public code v1
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#temporary files
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temp/
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test_results/
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test_scripts/
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cached_rules/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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||||||
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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||||||
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*.manifest
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*.spec
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||||||
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||||||
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# Installer logs
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||||||
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pip-log.txt
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||||||
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pip-delete-this-directory.txt
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||||||
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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|
*.log
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||||||
|
local_settings.py
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db.sqlite3
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db.sqlite3-journal
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||||||
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||||||
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# Flask stuff:
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||||||
|
instance/
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||||||
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.webassets-cache
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||||||
|
|
||||||
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# Scrapy stuff:
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||||||
|
.scrapy
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||||||
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|
||||||
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# Sphinx documentation
|
||||||
|
docs/_build/
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||||||
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|
||||||
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# PyBuilder
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||||||
|
target/
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||||||
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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||||||
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# pipenv
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||||||
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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||||||
|
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||||
|
# having no cross-platform support, pipenv may install dependencies that don't work, or not
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||||||
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# install all needed dependencies.
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||||||
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#Pipfile.lock
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||||||
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|
||||||
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# celery beat schedule file
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|
celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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||||||
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env.bak/
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||||||
|
venv.bak/
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||||||
|
|
||||||
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# Spyder project settings
|
||||||
|
.spyderproject
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||||||
|
.spyproject
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||||||
|
|
||||||
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# Rope project settings
|
||||||
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.ropeproject
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||||||
|
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||||||
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# mkdocs documentation
|
||||||
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/site
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||||||
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# mypy
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.mypy_cache/
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||||||
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.dmypy.json
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dmypy.json
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||||||
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|
||||||
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# Pyre type checker
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||||||
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.pyre/
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|
|
||||||
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# Datasets
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||||||
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src/data/
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|
||||||
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# Checkpoints
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||||||
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**/checkpoints/*
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||||||
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# Pycharm
|
||||||
|
**/.idea/
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|
.DS_Store
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|
|
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|
|
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.pytest_cache/
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||||||
|
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||||||
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||||||
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# ignore large test datasets
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||||||
|
notebooks/datasets/ml-32m/*
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datasets/ml-32m/*
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datasets/amazon/*
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import streamlit as st
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# Set the page configuration
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# This should be the first Streamlit command in your script
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st.set_page_config(
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page_title="PYGREX: Pyhton Explainable Group Recommendation",
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page_icon="👥",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# --- Welcome Page Content ---
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st.title("Welcome to PY-GREX! 👋")
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st.header("A Novel Library for Supporting Explanations in Group Recommendation")
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st.markdown("""
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Welcome to the interactive application for the **GREX** library. This tool allows you to step through the entire process of generating and explaining group recommendations.
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**GREX** is designed with a modular architecture, allowing you to flexibly combine different components like data handling, recommendation models, group aggregation strategies, and explanation methods.
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|
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||||||
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### How to Use This App:
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1. **📄 Data Preparation**: Use the sidebar to navigate to the Data Preparation page to upload your datasets.
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2. **🧠 Model Training**: Select and train a recommender model.
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3. **🎯 Group Recommendation**: Choose a group and an aggregation strategy to generate recommendations.
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4. **💬 Explanation & Evaluation**: View the generated explanations and analyze their quality.
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Use the navigation on the left to begin!
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|
# """)
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# st.sidebar.header("⚙️ App Controls")
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# st.sidebar.write("If results seem inconsistent, clear the app's memory.")
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||||||
|
# if st.sidebar.button("⚠️ Clear Cache & Rerun"):
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# # Clears all items from the session state (like data, models, etc.)
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|
# st.session_state.clear()
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# # Reruns the script from the top
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||||||
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# st.rerun()
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||||||
|
st.sidebar.success("Select a page above to start.")
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+21
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MIT License
|
||||||
|
|
||||||
|
Copyright (c) 2021 Ludovik Coba and Roberto Confalonieri
|
||||||
|
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
|
of this software and associated documentation files (the "Software"), to deal
|
||||||
|
in the Software without restriction, including without limitation the rights
|
||||||
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
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copies of the Software, and to permit persons to whom the Software is
|
||||||
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
|
The above copyright notice and this permission notice shall be included in all
|
||||||
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copies or substantial portions of the Software.
|
||||||
|
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||||||
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||||
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||||
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||||
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||||
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||||
|
SOFTWARE.
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<h3 align="center">PY-GREX: An Explainable Group Recommender Systems Toolkit</h3>
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||||||
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<div align="center">
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||||||
|
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||||||
|
[](https://github.com/toledomateus/pygrex)
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||||||
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[](/LICENSE.md)
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||||||
|
[](https://github.com/toledomateus/pygrex/issues)
|
||||||
|
[](https://github.com/toledomateus/pygrex/pulls)
|
||||||
|
[](https://badge.fury.io/py/pygrex)
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||||||
|
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||||||
|
</div>
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||||||
|
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||||||
|
---
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||||||
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||||||
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<p align="center"> A software toolkit for explainable group recommender systems, including several state-of-the-art explainability methods and evaluation metrics.
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||||||
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<br>
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||||||
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</p>
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||||||
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**➡️ [Platform live demo](https://pygrex.streamlit.app/)**
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||||||
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||||||
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||||||
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---
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||||||
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||||||
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## About
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||||||
|
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||||||
|
PY-GREX addresses this critical need, offering a modular Python toolkit equipped with multiple state-of-the-art explainability algorithms to facilitate research and development in eXplainable AI (XAI) for Recommender Systems.
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||||||
|
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||||||
|
---
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||||||
|
|
||||||
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## 🚀 Features
|
||||||
|
|
||||||
|
PY-GREX provides a modular, end-to-end pipeline for explainable group recommendations.
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||||||
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||||||
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- **Recommendation Models**:
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||||||
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- **Matrix Factorization**:
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- Alternating Least Squares (ALS)
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||||||
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- Singular Value Decomposition (SVD)
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||||||
|
- Bayesian Personalized Ranking (BPR)
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||||||
|
- Explainable Matrix Factorization (EMF)
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||||||
|
- **Neural Networks**:
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||||||
|
- Generalized Matrix Factorization (GMF)
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||||||
|
- Multi-Layer Perceptron (MLP)
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||||||
|
- Neural Collaborative Filtering (NCF)
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||||||
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- Deep Autoencoder
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||||||
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- **Memory-Based**:
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||||||
|
- Item-Based K-Nearest Neighbors
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||||||
|
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||||||
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- **Group Aggregation Strategies**:
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||||||
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- **Consensus-Based**:
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||||||
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- Additive Utilitarian
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||||||
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- Multiplicative Utilitarian
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||||||
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- Average Satisfaction
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||||||
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- **Majority-Based**:
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||||||
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- Borda Count
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||||||
|
- Plurality Voting
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||||||
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- **Fairness-Oriented**:
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||||||
|
- Least Misery
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||||||
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- Most Pleasure
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||||||
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- Most Respected Person
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||||||
|
|
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- **Explanation Methods**:
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||||||
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- **Counterfactual**:
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- Sliding Window Explainer (Counterfactual Explanations)
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|
- **Rule-Based**:
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|
- EXPGRS (Association Rules Explainer)
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||||||
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- **Local Explainers**:
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- LORE4Groups (Local Rule-Based Explanations)
|
||||||
|
|
||||||
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- **Evaluation Metrics**:
|
||||||
|
- **Accuracy**:
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- Hit Ratio (HR)
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||||||
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- Normalized Discounted Cumulative Gain (nDCG)
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- **Explainability**:
|
||||||
|
- Model Fidelity
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|
- Gaussian Intra-List Diversity (GILD)
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||||||
|
- Rule Support and Confidence
|
||||||
|
---
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||||||
|
|
||||||
|
## 🏁 Getting Started
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||||||
|
|
||||||
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### Installation
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||||||
|
|
||||||
|
You can install PY-GREX directly using pip:
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||||||
|
|
||||||
|
```bash
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||||||
|
pip install pygrex
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||||||
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```
|
||||||
|
|
||||||
|
This will install all the required dependencies automatically. PY-GREX requires Python 3.11 or higher.
|
||||||
|
|
||||||
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### Local Development
|
||||||
|
|
||||||
|
If you want to run the project locally for development:
|
||||||
|
|
||||||
|
1. **Prerequisites**:
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||||||
|
- Python 3.11 or higher
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||||||
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- Git
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||||||
|
- Conda (recommended)
|
||||||
|
|
||||||
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2. **Clone the repository**:
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||||||
|
```bash
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||||||
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git clone https://github.com/toledomateus/pygrex.git
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||||||
|
cd pygrex
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||||||
|
```
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||||||
|
|
||||||
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3. **Create and activate a Conda environment**:
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||||||
|
```bash
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||||||
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conda create -n pygrex python=3.11
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||||||
|
conda activate pygrex
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||||||
|
```
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||||||
|
|
||||||
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4. **Install in development mode**:
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||||||
|
```bash
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||||||
|
pip install -e .
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||||||
|
```
|
||||||
|
|
||||||
|
This will install the package in development mode, allowing you to modify the source code and see the changes immediately without reinstalling.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 🎈 Usage
|
||||||
|
|
||||||
|
### Running Locally
|
||||||
|
|
||||||
|
To run the Streamlit app locally:
|
||||||
|
|
||||||
|
1. **Install Streamlit**:
|
||||||
|
```bash
|
||||||
|
pip install streamlit
|
||||||
|
```
|
||||||
|
|
||||||
|
2. **Run the app**:
|
||||||
|
```bash
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||||||
|
streamlit run Home.py
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||||||
|
```
|
||||||
|
|
||||||
|
The app will be available at `http://localhost:8501`
|
||||||
|
|
||||||
|
### Interactive Web App
|
||||||
|
The easiest way to use PY-GREX is through the web application. It allows you to:
|
||||||
|
- **Upload or use default data** for users, items, and groups
|
||||||
|
- **Select and train** a variety of recommendation models
|
||||||
|
- **Generate group recommendations** using different aggregation strategies
|
||||||
|
- **Produce and evaluate explanations** for the recommendations
|
||||||
|
|
||||||
|
### Jupyter Notebooks
|
||||||
|
For detailed examples, check out the notebooks in the `notebooks/` directory:
|
||||||
|
- `expgrs_toy_example.ipynb`: Demonstrates the EXPGRS rule-based explainer with association rules
|
||||||
|
- `sliding_window_toy_example.ipynb`: Shows how to use counterfactual explanations with the Sliding Window method
|
||||||
|
- `lore4groups_toy_example.ipynb`: Illustrates local rule-based explanations using LORE4Groups
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 🤝 Contributing
|
||||||
|
|
||||||
|
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
|
||||||
|
|
||||||
|
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
|
||||||
|
|
||||||
|
1. Fork the Project
|
||||||
|
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
|
||||||
|
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
|
||||||
|
4. Push to the Branch (`git push origin feature/AmazingFeature`)
|
||||||
|
5. Open a Pull Request
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 📚 Citation
|
||||||
|
|
||||||
|
If you use PY-GREX in your research, please cite our paper:
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@inproceedings{Toledo2026GREX,
|
||||||
|
author = {Toledo, Mateus and Yera, Raciel and Barranco, Manuel J. and Dutta, Bapi},
|
||||||
|
title = {{GREX}: A Platform for Supporting Explanations in Group Recommender Systems},
|
||||||
|
booktitle = {Intelligent Data Engineering and Automated Learning -- {IDEAL} 2025},
|
||||||
|
year = {2026},
|
||||||
|
publisher = {Springer Nature Switzerland AG},
|
||||||
|
address = {Cham},
|
||||||
|
series = {Lecture Notes in Computer Science},
|
||||||
|
volume = {16239},
|
||||||
|
pages = {1--13},
|
||||||
|
doi = {10.1007/978-3-032-10489-2_9}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
---
|
||||||
|
|
||||||
|
## 📜 License
|
||||||
|
|
||||||
|
This project is licensed under the MIT License. See the `LICENSE` file for details.
|
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base:
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data:
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||||||
|
groups:
|
||||||
|
filepath_or_buffer:
|
||||||
|
- 'datasets/stratigis/groupsWithHighRatings5.txt'
|
||||||
|
- 'datasets/stratigis/groupsWithModerateRatings5untested.txt'
|
||||||
|
testdata:
|
||||||
|
filepath_or_buffer: 'datasets/fake_data.csv'
|
||||||
|
sep: '\t'
|
||||||
|
skiprows: 1
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
test:
|
||||||
|
filepath_or_buffer: 'datasets/stratigis/ratings.csv'
|
||||||
|
sep: ','
|
||||||
|
skiprows: 1
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
ml32m:
|
||||||
|
filepath_or_buffer: 'datasets/ml-32m/ratings.csv'
|
||||||
|
sep: ','
|
||||||
|
skiprows: 1
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
ml100k:
|
||||||
|
filepath_or_buffer: 'datasets/ml-100k/u.data'
|
||||||
|
sep: '\t'
|
||||||
|
skiprows: 0
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
ml1m:
|
||||||
|
filepath_or_buffer: 'datasets/ml-1m/ratings.dat'
|
||||||
|
sep: '::'
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp' ]
|
||||||
|
tags:
|
||||||
|
tags_file: 'datasets/stratigis/tags.csv'
|
||||||
|
model:
|
||||||
|
gmf:
|
||||||
|
learning_rate: 0.005
|
||||||
|
weight_decay: 0.0000001
|
||||||
|
latent_dim: 8
|
||||||
|
epochs: 30
|
||||||
|
num_negative: 10
|
||||||
|
batch_size: 1024
|
||||||
|
cuda: False
|
||||||
|
optimizer_name: 'adam'
|
||||||
|
mlp:
|
||||||
|
learning_rate: 0.005
|
||||||
|
weight_decay: 0.0000001
|
||||||
|
latent_dim: 8
|
||||||
|
epochs: 30
|
||||||
|
num_negative: 10
|
||||||
|
batch_size: 1024
|
||||||
|
cuda: False
|
||||||
|
optimizer_name: 'adam'
|
||||||
|
als:
|
||||||
|
learning_rate: 0.1
|
||||||
|
latent_dim: 100
|
||||||
|
epochs: 10
|
||||||
|
reg_term: 0.001
|
||||||
|
|
||||||
|
bpr:
|
||||||
|
learning_rate: 0.01
|
||||||
|
latent_dim: 100
|
||||||
|
epochs: 10
|
||||||
|
reg_term: 0.001
|
||||||
|
emf:
|
||||||
|
learning_rate: 0.01
|
||||||
|
reg_term: 0.001
|
||||||
|
expl_reg_term: 0.0
|
||||||
|
latent_dim: 80
|
||||||
|
epochs: 10
|
||||||
|
positive_threshold: 3
|
||||||
|
knn: 10
|
||||||
|
mf:
|
||||||
|
learning_rate: 0.01
|
||||||
|
reg_term: 0.001
|
||||||
|
expl_reg_term: 0.0
|
||||||
|
latent_dim: 80
|
||||||
|
epochs: 10
|
||||||
|
positive_threshold: 3
|
||||||
|
knn: 10
|
||||||
|
autoencoder:
|
||||||
|
learning_rate: 0.005
|
||||||
|
weight_decay: 0.0000001
|
||||||
|
hidden_layer_features: 8
|
||||||
|
epochs: 30
|
||||||
|
cuda: False
|
||||||
|
optimizer_name: 'adam'
|
||||||
|
positive_threshold: 3
|
||||||
|
knn: 10
|
||||||
|
expl: true
|
||||||
|
explainer:
|
||||||
|
lore4groups:
|
||||||
|
n_similar_for_tree: 500
|
||||||
|
rating_threshold_for_like: 2.8 # Increased significantly
|
||||||
|
max_tree_depth: 5
|
||||||
|
top_n_labels: 5000
|
||||||
|
min_rating_for_history: 1.0
|
||||||
|
similarity_threshold: 0.0000 # Added stricter similarity
|
||||||
|
tree_stop_threshold: 0.1 # Make sure this is present, as per the paper
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,157 @@
|
|||||||
|
SUMMARY & USAGE LICENSE
|
||||||
|
=============================================
|
||||||
|
|
||||||
|
MovieLens data sets were collected by the GroupLens Research Project
|
||||||
|
at the University of Minnesota.
|
||||||
|
|
||||||
|
This data set consists of:
|
||||||
|
* 100,000 ratings (1-5) from 943 users on 1682 movies.
|
||||||
|
* Each user has rated at least 20 movies.
|
||||||
|
* Simple demographic info for the users (age, gender, occupation, zip)
|
||||||
|
|
||||||
|
The data was collected through the MovieLens web site
|
||||||
|
(movielens.umn.edu) during the seven-month period from September 19th,
|
||||||
|
1997 through April 22nd, 1998. This data has been cleaned up - users
|
||||||
|
who had less than 20 ratings or did not have complete demographic
|
||||||
|
information were removed from this data set. Detailed descriptions of
|
||||||
|
the data file can be found at the end of this file.
|
||||||
|
|
||||||
|
Neither the University of Minnesota nor any of the researchers
|
||||||
|
involved can guarantee the correctness of the data, its suitability
|
||||||
|
for any particular purpose, or the validity of results based on the
|
||||||
|
use of the data set. The data set may be used for any research
|
||||||
|
purposes under the following conditions:
|
||||||
|
|
||||||
|
* The user may not state or imply any endorsement from the
|
||||||
|
University of Minnesota or the GroupLens Research Group.
|
||||||
|
|
||||||
|
* The user must acknowledge the use of the data set in
|
||||||
|
publications resulting from the use of the data set
|
||||||
|
(see below for citation information).
|
||||||
|
|
||||||
|
* The user may not redistribute the data without separate
|
||||||
|
permission.
|
||||||
|
|
||||||
|
* The user may not use this information for any commercial or
|
||||||
|
revenue-bearing purposes without first obtaining permission
|
||||||
|
from a faculty member of the GroupLens Research Project at the
|
||||||
|
University of Minnesota.
|
||||||
|
|
||||||
|
If you have any further questions or comments, please contact GroupLens
|
||||||
|
<grouplens-info@cs.umn.edu>.
|
||||||
|
|
||||||
|
CITATION
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
To acknowledge use of the dataset in publications, please cite the
|
||||||
|
following paper:
|
||||||
|
|
||||||
|
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets:
|
||||||
|
History and Context. ACM Transactions on Interactive Intelligent
|
||||||
|
Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages.
|
||||||
|
DOI=http://dx.doi.org/10.1145/2827872
|
||||||
|
|
||||||
|
|
||||||
|
ACKNOWLEDGEMENTS
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
Thanks to Al Borchers for cleaning up this data and writing the
|
||||||
|
accompanying scripts.
|
||||||
|
|
||||||
|
PUBLISHED WORK THAT HAS USED THIS DATASET
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic
|
||||||
|
Framework for Performing Collaborative Filtering. Proceedings of the
|
||||||
|
1999 Conference on Research and Development in Information
|
||||||
|
Retrieval. Aug. 1999.
|
||||||
|
|
||||||
|
FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
The GroupLens Research Project is a research group in the Department
|
||||||
|
of Computer Science and Engineering at the University of Minnesota.
|
||||||
|
Members of the GroupLens Research Project are involved in many
|
||||||
|
research projects related to the fields of information filtering,
|
||||||
|
collaborative filtering, and recommender systems. The project is lead
|
||||||
|
by professors John Riedl and Joseph Konstan. The project began to
|
||||||
|
explore automated collaborative filtering in 1992, but is most well
|
||||||
|
known for its world wide trial of an automated collaborative filtering
|
||||||
|
system for Usenet news in 1996. The technology developed in the
|
||||||
|
Usenet trial formed the base for the formation of Net Perceptions,
|
||||||
|
Inc., which was founded by members of GroupLens Research. Since then
|
||||||
|
the project has expanded its scope to research overall information
|
||||||
|
filtering solutions, integrating in content-based methods as well as
|
||||||
|
improving current collaborative filtering technology.
|
||||||
|
|
||||||
|
Further information on the GroupLens Research project, including
|
||||||
|
research publications, can be found at the following web site:
|
||||||
|
|
||||||
|
http://www.grouplens.org/
|
||||||
|
|
||||||
|
GroupLens Research currently operates a movie recommender based on
|
||||||
|
collaborative filtering:
|
||||||
|
|
||||||
|
http://www.movielens.org/
|
||||||
|
|
||||||
|
DETAILED DESCRIPTIONS OF DATA FILES
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
Here are brief descriptions of the data.
|
||||||
|
|
||||||
|
ml-data.tar.gz -- Compressed tar file. To rebuild the u data files do this:
|
||||||
|
gunzip ml-data.tar.gz
|
||||||
|
tar xvf ml-data.tar
|
||||||
|
mku.sh
|
||||||
|
|
||||||
|
u.data -- The full u data set, 100000 ratings by 943 users on 1682 items.
|
||||||
|
Each user has rated at least 20 movies. Users and items are
|
||||||
|
numbered consecutively from 1. The data is randomly
|
||||||
|
ordered. This is a tab separated list of
|
||||||
|
user id | item id | rating | timestamp.
|
||||||
|
The time stamps are unix seconds since 1/1/1970 UTC
|
||||||
|
|
||||||
|
u.info -- The number of users, items, and ratings in the u data set.
|
||||||
|
|
||||||
|
u.item -- Information about the items (movies); this is a tab separated
|
||||||
|
list of
|
||||||
|
movie id | movie title | release date | video release date |
|
||||||
|
IMDb URL | unknown | Action | Adventure | Animation |
|
||||||
|
Children's | Comedy | Crime | Documentary | Drama | Fantasy |
|
||||||
|
Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |
|
||||||
|
Thriller | War | Western |
|
||||||
|
The last 19 fields are the genres, a 1 indicates the movie
|
||||||
|
is of that genre, a 0 indicates it is not; movies can be in
|
||||||
|
several genres at once.
|
||||||
|
The movie ids are the ones used in the u.data data set.
|
||||||
|
|
||||||
|
u.genre -- A list of the genres.
|
||||||
|
|
||||||
|
u.user -- Demographic information about the users; this is a tab
|
||||||
|
separated list of
|
||||||
|
user id | age | gender | occupation | zip code
|
||||||
|
The user ids are the ones used in the u.data data set.
|
||||||
|
|
||||||
|
u.occupation -- A list of the occupations.
|
||||||
|
|
||||||
|
u1.base -- The data sets u1.base and u1.test through u5.base and u5.test
|
||||||
|
u1.test are 80%/20% splits of the u data into training and test data.
|
||||||
|
u2.base Each of u1, ..., u5 have disjoint test sets; this if for
|
||||||
|
u2.test 5 fold cross validation (where you repeat your experiment
|
||||||
|
u3.base with each training and test set and average the results).
|
||||||
|
u3.test These data sets can be generated from u.data by mku.sh.
|
||||||
|
u4.base
|
||||||
|
u4.test
|
||||||
|
u5.base
|
||||||
|
u5.test
|
||||||
|
|
||||||
|
ua.base -- The data sets ua.base, ua.test, ub.base, and ub.test
|
||||||
|
ua.test split the u data into a training set and a test set with
|
||||||
|
ub.base exactly 10 ratings per user in the test set. The sets
|
||||||
|
ub.test ua.test and ub.test are disjoint. These data sets can
|
||||||
|
be generated from u.data by mku.sh.
|
||||||
|
|
||||||
|
allbut.pl -- The script that generates training and test sets where
|
||||||
|
all but n of a users ratings are in the training data.
|
||||||
|
|
||||||
|
mku.sh -- A shell script to generate all the u data sets from u.data.
|
||||||
@@ -0,0 +1,34 @@
|
|||||||
|
#!/usr/local/bin/perl
|
||||||
|
|
||||||
|
# get args
|
||||||
|
if (@ARGV < 3) {
|
||||||
|
print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n";
|
||||||
|
exit 1;
|
||||||
|
}
|
||||||
|
$basename = shift;
|
||||||
|
$start = shift;
|
||||||
|
$stop = shift;
|
||||||
|
$maxtest = shift;
|
||||||
|
|
||||||
|
# open files
|
||||||
|
open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n";
|
||||||
|
open( BASEFILE, ">$basename.base" ) or die "Cannot open $basename.base for writing\n";
|
||||||
|
|
||||||
|
# init variables
|
||||||
|
$testcnt = 0;
|
||||||
|
|
||||||
|
while (<>) {
|
||||||
|
($user) = split;
|
||||||
|
if (! defined $ratingcnt{$user}) {
|
||||||
|
$ratingcnt{$user} = 0;
|
||||||
|
}
|
||||||
|
++$ratingcnt{$user};
|
||||||
|
if (($testcnt < $maxtest || $maxtest <= 0)
|
||||||
|
&& $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) {
|
||||||
|
++$testcnt;
|
||||||
|
print TESTFILE;
|
||||||
|
}
|
||||||
|
else {
|
||||||
|
print BASEFILE;
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,25 @@
|
|||||||
|
#!/bin/sh
|
||||||
|
|
||||||
|
trap `rm -f tmp.$$; exit 1` 1 2 15
|
||||||
|
|
||||||
|
for i in 1 2 3 4 5
|
||||||
|
do
|
||||||
|
head -`expr $i \* 20000` u.data | tail -20000 > tmp.$$
|
||||||
|
sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.test
|
||||||
|
head -`expr \( $i - 1 \) \* 20000` u.data > tmp.$$
|
||||||
|
tail -`expr \( 5 - $i \) \* 20000` u.data >> tmp.$$
|
||||||
|
sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.base
|
||||||
|
done
|
||||||
|
|
||||||
|
allbut.pl ua 1 10 100000 u.data
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ua.base > tmp.$$
|
||||||
|
mv tmp.$$ ua.base
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ua.test > tmp.$$
|
||||||
|
mv tmp.$$ ua.test
|
||||||
|
|
||||||
|
allbut.pl ub 11 20 100000 u.data
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ub.base > tmp.$$
|
||||||
|
mv tmp.$$ ub.base
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ub.test > tmp.$$
|
||||||
|
mv tmp.$$ ub.test
|
||||||
|
|
||||||
+100000
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,20 @@
|
|||||||
|
unknown|0
|
||||||
|
Action|1
|
||||||
|
Adventure|2
|
||||||
|
Animation|3
|
||||||
|
Children's|4
|
||||||
|
Comedy|5
|
||||||
|
Crime|6
|
||||||
|
Documentary|7
|
||||||
|
Drama|8
|
||||||
|
Fantasy|9
|
||||||
|
Film-Noir|10
|
||||||
|
Horror|11
|
||||||
|
Musical|12
|
||||||
|
Mystery|13
|
||||||
|
Romance|14
|
||||||
|
Sci-Fi|15
|
||||||
|
Thriller|16
|
||||||
|
War|17
|
||||||
|
Western|18
|
||||||
|
|
||||||
@@ -0,0 +1,3 @@
|
|||||||
|
943 users
|
||||||
|
1682 items
|
||||||
|
100000 ratings
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,21 @@
|
|||||||
|
administrator
|
||||||
|
artist
|
||||||
|
doctor
|
||||||
|
educator
|
||||||
|
engineer
|
||||||
|
entertainment
|
||||||
|
executive
|
||||||
|
healthcare
|
||||||
|
homemaker
|
||||||
|
lawyer
|
||||||
|
librarian
|
||||||
|
marketing
|
||||||
|
none
|
||||||
|
other
|
||||||
|
programmer
|
||||||
|
retired
|
||||||
|
salesman
|
||||||
|
scientist
|
||||||
|
student
|
||||||
|
technician
|
||||||
|
writer
|
||||||
@@ -0,0 +1,943 @@
|
|||||||
|
1|24|M|technician|85711
|
||||||
|
2|53|F|other|94043
|
||||||
|
3|23|M|writer|32067
|
||||||
|
4|24|M|technician|43537
|
||||||
|
5|33|F|other|15213
|
||||||
|
6|42|M|executive|98101
|
||||||
|
7|57|M|administrator|91344
|
||||||
|
8|36|M|administrator|05201
|
||||||
|
9|29|M|student|01002
|
||||||
|
10|53|M|lawyer|90703
|
||||||
|
11|39|F|other|30329
|
||||||
|
12|28|F|other|06405
|
||||||
|
13|47|M|educator|29206
|
||||||
|
14|45|M|scientist|55106
|
||||||
|
15|49|F|educator|97301
|
||||||
|
16|21|M|entertainment|10309
|
||||||
|
17|30|M|programmer|06355
|
||||||
|
18|35|F|other|37212
|
||||||
|
19|40|M|librarian|02138
|
||||||
|
20|42|F|homemaker|95660
|
||||||
|
21|26|M|writer|30068
|
||||||
|
22|25|M|writer|40206
|
||||||
|
23|30|F|artist|48197
|
||||||
|
24|21|F|artist|94533
|
||||||
|
25|39|M|engineer|55107
|
||||||
|
26|49|M|engineer|21044
|
||||||
|
27|40|F|librarian|30030
|
||||||
|
28|32|M|writer|55369
|
||||||
|
29|41|M|programmer|94043
|
||||||
|
30|7|M|student|55436
|
||||||
|
31|24|M|artist|10003
|
||||||
|
32|28|F|student|78741
|
||||||
|
33|23|M|student|27510
|
||||||
|
34|38|F|administrator|42141
|
||||||
|
35|20|F|homemaker|42459
|
||||||
|
36|19|F|student|93117
|
||||||
|
37|23|M|student|55105
|
||||||
|
38|28|F|other|54467
|
||||||
|
39|41|M|entertainment|01040
|
||||||
|
40|38|M|scientist|27514
|
||||||
|
41|33|M|engineer|80525
|
||||||
|
42|30|M|administrator|17870
|
||||||
|
43|29|F|librarian|20854
|
||||||
|
44|26|M|technician|46260
|
||||||
|
45|29|M|programmer|50233
|
||||||
|
46|27|F|marketing|46538
|
||||||
|
47|53|M|marketing|07102
|
||||||
|
48|45|M|administrator|12550
|
||||||
|
49|23|F|student|76111
|
||||||
|
50|21|M|writer|52245
|
||||||
|
51|28|M|educator|16509
|
||||||
|
52|18|F|student|55105
|
||||||
|
53|26|M|programmer|55414
|
||||||
|
54|22|M|executive|66315
|
||||||
|
55|37|M|programmer|01331
|
||||||
|
56|25|M|librarian|46260
|
||||||
|
57|16|M|none|84010
|
||||||
|
58|27|M|programmer|52246
|
||||||
|
59|49|M|educator|08403
|
||||||
|
60|50|M|healthcare|06472
|
||||||
|
61|36|M|engineer|30040
|
||||||
|
62|27|F|administrator|97214
|
||||||
|
63|31|M|marketing|75240
|
||||||
|
64|32|M|educator|43202
|
||||||
|
65|51|F|educator|48118
|
||||||
|
66|23|M|student|80521
|
||||||
|
67|17|M|student|60402
|
||||||
|
68|19|M|student|22904
|
||||||
|
69|24|M|engineer|55337
|
||||||
|
70|27|M|engineer|60067
|
||||||
|
71|39|M|scientist|98034
|
||||||
|
72|48|F|administrator|73034
|
||||||
|
73|24|M|student|41850
|
||||||
|
74|39|M|scientist|T8H1N
|
||||||
|
75|24|M|entertainment|08816
|
||||||
|
76|20|M|student|02215
|
||||||
|
77|30|M|technician|29379
|
||||||
|
78|26|M|administrator|61801
|
||||||
|
79|39|F|administrator|03755
|
||||||
|
80|34|F|administrator|52241
|
||||||
|
81|21|M|student|21218
|
||||||
|
82|50|M|programmer|22902
|
||||||
|
83|40|M|other|44133
|
||||||
|
84|32|M|executive|55369
|
||||||
|
85|51|M|educator|20003
|
||||||
|
86|26|M|administrator|46005
|
||||||
|
87|47|M|administrator|89503
|
||||||
|
88|49|F|librarian|11701
|
||||||
|
89|43|F|administrator|68106
|
||||||
|
90|60|M|educator|78155
|
||||||
|
91|55|M|marketing|01913
|
||||||
|
92|32|M|entertainment|80525
|
||||||
|
93|48|M|executive|23112
|
||||||
|
94|26|M|student|71457
|
||||||
|
95|31|M|administrator|10707
|
||||||
|
96|25|F|artist|75206
|
||||||
|
97|43|M|artist|98006
|
||||||
|
98|49|F|executive|90291
|
||||||
|
99|20|M|student|63129
|
||||||
|
100|36|M|executive|90254
|
||||||
|
101|15|M|student|05146
|
||||||
|
102|38|M|programmer|30220
|
||||||
|
103|26|M|student|55108
|
||||||
|
104|27|M|student|55108
|
||||||
|
105|24|M|engineer|94043
|
||||||
|
106|61|M|retired|55125
|
||||||
|
107|39|M|scientist|60466
|
||||||
|
108|44|M|educator|63130
|
||||||
|
109|29|M|other|55423
|
||||||
|
110|19|M|student|77840
|
||||||
|
111|57|M|engineer|90630
|
||||||
|
112|30|M|salesman|60613
|
||||||
|
113|47|M|executive|95032
|
||||||
|
114|27|M|programmer|75013
|
||||||
|
115|31|M|engineer|17110
|
||||||
|
116|40|M|healthcare|97232
|
||||||
|
117|20|M|student|16125
|
||||||
|
118|21|M|administrator|90210
|
||||||
|
119|32|M|programmer|67401
|
||||||
|
120|47|F|other|06260
|
||||||
|
121|54|M|librarian|99603
|
||||||
|
122|32|F|writer|22206
|
||||||
|
123|48|F|artist|20008
|
||||||
|
124|34|M|student|60615
|
||||||
|
125|30|M|lawyer|22202
|
||||||
|
126|28|F|lawyer|20015
|
||||||
|
127|33|M|none|73439
|
||||||
|
128|24|F|marketing|20009
|
||||||
|
129|36|F|marketing|07039
|
||||||
|
130|20|M|none|60115
|
||||||
|
131|59|F|administrator|15237
|
||||||
|
132|24|M|other|94612
|
||||||
|
133|53|M|engineer|78602
|
||||||
|
134|31|M|programmer|80236
|
||||||
|
135|23|M|student|38401
|
||||||
|
136|51|M|other|97365
|
||||||
|
137|50|M|educator|84408
|
||||||
|
138|46|M|doctor|53211
|
||||||
|
139|20|M|student|08904
|
||||||
|
140|30|F|student|32250
|
||||||
|
141|49|M|programmer|36117
|
||||||
|
142|13|M|other|48118
|
||||||
|
143|42|M|technician|08832
|
||||||
|
144|53|M|programmer|20910
|
||||||
|
145|31|M|entertainment|V3N4P
|
||||||
|
146|45|M|artist|83814
|
||||||
|
147|40|F|librarian|02143
|
||||||
|
148|33|M|engineer|97006
|
||||||
|
149|35|F|marketing|17325
|
||||||
|
150|20|F|artist|02139
|
||||||
|
151|38|F|administrator|48103
|
||||||
|
152|33|F|educator|68767
|
||||||
|
153|25|M|student|60641
|
||||||
|
154|25|M|student|53703
|
||||||
|
155|32|F|other|11217
|
||||||
|
156|25|M|educator|08360
|
||||||
|
157|57|M|engineer|70808
|
||||||
|
158|50|M|educator|27606
|
||||||
|
159|23|F|student|55346
|
||||||
|
160|27|M|programmer|66215
|
||||||
|
161|50|M|lawyer|55104
|
||||||
|
162|25|M|artist|15610
|
||||||
|
163|49|M|administrator|97212
|
||||||
|
164|47|M|healthcare|80123
|
||||||
|
165|20|F|other|53715
|
||||||
|
166|47|M|educator|55113
|
||||||
|
167|37|M|other|L9G2B
|
||||||
|
168|48|M|other|80127
|
||||||
|
169|52|F|other|53705
|
||||||
|
170|53|F|healthcare|30067
|
||||||
|
171|48|F|educator|78750
|
||||||
|
172|55|M|marketing|22207
|
||||||
|
173|56|M|other|22306
|
||||||
|
174|30|F|administrator|52302
|
||||||
|
175|26|F|scientist|21911
|
||||||
|
176|28|M|scientist|07030
|
||||||
|
177|20|M|programmer|19104
|
||||||
|
178|26|M|other|49512
|
||||||
|
179|15|M|entertainment|20755
|
||||||
|
180|22|F|administrator|60202
|
||||||
|
181|26|M|executive|21218
|
||||||
|
182|36|M|programmer|33884
|
||||||
|
183|33|M|scientist|27708
|
||||||
|
184|37|M|librarian|76013
|
||||||
|
185|53|F|librarian|97403
|
||||||
|
186|39|F|executive|00000
|
||||||
|
187|26|M|educator|16801
|
||||||
|
188|42|M|student|29440
|
||||||
|
189|32|M|artist|95014
|
||||||
|
190|30|M|administrator|95938
|
||||||
|
191|33|M|administrator|95161
|
||||||
|
192|42|M|educator|90840
|
||||||
|
193|29|M|student|49931
|
||||||
|
194|38|M|administrator|02154
|
||||||
|
195|42|M|scientist|93555
|
||||||
|
196|49|M|writer|55105
|
||||||
|
197|55|M|technician|75094
|
||||||
|
198|21|F|student|55414
|
||||||
|
199|30|M|writer|17604
|
||||||
|
200|40|M|programmer|93402
|
||||||
|
201|27|M|writer|E2A4H
|
||||||
|
202|41|F|educator|60201
|
||||||
|
203|25|F|student|32301
|
||||||
|
204|52|F|librarian|10960
|
||||||
|
205|47|M|lawyer|06371
|
||||||
|
206|14|F|student|53115
|
||||||
|
207|39|M|marketing|92037
|
||||||
|
208|43|M|engineer|01720
|
||||||
|
209|33|F|educator|85710
|
||||||
|
210|39|M|engineer|03060
|
||||||
|
211|66|M|salesman|32605
|
||||||
|
212|49|F|educator|61401
|
||||||
|
213|33|M|executive|55345
|
||||||
|
214|26|F|librarian|11231
|
||||||
|
215|35|M|programmer|63033
|
||||||
|
216|22|M|engineer|02215
|
||||||
|
217|22|M|other|11727
|
||||||
|
218|37|M|administrator|06513
|
||||||
|
219|32|M|programmer|43212
|
||||||
|
220|30|M|librarian|78205
|
||||||
|
221|19|M|student|20685
|
||||||
|
222|29|M|programmer|27502
|
||||||
|
223|19|F|student|47906
|
||||||
|
224|31|F|educator|43512
|
||||||
|
225|51|F|administrator|58202
|
||||||
|
226|28|M|student|92103
|
||||||
|
227|46|M|executive|60659
|
||||||
|
228|21|F|student|22003
|
||||||
|
229|29|F|librarian|22903
|
||||||
|
230|28|F|student|14476
|
||||||
|
231|48|M|librarian|01080
|
||||||
|
232|45|M|scientist|99709
|
||||||
|
233|38|M|engineer|98682
|
||||||
|
234|60|M|retired|94702
|
||||||
|
235|37|M|educator|22973
|
||||||
|
236|44|F|writer|53214
|
||||||
|
237|49|M|administrator|63146
|
||||||
|
238|42|F|administrator|44124
|
||||||
|
239|39|M|artist|95628
|
||||||
|
240|23|F|educator|20784
|
||||||
|
241|26|F|student|20001
|
||||||
|
242|33|M|educator|31404
|
||||||
|
243|33|M|educator|60201
|
||||||
|
244|28|M|technician|80525
|
||||||
|
245|22|M|student|55109
|
||||||
|
246|19|M|student|28734
|
||||||
|
247|28|M|engineer|20770
|
||||||
|
248|25|M|student|37235
|
||||||
|
249|25|M|student|84103
|
||||||
|
250|29|M|executive|95110
|
||||||
|
251|28|M|doctor|85032
|
||||||
|
252|42|M|engineer|07733
|
||||||
|
253|26|F|librarian|22903
|
||||||
|
254|44|M|educator|42647
|
||||||
|
255|23|M|entertainment|07029
|
||||||
|
256|35|F|none|39042
|
||||||
|
257|17|M|student|77005
|
||||||
|
258|19|F|student|77801
|
||||||
|
259|21|M|student|48823
|
||||||
|
260|40|F|artist|89801
|
||||||
|
261|28|M|administrator|85202
|
||||||
|
262|19|F|student|78264
|
||||||
|
263|41|M|programmer|55346
|
||||||
|
264|36|F|writer|90064
|
||||||
|
265|26|M|executive|84601
|
||||||
|
266|62|F|administrator|78756
|
||||||
|
267|23|M|engineer|83716
|
||||||
|
268|24|M|engineer|19422
|
||||||
|
269|31|F|librarian|43201
|
||||||
|
270|18|F|student|63119
|
||||||
|
271|51|M|engineer|22932
|
||||||
|
272|33|M|scientist|53706
|
||||||
|
273|50|F|other|10016
|
||||||
|
274|20|F|student|55414
|
||||||
|
275|38|M|engineer|92064
|
||||||
|
276|21|M|student|95064
|
||||||
|
277|35|F|administrator|55406
|
||||||
|
278|37|F|librarian|30033
|
||||||
|
279|33|M|programmer|85251
|
||||||
|
280|30|F|librarian|22903
|
||||||
|
281|15|F|student|06059
|
||||||
|
282|22|M|administrator|20057
|
||||||
|
283|28|M|programmer|55305
|
||||||
|
284|40|M|executive|92629
|
||||||
|
285|25|M|programmer|53713
|
||||||
|
286|27|M|student|15217
|
||||||
|
287|21|M|salesman|31211
|
||||||
|
288|34|M|marketing|23226
|
||||||
|
289|11|M|none|94619
|
||||||
|
290|40|M|engineer|93550
|
||||||
|
291|19|M|student|44106
|
||||||
|
292|35|F|programmer|94703
|
||||||
|
293|24|M|writer|60804
|
||||||
|
294|34|M|technician|92110
|
||||||
|
295|31|M|educator|50325
|
||||||
|
296|43|F|administrator|16803
|
||||||
|
297|29|F|educator|98103
|
||||||
|
298|44|M|executive|01581
|
||||||
|
299|29|M|doctor|63108
|
||||||
|
300|26|F|programmer|55106
|
||||||
|
301|24|M|student|55439
|
||||||
|
302|42|M|educator|77904
|
||||||
|
303|19|M|student|14853
|
||||||
|
304|22|F|student|71701
|
||||||
|
305|23|M|programmer|94086
|
||||||
|
306|45|M|other|73132
|
||||||
|
307|25|M|student|55454
|
||||||
|
308|60|M|retired|95076
|
||||||
|
309|40|M|scientist|70802
|
||||||
|
310|37|M|educator|91711
|
||||||
|
311|32|M|technician|73071
|
||||||
|
312|48|M|other|02110
|
||||||
|
313|41|M|marketing|60035
|
||||||
|
314|20|F|student|08043
|
||||||
|
315|31|M|educator|18301
|
||||||
|
316|43|F|other|77009
|
||||||
|
317|22|M|administrator|13210
|
||||||
|
318|65|M|retired|06518
|
||||||
|
319|38|M|programmer|22030
|
||||||
|
320|19|M|student|24060
|
||||||
|
321|49|F|educator|55413
|
||||||
|
322|20|M|student|50613
|
||||||
|
323|21|M|student|19149
|
||||||
|
324|21|F|student|02176
|
||||||
|
325|48|M|technician|02139
|
||||||
|
326|41|M|administrator|15235
|
||||||
|
327|22|M|student|11101
|
||||||
|
328|51|M|administrator|06779
|
||||||
|
329|48|M|educator|01720
|
||||||
|
330|35|F|educator|33884
|
||||||
|
331|33|M|entertainment|91344
|
||||||
|
332|20|M|student|40504
|
||||||
|
333|47|M|other|V0R2M
|
||||||
|
334|32|M|librarian|30002
|
||||||
|
335|45|M|executive|33775
|
||||||
|
336|23|M|salesman|42101
|
||||||
|
337|37|M|scientist|10522
|
||||||
|
338|39|F|librarian|59717
|
||||||
|
339|35|M|lawyer|37901
|
||||||
|
340|46|M|engineer|80123
|
||||||
|
341|17|F|student|44405
|
||||||
|
342|25|F|other|98006
|
||||||
|
343|43|M|engineer|30093
|
||||||
|
344|30|F|librarian|94117
|
||||||
|
345|28|F|librarian|94143
|
||||||
|
346|34|M|other|76059
|
||||||
|
347|18|M|student|90210
|
||||||
|
348|24|F|student|45660
|
||||||
|
349|68|M|retired|61455
|
||||||
|
350|32|M|student|97301
|
||||||
|
351|61|M|educator|49938
|
||||||
|
352|37|F|programmer|55105
|
||||||
|
353|25|M|scientist|28480
|
||||||
|
354|29|F|librarian|48197
|
||||||
|
355|25|M|student|60135
|
||||||
|
356|32|F|homemaker|92688
|
||||||
|
357|26|M|executive|98133
|
||||||
|
358|40|M|educator|10022
|
||||||
|
359|22|M|student|61801
|
||||||
|
360|51|M|other|98027
|
||||||
|
361|22|M|student|44074
|
||||||
|
362|35|F|homemaker|85233
|
||||||
|
363|20|M|student|87501
|
||||||
|
364|63|M|engineer|01810
|
||||||
|
365|29|M|lawyer|20009
|
||||||
|
366|20|F|student|50670
|
||||||
|
367|17|M|student|37411
|
||||||
|
368|18|M|student|92113
|
||||||
|
369|24|M|student|91335
|
||||||
|
370|52|M|writer|08534
|
||||||
|
371|36|M|engineer|99206
|
||||||
|
372|25|F|student|66046
|
||||||
|
373|24|F|other|55116
|
||||||
|
374|36|M|executive|78746
|
||||||
|
375|17|M|entertainment|37777
|
||||||
|
376|28|F|other|10010
|
||||||
|
377|22|M|student|18015
|
||||||
|
378|35|M|student|02859
|
||||||
|
379|44|M|programmer|98117
|
||||||
|
380|32|M|engineer|55117
|
||||||
|
381|33|M|artist|94608
|
||||||
|
382|45|M|engineer|01824
|
||||||
|
383|42|M|administrator|75204
|
||||||
|
384|52|M|programmer|45218
|
||||||
|
385|36|M|writer|10003
|
||||||
|
386|36|M|salesman|43221
|
||||||
|
387|33|M|entertainment|37412
|
||||||
|
388|31|M|other|36106
|
||||||
|
389|44|F|writer|83702
|
||||||
|
390|42|F|writer|85016
|
||||||
|
391|23|M|student|84604
|
||||||
|
392|52|M|writer|59801
|
||||||
|
393|19|M|student|83686
|
||||||
|
394|25|M|administrator|96819
|
||||||
|
395|43|M|other|44092
|
||||||
|
396|57|M|engineer|94551
|
||||||
|
397|17|M|student|27514
|
||||||
|
398|40|M|other|60008
|
||||||
|
399|25|M|other|92374
|
||||||
|
400|33|F|administrator|78213
|
||||||
|
401|46|F|healthcare|84107
|
||||||
|
402|30|M|engineer|95129
|
||||||
|
403|37|M|other|06811
|
||||||
|
404|29|F|programmer|55108
|
||||||
|
405|22|F|healthcare|10019
|
||||||
|
406|52|M|educator|93109
|
||||||
|
407|29|M|engineer|03261
|
||||||
|
408|23|M|student|61755
|
||||||
|
409|48|M|administrator|98225
|
||||||
|
410|30|F|artist|94025
|
||||||
|
411|34|M|educator|44691
|
||||||
|
412|25|M|educator|15222
|
||||||
|
413|55|M|educator|78212
|
||||||
|
414|24|M|programmer|38115
|
||||||
|
415|39|M|educator|85711
|
||||||
|
416|20|F|student|92626
|
||||||
|
417|27|F|other|48103
|
||||||
|
418|55|F|none|21206
|
||||||
|
419|37|M|lawyer|43215
|
||||||
|
420|53|M|educator|02140
|
||||||
|
421|38|F|programmer|55105
|
||||||
|
422|26|M|entertainment|94533
|
||||||
|
423|64|M|other|91606
|
||||||
|
424|36|F|marketing|55422
|
||||||
|
425|19|M|student|58644
|
||||||
|
426|55|M|educator|01602
|
||||||
|
427|51|M|doctor|85258
|
||||||
|
428|28|M|student|55414
|
||||||
|
429|27|M|student|29205
|
||||||
|
430|38|M|scientist|98199
|
||||||
|
431|24|M|marketing|92629
|
||||||
|
432|22|M|entertainment|50311
|
||||||
|
433|27|M|artist|11211
|
||||||
|
434|16|F|student|49705
|
||||||
|
435|24|M|engineer|60007
|
||||||
|
436|30|F|administrator|17345
|
||||||
|
437|27|F|other|20009
|
||||||
|
438|51|F|administrator|43204
|
||||||
|
439|23|F|administrator|20817
|
||||||
|
440|30|M|other|48076
|
||||||
|
441|50|M|technician|55013
|
||||||
|
442|22|M|student|85282
|
||||||
|
443|35|M|salesman|33308
|
||||||
|
444|51|F|lawyer|53202
|
||||||
|
445|21|M|writer|92653
|
||||||
|
446|57|M|educator|60201
|
||||||
|
447|30|M|administrator|55113
|
||||||
|
448|23|M|entertainment|10021
|
||||||
|
449|23|M|librarian|55021
|
||||||
|
450|35|F|educator|11758
|
||||||
|
451|16|M|student|48446
|
||||||
|
452|35|M|administrator|28018
|
||||||
|
453|18|M|student|06333
|
||||||
|
454|57|M|other|97330
|
||||||
|
455|48|M|administrator|83709
|
||||||
|
456|24|M|technician|31820
|
||||||
|
457|33|F|salesman|30011
|
||||||
|
458|47|M|technician|Y1A6B
|
||||||
|
459|22|M|student|29201
|
||||||
|
460|44|F|other|60630
|
||||||
|
461|15|M|student|98102
|
||||||
|
462|19|F|student|02918
|
||||||
|
463|48|F|healthcare|75218
|
||||||
|
464|60|M|writer|94583
|
||||||
|
465|32|M|other|05001
|
||||||
|
466|22|M|student|90804
|
||||||
|
467|29|M|engineer|91201
|
||||||
|
468|28|M|engineer|02341
|
||||||
|
469|60|M|educator|78628
|
||||||
|
470|24|M|programmer|10021
|
||||||
|
471|10|M|student|77459
|
||||||
|
472|24|M|student|87544
|
||||||
|
473|29|M|student|94708
|
||||||
|
474|51|M|executive|93711
|
||||||
|
475|30|M|programmer|75230
|
||||||
|
476|28|M|student|60440
|
||||||
|
477|23|F|student|02125
|
||||||
|
478|29|M|other|10019
|
||||||
|
479|30|M|educator|55409
|
||||||
|
480|57|M|retired|98257
|
||||||
|
481|73|M|retired|37771
|
||||||
|
482|18|F|student|40256
|
||||||
|
483|29|M|scientist|43212
|
||||||
|
484|27|M|student|21208
|
||||||
|
485|44|F|educator|95821
|
||||||
|
486|39|M|educator|93101
|
||||||
|
487|22|M|engineer|92121
|
||||||
|
488|48|M|technician|21012
|
||||||
|
489|55|M|other|45218
|
||||||
|
490|29|F|artist|V5A2B
|
||||||
|
491|43|F|writer|53711
|
||||||
|
492|57|M|educator|94618
|
||||||
|
493|22|M|engineer|60090
|
||||||
|
494|38|F|administrator|49428
|
||||||
|
495|29|M|engineer|03052
|
||||||
|
496|21|F|student|55414
|
||||||
|
497|20|M|student|50112
|
||||||
|
498|26|M|writer|55408
|
||||||
|
499|42|M|programmer|75006
|
||||||
|
500|28|M|administrator|94305
|
||||||
|
501|22|M|student|10025
|
||||||
|
502|22|M|student|23092
|
||||||
|
503|50|F|writer|27514
|
||||||
|
504|40|F|writer|92115
|
||||||
|
505|27|F|other|20657
|
||||||
|
506|46|M|programmer|03869
|
||||||
|
507|18|F|writer|28450
|
||||||
|
508|27|M|marketing|19382
|
||||||
|
509|23|M|administrator|10011
|
||||||
|
510|34|M|other|98038
|
||||||
|
511|22|M|student|21250
|
||||||
|
512|29|M|other|20090
|
||||||
|
513|43|M|administrator|26241
|
||||||
|
514|27|M|programmer|20707
|
||||||
|
515|53|M|marketing|49508
|
||||||
|
516|53|F|librarian|10021
|
||||||
|
517|24|M|student|55454
|
||||||
|
518|49|F|writer|99709
|
||||||
|
519|22|M|other|55320
|
||||||
|
520|62|M|healthcare|12603
|
||||||
|
521|19|M|student|02146
|
||||||
|
522|36|M|engineer|55443
|
||||||
|
523|50|F|administrator|04102
|
||||||
|
524|56|M|educator|02159
|
||||||
|
525|27|F|administrator|19711
|
||||||
|
526|30|M|marketing|97124
|
||||||
|
527|33|M|librarian|12180
|
||||||
|
528|18|M|student|55104
|
||||||
|
529|47|F|administrator|44224
|
||||||
|
530|29|M|engineer|94040
|
||||||
|
531|30|F|salesman|97408
|
||||||
|
532|20|M|student|92705
|
||||||
|
533|43|M|librarian|02324
|
||||||
|
534|20|M|student|05464
|
||||||
|
535|45|F|educator|80302
|
||||||
|
536|38|M|engineer|30078
|
||||||
|
537|36|M|engineer|22902
|
||||||
|
538|31|M|scientist|21010
|
||||||
|
539|53|F|administrator|80303
|
||||||
|
540|28|M|engineer|91201
|
||||||
|
541|19|F|student|84302
|
||||||
|
542|21|M|student|60515
|
||||||
|
543|33|M|scientist|95123
|
||||||
|
544|44|F|other|29464
|
||||||
|
545|27|M|technician|08052
|
||||||
|
546|36|M|executive|22911
|
||||||
|
547|50|M|educator|14534
|
||||||
|
548|51|M|writer|95468
|
||||||
|
549|42|M|scientist|45680
|
||||||
|
550|16|F|student|95453
|
||||||
|
551|25|M|programmer|55414
|
||||||
|
552|45|M|other|68147
|
||||||
|
553|58|M|educator|62901
|
||||||
|
554|32|M|scientist|62901
|
||||||
|
555|29|F|educator|23227
|
||||||
|
556|35|F|educator|30606
|
||||||
|
557|30|F|writer|11217
|
||||||
|
558|56|F|writer|63132
|
||||||
|
559|69|M|executive|10022
|
||||||
|
560|32|M|student|10003
|
||||||
|
561|23|M|engineer|60005
|
||||||
|
562|54|F|administrator|20879
|
||||||
|
563|39|F|librarian|32707
|
||||||
|
564|65|M|retired|94591
|
||||||
|
565|40|M|student|55422
|
||||||
|
566|20|M|student|14627
|
||||||
|
567|24|M|entertainment|10003
|
||||||
|
568|39|M|educator|01915
|
||||||
|
569|34|M|educator|91903
|
||||||
|
570|26|M|educator|14627
|
||||||
|
571|34|M|artist|01945
|
||||||
|
572|51|M|educator|20003
|
||||||
|
573|68|M|retired|48911
|
||||||
|
574|56|M|educator|53188
|
||||||
|
575|33|M|marketing|46032
|
||||||
|
576|48|M|executive|98281
|
||||||
|
577|36|F|student|77845
|
||||||
|
578|31|M|administrator|M7A1A
|
||||||
|
579|32|M|educator|48103
|
||||||
|
580|16|M|student|17961
|
||||||
|
581|37|M|other|94131
|
||||||
|
582|17|M|student|93003
|
||||||
|
583|44|M|engineer|29631
|
||||||
|
584|25|M|student|27511
|
||||||
|
585|69|M|librarian|98501
|
||||||
|
586|20|M|student|79508
|
||||||
|
587|26|M|other|14216
|
||||||
|
588|18|F|student|93063
|
||||||
|
589|21|M|lawyer|90034
|
||||||
|
590|50|M|educator|82435
|
||||||
|
591|57|F|librarian|92093
|
||||||
|
592|18|M|student|97520
|
||||||
|
593|31|F|educator|68767
|
||||||
|
594|46|M|educator|M4J2K
|
||||||
|
595|25|M|programmer|31909
|
||||||
|
596|20|M|artist|77073
|
||||||
|
597|23|M|other|84116
|
||||||
|
598|40|F|marketing|43085
|
||||||
|
599|22|F|student|R3T5K
|
||||||
|
600|34|M|programmer|02320
|
||||||
|
601|19|F|artist|99687
|
||||||
|
602|47|F|other|34656
|
||||||
|
603|21|M|programmer|47905
|
||||||
|
604|39|M|educator|11787
|
||||||
|
605|33|M|engineer|33716
|
||||||
|
606|28|M|programmer|63044
|
||||||
|
607|49|F|healthcare|02154
|
||||||
|
608|22|M|other|10003
|
||||||
|
609|13|F|student|55106
|
||||||
|
610|22|M|student|21227
|
||||||
|
611|46|M|librarian|77008
|
||||||
|
612|36|M|educator|79070
|
||||||
|
613|37|F|marketing|29678
|
||||||
|
614|54|M|educator|80227
|
||||||
|
615|38|M|educator|27705
|
||||||
|
616|55|M|scientist|50613
|
||||||
|
617|27|F|writer|11201
|
||||||
|
618|15|F|student|44212
|
||||||
|
619|17|M|student|44134
|
||||||
|
620|18|F|writer|81648
|
||||||
|
621|17|M|student|60402
|
||||||
|
622|25|M|programmer|14850
|
||||||
|
623|50|F|educator|60187
|
||||||
|
624|19|M|student|30067
|
||||||
|
625|27|M|programmer|20723
|
||||||
|
626|23|M|scientist|19807
|
||||||
|
627|24|M|engineer|08034
|
||||||
|
628|13|M|none|94306
|
||||||
|
629|46|F|other|44224
|
||||||
|
630|26|F|healthcare|55408
|
||||||
|
631|18|F|student|38866
|
||||||
|
632|18|M|student|55454
|
||||||
|
633|35|M|programmer|55414
|
||||||
|
634|39|M|engineer|T8H1N
|
||||||
|
635|22|M|other|23237
|
||||||
|
636|47|M|educator|48043
|
||||||
|
637|30|M|other|74101
|
||||||
|
638|45|M|engineer|01940
|
||||||
|
639|42|F|librarian|12065
|
||||||
|
640|20|M|student|61801
|
||||||
|
641|24|M|student|60626
|
||||||
|
642|18|F|student|95521
|
||||||
|
643|39|M|scientist|55122
|
||||||
|
644|51|M|retired|63645
|
||||||
|
645|27|M|programmer|53211
|
||||||
|
646|17|F|student|51250
|
||||||
|
647|40|M|educator|45810
|
||||||
|
648|43|M|engineer|91351
|
||||||
|
649|20|M|student|39762
|
||||||
|
650|42|M|engineer|83814
|
||||||
|
651|65|M|retired|02903
|
||||||
|
652|35|M|other|22911
|
||||||
|
653|31|M|executive|55105
|
||||||
|
654|27|F|student|78739
|
||||||
|
655|50|F|healthcare|60657
|
||||||
|
656|48|M|educator|10314
|
||||||
|
657|26|F|none|78704
|
||||||
|
658|33|M|programmer|92626
|
||||||
|
659|31|M|educator|54248
|
||||||
|
660|26|M|student|77380
|
||||||
|
661|28|M|programmer|98121
|
||||||
|
662|55|M|librarian|19102
|
||||||
|
663|26|M|other|19341
|
||||||
|
664|30|M|engineer|94115
|
||||||
|
665|25|M|administrator|55412
|
||||||
|
666|44|M|administrator|61820
|
||||||
|
667|35|M|librarian|01970
|
||||||
|
668|29|F|writer|10016
|
||||||
|
669|37|M|other|20009
|
||||||
|
670|30|M|technician|21114
|
||||||
|
671|21|M|programmer|91919
|
||||||
|
672|54|F|administrator|90095
|
||||||
|
673|51|M|educator|22906
|
||||||
|
674|13|F|student|55337
|
||||||
|
675|34|M|other|28814
|
||||||
|
676|30|M|programmer|32712
|
||||||
|
677|20|M|other|99835
|
||||||
|
678|50|M|educator|61462
|
||||||
|
679|20|F|student|54302
|
||||||
|
680|33|M|lawyer|90405
|
||||||
|
681|44|F|marketing|97208
|
||||||
|
682|23|M|programmer|55128
|
||||||
|
683|42|M|librarian|23509
|
||||||
|
684|28|M|student|55414
|
||||||
|
685|32|F|librarian|55409
|
||||||
|
686|32|M|educator|26506
|
||||||
|
687|31|F|healthcare|27713
|
||||||
|
688|37|F|administrator|60476
|
||||||
|
689|25|M|other|45439
|
||||||
|
690|35|M|salesman|63304
|
||||||
|
691|34|M|educator|60089
|
||||||
|
692|34|M|engineer|18053
|
||||||
|
693|43|F|healthcare|85210
|
||||||
|
694|60|M|programmer|06365
|
||||||
|
695|26|M|writer|38115
|
||||||
|
696|55|M|other|94920
|
||||||
|
697|25|M|other|77042
|
||||||
|
698|28|F|programmer|06906
|
||||||
|
699|44|M|other|96754
|
||||||
|
700|17|M|student|76309
|
||||||
|
701|51|F|librarian|56321
|
||||||
|
702|37|M|other|89104
|
||||||
|
703|26|M|educator|49512
|
||||||
|
704|51|F|librarian|91105
|
||||||
|
705|21|F|student|54494
|
||||||
|
706|23|M|student|55454
|
||||||
|
707|56|F|librarian|19146
|
||||||
|
708|26|F|homemaker|96349
|
||||||
|
709|21|M|other|N4T1A
|
||||||
|
710|19|M|student|92020
|
||||||
|
711|22|F|student|15203
|
||||||
|
712|22|F|student|54901
|
||||||
|
713|42|F|other|07204
|
||||||
|
714|26|M|engineer|55343
|
||||||
|
715|21|M|technician|91206
|
||||||
|
716|36|F|administrator|44265
|
||||||
|
717|24|M|technician|84105
|
||||||
|
718|42|M|technician|64118
|
||||||
|
719|37|F|other|V0R2H
|
||||||
|
720|49|F|administrator|16506
|
||||||
|
721|24|F|entertainment|11238
|
||||||
|
722|50|F|homemaker|17331
|
||||||
|
723|26|M|executive|94403
|
||||||
|
724|31|M|executive|40243
|
||||||
|
725|21|M|student|91711
|
||||||
|
726|25|F|administrator|80538
|
||||||
|
727|25|M|student|78741
|
||||||
|
728|58|M|executive|94306
|
||||||
|
729|19|M|student|56567
|
||||||
|
730|31|F|scientist|32114
|
||||||
|
731|41|F|educator|70403
|
||||||
|
732|28|F|other|98405
|
||||||
|
733|44|F|other|60630
|
||||||
|
734|25|F|other|63108
|
||||||
|
735|29|F|healthcare|85719
|
||||||
|
736|48|F|writer|94618
|
||||||
|
737|30|M|programmer|98072
|
||||||
|
738|35|M|technician|95403
|
||||||
|
739|35|M|technician|73162
|
||||||
|
740|25|F|educator|22206
|
||||||
|
741|25|M|writer|63108
|
||||||
|
742|35|M|student|29210
|
||||||
|
743|31|M|programmer|92660
|
||||||
|
744|35|M|marketing|47024
|
||||||
|
745|42|M|writer|55113
|
||||||
|
746|25|M|engineer|19047
|
||||||
|
747|19|M|other|93612
|
||||||
|
748|28|M|administrator|94720
|
||||||
|
749|33|M|other|80919
|
||||||
|
750|28|M|administrator|32303
|
||||||
|
751|24|F|other|90034
|
||||||
|
752|60|M|retired|21201
|
||||||
|
753|56|M|salesman|91206
|
||||||
|
754|59|F|librarian|62901
|
||||||
|
755|44|F|educator|97007
|
||||||
|
756|30|F|none|90247
|
||||||
|
757|26|M|student|55104
|
||||||
|
758|27|M|student|53706
|
||||||
|
759|20|F|student|68503
|
||||||
|
760|35|F|other|14211
|
||||||
|
761|17|M|student|97302
|
||||||
|
762|32|M|administrator|95050
|
||||||
|
763|27|M|scientist|02113
|
||||||
|
764|27|F|educator|62903
|
||||||
|
765|31|M|student|33066
|
||||||
|
766|42|M|other|10960
|
||||||
|
767|70|M|engineer|00000
|
||||||
|
768|29|M|administrator|12866
|
||||||
|
769|39|M|executive|06927
|
||||||
|
770|28|M|student|14216
|
||||||
|
771|26|M|student|15232
|
||||||
|
772|50|M|writer|27105
|
||||||
|
773|20|M|student|55414
|
||||||
|
774|30|M|student|80027
|
||||||
|
775|46|M|executive|90036
|
||||||
|
776|30|M|librarian|51157
|
||||||
|
777|63|M|programmer|01810
|
||||||
|
778|34|M|student|01960
|
||||||
|
779|31|M|student|K7L5J
|
||||||
|
780|49|M|programmer|94560
|
||||||
|
781|20|M|student|48825
|
||||||
|
782|21|F|artist|33205
|
||||||
|
783|30|M|marketing|77081
|
||||||
|
784|47|M|administrator|91040
|
||||||
|
785|32|M|engineer|23322
|
||||||
|
786|36|F|engineer|01754
|
||||||
|
787|18|F|student|98620
|
||||||
|
788|51|M|administrator|05779
|
||||||
|
789|29|M|other|55420
|
||||||
|
790|27|M|technician|80913
|
||||||
|
791|31|M|educator|20064
|
||||||
|
792|40|M|programmer|12205
|
||||||
|
793|22|M|student|85281
|
||||||
|
794|32|M|educator|57197
|
||||||
|
795|30|M|programmer|08610
|
||||||
|
796|32|F|writer|33755
|
||||||
|
797|44|F|other|62522
|
||||||
|
798|40|F|writer|64131
|
||||||
|
799|49|F|administrator|19716
|
||||||
|
800|25|M|programmer|55337
|
||||||
|
801|22|M|writer|92154
|
||||||
|
802|35|M|administrator|34105
|
||||||
|
803|70|M|administrator|78212
|
||||||
|
804|39|M|educator|61820
|
||||||
|
805|27|F|other|20009
|
||||||
|
806|27|M|marketing|11217
|
||||||
|
807|41|F|healthcare|93555
|
||||||
|
808|45|M|salesman|90016
|
||||||
|
809|50|F|marketing|30803
|
||||||
|
810|55|F|other|80526
|
||||||
|
811|40|F|educator|73013
|
||||||
|
812|22|M|technician|76234
|
||||||
|
813|14|F|student|02136
|
||||||
|
814|30|M|other|12345
|
||||||
|
815|32|M|other|28806
|
||||||
|
816|34|M|other|20755
|
||||||
|
817|19|M|student|60152
|
||||||
|
818|28|M|librarian|27514
|
||||||
|
819|59|M|administrator|40205
|
||||||
|
820|22|M|student|37725
|
||||||
|
821|37|M|engineer|77845
|
||||||
|
822|29|F|librarian|53144
|
||||||
|
823|27|M|artist|50322
|
||||||
|
824|31|M|other|15017
|
||||||
|
825|44|M|engineer|05452
|
||||||
|
826|28|M|artist|77048
|
||||||
|
827|23|F|engineer|80228
|
||||||
|
828|28|M|librarian|85282
|
||||||
|
829|48|M|writer|80209
|
||||||
|
830|46|M|programmer|53066
|
||||||
|
831|21|M|other|33765
|
||||||
|
832|24|M|technician|77042
|
||||||
|
833|34|M|writer|90019
|
||||||
|
834|26|M|other|64153
|
||||||
|
835|44|F|executive|11577
|
||||||
|
836|44|M|artist|10018
|
||||||
|
837|36|F|artist|55409
|
||||||
|
838|23|M|student|01375
|
||||||
|
839|38|F|entertainment|90814
|
||||||
|
840|39|M|artist|55406
|
||||||
|
841|45|M|doctor|47401
|
||||||
|
842|40|M|writer|93055
|
||||||
|
843|35|M|librarian|44212
|
||||||
|
844|22|M|engineer|95662
|
||||||
|
845|64|M|doctor|97405
|
||||||
|
846|27|M|lawyer|47130
|
||||||
|
847|29|M|student|55417
|
||||||
|
848|46|M|engineer|02146
|
||||||
|
849|15|F|student|25652
|
||||||
|
850|34|M|technician|78390
|
||||||
|
851|18|M|other|29646
|
||||||
|
852|46|M|administrator|94086
|
||||||
|
853|49|M|writer|40515
|
||||||
|
854|29|F|student|55408
|
||||||
|
855|53|M|librarian|04988
|
||||||
|
856|43|F|marketing|97215
|
||||||
|
857|35|F|administrator|V1G4L
|
||||||
|
858|63|M|educator|09645
|
||||||
|
859|18|F|other|06492
|
||||||
|
860|70|F|retired|48322
|
||||||
|
861|38|F|student|14085
|
||||||
|
862|25|M|executive|13820
|
||||||
|
863|17|M|student|60089
|
||||||
|
864|27|M|programmer|63021
|
||||||
|
865|25|M|artist|11231
|
||||||
|
866|45|M|other|60302
|
||||||
|
867|24|M|scientist|92507
|
||||||
|
868|21|M|programmer|55303
|
||||||
|
869|30|M|student|10025
|
||||||
|
870|22|M|student|65203
|
||||||
|
871|31|M|executive|44648
|
||||||
|
872|19|F|student|74078
|
||||||
|
873|48|F|administrator|33763
|
||||||
|
874|36|M|scientist|37076
|
||||||
|
875|24|F|student|35802
|
||||||
|
876|41|M|other|20902
|
||||||
|
877|30|M|other|77504
|
||||||
|
878|50|F|educator|98027
|
||||||
|
879|33|F|administrator|55337
|
||||||
|
880|13|M|student|83702
|
||||||
|
881|39|M|marketing|43017
|
||||||
|
882|35|M|engineer|40503
|
||||||
|
883|49|M|librarian|50266
|
||||||
|
884|44|M|engineer|55337
|
||||||
|
885|30|F|other|95316
|
||||||
|
886|20|M|student|61820
|
||||||
|
887|14|F|student|27249
|
||||||
|
888|41|M|scientist|17036
|
||||||
|
889|24|M|technician|78704
|
||||||
|
890|32|M|student|97301
|
||||||
|
891|51|F|administrator|03062
|
||||||
|
892|36|M|other|45243
|
||||||
|
893|25|M|student|95823
|
||||||
|
894|47|M|educator|74075
|
||||||
|
895|31|F|librarian|32301
|
||||||
|
896|28|M|writer|91505
|
||||||
|
897|30|M|other|33484
|
||||||
|
898|23|M|homemaker|61755
|
||||||
|
899|32|M|other|55116
|
||||||
|
900|60|M|retired|18505
|
||||||
|
901|38|M|executive|L1V3W
|
||||||
|
902|45|F|artist|97203
|
||||||
|
903|28|M|educator|20850
|
||||||
|
904|17|F|student|61073
|
||||||
|
905|27|M|other|30350
|
||||||
|
906|45|M|librarian|70124
|
||||||
|
907|25|F|other|80526
|
||||||
|
908|44|F|librarian|68504
|
||||||
|
909|50|F|educator|53171
|
||||||
|
910|28|M|healthcare|29301
|
||||||
|
911|37|F|writer|53210
|
||||||
|
912|51|M|other|06512
|
||||||
|
913|27|M|student|76201
|
||||||
|
914|44|F|other|08105
|
||||||
|
915|50|M|entertainment|60614
|
||||||
|
916|27|M|engineer|N2L5N
|
||||||
|
917|22|F|student|20006
|
||||||
|
918|40|M|scientist|70116
|
||||||
|
919|25|M|other|14216
|
||||||
|
920|30|F|artist|90008
|
||||||
|
921|20|F|student|98801
|
||||||
|
922|29|F|administrator|21114
|
||||||
|
923|21|M|student|E2E3R
|
||||||
|
924|29|M|other|11753
|
||||||
|
925|18|F|salesman|49036
|
||||||
|
926|49|M|entertainment|01701
|
||||||
|
927|23|M|programmer|55428
|
||||||
|
928|21|M|student|55408
|
||||||
|
929|44|M|scientist|53711
|
||||||
|
930|28|F|scientist|07310
|
||||||
|
931|60|M|educator|33556
|
||||||
|
932|58|M|educator|06437
|
||||||
|
933|28|M|student|48105
|
||||||
|
934|61|M|engineer|22902
|
||||||
|
935|42|M|doctor|66221
|
||||||
|
936|24|M|other|32789
|
||||||
|
937|48|M|educator|98072
|
||||||
|
938|38|F|technician|55038
|
||||||
|
939|26|F|student|33319
|
||||||
|
940|32|M|administrator|02215
|
||||||
|
941|20|M|student|97229
|
||||||
|
942|48|F|librarian|78209
|
||||||
|
943|22|M|student|77841
|
||||||
File diff suppressed because it is too large
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@@ -0,0 +1,170 @@
|
|||||||
|
SUMMARY
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
These files contain 1,000,209 anonymous ratings of approximately 3,900 movies
|
||||||
|
made by 6,040 MovieLens users who joined MovieLens in 2000.
|
||||||
|
|
||||||
|
USAGE LICENSE
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
Neither the University of Minnesota nor any of the researchers
|
||||||
|
involved can guarantee the correctness of the data, its suitability
|
||||||
|
for any particular purpose, or the validity of results based on the
|
||||||
|
use of the data set. The data set may be used for any research
|
||||||
|
purposes under the following conditions:
|
||||||
|
|
||||||
|
* The user may not state or imply any endorsement from the
|
||||||
|
University of Minnesota or the GroupLens Research Group.
|
||||||
|
|
||||||
|
* The user must acknowledge the use of the data set in
|
||||||
|
publications resulting from the use of the data set
|
||||||
|
(see below for citation information).
|
||||||
|
|
||||||
|
* The user may not redistribute the data without separate
|
||||||
|
permission.
|
||||||
|
|
||||||
|
* The user may not use this information for any commercial or
|
||||||
|
revenue-bearing purposes without first obtaining permission
|
||||||
|
from a faculty member of the GroupLens Research Project at the
|
||||||
|
University of Minnesota.
|
||||||
|
|
||||||
|
If you have any further questions or comments, please contact GroupLens
|
||||||
|
<grouplens-info@cs.umn.edu>.
|
||||||
|
|
||||||
|
CITATION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
To acknowledge use of the dataset in publications, please cite the following
|
||||||
|
paper:
|
||||||
|
|
||||||
|
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History
|
||||||
|
and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,
|
||||||
|
Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
|
||||||
|
|
||||||
|
|
||||||
|
ACKNOWLEDGEMENTS
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data
|
||||||
|
set.
|
||||||
|
|
||||||
|
FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
The GroupLens Research Project is a research group in the Department of
|
||||||
|
Computer Science and Engineering at the University of Minnesota. Members of
|
||||||
|
the GroupLens Research Project are involved in many research projects related
|
||||||
|
to the fields of information filtering, collaborative filtering, and
|
||||||
|
recommender systems. The project is lead by professors John Riedl and Joseph
|
||||||
|
Konstan. The project began to explore automated collaborative filtering in
|
||||||
|
1992, but is most well known for its world wide trial of an automated
|
||||||
|
collaborative filtering system for Usenet news in 1996. Since then the project
|
||||||
|
has expanded its scope to research overall information filtering solutions,
|
||||||
|
integrating in content-based methods as well as improving current collaborative
|
||||||
|
filtering technology.
|
||||||
|
|
||||||
|
Further information on the GroupLens Research project, including research
|
||||||
|
publications, can be found at the following web site:
|
||||||
|
|
||||||
|
http://www.grouplens.org/
|
||||||
|
|
||||||
|
GroupLens Research currently operates a movie recommender based on
|
||||||
|
collaborative filtering:
|
||||||
|
|
||||||
|
http://www.movielens.org/
|
||||||
|
|
||||||
|
RATINGS FILE DESCRIPTION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
All ratings are contained in the file "ratings.dat" and are in the
|
||||||
|
following format:
|
||||||
|
|
||||||
|
UserID::MovieID::Rating::Timestamp
|
||||||
|
|
||||||
|
- UserIDs range between 1 and 6040
|
||||||
|
- MovieIDs range between 1 and 3952
|
||||||
|
- Ratings are made on a 5-star scale (whole-star ratings only)
|
||||||
|
- Timestamp is represented in seconds since the epoch as returned by time(2)
|
||||||
|
- Each user has at least 20 ratings
|
||||||
|
|
||||||
|
USERS FILE DESCRIPTION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
User information is in the file "users.dat" and is in the following
|
||||||
|
format:
|
||||||
|
|
||||||
|
UserID::Gender::Age::Occupation::Zip-code
|
||||||
|
|
||||||
|
All demographic information is provided voluntarily by the users and is
|
||||||
|
not checked for accuracy. Only users who have provided some demographic
|
||||||
|
information are included in this data set.
