{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "from pathlib import Path\n", "from collections import OrderedDict" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[WindowsPath('../data/v1/WikiCSSH_categories.csv'),\n", " WindowsPath('../data/v1/WikiCSSH_category2page.csv'),\n", " WindowsPath('../data/v1/WikiCSSH_category_links.csv'),\n", " WindowsPath('../data/v1/WikiCSSH_category_links_all.csv'),\n", " WindowsPath('../data/v1/Wikicssh_core_categories.csv'),\n", " WindowsPath('../data/v1/WikiCSSH_page2redirect.csv')]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wikicssh_path = Path(\"../data/v1\")\n", "wikicssh_files = list(wikicssh_path.glob(\"./*.csv\"))\n", "wikicssh_files" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " category level\n", "0 Computer_science 1\n", "1 Mathematics 1\n", "2 Information_science 1\n", "3 Computer_engineering 1\n", "4 Statistics 1\n", "... ... ...\n", "7349 Techniques 20\n", "7350 Streptococcal_proteins 20\n", "7351 Catalysts 20\n", "7352 Flax 20\n", "7353 Gender 20\n", "\n", "[7354 rows x 2 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_categories" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(8400, 4)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_category_links_all = pd.concat([\n", " df_category_links,\n", " df_categories[df_categories.category.isin(root_child_cats)].rename(columns={\n", " \"category\": \"child_cat\", \n", " \"level\": \"child_level\", #\tparent_level\tchild_level\n", " }).assign(parent_cat=\"\", parent_level=0),\n", " pd.DataFrame({\n", " \"parent_cat\": [\"\"],\n", " \"child_cat\": [\"\"],\n", " \"parent_level\": [-1],\n", " \"child_level\": [0],\n", " })\n", "# df_categories[df_categories.category.isin(isolate_cats)].rename(columns={\n", "# \"category\": \"child_cat\", \n", "# \"level\": \"child_level\", #\tparent_level\tchild_level\n", "# }).assign(parent_cat=\"\", parent_level=0)\n", " \n", "], axis=0, sort=True).sort_values([\"parent_level\", \"child_level\"])\n", "df_category_links_all.shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
child_catchild_levelparent_catparent_level
85Artificial_intelligence3Areas_of_computer_science2
492Artificial_intelligence3Unsolved_problems_in_computer_science2
\n", "
" ], "text/plain": [ " child_cat child_level \\\n", "85 Artificial_intelligence 3 \n", "492 Artificial_intelligence 3 \n", "\n", " parent_cat parent_level \n", "85 Areas_of_computer_science 2 \n", "492 Unsolved_problems_in_computer_science 2 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_category_links_all[\n", " #(df_category_links_all.parent_cat==\"Artificial_intelligence\")\n", " (df_category_links_all.child_cat==\"Artificial_intelligence\")\n", "]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3389, 2)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_categories[df_categories.level<=5].shape" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "G = nx.DiGraph()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8400" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.add_edges_from(df_category_links_all.set_index([\"parent_cat\", \"child_cat\"]).to_dict(orient=\"index\", into=OrderedDict))\n", "G.size()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nx.algorithms.is_directed_acyclic_graph(G)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Fuzzy_logic',\n", " 'Machine_learning',\n", " 'Turing_tests',\n", " 'Rule_engines',\n", " 'Robots',\n", " 'Computer_vision',\n", " 'Logic_programming',\n", " 'Open_source_artificial_intelligence',\n", " 'Virtual_assistants',\n", " 'Cloud_robotics',\n", " 'Game_artificial_intelligence',\n", " 'AI_accelerators',\n", " 'Affective_computing',\n", " 'Cognitive_architecture',\n", " 'Artificial_intelligence_applications',\n", " 'Artificial_immune_systems',\n", " 'Robotics',\n", " 'Artificial_Intelligence_existential_risk',\n", " 'Evolutionary_computation']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "node = \"Artificial_intelligence\"\n", "list(G.neighbors(node))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Areas_of_computer_science', 'Unsolved_problems_in_computer_science']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(G.predecessors(node))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 1.17 s\n" ] } ], "source": [ "%%time\n", "cat2pages = pd.read_csv('../data/v1/WikiCSSH_category2page.csv').groupby(\"cat_title\").page_title.agg(list)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "from IPython.display import display, HTML" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "def show_context_of_category(node):\n", " parents = list(G.predecessors(node))\n", " children = list(G.neighbors(node))\n", " table_header = \"ParentsNodeChildren\"\n", " parents_list = \"\\n\".join([f\"
  • {i}
    • \" for i in parents])\n", " children_list = \"\\n\".join([f\"
  • {i}
    • \" for i in children])\n", " node_list = f\"
  • {node}
    • \"\n", " page_list = \" | \".join([\n", " f\"{i}\"\n", " for i in cat2pages[node]\n", " ])\n", " table_body = f\"\"\"\n", " \n", "
      {parents_list}
    \n", " {node_list}\n", "
      {children_list}
    \n", " \n", " Pages
    {page_list}\n", " \"\"\"\n", " div = f\"\"\"\n", " \n", "
    \n", " {table_header}{table_body}
    \n", "
    \"\"\"\n", " return HTML(div)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    ParentsNodeChildren
  • Artificial_intelligence
    • Pages
    User_behavior_analytics | FEDOR_(Armed_AI_bot) | Intelligent_word_recognition | Autonomous_agent | List_of_programming_languages_for_artificial_intelligence | Virtual_intelligence | Radiant_AI | Knowledge-based_recommender_system | Situated | Colloquis | Embodied_agent | Moral_Machine | Winner-take-all_in_action_selection | Onnx | Dartmouth_workshop | Fuzzy_logic | Knowledge_compilation | Music_and_artificial_intelligence | Trenchard_More | Artificial_empathy | Artificial_intelligence,_situated_approach | 3D_reconstruction_from_multiple_images | Roborace | Multi-Agent_Programming_Contest | VaultML | Fred_(chatterbot) | Automated_personal_assistant | Computer_audition | Stochastic_semantic_analysis | Structure_mapping_engine | Artificial_imagination | Game_theory | Algorithmic_probability | API.AI | Percept_(artificial_intelligence) | Maluuba | ADS-AC | SUPS | Automated_Mathematician | Web_intelligence | DeepDream | LIDA_(cognitive_architecture) | Shyster_(expert_system) | 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    " ], "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_context_of_category(node)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive usage" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from ipywidgets import interact_manual" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2d4a9781420c45319f11b3a246567e9e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Text(value='Computer_vision', description='node'), Button(description='Run Interact', st…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact_manual(show_context_of_category, node=\"Computer_vision\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }