notebooks/list_goldenset_entities.ipynb

{
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 "metadata": {
  "colab": {
   "provenance": [],
   "authorship_tag": "ABX9TyMaj53pj7FDWreOFLU0/FRl",
   "include_colab_link": true
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/vitor-faria/kgembeddings-binarization/blob/main/notebooks/list_goldenset_entities.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# List DBpedia entities in gold standard files\n",
    "\n",
    "This notebook consists of filtering DBpedia entities existing in both GEval and DLCC evaluation frameworks. It produces two pickle files: `geval_entities.pickle` and `dlcc_entities.pickle`, which are available in the `resources` folder.\n",
    "\n",
    "## List GEval entities"
   ],
   "metadata": {
    "id": "SJnOPVBtotkC"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!git clone /~https://github.com/mariaangelapellegrino/Evaluation-Framework.git geval"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X6C91bwAoGrP",
    "outputId": "000e521b-d1d0-4eac-f493-1bd781c874d4"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cloning into 'geval'...\n",
      "remote: Enumerating objects: 1129, done.\u001B[K\n",
      "remote: Counting objects: 100% (26/26), done.\u001B[K\n",
      "remote: Compressing objects: 100% (19/19), done.\u001B[K\n",
      "remote: Total 1129 (delta 11), reused 7 (delta 7), pack-reused 1103\u001B[K\n",
      "Receiving objects: 100% (1129/1129), 47.59 MiB | 20.46 MiB/s, done.\n",
      "Resolving deltas: 100% (777/777), done.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# !mv */* .\n",
    "# !find . -type d -empty -delete"
   ],
   "metadata": {
    "id": "e-bHuRrRoKTd"
   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9nQGNi6cm5d7",
    "outputId": "cc5cd887-dc1e-47c6-f0a7-7b8bdd36ff6a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "933 unique entities in dataset geval/evaluation_framework/Classification/data/AAUP.tsv\n",
      "933 unique entities in total.\n",
      "212 unique entities in dataset geval/evaluation_framework/Classification/data/Cities.tsv\n",
      "1145 unique entities in total.\n",
      "1585 unique entities in dataset geval/evaluation_framework/Classification/data/Forbes.tsv\n",
      "2730 unique entities in total.\n",
      "1592 unique entities in dataset geval/evaluation_framework/Classification/data/MetacriticAlbums.tsv\n",
      "4322 unique entities in total.\n",
      "2000 unique entities in dataset geval/evaluation_framework/Classification/data/MetacriticMovies.tsv\n",
      "6322 unique entities in total.\n",
      "4343 unique entities in dataset geval/evaluation_framework/Clustering/data/cities2000AndCountries_cluster.tsv\n",
      "10648 unique entities in total.\n",
      "11179 unique entities in dataset geval/evaluation_framework/Clustering/data/citiesAndCountries_cluster.tsv\n",
      "17434 unique entities in total.\n",
      "6322 unique entities in dataset geval/evaluation_framework/Clustering/data/citiesMoviesAlbumsCompaniesUni_cluster.tsv\n",
      "17944 unique entities in total.\n",
      "4206 unique entities in dataset geval/evaluation_framework/Clustering/data/teams_cluster.tsv\n",
      "22150 unique entities in total.\n",
      "933 unique entities in dataset geval/evaluation_framework/Regression/data/AAUP.tsv\n",
      "22150 unique entities in total.\n",
      "212 unique entities in dataset geval/evaluation_framework/Regression/data/Cities.tsv\n",