|
||||||
|
|
||||||
|
- Gender is denoted by a "M" for male and "F" for female
|
||||||
|
- Age is chosen from the following ranges:
|
||||||
|
|
||||||
|
* 1: "Under 18"
|
||||||
|
* 18: "18-24"
|
||||||
|
* 25: "25-34"
|
||||||
|
* 35: "35-44"
|
||||||
|
* 45: "45-49"
|
||||||
|
* 50: "50-55"
|
||||||
|
* 56: "56+"
|
||||||
|
|
||||||
|
- Occupation is chosen from the following choices:
|
||||||
|
|
||||||
|
* 0: "other" or not specified
|
||||||
|
* 1: "academic/educator"
|
||||||
|
* 2: "artist"
|
||||||
|
* 3: "clerical/admin"
|
||||||
|
* 4: "college/grad student"
|
||||||
|
* 5: "customer service"
|
||||||
|
* 6: "doctor/health care"
|
||||||
|
* 7: "executive/managerial"
|
||||||
|
* 8: "farmer"
|
||||||
|
* 9: "homemaker"
|
||||||
|
* 10: "K-12 student"
|
||||||
|
* 11: "lawyer"
|
||||||
|
* 12: "programmer"
|
||||||
|
* 13: "retired"
|
||||||
|
* 14: "sales/marketing"
|
||||||
|
* 15: "scientist"
|
||||||
|
* 16: "self-employed"
|
||||||
|
* 17: "technician/engineer"
|
||||||
|
* 18: "tradesman/craftsman"
|
||||||
|
* 19: "unemployed"
|
||||||
|
* 20: "writer"
|
||||||
|
|
||||||
|
MOVIES FILE DESCRIPTION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
Movie information is in the file "movies.dat" and is in the following
|
||||||
|
format:
|
||||||
|
|
||||||
|
MovieID::Title::Genres
|
||||||
|
|
||||||
|
- Titles are identical to titles provided by the IMDB (including
|
||||||
|
year of release)
|
||||||
|
- Genres are pipe-separated and are selected from the following genres:
|
||||||
|
|
||||||
|
* Action
|
||||||
|
* Adventure
|
||||||
|
* Animation
|
||||||
|
* Children's
|
||||||
|
* Comedy
|
||||||
|
* Crime
|
||||||
|
* Documentary
|
||||||
|
* Drama
|
||||||
|
* Fantasy
|
||||||
|
* Film-Noir
|
||||||
|
* Horror
|
||||||
|
* Musical
|
||||||
|
* Mystery
|
||||||
|
* Romance
|
||||||
|
* Sci-Fi
|
||||||
|
* Thriller
|
||||||
|
* War
|
||||||
|
* Western
|
||||||
|
|
||||||
|
- Some MovieIDs do not correspond to a movie due to accidental duplicate
|
||||||
|
entries and/or test entries
|
||||||
|
- Movies are mostly entered by hand, so errors and inconsistencies may exist
|
||||||
File diff suppressed because it is too large
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|
|||||||
|
94_102_117_174_584
|
||||||
|
14_156_45_89_28
|
||||||
|
522_385_234_452_594
|
||||||
|
522_385_234_246_428
|
||||||
|
452_246_220_586_82
|
||||||
|
452_246_220_586_198
|
||||||
|
452_246_220_586_50
|
||||||
|
220_586_73_263_372
|
||||||
|
220_586_73_263_365
|
||||||
|
220_586_73_263_6
|
||||||
|
73_263_563_119_66
|
||||||
|
73_263_563_4_312
|
||||||
|
73_263_563_4_354
|
||||||
|
14_156_45_580_560
|
||||||
|
14_156_45_560_318
|
||||||
|
14_156_45_560_606
|
||||||
|
14_156_517_462_448
|
||||||
|
14_156_517_89_28
|
||||||
@@ -0,0 +1,23 @@
|
|||||||
|
86_384_143_297_401
|
||||||
|
86_384_143_297_528
|
||||||
|
86_384_143_297_579
|
||||||
|
86_384_143_297_359
|
||||||
|
86_384_143_297_566
|
||||||
|
86_384_143_297_223
|
||||||
|
401_528_579_359_241
|
||||||
|
401_528_579_359_337
|
||||||
|
401_528_579_359_445
|
||||||
|
401_528_579_359_211
|
||||||
|
401_528_579_359_422
|
||||||
|
566_223_225_108_241
|
||||||
|
566_223_225_108_370
|
||||||
|
566_223_225_108_361
|
||||||
|
566_223_225_241_191
|
||||||
|
566_223_225_178_29
|
||||||
|
225_523_108_241_543
|
||||||
|
523_108_241_543_29
|
||||||
|
523_108_241_543_500
|
||||||
|
523_543_178_337_500
|
||||||
|
523_543_178_445_500
|
||||||
|
178_337_445_38_116
|
||||||
|
178_337_445_96_116
|
||||||
File diff suppressed because it is too large
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Load Diff
@@ -0,0 +1,96 @@
|
|||||||
|
base:
|
||||||
|
data:
|
||||||
|
groups:
|
||||||
|
filepath_or_buffer:
|
||||||
|
- 'datasets/stratigis/groupsWithHighRatings5.txt'
|
||||||
|
- 'datasets/stratigis/groupsWithModerateRatings5untested.txt'
|
||||||
|
testdata:
|
||||||
|
filepath_or_buffer: 'datasets/fake_data.csv'
|
||||||
|
sep: '\t'
|
||||||
|
skiprows: 1
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
test:
|
||||||
|
filepath_or_buffer: 'datasets/stratigis/ratings.csv'
|
||||||
|
sep: ','
|
||||||
|
skiprows: 1
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
ml32m:
|
||||||
|
filepath_or_buffer: 'datasets/ml-32m/ratings.csv'
|
||||||
|
sep: ','
|
||||||
|
skiprows: 1
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
ml100k:
|
||||||
|
filepath_or_buffer: 'datasets/ml-100k/u.data'
|
||||||
|
sep: '\t'
|
||||||
|
skiprows: 0
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp']
|
||||||
|
ml1m:
|
||||||
|
filepath_or_buffer: 'datasets/ml-1m/ratings.dat'
|
||||||
|
sep: '::'
|
||||||
|
names: [ 'userId', 'itemId', 'rating', 'timestamp' ]
|
||||||
|
tags:
|
||||||
|
tags_file: 'datasets/stratigis/tags.csv'
|
||||||
|
model:
|
||||||
|
gmf:
|
||||||
|
learning_rate: 0.005
|
||||||
|
weight_decay: 0.0000001
|
||||||
|
latent_dim: 8
|
||||||
|
epochs: 30
|
||||||
|
num_negative: 10
|
||||||
|
batch_size: 1024
|
||||||
|
cuda: False
|
||||||
|
optimizer_name: 'adam'
|
||||||
|
mlp:
|
||||||
|
learning_rate: 0.005
|
||||||
|
weight_decay: 0.0000001
|
||||||
|
latent_dim: 8
|
||||||
|
epochs: 30
|
||||||
|
num_negative: 10
|
||||||
|
batch_size: 1024
|
||||||
|
cuda: False
|
||||||
|
optimizer_name: 'adam'
|
||||||
|
als:
|
||||||
|
learning_rate: 0.1
|
||||||
|
latent_dim: 100
|
||||||
|
epochs: 10
|
||||||
|
reg_term: 0.001
|
||||||
|
|
||||||
|
bpr:
|
||||||
|
learning_rate: 0.01
|
||||||
|
latent_dim: 100
|
||||||
|
epochs: 10
|
||||||
|
reg_term: 0.001
|
||||||
|
emf:
|
||||||
|
learning_rate: 0.01
|
||||||
|
reg_term: 0.001
|
||||||
|
expl_reg_term: 0.0
|
||||||
|
latent_dim: 80
|
||||||
|
epochs: 10
|
||||||
|
positive_threshold: 3
|
||||||
|
knn: 10
|
||||||
|
mf:
|
||||||
|
learning_rate: 0.01
|
||||||
|
reg_term: 0.001
|
||||||
|
expl_reg_term: 0.0
|
||||||
|
latent_dim: 80
|
||||||
|
epochs: 10
|
||||||
|
positive_threshold: 3
|
||||||
|
knn: 10
|
||||||
|
autoencoder:
|
||||||
|
learning_rate: 0.005
|
||||||
|
weight_decay: 0.0000001
|
||||||
|
hidden_layer_features: 8
|
||||||
|
epochs: 30
|
||||||
|
cuda: False
|
||||||
|
optimizer_name: 'adam'
|
||||||
|
positive_threshold: 3
|
||||||
|
knn: 10
|
||||||
|
expl: true
|
||||||
|
explainer:
|
||||||
|
lore4groups:
|
||||||
|
n_similar_for_tree: 100
|
||||||
|
rating_threshold_for_like: 3.0
|
||||||
|
max_tree_depth: 5
|
||||||
|
top_n_labels: 5000
|
||||||
|
min_rating_for_history: 1.0
|
||||||
|
similarity_threshold: 0.1
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,157 @@
|
|||||||
|
SUMMARY & USAGE LICENSE
|
||||||
|
=============================================
|
||||||
|
|
||||||
|
MovieLens data sets were collected by the GroupLens Research Project
|
||||||
|
at the University of Minnesota.
|
||||||
|
|
||||||
|
This data set consists of:
|
||||||
|
* 100,000 ratings (1-5) from 943 users on 1682 movies.
|
||||||
|
* Each user has rated at least 20 movies.
|
||||||
|
* Simple demographic info for the users (age, gender, occupation, zip)
|
||||||
|
|
||||||
|
The data was collected through the MovieLens web site
|
||||||
|
(movielens.umn.edu) during the seven-month period from September 19th,
|
||||||
|
1997 through April 22nd, 1998. This data has been cleaned up - users
|
||||||
|
who had less than 20 ratings or did not have complete demographic
|
||||||
|
information were removed from this data set. Detailed descriptions of
|
||||||
|
the data file can be found at the end of this file.
|
||||||
|
|
||||||
|
Neither the University of Minnesota nor any of the researchers
|
||||||
|
involved can guarantee the correctness of the data, its suitability
|
||||||
|
for any particular purpose, or the validity of results based on the
|
||||||
|
use of the data set. The data set may be used for any research
|
||||||
|
purposes under the following conditions:
|
||||||
|
|
||||||
|
* The user may not state or imply any endorsement from the
|
||||||
|
University of Minnesota or the GroupLens Research Group.
|
||||||
|
|
||||||
|
* The user must acknowledge the use of the data set in
|
||||||
|
publications resulting from the use of the data set
|
||||||
|
(see below for citation information).
|
||||||
|
|
||||||
|
* The user may not redistribute the data without separate
|
||||||
|
permission.
|
||||||
|
|
||||||
|
* The user may not use this information for any commercial or
|
||||||
|
revenue-bearing purposes without first obtaining permission
|
||||||
|
from a faculty member of the GroupLens Research Project at the
|
||||||
|
University of Minnesota.
|
||||||
|
|
||||||
|
If you have any further questions or comments, please contact GroupLens
|
||||||
|
<grouplens-info@cs.umn.edu>.
|
||||||
|
|
||||||
|
CITATION
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
To acknowledge use of the dataset in publications, please cite the
|
||||||
|
following paper:
|
||||||
|
|
||||||
|
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets:
|
||||||
|
History and Context. ACM Transactions on Interactive Intelligent
|
||||||
|
Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages.
|
||||||
|
DOI=http://dx.doi.org/10.1145/2827872
|
||||||
|
|
||||||
|
|
||||||
|
ACKNOWLEDGEMENTS
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
Thanks to Al Borchers for cleaning up this data and writing the
|
||||||
|
accompanying scripts.
|
||||||
|
|
||||||
|
PUBLISHED WORK THAT HAS USED THIS DATASET
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic
|
||||||
|
Framework for Performing Collaborative Filtering. Proceedings of the
|
||||||
|
1999 Conference on Research and Development in Information
|
||||||
|
Retrieval. Aug. 1999.
|
||||||
|
|
||||||
|
FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
The GroupLens Research Project is a research group in the Department
|
||||||
|
of Computer Science and Engineering at the University of Minnesota.
|
||||||
|
Members of the GroupLens Research Project are involved in many
|
||||||
|
research projects related to the fields of information filtering,
|
||||||
|
collaborative filtering, and recommender systems. The project is lead
|
||||||
|
by professors John Riedl and Joseph Konstan. The project began to
|
||||||
|
explore automated collaborative filtering in 1992, but is most well
|
||||||
|
known for its world wide trial of an automated collaborative filtering
|
||||||
|
system for Usenet news in 1996. The technology developed in the
|
||||||
|
Usenet trial formed the base for the formation of Net Perceptions,
|
||||||
|
Inc., which was founded by members of GroupLens Research. Since then
|
||||||
|
the project has expanded its scope to research overall information
|
||||||
|
filtering solutions, integrating in content-based methods as well as
|
||||||
|
improving current collaborative filtering technology.
|
||||||
|
|
||||||
|
Further information on the GroupLens Research project, including
|
||||||
|
research publications, can be found at the following web site:
|
||||||
|
|
||||||
|
http://www.grouplens.org/
|
||||||
|
|
||||||
|
GroupLens Research currently operates a movie recommender based on
|
||||||
|
collaborative filtering:
|
||||||
|
|
||||||
|
http://www.movielens.org/
|
||||||
|
|
||||||
|
DETAILED DESCRIPTIONS OF DATA FILES
|
||||||
|
==============================================
|
||||||
|
|
||||||
|
Here are brief descriptions of the data.
|
||||||
|
|
||||||
|
ml-data.tar.gz -- Compressed tar file. To rebuild the u data files do this:
|
||||||
|
gunzip ml-data.tar.gz
|
||||||
|
tar xvf ml-data.tar
|
||||||
|
mku.sh
|
||||||
|
|
||||||
|
u.data -- The full u data set, 100000 ratings by 943 users on 1682 items.
|
||||||
|
Each user has rated at least 20 movies. Users and items are
|
||||||
|
numbered consecutively from 1. The data is randomly
|
||||||
|
ordered. This is a tab separated list of
|
||||||
|
user id | item id | rating | timestamp.
|
||||||
|
The time stamps are unix seconds since 1/1/1970 UTC
|
||||||
|
|
||||||
|
u.info -- The number of users, items, and ratings in the u data set.
|
||||||
|
|
||||||
|
u.item -- Information about the items (movies); this is a tab separated
|
||||||
|
list of
|
||||||
|
movie id | movie title | release date | video release date |
|
||||||
|
IMDb URL | unknown | Action | Adventure | Animation |
|
||||||
|
Children's | Comedy | Crime | Documentary | Drama | Fantasy |
|
||||||
|
Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |
|
||||||
|
Thriller | War | Western |
|
||||||
|
The last 19 fields are the genres, a 1 indicates the movie
|
||||||
|
is of that genre, a 0 indicates it is not; movies can be in
|
||||||
|
several genres at once.
|
||||||
|
The movie ids are the ones used in the u.data data set.
|
||||||
|
|
||||||
|
u.genre -- A list of the genres.
|
||||||
|
|
||||||
|
u.user -- Demographic information about the users; this is a tab
|
||||||
|
separated list of
|
||||||
|
user id | age | gender | occupation | zip code
|
||||||
|
The user ids are the ones used in the u.data data set.
|
||||||
|
|
||||||
|
u.occupation -- A list of the occupations.
|
||||||
|
|
||||||
|
u1.base -- The data sets u1.base and u1.test through u5.base and u5.test
|
||||||
|
u1.test are 80%/20% splits of the u data into training and test data.
|
||||||
|
u2.base Each of u1, ..., u5 have disjoint test sets; this if for
|
||||||
|
u2.test 5 fold cross validation (where you repeat your experiment
|
||||||
|
u3.base with each training and test set and average the results).
|
||||||
|
u3.test These data sets can be generated from u.data by mku.sh.
|
||||||
|
u4.base
|
||||||
|
u4.test
|
||||||
|
u5.base
|
||||||
|
u5.test
|
||||||
|
|
||||||
|
ua.base -- The data sets ua.base, ua.test, ub.base, and ub.test
|
||||||
|
ua.test split the u data into a training set and a test set with
|
||||||
|
ub.base exactly 10 ratings per user in the test set. The sets
|
||||||
|
ub.test ua.test and ub.test are disjoint. These data sets can
|
||||||
|
be generated from u.data by mku.sh.
|
||||||
|
|
||||||
|
allbut.pl -- The script that generates training and test sets where
|
||||||
|
all but n of a users ratings are in the training data.
|
||||||
|
|
||||||
|
mku.sh -- A shell script to generate all the u data sets from u.data.
|
||||||
@@ -0,0 +1,34 @@
|
|||||||
|
#!/usr/local/bin/perl
|
||||||
|
|
||||||
|
# get args
|
||||||
|
if (@ARGV < 3) {
|
||||||
|
print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n";
|
||||||
|
exit 1;
|
||||||
|
}
|
||||||
|
$basename = shift;
|
||||||
|
$start = shift;
|
||||||
|
$stop = shift;
|
||||||
|
$maxtest = shift;
|
||||||
|
|
||||||
|
# open files
|
||||||
|
open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n";
|
||||||
|
open( BASEFILE, ">$basename.base" ) or die "Cannot open $basename.base for writing\n";
|
||||||
|
|
||||||
|
# init variables
|
||||||
|
$testcnt = 0;
|
||||||
|
|
||||||
|
while (<>) {
|
||||||
|
($user) = split;
|
||||||
|
if (! defined $ratingcnt{$user}) {
|
||||||
|
$ratingcnt{$user} = 0;
|
||||||
|
}
|
||||||
|
++$ratingcnt{$user};
|
||||||
|
if (($testcnt < $maxtest || $maxtest <= 0)
|
||||||
|
&& $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) {
|
||||||
|
++$testcnt;
|
||||||
|
print TESTFILE;
|
||||||
|
}
|
||||||
|
else {
|
||||||
|
print BASEFILE;
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,25 @@
|
|||||||
|
#!/bin/sh
|
||||||
|
|
||||||
|
trap `rm -f tmp.$$; exit 1` 1 2 15
|
||||||
|
|
||||||
|
for i in 1 2 3 4 5
|
||||||
|
do
|
||||||
|
head -`expr $i \* 20000` u.data | tail -20000 > tmp.$$
|
||||||
|
sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.test
|
||||||
|
head -`expr \( $i - 1 \) \* 20000` u.data > tmp.$$
|
||||||
|
tail -`expr \( 5 - $i \) \* 20000` u.data >> tmp.$$
|
||||||
|
sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.base
|
||||||
|
done
|
||||||
|
|
||||||
|
allbut.pl ua 1 10 100000 u.data
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ua.base > tmp.$$
|
||||||
|
mv tmp.$$ ua.base
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ua.test > tmp.$$
|
||||||
|
mv tmp.$$ ua.test
|
||||||
|
|
||||||
|
allbut.pl ub 11 20 100000 u.data
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ub.base > tmp.$$
|
||||||
|
mv tmp.$$ ub.base
|
||||||
|
sort -t" " -k 1,1n -k 2,2n ub.test > tmp.$$
|
||||||
|
mv tmp.$$ ub.test
|
||||||
|
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,20 @@
|
|||||||
|
unknown|0
|
||||||
|
Action|1
|
||||||
|
Adventure|2
|
||||||
|
Animation|3
|
||||||
|
Children's|4
|
||||||
|
Comedy|5
|
||||||
|
Crime|6
|
||||||
|
Documentary|7
|
||||||
|
Drama|8
|
||||||
|
Fantasy|9
|
||||||
|
Film-Noir|10
|
||||||
|
Horror|11
|
||||||
|
Musical|12
|
||||||
|
Mystery|13
|
||||||
|
Romance|14
|
||||||
|
Sci-Fi|15
|
||||||
|
Thriller|16
|
||||||
|
War|17
|
||||||
|
Western|18
|
||||||
|
|
||||||
@@ -0,0 +1,3 @@
|
|||||||
|
943 users
|
||||||
|
1682 items
|
||||||
|
100000 ratings
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,21 @@
|
|||||||
|
administrator
|
||||||
|
artist
|
||||||
|
doctor
|
||||||
|
educator
|
||||||
|
engineer
|
||||||
|
entertainment
|
||||||
|
executive
|
||||||
|
healthcare
|
||||||
|
homemaker
|
||||||
|
lawyer
|
||||||
|
librarian
|
||||||
|
marketing
|
||||||
|
none
|
||||||
|
other
|
||||||
|
programmer
|
||||||
|
retired
|
||||||
|
salesman
|
||||||
|
scientist
|
||||||
|
student
|
||||||
|
technician
|
||||||
|
writer
|
||||||
@@ -0,0 +1,943 @@
|
|||||||
|
1|24|M|technician|85711
|
||||||
|
2|53|F|other|94043
|
||||||
|
3|23|M|writer|32067
|
||||||
|
4|24|M|technician|43537
|
||||||
|
5|33|F|other|15213
|
||||||
|
6|42|M|executive|98101
|
||||||
|
7|57|M|administrator|91344
|
||||||
|
8|36|M|administrator|05201
|
||||||
|
9|29|M|student|01002
|
||||||
|
10|53|M|lawyer|90703
|
||||||
|
11|39|F|other|30329
|
||||||
|
12|28|F|other|06405
|
||||||
|
13|47|M|educator|29206
|
||||||
|
14|45|M|scientist|55106
|
||||||
|
15|49|F|educator|97301
|
||||||
|
16|21|M|entertainment|10309
|
||||||
|
17|30|M|programmer|06355
|
||||||
|
18|35|F|other|37212
|
||||||
|
19|40|M|librarian|02138
|
||||||
|
20|42|F|homemaker|95660
|
||||||
|
21|26|M|writer|30068
|
||||||
|
22|25|M|writer|40206
|
||||||
|
23|30|F|artist|48197
|
||||||
|
24|21|F|artist|94533
|
||||||
|
25|39|M|engineer|55107
|
||||||
|
26|49|M|engineer|21044
|
||||||
|
27|40|F|librarian|30030
|
||||||
|
28|32|M|writer|55369
|
||||||
|
29|41|M|programmer|94043
|
||||||
|
30|7|M|student|55436
|
||||||
|
31|24|M|artist|10003
|
||||||
|
32|28|F|student|78741
|
||||||
|
33|23|M|student|27510
|
||||||
|
34|38|F|administrator|42141
|
||||||
|
35|20|F|homemaker|42459
|
||||||
|
36|19|F|student|93117
|
||||||
|
37|23|M|student|55105
|
||||||
|
38|28|F|other|54467
|
||||||
|
39|41|M|entertainment|01040
|
||||||
|
40|38|M|scientist|27514
|
||||||
|
41|33|M|engineer|80525
|
||||||
|
42|30|M|administrator|17870
|
||||||
|
43|29|F|librarian|20854
|
||||||
|
44|26|M|technician|46260
|
||||||
|
45|29|M|programmer|50233
|
||||||
|
46|27|F|marketing|46538
|
||||||
|
47|53|M|marketing|07102
|
||||||
|
48|45|M|administrator|12550
|
||||||
|
49|23|F|student|76111
|
||||||
|
50|21|M|writer|52245
|
||||||
|
51|28|M|educator|16509
|
||||||
|
52|18|F|student|55105
|
||||||
|
53|26|M|programmer|55414
|
||||||
|
54|22|M|executive|66315
|
||||||
|
55|37|M|programmer|01331
|
||||||
|
56|25|M|librarian|46260
|
||||||
|
57|16|M|none|84010
|
||||||
|
58|27|M|programmer|52246
|
||||||
|
59|49|M|educator|08403
|
||||||
|
60|50|M|healthcare|06472
|
||||||
|
61|36|M|engineer|30040
|
||||||
|
62|27|F|administrator|97214
|
||||||
|
63|31|M|marketing|75240
|
||||||
|
64|32|M|educator|43202
|
||||||
|
65|51|F|educator|48118
|
||||||
|
66|23|M|student|80521
|
||||||
|
67|17|M|student|60402
|
||||||
|
68|19|M|student|22904
|
||||||
|
69|24|M|engineer|55337
|
||||||
|
70|27|M|engineer|60067
|
||||||
|
71|39|M|scientist|98034
|
||||||
|
72|48|F|administrator|73034
|
||||||
|
73|24|M|student|41850
|
||||||
|
74|39|M|scientist|T8H1N
|
||||||
|
75|24|M|entertainment|08816
|
||||||
|
76|20|M|student|02215
|
||||||
|
77|30|M|technician|29379
|
||||||
|
78|26|M|administrator|61801
|
||||||
|
79|39|F|administrator|03755
|
||||||
|
80|34|F|administrator|52241
|
||||||
|
81|21|M|student|21218
|
||||||
|
82|50|M|programmer|22902
|
||||||
|
83|40|M|other|44133
|
||||||
|
84|32|M|executive|55369
|
||||||
|
85|51|M|educator|20003
|
||||||
|
86|26|M|administrator|46005
|
||||||
|
87|47|M|administrator|89503
|
||||||
|
88|49|F|librarian|11701
|
||||||
|
89|43|F|administrator|68106
|
||||||
|
90|60|M|educator|78155
|
||||||
|
91|55|M|marketing|01913
|
||||||
|
92|32|M|entertainment|80525
|
||||||
|
93|48|M|executive|23112
|
||||||
|
94|26|M|student|71457
|
||||||
|
95|31|M|administrator|10707
|
||||||
|
96|25|F|artist|75206
|
||||||
|
97|43|M|artist|98006
|
||||||
|
98|49|F|executive|90291
|
||||||
|
99|20|M|student|63129
|
||||||
|
100|36|M|executive|90254
|
||||||
|
101|15|M|student|05146
|
||||||
|
102|38|M|programmer|30220
|
||||||
|
103|26|M|student|55108
|
||||||
|
104|27|M|student|55108
|
||||||
|
105|24|M|engineer|94043
|
||||||
|
106|61|M|retired|55125
|
||||||
|
107|39|M|scientist|60466
|
||||||
|
108|44|M|educator|63130
|
||||||
|
109|29|M|other|55423
|
||||||
|
110|19|M|student|77840
|
||||||
|
111|57|M|engineer|90630
|
||||||
|
112|30|M|salesman|60613
|
||||||
|
113|47|M|executive|95032
|
||||||
|
114|27|M|programmer|75013
|
||||||
|
115|31|M|engineer|17110
|
||||||
|
116|40|M|healthcare|97232
|
||||||
|
117|20|M|student|16125
|
||||||
|
118|21|M|administrator|90210
|
||||||
|
119|32|M|programmer|67401
|
||||||
|
120|47|F|other|06260
|
||||||
|
121|54|M|librarian|99603
|
||||||
|
122|32|F|writer|22206
|
||||||
|
123|48|F|artist|20008
|
||||||
|
124|34|M|student|60615
|
||||||
|
125|30|M|lawyer|22202
|
||||||
|
126|28|F|lawyer|20015
|
||||||
|
127|33|M|none|73439
|
||||||
|
128|24|F|marketing|20009
|
||||||
|
129|36|F|marketing|07039
|
||||||
|
130|20|M|none|60115
|
||||||
|
131|59|F|administrator|15237
|
||||||
|
132|24|M|other|94612
|
||||||
|
133|53|M|engineer|78602
|
||||||
|
134|31|M|programmer|80236
|
||||||
|
135|23|M|student|38401
|
||||||
|
136|51|M|other|97365
|
||||||
|
137|50|M|educator|84408
|
||||||
|
138|46|M|doctor|53211
|
||||||
|
139|20|M|student|08904
|
||||||
|
140|30|F|student|32250
|
||||||
|
141|49|M|programmer|36117
|
||||||
|
142|13|M|other|48118
|
||||||
|
143|42|M|technician|08832
|
||||||
|
144|53|M|programmer|20910
|
||||||
|
145|31|M|entertainment|V3N4P
|
||||||
|
146|45|M|artist|83814
|
||||||
|
147|40|F|librarian|02143
|
||||||
|
148|33|M|engineer|97006
|
||||||
|
149|35|F|marketing|17325
|
||||||
|
150|20|F|artist|02139
|
||||||
|
151|38|F|administrator|48103
|
||||||
|
152|33|F|educator|68767
|
||||||
|
153|25|M|student|60641
|
||||||
|
154|25|M|student|53703
|
||||||
|
155|32|F|other|11217
|
||||||
|
156|25|M|educator|08360
|
||||||
|
157|57|M|engineer|70808
|
||||||
|
158|50|M|educator|27606
|
||||||
|
159|23|F|student|55346
|
||||||
|
160|27|M|programmer|66215
|
||||||
|
161|50|M|lawyer|55104
|
||||||
|
162|25|M|artist|15610
|
||||||
|
163|49|M|administrator|97212
|
||||||
|
164|47|M|healthcare|80123
|
||||||
|
165|20|F|other|53715
|
||||||
|
166|47|M|educator|55113
|
||||||
|
167|37|M|other|L9G2B
|
||||||
|
168|48|M|other|80127
|
||||||
|
169|52|F|other|53705
|
||||||
|
170|53|F|healthcare|30067
|
||||||
|
171|48|F|educator|78750
|
||||||
|
172|55|M|marketing|22207
|
||||||
|
173|56|M|other|22306
|
||||||
|
174|30|F|administrator|52302
|
||||||
|
175|26|F|scientist|21911
|
||||||
|
176|28|M|scientist|07030
|
||||||
|
177|20|M|programmer|19104
|
||||||
|
178|26|M|other|49512
|
||||||
|
179|15|M|entertainment|20755
|
||||||
|
180|22|F|administrator|60202
|
||||||
|
181|26|M|executive|21218
|
||||||
|
182|36|M|programmer|33884
|
||||||
|
183|33|M|scientist|27708
|
||||||
|
184|37|M|librarian|76013
|
||||||
|
185|53|F|librarian|97403
|
||||||
|
186|39|F|executive|00000
|
||||||
|
187|26|M|educator|16801
|
||||||
|
188|42|M|student|29440
|
||||||
|
189|32|M|artist|95014
|
||||||
|
190|30|M|administrator|95938
|
||||||
|
191|33|M|administrator|95161
|
||||||
|
192|42|M|educator|90840
|
||||||
|
193|29|M|student|49931
|
||||||
|
194|38|M|administrator|02154
|
||||||
|
195|42|M|scientist|93555
|
||||||
|
196|49|M|writer|55105
|
||||||
|
197|55|M|technician|75094
|
||||||
|
198|21|F|student|55414
|
||||||
|
199|30|M|writer|17604
|
||||||
|
200|40|M|programmer|93402
|
||||||
|
201|27|M|writer|E2A4H
|
||||||
|
202|41|F|educator|60201
|
||||||
|
203|25|F|student|32301
|
||||||
|
204|52|F|librarian|10960
|
||||||
|
205|47|M|lawyer|06371
|
||||||
|
206|14|F|student|53115
|
||||||
|
207|39|M|marketing|92037
|
||||||
|
208|43|M|engineer|01720
|
||||||
|
209|33|F|educator|85710
|
||||||
|
210|39|M|engineer|03060
|
||||||
|
211|66|M|salesman|32605
|
||||||
|
212|49|F|educator|61401
|
||||||
|
213|33|M|executive|55345
|
||||||
|
214|26|F|librarian|11231
|
||||||
|
215|35|M|programmer|63033
|
||||||
|
216|22|M|engineer|02215
|
||||||
|
217|22|M|other|11727
|
||||||
|
218|37|M|administrator|06513
|
||||||
|
219|32|M|programmer|43212
|
||||||
|
220|30|M|librarian|78205
|
||||||
|
221|19|M|student|20685
|
||||||
|
222|29|M|programmer|27502
|
||||||
|
223|19|F|student|47906
|
||||||
|
224|31|F|educator|43512
|
||||||
|
225|51|F|administrator|58202
|
||||||
|
226|28|M|student|92103
|
||||||
|
227|46|M|executive|60659
|
||||||
|
228|21|F|student|22003
|
||||||
|
229|29|F|librarian|22903
|
||||||
|
230|28|F|student|14476
|
||||||
|
231|48|M|librarian|01080
|
||||||
|
232|45|M|scientist|99709
|
||||||
|
233|38|M|engineer|98682
|
||||||
|
234|60|M|retired|94702
|
||||||
|
235|37|M|educator|22973
|
||||||
|
236|44|F|writer|53214
|
||||||
|
237|49|M|administrator|63146
|
||||||
|
238|42|F|administrator|44124
|
||||||
|
239|39|M|artist|95628
|
||||||
|
240|23|F|educator|20784
|
||||||
|
241|26|F|student|20001
|
||||||
|
242|33|M|educator|31404
|
||||||
|
243|33|M|educator|60201
|
||||||
|
244|28|M|technician|80525
|
||||||
|
245|22|M|student|55109
|
||||||
|
246|19|M|student|28734
|
||||||
|
247|28|M|engineer|20770
|
||||||
|
248|25|M|student|37235
|
||||||
|
249|25|M|student|84103
|
||||||
|
250|29|M|executive|95110
|
||||||
|
251|28|M|doctor|85032
|
||||||
|
252|42|M|engineer|07733
|
||||||
|
253|26|F|librarian|22903
|
||||||
|
254|44|M|educator|42647
|
||||||
|
255|23|M|entertainment|07029
|
||||||
|
256|35|F|none|39042
|
||||||
|
257|17|M|student|77005
|
||||||
|
258|19|F|student|77801
|
||||||
|
259|21|M|student|48823
|
||||||
|
260|40|F|artist|89801
|
||||||
|
261|28|M|administrator|85202
|
||||||
|
262|19|F|student|78264
|
||||||
|
263|41|M|programmer|55346
|
||||||
|
264|36|F|writer|90064
|
||||||
|
265|26|M|executive|84601
|
||||||
|
266|62|F|administrator|78756
|
||||||
|
267|23|M|engineer|83716
|
||||||
|
268|24|M|engineer|19422
|
||||||
|
269|31|F|librarian|43201
|
||||||
|
270|18|F|student|63119
|
||||||
|
271|51|M|engineer|22932
|
||||||
|
272|33|M|scientist|53706
|
||||||
|
273|50|F|other|10016
|
||||||
|
274|20|F|student|55414
|
||||||
|
275|38|M|engineer|92064
|
||||||
|
276|21|M|student|95064
|
||||||
|
277|35|F|administrator|55406
|
||||||
|
278|37|F|librarian|30033
|
||||||
|
279|33|M|programmer|85251
|
||||||
|
280|30|F|librarian|22903
|
||||||
|
281|15|F|student|06059
|
||||||
|
282|22|M|administrator|20057
|
||||||
|
283|28|M|programmer|55305
|
||||||
|
284|40|M|executive|92629
|
||||||
|
285|25|M|programmer|53713
|
||||||
|
286|27|M|student|15217
|
||||||
|
287|21|M|salesman|31211
|
||||||
