example/dependency/load-dependency.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.5 s, sys: 1.84 s, total: 13.3 s\n",
      "Wall time: 18.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import malaya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List available deep learning Dependency models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['concat', 'bahdanau', 'luong', 'attention-is-all-you-need']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.dependency.available_deep_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe supported dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acl - clausal modifier of noun\n",
      "advcl - adverbial clause modifier\n",
      "advmod - adverbial modifier\n",
      "amod - adjectival modifier\n",
      "appos - appositional modifier\n",
      "aux - auxiliary\n",
      "case - case marking\n",
      "ccomp - clausal complement\n",
      "compound - compound\n",
      "compound:plur - plural compound\n",
      "conj - conjunct\n",
      "cop - cop\n",
      "csubj - clausal subject\n",
      "dep - dependent\n",
      "det - determiner\n",
      "fixed - multi-word expression\n",
      "flat - name\n",
      "iobj - indirect object\n",
      "mark - marker\n",
      "nmod - nominal modifier\n",
      "nsubj - nominal subject\n",
      "obj - direct object\n",
      "parataxis - parataxis\n",
      "root - root\n",
      "xcomp - open clausal complement\n",
      "you can read more from https://universaldependencies.org/en/dep/xcomp.html\n"
     ]
    }
   ],
   "source": [
    "malaya.describe_dependency()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load CRF model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0.00/19.0 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found old version of /Users/huseinzol/Malaya/dependency/crf, deleting..\n",
      "Done.\n",
      "downloading frozen /Users/huseinzol/Malaya/dependency/crf model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19.0MB [00:04, 3.91MB/s]                          \n",
      "  0%|          | 0.00/40.8 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading frozen /Users/huseinzol/Malaya/dependency/crf depend\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "41.0MB [00:10, 4.14MB/s]                          \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([('Dr', 'det'),\n",
       "  ('Mahathir', 'nsubj'),\n",
       "  ('menasihati', 'conj'),\n",
       "  ('mereka', 'obj'),\n",
       "  ('supaya', 'case'),\n",
       "  ('berhenti', 'xcomp'),\n",
       "  ('berehat', 'advcl'),\n",
       "  ('dan', 'cc'),\n",
       "  ('tidur', 'conj'),\n",
       "  ('sebentar', 'advmod'),\n",
       "  ('sekiranya', 'advmod'),\n",
       "  ('mengantuk', 'UNK'),\n",
       "  ('ketika', 'case'),\n",
       "  ('memandu', 'xcomp')],\n",
       " [('Dr', 5),\n",
       "  ('Mahathir', 10),\n",
       "  ('menasihati', 8),\n",
       "  ('mereka', 8),\n",
       "  ('supaya', 8),\n",
       "  ('berhenti', 10),\n",
       "  ('berehat', 10),\n",
       "  ('dan', 14),\n",
       "  ('tidur', 4),\n",
       "  ('sebentar', 12),\n",
       "  ('sekiranya', 9),\n",
       "  ('mengantuk', 1),\n",
       "  ('ketika', 9),\n",
       "  ('memandu', 7)])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crf = malaya.dependency.crf()\n",
    "d_object, tagging, indexing = crf.predict(string)\n",
    "tagging, indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize graph for dependency output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make sure you already installed graphvis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "d_object.to_graphvis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print important features from CRF model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-10 tagging positive:\n",
      "12.547764 conj     next_word:perlunya\n",
      "12.367710 punct    word:mengembanfkan\n",
      "11.793032 nummod   word:mengembanfkan\n",
      "10.094637 det      word:berbagai\n",
      "9.573215 case     word:dari\n",
      "9.210329 appos    word:dimekarkannya\n",
      "9.153894 det      word:para\n",
      "9.128825 root     word:fasilitator\n",
      "9.055041 obl      next_word:ditafsirkan\n",
      "9.013166 case     word:dengan\n",
      "\n",
      "Top-10 tagging negative:\n",
      "-4.373424 flat     prefix-2:ak\n",
      "-4.400194 cc       next_word:ketumbar\n",
      "-4.465530 obj      next_word:cara\n",
      "-4.517578 conj     prefix-2:Mr\n",
      "-4.546550 compound word:masih\n",
      "-4.644113 case     prev_word:harus\n",
      "-4.800643 compound word:telah\n",
      "-5.301659 compound word:akan\n",
      "-5.808335 compound word:pernah\n",
      "-6.553638 flat     is_first\n"
     ]
    }
   ],