      "22150 unique entities in total.\n",
      "1585 unique entities in dataset geval/evaluation_framework/Regression/data/Forbes.tsv\n",
      "22150 unique entities in total.\n",
      "1592 unique entities in dataset geval/evaluation_framework/Regression/data/MetacriticAlbums.tsv\n",
      "22150 unique entities in total.\n",
      "2000 unique entities in dataset geval/evaluation_framework/Regression/data/MetacriticMovies.tsv\n",
      "22150 unique entities in total.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "datasets = [\n",
    "    # Classification\n",
    "    'geval/evaluation_framework/Classification/data/AAUP.tsv',\n",
    "    'geval/evaluation_framework/Classification/data/Cities.tsv',\n",
    "    'geval/evaluation_framework/Classification/data/Forbes.tsv',\n",
    "    'geval/evaluation_framework/Classification/data/MetacriticAlbums.tsv',\n",
    "    'geval/evaluation_framework/Classification/data/MetacriticMovies.tsv',\n",
    "    # Clustering\n",
    "    'geval/evaluation_framework/Clustering/data/cities2000AndCountries_cluster.tsv',\n",
    "    'geval/evaluation_framework/Clustering/data/citiesAndCountries_cluster.tsv',\n",
    "    'geval/evaluation_framework/Clustering/data/citiesMoviesAlbumsCompaniesUni_cluster.tsv',\n",
    "    'geval/evaluation_framework/Clustering/data/teams_cluster.tsv',\n",
    "    # Regression\n",
    "    'geval/evaluation_framework/Regression/data/AAUP.tsv',\n",
    "    'geval/evaluation_framework/Regression/data/Cities.tsv',\n",
    "    'geval/evaluation_framework/Regression/data/Forbes.tsv',\n",
    "    'geval/evaluation_framework/Regression/data/MetacriticAlbums.tsv',\n",
    "    'geval/evaluation_framework/Regression/data/MetacriticMovies.tsv',\n",
    "]\n",
    "\n",
    "geval_entity_set = set()\n",
    "\n",
    "for dataset in datasets:\n",
    "    df = pd.read_csv(dataset, sep='\\t')\n",
    "    dataset_entities = set(df['DBpedia_URI'].unique())\n",
    "    print(f'{len(dataset_entities)} unique entities in dataset {dataset}')\n",
    "    geval_entity_set.update(dataset_entities)\n",
    "    print(f'{len(geval_entity_set)} unique entities in total.')"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "semantic_datasets = [\n",
    "    'geval/evaluation_framework/SemanticAnalogies/data/all_capital_country_entities.txt',\n",
    "    'geval/evaluation_framework/SemanticAnalogies/data/capital_country_entities.txt',\n",
    "    'geval/evaluation_framework/SemanticAnalogies/data/city_state_entities.txt',\n",
    "    'geval/evaluation_framework/SemanticAnalogies/data/currency_entities.txt',\n",
    "]\n",
    "\n",
    "semantic_entity_set = set()\n",
    "for dataset in semantic_datasets:\n",
    "    dataset_entities = set()\n",
    "    df = pd.read_csv(\n",
    "        dataset,\n",
    "        sep=' ',\n",
    "        names=['entity_1', 'entity_2', 'entity_3', 'entity_4'],\n",
    "    )\n",
    "    dataset_entities.update(set(df['entity_1'].unique()))\n",
    "    dataset_entities.update(set(df['entity_2'].unique()))\n",
    "    dataset_entities.update(set(df['entity_3'].unique()))\n",
    "    dataset_entities.update(set(df['entity_4'].unique()))\n",
    "    print(f'{len(dataset_entities)} unique entities in dataset {dataset}')\n",
    "    semantic_entity_set.update(dataset_entities)\n",
    "    print(f'{len(semantic_entity_set)} unique entities in total.')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jNpz_IyOnx5q",
