tm-qa-1-alpha.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(180000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 180 seconds\n"
     ]
    }
   ],
   "source": [
    "%autosave 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import convex as cx\n",
    "import requests\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_lg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_nlp(sentence):\n",
    "    return nlp(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdt import HDTDocument\n",
    "hdt_wd = HDTDocument(\"data/kb/wikidata2018_09_11.hdt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = [ \n",
    "    \"Which actor voiced the Unicorn in The Last Unicorn?\",\n",
    "    \"And Alan Arkin was behind...?\",\n",
    "    \"And Alan Arkin be behind...? Why How when which was happy make fun\",\n",
    "    \"Who is the composer of the soundtrack?\",\n",
    "    \"So who performed the songs?\",\n",
    "    \"Genre of this band's music?\",\n",
    "    \"By the way, who was the director?\"\n",
    "            ]\n",
    "\n",
    "q_test = str(\"Which actor voiced the Unicorn in The Last Unicorn? \"+\n",
    "    \"And Alan Arkin was behind...? \"+\n",
    "    \"And Alan Arkin be behind...? Why How when which was happy make fun. \"+\n",
    "    \"Who is the composer of the soundtrack? \"+\n",
    "    \"So who performed songs? \"+\n",
    "    \"Genre of this band's music? \"+\n",
    "    \"By the way, who was the director? \")\n",
    "\n",
    "q_test_2 = \"Who is the wife of Barack Obama?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Which actor voiced the Unicorn in The Last Unicorn?"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q0_nlp = get_nlp(questions[0])\n",
    "q0_nlp_test = get_nlp(q_test)\n",
    "q0_nlp_test_2 = get_nlp(q_test_2)\n",
    "q0_nlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "def is_wd_entity(to_check):\n",
    "    pattern = re.compile('^Q[0-9]*$')\n",
    "    if pattern.match(to_check.strip()): return True\n",
    "    else: return False\n",
    "\n",
    "def is_wd_predicate(to_check):\n",
    "    pattern = re.compile('^P[0-9]*$')\n",
    "    if pattern.match(to_check.strip()): return True\n",
    "    else: return False\n",
    "    \n",
    "def is_valide_wd_id(to_check):\n",
    "    if is_wd_entity(to_check) or is_wd_predicate(to_check): return True\n",
    "    else: return False\n",
    "\n",
    "print(is_valide_wd_id(\"P8765\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q7907375']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_wd_ids_online(name, is_predicate=False, top_k=3):\n",
    "    name = name.split('(')[0]\n",
    "\n",
    "    request_successfull = False\n",
    "    entity_ids = \"\"\n",
    "    while not request_successfull:\n",
    "        try:\n",
    "            if is_predicate:\n",
    "                entity_ids = requests.get('https://www.wikidata.org/w/api.php?action=wbsearchentities&format=json&language=en&type=property&limit=' + str(top_k) + '&search='+name).json()\n",
    "            else:\n",
    "                entity_ids = requests.get('https://www.wikidata.org/w/api.php?action=wbsearchentities&format=json&language=en&limit=' + str(top_k) + '&search='+name).json()\n",
    "            request_successfull = True\n",
    "        except:\n",
    "            time.sleep(5)\n",
    "    results = entity_ids.get(\"search\")\n",
    "    if not results:\n",
    "        return \"\"\n",
    "    if not len(results):\n",
    "        return \"\"\n",
    "    res = []\n",
    "    for result in results:\n",
    "        res.append(result['id'])\n",
    "    return res\n",
    "get_wd_ids_online(\"voiced\", is_predicate=False, top_k=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('voicing', 0.7561705), ('voices', 0.6340212), ('voice', 0.5594842)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# very computational\n",
    "def get_most_similar(word, topn=5):\n",
    "  word = nlp.vocab[str(word)]\n",
    "  queries = [w for w in word.vocab if w.is_lower == word.is_lower and w.prob >= -15]\n",
    "  by_similarity = sorted(queries, key=lambda w: word.similarity(w), reverse=True)\n",
    "  return [(w.lower_,w.similarity(word)) for w in by_similarity[:topn+1] if w.lower_ != word.lower_]\n",
    "\n",
    "get_most_similar(\"voiced\", topn=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q16614390', 'Q15628943', 'Q30060419', 'Q176198']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_wd_ids(word, top_k=3, limit=10):\n",