|
288|34|M|marketing|23226
|
||||||
|
289|11|M|none|94619
|
||||||
|
290|40|M|engineer|93550
|
||||||
|
291|19|M|student|44106
|
||||||
|
292|35|F|programmer|94703
|
||||||
|
293|24|M|writer|60804
|
||||||
|
294|34|M|technician|92110
|
||||||
|
295|31|M|educator|50325
|
||||||
|
296|43|F|administrator|16803
|
||||||
|
297|29|F|educator|98103
|
||||||
|
298|44|M|executive|01581
|
||||||
|
299|29|M|doctor|63108
|
||||||
|
300|26|F|programmer|55106
|
||||||
|
301|24|M|student|55439
|
||||||
|
302|42|M|educator|77904
|
||||||
|
303|19|M|student|14853
|
||||||
|
304|22|F|student|71701
|
||||||
|
305|23|M|programmer|94086
|
||||||
|
306|45|M|other|73132
|
||||||
|
307|25|M|student|55454
|
||||||
|
308|60|M|retired|95076
|
||||||
|
309|40|M|scientist|70802
|
||||||
|
310|37|M|educator|91711
|
||||||
|
311|32|M|technician|73071
|
||||||
|
312|48|M|other|02110
|
||||||
|
313|41|M|marketing|60035
|
||||||
|
314|20|F|student|08043
|
||||||
|
315|31|M|educator|18301
|
||||||
|
316|43|F|other|77009
|
||||||
|
317|22|M|administrator|13210
|
||||||
|
318|65|M|retired|06518
|
||||||
|
319|38|M|programmer|22030
|
||||||
|
320|19|M|student|24060
|
||||||
|
321|49|F|educator|55413
|
||||||
|
322|20|M|student|50613
|
||||||
|
323|21|M|student|19149
|
||||||
|
324|21|F|student|02176
|
||||||
|
325|48|M|technician|02139
|
||||||
|
326|41|M|administrator|15235
|
||||||
|
327|22|M|student|11101
|
||||||
|
328|51|M|administrator|06779
|
||||||
|
329|48|M|educator|01720
|
||||||
|
330|35|F|educator|33884
|
||||||
|
331|33|M|entertainment|91344
|
||||||
|
332|20|M|student|40504
|
||||||
|
333|47|M|other|V0R2M
|
||||||
|
334|32|M|librarian|30002
|
||||||
|
335|45|M|executive|33775
|
||||||
|
336|23|M|salesman|42101
|
||||||
|
337|37|M|scientist|10522
|
||||||
|
338|39|F|librarian|59717
|
||||||
|
339|35|M|lawyer|37901
|
||||||
|
340|46|M|engineer|80123
|
||||||
|
341|17|F|student|44405
|
||||||
|
342|25|F|other|98006
|
||||||
|
343|43|M|engineer|30093
|
||||||
|
344|30|F|librarian|94117
|
||||||
|
345|28|F|librarian|94143
|
||||||
|
346|34|M|other|76059
|
||||||
|
347|18|M|student|90210
|
||||||
|
348|24|F|student|45660
|
||||||
|
349|68|M|retired|61455
|
||||||
|
350|32|M|student|97301
|
||||||
|
351|61|M|educator|49938
|
||||||
|
352|37|F|programmer|55105
|
||||||
|
353|25|M|scientist|28480
|
||||||
|
354|29|F|librarian|48197
|
||||||
|
355|25|M|student|60135
|
||||||
|
356|32|F|homemaker|92688
|
||||||
|
357|26|M|executive|98133
|
||||||
|
358|40|M|educator|10022
|
||||||
|
359|22|M|student|61801
|
||||||
|
360|51|M|other|98027
|
||||||
|
361|22|M|student|44074
|
||||||
|
362|35|F|homemaker|85233
|
||||||
|
363|20|M|student|87501
|
||||||
|
364|63|M|engineer|01810
|
||||||
|
365|29|M|lawyer|20009
|
||||||
|
366|20|F|student|50670
|
||||||
|
367|17|M|student|37411
|
||||||
|
368|18|M|student|92113
|
||||||
|
369|24|M|student|91335
|
||||||
|
370|52|M|writer|08534
|
||||||
|
371|36|M|engineer|99206
|
||||||
|
372|25|F|student|66046
|
||||||
|
373|24|F|other|55116
|
||||||
|
374|36|M|executive|78746
|
||||||
|
375|17|M|entertainment|37777
|
||||||
|
376|28|F|other|10010
|
||||||
|
377|22|M|student|18015
|
||||||
|
378|35|M|student|02859
|
||||||
|
379|44|M|programmer|98117
|
||||||
|
380|32|M|engineer|55117
|
||||||
|
381|33|M|artist|94608
|
||||||
|
382|45|M|engineer|01824
|
||||||
|
383|42|M|administrator|75204
|
||||||
|
384|52|M|programmer|45218
|
||||||
|
385|36|M|writer|10003
|
||||||
|
386|36|M|salesman|43221
|
||||||
|
387|33|M|entertainment|37412
|
||||||
|
388|31|M|other|36106
|
||||||
|
389|44|F|writer|83702
|
||||||
|
390|42|F|writer|85016
|
||||||
|
391|23|M|student|84604
|
||||||
|
392|52|M|writer|59801
|
||||||
|
393|19|M|student|83686
|
||||||
|
394|25|M|administrator|96819
|
||||||
|
395|43|M|other|44092
|
||||||
|
396|57|M|engineer|94551
|
||||||
|
397|17|M|student|27514
|
||||||
|
398|40|M|other|60008
|
||||||
|
399|25|M|other|92374
|
||||||
|
400|33|F|administrator|78213
|
||||||
|
401|46|F|healthcare|84107
|
||||||
|
402|30|M|engineer|95129
|
||||||
|
403|37|M|other|06811
|
||||||
|
404|29|F|programmer|55108
|
||||||
|
405|22|F|healthcare|10019
|
||||||
|
406|52|M|educator|93109
|
||||||
|
407|29|M|engineer|03261
|
||||||
|
408|23|M|student|61755
|
||||||
|
409|48|M|administrator|98225
|
||||||
|
410|30|F|artist|94025
|
||||||
|
411|34|M|educator|44691
|
||||||
|
412|25|M|educator|15222
|
||||||
|
413|55|M|educator|78212
|
||||||
|
414|24|M|programmer|38115
|
||||||
|
415|39|M|educator|85711
|
||||||
|
416|20|F|student|92626
|
||||||
|
417|27|F|other|48103
|
||||||
|
418|55|F|none|21206
|
||||||
|
419|37|M|lawyer|43215
|
||||||
|
420|53|M|educator|02140
|
||||||
|
421|38|F|programmer|55105
|
||||||
|
422|26|M|entertainment|94533
|
||||||
|
423|64|M|other|91606
|
||||||
|
424|36|F|marketing|55422
|
||||||
|
425|19|M|student|58644
|
||||||
|
426|55|M|educator|01602
|
||||||
|
427|51|M|doctor|85258
|
||||||
|
428|28|M|student|55414
|
||||||
|
429|27|M|student|29205
|
||||||
|
430|38|M|scientist|98199
|
||||||
|
431|24|M|marketing|92629
|
||||||
|
432|22|M|entertainment|50311
|
||||||
|
433|27|M|artist|11211
|
||||||
|
434|16|F|student|49705
|
||||||
|
435|24|M|engineer|60007
|
||||||
|
436|30|F|administrator|17345
|
||||||
|
437|27|F|other|20009
|
||||||
|
438|51|F|administrator|43204
|
||||||
|
439|23|F|administrator|20817
|
||||||
|
440|30|M|other|48076
|
||||||
|
441|50|M|technician|55013
|
||||||
|
442|22|M|student|85282
|
||||||
|
443|35|M|salesman|33308
|
||||||
|
444|51|F|lawyer|53202
|
||||||
|
445|21|M|writer|92653
|
||||||
|
446|57|M|educator|60201
|
||||||
|
447|30|M|administrator|55113
|
||||||
|
448|23|M|entertainment|10021
|
||||||
|
449|23|M|librarian|55021
|
||||||
|
450|35|F|educator|11758
|
||||||
|
451|16|M|student|48446
|
||||||
|
452|35|M|administrator|28018
|
||||||
|
453|18|M|student|06333
|
||||||
|
454|57|M|other|97330
|
||||||
|
455|48|M|administrator|83709
|
||||||
|
456|24|M|technician|31820
|
||||||
|
457|33|F|salesman|30011
|
||||||
|
458|47|M|technician|Y1A6B
|
||||||
|
459|22|M|student|29201
|
||||||
|
460|44|F|other|60630
|
||||||
|
461|15|M|student|98102
|
||||||
|
462|19|F|student|02918
|
||||||
|
463|48|F|healthcare|75218
|
||||||
|
464|60|M|writer|94583
|
||||||
|
465|32|M|other|05001
|
||||||
|
466|22|M|student|90804
|
||||||
|
467|29|M|engineer|91201
|
||||||
|
468|28|M|engineer|02341
|
||||||
|
469|60|M|educator|78628
|
||||||
|
470|24|M|programmer|10021
|
||||||
|
471|10|M|student|77459
|
||||||
|
472|24|M|student|87544
|
||||||
|
473|29|M|student|94708
|
||||||
|
474|51|M|executive|93711
|
||||||
|
475|30|M|programmer|75230
|
||||||
|
476|28|M|student|60440
|
||||||
|
477|23|F|student|02125
|
||||||
|
478|29|M|other|10019
|
||||||
|
479|30|M|educator|55409
|
||||||
|
480|57|M|retired|98257
|
||||||
|
481|73|M|retired|37771
|
||||||
|
482|18|F|student|40256
|
||||||
|
483|29|M|scientist|43212
|
||||||
|
484|27|M|student|21208
|
||||||
|
485|44|F|educator|95821
|
||||||
|
486|39|M|educator|93101
|
||||||
|
487|22|M|engineer|92121
|
||||||
|
488|48|M|technician|21012
|
||||||
|
489|55|M|other|45218
|
||||||
|
490|29|F|artist|V5A2B
|
||||||
|
491|43|F|writer|53711
|
||||||
|
492|57|M|educator|94618
|
||||||
|
493|22|M|engineer|60090
|
||||||
|
494|38|F|administrator|49428
|
||||||
|
495|29|M|engineer|03052
|
||||||
|
496|21|F|student|55414
|
||||||
|
497|20|M|student|50112
|
||||||
|
498|26|M|writer|55408
|
||||||
|
499|42|M|programmer|75006
|
||||||
|
500|28|M|administrator|94305
|
||||||
|
501|22|M|student|10025
|
||||||
|
502|22|M|student|23092
|
||||||
|
503|50|F|writer|27514
|
||||||
|
504|40|F|writer|92115
|
||||||
|
505|27|F|other|20657
|
||||||
|
506|46|M|programmer|03869
|
||||||
|
507|18|F|writer|28450
|
||||||
|
508|27|M|marketing|19382
|
||||||
|
509|23|M|administrator|10011
|
||||||
|
510|34|M|other|98038
|
||||||
|
511|22|M|student|21250
|
||||||
|
512|29|M|other|20090
|
||||||
|
513|43|M|administrator|26241
|
||||||
|
514|27|M|programmer|20707
|
||||||
|
515|53|M|marketing|49508
|
||||||
|
516|53|F|librarian|10021
|
||||||
|
517|24|M|student|55454
|
||||||
|
518|49|F|writer|99709
|
||||||
|
519|22|M|other|55320
|
||||||
|
520|62|M|healthcare|12603
|
||||||
|
521|19|M|student|02146
|
||||||
|
522|36|M|engineer|55443
|
||||||
|
523|50|F|administrator|04102
|
||||||
|
524|56|M|educator|02159
|
||||||
|
525|27|F|administrator|19711
|
||||||
|
526|30|M|marketing|97124
|
||||||
|
527|33|M|librarian|12180
|
||||||
|
528|18|M|student|55104
|
||||||
|
529|47|F|administrator|44224
|
||||||
|
530|29|M|engineer|94040
|
||||||
|
531|30|F|salesman|97408
|
||||||
|
532|20|M|student|92705
|
||||||
|
533|43|M|librarian|02324
|
||||||
|
534|20|M|student|05464
|
||||||
|
535|45|F|educator|80302
|
||||||
|
536|38|M|engineer|30078
|
||||||
|
537|36|M|engineer|22902
|
||||||
|
538|31|M|scientist|21010
|
||||||
|
539|53|F|administrator|80303
|
||||||
|
540|28|M|engineer|91201
|
||||||
|
541|19|F|student|84302
|
||||||
|
542|21|M|student|60515
|
||||||
|
543|33|M|scientist|95123
|
||||||
|
544|44|F|other|29464
|
||||||
|
545|27|M|technician|08052
|
||||||
|
546|36|M|executive|22911
|
||||||
|
547|50|M|educator|14534
|
||||||
|
548|51|M|writer|95468
|
||||||
|
549|42|M|scientist|45680
|
||||||
|
550|16|F|student|95453
|
||||||
|
551|25|M|programmer|55414
|
||||||
|
552|45|M|other|68147
|
||||||
|
553|58|M|educator|62901
|
||||||
|
554|32|M|scientist|62901
|
||||||
|
555|29|F|educator|23227
|
||||||
|
556|35|F|educator|30606
|
||||||
|
557|30|F|writer|11217
|
||||||
|
558|56|F|writer|63132
|
||||||
|
559|69|M|executive|10022
|
||||||
|
560|32|M|student|10003
|
||||||
|
561|23|M|engineer|60005
|
||||||
|
562|54|F|administrator|20879
|
||||||
|
563|39|F|librarian|32707
|
||||||
|
564|65|M|retired|94591
|
||||||
|
565|40|M|student|55422
|
||||||
|
566|20|M|student|14627
|
||||||
|
567|24|M|entertainment|10003
|
||||||
|
568|39|M|educator|01915
|
||||||
|
569|34|M|educator|91903
|
||||||
|
570|26|M|educator|14627
|
||||||
|
571|34|M|artist|01945
|
||||||
|
572|51|M|educator|20003
|
||||||
|
573|68|M|retired|48911
|
||||||
|
574|56|M|educator|53188
|
||||||
|
575|33|M|marketing|46032
|
||||||
|
576|48|M|executive|98281
|
||||||
|
577|36|F|student|77845
|
||||||
|
578|31|M|administrator|M7A1A
|
||||||
|
579|32|M|educator|48103
|
||||||
|
580|16|M|student|17961
|
||||||
|
581|37|M|other|94131
|
||||||
|
582|17|M|student|93003
|
||||||
|
583|44|M|engineer|29631
|
||||||
|
584|25|M|student|27511
|
||||||
|
585|69|M|librarian|98501
|
||||||
|
586|20|M|student|79508
|
||||||
|
587|26|M|other|14216
|
||||||
|
588|18|F|student|93063
|
||||||
|
589|21|M|lawyer|90034
|
||||||
|
590|50|M|educator|82435
|
||||||
|
591|57|F|librarian|92093
|
||||||
|
592|18|M|student|97520
|
||||||
|
593|31|F|educator|68767
|
||||||
|
594|46|M|educator|M4J2K
|
||||||
|
595|25|M|programmer|31909
|
||||||
|
596|20|M|artist|77073
|
||||||
|
597|23|M|other|84116
|
||||||
|
598|40|F|marketing|43085
|
||||||
|
599|22|F|student|R3T5K
|
||||||
|
600|34|M|programmer|02320
|
||||||
|
601|19|F|artist|99687
|
||||||
|
602|47|F|other|34656
|
||||||
|
603|21|M|programmer|47905
|
||||||
|
604|39|M|educator|11787
|
||||||
|
605|33|M|engineer|33716
|
||||||
|
606|28|M|programmer|63044
|
||||||
|
607|49|F|healthcare|02154
|
||||||
|
608|22|M|other|10003
|
||||||
|
609|13|F|student|55106
|
||||||
|
610|22|M|student|21227
|
||||||
|
611|46|M|librarian|77008
|
||||||
|
612|36|M|educator|79070
|
||||||
|
613|37|F|marketing|29678
|
||||||
|
614|54|M|educator|80227
|
||||||
|
615|38|M|educator|27705
|
||||||
|
616|55|M|scientist|50613
|
||||||
|
617|27|F|writer|11201
|
||||||
|
618|15|F|student|44212
|
||||||
|
619|17|M|student|44134
|
||||||
|
620|18|F|writer|81648
|
||||||
|
621|17|M|student|60402
|
||||||
|
622|25|M|programmer|14850
|
||||||
|
623|50|F|educator|60187
|
||||||
|
624|19|M|student|30067
|
||||||
|
625|27|M|programmer|20723
|
||||||
|
626|23|M|scientist|19807
|
||||||
|
627|24|M|engineer|08034
|
||||||
|
628|13|M|none|94306
|
||||||
|
629|46|F|other|44224
|
||||||
|
630|26|F|healthcare|55408
|
||||||
|
631|18|F|student|38866
|
||||||
|
632|18|M|student|55454
|
||||||
|
633|35|M|programmer|55414
|
||||||
|
634|39|M|engineer|T8H1N
|
||||||
|
635|22|M|other|23237
|
||||||
|
636|47|M|educator|48043
|
||||||
|
637|30|M|other|74101
|
||||||
|
638|45|M|engineer|01940
|
||||||
|
639|42|F|librarian|12065
|
||||||
|
640|20|M|student|61801
|
||||||
|
641|24|M|student|60626
|
||||||
|
642|18|F|student|95521
|
||||||
|
643|39|M|scientist|55122
|
||||||
|
644|51|M|retired|63645
|
||||||
|
645|27|M|programmer|53211
|
||||||
|
646|17|F|student|51250
|
||||||
|
647|40|M|educator|45810
|
||||||
|
648|43|M|engineer|91351
|
||||||
|
649|20|M|student|39762
|
||||||
|
650|42|M|engineer|83814
|
||||||
|
651|65|M|retired|02903
|
||||||
|
652|35|M|other|22911
|
||||||
|
653|31|M|executive|55105
|
||||||
|
654|27|F|student|78739
|
||||||
|
655|50|F|healthcare|60657
|
||||||
|
656|48|M|educator|10314
|
||||||
|
657|26|F|none|78704
|
||||||
|
658|33|M|programmer|92626
|
||||||
|
659|31|M|educator|54248
|
||||||
|
660|26|M|student|77380
|
||||||
|
661|28|M|programmer|98121
|
||||||
|
662|55|M|librarian|19102
|
||||||
|
663|26|M|other|19341
|
||||||
|
664|30|M|engineer|94115
|
||||||
|
665|25|M|administrator|55412
|
||||||
|
666|44|M|administrator|61820
|
||||||
|
667|35|M|librarian|01970
|
||||||
|
668|29|F|writer|10016
|
||||||
|
669|37|M|other|20009
|
||||||
|
670|30|M|technician|21114
|
||||||
|
671|21|M|programmer|91919
|
||||||
|
672|54|F|administrator|90095
|
||||||
|
673|51|M|educator|22906
|
||||||
|
674|13|F|student|55337
|
||||||
|
675|34|M|other|28814
|
||||||
|
676|30|M|programmer|32712
|
||||||
|
677|20|M|other|99835
|
||||||
|
678|50|M|educator|61462
|
||||||
|
679|20|F|student|54302
|
||||||
|
680|33|M|lawyer|90405
|
||||||
|
681|44|F|marketing|97208
|
||||||
|
682|23|M|programmer|55128
|
||||||
|
683|42|M|librarian|23509
|
||||||
|
684|28|M|student|55414
|
||||||
|
685|32|F|librarian|55409
|
||||||
|
686|32|M|educator|26506
|
||||||
|
687|31|F|healthcare|27713
|
||||||
|
688|37|F|administrator|60476
|
||||||
|
689|25|M|other|45439
|
||||||
|
690|35|M|salesman|63304
|
||||||
|
691|34|M|educator|60089
|
||||||
|
692|34|M|engineer|18053
|
||||||
|
693|43|F|healthcare|85210
|
||||||
|
694|60|M|programmer|06365
|
||||||
|
695|26|M|writer|38115
|
||||||
|
696|55|M|other|94920
|
||||||
|
697|25|M|other|77042
|
||||||
|
698|28|F|programmer|06906
|
||||||
|
699|44|M|other|96754
|
||||||
|
700|17|M|student|76309
|
||||||
|
701|51|F|librarian|56321
|
||||||
|
702|37|M|other|89104
|
||||||
|
703|26|M|educator|49512
|
||||||
|
704|51|F|librarian|91105
|
||||||
|
705|21|F|student|54494
|
||||||
|
706|23|M|student|55454
|
||||||
|
707|56|F|librarian|19146
|
||||||
|
708|26|F|homemaker|96349
|
||||||
|
709|21|M|other|N4T1A
|
||||||
|
710|19|M|student|92020
|
||||||
|
711|22|F|student|15203
|
||||||
|
712|22|F|student|54901
|
||||||
|
713|42|F|other|07204
|
||||||
|
714|26|M|engineer|55343
|
||||||
|
715|21|M|technician|91206
|
||||||
|
716|36|F|administrator|44265
|
||||||
|
717|24|M|technician|84105
|
||||||
|
718|42|M|technician|64118
|
||||||
|
719|37|F|other|V0R2H
|
||||||
|
720|49|F|administrator|16506
|
||||||
|
721|24|F|entertainment|11238
|
||||||
|
722|50|F|homemaker|17331
|
||||||
|
723|26|M|executive|94403
|
||||||
|
724|31|M|executive|40243
|
||||||
|
725|21|M|student|91711
|
||||||
|
726|25|F|administrator|80538
|
||||||
|
727|25|M|student|78741
|
||||||
|
728|58|M|executive|94306
|
||||||
|
729|19|M|student|56567
|
||||||
|
730|31|F|scientist|32114
|
||||||
|
731|41|F|educator|70403
|
||||||
|
732|28|F|other|98405
|
||||||
|
733|44|F|other|60630
|
||||||
|
734|25|F|other|63108
|
||||||
|
735|29|F|healthcare|85719
|
||||||
|
736|48|F|writer|94618
|
||||||
|
737|30|M|programmer|98072
|
||||||
|
738|35|M|technician|95403
|
||||||
|
739|35|M|technician|73162
|
||||||
|
740|25|F|educator|22206
|
||||||
|
741|25|M|writer|63108
|
||||||
|
742|35|M|student|29210
|
||||||
|
743|31|M|programmer|92660
|
||||||
|
744|35|M|marketing|47024
|
||||||
|
745|42|M|writer|55113
|
||||||
|
746|25|M|engineer|19047
|
||||||
|
747|19|M|other|93612
|
||||||
|
748|28|M|administrator|94720
|
||||||
|
749|33|M|other|80919
|
||||||
|
750|28|M|administrator|32303
|
||||||
|
751|24|F|other|90034
|
||||||
|
752|60|M|retired|21201
|
||||||
|
753|56|M|salesman|91206
|
||||||
|
754|59|F|librarian|62901
|
||||||
|
755|44|F|educator|97007
|
||||||
|
756|30|F|none|90247
|
||||||
|
757|26|M|student|55104
|
||||||
|
758|27|M|student|53706
|
||||||
|
759|20|F|student|68503
|
||||||
|
760|35|F|other|14211
|
||||||
|
761|17|M|student|97302
|
||||||
|
762|32|M|administrator|95050
|
||||||
|
763|27|M|scientist|02113
|
||||||
|
764|27|F|educator|62903
|
||||||
|
765|31|M|student|33066
|
||||||
|
766|42|M|other|10960
|
||||||
|
767|70|M|engineer|00000
|
||||||
|
768|29|M|administrator|12866
|
||||||
|
769|39|M|executive|06927
|
||||||
|
770|28|M|student|14216
|
||||||
|
771|26|M|student|15232
|
||||||
|
772|50|M|writer|27105
|
||||||
|
773|20|M|student|55414
|
||||||
|
774|30|M|student|80027
|
||||||
|
775|46|M|executive|90036
|
||||||
|
776|30|M|librarian|51157
|
||||||
|
777|63|M|programmer|01810
|
||||||
|
778|34|M|student|01960
|
||||||
|
779|31|M|student|K7L5J
|
||||||
|
780|49|M|programmer|94560
|
||||||
|
781|20|M|student|48825
|
||||||
|
782|21|F|artist|33205
|
||||||
|
783|30|M|marketing|77081
|
||||||
|
784|47|M|administrator|91040
|
||||||
|
785|32|M|engineer|23322
|
||||||
|
786|36|F|engineer|01754
|
||||||
|
787|18|F|student|98620
|
||||||
|
788|51|M|administrator|05779
|
||||||
|
789|29|M|other|55420
|
||||||
|
790|27|M|technician|80913
|
||||||
|
791|31|M|educator|20064
|
||||||
|
792|40|M|programmer|12205
|
||||||
|
793|22|M|student|85281
|
||||||
|
794|32|M|educator|57197
|
||||||
|
795|30|M|programmer|08610
|
||||||
|
796|32|F|writer|33755
|
||||||
|
797|44|F|other|62522
|
||||||
|
798|40|F|writer|64131
|
||||||
|
799|49|F|administrator|19716
|
||||||
|
800|25|M|programmer|55337
|
||||||
|
801|22|M|writer|92154
|
||||||
|
802|35|M|administrator|34105
|
||||||
|
803|70|M|administrator|78212
|
||||||
|
804|39|M|educator|61820
|
||||||
|
805|27|F|other|20009
|
||||||
|
806|27|M|marketing|11217
|
||||||
|
807|41|F|healthcare|93555
|
||||||
|
808|45|M|salesman|90016
|
||||||
|
809|50|F|marketing|30803
|
||||||
|
810|55|F|other|80526
|
||||||
|
811|40|F|educator|73013
|
||||||
|
812|22|M|technician|76234
|
||||||
|
813|14|F|student|02136
|
||||||
|
814|30|M|other|12345
|
||||||
|
815|32|M|other|28806
|
||||||
|
816|34|M|other|20755
|
||||||
|
817|19|M|student|60152
|
||||||
|
818|28|M|librarian|27514
|
||||||
|
819|59|M|administrator|40205
|
||||||
|
820|22|M|student|37725
|
||||||
|
821|37|M|engineer|77845
|
||||||
|
822|29|F|librarian|53144
|
||||||
|
823|27|M|artist|50322
|
||||||
|
824|31|M|other|15017
|
||||||
|
825|44|M|engineer|05452
|
||||||
|
826|28|M|artist|77048
|
||||||
|
827|23|F|engineer|80228
|
||||||
|
828|28|M|librarian|85282
|
||||||
|
829|48|M|writer|80209
|
||||||
|
830|46|M|programmer|53066
|
||||||
|
831|21|M|other|33765
|
||||||
|
832|24|M|technician|77042
|
||||||
|
833|34|M|writer|90019
|
||||||
|
834|26|M|other|64153
|
||||||
|
835|44|F|executive|11577
|
||||||
|
836|44|M|artist|10018
|
||||||
|
837|36|F|artist|55409
|
||||||
|
838|23|M|student|01375
|
||||||
|
839|38|F|entertainment|90814
|
||||||
|
840|39|M|artist|55406
|
||||||
|
841|45|M|doctor|47401
|
||||||
|
842|40|M|writer|93055
|
||||||
|
843|35|M|librarian|44212
|
||||||
|
844|22|M|engineer|95662
|
||||||
|
845|64|M|doctor|97405
|
||||||
|
846|27|M|lawyer|47130
|
||||||
|
847|29|M|student|55417
|
||||||
|
848|46|M|engineer|02146
|
||||||
|
849|15|F|student|25652
|
||||||
|
850|34|M|technician|78390
|
||||||
|
851|18|M|other|29646
|
||||||
|
852|46|M|administrator|94086
|
||||||
|
853|49|M|writer|40515
|
||||||
|
854|29|F|student|55408
|
||||||
|
855|53|M|librarian|04988
|
||||||
|
856|43|F|marketing|97215
|
||||||
|
857|35|F|administrator|V1G4L
|
||||||
|
858|63|M|educator|09645
|
||||||
|
859|18|F|other|06492
|
||||||
|
860|70|F|retired|48322
|
||||||
|
861|38|F|student|14085
|
||||||
|
862|25|M|executive|13820
|
||||||
|
863|17|M|student|60089
|
||||||
|
864|27|M|programmer|63021
|
||||||
|
865|25|M|artist|11231
|
||||||
|
866|45|M|other|60302
|
||||||
|
867|24|M|scientist|92507
|
||||||
|
868|21|M|programmer|55303
|
||||||
|
869|30|M|student|10025
|
||||||
|
870|22|M|student|65203
|
||||||
|
871|31|M|executive|44648
|
||||||
|
872|19|F|student|74078
|
||||||
|
873|48|F|administrator|33763
|
||||||
|
874|36|M|scientist|37076
|
||||||
|
875|24|F|student|35802
|
||||||
|
876|41|M|other|20902
|
||||||
|
877|30|M|other|77504
|
||||||
|
878|50|F|educator|98027
|
||||||
|
879|33|F|administrator|55337
|
||||||
|
880|13|M|student|83702
|
||||||
|
881|39|M|marketing|43017
|
||||||
|
882|35|M|engineer|40503
|
||||||
|
883|49|M|librarian|50266
|
||||||
|
884|44|M|engineer|55337
|
||||||
|
885|30|F|other|95316
|
||||||
|
886|20|M|student|61820
|
||||||
|
887|14|F|student|27249
|
||||||
|
888|41|M|scientist|17036
|
||||||
|
889|24|M|technician|78704
|
||||||
|
890|32|M|student|97301
|
||||||
|
891|51|F|administrator|03062
|
||||||
|
892|36|M|other|45243
|
||||||
|
893|25|M|student|95823
|
||||||
|
894|47|M|educator|74075
|
||||||
|
895|31|F|librarian|32301
|
||||||
|
896|28|M|writer|91505
|
||||||
|
897|30|M|other|33484
|
||||||
|
898|23|M|homemaker|61755
|
||||||
|
899|32|M|other|55116
|
||||||
|
900|60|M|retired|18505
|
||||||
|
901|38|M|executive|L1V3W
|
||||||
|
902|45|F|artist|97203
|
||||||
|
903|28|M|educator|20850
|
||||||
|
904|17|F|student|61073
|
||||||
|
905|27|M|other|30350
|
||||||
|
906|45|M|librarian|70124
|
||||||
|
907|25|F|other|80526
|
||||||
|
908|44|F|librarian|68504
|
||||||
|
909|50|F|educator|53171
|
||||||
|
910|28|M|healthcare|29301
|
||||||
|
911|37|F|writer|53210
|
||||||
|
912|51|M|other|06512
|
||||||
|
913|27|M|student|76201
|
||||||
|
914|44|F|other|08105
|
||||||
|
915|50|M|entertainment|60614
|
||||||
|
916|27|M|engineer|N2L5N
|
||||||
|
917|22|F|student|20006
|
||||||
|
918|40|M|scientist|70116
|
||||||
|
919|25|M|other|14216
|
||||||
|
920|30|F|artist|90008
|
||||||
|
921|20|F|student|98801
|
||||||
|
922|29|F|administrator|21114
|
||||||
|
923|21|M|student|E2E3R
|
||||||
|
924|29|M|other|11753
|
||||||
|
925|18|F|salesman|49036
|
||||||
|
926|49|M|entertainment|01701
|
||||||
|
927|23|M|programmer|55428
|
||||||
|
928|21|M|student|55408
|
||||||
|
929|44|M|scientist|53711
|
||||||
|
930|28|F|scientist|07310
|
||||||
|
931|60|M|educator|33556
|
||||||
|
932|58|M|educator|06437
|
||||||
|
933|28|M|student|48105
|
||||||
|
934|61|M|engineer|22902
|
||||||
|
935|42|M|doctor|66221
|
||||||
|
936|24|M|other|32789
|
||||||
|
937|48|M|educator|98072
|
||||||
|
938|38|F|technician|55038
|
||||||
|
939|26|F|student|33319
|
||||||
|
940|32|M|administrator|02215
|
||||||
|
941|20|M|student|97229
|
||||||
|
942|48|F|librarian|78209
|
||||||
|
943|22|M|student|77841
|
||||||
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@@ -0,0 +1,170 @@
|
|||||||
|
SUMMARY
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
These files contain 1,000,209 anonymous ratings of approximately 3,900 movies
|
||||||
|
made by 6,040 MovieLens users who joined MovieLens in 2000.
|
||||||
|
|
||||||
|
USAGE LICENSE
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
Neither the University of Minnesota nor any of the researchers
|
||||||
|
involved can guarantee the correctness of the data, its suitability
|
||||||
|
for any particular purpose, or the validity of results based on the
|
||||||
|
use of the data set. The data set may be used for any research
|
||||||
|
purposes under the following conditions:
|
||||||
|
|
||||||
|
* The user may not state or imply any endorsement from the
|
||||||
|
University of Minnesota or the GroupLens Research Group.
|
||||||
|
|
||||||
|
* The user must acknowledge the use of the data set in
|
||||||
|
publications resulting from the use of the data set
|
||||||
|
(see below for citation information).
|
||||||
|
|
||||||
|
* The user may not redistribute the data without separate
|
||||||
|
permission.
|
||||||
|
|
||||||
|
* The user may not use this information for any commercial or
|
||||||
|
revenue-bearing purposes without first obtaining permission
|
||||||
|
from a faculty member of the GroupLens Research Project at the
|
||||||
|
University of Minnesota.
|
||||||
|
|
||||||
|
If you have any further questions or comments, please contact GroupLens
|
||||||
|
<grouplens-info@cs.umn.edu>.
|
||||||
|
|
||||||
|
CITATION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
To acknowledge use of the dataset in publications, please cite the following
|
||||||
|
paper:
|
||||||
|
|
||||||
|
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History
|
||||||
|
and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,
|
||||||
|
Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
|
||||||
|
|
||||||
|
|
||||||
|
ACKNOWLEDGEMENTS
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data
|
||||||
|
set.
|
||||||
|
|
||||||
|
FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
The GroupLens Research Project is a research group in the Department of
|
||||||
|
Computer Science and Engineering at the University of Minnesota. Members of
|
||||||
|
the GroupLens Research Project are involved in many research projects related
|
||||||
|
to the fields of information filtering, collaborative filtering, and
|
||||||
|
recommender systems. The project is lead by professors John Riedl and Joseph
|
||||||
|
Konstan. The project began to explore automated collaborative filtering in
|
||||||
|
1992, but is most well known for its world wide trial of an automated
|
||||||
|
collaborative filtering system for Usenet news in 1996. Since then the project
|
||||||
|
has expanded its scope to research overall information filtering solutions,
|
||||||
|
integrating in content-based methods as well as improving current collaborative
|
||||||
|
filtering technology.
|
||||||
|
|
||||||
|
Further information on the GroupLens Research project, including research
|
||||||
|
publications, can be found at the following web site:
|
||||||
|
|
||||||
|
http://www.grouplens.org/
|
||||||
|
|
||||||
|
GroupLens Research currently operates a movie recommender based on
|
||||||
|
collaborative filtering:
|
||||||
|
|
||||||
|
http://www.movielens.org/
|
||||||
|
|
||||||
|
RATINGS FILE DESCRIPTION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
All ratings are contained in the file "ratings.dat" and are in the
|
||||||
|
following format:
|
||||||
|
|
||||||
|
UserID::MovieID::Rating::Timestamp
|
||||||
|
|
||||||
|
- UserIDs range between 1 and 6040
|
||||||
|
- MovieIDs range between 1 and 3952
|
||||||
|
- Ratings are made on a 5-star scale (whole-star ratings only)
|
||||||
|
- Timestamp is represented in seconds since the epoch as returned by time(2)
|
||||||
|
- Each user has at least 20 ratings
|
||||||
|
|
||||||
|
USERS FILE DESCRIPTION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
User information is in the file "users.dat" and is in the following
|
||||||
|
format:
|
||||||
|
|
||||||
|
UserID::Gender::Age::Occupation::Zip-code
|
||||||
|
|
||||||
|
All demographic information is provided voluntarily by the users and is
|
||||||
|
not checked for accuracy. Only users who have provided some demographic
|
||||||
|
information are included in this data set.