   "source": [
    "crf.print_features(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print important tagging transitions from CRF Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-10 likely tagging transitions:\n",
      "case   -> obl     5.195645\n",
      "aux    -> aux     4.643113\n",
      "cc     -> conj    4.223698\n",
      "case   -> nmod    4.180786\n",
      "flat   -> flat    4.040346\n",
      "cop    -> root    3.771183\n",
      "punct  -> compound:plur 3.497424\n",
      "punct  -> fixed   3.416479\n",
      "appos  -> flat    3.031077\n",
      "nmod   -> flat    2.940859\n",
      "\n",
      "Top-10 unlikely tagging transitions:\n",
      "appos  -> advcl   -4.389181\n",
      "root   -> parataxis -4.557102\n",
      "cop    -> nmod    -4.594469\n",
      "cop    -> obl     -4.752784\n",
      "aux    -> acl     -4.981520\n",
      "case   -> fixed   -5.202611\n",
      "nummod -> compound:plur -5.284053\n",
      "compound:plur -> cop     -5.808322\n",
      "root   -> root    -6.301090\n",
      "cop    -> obj     -7.429950\n"
     ]
    }
   ],
   "source": [
    "crf.print_transitions_tag(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print important indexing transitions from CRF Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-10 likely indexing transitions:\n",
      "117    -> 117     6.829730\n",
      "182    -> 176     6.767373\n",
      "1      -> 0       6.499095\n",
      "108    -> 109     6.303625\n",
      "95     -> 99      6.039325\n",
      "138    -> 138     6.011194\n",
      "97     -> 97      5.971080\n",
      "103    -> 77      5.789712\n",
      "79     -> 79      5.758269\n",
      "137    -> 140     5.705340\n",
      "\n",
      "Top-10 unlikely indexing transitions:\n",
      "0      -> 30      -5.182860\n",
      "2      -> 39      -5.200419\n",
      "33     -> 1       -5.227053\n",
      "30     -> 0       -5.265685\n",
      "36     -> 0       -5.269204\n",
      "34     -> 0       -5.398857\n",
      "1      -> 40      -5.499971\n",
      "49     -> 2       -5.615010\n",
      "1      -> 38      -5.627040\n",
      "1      -> 39      -5.758355\n"
     ]
    }
   ],
   "source": [
    "crf.print_transitions_index(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load deep learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0.00/37.7 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing concat model\n",
      "Found old version of /Users/huseinzol/Malaya/dependency/concat, deleting..\n",
      "Done.\n",
      "downloading frozen /Users/huseinzol/Malaya/dependency/concat model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "38.0MB [00:10, 3.43MB/s]                          \n",
      "  0%|          | 0.00/2.45 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading frozen /Users/huseinzol/Malaya/dependency/concat setting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3.00MB [00:00, 3.96MB/s]                          \n",
      "  0%|          | 0.00/38.6 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<malaya._utils._parse_dependency.DependencyGraph object at 0x152457860>, [('Dr', 'nsubj'), ('Mahathir', 'flat'), ('menasihati', 'root'), ('mereka', 'obj'), ('supaya', 'case'), ('berhenti', 'xcomp'), ('berehat', 'xcomp'), ('dan', 'cc'), ('tidur', 'conj'), ('sebentar', 'det'), ('sekiranya', 'mark'), ('mengantuk', 'amod'), ('ketika', 'case'), ('memandu', 'xcomp')], [('Dr', 3), ('Mahathir', 1), ('menasihati', 0), ('mereka', 3), ('supaya', 6), ('berhenti', 3), ('berehat', 6), ('dan', 9), ('tidur', 7), ('sebentar', 9), ('sekiranya', 12), ('mengantuk', 9), ('ketika', 13), ('memandu', 13)])\n",
      "\n",
      "Testing bahdanau model\n",
      "Found old version of /Users/huseinzol/Malaya/dependency/bahdanau, deleting..\n",
      "Done.\n",
      "downloading frozen /Users/huseinzol/Malaya/dependency/bahdanau model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "39.0MB [00:11, 4.38MB/s]                          \n",
      "  0%|          | 0.00/2.45 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading frozen /Users/huseinzol/Malaya/dependency/bahdanau setting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3.00MB [00:00, 4.00MB/s]                          \n",
      "  0%|          | 0.00/38.6 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<malaya._utils._parse_dependency.DependencyGraph object at 0x1515ceac8>, [('Dr', 'flat'), ('Mahathir', 'flat'), ('menasihati', 'root'), ('mereka', 'det'), ('supaya', 'case'), ('berhenti', 'xcomp'), ('berehat', 'ccomp'), ('dan', 'cc'), ('tidur', 'conj'), ('sebentar', 'case'), ('sekiranya', 'mark'), ('mengantuk', 'advmod'), ('ketika', 'mark'), ('memandu', 'ccomp')], [('Dr', 2), ('Mahathir', 2), ('menasihati', 0), ('mereka', 3), ('supaya', 6), ('berhenti', 3), ('berehat', 6), ('dan', 9), ('tidur', 3), ('sebentar', 9), ('sekiranya', 12), ('mengantuk', 10), ('ketika', 13), ('memandu', 12)])\n",