    "    language = \"en\"\n",
    "    word_formated = str(\"\\\"\"+word+\"\\\"\"+\"@\"+language)\n",
    "    to_remove = len(\"http://www.wikidata.org/entity/\")\n",
    "    t_name, card_name = hdt_wd.search_triples(\"\", \"http://schema.org/name\", word_formated, limit=top_k)\n",
    "    #print(\"names cardinality of \\\"\" + word+\"\\\": %i\" % card_name)\n",
    "    t_alt, card_alt = hdt_wd.search_triples(\"\", 'http://www.w3.org/2004/02/skos/core#altLabel', word_formated, limit=top_k)\n",
    "    #print(\"alternative names cardinality of \\\"\" + word+\"\\\": %i\" % card_alt)\n",
    "    \n",
    "    return list(set(\n",
    "        [t[0][to_remove:] for t in t_name if is_valide_wd_id(t[0][to_remove:])] + \n",
    "        [t[0][to_remove:] for t in t_alt if is_valide_wd_id(t[0][to_remove:])]\n",
    "           ))[:limit]\n",
    "    \n",
    "get_wd_ids(\"The Last Unicorn\", top_k=3, limit=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'voice actor'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_wd_label(from_id):\n",
    "    #print(\"from_id\",from_id)\n",
    "    if is_valide_wd_id(from_id):\n",
    "        language = \"en\"\n",
    "        id_url = \"http://www.wikidata.org/entity/\"+from_id\n",
    "        t_name, card_name = hdt_wd.search_triples(id_url, \"http://schema.org/name\", \"\")\n",
    "        name = [t[2].split('\\\"@en')[0].replace(\"\\\"\", \"\") for t in t_name if \"@\"+language in t[2]]\n",
    "        return name[0] if name else ''\n",
    "    else:\n",
    "        return ''\n",
    "    \n",
    "get_wd_label(\"P725\")\n",
    "#get_wd_label(\"Q20789322\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Building colors from graph\n",
    "def get_color(node_type):\n",
    "    if node_type == \"entity\": return \"violet\"#\"cornflowerblue\"\n",
    "    elif node_type == \"predicate\": return \"yellow\"\n",
    "    else: return \"red\"\n",
    "\n",
    "# Building labels for graph\n",
    "def get_elements_from_graph(graph):\n",
    "    node_names = nx.get_node_attributes(graph,\"name\")\n",
    "    node_types = nx.get_node_attributes(graph,\"type\")\n",
    "    colors = [get_color(node_types[n]) for n in node_names]\n",
    "    return node_names, colors\n",
    "\n",
    "# Plotting the graph\n",
    "def plot_graph(graph, name, title=\"Graph\"):\n",
    "    fig = plt.figure(figsize=(14,14))\n",
    "    ax = plt.subplot(111)\n",
    "    ax.set_title(str(\"answer: \"+title), fontsize=10)\n",
    "    pos = nx.spring_layout(graph)\n",
    "    labels, colors = get_elements_from_graph(graph)\n",
    "    nx.draw(graph, pos, node_size=30, node_color=colors, font_size=10, font_weight='bold', with_labels=True, labels=labels)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(str(name)+\".png\", format=\"PNG\", dpi = 300)\n",
    "    plt.show()\n",
    "    \n",
    "#plot_graph(graph, \"file_name_graph\", \"Graph_title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "def make_statements_graph(statements, indexing_predicates=True):\n",
    "    graph = nx.Graph()\n",
    "    turn=0\n",
    "    predicate_nodes = {}\n",
    "\n",
    "    for statement in statements:\n",
    "        #print(statement)\n",
    "        if not statement['entity']['id'] in graph:\n",
    "            graph.add_node(statement['entity']['id'], name=get_wd_label(statement['entity']['id']), type='entity', turn=turn)\n",
    "        if not statement['object']['id'] in graph:\n",
    "            graph.add_node(statement['object']['id'], name=get_wd_label(statement['object']['id']), type='entity', turn=turn)\n",
    "\n",
    "        # increment index of predicate or set it at 0\n",
    "        if not statement['predicate']['id'] in predicate_nodes or not indexing_predicates:\n",
    "            predicate_nodes_index = 1\n",
    "            predicate_nodes[statement['predicate']['id']] = 1\n",
    "        else:\n",
    "            predicate_nodes[statement['predicate']['id']] += 1\n",
    "            predicate_nodes_index = predicate_nodes[statement['predicate']['id']]\n",
    "\n",
    "        # add the predicate node\n",
    "        predicate_node_id = (statement['predicate']['id'])\n",
    "        if indexing_predicates: predicate_node_id += \"-\" + str(predicate_nodes_index)\n",
    "        \n",
    "        graph.add_node(predicate_node_id, name=get_wd_label(statement['predicate']['id']), type='predicate', turn=turn)\n",
    "\n",
    "        # add the two edges (entity->predicate->object)\n",