|
||||||
|
|
||||||
|
- Gender is denoted by a "M" for male and "F" for female
|
||||||
|
- Age is chosen from the following ranges:
|
||||||
|
|
||||||
|
* 1: "Under 18"
|
||||||
|
* 18: "18-24"
|
||||||
|
* 25: "25-34"
|
||||||
|
* 35: "35-44"
|
||||||
|
* 45: "45-49"
|
||||||
|
* 50: "50-55"
|
||||||
|
* 56: "56+"
|
||||||
|
|
||||||
|
- Occupation is chosen from the following choices:
|
||||||
|
|
||||||
|
* 0: "other" or not specified
|
||||||
|
* 1: "academic/educator"
|
||||||
|
* 2: "artist"
|
||||||
|
* 3: "clerical/admin"
|
||||||
|
* 4: "college/grad student"
|
||||||
|
* 5: "customer service"
|
||||||
|
* 6: "doctor/health care"
|
||||||
|
* 7: "executive/managerial"
|
||||||
|
* 8: "farmer"
|
||||||
|
* 9: "homemaker"
|
||||||
|
* 10: "K-12 student"
|
||||||
|
* 11: "lawyer"
|
||||||
|
* 12: "programmer"
|
||||||
|
* 13: "retired"
|
||||||
|
* 14: "sales/marketing"
|
||||||
|
* 15: "scientist"
|
||||||
|
* 16: "self-employed"
|
||||||
|
* 17: "technician/engineer"
|
||||||
|
* 18: "tradesman/craftsman"
|
||||||
|
* 19: "unemployed"
|
||||||
|
* 20: "writer"
|
||||||
|
|
||||||
|
MOVIES FILE DESCRIPTION
|
||||||
|
================================================================================
|
||||||
|
|
||||||
|
Movie information is in the file "movies.dat" and is in the following
|
||||||
|
format:
|
||||||
|
|
||||||
|
MovieID::Title::Genres
|
||||||
|
|
||||||
|
- Titles are identical to titles provided by the IMDB (including
|
||||||
|
year of release)
|
||||||
|
- Genres are pipe-separated and are selected from the following genres:
|
||||||
|
|
||||||
|
* Action
|
||||||
|
* Adventure
|
||||||
|
* Animation
|
||||||
|
* Children's
|
||||||
|
* Comedy
|
||||||
|
* Crime
|
||||||
|
* Documentary
|
||||||
|
* Drama
|
||||||
|
* Fantasy
|
||||||
|
* Film-Noir
|
||||||
|
* Horror
|
||||||
|
* Musical
|
||||||
|
* Mystery
|
||||||
|
* Romance
|
||||||
|
* Sci-Fi
|
||||||
|
* Thriller
|
||||||
|
* War
|
||||||
|
* Western
|
||||||
|
|
||||||
|
- Some MovieIDs do not correspond to a movie due to accidental duplicate
|
||||||
|
entries and/or test entries
|
||||||
|
- Movies are mostly entered by hand, so errors and inconsistencies may exist
|
||||||
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|
|||||||
|
522_385_234_452_594
|
||||||
|
522_385_234_246_428
|
||||||
|
452_246_220_586_82
|
||||||
|
452_246_220_586_198
|
||||||
|
452_246_220_586_50
|
||||||
|
220_586_73_263_372
|
||||||
|
220_586_73_263_365
|
||||||
|
220_586_73_263_6
|
||||||
|
73_263_563_119_66
|
||||||
|
73_263_563_4_312
|
||||||
|
73_263_563_4_354
|
||||||
|
14_156_45_580_560
|
||||||
|
14_156_45_560_318
|
||||||
|
14_156_45_560_606
|
||||||
|
14_156_45_89_28
|
||||||
|
14_156_517_462_448
|
||||||
|
14_156_517_89_28
|
||||||
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|
|||||||
|
86_384_143_297_401
|
||||||
|
86_384_143_297_528
|
||||||
|
86_384_143_297_579
|
||||||
|
86_384_143_297_359
|
||||||
|
86_384_143_297_566
|
||||||
|
86_384_143_297_223
|
||||||
|
401_528_579_359_241
|
||||||
|
401_528_579_359_337
|
||||||
|
401_528_579_359_445
|
||||||
|
401_528_579_359_211
|
||||||
|
401_528_579_359_422
|
||||||
|
566_223_225_108_241
|
||||||
|
566_223_225_108_370
|
||||||
|
566_223_225_108_361
|
||||||
|
566_223_225_241_191
|
||||||
|
566_223_225_178_29
|
||||||
|
225_523_108_241_543
|
||||||
|
523_108_241_543_29
|
||||||
|
523_108_241_543_500
|
||||||
|
523_543_178_337_500
|
||||||
|
523_543_178_445_500
|
||||||
|
178_337_445_38_116
|
||||||
|
178_337_445_96_116
|
||||||
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|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "7fa3d250",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Imports"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"id": "6b55c6e8",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Local application/library specific imports\n",
|
||||||
|
"from pygrex.config import cfg\n",
|
||||||
|
"from pygrex.data_reader import DataReader, GroupInteractionHandler\n",
|
||||||
|
"# from pygrex.evaluator import SlidingWindowEvaluator\n",
|
||||||
|
"from pygrex.explain import RuleBasedGroupRecExplainer\n",
|
||||||
|
"from pygrex.models import ALS\n",
|
||||||
|
"from pygrex.recommender import GroupRecommender\n",
|
||||||
|
"from pygrex.utils import AggregationStrategy\n",
|
||||||
|
"from pygrex.evaluator import ExplanationEvaluator\n",
|
||||||
|
"\n",
|
||||||
|
"import time\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import pickle\n",
|
||||||
|
"import os\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"id": "adbf9967",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"✅ Data preparation complete.\n",
|
||||||
|
"\n",
|
||||||
|
"--- Data Summary ---\n",
|
||||||
|
"👥 Unique Users: 610\n",
|
||||||
|
"📦 Unique Items: 9,724\n",
|
||||||
|
"⭐ Total Ratings: 100,836\n",
|
||||||
|
"👨👩👧👦 Number of Groups: 17\n",
|
||||||
|
"\n",
|
||||||
|
"Processed Ratings DataFrame Head:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>userId</th>\n",
|
||||||
|
" <th>itemId</th>\n",
|
||||||
|
" <th>rating</th>\n",
|
||||||
|
" <th>timestamp</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>964982703</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>2</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>964981247</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>5</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>964982224</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>43</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>964983815</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>46</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>964982931</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" userId itemId rating timestamp\n",
|
||||||
|
"0 0 0 1 964982703\n",
|
||||||
|
"1 0 2 1 964981247\n",
|
||||||
|
"2 0 5 1 964982224\n",
|
||||||
|
"3 0 43 1 964983815\n",
|
||||||
|
"4 0 46 1 964982931"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Read the ratings file.\n",
|
||||||
|
"data = DataReader(**cfg.data.test)\n",
|
||||||
|
"data.make_consecutive_ids_in_dataset()\n",
|
||||||
|
"data.binarize(binary_threshold=1)\n",
|
||||||
|
"\n",
|
||||||
|
"# Read the file with the group ids\n",
|
||||||
|
"group_handler = GroupInteractionHandler(**cfg.data.groups)\n",
|
||||||
|
"available_groups = group_handler.read_groups(\"groupsWithHighRatings5.txt\")\n",
|
||||||
|
"print(\"✅ Data preparation complete.\\n\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Display Data Summary ---\n",
|
||||||
|
"print(\"--- Data Summary ---\")\n",
|
||||||
|
"print(f\"👥 Unique Users: {data.num_user:,}\")\n",
|
||||||
|
"print(f\"📦 Unique Items: {data.num_item:,}\")\n",
|
||||||
|
"print(f\"⭐ Total Ratings: {len(data.get_raw_dataset()):,}\")\n",
|
||||||
|
"print(f\"👨👩👧👦 Number of Groups: {len(available_groups):,}\")\n",
|
||||||
|
"print(\"\\nProcessed Ratings DataFrame Head:\")\n",
|
||||||
|
"display(data.dataset.head())"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5fc94aef",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 2: Model Training & Evaluation\n",
|
||||||
|
"\n",
|
||||||
|
"With the data prepared, we now select and train a recommendation model. We will use **Alternating Least Squares (ALS)**, a matrix factorization technique for implicit feedback. After training, we will evaluate its performance using a train/test split to measure its Hit Ratio and NDCG."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"id": "8c13c283",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 2.1 Model Training ---\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"c:\\Users\\usuar\\miniconda3\\envs\\pygrex-exp-grs\\Lib\\site-packages\\implicit\\cpu\\als.py:95: RuntimeWarning: OpenBLAS is configured to use 8 threads. It is highly recommended to disable its internal threadpool by setting the environment variable 'OPENBLAS_NUM_THREADS=1' or by calling 'threadpoolctl.threadpool_limits(1, \"blas\")'. Having OpenBLAS use a threadpool can lead to severe performance issues here.\n",
|
||||||
|
" check_blas_config()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.jupyter.widget-view+json": {
|
||||||
|
"model_id": "4a7883a7b94a4a13952cb1d9cf9a33a4",
|
||||||
|
"version_major": 2,
|
||||||
|
"version_minor": 0
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
" 0%| | 0/10 [00:00<?, ?it/s]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"✅ Model trained successfully in 1.00 seconds!\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 2.1 Model Training ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Train the recommendation model\n",
|
||||||
|
"model = ALS(**cfg.model.als)\n",
|
||||||
|
"\n",
|
||||||
|
"# Train the model\n",
|
||||||
|
"start_time = time.time()\n",
|
||||||
|
"model.fit(data)\n",
|
||||||
|
"end_time = time.time()\n",
|
||||||
|
"training_time = end_time - start_time\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"✅ Model trained successfully in {training_time:.2f} seconds!\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "047fe521",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"print(\"\\n--- 2.2 Offline Model Evaluation ---\")\n",
|
||||||
|
"# For evaluation, a new model instance must be created.\n",
|
||||||
|
"# The evaluation function handles its own internal data splitting and training.\n",
|
||||||
|
"eval_model = ALS(**cfg.model.als)\n",
|
||||||
|
"\n",
|
||||||
|
"# Define evaluation parameters\n",
|
||||||
|
"test_size = 0.2\n",
|
||||||
|
"top_n = 10\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Running evaluation with a {test_size*100:.0f}% test split (Top-{top_n})...\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Run the evaluation\n",
|
||||||
|
"evaluation_scores = run_evaluation_with_proper_split(\n",
|
||||||
|
" data_reader=data,\n",
|
||||||
|
" model=eval_model,\n",
|
||||||
|
" test_size=test_size,\n",
|
||||||
|
" top_n=top_n,\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# Display evaluation results\n",
|
||||||
|
"print(\"\\n--- Evaluation Results ---\")\n",
|
||||||
|
"print(f\"Hit Ratio @{top_n}: {evaluation_scores.get('Hit Ratio', 0.0):.2%}\")\n",
|
||||||
|
"print(f\"NDCG @{top_n}: {evaluation_scores.get('NDCG', 0.0):.4f}\")\n",
|
||||||
|
"print(f\"Evaluation Time: {evaluation_scores.get('evaluation_time', 0):.1f}s\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "49cb2659",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 3: Group Recommendation\n",
|
||||||
|
"\n",
|
||||||
|
"Now that we have a trained model, we can generate recommendations for a group. We will select a group, choose an aggregation strategy to combine individual member preferences, and generate a Top-10 list of recommended items."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"id": "0a138815",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 3. Group Recommendation ---\n",
|
||||||
|
"Generating Top-10 recommendations for group: 522_385_234_452_594\n",
|
||||||
|
"👥 Group Members: [522, 385, 234, 452, 594]\n",
|
||||||
|
"📊 Aggregation Strategy: AVG_PREDICTIONS\n",
|
||||||
|
"\n",
|
||||||
|
"✅ Recommendations generated successfully!\n",
|
||||||
|
"\n",
|
||||||
|
"Top 10 Recommended Items:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>Rank</th>\n",
|
||||||
|
" <th>Item ID</th>\n",
|
||||||
|
" <th>Aggregated Score</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>543</td>\n",
|
||||||
|
" <td>4.636274</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>2</td>\n",
|
||||||
|
" <td>757</td>\n",
|
||||||
|
" <td>4.582981</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>3</td>\n",
|
||||||
|
" <td>564</td>\n",
|
||||||
|
" <td>4.504107</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>4</td>\n",
|
||||||
|
" <td>441</td>\n",
|
||||||
|
" <td>4.488708</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>5</td>\n",
|
||||||
|
" <td>379</td>\n",
|
||||||
|
" <td>4.341830</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5</th>\n",
|
||||||
|
" <td>6</td>\n",
|
||||||
|
" <td>475</td>\n",
|
||||||
|
" <td>4.279482</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>6</th>\n",
|
||||||
|
" <td>7</td>\n",
|
||||||
|
" <td>43</td>\n",
|
||||||
|
" <td>4.268454</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7</th>\n",
|
||||||
|
" <td>8</td>\n",
|
||||||
|
" <td>19</td>\n",
|
||||||
|
" <td>4.225248</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>8</th>\n",
|
||||||
|
" <td>9</td>\n",
|
||||||
|
" <td>748</td>\n",
|
||||||
|
" <td>4.178329</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>9</th>\n",
|
||||||
|
" <td>10</td>\n",
|
||||||
|
" <td>64</td>\n",
|
||||||
|
" <td>4.147735</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Rank Item ID Aggregated Score\n",
|
||||||
|
"0 1 543 4.636274\n",
|
||||||
|
"1 2 757 4.582981\n",
|
||||||
|
"2 3 564 4.504107\n",
|
||||||
|
"3 4 441 4.488708\n",
|
||||||
|
"4 5 379 4.341830\n",
|
||||||
|
"5 6 475 4.279482\n",
|
||||||
|
"6 7 43 4.268454\n",
|
||||||
|
"7 8 19 4.225248\n",
|
||||||
|
"8 9 748 4.178329\n",
|
||||||
|
"9 10 64 4.147735"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 3. Group Recommendation ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Select a group and strategy\n",
|
||||||
|
"selected_group_id = available_groups[0] # Let's use the first group as an example\n",
|
||||||
|
"group_members = group_handler.parse_group_members(selected_group_id)\n",
|
||||||
|
"aggregation_strategy = AggregationStrategy.AVG_PREDICTIONS # Use the simple average strategy\n",
|
||||||
|
"top_k = 10\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Generating Top-{top_k} recommendations for group: {selected_group_id}\")\n",
|
||||||
|
"print(f\"👥 Group Members: {group_members}\")\n",
|
||||||
|
"print(f\"📊 Aggregation Strategy: {aggregation_strategy.name}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Generate Recommendations ---\n",
|
||||||
|
"# 1. Instantiate the GroupRecommender\n",
|
||||||
|
"group_recommender = GroupRecommender(data=data)\n",
|
||||||
|
"\n",
|
||||||
|
"# 2. Setup the recommendation process\n",
|
||||||
|
"group_recommender.setup_recommendation(\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" members=group_members, # type: ignore\n",
|
||||||
|
" data=data,\n",
|
||||||
|
" aggregation_strategy=aggregation_strategy,\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# 3. Get the final recommendation list\n",
|
||||||
|
"recommended_items = group_recommender.get_group_recommendations(top_k=top_k)\n",
|
||||||
|
"recommendation_scores = group_recommender.get_recommendation_scores()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\n✅ Recommendations generated successfully!\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Display Results ---\n",
|
||||||
|
"rec_data = [\n",
|
||||||
|
" {\n",
|
||||||
|
" \"Rank\": i + 1,\n",
|
||||||
|
" \"Item ID\": item_id,\n",
|
||||||
|
" \"Aggregated Score\": recommendation_scores.get(item_id, 0.0),\n",
|
||||||
|
" }\n",
|
||||||
|
" for i, item_id in enumerate(recommended_items) # type: ignore\n",
|
||||||
|
"]\n",
|
||||||
|
"\n",
|
||||||
|
"rec_df = pd.DataFrame(rec_data)\n",
|
||||||
|
"print(f\"\\nTop {top_k} Recommended Items:\")\n",
|
||||||
|
"display(rec_df)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "6268a2ed",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 4: Explanation (EXPGRS)\n",
|
||||||
|
"\n",
|
||||||
|
"Finally, we generate an explanation for one of the recommendations. We will use the **EXPGRS** method to find a ruled based explanation. This method calculates the Model Fidelity: the percentage of the Top-N list that can be explained by pre-computed association rules from cached files.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"id": "367063db",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 4. Rule based Explanation (EXPGRS) ---\n",
|
||||||
|
"Explanation Fidelity:\n",
|
||||||
|
"10.00%\n",
|
||||||
|
"--------------------\n",
|
||||||
|
"Advanced Explanation Fidelity:\n",
|
||||||
|
"0.00%\n",
|
||||||
|
"--------------------\n",
|
||||||
|
"Explanation Diversity (GILD):\n",
|
||||||
|
"0.0000\n",
|
||||||
|
"--------------------\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 4. Rule based Explanation (EXPGRS) ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def load_cached_data_rules(min_support, min_confidence, rating_threshold):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" Loads pre-computed association rules from the cached_rules folder.\n",
|
||||||
|
" Returns the loaded object (typically a dict with key \"rules\") if found, None otherwise.\n",
|
||||||
|
" Searches several common locations to be robust in notebooks.\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" from pathlib import Path\n",
|
||||||
|
"\n",
|
||||||
|
" filename = f\"rules_sup{min_support:.2f}_conf{min_confidence:.1f}_rating{rating_threshold:.0f}\"\n",
|
||||||
|
" possible_extensions = [\".pkl\", \".pickle\", \".json\"]\n",
|
||||||
|
"\n",
|
||||||
|
" cwd = Path.cwd()\n",
|
||||||
|
" search_dirs = [\n",
|
||||||
|
" cwd / \"cached_rules\", # current working directory\n",
|
||||||
|
" cwd.parent / \"cached_rules\", # parent (useful when running from notebooks/)\n",
|
||||||
|
" Path(__file__).resolve().parent / \"cached_rules\" if '__file__' in globals() else None, # script dir if available\n",
|
||||||
|
" ]\n",
|
||||||
|
" search_dirs = [p for p in search_dirs if p is not None]\n",
|
||||||
|
"\n",
|
||||||
|
" tried_paths = []\n",
|
||||||
|
" for base in search_dirs:\n",
|
||||||
|
" for ext in possible_extensions:\n",
|
||||||
|
" filepath = base / f\"{filename}{ext}\"\n",
|
||||||
|
" tried_paths.append(str(filepath))\n",
|
||||||
|
" if filepath.exists():\n",
|
||||||
|
" try:\n",
|
||||||
|
" if ext in [\".pkl\", \".pickle\"]:\n",
|
||||||
|
" with open(filepath, \"rb\") as f:\n",
|
||||||
|
" return pickle.load(f)\n",
|
||||||
|
" elif ext == \".json\":\n",
|
||||||
|
" import json\n",
|
||||||
|
" with open(filepath, \"r\") as f:\n",
|
||||||
|
" return json.load(f)\n",
|
||||||
|
" except Exception as e:\n",
|
||||||
|
" print(f\"Error loading cached rules from {filepath}: {e}\")\n",
|
||||||
|
" continue\n",
|
||||||
|
"\n",
|
||||||
|
" print(\"Cached rules not found. Tried paths:\")\n",
|
||||||
|
" for p in tried_paths:\n",
|
||||||
|
" print(\" -\", p)\n",
|
||||||
|
" return None\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def get_user_history(rating_threshold):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" Generates the user interaction history based only on the rating threshold.\n",
|
||||||
|
" The keys of the returned dictionary are the ORIGINAL user IDs.\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" df_filtered = data.dataset[data.dataset[\"rating\"] >= rating_threshold]\n",
|
||||||
|
"\n",
|
||||||
|
" # Group by the 'userId' column (which contains the new, consecutive IDs)\n",
|
||||||
|
" history_by_new_id = df_filtered.groupby(\"userId\")[\"itemId\"].apply(set).to_dict()\n",
|
||||||
|
"\n",
|
||||||
|
" # Create the final dictionary mapping original user IDs to sets of new item IDs\n",
|
||||||
|
" history_by_original_id = {}\n",
|
||||||
|
" for new_id, item_set in history_by_new_id.items():\n",
|
||||||
|
" try:\n",
|
||||||
|
" original_id = data.get_original_user_id(int(new_id))\n",
|
||||||
|
" # The explainer needs the item IDs to be strings to match the rules\n",
|
||||||
|
" history_by_original_id[original_id] = {str(item) for item in item_set}\n",
|
||||||
|
" except (ValueError, KeyError):\n",
|
||||||
|
" continue\n",
|
||||||
|
"\n",
|
||||||
|
" return history_by_original_id\n",
|
||||||
|
"\n",
|
||||||
|
"# ----------------------------------------------------------------------- #\n",
|
||||||
|
"\n",
|
||||||
|
"min_support = 0.1\n",
|
||||||
|
"min_confidence = 0.1\n",
|
||||||
|
"rating_threshold = 1\n",
|
||||||
|
"minimum_members = 1\n",
|
||||||
|
"\n",
|
||||||
|
"# Load cached rules (no Streamlit dependencies)\n",
|
||||||
|
"expected_filename = f\"rules_sup{min_support:.2f}_conf{min_confidence:.1f}_rating{rating_threshold:.0f}\"\n",
|
||||||
|
"cached_data_rules = load_cached_data_rules(min_support, min_confidence, rating_threshold)\n",
|
||||||
|
"if cached_data_rules is None:\n",
|
||||||
|
" print(\"⚠️ Cached rules not found.\")\n",
|
||||||
|
" print(\"Looked for:\", \", \".join(\n",
|
||||||
|
" [os.path.join(\"cached_rules\", expected_filename + ext) for ext in [\".pkl\", \".pickle\", \".json\"]]\n",
|
||||||
|
" ))\n",
|
||||||
|
" raise SystemExit(\"Please place the cached rules file in the 'cached_rules/' folder.\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Extract rules from loaded structure\n",
|
||||||
|
"cached_rules = cached_data_rules.get(\"rules\") if isinstance(cached_data_rules, dict) else None\n",
|
||||||
|
"if cached_rules is None:\n",
|
||||||
|
" raise ValueError(\n",
|
||||||
|
" \"Loaded cached rules file does not contain a 'rules' key. Check the file format.\"\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
"# Get user history\n",
|
||||||
|
"user_history = get_user_history(rating_threshold)\n",
|
||||||
|
"\n",
|
||||||
|
"# Create explainer with cached rules\n",
|
||||||
|
"explainer = RuleBasedGroupRecExplainer(\n",
|
||||||
|
" rules=cached_rules,\n",
|
||||||
|
" data=data,\n",
|
||||||
|
" pool_recommendations=recommended_items,\n",
|
||||||
|
" members=group_members,\n",
|
||||||
|
" user_history=user_history,\n",
|
||||||
|
" min_members_threshold=minimum_members,\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# Compute explanations and metrics\n",
|
||||||
|
"fidelity_score = explainer.find_explanation()\n",
|
||||||
|
"advanced_fidelity_score = explainer.compute_group_fidelity_advanced()\n",
|
||||||
|
"explanation_details = explainer.get_explanation_details()\n",
|
||||||
|
"\n",
|
||||||
|
"explanation_results = {\n",
|
||||||
|
" \"fidelity\": fidelity_score,\n",
|
||||||
|
" \"advanced_fidelity\": advanced_fidelity_score,\n",
|
||||||
|
" \"details\": explanation_details,\n",
|
||||||
|
"}\n",
|
||||||
|
"\n",
|
||||||
|
"# Evaluate results\n",
|
||||||
|
"_evaluator = ExplanationEvaluator()\n",
|
||||||
|
"metrics = _evaluator.evaluate(explanation_results, explainer_type=\"EXPGRS\")\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Explanation Fidelity:\")\n",
|
||||||
|
"print(f\"{metrics.get('fidelity', 0.0):.2%}\")\n",
|
||||||
|
"print(\"-\" * 20)\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Advanced Explanation Fidelity:\")\n",
|
||||||
|
"print(f\"{explanation_results.get('advanced_fidelity', 0.0):.2%}\")\n",
|
||||||
|
"print(\"-\" * 20)\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Explanation Diversity (GILD):\")\n",
|
||||||
|
"print(f\"{metrics.get('gild', 0.0):.4f}\")\n",
|
||||||
|
"print(\"-\" * 20)\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "pygrex-exp-grs",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
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|
"version": 3
|
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|
},
|
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|
"file_extension": ".py",
|
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|
"mimetype": "text/x-python",
|
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"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.11"
|
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|
}
|
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|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
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|
}
|
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@@ -0,0 +1,616 @@
|
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|
{
|
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|
"cells": [
|
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|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "7fa3d250",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Imports"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"id": "6b55c6e8",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Local application/library specific imports\n",
|
||||||
|
"from pygrex.config import cfg\n",
|
||||||
|
"from pygrex.data_reader import DataReader, GroupInteractionHandler\n",
|
||||||
|
"from pygrex.evaluator import run_evaluation_with_proper_split\n",
|
||||||
|
"from pygrex.explain.groups.lore4groups_explainer import LORE4GroupsExplainer\n",
|
||||||
|
"from pygrex.models import ALS\n",
|
||||||
|
"from pygrex.recommender import GroupRecommender\n",
|
||||||
|
"from pygrex.utils import AggregationStrategy\n",
|
||||||
|
"from pygrex.evaluator import ExplanationEvaluator\n",
|
||||||
|
"\n",
|
||||||
|
"import time\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import os\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"id": "adbf9967",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"✅ Data preparation complete.\n",
|
||||||
|
"\n",
|
||||||
|
"--- Data Summary ---\n",
|
||||||
|
"👥 Unique Users: 610\n",
|
||||||
|
"📦 Unique Items: 9,724\n",
|
||||||
|
"⭐ Total Ratings: 100,836\n",
|
||||||
|
"👨👩👧👦 Number of Groups: 17\n",
|
||||||
|
"\n",
|
||||||
|
"Processed Ratings DataFrame Head:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
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|
" vertical-align: middle;\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>userId</th>\n",
|
||||||
|
" <th>itemId</th>\n",
|
||||||
|
" <th>rating</th>\n",
|
||||||
|
" <th>timestamp</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>4.0</td>\n",
|
||||||
|
" <td>964982703</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>2</td>\n",
|
||||||
|
" <td>4.0</td>\n",
|
||||||
|
" <td>964981247</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>5</td>\n",
|
||||||
|
" <td>4.0</td>\n",
|
||||||
|
" <td>964982224</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>43</td>\n",
|
||||||
|
" <td>5.0</td>\n",
|
||||||
|
" <td>964983815</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>0</td>\n",
|
||||||
|
" <td>46</td>\n",
|
||||||
|
" <td>5.0</td>\n",
|
||||||
|
" <td>964982931</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" userId itemId rating timestamp\n",
|
||||||
|
"0 0 0 4.0 964982703\n",
|
||||||
|
"1 0 2 4.0 964981247\n",
|
||||||
|
"2 0 5 4.0 964982224\n",
|
||||||
|
"3 0 43 5.0 964983815\n",
|
||||||
|
"4 0 46 5.0 964982931"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Read the ratings file.\n",
|
||||||
|
"data = DataReader(**cfg.data.test)\n",
|
||||||
|
"data.make_consecutive_ids_in_dataset()\n",
|
||||||
|
"# data.binarize(binary_threshold=1)\n",
|
||||||
|
"\n",
|
||||||
|
"# Read the file with the group ids\n",
|
||||||
|
"group_handler = GroupInteractionHandler(**cfg.data.groups)\n",
|
||||||
|
"available_groups = group_handler.read_groups(\"groupsWithHighRatings5.txt\")\n",
|
||||||
|
"print(\"✅ Data preparation complete.\\n\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Display Data Summary ---\n",
|
||||||
|
"print(\"--- Data Summary ---\")\n",
|
||||||
|
"print(f\"👥 Unique Users: {data.num_user:,}\")\n",
|
||||||
|
"print(f\"📦 Unique Items: {data.num_item:,}\")\n",
|
||||||
|
"print(f\"⭐ Total Ratings: {len(data.get_raw_dataset()):,}\")\n",
|
||||||
|
"print(f\"👨👩👧👦 Number of Groups: {len(available_groups):,}\")\n",
|
||||||
|
"print(\"\\nProcessed Ratings DataFrame Head:\")\n",
|
||||||
|
"display(data.dataset.head())"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5fc94aef",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 2: Model Training & Evaluation\n",
|
||||||
|
"\n",
|
||||||
|
"With the data prepared, we now select and train a recommendation model. We will use **Alternating Least Squares (ALS)**, a matrix factorization technique for implicit feedback. After training, we will evaluate its performance using a train/test split to measure its Hit Ratio and NDCG."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"id": "8c13c283",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 2.1 Model Training ---\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"c:\\Users\\usuar\\miniconda3\\envs\\pygrex-exp-grs\\Lib\\site-packages\\implicit\\cpu\\als.py:95: RuntimeWarning: OpenBLAS is configured to use 8 threads. It is highly recommended to disable its internal threadpool by setting the environment variable 'OPENBLAS_NUM_THREADS=1' or by calling 'threadpoolctl.threadpool_limits(1, \"blas\")'. Having OpenBLAS use a threadpool can lead to severe performance issues here.\n",
|
||||||
|
" check_blas_config()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.jupyter.widget-view+json": {
|
||||||
|
"model_id": "40a7ec46978a413e80b045c0f60fbce6",
|
||||||
|
"version_major": 2,
|
||||||
|
"version_minor": 0
|
||||||
|
},
|
||||||
|
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|
||||||
|
" 0%| | 0/10 [00:00<?, ?it/s]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"✅ Model trained successfully in 0.95 seconds!\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 2.1 Model Training ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Train the recommendation model\n",
|
||||||
|
"model = ALS(**cfg.model.als)\n",
|
||||||
|
"\n",
|
||||||
|
"# Train the model\n",
|
||||||
|
"start_time = time.time()\n",
|
||||||
|
"model.fit(data)\n",
|
||||||
|
"end_time = time.time()\n",
|
||||||
|
"training_time = end_time - start_time\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"✅ Model trained successfully in {training_time:.2f} seconds!\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "047fe521",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"print(\"\\n--- 2.2 Offline Model Evaluation ---\")\n",
|
||||||
|
"# For evaluation, a new model instance must be created.\n",
|
||||||
|
"# The evaluation function handles its own internal data splitting and training.\n",
|
||||||
|
"eval_model = ALS(**cfg.model.als)\n",
|
||||||
|
"\n",
|
||||||
|
"# Define evaluation parameters\n",
|
||||||
|
"test_size = 0.2\n",
|
||||||
|
"top_n = 10\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Running evaluation with a {test_size*100:.0f}% test split (Top-{top_n})...\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Run the evaluation\n",
|
||||||
|
"evaluation_scores = run_evaluation_with_proper_split(\n",
|
||||||
|
" data_reader=data,\n",
|
||||||
|
" model=eval_model,\n",
|
||||||
|
" test_size=test_size,\n",
|
||||||
|
" top_n=top_n,\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# Display evaluation results\n",
|
||||||
|
"print(\"\\n--- Evaluation Results ---\")\n",
|
||||||
|
"print(f\"Hit Ratio @{top_n}: {evaluation_scores.get('Hit Ratio', 0.0):.2%}\")\n",
|
||||||
|
"print(f\"NDCG @{top_n}: {evaluation_scores.get('NDCG', 0.0):.4f}\")\n",
|
||||||
|
"print(f\"Evaluation Time: {evaluation_scores.get('evaluation_time', 0):.1f}s\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "49cb2659",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 3: Group Recommendation\n",
|
||||||
|
"\n",
|
||||||
|
"Now that we have a trained model, we can generate recommendations for a group. We will select a group, choose an aggregation strategy to combine individual member preferences, and generate a Top-10 list of recommended items."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"id": "0a138815",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 3. Group Recommendation ---\n",
|
||||||
|
"Generating Top-10 recommendations for group: 522_385_234_452_594\n",
|
||||||
|
"👥 Group Members: [522, 385, 234, 452, 594]\n",
|
||||||
|
"📊 Aggregation Strategy: AVG_PREDICTIONS\n",
|
||||||
|
"\n",
|
||||||
|
"✅ Recommendations generated successfully!\n",
|
||||||
|
"\n",
|
||||||
|
"Top 10 Recommended Items:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>Rank</th>\n",
|
||||||
|
" <th>Item ID</th>\n",
|
||||||
|
" <th>Aggregated Score</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>543</td>\n",
|
||||||
|
" <td>4.636274</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>2</td>\n",
|
||||||
|
" <td>757</td>\n",
|
||||||
|
" <td>4.582981</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>3</td>\n",
|
||||||
|
" <td>564</td>\n",
|
||||||
|
" <td>4.504107</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>4</td>\n",
|
||||||
|
" <td>441</td>\n",
|
||||||
|
" <td>4.488708</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>5</td>\n",
|
||||||
|
" <td>379</td>\n",
|
||||||
|
" <td>4.341830</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5</th>\n",
|
||||||
|
" <td>6</td>\n",
|
||||||
|
" <td>475</td>\n",
|
||||||
|
" <td>4.279482</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>6</th>\n",
|
||||||
|
" <td>7</td>\n",
|
||||||
|
" <td>43</td>\n",
|
||||||
|
" <td>4.268454</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7</th>\n",
|
||||||
|
" <td>8</td>\n",
|
||||||
|
" <td>19</td>\n",
|
||||||
|
" <td>4.225248</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>8</th>\n",
|
||||||
|
" <td>9</td>\n",
|
||||||
|
" <td>748</td>\n",
|
||||||
|
" <td>4.178329</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>9</th>\n",
|
||||||
|
" <td>10</td>\n",
|
||||||
|
" <td>64</td>\n",
|
||||||
|
" <td>4.147735</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Rank Item ID Aggregated Score\n",
|
||||||
|
"0 1 543 4.636274\n",
|
||||||
|
"1 2 757 4.582981\n",
|
||||||
|
"2 3 564 4.504107\n",
|
||||||
|
"3 4 441 4.488708\n",
|
||||||
|
"4 5 379 4.341830\n",
|
||||||
|
"5 6 475 4.279482\n",
|
||||||
|
"6 7 43 4.268454\n",
|
||||||
|
"7 8 19 4.225248\n",
|
||||||
|
"8 9 748 4.178329\n",
|
||||||
|
"9 10 64 4.147735"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 3. Group Recommendation ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Select a group and strategy\n",
|
||||||
|
"selected_group_id = available_groups[0] # Let's use the first group as an example\n",
|
||||||
|
"group_members = group_handler.parse_group_members(selected_group_id)\n",
|
||||||
|
"aggregation_strategy = AggregationStrategy.AVG_PREDICTIONS # Use the simple average strategy\n",
|
||||||
|
"top_k = 10\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Generating Top-{top_k} recommendations for group: {selected_group_id}\")\n",
|
||||||
|
"print(f\"👥 Group Members: {group_members}\")\n",
|
||||||
|
"print(f\"📊 Aggregation Strategy: {aggregation_strategy.name}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Generate Recommendations ---\n",
|
||||||
|
"# 1. Instantiate the GroupRecommender\n",
|
||||||
|
"group_recommender = GroupRecommender(data=data)\n",
|
||||||
|
"\n",
|
||||||
|
"# 2. Setup the recommendation process\n",
|
||||||
|
"group_recommender.setup_recommendation(\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" members=group_members, # type: ignore\n",
|
||||||
|
" data=data,\n",
|
||||||
|
" aggregation_strategy=aggregation_strategy,\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# 3. Get the final recommendation list\n",
|
||||||
|
"recommended_items = group_recommender.get_group_recommendations(top_k=top_k)\n",
|
||||||
|
"recommendation_scores = group_recommender.get_recommendation_scores()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\n✅ Recommendations generated successfully!\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Display Results ---\n",
|
||||||
|
"rec_data = [\n",
|
||||||
|
" {\n",
|
||||||
|
" \"Rank\": i + 1,\n",
|
||||||
|
" \"Item ID\": item_id,\n",
|
||||||
|
" \"Aggregated Score\": recommendation_scores.get(item_id, 0.0),\n",
|
||||||
|
" }\n",
|
||||||
|
" for i, item_id in enumerate(recommended_items) # type: ignore\n",
|
||||||
|
"]\n",
|
||||||
|
"\n",
|
||||||
|
"rec_df = pd.DataFrame(rec_data)\n",
|
||||||
|
"print(f\"\\nTop {top_k} Recommended Items:\")\n",
|
||||||
|
"display(rec_df)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "6268a2ed",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 4: Explanation (LORE4Groups)\n",
|
||||||
|
"\n",
|
||||||
|
"Finally, we generate an explanation for the recommended items using **LORE4Groups**, a local rule-based method. It:\n",
|
||||||
|
"- builds a local neighborhood of similar items using tag profiles\n",
|
||||||
|
"- trains a simple decision tree per item to predict 'like' vs 'not like'\n",
|
||||||
|
"- extracts interpretable rules explaining why items were recommended.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"id": "367063db",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 4. Local Rule-Based Explanation (LORE4Groups) ---\n",
|
||||||
|
"Explanation Fidelity:\n",
|
||||||
|
"25.00%\n",
|
||||||
|
"--------------------\n",
|
||||||
|
"Explanation Diversity (GILD):\n",
|
||||||
|
"0.8871\n",
|
||||||
|
"--------------------\n",
|
||||||
|
"Items with explanations:\n",
|
||||||
|
"['475', '43']\n",
|
||||||
|
"--------------------\n",
|
||||||
|
"Sample item: 475\n",
|
||||||
|
"Decision Path (rules): ['nudity (rear) <= 0.50', 'twins <= 0.50']\n",
|
||||||
|
"Group Factual Rules: {'unanimous': [], 'majority': [], 'minority': ['70mm <= 0.50 (1/5 members)', 'franchise <= 0.50 (1/5 members)', 'futuristmoviescom <= 0.50 (1/5 members)', 'nudity (rear) <= 0.50 (1/5 members)', 'owned <= 0.50 (1/5 members)', 'seen at the cinema <= 0.50 (1/5 members)', 'sequel <= 0.50 (1/5 members)', 'twins <= 0.50 (1/5 members)']}\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 4. Local Rule-Based Explanation (LORE4Groups) ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# 1) Build tag-based item profiles aligned with the ratings dataset\n",
|
||||||
|
"# ---------------------------------------------------------------\n",
|
||||||
|
"# Read tags file from config\n",
|
||||||
|
"_tags_path = cfg.data.tags.tags_file\n",
|
||||||
|
"if not os.path.exists(_tags_path):\n",
|
||||||
|
" raise SystemExit(f\"Tags file not found at: {_tags_path}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Read tags and align original -> consecutive item ids\n",
|
||||||
|
"tags_df = pd.read_csv(_tags_path)\n",
|
||||||
|
"consecutive_items = set(data.dataset[\"itemId\"].unique())\n",
|
||||||
|
"original_to_consecutive = {}\n",
|
||||||
|
"for item_consec in consecutive_items:\n",
|
||||||
|
" try:\n",
|
||||||
|
" item_orig = data.get_original_item_id(int(item_consec))\n",
|
||||||
|
" original_to_consecutive[item_orig] = int(item_consec)\n",
|
||||||
|
" except (ValueError, KeyError):\n",
|
||||||
|
" continue\n",
|
||||||
|
"\n",
|
||||||
|
"# Keep only tags for items present in ratings\n",
|
||||||
|
"tags_df = tags_df[tags_df[\"movieId\"].isin(original_to_consecutive.keys())].copy()\n",
|
||||||
|
"if len(tags_df) == 0:\n",
|
||||||
|
" raise SystemExit(\"No tag data matches items in ratings dataset.\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Normalize labels (keep full label as tag, lowercase)\n",
|
||||||
|
"tags_df[\"label\"] = tags_df[\"label\"].astype(str).str.lower().str.strip()\n",
|
||||||
|
"# Map to consecutive ids\n",
|
||||||
|
"tags_df[\"movieId\"] = tags_df[\"movieId\"].map(original_to_consecutive).astype(int)\n",
|
||||||
|
"\n",
|
||||||
|
"# Keep top-N most frequent labels to reduce sparsity\n",
|
||||||
|
"_top_n = cfg.explainer.lore4groups.top_n_labels\n",
|
||||||
|
"top_labels = (\n",
|
||||||
|
" tags_df[\"label\"].value_counts().nlargest(_top_n).index.tolist()\n",
|
||||||
|
")\n",
|
||||||
|
"tags_final = tags_df[tags_df[\"label\"].isin(top_labels)].copy()\n",
|
||||||
|
"\n",
|
||||||
|
"# Item profiles: {str(itemId): set(labels)}\n",
|
||||||
|
"item_profiles = (\n",
|
||||||
|
" tags_final.groupby(\"movieId\")[\"label\"].apply(set).to_dict()\n",
|
||||||
|
")\n",
|
||||||
|
"item_profiles = {str(k): v for k, v in item_profiles.items()}\n",
|
||||||
|
"\n",
|
||||||
|
"# Item-label matrix (rows: itemId as str, cols: labels, values: 0/1)\n",
|
||||||
|
"item_label_matrix = tags_final.assign(value=1).pivot_table(\n",
|
||||||
|
" index=\"movieId\", columns=\"label\", values=\"value\", fill_value=0\n",
|
||||||
|
")\n",
|
||||||
|
"item_label_matrix.index = item_label_matrix.index.astype(str)\n",
|
||||||
|
"\n",
|
||||||
|
"# 2) Prepare user history in required format\n",
|
||||||
|
"# ------------------------------------------\n",
|
||||||
|
"user_hist = {}\n",
|
||||||
|
"for user_id_orig in group_members:\n",
|
||||||
|
" try:\n",
|
||||||
|
" user_id_consec = data.get_new_user_id(user_id_orig)\n",
|
||||||
|
" hist_items = set(\n",
|
||||||
|
" data.dataset[data.dataset[\"userId\"] == user_id_consec][\"itemId\"].astype(str)\n",
|
||||||
|
" )\n",
|
||||||
|
" user_hist[user_id_orig] = hist_items\n",
|
||||||
|
" except Exception:\n",
|
||||||
|
" user_hist[user_id_orig] = set()\n",
|
||||||
|
"\n",
|
||||||
|
"# Filter recommendations to those we can explain (must exist in profiles)\n",
|
||||||
|
"explainable_recs = [str(i) for i in recommended_items if str(i) in item_profiles]\n",
|
||||||
|
"if not explainable_recs:\n",
|
||||||
|
" print(\"⚠️ No recommended items have sufficient tag data for explanation.\")\n",
|
||||||
|
"else:\n",
|
||||||
|
" # 3) Run LORE4Groups explainer\n",
|
||||||
|
" explainer = LORE4GroupsExplainer(\n",
|
||||||
|
" item_profiles=item_profiles,\n",
|
||||||
|
" item_label_matrix=item_label_matrix,\n",
|
||||||
|
" config=cfg,\n",
|
||||||
|
" genre_profiles=None, # optional, omitted for toy example\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
" results = explainer.find_explanation(\n",
|
||||||
|
" explainable_recs,\n",
|
||||||
|
" group_members,\n",
|
||||||
|
" user_hist,\n",
|
||||||
|
" data.dataset,\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" data_reader=data,\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
" fidelity = results.get(\"fidelity\", 0.0)\n",
|
||||||
|
" details = results.get(\"details\", {})\n",
|
||||||
|
"\n",
|
||||||
|
" print(\"Explanation Fidelity:\")\n",
|
||||||
|
" print(f\"{fidelity:.2%}\")\n",
|
||||||
|
" print(\"-\" * 20)\n",
|
||||||
|
"\n",
|
||||||
|
" # Compute GILD diversity like in the app\n",
|
||||||
|
" evaluator = ExplanationEvaluator()\n",
|
||||||
|
" metrics = evaluator.evaluate({\"fidelity\": fidelity, \"details\": details}, explainer_type=\"LORE4Groups\")\n",
|
||||||
|
" print(\"Explanation Diversity (GILD):\")\n",
|
||||||
|
" print(f\"{metrics.get('gild', 0.0):.4f}\")\n",
|
||||||
|
" print(\"-\" * 20)\n",
|
||||||
|
"\n",
|
||||||
|
" print(\"Items with explanations:\")\n",
|
||||||
|
" print(list(details.keys()))\n",
|
||||||
|
" print(\"-\" * 20)\n",
|
||||||
|
"\n",
|
||||||
|
" # Optionally preview one item's explanation summary if available\n",
|
||||||
|
" if details:\n",
|
||||||
|
" first_item, exp = next(iter(details.items()))\n",
|
||||||
|
" decision_path = exp.get(\"decision_path\", [])\n",
|
||||||
|
" group_factual = exp.get(\"group_factual_rule\", [])\n",
|
||||||
|
" print(f\"Sample item: {first_item}\")\n",
|
||||||
|
" print(\"Decision Path (rules):\", decision_path)\n",
|
||||||
|
" print(\"Group Factual Rules:\", group_factual)\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "pygrex-exp-grs",
|
||||||
|
"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": "3.11.11"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@@ -0,0 +1,575 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "7fa3d250",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Imports"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"id": "6b55c6e8",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Local application/library specific imports\n",
|
||||||
|
"from pygrex.config import cfg\n",
|
||||||
|
"from pygrex.data_reader import DataReader, GroupInteractionHandler\n",
|
||||||
|
"# from pygrex.evaluator import SlidingWindowEvaluator\n",
|
||||||
|
"from pygrex.explain import SlidingWindowExplainer\n",
|
||||||
|
"from pygrex.models import ALS\n",
|
||||||
|
"from pygrex.recommender import GroupRecommender\n",
|
||||||
|
"from pygrex.utils import SlidingWindow, AggregationStrategy\n",
|
||||||
|
"from pygrex.evaluator import run_evaluation_with_proper_split\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"import time\n",
|
||||||
|
"import pandas as pd\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"id": "adbf9967",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"✅ Data preparation complete.\n",
|
||||||
|
"\n",
|
||||||
|
"--- Data Summary ---\n",
|
||||||
|
"👥 Unique Users: 610\n",
|
||||||
|
"📦 Unique Items: 9,724\n",
|
||||||
|
"⭐ Total Ratings: 100,836\n",
|
||||||
|
"👨👩👧👦 Number of Groups: 17\n",
|
||||||
|
"\n",
|
||||||
|