      "\n",
      "Testing luong model\n",
      "Found old version of /Users/huseinzol/Malaya/dependency/luong, deleting..\n",
      "Done.\n",
      "downloading frozen /Users/huseinzol/Malaya/dependency/luong model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "39.0MB [00:10, 4.43MB/s]                          \n",
      "  0%|          | 0.00/2.45 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading frozen /Users/huseinzol/Malaya/dependency/luong setting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3.00MB [00:00, 3.18MB/s]                          \n",
      "  0%|          | 0.00/52.9 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<malaya._utils._parse_dependency.DependencyGraph object at 0x152eb4d30>, [('Dr', 'flat'), ('Mahathir', 'flat'), ('menasihati', 'root'), ('mereka', 'det'), ('supaya', 'mark'), ('berhenti', 'advcl'), ('berehat', 'fixed'), ('dan', 'cc'), ('tidur', 'conj'), ('sebentar', 'ccomp'), ('sekiranya', 'mark'), ('mengantuk', 'advcl'), ('ketika', 'mark'), ('memandu', 'advcl')], [('Dr', 3), ('Mahathir', 1), ('menasihati', 0), ('mereka', 2), ('supaya', 6), ('berhenti', 4), ('berehat', 6), ('dan', 9), ('tidur', 9), ('sebentar', 9), ('sekiranya', 12), ('mengantuk', 9), ('ketika', 12), ('memandu', 12)])\n",
      "\n",
      "Testing attention-is-all-you-need model\n",
      "downloading frozen /Users/huseinzol/Malaya/dependency/attention model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "53.0MB [00:14, 4.04MB/s]                          \n",
      "  0%|          | 0.00/2.45 [00:00<?, ?MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading frozen /Users/huseinzol/Malaya/dependency/attention setting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3.00MB [00:00, 4.07MB/s]                          \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<malaya._utils._parse_dependency.DependencyGraph object at 0x159ce8780>, [('Dr', 'mark'), ('Mahathir', 'nsubj'), ('menasihati', 'advmod'), ('mereka', 'nsubj'), ('supaya', 'det'), ('berhenti', 'nsubj'), ('berehat', 'UNK'), ('dan', 'mark'), ('tidur', 'nsubj'), ('sebentar', 'advmod'), ('sekiranya', 'nsubj'), ('mengantuk', 'advmod'), ('ketika', 'nsubj'), ('memandu', 'advmod')], [('Dr', 3), ('Mahathir', 3), ('menasihati', 3), ('mereka', 3), ('supaya', 3), ('berhenti', 3), ('berehat', 3), ('dan', 3), ('tidur', 3), ('sebentar', 3), ('sekiranya', 3), ('mengantuk', 3), ('ketika', 3), ('memandu', 3)])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in malaya.dependency.available_deep_model():\n",
    "    print('Testing %s model'%(i))\n",
    "    model = malaya.dependency.deep_model(i)\n",
    "    print(model.predict(string))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print important features from deep learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-10 positive:\n",
      "disekitarnya: 15.197718\n",
      "Atau: 13.290402\n",
      "ektoderma: 13.264775\n",
      "sepedanya: 12.893903\n",
      "Kitchee: 12.608871\n",
      "harafiah: 11.988880\n",
      "Naito: 11.850497\n",
      "Ukraina: 11.662189\n",
      "romantis: 11.286506\n",
      "peluncuran: 11.277926\n",
      "\n",
      "Top-10 negative:\n",
      "basin: -16.109232\n",
      "Bernard: -14.093240\n",
      "antisipasi: -13.886290\n",
      "diminum: -13.461384\n",
      "sebelah: -12.955760\n",
      "Kusni: -12.772882\n",
      "minta: -12.678089\n",
      "10Am: -12.510218\n",
      "uangnya: -12.470654\n",
      "Hati: -12.458626\n"
     ]
    }
   ],
   "source": [
    "attention_is_all_you_need = malaya.dependency.deep_model('attention-is-all-you-need')\n",
    "attention_is_all_you_need.print_features(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bahdanau = malaya.dependency.deep_model('bahdanau')\n",
    "d_object, tagging, indexing = bahdanau.predict(string)\n",
    "d_object.to_graphvis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print important tagging transitions from deep learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-10 likely transitions:\n",
      "cc -> conj: 3.898244\n",
      "punct -> compound:plur: 2.816386\n",
      "case -> obl: 2.744200\n",
      "case -> xcomp: 2.671549\n",
      "case -> nmod: 2.591420\n",
      "compound:plur -> amod: 1.975937\n",
      "nsubj:pass -> acl: 1.963712\n",
      "cop -> ccomp: 1.963573\n",
      "punct -> fixed: 1.794608\n",
      "xcomp -> obj: 1.712421\n",
      "\n",
      "Top-10 unlikely transitions:\n",
      "root -> root: -8.622316\n",
      "nsubj:pass -> compound:plur: -7.600410\n",
      "obl -> compound:plur: -7.044655\n",
      "obj -> compound:plur: -6.751858\n",
      "case -> iobj: -6.546042\n",