    "        graph.add_edge(statement['entity']['id'], predicate_node_id)\n",
    "        graph.add_edge(predicate_node_id, statement['object']['id'])\n",
    "    \n",
    "    return graph, predicate_nodes\n",
    "\n",
    "#test_graph = make_statements_graph(test_unduplicate_statements, indexing_predicates=False)\n",
    "#print(test_graph[1])\n",
    "#plot_graph(test_graph[0],\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 4), (2, 5), (3, 6)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def merge_lists(list_1, list_2):\n",
    "    if len(list_1) == len(list_2):\n",
    "        return [(list_1[i], list_2[i]) for i in range(0, len(list_1))]\n",
    "    else:\n",
    "        return \"Error: lists are not the same lenght\"\n",
    "\n",
    "merge_lists([1,2,3],[4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(The Last Unicorn, ['Q16614390', 'Q15628943', 'Q30060419', 'Q176198'])],\n",
       " [Which actor, the Unicorn])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_themes(nlp_question, top_k=3):\n",
    "    themes = []\n",
    "    theme_complements = []\n",
    "    \n",
    "    noun_chunks = [chunk for chunk in nlp_question.noun_chunks]\n",
    "    theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks]\n",
    "\n",
    "    for i, chunk in enumerate(theme_ids):\n",
    "        if chunk: themes.append((noun_chunks[i], chunk))\n",
    "        else: theme_complements.append(noun_chunks[i])\n",
    "    \n",
    "    return themes, theme_complements\n",
    "\n",
    "q0_themes = get_themes(q0_nlp, top_k=3)\n",
    "q0_themes_test = get_themes(q0_nlp_test)\n",
    "q0_themes_test_2 = get_themes(q0_nlp_test_2)\n",
    "q0_themes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(voiced, ['P725'])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_predicates_online(nlp_sentence, top_k=3):\n",
    "    predicates = [p for p in nlp_sentence if p.pos_ == \"VERB\" or p.pos_ == \"AUX\"]\n",
    "    predicates_ids = [get_wd_ids_online(p.text, is_predicate=True, top_k=top_k) for p in predicates]\n",
    "    return merge_lists(predicates, predicates_ids)\n",
    "\n",
    "q0_predicates = get_predicates_online(q0_nlp, top_k=3)\n",
    "q0_predicates_test_2 = get_predicates_online(q0_nlp_test_2, top_k=3)\n",
    "q0_predicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(actor, ['Q4677712', 'Q33999', 'Q421946', 'P161', 'Q10798782'])]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_focused_parts(nlp_sentence, top_k=3):\n",
    "    focused_parts = [t.head for t in nlp_sentence if t.tag_ == \"WDT\" or t.tag_ == \"WP\" or t.tag_ == \"WP$\" or t.tag_ == \"WRB\"] \n",
    "    focused_parts_ids = [get_wd_ids(p.text, top_k=top_k) for p in focused_parts]\n",
    "    return merge_lists(focused_parts, focused_parts_ids)\n",
    "\n",
    "q0_focused_parts = get_focused_parts(q0_nlp)\n",
    "q0_focused_parts_test_2 = get_focused_parts(q0_nlp_test_2)\n",
    "q0_focused_parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q4677712', 'Q33999', 'Q421946', 'P161', 'Q10798782']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import chain\n",
    "def extract_ids(to_extract):\n",
    "    return [i for i in chain.from_iterable([id[1] for id in to_extract])]\n",
    "#extract_ids([('name', ['id'])]) #q0_themes[0] #q0_focused_parts #q0_predicates\n",
    "#extract_ids([(\"The Last Unicorn\", ['Q16614390']),(\"Second Theme\", ['Q12345'])])\n",
    "extract_ids(q0_focused_parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6516460644618532"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_similarity_by_words(nlp_word_from, nlp_word_to):\n",
    "    if not nlp_word_from or not nlp_word_to:\n",
    "        return 0\n",
    "    elif not nlp_word_from.vector_norm or not nlp_word_to.vector_norm:\n",
    "        return 0\n",
    "    else:\n",
    "        return nlp_word_from.similarity(nlp_word_to)\n",
    "\n",
    "get_similarity_by_words(get_nlp(\"character role\"), get_nlp(\"voice actor\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6516460644618532"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_similarity_by_ids(word_id_from, word_id_to):\n",
    "    nlp_word_from = get_nlp(get_wd_label(word_id_from))\n",
    "    nlp_word_to = get_nlp(get_wd_label(word_id_to))\n",
    "    return get_similarity_by_words(nlp_word_from, nlp_word_to)\n",
    "\n",
    "get_similarity_by_ids(\"P453\", \"P725\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36831644893980314,\n",
