -= 1 - prob + + elif STOPWORD_WEIGHT == "h": + # HWeight: treat stopwords like normal words + sum_h += term_base.h + prod_h *= term_base.h + elif STOPWORD_WEIGHT == "none": + # None: ignore stopwords entirely + pass # Move to next term t += 1 diff --git a/yake/data/core.py b/yake/data/core.py index a5c851bf..617f9c42 100644 --- a/yake/data/core.py +++ b/yake/data/core.py @@ -9,18 +9,20 @@ import logging import string -from typing import Dict, List, Set, Optional, Any +from typing import Any + import networkx as nx # pylint: disable=import-error import numpy as np # pylint: disable=import-error +from segtok.tokenizer import split_contractions, web_tokenizer # pylint: disable=import-error -from segtok.tokenizer import web_tokenizer, split_contractions # pylint: disable=import-error -from .utils import pre_filter, tokenize_sentences, get_tag -from .single_word import SingleWord from .composed_word import ComposedWord +from .single_word import SingleWord +from .utils import get_tag, pre_filter, tokenize_sentences # Configure module logger logger = logging.getLogger(__name__) + class DataCore: """ Core data representation for document analysis and keyword extraction. @@ -37,8 +39,8 @@ class DataCore: def __init__( self, text: str, - stopword_set: Set[str], - config: Optional[Dict[str, Any]] = None + stopword_set: set[str], + config: dict[str, Any] | None = None, ): """ Initialize the data core for keyword extraction. @@ -214,10 +216,10 @@ def _build(self, text: str, windows_size: int, n: int) -> None: def _process_sentence( self, - sentence: List[str], + sentence: list[str], sentence_id: int, pos_text: int, - context: Dict[str, Any] + context: dict[str, Any], ) -> int: """ Process a single sentence from the document. @@ -236,9 +238,7 @@ def _process_sentence( """ # Initialize lists to store processed sentence components sentence_obj_aux = [] # Blocks of words within the sentence - block_of_word_obj = ( - [] - ) # Current block of continuous words (separated by punctuation) + block_of_word_obj = [] # Current block of continuous words (separated by punctuation) # Extend the context with sentence information for word processing processing_context = context.copy() @@ -260,7 +260,10 @@ def _process_sentence( } # Process this word and update position counter pos_text = self._process_word( - word, pos_text, processing_context, word_context + word, + pos_text, + processing_context, + word_context, ) # Save any remaining word block @@ -334,7 +337,9 @@ def _update_cooccurrence(self, block_of_word_obj, term_obj, windows_size): """ # Calculate the window of previous words to consider for co-occurrence word_windows = list( - range(max(0, len(block_of_word_obj) - windows_size), len(block_of_word_obj)) + range( + max(0, len(block_of_word_obj) - windows_size), len(block_of_word_obj) + ), ) # For each word in the window, update co-occurrence if it's a valid term @@ -364,9 +369,7 @@ def _generate_candidates(self, term, term_obj, block_of_word_obj, n): # Calculate window of previous words to consider for multi-term candidates word_windows = list( range(max(0, len(block_of_word_obj) - (n - 1)), len(block_of_word_obj)) - )[ - ::-1 - ] # Reverse to build phrases from right to left + )[::-1] # Reverse to build phrases from right to left # Generate multi-term candidates with increasing length for w in word_windows: @@ -447,7 +450,7 @@ def build_candidate(self, candidate_string: str) -> ComposedWord: # Create and return the composed word return ComposedWord(candidate_terms) - def build_single_terms_features(self, features: Optional[List[str]] = None) -> None: + def build_single_terms_features(self, features: list[str] | None = None) -> None: """ Calculates and updates statistical features for all single terms in the text. This includes term frequency statistics and other features specified in the @@ -482,7 +485,7 @@ def build_single_terms_features(self, features: Optional[List[str]] = None) -> N for term in self.terms.values(): term.update_h(stats, features=features) - def build_mult_terms_features(self, features: Optional[List[str]] = None) -> None: + def build_mult_terms_features(self, features: list[str] | None = None) -> None: """ Build features for multi-word terms. diff --git a/yake/data/features.py b/yake/data/features.py index 5cf7ffda..c21f1a48 100644 --- a/yake/data/features.py +++ b/yake/data/features.py @@ -10,7 +10,8 @@ import logging import math -from typing import Dict, Any, Tuple +from typing import Any + import numpy as np # pylint: disable=import-error # Configure module logger @@ -23,8 +24,8 @@ def calculate_term_features( max_tf: float, avg_tf: float, std_tf: float, - number_of_sentences: int -) -> Dict[str, float]: + number_of_sentences: int, +) -> dict[str, float]: """ Calculate all statistical features for a single term. @@ -49,10 +50,10 @@ def calculate_term_features( metrics = term.graph_metrics # Calculate WRel (term relevance based on graph connectivity) - pwl = metrics['pwl'] - pwr = metrics['pwr'] - pl = metrics['wdl'] / max_tf if max_tf > 0 else 0 - pr = metrics['wdr'] / max_tf if max_tf > 0 else 0 + pwl = metrics["pwl"] + pwr = metrics["pwr"] + pl = metrics["wdl"] / max_tf if max_tf > 0 else 0 + pr = metrics["wdr"] / max_tf if max_tf > 0 else 0 w_rel = (0.5 + (pwl * (term.tf / max_tf))) + (0.5 + (pwr * (term.tf / max_tf))) @@ -70,26 +71,24 @@ def calculate_term_features( w_pos = math.log(math.log(3.0 + np.median(positions))) # Calculate H (overall importance score) - h_score = (w_pos * w_rel) / ( - w_case + (w_freq / w_rel) + (w_spread / w_rel) - ) + h_score = (w_pos * w_rel) / (w_case + (w_freq / w_rel) + (w_spread / w_rel)) return { - 'w_rel': w_rel, - 'w_freq': w_freq, - 'w_spread': w_spread, - 'w_case': w_case, - 'w_pos': w_pos, - 'pl': pl, - 'pr': pr, - 'h': h_score + "w_rel": w_rel, + "w_freq": w_freq, + "w_spread": w_spread, + "w_case": w_case, + "w_pos": w_pos, + "pl": pl, + "pr": pr, + "h": h_score, } def calculate_composed_features( composed_word: Any, - stopword_weight: str = 'bi' -) -> Dict[str, float]: + stopword_weight: str = "bi", +) -> dict[str, float]: """ Calculate features for multi-word expressions (n-grams). @@ -112,32 +111,32 @@ def calculate_composed_features( # Non-stopwords: directly contribute their H scores sum_h += term.h prod_h *= term.h - else: - # Stopwords: weight by connection probability - if stopword_weight == 'bi': - prob_t1 = prob_t2 = 0.0 - - # Probability from previous term to current stopword - if t > 0 and term.g.has_edge(composed_word.terms[t-1].id, term.id): - edge_data = term.g[composed_word.terms[t-1].id][term.id] - prob_t1 = edge_data['tf'] / composed_word.terms[t-1].tf - - # Probability from current stopword to next term - if t < len(composed_word.terms) - 1 and term.g.has_edge( - term.id, composed_word.terms[t+1].id - ): - edge_data = term.g[term.id][composed_word.terms[t+1].id] - prob_t2 = edge_data['tf'] / composed_word.terms[t+1].tf - - # Combined probability affects the score - prob = prob_t1 * prob_t2 - prod_h *= 1 + (1 - prob) - sum_h -= 1 - prob - - elif stopword_weight == 'h': - # Alternative: include stopword's H value - sum_h += term.h - prod_h *= term.h + # Stopwords: weight by connection probability + elif stopword_weight == "bi": + prob_t1 = prob_t2 = 0.0 + + # Probability from previous term to current stopword + if t > 0 and term.g.has_edge(composed_word.terms[t - 1].id, term.id): + edge_data = term.g[composed_word.terms[t - 1].id][term.id] + prob_t1 = edge_data["tf"] / composed_word.terms[t - 1].tf + + # Probability from current stopword to next term + if t < len(composed_word.terms) - 1 and term.g.has_edge( + term.id, + composed_word.terms[t + 1].id, + ): + edge_data = term.g[term.id][composed_word.terms[t + 1].id] + prob_t2 = edge_data["tf"] / composed_word.terms[t + 1].tf + + # Combined probability affects the score + prob = prob_t1 * prob_t2 + prod_h *= 1 + (1 - prob) + sum_h -= 1 - prob + + elif stopword_weight == "h": + # Alternative: include stopword's H value + sum_h += term.h + prod_h *= term.h # If 'none', stopwords are ignored (no contribution) # Use term frequency @@ -146,19 +145,14 @@ def calculate_composed_features( # Calculate final H score h_score = prod_h / ((sum_h + 1) * tf_used) if tf_used > 0 else 0 - return { - 'prod_h': prod_h, - 'sum_h': sum_h, - 'tf_used': tf_used, - 'h': h_score - } + return {"prod_h": prod_h, "sum_h": sum_h, "tf_used": tf_used, "h": h_score} def get_feature_aggregation( composed_word: Any, feature_name: str, - exclude_stopwords: bool = True -) -> Tuple[float, float, float]: + exclude_stopwords: bool = True, +) -> tuple[float, float, float]: """ Aggregate a specific feature across all terms in a composed word. diff --git a/yake/data/single_word.py b/yake/data/single_word.py index fe63369c..680afc4e 100644 --- a/yake/data/single_word.py +++ b/yake/data/single_word.py @@ -10,8 +10,9 @@ import logging import math from typing import Any -import numpy as np # pylint: disable=import-error + import networkx as nx # pylint: disable=import-error +import numpy as np # pylint: disable=import-error # Configure module logger logger = logging.getLogger(__name__) @@ -31,7 +32,7 @@ class SingleWord: """ # Use __slots__ to reduce memory overhead per instance - __slots__ = ('id', 'g', 'data', '_graph_metrics_cache', '_graph_version') + __slots__ = ("id", "g", "data", "_graph_metrics_cache", "_graph_version") def __init__(self, unique: str, idx: int, graph: nx.DiGraph): """ @@ -189,7 +190,7 @@ def set_metric(self, name, value): def invalidate_graph_cache(self): """ Invalidate the cached graph metrics. - + Call this method when the graph structure changes to force recalculation of metrics on next access. """ @@ -230,8 +231,12 @@ def get_graph_metrics(self): # Cache the results self._graph_metrics_cache = { - "wdr": wdr, "wir": wir, "pwr": pwr, - "wdl": wdl, "wil": wil, "pwl": pwl + "wdr": wdr, + "wir": wir, + "pwr": pwr, + "wdl": wdl, + "wil": wil, + "pwl": pwl, } return self._graph_metrics_cache diff --git a/yake/data/utils.py b/yake/data/utils.py index d1705e33..eda2d41f 100644 --- a/yake/data/utils.py +++ b/yake/data/utils.py @@ -9,8 +9,9 @@ import re from functools import lru_cache + from segtok.segmenter import split_multi # pylint: disable=import-error -from segtok.tokenizer import web_tokenizer, split_contractions # pylint: disable=import-error +from segtok.tokenizer import split_contractions, web_tokenizer # pylint: disable=import-error # Pre-compiled regex patterns for better performance _CAPITAL_LETTER_PATTERN = re.compile(r"^(\s*([A-Z]))")