    "outputId": "0eecaffe-cbb8-4f13-defa-c647e1ee7ee1"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "232 unique entities in dataset geval/evaluation_framework/SemanticAnalogies/data/all_capital_country_entities.txt\n",
      "232 unique entities in total.\n",
      "46 unique entities in dataset geval/evaluation_framework/SemanticAnalogies/data/capital_country_entities.txt\n",
      "232 unique entities in total.\n",
      "94 unique entities in dataset geval/evaluation_framework/SemanticAnalogies/data/city_state_entities.txt\n",
      "325 unique entities in total.\n",
      "58 unique entities in dataset geval/evaluation_framework/SemanticAnalogies/data/currency_entities.txt\n",
      "363 unique entities in total.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import json\n",
    "\n",
    "with open('geval/evaluation_framework/DocumentSimilarity/data/LP50_entities.json', 'r') as file:\n",
    "    doc_sim_dataset = json.load(file)\n",
    "\n",
    "doc_sim_entity_set = set()\n",
    "for text in doc_sim_dataset:\n",
    "    for annotation in text['annotations']:\n",
    "        doc_sim_entity_set.add(annotation['entity'])\n",
    "\n",
    "len(doc_sim_entity_set)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E0lLP6Vin3vA",
    "outputId": "9fb84019-69c4-405d-dfc1-1c7b61c672ec"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "401"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "ent_rel_df = pd.read_csv(\n",
    "    'geval/evaluation_framework/EntityRelatedness/data/KORE.txt',\n",
    "    sep='\\t',\n",
    "    names=['index', 'DBpedia_URI']\n",
    ")\n",
    "\n",
    "ent_rel_set = set()\n",
    "ent_rel_set.update(set([\n",
    "    ent for ent in ent_rel_df['index'].tolist() if isinstance(ent, str)\n",
    "]))\n",
    "ent_rel_set.update(set([\n",
    "    ent for ent in ent_rel_df['DBpedia_URI'].tolist() if isinstance(ent, str)\n",
    "]))\n",
    "\n",
    "len(ent_rel_set)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g6afPbZZn8hl",
    "outputId": "6e935013-d39b-469f-ab28-38601d29d630"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "414"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "geval_entity_set.update(semantic_entity_set)\n",
    "geval_entity_set.update(doc_sim_entity_set)\n",
    "geval_entity_set.update(ent_rel_set)\n",
    "len(geval_entity_set)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QMTx7fM8n_gm",
    "outputId": "09c6bae1-6bab-4032-aff3-40d1ec71e85b"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "23004"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import pickle\n",
    "\n",
    "with open('geval_entities.pickle', 'wb') as fp:\n",
    "    pickle.dump(geval_entity_set, fp)"
   ],
   "metadata": {
    "id": "IGD88dR8y72Z"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## List DLCC entities"
   ],
   "metadata": {
    "id": "lHbOSKUqwE6B"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!git clone /~https://github.com/janothan/DL-TC-Generator.git dlcc"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MQHJCRTsqWed",
    "outputId": "e6a0157d-0d6f-4ee7-c2e4-2864654daaf4"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cloning into 'dlcc'...\n",
      "remote: Enumerating objects: 8802, done.\u001B[K\n",
      "remote: Counting objects: 100% (9/9), done.\u001B[K\n",
      "remote: Compressing objects: 100% (9/9), done.\u001B[K\n",
      "remote: Total 8802 (delta 4), reused 0 (delta 0), pack-reused 8793\u001B[K\n",