       " {'entity': {'id': 'Q176198'},\n",
       "  'predicate': {'id': 'P136'},\n",
       "  'object': {'id': 'Q28968511'},\n",
       "  'qualifiers': []})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top_similar_statements(statements, from_token_id, similar_to_name, top_k=3, qualifier=False, statement_type=\"object\"):\n",
    "    highest_matching_similarity = -1\n",
    "    top_statements = []\n",
    "    nlp_name = get_nlp(similar_to_name)\n",
    "    \n",
    "    if get_wd_label(from_token_id):\n",
    "        for statement in statements:\n",
    "            if qualifier:\n",
    "                if statement.get('qualifiers'):\n",
    "                    for qualifier in statement['qualifiers']:\n",
    "                        nlp_word_to = get_nlp(get_wd_label(qualifier[statement_type]['id']))\n",
    "                        matching_similarity = get_similarity_by_words(nlp_name, nlp_word_to)\n",
    "                        if highest_matching_similarity == -1 or matching_similarity > highest_matching_similarity:\n",
    "                            highest_matching_similarity = matching_similarity\n",
    "                            best_statement = statement\n",
    "                            top_statements.append((highest_matching_similarity, best_statement))\n",
    "            else:\n",
    "                nlp_word_to = get_nlp(get_wd_label(statement[statement_type]['id']))\n",
    "                matching_similarity = get_similarity_by_words(nlp_name, nlp_word_to)\n",
    "                if highest_matching_similarity == -1 or matching_similarity > highest_matching_similarity:\n",
    "                    highest_matching_similarity = matching_similarity\n",
    "                    best_statement = statement\n",
    "                    top_statements.append((highest_matching_similarity, best_statement))\n",
    "            \n",
    "    return sorted(top_statements, key=lambda x: x[0], reverse=True)[:top_k]\n",
    "\n",
    "statements = cx.wd.get_all_statements_of_entity('Q176198')\n",
    "top_similar_statements = get_top_similar_statements(statements, 'Q176198', 'voiced')\n",
    "top_similar_statements[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3872851385988705,\n",
       " {'entity': {'id': 'Q30060419'},\n",
       "  'predicate': {'id': 'P1441'},\n",
       "  'object': {'id': 'Q176198'},\n",
       "  'qualifiers': [{'qualifier_predicate': {'id': 'P2868'},\n",
       "    'qualifier_object': {'id': 'Q215972'}},\n",
       "   {'qualifier_predicate': {'id': 'P2868'},\n",
       "    'qualifier_object': {'id': 'Q3246821'}}]})"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_best_similar_statements_by_word(from_token_ids, similar_to_name, top_k=3, qualifier=False, statement_type=\"object\"):\n",
    "    best_statements = []\n",
    "    for token in from_token_ids:\n",
    "        statements = cx.wd.get_all_statements_of_entity(token)\n",
    "        if statements: best_statements += get_top_similar_statements(statements, token, similar_to_name, top_k=top_k, qualifier=qualifier, statement_type=statement_type)\n",
    "\n",
    "    return sorted(best_statements, key=lambda x: x[0], reverse=True)[:top_k]\n",
    "\n",
    "best_similar_statements = get_best_similar_statements_by_word(extract_ids(q0_themes[0]), 'voiced', top_k=3, qualifier=True, statement_type=\"qualifier_object\")\n",
    "best_similar_statements[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The Unicorn', 'The Last Unicorn', 'The Last Unicorn']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_statements_subjects_labels(statements):\n",
    "    return [get_wd_label(t[1]['entity']['id']) for t in statements]\n",
    "get_statements_subjects_labels(best_similar_statements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['present in work', 'voice actor', 'voice actor']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_statements_predicates_labels(statements):\n",
    "    return [get_wd_label(t[1]['predicate']['id']) for t in statements]\n",
    "get_statements_predicates_labels(best_similar_statements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The Last Unicorn', 'Don Messick', 'Christopher Lee']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_statements_objects_labels(statements):\n",
    "    return [get_wd_label(t[1]['object']['id']) for t in statements]\n",