"Processed Ratings DataFrame Head:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
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|
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|
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|
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|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>userId</th>\n",
|
||||||
|
" <th>itemId</th>\n",
|
||||||
|
" <th>rating</th>\n",
|
||||||
|
" <th>timestamp</th>\n",
|
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|
" </tr>\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
" <td>964982224</td>\n",
|
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|
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|
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|
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|
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|
" <th>3</th>\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
" <td>964982931</td>\n",
|
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|
" </tr>\n",
|
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|
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|
||||||
|
"</table>\n",
|
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|
"</div>"
|
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|
],
|
||||||
|
"text/plain": [
|
||||||
|
" userId itemId rating timestamp\n",
|
||||||
|
"0 0 0 1 964982703\n",
|
||||||
|
"1 0 2 1 964981247\n",
|
||||||
|
"2 0 5 1 964982224\n",
|
||||||
|
"3 0 43 1 964983815\n",
|
||||||
|
"4 0 46 1 964982931"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Read the ratings file.\n",
|
||||||
|
"data = DataReader(**cfg.data.test)\n",
|
||||||
|
"data.make_consecutive_ids_in_dataset()\n",
|
||||||
|
"data.binarize(binary_threshold=1)\n",
|
||||||
|
"\n",
|
||||||
|
"# Read the file with the group ids\n",
|
||||||
|
"group_handler = GroupInteractionHandler(**cfg.data.groups)\n",
|
||||||
|
"available_groups = group_handler.read_groups(\"groupsWithHighRatings5.txt\")\n",
|
||||||
|
"print(\"✅ Data preparation complete.\\n\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Display Data Summary ---\n",
|
||||||
|
"print(\"--- Data Summary ---\")\n",
|
||||||
|
"print(f\"👥 Unique Users: {data.num_user:,}\")\n",
|
||||||
|
"print(f\"📦 Unique Items: {data.num_item:,}\")\n",
|
||||||
|
"print(f\"⭐ Total Ratings: {len(data.get_raw_dataset()):,}\")\n",
|
||||||
|
"print(f\"👨👩👧👦 Number of Groups: {len(available_groups):,}\")\n",
|
||||||
|
"print(\"\\nProcessed Ratings DataFrame Head:\")\n",
|
||||||
|
"display(data.dataset.head())"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5fc94aef",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 2: Model Training & Evaluation\n",
|
||||||
|
"\n",
|
||||||
|
"With the data prepared, we now select and train a recommendation model. We will use **Alternating Least Squares (ALS)**, a matrix factorization technique for implicit feedback. After training, we will evaluate its performance using a train/test split to measure its Hit Ratio and NDCG."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"id": "8c13c283",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 2.1 Model Training ---\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"c:\\Users\\usuar\\miniconda3\\envs\\pygrex-exp-grs\\Lib\\site-packages\\implicit\\cpu\\als.py:95: RuntimeWarning: OpenBLAS is configured to use 8 threads. It is highly recommended to disable its internal threadpool by setting the environment variable 'OPENBLAS_NUM_THREADS=1' or by calling 'threadpoolctl.threadpool_limits(1, \"blas\")'. Having OpenBLAS use a threadpool can lead to severe performance issues here.\n",
|
||||||
|
" check_blas_config()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.jupyter.widget-view+json": {
|
||||||
|
"model_id": "4a2c30b182994b868f98ba8d9d2d7d8f",
|
||||||
|
"version_major": 2,
|
||||||
|
"version_minor": 0
|
||||||
|
},
|
||||||
|
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|
||||||
|
" 0%| | 0/10 [00:00<?, ?it/s]"
|
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|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"✅ Model trained successfully in 1.08 seconds!\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 2.1 Model Training ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Train the recommendation model\n",
|
||||||
|
"model = ALS(**cfg.model.als)\n",
|
||||||
|
"\n",
|
||||||
|
"# Train the model\n",
|
||||||
|
"start_time = time.time()\n",
|
||||||
|
"model.fit(data)\n",
|
||||||
|
"end_time = time.time()\n",
|
||||||
|
"training_time = end_time - start_time\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"✅ Model trained successfully in {training_time:.2f} seconds!\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "047fe521",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"print(\"\\n--- 2.2 Offline Model Evaluation ---\")\n",
|
||||||
|
"# For evaluation, a new model instance must be created.\n",
|
||||||
|
"# The evaluation function handles its own internal data splitting and training.\n",
|
||||||
|
"eval_model = ALS(**cfg.model.als)\n",
|
||||||
|
"\n",
|
||||||
|
"# Define evaluation parameters\n",
|
||||||
|
"test_size = 0.2\n",
|
||||||
|
"top_n = 10\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Running evaluation with a {test_size*100:.0f}% test split (Top-{top_n})...\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Run the evaluation\n",
|
||||||
|
"evaluation_scores = run_evaluation_with_proper_split(\n",
|
||||||
|
" data_reader=data,\n",
|
||||||
|
" model=eval_model,\n",
|
||||||
|
" test_size=test_size,\n",
|
||||||
|
" top_n=top_n,\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# Display evaluation results\n",
|
||||||
|
"print(\"\\n--- Evaluation Results ---\")\n",
|
||||||
|
"print(f\"Hit Ratio @{top_n}: {evaluation_scores.get('Hit Ratio', 0.0):.2%}\")\n",
|
||||||
|
"print(f\"NDCG @{top_n}: {evaluation_scores.get('NDCG', 0.0):.4f}\")\n",
|
||||||
|
"print(f\"Evaluation Time: {evaluation_scores.get('evaluation_time', 0):.1f}s\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "49cb2659",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 3: Group Recommendation\n",
|
||||||
|
"\n",
|
||||||
|
"Now that we have a trained model, we can generate recommendations for a group. We will select a group, choose an aggregation strategy to combine individual member preferences, and generate a Top-10 list of recommended items."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"id": "0a138815",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 3. Group Recommendation ---\n",
|
||||||
|
"Generating Top-10 recommendations for group: 522_385_234_452_594\n",
|
||||||
|
"👥 Group Members: [522, 385, 234, 452, 594]\n",
|
||||||
|
"📊 Aggregation Strategy: AVG_PREDICTIONS\n",
|
||||||
|
"\n",
|
||||||
|
"✅ Recommendations generated successfully!\n",
|
||||||
|
"\n",
|
||||||
|
"Top 10 Recommended Items:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>Rank</th>\n",
|
||||||
|
" <th>Item ID</th>\n",
|
||||||
|
" <th>Aggregated Score</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>543</td>\n",
|
||||||
|
" <td>4.636274</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>2</td>\n",
|
||||||
|
" <td>757</td>\n",
|
||||||
|
" <td>4.582981</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>3</td>\n",
|
||||||
|
" <td>564</td>\n",
|
||||||
|
" <td>4.504107</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>4</td>\n",
|
||||||
|
" <td>441</td>\n",
|
||||||
|
" <td>4.488708</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>5</td>\n",
|
||||||
|
" <td>379</td>\n",
|
||||||
|
" <td>4.341830</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5</th>\n",
|
||||||
|
" <td>6</td>\n",
|
||||||
|
" <td>475</td>\n",
|
||||||
|
" <td>4.279482</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>6</th>\n",
|
||||||
|
" <td>7</td>\n",
|
||||||
|
" <td>43</td>\n",
|
||||||
|
" <td>4.268454</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7</th>\n",
|
||||||
|
" <td>8</td>\n",
|
||||||
|
" <td>19</td>\n",
|
||||||
|
" <td>4.225248</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>8</th>\n",
|
||||||
|
" <td>9</td>\n",
|
||||||
|
" <td>748</td>\n",
|
||||||
|
" <td>4.178329</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>9</th>\n",
|
||||||
|
" <td>10</td>\n",
|
||||||
|
" <td>64</td>\n",
|
||||||
|
" <td>4.147735</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Rank Item ID Aggregated Score\n",
|
||||||
|
"0 1 543 4.636274\n",
|
||||||
|
"1 2 757 4.582981\n",
|
||||||
|
"2 3 564 4.504107\n",
|
||||||
|
"3 4 441 4.488708\n",
|
||||||
|
"4 5 379 4.341830\n",
|
||||||
|
"5 6 475 4.279482\n",
|
||||||
|
"6 7 43 4.268454\n",
|
||||||
|
"7 8 19 4.225248\n",
|
||||||
|
"8 9 748 4.178329\n",
|
||||||
|
"9 10 64 4.147735"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 3. Group Recommendation ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Select a group and strategy\n",
|
||||||
|
"selected_group_id = available_groups[0] # Let's use the first group as an example\n",
|
||||||
|
"group_members = group_handler.parse_group_members(selected_group_id)\n",
|
||||||
|
"aggregation_strategy = AggregationStrategy.AVG_PREDICTIONS # Use the simple average strategy\n",
|
||||||
|
"top_k = 10\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Generating Top-{top_k} recommendations for group: {selected_group_id}\")\n",
|
||||||
|
"print(f\"👥 Group Members: {group_members}\")\n",
|
||||||
|
"print(f\"📊 Aggregation Strategy: {aggregation_strategy.name}\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Generate Recommendations ---\n",
|
||||||
|
"# 1. Instantiate the GroupRecommender\n",
|
||||||
|
"group_recommender = GroupRecommender(data=data)\n",
|
||||||
|
"\n",
|
||||||
|
"# 2. Setup the recommendation process\n",
|
||||||
|
"group_recommender.setup_recommendation(\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" members=group_members, # type: ignore\n",
|
||||||
|
" data=data,\n",
|
||||||
|
" aggregation_strategy=aggregation_strategy,\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# 3. Get the final recommendation list\n",
|
||||||
|
"recommended_items = group_recommender.get_group_recommendations(top_k=top_k)\n",
|
||||||
|
"recommendation_scores = group_recommender.get_recommendation_scores()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\n✅ Recommendations generated successfully!\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Display Results ---\n",
|
||||||
|
"rec_data = [\n",
|
||||||
|
" {\n",
|
||||||
|
" \"Rank\": i + 1,\n",
|
||||||
|
" \"Item ID\": item_id,\n",
|
||||||
|
" \"Aggregated Score\": recommendation_scores.get(item_id, 0.0),\n",
|
||||||
|
" }\n",
|
||||||
|
" for i, item_id in enumerate(recommended_items) # type: ignore\n",
|
||||||
|
"]\n",
|
||||||
|
"\n",
|
||||||
|
"rec_df = pd.DataFrame(rec_data)\n",
|
||||||
|
"print(f\"\\nTop {top_k} Recommended Items:\")\n",
|
||||||
|
"display(rec_df)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "6268a2ed",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 4: Explanation (Sliding Window)\n",
|
||||||
|
"\n",
|
||||||
|
"Finally, we generate an explanation for one of the recommendations. We will use the **Sliding Window** method to find a counterfactual explanation. This method answers the question: *\"Which minimal set of items, if removed from the group's history, would cause our target item to disappear from the recommendation list?\"*\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"id": "367063db",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"--- 4. Counterfactual Explanation (Sliding Window) ---\n",
|
||||||
|
"Generating explanation for recommended item: 543\n",
|
||||||
|
"Sliding Window Size: 3\n",
|
||||||
|
"\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.jupyter.widget-view+json": {
|
||||||
|
"model_id": "6c1d8da938db475ab005c2378f99feae",
|
||||||
|
"version_major": 2,
|
||||||
|
"version_minor": 0
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
" 0%| | 0/10 [00:00<?, ?it/s]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"application/vnd.jupyter.widget-view+json": {
|
||||||
|
"model_id": "221213362ff442f7b9e736536844c6b9",
|
||||||
|
"version_major": 2,
|
||||||
|
"version_minor": 0
|
||||||
|
},
|
||||||
|
"text/plain": [
|
||||||
|
" 0%| | 0/10 [00:00<?, ?it/s]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"If the group had not interacted with these items [np.int64(480)],\n",
|
||||||
|
"the item of interest 543 would not have appeared on the recommendation list;\n",
|
||||||
|
"instead, 303 would have been recommended.\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(\"--- 4. Counterfactual Explanation (Sliding Window) ---\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Select a target item from our recommendation list to explain\n",
|
||||||
|
"target_item = recommended_items[0]\n",
|
||||||
|
"# Configure the explainer\n",
|
||||||
|
"window_size = 3\n",
|
||||||
|
"# These weights determine how to rank items from the group's history\n",
|
||||||
|
"# before attempting to remove them to find an explanation.\n",
|
||||||
|
"ranking_weights = {\n",
|
||||||
|
" \"popularity\": 1.0,\n",
|
||||||
|
" \"intensity\": 1.0,\n",
|
||||||
|
" \"rating\": 1.0,\n",
|
||||||
|
" \"relevance\": 1.0,\n",
|
||||||
|
" \"trend\": 1.0,\n",
|
||||||
|
"}\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Generating explanation for recommended item: {target_item}\")\n",
|
||||||
|
"print(f\"Sliding Window Size: {window_size}\\n\")\n",
|
||||||
|
"\n",
|
||||||
|
"# --- Generate Explanation ---\n",
|
||||||
|
"# 1. Get all items previously rated by the group\n",
|
||||||
|
"items_rated_by_group = group_handler.get_rated_items_by_all_group_members(\n",
|
||||||
|
" group=group_members, original_data=data\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# 2. Instantiate the explainer\n",
|
||||||
|
"explainer = SlidingWindowExplainer(\n",
|
||||||
|
" config=cfg, # Not needed for this explainer\n",
|
||||||
|
" data=data,\n",
|
||||||
|
" group_handler=group_handler,\n",
|
||||||
|
" members=group_members,\n",
|
||||||
|
" target_item=target_item,\n",
|
||||||
|
" aggregation_strategy=aggregation_strategy,\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" window_size=window_size,\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# 3. Find the explanation\n",
|
||||||
|
"explanations = explainer.find_explanation(\n",
|
||||||
|
" items_rated_by_group=items_rated_by_group,\n",
|
||||||
|
" group_predictions=group_recommender.get_individual_predictions(),\n",
|
||||||
|
" top_recommendation=group_recommender.get_top_recommendation(),\n",
|
||||||
|
" ranking_weights=ranking_weights,\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "pygrex-exp-grs",
|
||||||
|
"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": "3.11.11"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@@ -0,0 +1,162 @@
|
|||||||
|
import streamlit as st
|
||||||
|
import pandas as pd
|
||||||
|
import os
|
||||||
|
from io import StringIO
|
||||||
|
|
||||||
|
# Library Imports
|
||||||
|
from pygrex.data_reader import DataReader, GroupInteractionHandler
|
||||||
|
|
||||||
|
# Page Configuration
|
||||||
|
st.set_page_config(page_title="Data Preparation", page_icon="📄", layout="wide")
|
||||||
|
|
||||||
|
st.title("📄 Data Preparation")
|
||||||
|
|
||||||
|
# Default File Paths
|
||||||
|
DEFAULT_RATINGS_PATH = "datasets/stratigis/ratings.csv"
|
||||||
|
DEFAULT_GROUPS_PATH = "datasets/stratigis/groupsWithHighRatings5.txt"
|
||||||
|
|
||||||
|
# Session State Initialization
|
||||||
|
if "data_loaded" not in st.session_state:
|
||||||
|
st.session_state.data_loaded = False
|
||||||
|
st.session_state.data_reader = None
|
||||||
|
st.session_state.group_handler = None
|
||||||
|
st.session_state.num_groups = 0
|
||||||
|
|
||||||
|
# DATA INPUT SECTION
|
||||||
|
|
||||||
|
# Ratings Input
|
||||||
|
st.header("1. Ratings Data")
|
||||||
|
st.markdown(
|
||||||
|
"You can upload your own ratings file or use the default **MovieLens 100k** dataset."
|
||||||
|
)
|
||||||
|
ratings_file_buffer = st.file_uploader(
|
||||||
|
"Upload Your Ratings Data (Optional)", type=["csv"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Group Input
|
||||||
|
st.header("2. Group Data")
|
||||||
|
group_input_method = st.radio(
|
||||||
|
"Choose group input method:",
|
||||||
|
("Enter groups manually", "Upload a group file"),
|
||||||
|
horizontal=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Load default group data for the text area
|
||||||
|
default_group_text = ""
|
||||||
|
if os.path.exists(DEFAULT_GROUPS_PATH) and ratings_file_buffer is None:
|
||||||
|
with open(DEFAULT_GROUPS_PATH, "r") as f:
|
||||||
|
default_group_text = f.read()
|
||||||
|
|
||||||
|
if group_input_method == "Enter groups manually":
|
||||||
|
group_text_input = st.text_area(
|
||||||
|
"Enter group members (one group per line, members separated by '_')",
|
||||||
|
value=default_group_text,
|
||||||
|
height=150,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
groups_file_buffer = st.file_uploader(
|
||||||
|
"Upload Your Group Data (Optional)", type=["txt"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Preprocessing Options
|
||||||
|
st.header("3. Preprocessing")
|
||||||
|
binarize_data = st.checkbox(
|
||||||
|
"Binarize ratings (for implicit feedback models)", value=True
|
||||||
|
)
|
||||||
|
if binarize_data:
|
||||||
|
binary_threshold = st.number_input(
|
||||||
|
"Rating threshold for binarization", min_value=0.0, value=1.0, step=0.5
|
||||||
|
)
|
||||||
|
|
||||||
|
# Main Loading Logic
|
||||||
|
st.header("4. Load and Process")
|
||||||
|
if st.button("Load and Process Data", type="primary"):
|
||||||
|
with st.spinner("Processing data..."):
|
||||||
|
try:
|
||||||
|
desired_columns = ["userId", "itemId", "rating", "timestamp"]
|
||||||
|
# Determine which ratings file to use
|
||||||
|
if ratings_file_buffer:
|
||||||
|
ratings_df = pd.read_csv(
|
||||||
|
StringIO(ratings_file_buffer.getvalue().decode("utf-8")),
|
||||||
|
sep=",",
|
||||||
|
usecols=lambda column: column in desired_columns,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
if not os.path.exists(DEFAULT_RATINGS_PATH):
|
||||||
|
st.error(
|
||||||
|
f"Default ratings file not found at: `{DEFAULT_RATINGS_PATH}`"
|
||||||
|
)
|
||||||
|
st.stop()
|
||||||
|
ratings_df = pd.read_csv(
|
||||||
|
DEFAULT_RATINGS_PATH,
|
||||||
|
sep=",",
|
||||||
|
names=desired_columns,
|
||||||
|
skiprows=1,
|
||||||
|
)
|
||||||
|
ratings_df = ratings_df[desired_columns]
|
||||||
|
|
||||||
|
# Determine which group data to use and prepare it for the handler
|
||||||
|
temp_dir = "temp/group_data"
|
||||||
|
os.makedirs(temp_dir, exist_ok=True)
|
||||||
|
groups_filepath = os.path.join(temp_dir, "current_groups.txt")
|
||||||
|
|
||||||
|
if group_input_method == "Enter groups manually":
|
||||||
|
with open(groups_filepath, "w") as f:
|
||||||
|
f.write(group_text_input) # type: ignore
|
||||||
|
st.session_state.group_filename = os.path.basename(groups_filepath)
|
||||||
|
else: # File upload method
|
||||||
|
if groups_file_buffer: # type: ignore
|
||||||
|
with open(groups_filepath, "wb") as f:
|
||||||
|
f.write(groups_file_buffer.getbuffer())
|
||||||
|
st.session_state.group_filename = groups_file_buffer.name
|
||||||
|
else: # Fallback to default if no file is uploaded
|
||||||
|
if not os.path.exists(DEFAULT_GROUPS_PATH):
|
||||||
|
st.error(
|
||||||
|
f"Default groups file not found at: `{DEFAULT_GROUPS_PATH}`"
|
||||||
|
)
|
||||||
|
st.stop()
|
||||||
|
groups_filepath = DEFAULT_GROUPS_PATH
|
||||||
|
st.session_state.group_filename = os.path.basename(groups_filepath)
|
||||||
|
|
||||||
|
# Instantiate library classes and process data
|
||||||
|
data_reader = DataReader(dataframe=ratings_df)
|
||||||
|
group_handler = GroupInteractionHandler(filepath_or_buffer=groups_filepath)
|
||||||
|
|
||||||
|
if binarize_data:
|
||||||
|
data_reader.binarize(binary_threshold=binary_threshold) # type: ignore
|
||||||
|
data_reader.make_consecutive_ids_in_dataset()
|
||||||
|
|
||||||
|
available_groups = group_handler.read_groups(
|
||||||
|
filename=st.session_state.group_filename
|
||||||
|
)
|
||||||
|
|
||||||
|
# Store results in session state
|
||||||
|
st.session_state.data_reader = data_reader
|
||||||
|
st.session_state.group_handler = group_handler
|
||||||
|
st.session_state.num_groups = len(available_groups)
|
||||||
|
st.session_state.data_loaded = True
|
||||||
|
|
||||||
|
st.success("✅ Data loaded and processed successfully!")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"An error occurred: {e}")
|
||||||
|
st.session_state.data_loaded = False
|
||||||
|
|
||||||
|
|
||||||
|
# Enhanced Data Summary
|
||||||
|
if st.session_state.data_loaded:
|
||||||
|
st.markdown("")
|
||||||
|
st.header("Data Summary")
|
||||||
|
|
||||||
|
dr = st.session_state.data_reader
|
||||||
|
|
||||||
|
col1, col2 = st.columns(2)
|
||||||
|
with col1:
|
||||||
|
st.metric("👥 Unique Users", f"{dr.num_user:,}") # type: ignore
|
||||||
|
st.metric("📦 Unique Items", f"{dr.num_item:,}") # type: ignore
|
||||||
|
with col2:
|
||||||
|
st.metric("⭐ Total Ratings", f"{len(dr.get_raw_dataset()):,}") # type: ignore
|
||||||
|
st.metric("👨👩👧👦 Number of Groups", f"{st.session_state.num_groups:,}")
|
||||||
|
|
||||||
|
with st.expander("Processed Ratings DataFrame Head:", expanded=True):
|
||||||
|
st.dataframe(dr.dataset.head(), hide_index=True) # type: ignore
|
||||||
@@ -0,0 +1,956 @@
|
|||||||
|
import streamlit as st
|
||||||
|
import time
|
||||||
|
|
||||||
|
# Library Imports
|
||||||
|
from pygrex.models import (
|
||||||
|
ALS,
|
||||||
|
BPR,
|
||||||
|
ExplAutoencoderTorch,
|
||||||
|
EMFModel,
|
||||||
|
GMFModel,
|
||||||
|
MLPModel,
|
||||||
|
SVD,
|
||||||
|
KNNBasic,
|
||||||
|
)
|
||||||
|
from pygrex.evaluator import (
|
||||||
|
run_leave_one_out_evaluation,
|
||||||
|
run_evaluation_with_proper_split,
|
||||||
|
)
|
||||||
|
|
||||||
|
st.set_page_config(page_title="Model Training", page_icon="🧠", layout="wide")
|
||||||
|
|
||||||
|
st.title("🧠 Model Selection & Training")
|
||||||
|
|
||||||
|
# Check if data is loaded
|
||||||
|
if not st.session_state.get("data_loaded", False):
|
||||||
|
st.warning("⚠️ Please load data on the **📄 Data Preparation** page first.")
|
||||||
|
st.stop() # Stop execution if no data is loaded
|
||||||
|
|
||||||
|
# Model Selection
|
||||||
|
st.header("1. Select a Model")
|
||||||
|
# As you add more models to your library, you can add them to this list.
|
||||||
|
model_option = st.selectbox(
|
||||||
|
"Choose a recommendation model:",
|
||||||
|
("ALS", "BPR", "Autoencoder", "EMF", "GMF", "MLP", "KNN", "SVD"),
|
||||||
|
)
|
||||||
|
|
||||||
|
# Hyperparameter Configuration
|
||||||
|
st.header("2. Configure Hyperparameters")
|
||||||
|
model_params = {}
|
||||||
|
|
||||||
|
if model_option == "ALS":
|
||||||
|
st.subheader("ALS (Alternating Least Squares) Parameters")
|
||||||
|
|
||||||
|
# Create columns for a cleaner layout
|
||||||
|
col1, col2, col3 = st.columns(3)
|
||||||
|
with col1:
|
||||||
|
latent_dim = st.number_input(
|
||||||
|
"Latent Dimensions (factors)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=500,
|
||||||
|
value=100,
|
||||||
|
step=10,
|
||||||
|
help="The number of latent factors to compute.",
|
||||||
|
)
|
||||||
|
with col2:
|
||||||
|
reg_term = st.number_input(
|
||||||
|
"Regularization Term",
|
||||||
|
min_value=0.001,
|
||||||
|
max_value=1.0,
|
||||||
|
value=0.001,
|
||||||
|
step=0.001,
|
||||||
|
format="%.3f",
|
||||||
|
help="The regularization factor.",
|
||||||
|
)
|
||||||
|
with col3:
|
||||||
|
epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=200,
|
||||||
|
value=10,
|
||||||
|
step=5,
|
||||||
|
help="The number of ALS iterations.",
|
||||||
|
)
|
||||||
|
model_params = {
|
||||||
|
"latent_dim": latent_dim,
|
||||||
|
"reg_term": reg_term,
|
||||||
|
"epochs": epochs,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
elif model_option == "BPR":
|
||||||
|
st.subheader("BPR (Bayesian Personalised Ranking) Parameters")
|
||||||
|
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
with col1_r1:
|
||||||
|
latent_dim = st.number_input(
|
||||||
|
"Latent Dimensions (factors)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=500,
|
||||||
|
value=100,
|
||||||
|
step=10,
|
||||||
|
help="The number of latent factors to compute.",
|
||||||
|
)
|
||||||
|
with col2_r1:
|
||||||
|
reg_term = st.number_input(
|
||||||
|
"Regularization Term",
|
||||||
|
min_value=0.001,
|
||||||
|
max_value=1.0,
|
||||||
|
value=0.001,
|
||||||
|
step=0.001,
|
||||||
|
format="%.3f",
|
||||||
|
help="The regularization factor.",
|
||||||
|
)
|
||||||
|
with col3_r1:
|
||||||
|
epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=200,
|
||||||
|
value=10,
|
||||||
|
step=5,
|
||||||
|
help="The number of ALS iterations.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
learning_rate = st.number_input(
|
||||||
|
"Learning Rate",
|
||||||
|
min_value=0.0,
|
||||||
|
max_value=0.1,
|
||||||
|
value=0.01,
|
||||||
|
step=0.01,
|
||||||
|
format="%.2f",
|
||||||
|
help="The step size at each iteration while moving toward a minimum of the loss function.",
|
||||||
|
)
|
||||||
|
model_params = {
|
||||||
|
"latent_dim": latent_dim,
|
||||||
|
"reg_term": reg_term,
|
||||||
|
"epochs": epochs,
|
||||||
|
"learning_rate": learning_rate,
|
||||||
|
}
|
||||||
|
|
||||||
|
elif model_option == "Autoencoder":
|
||||||
|
st.subheader("Autoencoder Parameters")
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r1:
|
||||||
|
learning_rate = st.number_input(
|
||||||
|
"Learning Rate",
|
||||||
|
min_value=0.0001,
|
||||||
|
max_value=0.1,
|
||||||
|
value=0.005,
|
||||||
|
step=0.001,
|
||||||
|
format="%.4f",
|
||||||
|
help="The step size at each iteration while moving toward a minimum of the loss function.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r1:
|
||||||
|
weight_decay = st.number_input(
|
||||||
|
"Weight Decay",
|
||||||
|
min_value=0.0000001,
|
||||||
|
max_value=0.0001,
|
||||||
|
value=0.0000001,
|
||||||
|
step=0.0000001,
|
||||||
|
format="%.7f",
|
||||||
|
help="The regularization factor to prevent overfitting by penalizing large weights.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r1:
|
||||||
|
hidden_layer_features = st.number_input(
|
||||||
|
"Hidden Layer Features",
|
||||||
|
min_value=4,
|
||||||
|
max_value=128,
|
||||||
|
value=8,
|
||||||
|
step=4,
|
||||||
|
help="The number of features in the hidden layers of the neural network.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=200,
|
||||||
|
value=30,
|
||||||
|
step=5,
|
||||||
|
help="The number of complete passes through the entire training dataset.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r2:
|
||||||
|
cuda = st.checkbox(
|
||||||
|
"Use CUDA (GPU)",
|
||||||
|
value=False,
|
||||||
|
help="Check to use NVIDIA CUDA for GPU acceleration if available.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r2:
|
||||||
|
optimizer_name = st.selectbox(
|
||||||
|
"Optimizer",
|
||||||
|
options=["adam", "sgd", "rmsprop"],
|
||||||
|
index=0, # 'adam'
|
||||||
|
help="The optimization algorithm to use for training the model.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Third Row
|
||||||
|
col1_r3, col2_r3, col3_r3 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r3:
|
||||||
|
positive_threshold = st.number_input(
|
||||||
|
"Positive Threshold",
|
||||||
|
min_value=1,
|
||||||
|
max_value=5,
|
||||||
|
value=3,
|
||||||
|
step=1,
|
||||||
|
help="The minimum rating value considered as a 'positive' interaction.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r3:
|
||||||
|
knn = st.number_input(
|
||||||
|
"K-Nearest Neighbors (KNN)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=50,
|
||||||
|
value=10,
|
||||||
|
step=1,
|
||||||
|
help="The number of nearest neighbors to consider for KNN-based models.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r3:
|
||||||
|
expl = st.checkbox(
|
||||||
|
"Enable Explanations",
|
||||||
|
value=True,
|
||||||
|
help="Check to enable model explanations or interpretability features.",
|
||||||
|
)
|
||||||
|
model_params = {
|
||||||
|
"learning_rate": learning_rate,
|
||||||
|
"weight_decay": weight_decay,
|
||||||
|
"hidden_layer_features": hidden_layer_features,
|
||||||
|
"epochs": epochs,
|
||||||
|
"cuda": cuda,
|
||||||
|
"optimizer_name": optimizer_name,
|
||||||
|
"positive_threshold": positive_threshold,
|
||||||
|
"knn": knn,
|
||||||
|
"expl": expl,
|
||||||
|
}
|
||||||
|
|
||||||
|
elif model_option == "EMF":
|
||||||
|
st.subheader("EMF (Explainable Matrix Factorisation) Parameters")
|
||||||
|
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r1:
|
||||||
|
learning_rate = st.number_input(
|
||||||
|
"Learning Rate",
|
||||||
|
min_value=0.0001,
|
||||||
|
max_value=0.1,
|
||||||
|
value=0.01,
|
||||||
|
step=0.001,
|
||||||
|
format="%.4f",
|
||||||
|
help="The step size at each iteration for the EMF model.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r1:
|
||||||
|
reg_term = st.number_input(
|
||||||
|
"Regularization Term",
|
||||||
|
min_value=0.0001,
|
||||||
|
max_value=1.0,
|
||||||
|
value=0.001,
|
||||||
|
step=0.001,
|
||||||
|
format="%.4f",
|
||||||
|
help="The regularization factor for the main matrix factorization components.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r1:
|
||||||
|
expl_reg_term = st.number_input(
|
||||||
|
"Explanation Regularization Term",
|
||||||
|
min_value=0.0,
|
||||||
|
max_value=1.0,
|
||||||
|
value=0.0,
|
||||||
|
step=0.001,
|
||||||
|
format="%.4f",
|
||||||
|
help="The regularization factor for the explanation components in EMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
latent_dim = st.number_input(
|
||||||
|
"Latent Dimension",
|
||||||
|
min_value=10,
|
||||||
|
max_value=200,
|
||||||
|
value=80,
|
||||||
|
step=10,
|
||||||
|
help="The number of latent factors used in the matrix factorization.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r2:
|
||||||
|
epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=200,
|
||||||
|
value=10,
|
||||||
|
step=5,
|
||||||
|
help="The number of complete passes through the entire training dataset for EMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r2:
|
||||||
|
positive_threshold = st.number_input(
|
||||||
|
"Positive Threshold",
|
||||||
|
min_value=1,
|
||||||
|
max_value=5,
|
||||||
|
value=3,
|
||||||
|
step=1,
|
||||||
|
help="The minimum rating value considered as a 'positive' interaction for EMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Third Row
|
||||||
|
col1_r3, col2_r3, col3_r3 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r3:
|
||||||
|
knn = st.number_input(
|
||||||
|
"K-Nearest Neighbors (KNN)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=50,
|
||||||
|
value=10,
|
||||||
|
step=1,
|
||||||
|
help="The number of nearest neighbors to consider for KNN-based aspects of EMF.",
|
||||||
|
)
|
||||||
|
model_params = {
|
||||||
|
"learning_rate": learning_rate,
|
||||||
|
"reg_term": reg_term,
|
||||||
|
"expl_reg_term": expl_reg_term,
|
||||||
|
"latent_dim": latent_dim,
|
||||||
|
"epochs": epochs,
|
||||||
|
"positive_threshold": positive_threshold,
|
||||||
|
"knn": knn,
|
||||||
|
}
|
||||||
|
|
||||||
|
elif model_option == "GMF":
|
||||||
|
st.subheader("GMF (Generalised Matrix Factorisation) Parameters")
|
||||||
|
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r1:
|
||||||
|
learning_rate = st.number_input(
|
||||||
|
"Learning Rate",
|
||||||
|
min_value=0.0001,
|
||||||
|
max_value=0.1,
|
||||||
|
value=0.005,
|
||||||
|
step=0.001,
|
||||||
|
format="%.4f",
|
||||||
|
help="The step size at each iteration for the GMF model.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r1:
|
||||||
|
weight_decay = st.number_input(
|
||||||
|
"Weight Decay",
|
||||||
|
min_value=0.0000001,
|
||||||
|
max_value=0.0001,
|
||||||
|
value=0.0000001,
|
||||||
|
step=0.0000001,
|
||||||
|
format="%.7f",
|
||||||
|
help="The regularization factor to prevent overfitting in GMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r1:
|
||||||
|
latent_dim = st.number_input(
|
||||||
|
"Latent Dimension",
|
||||||
|
min_value=4,
|
||||||
|
max_value=128,
|
||||||
|
value=8,
|
||||||
|
step=4,
|
||||||
|
help="The number of latent factors for users and items in GMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=200,
|
||||||
|
value=30,
|
||||||
|
step=5,
|
||||||
|
help="The number of complete passes through the training data for GMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r2:
|
||||||
|
num_negative = st.number_input(
|
||||||
|
"Number of Negative Samples",
|
||||||
|
min_value=1,
|
||||||
|
max_value=100,
|
||||||
|
value=10,
|
||||||
|
step=1,
|
||||||
|
help="The number of negative samples per positive interaction during training.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r2:
|
||||||
|
batch_size = st.number_input(
|
||||||
|
"Batch Size",
|
||||||
|
min_value=64,
|
||||||
|
max_value=4096,
|
||||||
|
value=1024,
|
||||||
|
step=64,
|
||||||
|
help="The number of samples per gradient update.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Third Row
|
||||||
|
col1_r3, col2_r3, col3_r3 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r3:
|
||||||
|
cuda = st.checkbox(
|
||||||
|
"Use CUDA (GPU)",
|
||||||
|
value=False,
|
||||||
|
help="Check to use NVIDIA CUDA for GPU acceleration if available for GMF.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r3:
|
||||||
|
optimizer_name = st.selectbox(
|
||||||
|
"Optimizer",
|
||||||
|
options=["adam", "sgd", "rmsprop"],
|
||||||
|
index=0, # 'adam'
|
||||||
|
help="The optimization algorithm to use for training the GMF model.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# col3_r3 is left empty here if no further parameters for GMF
|
||||||
|
model_params = {
|
||||||
|
"learning_rate": learning_rate,
|
||||||
|
"weight_decay": weight_decay,
|
||||||
|
"latent_dim": latent_dim,
|
||||||
|
"epochs": epochs,
|
||||||
|
"num_negative": num_negative,
|
||||||
|
"batch_size": batch_size,
|
||||||
|
"cuda": cuda,
|
||||||
|
"optimizer_name": optimizer_name,
|
||||||
|
}
|
||||||
|
|
||||||
|
elif model_option == "MLP":
|
||||||
|
st.subheader("MLP (Multi-Layer Perceptron) Parameters")
|
||||||
|
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r1:
|
||||||
|
learning_rate = st.number_input(
|
||||||
|
"Learning Rate",
|
||||||
|
min_value=0.0001,
|
||||||
|
max_value=0.1,
|
||||||
|
value=0.005,
|
||||||
|
step=0.001,
|
||||||
|
format="%.4f",
|
||||||
|
help="The step size at each iteration for the MLP model.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r1:
|
||||||
|
weight_decay = st.number_input(
|
||||||
|
"Weight Decay",
|
||||||
|
min_value=0.0000001,
|
||||||
|
max_value=0.0001,
|
||||||
|
value=0.0000001,
|
||||||
|
step=0.0000001,
|
||||||
|
format="%.7f",
|
||||||
|
help="The regularization factor to prevent overfitting in MLP.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r1:
|
||||||
|
latent_dim = st.number_input(
|
||||||
|
"Latent Dimension",
|
||||||
|
min_value=4,
|
||||||
|
max_value=128,
|
||||||
|
value=8,
|
||||||
|
step=4,
|
||||||
|
help="The number of latent factors for users and items in MLP.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=200,
|
||||||
|
value=30,
|
||||||
|
step=5,
|
||||||
|
help="The number of complete passes through the training data for MLP.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r2:
|
||||||
|
num_negative = st.number_input(
|
||||||
|
"Number of Negative Samples",
|
||||||
|
min_value=1,
|
||||||
|
max_value=100,
|
||||||
|
value=10,
|
||||||
|
step=1,
|
||||||
|
help="The number of negative samples per positive interaction during MLP training.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r2:
|
||||||
|
batch_size = st.number_input(
|
||||||
|
"Batch Size",
|
||||||
|
min_value=64,
|
||||||
|
max_value=4096,
|
||||||
|
value=1024,
|
||||||
|
step=64,
|
||||||
|
help="The number of samples per gradient update for MLP.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Third Row
|
||||||
|
col1_r3, col2_r3, col3_r3 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r3:
|
||||||
|
cuda = st.checkbox(
|
||||||
|
"Use CUDA (GPU)",
|
||||||
|
value=False,
|
||||||
|
help="Check to use NVIDIA CUDA for GPU acceleration if available for MLP.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r3:
|
||||||
|
optimizer_name = st.selectbox(
|
||||||
|
"Optimizer",
|
||||||
|
options=["adam", "sgd", "rmsprop"],
|
||||||
|
index=0, # 'adam'
|
||||||
|
help="The optimization algorithm to use for training the MLP model.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# col3_r3 is left empty here if no further parameters for MLP
|
||||||
|
model_params = {
|
||||||
|
"learning_rate": learning_rate,
|
||||||
|
"weight_decay": weight_decay,
|
||||||
|
"latent_dim": latent_dim,
|
||||||
|
"epochs": epochs,
|
||||||
|
"num_negative": num_negative,
|
||||||
|
"batch_size": batch_size,
|
||||||
|
"cuda": cuda,
|
||||||
|
"optimizer_name": optimizer_name,
|
||||||
|
}
|
||||||
|
|
||||||
|
elif model_option == "KNN":
|
||||||
|
st.subheader("KNN (K-Nearest Neighbors) Parameters")
|
||||||
|
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r1:
|
||||||
|
k_neighbors = st.number_input(
|
||||||
|
"Number of Neighbors (k)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=100,
|
||||||
|
value=50,
|
||||||
|
step=1,
|
||||||
|
help="The number of nearest neighbors to consider for making predictions.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r1:
|
||||||
|
min_k_neighbors = st.number_input(
|
||||||
|
"Minimum Number of Neighbors",
|
||||||
|
min_value=1,
|
||||||
|
max_value=20,
|
||||||
|
value=3,
|
||||||
|
step=1,
|
||||||
|
help="The minimum number of neighbors required to make a prediction.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r1:
|
||||||
|
similarity_type = st.selectbox(
|
||||||
|
"Similarity Metric",
|
||||||
|
options=["cosine", "pearson"],
|
||||||
|
index=1, # 'pearson'
|
||||||
|
help="The similarity metric to use for finding nearest neighbors.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
boolean_user_based = st.checkbox(
|
||||||
|
"User-Based Collaborative Filtering",
|
||||||
|
value=True,
|
||||||
|
help="Check to use user-based collaborative filtering; uncheck for item-based.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# col2_r2 and col3_r2 is left empty here if no further parameters for KNN
|
||||||
|
model_params = {
|
||||||
|
"k_neighbors": k_neighbors,
|
||||||
|
"min_k_neighbors": min_k_neighbors,
|
||||||
|
"similarity_type": similarity_type,
|
||||||
|
"boolean_user_based": boolean_user_based,
|
||||||
|
}
|
||||||
|
|
||||||
|
elif model_option == "SVD":
|
||||||
|
st.subheader("SVD Parameters")
|
||||||
|
N_FACTORS = 64
|
||||||
|
N_EPOCHS = 30
|
||||||
|
LEARNING_RATE = 0.005
|
||||||
|
REGULARIZATION = 0.08
|
||||||
|
RANDOM_STATE = 42
|
||||||
|
# First Row
|
||||||
|
col1_r1, col2_r1, col3_r1 = st.columns(3)
|
||||||
|
with col1_r1:
|
||||||
|
n_factors = st.number_input(
|
||||||
|
"Latent Dimensions (factors)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=100,
|
||||||
|
value=64,
|
||||||
|
step=1,
|
||||||
|
help="The number of latent factors to compute.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2_r1:
|
||||||
|
n_epochs = st.number_input(
|
||||||
|
"Epochs (iterations)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=35,
|
||||||
|
value=30,
|
||||||
|
step=1,
|
||||||
|
help="The number of model iterations.",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3_r1:
|
||||||
|
learning_rater = st.number_input(
|
||||||
|
"Learning Rate",
|
||||||
|
min_value=0.001,
|
||||||
|
max_value=0.050,
|
||||||
|
value=0.005,
|
||||||
|
help="The step size at each iteration while moving toward a minimum of the loss function.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Second Row
|
||||||
|
col1_r2, col2_r2, col3_r2 = st.columns(3)
|
||||||
|
|
||||||
|
with col1_r2:
|
||||||
|
early_stopping = st.checkbox(
|
||||||
|
"Enable Early Stopping",
|
||||||
|
value=False,
|
||||||
|
help="Check to stop training when validation performance degrades.",
|
||||||
|
)
|
||||||
|
with col2_r2:
|
||||||
|
reg = st.number_input(
|
||||||
|
"Regularization Term",
|
||||||
|
min_value=0.01,
|
||||||
|
max_value=0.50,
|
||||||
|
value=0.08,
|
||||||
|
step=0.01,
|
||||||
|
format="%.3f",
|
||||||
|
help="The regularization factor.",
|
||||||
|
)
|
||||||
|
with col3_r2:
|
||||||
|
init_mean = st.number_input(
|
||||||
|
"Initialization Mean",
|
||||||
|
min_value=0.0,
|
||||||
|
max_value=0.5,
|
||||||
|
value=0.0,
|
||||||
|
step=0.01,
|
||||||
|
format="%.2f",
|
||||||
|
help="The mean for initializing latent factors.",
|
||||||
|
)
|
||||||
|
# Third Row
|
||||||
|
|
||||||
|
col1_r3, col2_r3, col3_r3 = st.columns(3)
|
||||||
|
with col1_r3:
|
||||||
|
init_std = st.number_input(
|
||||||
|
"Initialization Standard Deviation",
|
||||||
|
min_value=0.00,
|
||||||
|
max_value=0.5,
|
||||||
|
value=0.0,
|
||||||
|
step=0.01,
|
||||||
|
format="%.2f",
|
||||||
|
help="The standard deviation for initializing latent factors.",
|
||||||
|
)
|
||||||
|
with col2_r3:
|
||||||
|
random_state = st.number_input(
|
||||||
|
"Random State (Seed)",
|
||||||
|
min_value=1,
|
||||||
|
max_value=100,
|
||||||
|
value=42,
|
||||||
|
step=1,
|
||||||
|
help="The seed for random number generation to ensure reproducibility.",
|
||||||
|
)
|
||||||
|
# col3_r3 is left empty here if no further parameters for SVD
|
||||||
|
model_params = {
|
||||||
|
"n_factors": n_factors,
|
||||||
|
"n_epochs": n_epochs,
|
||||||
|
"learning_rater": learning_rater,
|
||||||
|
"reg": reg,
|
||||||
|
"init_mean": init_mean,
|
||||||
|
"init_std": init_std,
|
||||||
|
"random_state": random_state,
|
||||||
|
"early_stopping": early_stopping,
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
st.info(f"Configuration for **{model_option}** is not yet implemented.")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
# Model Training
|
||||||
|
st.header("3. Train the Model")
|
||||||
|
|
||||||
|
if st.button("Train Model", type="primary"):
|
||||||
|
with st.spinner(f"Training **{model_option}** model... This may take a moment."):
|
||||||
|
try:
|
||||||
|
# Retrieve the data_reader object from session state
|
||||||
|
data_reader = st.session_state.data_reader
|
||||||
|
model = None
|
||||||
|
# 1. Instantiate the model with user-defined hyperparameters
|
||||||
|
if model_option == "ALS":
|
||||||
|
model = ALS(**model_params)
|
||||||
|
elif model_option == "BPR":
|
||||||
|
model = BPR(**model_params)
|
||||||
|
elif model_option == "Autoencoder":
|
||||||
|
autoencoder_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
model = ExplAutoencoderTorch(**autoencoder_params)
|
||||||
|
elif model_option == "EMF":
|
||||||
|
emf_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
model = EMFModel(**emf_params)
|
||||||
|
elif model_option == "GMF":
|
||||||
|
gmf_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
model = GMFModel(**gmf_params)
|
||||||
|
elif model_option == "MLP":
|
||||||
|
mlp_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
model = MLPModel(**mlp_params)
|
||||||
|
elif model_option == "KNN":
|
||||||
|
if "k_neighbors" in model_params:
|
||||||
|
model_params["k"] = model_params.pop("k_neighbors")
|
||||||
|
knn_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
model = KNNBasic(**knn_params)
|
||||||
|
elif model_option == "SVD":
|
||||||
|
if "learning_rater" in model_params:
|
||||||
|
model_params["lr"] = model_params.pop("learning_rater")
|
||||||
|
svd_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
model = SVD(**svd_params)
|
||||||
|
if model:
|
||||||
|
start_time = time.time()
|
||||||
|
# 2. Fit the model using the processed dataset
|
||||||
|
model.fit(data_reader)
|
||||||
|
end_time = time.time()
|
||||||
|
training_time = end_time - start_time
|
||||||
|
# 3. Store the trained model in session state for the next page
|
||||||
|
st.session_state.trained_model = model
|
||||||
|
st.session_state.model_name = model_option
|
||||||
|
|
||||||
|
st.success(
|
||||||
|
f"✅ **{model_option}** model trained successfully in {training_time:.2f} seconds!"