      "Receiving objects: 100% (8802/8802), 119.76 MiB | 12.64 MiB/s, done.\n",
      "Resolving deltas: 100% (4379/4379), done.\n",
      "Updating files: 100% (1473/1473), done.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "dlcc_entities = set()\n",
    "directory = 'dlcc/results/v1/dbpedia'\n",
    "for dirpath, dirnames, filenames in os.walk(directory):\n",
    "    for filename in filenames:\n",
    "        if filename.endswith('.txt'):\n",
    "            print(os.path.join(dirpath, filename))\n",
    "            with open(os.path.join(dirpath, filename)) as f:\n",
    "                file_entities = set(f.read().split())\n",
    "                dlcc_entities.update(file_entities)\n",
    "                print(len(dlcc_entities))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LfIH3Qt4tIQd",
    "outputId": "cf7392d5-4294-4a7a-9329-a0d65cd1d1c3"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "dlcc/results/v1/dbpedia/tc07/species/500/negatives.txt\n",
      "500\n",
      "dlcc/results/v1/dbpedia/tc07/species/500/positives.txt\n",
      "1000\n",
      "dlcc/results/v1/dbpedia/tc07/species/500/train_test/train.txt\n",
      "1002\n",
      "dlcc/results/v1/dbpedia/tc07/species/500/train_test/test.txt\n",
      "1002\n",
      "dlcc/results/v1/dbpedia/tc07/species/5000/negatives.txt\n",
      "5982\n",
      "dlcc/results/v1/dbpedia/tc07/species/5000/positives.txt\n",
      "9177\n",
      "dlcc/results/v1/dbpedia/tc07/species/5000/train_test/train.txt\n",
      "9177\n",
      "dlcc/results/v1/dbpedia/tc07/species/5000/train_test/test.txt\n",
      "9177\n",
      "dlcc/results/v1/dbpedia/tc07/species/50/negatives.txt\n",
      "9225\n",
      "dlcc/results/v1/dbpedia/tc07/species/50/positives.txt\n",
      "9225\n",
      "dlcc/results/v1/dbpedia/tc07/species/50/train_test/train.txt\n",
      "9225\n",
      "dlcc/results/v1/dbpedia/tc07/species/50/train_test/test.txt\n",
      "9225\n",
      "dlcc/results/v1/dbpedia/tc07/people/500/negatives.txt\n",
      "9725\n",
      "dlcc/results/v1/dbpedia/tc07/people/500/positives.txt\n",
      "10225\n",
      "dlcc/results/v1/dbpedia/tc07/people/500/train_test/train.txt\n",
      "10225\n",
      "dlcc/results/v1/dbpedia/tc07/people/500/train_test/test.txt\n",
      "10225\n",
      "dlcc/results/v1/dbpedia/tc07/people/5000/negatives.txt\n",
      "15221\n",
      "dlcc/results/v1/dbpedia/tc07/people/5000/positives.txt\n",
      "19743\n",
      "dlcc/results/v1/dbpedia/tc07/people/5000/train_test/train.txt\n",
      "19743\n",
      "dlcc/results/v1/dbpedia/tc07/people/5000/train_test/test.txt\n",
      "19743\n",
      "dlcc/results/v1/dbpedia/tc07/people/50/negatives.txt\n",
      "19793\n",
      "dlcc/results/v1/dbpedia/tc07/people/50/positives.txt\n",
      "19794\n",
      "dlcc/results/v1/dbpedia/tc07/people/50/train_test/train.txt\n",
      "19794\n",
      "dlcc/results/v1/dbpedia/tc07/people/50/train_test/test.txt\n",
      "19794\n",
      "dlcc/results/v1/dbpedia/tc05/movies/500/negatives.txt\n",
      "20294\n",
      "dlcc/results/v1/dbpedia/tc05/movies/500/positives.txt\n",
      "20794\n",
      "dlcc/results/v1/dbpedia/tc05/movies/500/train_test/train.txt\n",
      "20794\n",
      "dlcc/results/v1/dbpedia/tc05/movies/500/train_test/test.txt\n",
      "20794\n",
      "dlcc/results/v1/dbpedia/tc05/movies/5000/negatives.txt\n",
      "25768\n",
      "dlcc/results/v1/dbpedia/tc05/movies/5000/positives.txt\n",
      "30600\n",
      "dlcc/results/v1/dbpedia/tc05/movies/5000/train_test/train.txt\n",