    "get_statements_objects_labels(best_similar_statements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['subject has role', 'character role', 'character role']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_statements_qualifier_predicates_labels(statements):\n",
    "    return [get_wd_label(t[1]['qualifiers'][0]['qualifier_predicate']['id']) for t in statements]\n",
    "get_statements_qualifier_predicates_labels(best_similar_statements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['protagonist', 'The cat', 'King Haggard']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_statements_qualifier_objects_labels(statements):\n",
    "    return [get_wd_label(t[1]['qualifiers'][0]['qualifier_object']['id']) for t in statements]\n",
    "get_statements_qualifier_objects_labels(best_similar_statements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4414132775507227,\n",
       " {'entity': {'id': 'Q176198'},\n",
       "  'predicate': {'id': 'P725'},\n",
       "  'object': {'id': 'Q202725'},\n",
       "  'qualifiers': [{'qualifier_predicate': {'id': 'P453'},\n",
       "    'qualifier_object': {'id': 'Q30060419'}}]})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cluster_extend_by_words(cluster_root_ids, extending_words, top_k=3):\n",
    "    cluster = []\n",
    "    \n",
    "    for name in extending_words:\n",
    "        cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=True, statement_type=\"qualifier_predicate\")\n",
    "        cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=True, statement_type=\"qualifier_object\")\n",
    "        cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=False, statement_type=\"predicate\")\n",
    "        cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=False, statement_type=\"object\")\n",
    "        \n",
    "    return cluster\n",
    "    \n",
    "test_cluster = cluster_extend_by_words(extract_ids(q0_themes[0]), ['voiced'], top_k=2)\n",
    "test_cluster_test_2 = cluster_extend_by_words(extract_ids(q0_themes_test_2[0]), ['birth'], top_k=2)\n",
    "test_cluster[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5974342416487763,\n",
       " {'entity': {'id': 'Q176198'},\n",
       "  'predicate': {'id': 'P725'},\n",
       "  'object': {'id': 'Q202725'},\n",
       "  'qualifiers': [{'qualifier_predicate': {'id': 'P453'},\n",
       "    'qualifier_object': {'id': 'Q30060419'}}]})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorts by the similarity value of statements[0]\n",
    "def sort_statements_by_similarity(statements):\n",
    "    return [s for s in sorted(statements, key=lambda x: x[0], reverse=True)]\n",
    "\n",
    "test_sorted_statements = sort_statements_by_similarity(test_cluster)\n",
    "test_sorted_statements_test_2 = sort_statements_by_similarity(test_cluster_test_2)\n",
    "test_sorted_statements[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'entity': {'id': 'Q176198'},\n",
       " 'predicate': {'id': 'P453'},\n",
       " 'object': {'id': 'Q30060419'}}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from copy import copy\n",
    "\n",
    "# appends spo from qualifiers, removes qualifier tags, and removes similarity scores\n",
    "def statements_flatter(statements):\n",
    "    best_statements_to_graph = []\n",
    "    for statement in statements:\n",
    "        tmp_statement = copy(statement)\n",
    "        if tmp_statement.get('qualifiers'):\n",
    "            #print(\"statement\", statement)\n",
    "            for q in tmp_statement['qualifiers']:\n",
    "                qualifier_statement = {'entity': {'id': tmp_statement['entity']['id']}}\n",
    "                qualifier_statement['predicate'] = {'id': q['qualifier_predicate']['id']}\n",
    "                qualifier_statement['object'] = {'id': q['qualifier_object']['id']}\n",
    "                best_statements_to_graph.append(qualifier_statement)\n",
    "            del(tmp_statement['qualifiers'])\n",
    "        else: \n",
    "            #print(\"tmp_statement\", tmp_statement)\n",
    "            if ('qualifiers' in tmp_statement): del(tmp_statement['qualifiers'])\n",
    "        if tmp_statement not in best_statements_to_graph:\n",
    "            #print(\"best_statements_to_graph\", tmp_statement)\n",
    "            best_statements_to_graph.append(tmp_statement)\n",
    "    return best_statements_to_graph\n",
    "\n",
    "test_flatten_statements = statements_flatter([s[1] for s in test_sorted_statements])\n",
    "#test_flatten_statements_test_2 = statements_flatter([s[1] for s in test_sorted_statements_test_2])\n",
    "test_flatten_statements[0]\n",