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"An error occurred during model training: {e}")
|
||||||
|
if "trained_model" in st.session_state:
|
||||||
|
del st.session_state.trained_model
|
||||||
|
|
||||||
|
if "trained_model" in st.session_state:
|
||||||
|
st.markdown("")
|
||||||
|
st.header("4. Offline Model Evaluation")
|
||||||
|
|
||||||
|
with st.expander("🔬 Run Model Evaluation", expanded=True):
|
||||||
|
st.markdown("""
|
||||||
|
Choose your evaluation method:
|
||||||
|
- **Leave-One-Out**: More thorough but slower (recommended for final evaluation)
|
||||||
|
- **Train/Test Split**: Faster and practical for iterative testing
|
||||||
|
|
||||||
|
**Metrics Explained:**
|
||||||
|
- **Hit Ratio @10**: Percentage of users for whom we found at least one relevant item in top-10
|
||||||
|
- **NDCG @10**: Measures ranking quality - higher values mean better ranking of relevant items
|
||||||
|
""")
|
||||||
|
|
||||||
|
# Evaluation method selection
|
||||||
|
eval_method = st.radio(
|
||||||
|
"Select Evaluation Method:",
|
||||||
|
["Train/Test Split (Fast)", "Leave-One-Out (Thorough)"],
|
||||||
|
index=0,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Parameters
|
||||||
|
col1, col2 = st.columns(2)
|
||||||
|
with col1:
|
||||||
|
test_size = 0.2 # Default value
|
||||||
|
if eval_method == "Train/Test Split (Fast)":
|
||||||
|
test_size = st.slider("Test Set Size (%)", 10, 30, 20) / 100
|
||||||
|
eval_top_n = st.number_input("Top-N for evaluation", 1, 20, 10)
|
||||||
|
|
||||||
|
with col2:
|
||||||
|
if eval_method == "Leave-One-Out (Thorough)":
|
||||||
|
st.info("Leave-one-out will use 1 item per user for testing")
|
||||||
|
|
||||||
|
# Run evaluation button
|
||||||
|
eval_button_key = f"run_eval_{eval_method.replace(' ', '_').replace('(', '').replace(')', '')}"
|
||||||
|
|
||||||
|
if st.button("Run Evaluation", key=eval_button_key, type="primary"):
|
||||||
|
with st.spinner(
|
||||||
|
f"Running {eval_method.lower()} evaluation... Please wait."
|
||||||
|
):
|
||||||
|
try:
|
||||||
|
# Get the model configuration for re-instantiation
|
||||||
|
model_name = st.session_state.model_name
|
||||||
|
data_reader = st.session_state.data_reader
|
||||||
|
|
||||||
|
# Re-instantiate model with same parameters
|
||||||
|
if model_option == "ALS":
|
||||||
|
eval_model = ALS(**model_params)
|
||||||
|
elif model_option == "BPR":
|
||||||
|
eval_model = BPR(**model_params)
|
||||||
|
elif model_option == "Autoencoder":
|
||||||
|
autoencoder_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
eval_model = ExplAutoencoderTorch(**autoencoder_params)
|
||||||
|
elif model_option == "EMF":
|
||||||
|
emf_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
eval_model = EMFModel(**emf_params)
|
||||||
|
elif model_option == "GMF":
|
||||||
|
gmf_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
eval_model = GMFModel(**gmf_params)
|
||||||
|
elif model_option == "MLP":
|
||||||
|
mlp_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
eval_model = MLPModel(**mlp_params)
|
||||||
|
elif model_option == "KNN":
|
||||||
|
if "k_neighbors" in model_params:
|
||||||
|
model_params["k"] = model_params.pop("k_neighbors")
|
||||||
|
knn_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
eval_model = KNNBasic(**knn_params)
|
||||||
|
elif model_option == "SVD":
|
||||||
|
if "learning_rater" in model_params:
|
||||||
|
model_params["lr"] = model_params.pop("learning_rater")
|
||||||
|
svd_params = {
|
||||||
|
k: v
|
||||||
|
for k, v in model_params.items()
|
||||||
|
if k not in ["num_users", "num_items"]
|
||||||
|
}
|
||||||
|
eval_model = SVD(**svd_params)
|
||||||
|
else:
|
||||||
|
st.error(f"Evaluation not implemented for {model_name}")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
# Run the appropriate evaluation
|
||||||
|
if eval_method == "Leave-One-Out (Thorough)":
|
||||||
|
evaluation_scores = run_leave_one_out_evaluation(
|
||||||
|
data_reader=data_reader,
|
||||||
|
model=eval_model,
|
||||||
|
top_n=eval_top_n,
|
||||||
|
)
|
||||||
|
else: # Train/Test Split
|
||||||
|
evaluation_scores = run_evaluation_with_proper_split(
|
||||||
|
data_reader=data_reader,
|
||||||
|
model=eval_model,
|
||||||
|
test_size=test_size,
|
||||||
|
top_n=eval_top_n,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Store results
|
||||||
|
st.session_state.evaluation_scores = evaluation_scores
|
||||||
|
st.session_state.eval_method = eval_method
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"Evaluation failed: {str(e)}")
|
||||||
|
st.exception(e)
|
||||||
|
|
||||||
|
# Display results if available
|
||||||
|
if "evaluation_scores" in st.session_state:
|
||||||
|
st.markdown("")
|
||||||
|
st.subheader("📊 Evaluation Results")
|
||||||
|
|
||||||
|
scores = st.session_state.evaluation_scores
|
||||||
|
method = st.session_state.get("eval_method", "")
|
||||||
|
|
||||||
|
# Metrics display
|
||||||
|
col1, col2, col3 = st.columns(3)
|
||||||
|
|
||||||
|
with col1:
|
||||||
|
st.metric(
|
||||||
|
label=f"Hit Ratio @{eval_top_n}",
|
||||||
|
value=f"{scores.get('Hit Ratio', 0.0):.2%}",
|
||||||
|
help="Percentage of test users for whom at least one relevant item was found in top-10",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col2:
|
||||||
|
ndcg_value = scores.get("NDCG", scores.get("eNDCG", 0.0))
|
||||||
|
st.metric(
|
||||||
|
label=f"NDCG @{eval_top_n}",
|
||||||
|
value=f"{ndcg_value:.4f}",
|
||||||
|
help="Normalized Discounted Cumulative Gain - measures ranking quality",
|
||||||
|
)
|
||||||
|
|
||||||
|
with col3:
|
||||||
|
st.metric(
|
||||||
|
label="Evaluation Time",
|
||||||
|
value=f"{scores.get('evaluation_time', 0):.1f}s",
|
||||||
|
help="Time taken to complete the evaluation",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Additional info
|
||||||
|
if "test_interactions" in scores:
|
||||||
|
st.info(
|
||||||
|
f"📈 Evaluated on {scores['test_interactions']:,} test interactions using {method}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Performance interpretation
|
||||||
|
hit_ratio = scores.get("Hit Ratio", 0.0)
|
||||||
|
ndcg = ndcg_value
|
||||||
|
|
||||||
|
st.markdown("### 🎯 Performance Interpretation")
|
||||||
|
|
||||||
|
if hit_ratio > 0.15 and ndcg > 0.08:
|
||||||
|
st.success(
|
||||||
|
"🎉 Excellent performance! Your model shows strong recommendation capability."
|
||||||
|
)
|
||||||
|
elif hit_ratio > 0.08 and ndcg > 0.04:
|
||||||
|
st.success("✅ Good performance! Your model is working well.")
|
||||||
|
elif hit_ratio > 0.03 and ndcg > 0.02:
|
||||||
|
st.warning(
|
||||||
|
"⚠️ Moderate performance. Consider tuning hyperparameters or trying a different model."
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
st.error(
|
||||||
|
"❌ Poor performance. The model may need significant improvements."
|
||||||
|
)
|
||||||
|
|
||||||
|
st.info("Navigate to the **🎯 Group Recommendation** page to continue.")
|
||||||
@@ -0,0 +1,156 @@
|
|||||||
|
import streamlit as st
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
from pygrex.recommender import GroupRecommender
|
||||||
|
from pygrex.utils import AggregationStrategy
|
||||||
|
|
||||||
|
st.set_page_config(page_title="Group Recommendation", page_icon="🎯", layout="wide")
|
||||||
|
st.title("🎯 Group Recommendation")
|
||||||
|
|
||||||
|
# Session State Checks
|
||||||
|
# Ensure data is loaded and a model is trained before proceeding.
|
||||||
|
if not st.session_state.get("data_loaded", False):
|
||||||
|
st.warning("⚠️ Please load data on the **📄 Data Preparation** page first.")
|
||||||
|
st.stop()
|
||||||
|
if not st.session_state.get("trained_model", False):
|
||||||
|
st.warning("⚠️ Please train a model on the **🧠 Model Training** page first.")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
# Retrieve objects from session state
|
||||||
|
data_reader = st.session_state.data_reader
|
||||||
|
group_handler = st.session_state.group_handler
|
||||||
|
model = st.session_state.trained_model
|
||||||
|
model_name = st.session_state.model_name
|
||||||
|
|
||||||
|
# Recommendation Setup
|
||||||
|
st.header("1. Select a Group and Strategy")
|
||||||
|
|
||||||
|
group_filename = st.session_state.group_filename
|
||||||
|
|
||||||
|
try:
|
||||||
|
available_groups = group_handler.read_groups(filename=group_filename)
|
||||||
|
|
||||||
|
col1, col2 = st.columns(2)
|
||||||
|
with col1:
|
||||||
|
selected_group_id = st.selectbox(
|
||||||
|
"Choose a group:",
|
||||||
|
options=available_groups,
|
||||||
|
help="These groups were loaded from your group data file.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Parse and display members of the selected group
|
||||||
|
if selected_group_id:
|
||||||
|
group_members = group_handler.parse_group_members(selected_group_id)
|
||||||
|
st.write("👥 **Group Members:**", ", ".join(map(str, group_members)))
|
||||||
|
|
||||||
|
with col2:
|
||||||
|
# Use the AggregationStrategy Enum to populate the selectbox
|
||||||
|
agg_strategy_enum = st.selectbox(
|
||||||
|
"Choose an aggregation strategy:",
|
||||||
|
options=list(AggregationStrategy),
|
||||||
|
format_func=lambda x: x.name.replace("_", " ").title(),
|
||||||
|
help="Select the method for combining individual member preferences.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Conditional Input for Most Respected Person
|
||||||
|
mrp_id = None
|
||||||
|
if agg_strategy_enum == AggregationStrategy.MOST_RESPECTED_PERSON:
|
||||||
|
mrp_id = st.selectbox(
|
||||||
|
"Select the Most Respected Person:",
|
||||||
|
options=group_members, # type: ignore
|
||||||
|
help="This user's preferences will solely determine the group recommendation.",
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"Could not read groups from file '{group_filename}'. Error: {e}")
|
||||||
|
st.stop()
|
||||||
|
|
||||||
|
# Top-K Configuration
|
||||||
|
st.header("2. Specify Number of Recommendations")
|
||||||
|
top_k = st.slider(
|
||||||
|
"Number of items to recommend (Top-K):",
|
||||||
|
min_value=1,
|
||||||
|
max_value=50,
|
||||||
|
value=10,
|
||||||
|
help="Adjust the slider to change the length of the final recommendation list.",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Generate Recommendations
|
||||||
|
st.header("3. Generate and View Recommendations")
|
||||||
|
|
||||||
|
if st.button("Generate Group Recommendations", type="primary"):
|
||||||
|
if not selected_group_id:
|
||||||
|
st.warning("Please select a group first.")
|
||||||
|
else:
|
||||||
|
with st.spinner("Generating recommendations..."):
|
||||||
|
try:
|
||||||
|
# 1. Instantiate the GroupRecommender
|
||||||
|
group_recommender = GroupRecommender(data=data_reader)
|
||||||
|
|
||||||
|
# 2. Setup the recommendation process
|
||||||
|
group_recommender.setup_recommendation(
|
||||||
|
model=model,
|
||||||
|
members=group_members, # type: ignore
|
||||||
|
data=data_reader,
|
||||||
|
aggregation_strategy=agg_strategy_enum,
|
||||||
|
most_respected_person=mrp_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
# 3. Get the final recommendation list
|
||||||
|
recommended_items = group_recommender.get_group_recommendations(
|
||||||
|
top_k=top_k
|
||||||
|
)
|
||||||
|
|
||||||
|
# Store the recommender instance for the explanation page
|
||||||
|
st.session_state.group_recommender = group_recommender
|
||||||
|
st.session_state.recommended_items = recommended_items
|
||||||
|
|
||||||
|
st.success("✅ Recommendations generated successfully!")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
st.error(f"An error occurred while generating recommendations: {e}")
|
||||||
|
|
||||||
|
|
||||||
|
# Display Results
|
||||||
|
if "recommended_items" in st.session_state:
|
||||||
|
st.markdown("")
|
||||||
|
st.subheader(f"Top {top_k} Recommended Items")
|
||||||
|
|
||||||
|
recommender = st.session_state.group_recommender
|
||||||
|
scores = recommender.get_recommendation_scores()
|
||||||
|
|
||||||
|
# Create a DataFrame for nice display
|
||||||
|
rec_data = []
|
||||||
|
for i, item_id in enumerate(st.session_state.recommended_items): # type: ignore
|
||||||
|
rec_data.append(
|
||||||
|
{
|
||||||
|
"Rank": i + 1,
|
||||||
|
"Item ID": item_id,
|
||||||
|
"Aggregated Score": scores.get(item_id, 0.0),
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
if not rec_data:
|
||||||
|
st.info("No recommendations were generated for this group.")
|
||||||
|
else:
|
||||||
|
st.dataframe(pd.DataFrame(rec_data), use_container_width=True, hide_index=True)
|
||||||
|
|
||||||
|
# Show detailed individual predictions
|
||||||
|
with st.expander("🔍 View Individual Predictions"):
|
||||||
|
individual_preds = recommender.get_individual_predictions()
|
||||||
|
if individual_preds:
|
||||||
|
# Convert to a more readable DataFrame
|
||||||
|
df_preds = pd.DataFrame(
|
||||||
|
individual_preds
|
||||||
|
).T # Transpose to have users as rows
|
||||||
|
df_preds.index.name = "User ID"
|
||||||
|
st.write(
|
||||||
|
"Predicted scores (1-5 scale) for each user on items in the candidate pool:"
|
||||||
|
)
|
||||||
|
st.dataframe(df_preds.head(10))
|
||||||
|
else:
|
||||||
|
st.write("No individual predictions available.")
|
||||||
|
|
||||||
|
st.info(
|
||||||
|
"Navigate to the **💬 Explanation & Evaluation** page to analyze these recommendations."
|
||||||
|
)
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,7 @@
|
|||||||
|
import yaml
|
||||||
|
from box import Box
|
||||||
|
|
||||||
|
with open("configs/config.yml", "r") as yml_file:
|
||||||
|
full_cfg = yaml.safe_load(yml_file)
|
||||||
|
|
||||||
|
cfg = Box({**full_cfg["base"]}, default_box=True, default_box_attr=None)
|
||||||
@@ -0,0 +1,11 @@
|
|||||||
|
from .data_reader import DataReader
|
||||||
|
from .user_item_rating_dataset import UserItemRatingDataset
|
||||||
|
from .group_interaction_handler import GroupInteractionHandler
|
||||||
|
from .user_item_dict import UserItemDict
|
||||||
|
|
||||||
|
__all__ = [
|
||||||
|
"DataReader",
|
||||||
|
"UserItemRatingDataset",
|
||||||
|
"GroupInteractionHandler",
|
||||||
|
"UserItemDict",
|
||||||
|
]
|
||||||
@@ -0,0 +1,416 @@
|
|||||||
|
from typing import List, Optional, Union, cast
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import warnings
|
||||||
|
|
||||||
|
|
||||||
|
class DataReader:
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
filepath_or_buffer: Optional[str] = None,
|
||||||
|
sep: Optional[str] = None,
|
||||||
|
names: Optional[List[str]] = None,
|
||||||
|
skiprows: int = 0,
|
||||||
|
dataframe: Optional[pd.DataFrame] = None,
|
||||||
|
) -> None:
|
||||||
|
"""
|
||||||
|
Initialize the DataReader with either a DataFrame or file parameters.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
filepath_or_buffer (Optional[str]): Path to the CSV file or buffer.
|
||||||
|
sep (Optional[str]): Separator used in the CSV file.
|
||||||
|
names (Optional[List[str]]): List of column names for the CSV file.
|
||||||
|
skiprows (int, optional): Number of rows to skip in the CSV file. Defaults to 0.
|
||||||
|
dataframe (Optional[pd.DataFrame], optional): A DataFrame to use directly. Defaults to None.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If neither `dataframe` nor valid file parameters are provided.
|
||||||
|
FileNotFoundError: If the file cannot be found when loading from file.
|
||||||
|
pd.errors.ParserError: If the CSV file cannot be parsed when loading from file.
|
||||||
|
|
||||||
|
Note:
|
||||||
|
If `dataframe` is provided, it takes precedence, and file-related parameters
|
||||||
|
are ignored but stored for reference. A warning is issued in this case.
|
||||||
|
The DataFrame must contain columns: 'userId', 'itemId', 'rating', 'timestamp'.
|
||||||
|
"""
|
||||||
|
if dataframe is None and (not filepath_or_buffer or not sep or not names):
|
||||||
|
raise ValueError(
|
||||||
|
"Must provide either a DataFrame or valid file parameters."
|
||||||
|
)
|
||||||
|
|
||||||
|
self.filepath_or_buffer = filepath_or_buffer
|
||||||
|
self.sep = sep
|
||||||
|
self.names = names
|
||||||
|
self.skiprows = skiprows
|
||||||
|
self._dataset = None
|
||||||
|
self._raw_dataset = None
|
||||||
|
self._num_user: Optional[int] = None
|
||||||
|
self._num_item: Optional[int] = None
|
||||||
|
self.original_user_id: Optional[pd.DataFrame] = None
|
||||||
|
self.original_item_id: Optional[pd.DataFrame] = None
|
||||||
|
self.new_user_id: Optional[pd.DataFrame] = None
|
||||||
|
self.new_item_id: Optional[pd.DataFrame] = None
|
||||||
|
|
||||||
|
if dataframe is not None:
|
||||||
|
if any(param is not None for param in [filepath_or_buffer, sep, names]):
|
||||||
|
warnings.warn(
|
||||||
|
"DataFrame provided; file parameters (filepath_or_buffer, sep, names) are ignored.",
|
||||||
|
UserWarning,
|
||||||
|
)
|
||||||
|
self.dataset = dataframe
|
||||||
|
|
||||||
|
elif filepath_or_buffer and sep and names:
|
||||||
|
# Eagerly load data if file parameters are provided
|
||||||
|
try:
|
||||||
|
assert self.filepath_or_buffer is not None
|
||||||
|
|
||||||
|
loaded_df = pd.read_csv(
|
||||||
|
filepath_or_buffer=self.filepath_or_buffer,
|
||||||
|
sep=self.sep,
|
||||||
|
names=self.names,
|
||||||
|
skiprows=self.skiprows,
|
||||||
|
engine="python",
|
||||||
|
)
|
||||||
|
self._raw_dataset = loaded_df.copy()
|
||||||
|
# Use the setter to handle dataset validation and setting _num_user/_num_item
|
||||||
|
self.dataset = loaded_df
|
||||||
|
except FileNotFoundError:
|
||||||
|
raise FileNotFoundError(f"File not found: {self.filepath_or_buffer}")
|
||||||
|
except pd.errors.ParserError as e:
|
||||||
|
raise pd.errors.ParserError(f"Failed to parse CSV: {str(e)}")
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"Must provide either a DataFrame or valid file parameters."
|
||||||
|
)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def dataset(self) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Get the dataset DataFrame.
|
||||||
|
"""
|
||||||
|
if self._dataset is None:
|
||||||
|
if self._dataset is None:
|
||||||
|
# If it reach here and _dataset is None, it means initialization failed
|
||||||
|
# or an empty DataFrame was set.
|
||||||
|
# This state should ideally not be reached with eager loading if file params were valid.
|
||||||
|
raise ValueError("Dataset is not loaded or is not valid.")
|
||||||
|
return self._dataset
|
||||||
|
|
||||||
|
@dataset.setter
|
||||||
|
def dataset(self, new_data: pd.DataFrame) -> None:
|
||||||
|
"""
|
||||||
|
Set the dataset and compute the number of unique users and items.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
new_data (pd.DataFrame): The new dataset to set.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the DataFrame is None, empty, lacks required columns,
|
||||||
|
or contains invalid data types/missing values.
|
||||||
|
"""
|
||||||
|
if new_data is None:
|
||||||
|
raise ValueError("DataFrame cannot be None")
|
||||||
|
if new_data.empty:
|
||||||
|
raise ValueError("DataFrame cannot be empty")
|
||||||
|
|
||||||
|
# Validate data types
|
||||||
|
for col in ["userId", "itemId", "rating"]:
|
||||||
|
if not pd.api.types.is_numeric_dtype(new_data[col]):
|
||||||
|
warnings.warn(
|
||||||
|
f"Column '{col}' is not numeric. Attempting conversion.",
|
||||||
|
UserWarning,
|
||||||
|
)
|
||||||
|
try:
|
||||||
|
new_data[col] = pd.to_numeric(new_data[col])
|
||||||
|
except ValueError:
|
||||||
|
raise ValueError(
|
||||||
|
f"Column '{col}' cannot be converted to a numeric type."
|
||||||
|
)
|
||||||
|
|
||||||
|
# Check for missing values in essential columns
|
||||||
|
if new_data[["userId", "itemId", "rating"]].isnull().any().any():
|
||||||
|
raise ValueError(
|
||||||
|
"DataFrame contains missing values in essential columns (userId, itemId, rating)."
|
||||||
|
)
|
||||||
|
|
||||||
|
self._dataset = new_data
|
||||||
|
self._raw_dataset = new_data.copy()
|
||||||
|
self._num_user = int(self._dataset["userId"].nunique())
|
||||||
|
self._num_item = int(self._dataset["itemId"].nunique())
|
||||||
|
# Set the index to userId and itemId for easier access
|
||||||
|
# Reset id mappings as they are now invalid for the new dataset
|
||||||
|
self.original_user_id = None
|
||||||
|
self.original_item_id = None
|
||||||
|
self.new_user_id = None
|
||||||
|
self.new_item_id = None
|
||||||
|
|
||||||
|
def get_raw_dataset(self) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Get the raw dataset as loaded from the file or initially set.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: The raw dataset.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the raw dataset is not set.
|
||||||
|
"""
|
||||||
|
if self._raw_dataset is None:
|
||||||
|
raise ValueError(
|
||||||
|
"Raw dataset is not set. Load data from file or set a DataFrame first."
|
||||||
|
)
|
||||||
|
return self._raw_dataset
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _create_id_mapping(column: pd.Series, new_column_name: str) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Create a mapping for consecutive IDs.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
column (pd.Series): The column to map.
|
||||||
|
new_column_name (str): The name of the new column for consecutive IDs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: A DataFrame with the original and mapped IDs.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the column is empty.
|
||||||
|
"""
|
||||||
|
if column.empty:
|
||||||
|
raise ValueError("Cannot create ID mapping for an empty column")
|
||||||
|
unique_values = column.drop_duplicates().reset_index(drop=True)
|
||||||
|
mapping = pd.DataFrame(
|
||||||
|
{column.name: unique_values, new_column_name: np.arange(len(unique_values))}
|
||||||
|
)
|
||||||
|
return mapping
|
||||||
|
|
||||||
|
def make_consecutive_ids_in_dataset(self) -> None:
|
||||||
|
"""
|
||||||
|
Map user and item IDs to consecutive integers starting from 0 in a deterministic way.
|
||||||
|
Modifies the dataset in-place and stores mappings for original and new IDs.
|
||||||
|
"""
|
||||||
|
if self._dataset is None:
|
||||||
|
raise ValueError("Dataset must be loaded or set before mapping IDs")
|
||||||
|
|
||||||
|
dataset = self.dataset.copy()
|
||||||
|
|
||||||
|
# Get unique IDs and SORT them to ensure the mapping is identical every time.
|
||||||
|
sorted_unique_users = sorted(dataset["userId"].unique())
|
||||||
|
sorted_unique_items = sorted(dataset["itemId"].unique())
|
||||||
|
|
||||||
|
# Create user ID mapping from the sorted list
|
||||||
|
user_id_mapping = pd.DataFrame(
|
||||||
|
{
|
||||||
|
"userId": sorted_unique_users,
|
||||||
|
"new_userId": range(len(sorted_unique_users)),
|
||||||
|
}
|
||||||
|
)
|
||||||
|
dataset["userId"] = dataset["userId"].map(
|
||||||
|
user_id_mapping.set_index("userId")["new_userId"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Create item ID mapping from the sorted list
|
||||||
|
item_id_mapping = pd.DataFrame(
|
||||||
|
{
|
||||||
|
"itemId": sorted_unique_items,
|
||||||
|
"new_itemId": range(len(sorted_unique_items)),
|
||||||
|
}
|
||||||
|
)
|
||||||
|
dataset["itemId"] = dataset["itemId"].map(
|
||||||
|
item_id_mapping.set_index("itemId")["new_itemId"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Store mappings for lookups
|
||||||
|
self.original_user_id = user_id_mapping.set_index("new_userId")
|
||||||
|
self.original_item_id = item_id_mapping.set_index("new_itemId")
|
||||||
|
self.new_user_id = user_id_mapping.set_index("userId")
|
||||||
|
self.new_item_id = item_id_mapping.set_index("itemId")
|
||||||
|
|
||||||
|
# Update the internal dataset
|
||||||
|
dataset["userId"] = dataset["userId"].astype(int)
|
||||||
|
dataset["itemId"] = dataset["itemId"].astype(int)
|
||||||
|
self._dataset = dataset
|
||||||
|
|
||||||
|
self._num_user = self._dataset["userId"].max() + 1
|
||||||
|
self._num_item = self._dataset["itemId"].max() + 1
|
||||||
|
|
||||||
|
def binarize(
|
||||||
|
self, binary_threshold: float = 1, inplace: bool = True
|
||||||
|
) -> Optional[pd.DataFrame]:
|
||||||
|
"""
|
||||||
|
Binarize ratings into 0 or 1 based on a threshold (implicit feedback).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
binary_threshold (float, optional): Threshold for binarization. Defaults to 1.0.
|
||||||
|
inplace (bool, optional): If True, modify the dataset in-place. If False, return a new DataFrame.
|
||||||
|
Defaults to True.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Optional[pd.DataFrame]: The binarized dataset if inplace=False, else None.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the dataset is not set or binary_threshold is invalid.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
Ratings [0.5, 2.0, 3.0] with threshold=1.0 -> [0, 1, 1]
|
||||||
|
"""
|
||||||
|
if self._dataset is None:
|
||||||
|
raise ValueError("Dataset must be loaded or set before binarization")
|
||||||
|
if not isinstance(binary_threshold, (int, float)):
|
||||||
|
raise ValueError("binary_threshold must be a number")
|
||||||
|
|
||||||
|
dataset = self._dataset if inplace else self._dataset.copy()
|
||||||
|
dataset["rating"] = (dataset["rating"] > binary_threshold).astype(int)
|
||||||
|
|
||||||
|
if not inplace:
|
||||||
|
return dataset
|
||||||
|
self._dataset = dataset
|
||||||
|
return None
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_user(self) -> int:
|
||||||
|
"""
|
||||||
|
Get the number of unique users.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
int: Number of unique users.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the dataset is not set.
|
||||||
|
"""
|
||||||
|
if self._num_user is None:
|
||||||
|
raise ValueError("Dataset must be loaded or set to compute num_user")
|
||||||
|
return self._num_user
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_item(self) -> int:
|
||||||
|
"""
|
||||||
|
Get the number of unique items.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
int: Number of unique items.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the dataset is not set.
|
||||||
|
"""
|
||||||
|
if self._num_item is None:
|
||||||
|
raise ValueError("Dataset must be loaded or set to compute num_item")
|
||||||
|
return self._num_item
|
||||||
|
|
||||||
|
def get_original_user_id(self, u: Union[int, List[int]]) -> Union[int, List[int]]:
|
||||||
|
"""
|
||||||
|
Get the original user ID(s) from the new (consecutive) ID(s).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
u (Union[int, List[int]]): New user ID(s).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Union[int, List[int]]: Original user ID(s).
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If ID mapping is not set or if any ID is not found.
|
||||||
|
"""
|
||||||
|
if self.original_user_id is None:
|
||||||
|
raise ValueError(
|
||||||
|
"ID mapping not set. Call make_consecutive_ids_in_dataset first"
|
||||||
|
)
|
||||||
|
try:
|
||||||
|
if isinstance(u, (int, np.integer)):
|
||||||
|
return int(self.original_user_id.loc[u, "userId"]) # type: ignore
|
||||||
|
series = cast(pd.Series, self.original_user_id.loc[u, "userId"])
|
||||||
|
return series.tolist()
|
||||||
|
except KeyError as e:
|
||||||
|
raise ValueError(f"User ID(s) not found: {e}")
|
||||||
|
|
||||||
|
def get_original_item_id(self, i: Union[int, List[int]]) -> Union[int, List[int]]:
|
||||||
|
"""
|
||||||
|
Get the original item ID(s) from the new (consecutive) ID(s).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
i (Union[int, List[int]]): New item ID(s).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Union[int, List[int]]: Original item ID(s).
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If ID mapping is not set or if any ID is not found.
|
||||||
|
"""
|
||||||
|
if self.original_item_id is None:
|
||||||
|
raise ValueError(
|
||||||
|
"ID mapping not set. Call make_consecutive_ids_in_dataset first"
|
||||||
|
)
|
||||||
|
try:
|
||||||
|
if isinstance(i, (int, np.integer)):
|
||||||
|
return int(self.original_item_id.loc[i, "itemId"]) # type: ignore
|
||||||
|
|
||||||
|
series = cast(pd.Series, self.original_item_id.loc[i, "itemId"])
|
||||||
|
return series.tolist()
|
||||||
|
except KeyError as e:
|
||||||
|
raise ValueError(f"Item ID(s) not found: {e}")
|
||||||
|
|
||||||
|
def get_new_user_id(
|
||||||
|
self, u: Union[Union[str, int], List[Union[str, int]]]
|
||||||
|
) -> Union[int, List[int]]:
|
||||||
|
"""
|
||||||
|
Get the new (consecutive) user ID(s) from the original ID(s).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
u: Original user ID(s).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
New user ID(s).
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If ID mapping is not set or if any ID is not found.
|
||||||
|
"""
|
||||||
|
if self.new_user_id is None:
|
||||||
|
raise ValueError(
|
||||||
|
"ID mapping not set. Call make_consecutive_ids_in_dataset first"
|
||||||
|
)
|
||||||
|
try:
|
||||||
|
if isinstance(u, str):
|
||||||
|
u = int(u)
|
||||||
|
return int(self.new_user_id.loc[u, "new_userId"]) # type: ignore
|
||||||
|
if isinstance(u, list) and all(isinstance(x, str) for x in u):
|
||||||
|
u = [int(x) for x in u]
|
||||||
|
series = cast(pd.Series, self.new_user_id.loc[u, "new_userId"])
|
||||||
|
return series.tolist()
|
||||||
|
if isinstance(u, (int, np.integer)):
|
||||||
|
return int(self.new_user_id.loc[u, "new_userId"]) # type: ignore
|
||||||
|
series = cast(pd.Series, self.new_user_id.loc[u, "new_userId"])
|
||||||
|
return series.tolist()
|
||||||
|
except KeyError as e:
|
||||||
|
raise ValueError(f"User ID(s) not found: {e}")
|
||||||
|
|
||||||
|
def get_new_item_id(
|
||||||
|
self, i: Union[Union[str, int], List[Union[str, int]]]
|
||||||
|
) -> Union[int, List[int]]:
|
||||||
|
"""
|
||||||
|
Get the new (consecutive) item ID(s) from the original ID(s).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
i: Original item ID(s).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
New item ID(s).