      "30600\n",
      "dlcc/results/v1/dbpedia/tc05/movies/5000/train_test/test.txt\n",
      "30600\n",
      "dlcc/results/v1/dbpedia/tc05/movies/50/negatives.txt\n",
      "30647\n",
      "dlcc/results/v1/dbpedia/tc05/movies/50/positives.txt\n",
      "30682\n",
      "dlcc/results/v1/dbpedia/tc05/movies/50/train_test/train.txt\n",
      "30682\n",
      "dlcc/results/v1/dbpedia/tc05/movies/50/train_test/test.txt\n",
      "30682\n",
      "dlcc/results/v1/dbpedia/tc05/species/500/negatives.txt\n",
      "31147\n",
      "dlcc/results/v1/dbpedia/tc05/species/500/positives.txt\n",
      "31623\n",
      "dlcc/results/v1/dbpedia/tc05/species/500/train_test/train.txt\n",
      "31623\n",
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      "31623\n",
      "dlcc/results/v1/dbpedia/tc05/species/5000/negatives.txt\n",
      "36315\n",
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      "37354\n",
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      "37354\n",
      "dlcc/results/v1/dbpedia/tc05/species/5000/train_test/test.txt\n",
      "37354\n",
      "dlcc/results/v1/dbpedia/tc05/species/50/negatives.txt\n",
      "37399\n",
      "dlcc/results/v1/dbpedia/tc05/species/50/positives.txt\n",
      "37399\n",
      "dlcc/results/v1/dbpedia/tc05/species/50/train_test/train.txt\n",
      "37399\n",
      "dlcc/results/v1/dbpedia/tc05/species/50/train_test/test.txt\n",
      "37399\n",
      "dlcc/results/v1/dbpedia/tc05/albums/500/negatives.txt\n",
      "37899\n",
      "dlcc/results/v1/dbpedia/tc05/albums/500/positives.txt\n",
      "37906\n",
      "dlcc/results/v1/dbpedia/tc05/albums/500/train_test/train.txt\n",
      "37906\n",
      "dlcc/results/v1/dbpedia/tc05/albums/500/train_test/test.txt\n",
      "37906\n",
      "dlcc/results/v1/dbpedia/tc05/albums/5000/negatives.txt\n",
      "42886\n",
      "dlcc/results/v1/dbpedia/tc05/albums/5000/positives.txt\n",
      "42886\n",
      "dlcc/results/v1/dbpedia/tc05/albums/5000/train_test/train.txt\n",
      "42886\n",
      "dlcc/results/v1/dbpedia/tc05/albums/5000/train_test/test.txt\n",
      "42886\n",
      "dlcc/results/v1/dbpedia/tc05/albums/50/negatives.txt\n",
      "42934\n",
      "dlcc/results/v1/dbpedia/tc05/albums/50/positives.txt\n",
      "42934\n",
      "dlcc/results/v1/dbpedia/tc05/albums/50/train_test/train.txt\n",
      "42934\n",
      "dlcc/results/v1/dbpedia/tc05/albums/50/train_test/test.txt\n",
      "42934\n",
      "dlcc/results/v1/dbpedia/tc05/people/500/negatives.txt\n",
      "43431\n",
      "dlcc/results/v1/dbpedia/tc05/people/500/positives.txt\n",
      "43930\n",
      "dlcc/results/v1/dbpedia/tc05/people/500/train_test/train.txt\n",
      "43930\n",
      "dlcc/results/v1/dbpedia/tc05/people/500/train_test/test.txt\n",
      "43930\n",
      "dlcc/results/v1/dbpedia/tc05/people/5000/negatives.txt\n",
      "48893\n",
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      "53820\n",
      "dlcc/results/v1/dbpedia/tc05/people/5000/train_test/train.txt\n",
      "53820\n",
      "dlcc/results/v1/dbpedia/tc05/people/5000/train_test/test.txt\n",
      "53820\n",
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     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import pickle\n",
    "\n",
    "with open('dlcc_entities.pickle', 'wb') as fp:\n",
    "    pickle.dump(dlcc_entities, fp)"
   ],
   "metadata": {
    "id": "cLku5Hpcyz3v"
   },
   "execution_count": 11,
   "outputs": []
  }
 ]
}