    "#test_flatten_statements_test_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'entity': {'id': 'Q176198'},\n",
       " 'predicate': {'id': 'P453'},\n",
       " 'object': {'id': 'Q30060419'}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove duplicates from statements\n",
    "def unduplicate_statements(statements):\n",
    "    filtered_statements = []\n",
    "    [filtered_statements.append(s) for s in statements if s not in [e for e in filtered_statements]]\n",
    "    return filtered_statements\n",
    "\n",
    "test_unduplicate_statements = unduplicate_statements(test_flatten_statements)\n",
    "print(len(test_flatten_statements))\n",
    "print(len(test_unduplicate_statements))\n",
    "test_unduplicate_statements[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'P453': 2, 'P725': 2, 'P31': 1, 'P2868': 2, 'P1441': 1, 'P642': 2}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_graph = make_statements_graph(test_unduplicate_statements)\n",
    "print(test_graph[1])\n",
    "plot_graph(test_graph[0], \"file_name_graph\", \"Graph_title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_statements_by_id(statements, from_token_id, to_id, qualifier=False, statement_type=\"predicate\"):\n",
    "    id_statements = []\n",
    "    if not statements:\n",
    "        return id_statements\n",
    "    if cx.wd.wikidata_id_to_label(from_token_id):\n",
    "        for statement in statements:\n",
    "            if qualifier:\n",
    "                if statement.get('qualifiers'):\n",
    "                    for s in statement['qualifiers']:\n",
    "                        if to_id == s[statement_type]['id']:\n",
    "                            id_statements.append(statement)\n",
    "            else:\n",
    "                if to_id == statement[statement_type]['id']:\n",
    "                    id_statements.append(statement)\n",
    "    \n",
    "    return id_statements\n",
    "\n",
    "#statements_test = cx.wd.get_all_statements_of_entity('Q176198')\n",
    "#id_statements_test = get_statements_by_id(statements_test, 'Q176198', 'P725')\n",
    "#id_statements_test[0]\n",
    "\n",
    "#get_statements_by_id(root_statements, cluster_root_id, predicate_id, qualifier=False, statement_type=\"predicate\")\n",
    "#statements_test = cx.wd.get_all_statements_of_entity('Q176198')\n",
    "#id_statements_test = get_statements_by_id(statements_test, 'Q176198', 'P725')\n",
    "#id_statements_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'entity': {'id': 'Q76'},\n",
       " 'predicate': {'id': 'P19'},\n",
       " 'object': {'id': 'Q6366688'},\n",
       " 'qualifiers': [{'qualifier_predicate': {'id': 'P131'},\n",
       "   'qualifier_object': {'id': 'Q782'}}]}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters\n",
    "# cluster_root_ids: ['Qcode']\n",
    "# predicates_ids: ['Pcode']\n",
    "def cluster_extend_by_predicates_ids(cluster_root_ids, predicates_ids):\n",
    "    cluster = []\n",
    "    \n",
    "    for cluster_root_id in cluster_root_ids:\n",
    "        root_statements = cx.wd.get_all_statements_of_entity(cluster_root_id)\n",
    "        #print(\"root_statements\", root_statements)\n",
    "        for predicate_id in predicates_ids:\n",
    "            cluster += get_statements_by_id(root_statements, cluster_root_id, predicate_id, qualifier=True, statement_type=\"qualifier_predicate\")\n",
    "            cluster += get_statements_by_id(root_statements, cluster_root_id, predicate_id, qualifier=False, statement_type=\"predicate\")\n",
    "\n",
    "    return cluster\n",
    "    \n",
    "#test_predicate_clusters = cluster_extend_by_predicates_ids(extract_ids(q0_themes[0]), extract_ids(q0_predicates))\n",
    "#print(len(test_predicate_clusters))\n",
    "#test_predicate_clusters[0]\n",
    "\n",
    "test_predicate_clusters_test_2 = cluster_extend_by_predicates_ids(extract_ids(q0_themes_test_2[0]), extract_ids(q0_predicates_test_2))\n",
    "print(len(test_predicate_clusters_test_2))\n",
    "test_predicate_clusters_test_2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'P453': 14, 'P725': 13, 'P31': 3, 'P2868': 2, 'P1441': 1, 'P642': 2, 'P136': 3, 'P58': 1, 'P162': 1, 'P361': 1, 'P175': 2, 'P1040': 1, 'P21': 1, 'P1340': 1, 'P646': 2}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameter\n",
    "# question: nlp_string\n",
    "def build_graph(nlp, themes, predicates, deep_k=50):\n",
    "    init_clusters = cluster_extend_by_words(extract_ids(themes[0]), [p[0].text for p in predicates], top_k=deep_k)\n",