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If ID mapping is not set or if any ID is not found.
|
||||||
|
"""
|
||||||
|
if self.new_item_id is None:
|
||||||
|
raise ValueError(
|
||||||
|
"ID mapping not set. Call make_consecutive_ids_in_dataset first"
|
||||||
|
)
|
||||||
|
try:
|
||||||
|
if isinstance(i, str):
|
||||||
|
i = int(i)
|
||||||
|
return int(self.new_item_id.loc[i, "new_itemId"]) # type: ignore
|
||||||
|
if isinstance(i, list) and all(isinstance(x, str) for x in i):
|
||||||
|
i = [int(x) for x in i]
|
||||||
|
series = cast(pd.Series, self.new_item_id.loc[i, "new_itemId"])
|
||||||
|
return series.tolist()
|
||||||
|
if isinstance(i, (int, np.integer)):
|
||||||
|
return int(self.new_item_id.loc[i, "new_itemId"]) # type: ignore
|
||||||
|
series = cast(pd.Series, self.new_item_id.loc[i, "new_itemId"])
|
||||||
|
return series.tolist()
|
||||||
|
except KeyError as e:
|
||||||
|
raise ValueError(f"Item ID(s) not found: {e}")
|
||||||
@@ -0,0 +1,289 @@
|
|||||||
|
from typing import List, Optional, Union
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
from pygrex.data_reader.data_reader import DataReader
|
||||||
|
|
||||||
|
|
||||||
|
class GroupInteractionHandler:
|
||||||
|
def __init__(self, filepath_or_buffer: Union[str, Path, List[Union[str, Path]]]):
|
||||||
|
"""
|
||||||
|
Initialize the GroupInteractionHandler.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
filepath_or_buffer: Path to directory containing group files or list of file paths
|
||||||
|
"""
|
||||||
|
# Convert to Path objects
|
||||||
|
if isinstance(filepath_or_buffer, (str, Path)):
|
||||||
|
path = Path(filepath_or_buffer)
|
||||||
|
# If a single directory path is provided, get all files in it
|
||||||
|
if path.is_dir():
|
||||||
|
self.filepath_or_buffer = [
|
||||||
|
str(file) for file in path.iterdir() if file.is_file()
|
||||||
|
]
|
||||||
|
else:
|
||||||
|
self.filepath_or_buffer = [str(path)]
|
||||||
|
else:
|
||||||
|
# If a list of paths is provided, convert all to Path and then to strings
|
||||||
|
self.filepath_or_buffer = [str(Path(p)) for p in filepath_or_buffer]
|
||||||
|
|
||||||
|
def _get_group_filepath(self, filename: str) -> str:
|
||||||
|
"""
|
||||||
|
Get a specific group file path by matching the filename.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
filename (str): The name of the file to search for.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
str: The matched file path.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: Error: File does not exist
|
||||||
|
ValueError: No file found containing '{filename}' in its name.
|
||||||
|
"""
|
||||||
|
for path_str in self.filepath_or_buffer:
|
||||||
|
if filename in path_str: # Check if filename is part of the path
|
||||||
|
path = Path(path_str).resolve()
|
||||||
|
if path.exists():
|
||||||
|
return str(path)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Error: File does not exist: {path}")
|
||||||
|
|
||||||
|
raise ValueError(f"Error: No file found containing '{filename}' in its name.")
|
||||||
|
|
||||||
|
def read_groups(self, filename: str) -> List[str]:
|
||||||
|
"""
|
||||||
|
Method to read group IDs from a specified file.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
filename (str): Name of the file containing group IDs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List[str]: List of group IDs.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If groups path is not specified in configuration
|
||||||
|
"""
|
||||||
|
if not filename:
|
||||||
|
raise ValueError("Groups path not specified in configuration")
|
||||||
|
|
||||||
|
filepath = self._get_group_filepath(filename)
|
||||||
|
|
||||||
|
# Use Path for file reading
|
||||||
|
path = Path(filepath)
|
||||||
|
return [line.strip() for line in path.read_text().splitlines()]
|
||||||
|
|
||||||
|
def parse_group_members(self, group: str) -> List[int]:
|
||||||
|
"""
|
||||||
|
Parse group ID to get member IDs.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
group: Group ID string
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of member IDs
|
||||||
|
"""
|
||||||
|
group = group.strip()
|
||||||
|
members = group.split("_")
|
||||||
|
return [int(m) for m in members]
|
||||||
|
|
||||||
|
def get_group_members(self, group: Union[List[Union[int, str]], str]) -> List[int]:
|
||||||
|
"""
|
||||||
|
Get group members from a group ID string or list.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
group: Group ID string in format "id1_id2_id3" or list of IDs
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of member IDs as integers
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If any member ID cannot be converted to an integer
|
||||||
|
TypeError: If group is neither a string nor a list
|
||||||
|
"""
|
||||||
|
|
||||||
|
if isinstance(group, list):
|
||||||
|
return [int(member) for member in group]
|
||||||
|
|
||||||
|
if not isinstance(group, str):
|
||||||
|
raise TypeError(f"Expected string or list, got {type(group).__name__}")
|
||||||
|
|
||||||
|
group = group.strip()
|
||||||
|
if not group:
|
||||||
|
return []
|
||||||
|
|
||||||
|
try:
|
||||||
|
return [int(member) for member in group.split("_")]
|
||||||
|
except ValueError as e:
|
||||||
|
raise ValueError(f"Invalid member ID in group: {str(e)}")
|
||||||
|
|
||||||
|
def create_modified_dataset(
|
||||||
|
self,
|
||||||
|
original_data: Union[pd.DataFrame, DataReader],
|
||||||
|
group_ids: List[Union[int, str]],
|
||||||
|
item_ids: List[Union[int, str]],
|
||||||
|
data: Optional[DataReader] = None,
|
||||||
|
) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Creates a modified dataset by removing interactions between specified groups and items.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
original_data: Either a pandas DataFrame or a DataReader object containing the dataset
|
||||||
|
group_ids: List of group IDs to consider for removal
|
||||||
|
item_ids: List of item IDs to consider for removal
|
||||||
|
data: Optional DataReader object if original_data is a DataFrame
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: A pandas DataFrame with the specified interactions removed
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If input data types are incorrect
|
||||||
|
"""
|
||||||
|
# Determine the data source and target dataset
|
||||||
|
if isinstance(original_data, DataReader):
|
||||||
|
data_reader = original_data
|
||||||
|
dataset = original_data.dataset
|
||||||
|
elif isinstance(original_data, pd.DataFrame) and isinstance(data, DataReader):
|
||||||
|
data_reader = data
|
||||||
|
dataset = original_data
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"Either original_data must be a DataReader or data must be provided as a DataReader"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Convert IDs to internal representation
|
||||||
|
new_group_ids = [
|
||||||
|
data_reader.get_new_user_id(
|
||||||
|
int(g) if isinstance(g, (int, np.integer)) else g
|
||||||
|
)
|
||||||
|
for g in group_ids
|
||||||
|
]
|
||||||
|
|
||||||
|
new_item_ids = [
|
||||||
|
data_reader.get_new_item_id(
|
||||||
|
int(i) if isinstance(i, (int, np.integer)) else i
|
||||||
|
)
|
||||||
|
for i in item_ids
|
||||||
|
]
|
||||||
|
|
||||||
|
# Create mask for rows to keep (inverse of rows to drop)
|
||||||
|
mask = ~(dataset.itemId.isin(new_item_ids) & dataset.userId.isin(new_group_ids))
|
||||||
|
|
||||||
|
return dataset[mask]
|
||||||
|
|
||||||
|
def get_rated_items_by_all_group_members(
|
||||||
|
self, group: List[Union[int, str]], original_data: DataReader
|
||||||
|
) -> np.ndarray:
|
||||||
|
"""
|
||||||
|
Get all items rated by any member of the group.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
group: List of user IDs
|
||||||
|
original_data: Data object with mapping methods
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.ndarray: Array of original item IDs rated by any group member
|
||||||
|
"""
|
||||||
|
# Convert group members to new user IDs
|
||||||
|
new_group = [
|
||||||
|
original_data.get_new_user_id(
|
||||||
|
int(g) if isinstance(g, (int, np.integer)) else g
|
||||||
|
)
|
||||||
|
for g in group
|
||||||
|
]
|
||||||
|
|
||||||
|
# Get unique items rated by any group member
|
||||||
|
group_items = original_data.dataset[
|
||||||
|
original_data.dataset.userId.isin(new_group)
|
||||||
|
]["itemId"].unique()
|
||||||
|
|
||||||
|
# Convert back to original item IDs
|
||||||
|
original_ids = original_data.get_original_item_id(group_items.tolist())
|
||||||
|
return np.array(original_ids)
|
||||||
|
|
||||||
|
def get_common_rated_items(
|
||||||
|
self, group: List[Union[int, str]], original_data: DataReader
|
||||||
|
) -> np.ndarray:
|
||||||
|
"""
|
||||||
|
Get items rated by all members of the group (intersection of rated items).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
group: List of user IDs
|
||||||
|
original_data: DataReader object with mapping methods
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.ndarray: Array of original item IDs rated by all group members
|
||||||
|
"""
|
||||||
|
# Convert group members to new user IDs
|
||||||
|
new_group = [
|
||||||
|
original_data.get_new_user_id(
|
||||||
|
int(g) if isinstance(g, (int, np.integer)) else g
|
||||||
|
)
|
||||||
|
for g in group
|
||||||
|
]
|
||||||
|
|
||||||
|
# Get items rated by each group member
|
||||||
|
rated_items_per_member = []
|
||||||
|
for user_id in new_group:
|
||||||
|
user_items = original_data.dataset[original_data.dataset.userId == user_id][
|
||||||
|
"itemId"
|
||||||
|
].unique()
|
||||||
|
rated_items_per_member.append(set(user_items))
|
||||||
|
|
||||||
|
# Find intersection of all rated items
|
||||||
|
if rated_items_per_member:
|
||||||
|
common_items = set.intersection(*rated_items_per_member)
|
||||||
|
common_items_array = np.array(list(common_items))
|
||||||
|
# Convert back to original item IDs
|
||||||
|
original_ids = original_data.get_original_item_id(
|
||||||
|
common_items_array.tolist()
|
||||||
|
)
|
||||||
|
return np.array(original_ids)
|
||||||
|
else:
|
||||||
|
return np.array([])
|
||||||
|
|
||||||
|
def get_items_for_group_recommendation(
|
||||||
|
self, data: pd.DataFrame, item_ids: np.ndarray, group: List[int]
|
||||||
|
) -> np.ndarray:
|
||||||
|
"""
|
||||||
|
Get items for group recommendation (those not interacted with by any group member).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data: DataFrame with interaction data
|
||||||
|
item_ids: Array of all item IDs
|
||||||
|
group: List of group member IDs
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Array of item IDs not interacted with by any group member
|
||||||
|
"""
|
||||||
|
item_ids_group = data.loc[data.userId.isin(group), "itemId"]
|
||||||
|
return np.setdiff1d(item_ids, item_ids_group)
|
||||||
|
|
||||||
|
def get_group_preferences(
|
||||||
|
self, group: List[Union[int, str]], data_reader: DataReader
|
||||||
|
) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Get all preferences (ratings) by all members of the group.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
group: List of user IDs
|
||||||
|
data_reader: DataReader object with the dataset
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: DataFrame containing all preferences by group members
|
||||||
|
"""
|
||||||
|
# Convert group members to new user IDs
|
||||||
|
new_group = [
|
||||||
|
data_reader.get_new_user_id(
|
||||||
|
int(g) if isinstance(g, (int, np.integer)) else g
|
||||||
|
)
|
||||||
|
for g in group
|
||||||
|
]
|
||||||
|
|
||||||
|
# Get all interactions by group members
|
||||||
|
group_preferences = data_reader.dataset[
|
||||||
|
data_reader.dataset.userId.isin(new_group)
|
||||||
|
].copy()
|
||||||
|
|
||||||
|
return group_preferences
|
||||||
@@ -0,0 +1,36 @@
|
|||||||
|
from torch.utils.data import Dataset
|
||||||
|
import torch
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
class UserItemDict(Dataset):
|
||||||
|
"""Wrapper, convert <user, item, rating> Tensor into Pytorch Dataset"""
|
||||||
|
|
||||||
|
def __init__(self, data, expl_matrix, expl):
|
||||||
|
"""
|
||||||
|
args:
|
||||||
|
|
||||||
|
target_tensor: torch.Tensor, the corresponding rating for <user, item> pair
|
||||||
|
"""
|
||||||
|
|
||||||
|
grp_data = data.groupby('userId')
|
||||||
|
self.users_dict = dict()
|
||||||
|
for userId, itemId_rating in grp_data:
|
||||||
|
self.users_dict[userId] = {'items': list(itemId_rating.itemId),
|
||||||
|
'rating': list(itemId_rating.rating)}
|
||||||
|
self.n_items = data.itemId.nunique()
|
||||||
|
self.n_users = data.userId.nunique()
|
||||||
|
self.expl_matrix = expl_matrix
|
||||||
|
self.expl = expl
|
||||||
|
|
||||||
|
def __getitem__(self, index):
|
||||||
|
ratings = np.zeros(self.n_items)
|
||||||
|
ratings[self.users_dict[index]['items']] = self.users_dict[index]['rating']
|
||||||
|
if self.expl:
|
||||||
|
return torch.tensor(ratings) + self.expl_matrix[index, :]
|
||||||
|
else:
|
||||||
|
return torch.tensor(ratings)
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return self.n_users
|
||||||
|
|
||||||
@@ -0,0 +1,21 @@
|
|||||||
|
from torch.utils.data import Dataset
|
||||||
|
|
||||||
|
|
||||||
|
class UserItemRatingDataset(Dataset):
|
||||||
|
"""Wrapper, convert <user, item, rating> Tensor into Pytorch Dataset"""
|
||||||
|
|
||||||
|
def __init__(self, user_tensor, item_tensor, target_tensor):
|
||||||
|
"""
|
||||||
|
args:
|
||||||
|
|
||||||
|
target_tensor: torch.Tensor, the corresponding rating for <user, item> pair
|
||||||
|
"""
|
||||||
|
self.user_tensor = user_tensor
|
||||||
|
self.item_tensor = item_tensor
|
||||||
|
self.target_tensor = target_tensor
|
||||||
|
|
||||||
|
def __getitem__(self, index):
|
||||||
|
return self.user_tensor[index], self.item_tensor[index], self.target_tensor[index]
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return self.user_tensor.size(0)
|
||||||
@@ -0,0 +1,15 @@
|
|||||||
|
from .splitter import Splitter
|
||||||
|
from .model_evaluator import ModelEvaluator
|
||||||
|
from .explainer_evaluator import ExplanationEvaluator
|
||||||
|
from .evaluation_pipelines import (
|
||||||
|
run_evaluation_with_proper_split,
|
||||||
|
run_leave_one_out_evaluation,
|
||||||
|
)
|
||||||
|
|
||||||
|
__all__ = [
|
||||||
|
"Splitter",
|
||||||
|
"ModelEvaluator",
|
||||||
|
"ExplanationEvaluator",
|
||||||
|
"run_evaluation_with_proper_split",
|
||||||
|
"run_leave_one_out_evaluation",
|
||||||
|
]
|
||||||
@@ -0,0 +1,251 @@
|
|||||||
|
import time
|
||||||
|
from typing import Dict
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from pygrex.data_reader.data_reader import DataReader
|
||||||
|
from pygrex.evaluator import Splitter, ModelEvaluator
|
||||||
|
|
||||||
|
|
||||||
|
def run_leave_one_out_evaluation(
|
||||||
|
data_reader: DataReader, model, top_n: int = 10
|
||||||
|
) -> Dict:
|
||||||
|
print("Starting leave-one-out evaluation...")
|
||||||
|
start_time = time.time()
|
||||||
|
|
||||||
|
# 1. Proper leave-one-out split (one item per user)
|
||||||
|
train_dr, test_df = Splitter.split_leave_n_out(
|
||||||
|
data_reader, n=1
|
||||||
|
) # n=1 for true leave-one-out
|
||||||
|
print(f"Split completed: {len(test_df)} test interactions")
|
||||||
|
|
||||||
|
train_users = set(train_dr.dataset["userId"].unique())
|
||||||
|
train_items = set(train_dr.dataset["itemId"].unique())
|
||||||
|
|
||||||
|
original_test_len = len(test_df)
|
||||||
|
test_df = test_df[
|
||||||
|
test_df["userId"].isin(train_users) & test_df["itemId"].isin(train_items)
|
||||||
|
]
|
||||||
|
print(
|
||||||
|
f"Filtered test set: {len(test_df)} interactions remaining from {original_test_len}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 2. Train model on training data
|
||||||
|
print("Training model on reduced dataset...")
|
||||||
|
train_start = time.time()
|
||||||
|
model.fit(train_dr)
|
||||||
|
train_time = time.time() - train_start
|
||||||
|
print(f"Model training completed in {train_time:.2f} seconds")
|
||||||
|
|
||||||
|
# 3. Generate recommendations efficiently
|
||||||
|
print("Generating recommendations...")
|
||||||
|
rec_start = time.time()
|
||||||
|
recommendations = generate_recommendations_batch(model, train_dr, test_df, top_n)
|
||||||
|
rec_time = time.time() - rec_start
|
||||||
|
print(f"Recommendations generated in {rec_time:.2f} seconds")
|
||||||
|
|
||||||
|
# 4. Use the existing Evaluator class
|
||||||
|
evaluator = ModelEvaluator(test_df, top_n=top_n)
|
||||||
|
|
||||||
|
# Calculate metrics
|
||||||
|
hit_ratio = evaluator.cal_hit_ratio(recommendations)
|
||||||
|
ndcg = evaluator.cal_ndcg(recommendations)
|
||||||
|
|
||||||
|
total_time = time.time() - start_time
|
||||||
|
print(f"Total evaluation time: {total_time:.2f} seconds")
|
||||||
|
|
||||||
|
return {
|
||||||
|
"Hit Ratio": hit_ratio,
|
||||||
|
"NDCG": ndcg, # Using standard NDCG instead of eNDCG for now
|
||||||
|
"evaluation_time": total_time,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def generate_recommendations_batch(
|
||||||
|
model, train_dr: DataReader, test_df: pd.DataFrame, top_n: int
|
||||||
|
) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Generate recommendations in batch mode for efficiency.
|
||||||
|
Returns DataFrame with columns: ['userId', 'itemId', 'rank', 'score']
|
||||||
|
"""
|
||||||
|
all_items = set(train_dr.dataset["itemId"].unique())
|
||||||
|
recommendations = []
|
||||||
|
|
||||||
|
test_users = test_df["userId"].unique()
|
||||||
|
print(f"Generating recommendations for {len(test_users)} users...")
|
||||||
|
|
||||||
|
for i, user_id in enumerate(test_users):
|
||||||
|
if i % 100 == 0: # Progress indicator
|
||||||
|
print(f"Processing user {i}/{len(test_users)}")
|
||||||
|
|
||||||
|
# Get items the user has already interacted with
|
||||||
|
user_items = set(
|
||||||
|
train_dr.dataset[train_dr.dataset["userId"] == user_id]["itemId"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Candidate items (unseen items)
|
||||||
|
candidate_items = list(all_items - user_items)
|
||||||
|
|
||||||
|
# For efficiency, limit candidates if there are too many
|
||||||
|
if len(candidate_items) > 10000: # Adjust this threshold based on your needs
|
||||||
|
candidate_items = np.random.choice(
|
||||||
|
candidate_items, 10000, replace=False
|
||||||
|
).tolist()
|
||||||
|
|
||||||
|
# Generate predictions - try to use batch prediction if available
|
||||||
|
try:
|
||||||
|
# Check if model has batch prediction capability
|
||||||
|
if hasattr(model, "predict_batch") or hasattr(model, "recommend"):
|
||||||
|
user_recs = generate_recommendations_efficient(
|
||||||
|
model, user_id, candidate_items, top_n
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
# Fall back to individual predictions (slower)
|
||||||
|
user_recs = generate_recommendations_individual(
|
||||||
|
model, user_id, candidate_items, top_n
|
||||||
|
)
|
||||||
|
|
||||||
|
recommendations.extend(user_recs)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Error generating recommendations for user {user_id}: {e}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Convert to DataFrame
|
||||||
|
if recommendations:
|
||||||
|
rec_df = pd.DataFrame(
|
||||||
|
recommendations, columns=["userId", "itemId", "rank", "score"]
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
# Return empty DataFrame with correct structure
|
||||||
|
rec_df = pd.DataFrame(columns=["userId", "itemId", "rank", "score"])
|
||||||
|
|
||||||
|
return rec_df
|
||||||
|
|
||||||
|
|
||||||
|
def generate_recommendations_efficient(
|
||||||
|
model, user_id: int, candidate_items: list, top_n: int
|
||||||
|
) -> list:
|
||||||
|
"""
|
||||||
|
Try to use efficient recommendation methods if available.
|
||||||
|
"""
|
||||||
|
recommendations = []
|
||||||
|
|
||||||
|
# Try different efficient methods based on model type
|
||||||
|
if hasattr(model, "recommend"):
|
||||||
|
# Some models have a recommend method
|
||||||
|
try:
|
||||||
|
recs = model.recommend(user_id, candidate_items, top_n)
|
||||||
|
for rank, (item_id, score) in enumerate(recs, 1):
|
||||||
|
recommendations.append((user_id, item_id, rank, score))
|
||||||
|
except Exception:
|
||||||
|
# Fall back to individual predictions
|
||||||
|
return generate_recommendations_individual(
|
||||||
|
model, user_id, candidate_items, top_n
|
||||||
|
)
|
||||||
|
|
||||||
|
elif hasattr(model, "predict_batch"):
|
||||||
|
# Batch prediction if available
|
||||||
|
try:
|
||||||
|
user_items_batch = [(user_id, item_id) for item_id in candidate_items]
|
||||||
|
scores = model.predict_batch(user_items_batch)
|
||||||
|
|
||||||
|
# Sort by score and get top-N
|
||||||
|
scored_items = list(zip(candidate_items, scores))
|
||||||
|
scored_items.sort(key=lambda x: x[1], reverse=True)
|
||||||
|
|
||||||
|
for rank, (item_id, score) in enumerate(scored_items[:top_n], 1):
|
||||||
|
recommendations.append((user_id, item_id, rank, score))
|
||||||
|
except Exception:
|
||||||
|
return generate_recommendations_individual(
|
||||||
|
model, user_id, candidate_items, top_n
|
||||||
|
)
|
||||||
|
|
||||||
|
else:
|
||||||
|
return generate_recommendations_individual(
|
||||||
|
model, user_id, candidate_items, top_n
|
||||||
|
)
|
||||||
|
|
||||||
|
return recommendations
|
||||||
|
|
||||||
|
|
||||||
|
def generate_recommendations_individual(
|
||||||
|
model, user_id: int, candidate_items: list, top_n: int
|
||||||
|
) -> list:
|
||||||
|
"""
|
||||||
|
Fall back to individual predictions (slower but works with any model).
|
||||||
|
"""
|
||||||
|
predictions = []
|
||||||
|
|
||||||
|
# Batch the individual predictions for better performance
|
||||||
|
batch_size = 100
|
||||||
|
for i in range(0, len(candidate_items), batch_size):
|
||||||
|
batch_items = candidate_items[i : i + batch_size]
|
||||||
|
|
||||||
|
for item_id in batch_items:
|
||||||
|
try:
|
||||||
|
score = model.predict(user_id, item_id)
|
||||||
|
predictions.append((item_id, score))
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Prediction error for user {user_id}, item {item_id}: {e}")
|
||||||
|
# Skip items that cause prediction errors
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Sort by score and get top-N
|
||||||
|
predictions.sort(key=lambda x: x[1], reverse=True)
|
||||||
|
top_predictions = predictions[:top_n]
|
||||||
|
|
||||||
|
recommendations = []
|
||||||
|
for rank, (item_id, score) in enumerate(top_predictions, 1):
|
||||||
|
recommendations.append((user_id, item_id, rank, score))
|
||||||
|
|
||||||
|
return recommendations
|
||||||
|
|
||||||
|
|
||||||
|
def run_evaluation_with_proper_split(
|
||||||
|
data_reader: DataReader, model, test_size: float = 0.2, top_n: int = 10
|
||||||
|
) -> Dict:
|
||||||
|
"""
|
||||||
|
Alternative evaluation using a proper train/test split instead of leave-one-out.
|
||||||
|
"""
|
||||||
|
print(f"Starting evaluation with {test_size * 100}% test split...")
|
||||||
|
start_time = time.time()
|
||||||
|
|
||||||
|
# 1. Split data into train/test
|
||||||
|
train_dr, test_df = Splitter.split_leave_n_out(data_reader, frac=test_size)
|
||||||
|
print(f"Split completed: {len(test_df)} test interactions")
|
||||||
|
|
||||||
|
# 2. Filter test set to ensure all users/items exist in the training set
|
||||||
|
train_users = set(train_dr.dataset["userId"].unique())
|
||||||
|
train_items = set(train_dr.dataset["itemId"].unique())
|
||||||
|
|
||||||
|
original_test_len = len(test_df)
|
||||||
|
test_df = test_df[
|
||||||
|
test_df["userId"].isin(train_users) & test_df["itemId"].isin(train_items)
|
||||||
|
]
|
||||||
|
print(
|
||||||
|
f"Filtered test set: {len(test_df)} interactions remaining from {original_test_len}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 2. Train model
|
||||||
|
print("Training model...")
|
||||||
|
model.fit(train_dr)
|
||||||
|
|
||||||
|
# 3. Generate recommendations
|
||||||
|
print("Generating recommendations...")
|
||||||
|
recommendations = generate_recommendations_batch(model, train_dr, test_df, top_n)
|
||||||
|
|
||||||
|
# 4. Evaluate
|
||||||
|
evaluator = ModelEvaluator(test_df, top_n=top_n)
|
||||||
|
hit_ratio = evaluator.cal_hit_ratio(recommendations)
|
||||||
|
ndcg = evaluator.cal_ndcg(recommendations)
|
||||||
|
|
||||||
|
total_time = time.time() - start_time
|
||||||
|
print(f"Evaluation completed in {total_time:.2f} seconds")
|
||||||
|
|
||||||
|
return {
|
||||||
|
"Hit Ratio": hit_ratio,
|
||||||
|
"NDCG": ndcg,
|
||||||
|
"evaluation_time": total_time,
|
||||||
|
"test_interactions": len(test_df),
|
||||||
|
"total_recommendations": len(recommendations),
|
||||||
|
}
|
||||||
@@ -0,0 +1,68 @@
|
|||||||
|
from typing import Dict, Any
|
||||||
|
|
||||||
|
from pygrex.utils import calculate_gild_for_explanations
|
||||||
|
|
||||||
|
|
||||||
|
class ExplanationEvaluator:
|
||||||
|
"""
|
||||||
|
A unified evaluator for different explanation methods.
|
||||||
|
|
||||||
|
This class takes the results generated by an explainer and calculates
|
||||||
|
a standard set of quality metrics, such as Fidelity and Diversity (GILD).
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
"""Initializes the ExplanationEvaluator."""
|
||||||
|
# This class is stateless, so __init__ is simple.
|
||||||
|
pass
|
||||||
|
|
||||||
|
def evaluate(
|
||||||
|
self, explanation_results: Dict[str, Any], explainer_type: str
|
||||||
|
) -> Dict[str, float]:
|
||||||
|
"""
|
||||||
|
Calculates all relevant metrics for a given explanation result.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
explanation_results: The dictionary returned by an explainer's
|
||||||
|
`find_explanation` method.
|
||||||
|
explainer_type: A string identifier for the explainer used
|
||||||
|
(e.g., "LORE4Groups", "EXPGRS").
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A dictionary containing the calculated metric scores.
|
||||||
|
"""
|
||||||
|
if not explanation_results:
|
||||||
|
return {"fidelity": 0.0, "gild": 0.0}
|
||||||
|
|
||||||
|
fidelity = self._calculate_fidelity(explanation_results)
|
||||||
|
gild = self._calculate_gild(explanation_results, explainer_type)
|
||||||
|
|
||||||
|
return {"fidelity": fidelity, "gild": gild}
|
||||||
|
|
||||||
|
def _calculate_fidelity(self, explanation_results: Dict[str, Any]) -> float:
|
||||||
|
"""
|
||||||
|
Extracts the fidelity score from the explanation results.
|
||||||
|
|
||||||
|
Fidelity is computed by the explainer itself, as it's the ratio of
|
||||||
|
items it was able to explain. This method standardizes its retrieval.
|
||||||
|
"""
|
||||||
|
return explanation_results.get("fidelity", 0.0)
|
||||||
|
|
||||||
|
def _calculate_gild(
|
||||||
|
self, explanation_results: Dict[str, Any], explainer_type: str
|
||||||
|
) -> float:
|
||||||
|
"""
|
||||||
|
Calculates the Gaussian Inter-List Diversity (GILD) of the explanations.
|
||||||
|
|
||||||
|
This is a wrapper around the utility function that handles the details.
|
||||||
|
It uses the 'details' part of the explanation results.
|
||||||
|
"""
|
||||||
|
explanation_details = explanation_results.get("details", {})
|
||||||
|
if not explanation_details:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
# The GILD function is now called from a central, logical place.
|
||||||
|
gild_score = calculate_gild_for_explanations(
|
||||||
|
explanation_details, explainer_type
|
||||||
|
)
|
||||||
|
return gild_score
|
||||||
@@ -0,0 +1,179 @@
|
|||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
|
||||||
|
class ModelEvaluator:
|
||||||
|
disc_functions = ["log", "linear"]
|
||||||
|
|
||||||
|
def __init__(self, test_set, top_n: int = 10, discount_function: str = "log"):
|
||||||
|
self.test_set = test_set
|
||||||
|
self._top_n = top_n
|
||||||
|
assert discount_function in self.disc_functions, "Wrong Discount Function."
|
||||||
|
self._discount_function = discount_function
|
||||||
|
self.num_users = self.test_set.userId.nunique()
|
||||||
|
|
||||||
|
@property
|
||||||
|
def top_n(self):
|
||||||
|
return self._top_n
|
||||||
|
|
||||||
|
@top_n.setter
|
||||||
|
def top_n(self, top_n: int):
|
||||||
|
self._top_n = top_n
|
||||||
|
|
||||||
|
@property
|
||||||
|
def discount_function(self):
|
||||||
|
return self._discount_function
|
||||||
|
|
||||||
|
@discount_function.setter
|
||||||
|
def discount_function(self, discount_function: str):
|
||||||
|
assert discount_function in self.disc_functions, "Wrong Discount Function."
|
||||||
|
self._discount_function = discount_function
|
||||||
|
|
||||||
|
def cal_hit_ratio(self, recommendations):
|
||||||
|
"""
|
||||||
|
Hit Ratio
|
||||||
|
:param recommendations: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
:return: hit rate.
|
||||||
|
"""
|
||||||
|
test_in_top_n = self.get_hits(recommendations)
|
||||||
|
# count hits per user
|
||||||
|
hits_per_user = self.count_positives(test_in_top_n)
|
||||||
|
# merge with the entire list of positive items for user
|
||||||
|
hits_per_user = hits_per_user.merge(
|
||||||
|
self.count_positives(self.test_set),
|
||||||
|
on="userId",
|
||||||
|
suffixes=("_true", ""),
|
||||||
|
how="right",
|
||||||
|
)
|
||||||
|
# if there are users with 0 hits the merge will have NA.
|
||||||
|
hits_per_user = hits_per_user.fillna(0)
|
||||||
|
# get the hit rate per user
|
||||||
|
hit_rate = hits_per_user.positive_true / hits_per_user.positive
|
||||||
|
# average
|
||||||
|
hit_rate = hit_rate.mean()
|
||||||
|
return hit_rate
|
||||||
|
|
||||||
|
def get_hits(self, recommendations):
|
||||||
|
"""
|
||||||
|
Find which items in the test set have a hit on the recommendations.
|
||||||
|
:param recommendations: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
:return: dataframe, removing the rows missing in the test set.
|
||||||
|
"""
|
||||||
|
# check whether there are top_n items per user
|
||||||
|
top_n_recommendations = self.filter_to_top_n(recommendations)
|
||||||
|
# find the hits
|
||||||
|
test_in_top_n = pd.merge(
|
||||||
|
top_n_recommendations, self.test_set, on=["userId", "itemId"]
|
||||||
|
)
|
||||||
|
return test_in_top_n
|
||||||
|
|
||||||
|
def filter_to_top_n(self, dataset):
|
||||||
|
"""
|
||||||
|
if rank > top_n, we do not use it for evaluation
|
||||||
|
:param dataset: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
:return: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
"""
|
||||||
|
return dataset[dataset["rank"] <= self.top_n]
|
||||||
|
|
||||||
|
def cal_ndcg(self, recommendations):
|
||||||
|
r"""
|
||||||
|
For evaluating the top-N recommendation list, we also provide the normalized Discounted Cumulative Gain at N
|
||||||
|
recommendation (nDCG@N) computed as the ratio of the Discounted Cumulative Gain(DCG) with the ideal Discounted
|
||||||
|
Cumulative Gain(IDCG):
|
||||||
|
DGC_{pos} = rel_1 + \sum_{i=2}^{pos} \frac{rel_i}{\log_2i} \qquad \qquad
|
||||||
|
IDGC_{pos} = rel_1 + \sum_{i=2}^{|h|-1} \frac{rel_i}{\log_2i} \\
|
||||||
|
nDCG_{pos} = \frac{DCG}{IDCG}
|
||||||
|
where pos denotes the position up to which relevance is accumulated, and $rel_i$ is the relevance of the recommended item at position \textit{i}.
|
||||||
|
Ref: Y. Wang, L. Wang, Y. Li, D. He, T.-Y. Liu, and W. Chen.
|
||||||
|
A theoretical analysis of ndcgtype ranking measures.
|
||||||
|
:param recommendations: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
:return: nDCG
|
||||||
|
"""
|
||||||
|
# get hits
|
||||||
|
hits = self.get_hits(recommendations)
|
||||||
|
|
||||||
|
DCG = self.cal_dcg(hits)
|
||||||
|
iDCG = self.cal_idcg()
|
||||||
|
|
||||||
|
# join to check if there are users in the test without hits
|
||||||
|
nDCG = iDCG.merge(DCG, on="userId", how="left")
|
||||||
|
nDCG = nDCG.fillna(0)
|
||||||
|
# normalize
|
||||||
|
nDCG["ndcg"] = nDCG["dcg"] / nDCG["idcg"]
|
||||||
|
|
||||||
|
return nDCG["ndcg"].mean()
|
||||||
|
|
||||||
|
def cal_dcg(self, hits):
|
||||||
|
"""
|
||||||
|
Discounted Comulative Gain
|
||||||
|
:param hits: recommendations: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
:return: DCG
|
||||||
|
"""
|
||||||
|
# todo: the gain so far is set to a constant.
|
||||||
|
|
||||||
|
if self.discount_function == "log":
|
||||||
|
hits["discounted_gain"] = np.log(2) / np.log(hits["rank"] + 1)
|
||||||
|
elif self.discount_function == "linear":
|
||||||
|
hits["discounted_gain"] = 1 / hits["rank"]
|
||||||
|
|
||||||
|
DCG = hits.groupby("userId")["discounted_gain"].sum()
|
||||||
|
|
||||||
|
return pd.DataFrame(
|
||||||
|
{"userId": hits["userId"].unique(), "dcg": DCG}
|
||||||
|
).reset_index(drop=True)
|
||||||
|
|
||||||
|
def cal_idcg(self):
|
||||||
|
"""
|
||||||
|
the Ideal DCG, is the DCG for the best ranking possible (i.e. all true positives were recommended first).
|
||||||
|
:return: iDCG
|
||||||
|
"""
|
||||||
|
# create a fake ranking for test set items.
|
||||||
|
# We assume that the items in the test set are all on the Top-N list.
|
||||||
|
count_positives = self.count_positives(self.test_set)
|
||||||
|
ideal_rank = [i for x in count_positives["positive"] for i in (range(1, x + 1))]
|
||||||
|
test_ideal_ranking = self.test_set.copy()
|
||||||
|
test_ideal_ranking["rank"] = ideal_rank
|
||||||
|
# Filter to have at most top-N items.
|
||||||
|
test_ideal_ranking = self.filter_to_top_n(test_ideal_ranking)
|
||||||
|
# get the dcg for the ideal ranking
|
||||||
|
idcg = self.cal_dcg(test_ideal_ranking)
|
||||||
|
idcg = idcg.rename(columns={"dcg": "idcg"})
|
||||||
|
return idcg
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def count_positives(dataset):
|
||||||
|
"""
|
||||||
|
Returns the positives count.
|
||||||
|
:param dataset: dataframe, columns = ['userId', 'itemId', 'rank']
|
||||||
|
:return: dataframe, columns = ['userId', 'positive']
|
||||||
|
"""
|
||||||
|
users_with_positives = dataset.userId.unique()
|
||||||
|
positives_per_user = dataset.groupby("userId")["itemId"].count()
|
||||||
|
positives_per_user = pd.DataFrame(
|
||||||
|
{"userId": users_with_positives, "positive": positives_per_user}
|
||||||
|
)
|
||||||
|
|
||||||
|
return positives_per_user.reset_index(drop=True)
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
## recoms = pd.DataFrame({
|
||||||
|
# 'userId': [1, 1, 1, 2, 2, 2, 3, 3, 3],
|
||||||
|
# 'itemId': [1, 2, 3, 4, 1, 2, 2, 3, 4],
|
||||||
|
# 'rank': [1, 2, 3, 1, 2, 3, 1, 2, 3]
|
||||||
|
# })
|
||||||
|
|
||||||
|
# test = pd.DataFrame({
|
||||||
|
# 'userId': [1, 1, 2, 3],
|
||||||
|
# 'itemId': [1, 4, 1, 5]
|
||||||
|
# })
|
||||||
|
|
||||||
|
# eval = Evaluator(test_set=test, top_n=2)
|
||||||
|
|
||||||
|
# assert eval.num_users == 3, 'number of users'
|
||||||
|
# assert eval.top_n == 2, 'number of top n'
|
||||||
|
# eval.top_n = 3
|
||||||
|
# assert eval.top_n == 3, 'changing of top n'
|
||||||
|
|
||||||
|
# print(eval.cal_hit_ratio(recoms))
|
||||||
|
# print(eval.cal_ndcg(recoms))
|
||||||
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Reference in New Issue
Block a user