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new version of pip setup
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config_files/test_entity2rec.yml

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experiment:
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# dataset:
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# name: ml-100k
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# item: # infos related to item dataset (mandatory, at least item_id)
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# path: datasets/ml-100k/processed/item.csv
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# extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
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# user: # mandatory (at least user_id)
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# path: datasets/ml-100k/processed/user.csv
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# extra_features: [gender, occupation] # features beside user_id
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# ratings: # mandatory (at least [user_id, item_id, rating])
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# path: datasets/ml-100k/processed/rating.csv
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# timestamp: True
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# enrich:
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# map_path: datasets/ml-100k/processed/map.csv
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# enrich_path: datasets/ml-100k/processed/enriched.csv
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# remove_unmatched: False
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# properties: [subject, director, abstract]
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dataset:
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name: ml-100k
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item: # infos related to item dataset (mandatory, at least item_id)
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path: datasets/ml-100k/processed/item.csv
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extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
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user: # mandatory (at least user_id)
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path: datasets/ml-100k/processed/user.csv
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extra_features: [gender, occupation] # features beside user_id
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ratings: # mandatory (at least [user_id, item_id, rating])
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path: datasets/ml-100k/processed/rating.csv
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timestamp: True
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enrich:
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map_path: datasets/ml-100k/processed/map.csv
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enrich_path: datasets/ml-100k/processed/enriched.csv
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remove_unmatched: False
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properties: [subject, director, abstract]
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# dataset:
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# name: ml-100k
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# item: # infos related to item dataset (mandatory, at least item_id)
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# enrich_path: datasets/douban-movie/processed/enriched.csv
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# remove_unmatched: False
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# properties: [subject, director, abstract]
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dataset:
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name: ml-1m
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item: # infos related to item dataset (mandatory, at least item_id)
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path: datasets/ml-1m/processed/item.csv
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extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
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user: # mandatory (at least user_id)
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path: datasets/ml-1m/processed/user.csv
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extra_features: [gender, occupation] # features beside user_id
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ratings: # mandatory (at least [user_id, item_id, rating])
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path: datasets/ml-1m/processed/rating.csv
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timestamp: True
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# dataset:
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# name: ml-1m
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# item: # infos related to item dataset (mandatory, at least item_id)
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# path: datasets/ml-1m/processed/item.csv
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# extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
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# user: # mandatory (at least user_id)
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# path: datasets/ml-1m/processed/user.csv
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# extra_features: [gender, occupation] # features beside user_id
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# ratings: # mandatory (at least [user_id, item_id, rating])
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# path: datasets/ml-1m/processed/rating.csv
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# timestamp: True
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preprocess:
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# - method: filter_by_rating

setup.py

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setup(
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name="FranKGraphBench",
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version="0.1.0",
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version="0.1.1",
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author="Paulo do Carmo and Alvaro Lopes",
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author_email='[email protected]',
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url='https://github.com/AKSW/frankgraphbench/tree/main/',
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'gensim', 'joblib', 'networkx==3.1', 'numpy==1.24', 'pandas==2.0', 'python-dateutil', 'pytz', 'PyYAML', 'scikit-learn==1.3', 'scipy==1.10', 'six', 'smart-open',
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'threadpoolctl', 'tqdm', 'tzdata', 'pykeen', 'torch==2.1.2', 'py-cpuinfo', 'gputil', 'psutil', 'sentence-transformers', 'graph-walker', 'isodate==0.6',
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'Levenshtein==0.21', 'pyparsing==3.0', 'python-Levenshtein==0.21', 'rapidfuzz==3.0', 'rdflib==6.3', 'SPARQLWrapper==2.0', 'thefuzz==0.19', 'tzdata==2023.3',
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'beautifulsoup4', 'matplotlib==3.7', 'contourpy==1.1'
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'beautifulsoup4', 'matplotlib==3.7', 'contourpy==1.1', 'SPARQLWrapper', 'multiprocess'
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],
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entry_points={
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'console_scripts': [

src/chart_generation/__init__.py

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from chart_generation.chart2class import chart2class
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__version__ = "0.1.0"
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__version__ = "0.1.1"
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__author__ = "Paulo do Carmo and Alvaro Lopes"
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def get_chart_class(dataset):

src/data_integration/__init__.py

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from data_integration.dataset2class import dataset2class
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__version__ = "0.1.0"
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__version__ = "0.1.1"
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__author__ = "Paulo do Carmo and Alvaro Lopes"
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def get_dataset_class(dataset):

src/framework/__init__.py

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import argparse
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from framework.experiment import run
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__version__ = "0.1.0"
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__version__ = "0.1.1"
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__author__ = "Paulo do Carmo and Alvaro Lopes"
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def framework():

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