    "    init_sorted_statements = sort_statements_by_similarity(init_clusters)\n",
    "    init_flatten_statements = statements_flatter([s[1] for s in init_sorted_statements])\n",
    "    \n",
    "    predicate_ids_clusters = cluster_extend_by_predicates_ids(extract_ids(themes[0]), extract_ids(predicates))\n",
    "    predicate_ids_flatten_statements = statements_flatter(predicate_ids_clusters)\n",
    "    clusters = init_flatten_statements+predicate_ids_flatten_statements\n",
    "    filtered_statements = unduplicate_statements(clusters)\n",
    "    \n",
    "    graph = make_statements_graph(filtered_statements)\n",
    "    \n",
    "    #print(\"clusters:\", len(clusters))\n",
    "    #print(\"filtered_statements:\", len(filtered_statements))\n",
    "    return graph\n",
    "\n",
    "#q0_test = questions[0]\n",
    "q0_test = \"Which actor voiced the Unicorn in The Last Unicorn?\"\n",
    "q0_nlp_test = get_nlp(q0_test)\n",
    "q0_themes_test = get_themes(q0_nlp_test, top_k=3)\n",
    "q0_predicates_test = get_predicates_online(q0_nlp_test, top_k=3)\n",
    "q0_focused_parts_test = get_focused_parts(q0_nlp_test)\n",
    "graph, predicates_dict = build_graph(q0_nlp_test, q0_themes_test, q0_predicates_test)\n",
    "print(predicates_dict)\n",
    "plot_graph(graph, \"file_name_graph\", \"Graph_title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q30060419']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the graph for complements\n",
    "# parameters\n",
    "# name: string\n",
    "def find_name_in_graph(graph, name):\n",
    "    return [x for x,y in graph.nodes(data=True) if y['name'].lower() == name.lower()]\n",
    "\n",
    "#[find_name_in_graph(c.text) for c in q0_themes[1]]\n",
    "find_name_in_graph(graph, \"the unicorn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q30060419']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: clean the complements by removing stopwords etc.\n",
    "def find_theme_complement(graph, themes):\n",
    "    return [i for i in chain.from_iterable(\n",
    "        [id for id in [c for c in [find_name_in_graph(graph, t.text) for t in themes[1]] if c]])]\n",
    "\n",
    "find_theme_complement(graph, q0_themes_test)\n",
    "#[i for i in chain.from_iterable([id for id in check_theme_complement(graph, q0_themes)])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Q176198', 'P453-1', 'Q30060419', 'P175-2', 'Q202725'],\n",
       " ['Q176198', 'P725-1', 'Q202725'],\n",
       " ['Q176198', 'P1441-1', 'Q30060419', 'P175-2', 'Q202725'],\n",
       " ['Q176198', 'P642-1', 'Q30060419', 'P175-2', 'Q202725']]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_paths_in_graph(graph, node_start, node_end):\n",
    "    return [p for p in nx.all_simple_paths(graph, source=node_start, target=node_end)]\n",
    "        \n",
    "test_paths = find_paths_in_graph(graph, \"Q176198\", \"Q202725\")\n",
    "test_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_id_in_graph(graph, node_id):\n",
    "    return graph.has_node(node_id)\n",
    "is_id_in_graph(graph, \"Q176198\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_name_in_graph(graph, node_name):\n",
    "    return find_name_in_graph(graph, node_name) != []\n",
    "is_name_in_graph(graph, \"the Unicorn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Q176198', 'P453-1', 'Q30060419'],\n",
       " ['Q176198', 'P725-1', 'Q202725', 'P175-2', 'Q30060419'],\n",
       " ['Q176198', 'P1441-1', 'Q30060419'],\n",
       " ['Q176198', 'P642-1', 'Q30060419']]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_paths_for_themes(graph, themes):\n",
    "    themes_ids = [t for t in  extract_ids(themes[0])]\n",
    "    complements_ids = find_theme_complement(graph, themes)\n",
    "    paths = []\n",
    "    for t_id in themes_ids:\n",
    "        if is_id_in_graph(graph, t_id):\n",
    "            for c_id in complements_ids:\n",
    "                if is_id_in_graph(graph, c_id):\n",
    "                    path = find_paths_in_graph(graph, t_id, c_id)\n",
    "                    if path:\n",
    "                        paths.append(path)\n",
    "    paths = [i for i in chain.from_iterable(\n",
    "        [id for id in paths])]\n",
    "    \n",
    "    return paths\n",
    "find_paths_for_themes(graph, q0_themes_test)\n",
    "find_paths_for_themes(graph, q0_themes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['P453', 'P175', 'P725', 'P1441', 'P642']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_node_predicates_from_path(paths):\n",
    "    predicates = []\n",
    "    for p in paths:\n",
    "        [predicates.append(i[:i.find(\"-\")]) for i in p if is_wd_predicate(i[:i.find(\"-\")]) and i[:i.find(\"-\")] not in predicates]\n",
    "    return predicates\n",
    "\n",
    "test_node_predicates = get_node_predicates_from_path(test_paths)\n",
    "test_node_predicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('P725', 1.0),\n",
       " ('P453', 0.6516460644618532),\n",
       " ('P175', 0.5508198537586991),\n",
       " ('P1441', 0.34966764303274056),\n",
       " ('P642', 0.2361562242692872)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_node_predicate_similarity_from_path(paths, predicates):\n",
    "    path_predicates = get_node_predicates_from_path(paths)\n",
    "    return sorted([(pp, get_similarity_by_ids(p2, pp)) for p in predicates for p2 in p[1] for pp in path_predicates], key=lambda x: x[-1], reverse=True)\n",
    "\n",
    "test_node_pedicate_similarities = get_node_predicate_similarity_from_path(test_paths, q0_predicates)\n",
    "test_node_pedicate_similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Mia Farrow', 'Q202725')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: make the predicate search go further in the path list for the !i%2\n",
    "def find_anwser_from_graph(graph, q_nlp, themes, predicates):\n",
    "    initial_paths = find_paths_for_themes(graph, themes)\n",
    "    predicate_id_similarities = get_node_predicate_similarity_from_path(initial_paths, predicates)\n",
    "    best_path = [p for p in initial_paths if predicate_id_similarities[0][0] == p[1][:p[1].find(\"-\")]]\n",
    "    path_answer = get_wd_label(best_path[0][2]) if best_path else []\n",
    "    \n",
    "    return path_answer, best_path[0][2] if path_answer else (False, False)\n",
    "\n",
    "find_anwser_from_graph(graph, q0_nlp, q0_themes, q0_predicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_initial_question(question):\n",
    "    q_nlp = get_nlp(question)\n",
    "    q_themes = get_themes(q_nlp, top_k=3)\n",
    "    q_predicates = get_predicates_online(q_nlp, top_k=3)\n",
    "    q_focused_parts = get_focused_parts(q_nlp)\n",
    "    #print(q_nlp, q_themes, q_predicates, q_focused_parts)\n",
    "    graph, predicates_dict = build_graph(q_nlp, q_themes, q_predicates)\n",
    "    #print(predicates_dict)\n",
    "    #plot_graph(graph, \"main_graph\", \"Main_graph_title\")\n",
    "    return find_anwser_from_graph(graph, q0_nlp, q0_themes, q0_predicates)\n",
    "\n",
    "#answer_initial_question(\"Which actor voiced the Unicorn in The Last Unicorn?\")\n",
    "#answer_initial_question(\"Who is the author of Le Petit Prince?\")\n",
    "#answer_initial_question(\"Who made the soundtrack of the The Last Unicorn movie?\")\n",
    "#answer_initial_question(\"When was produced the first Matrix movie?\")\n",
    "#answer_initial_question(\"Who is the president of the United States?\")\n",
    "#answer_initial_question(\"Who is the wife of Barack Obama?\")\n",
    "answer_initial_question(\"what was the cause of death of yves klein\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subgraphs = [graph.subgraph(c) for c in nx.connected_components(graph)]\n",
    "len(subgraphs[0].nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def search_focused_part(graph):\n",
    "    return 0\n",
    "\n",
    "search_focused_part(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for path in nx.all_simple_paths(graph, source=\"Q176198\", target=\"Q202725\"):\n",
    "    print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.shortest_path(graph, source=\"Q176198\", target=\"Q202725\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[y['name'] for x,y in graph.nodes(data=True) if y['name'].lower() == \"The Unicorn\".lower()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for complement in q0_themes[1]:\n",
    "    print(complement)\n",
    "    for e in complement:\n",
    "        print(e, e.pos_, e.tag_, e.dep_, e.head)\n",
    "\n",
    "print(\"\\n\")\n",
    "for e in q0_nlp:\n",
    "    print(e, e.pos_, e.tag_, e.dep_, e.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(nx.dfs_labeled_edges(graph, source=get_themes(question_0_nlp, top_k=3)[0][0][1][0], depth_limit=4))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_themes(question_0_nlp, top_k=3)[0][0][1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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