      "case -> parataxis: -6.536805\n",
      "compound:plur -> compound:plur: -6.359083\n",
      "nummod -> compound:plur: -6.165409\n",
      "parataxis -> advcl: -6.097473\n",
      "nmod -> compound:plur: -6.087421\n"
     ]
    }
   ],
   "source": [
    "bahdanau.print_transitions_tag(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print important indexing transitions from deep learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-10 likely transitions:\n",
      "133 -> 136: 2.720363\n",
      "108 -> 109: 2.385479\n",
      "126 -> 127: 2.302784\n",
      "153 -> 56: 2.086003\n",
      "88 -> 89: 2.082119\n",
      "56 -> 158: 2.057645\n",
      "100 -> 103: 2.021965\n",
      "136 -> 122: 1.989143\n",
      "112 -> 113: 1.980897\n",
      "128 -> 129: 1.962656\n",
      "\n",
      "Top-10 unlikely transitions:\n",
      "46 -> 45: -7.945712\n",
      "1 -> 1: -7.238533\n",
      "63 -> 62: -6.313935\n",
      "41 -> 53: -6.211363\n",
      "37 -> 47: -6.163178\n",
      "68 -> 67: -6.078556\n",
      "64 -> 63: -6.066933\n",
      "27 -> 41: -6.057477\n",
      "51 -> 59: -6.047529\n",
      "45 -> 55: -6.034111\n"
     ]
    }
   ],
   "source": [
    "bahdanau.print_transitions_index(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting stack model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"312.4692\" y=\"-443.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mark</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x16c58f0f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat = malaya.dependency.deep_model('concat')\n",
    "bahdanau = malaya.dependency.deep_model('bahdanau')\n",
    "luong = malaya.dependency.deep_model('luong')\n",
    "tagging, indexing = malaya.stack.voting_stack([concat, bahdanau, luong], string)\n",
    "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependency graph object\n",
    "\n",
    "To initiate a dependency graph from dependency models, you need to call `malaya.dependency.dependency_graph`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<malaya._utils._parse_dependency.DependencyGraph at 0x16cc8beb8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = malaya.dependency.dependency_graph(tagging, indexing)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### generate graphvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
       " -->\n",
       "<!-- Title: G Pages: 1 -->\n",
       "<svg width=\"513pt\" height=\"474pt\"\n",
       " viewBox=\"0.00 0.00 512.59 474.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 470)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-470 508.5864,-470 508.5864,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<text text-anchor=\"middle\" x=\"151.5708\" y=\"-443.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">0 (None)</text>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>3</title>\n",
       "<text text-anchor=\"middle\" x=\"151.5708\" y=\"-357.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">3 (menasihati)</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;3 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M151.5708,-429.7616C151.5708,-418.3597 151.5708,-403.4342 151.5708,-390.494\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"155.0709,-390.2121 151.5708,-380.2121 148.0709,-390.2121 155.0709,-390.2121\"/>\n",
       "<text text-anchor=\"middle\" x=\"162.8467\" y=\"-400.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">root</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>1</title>\n",
       "<text text-anchor=\"middle\" x=\"45.5708\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">1 (Dr)</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;1 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>3&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M128.4413,-343.7669C121.2988,-338.101 113.4216,-331.8158 106.2329,-326 96.1199,-317.8183 85.1284,-308.798 75.3145,-300.6977\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"77.3302,-297.8229 67.3929,-294.1485 72.8698,-303.2179 77.3302,-297.8229\"/>\n",
       "<text text-anchor=\"middle\" x=\"121.7397\" y=\"-314.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<text text-anchor=\"middle\" x=\"131.5708\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">4 (mereka)</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>3&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M147.3293,-343.7616C144.6512,-332.2456 141.1372,-317.1353 138.1072,-304.1064\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"141.4804,-303.1594 135.8062,-294.2121 134.6624,-304.745 141.4804,-303.1594\"/>\n",
       "<text text-anchor=\"middle\" x=\"152.5156\" y=\"-314.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obj</text>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>6</title>\n",
       "<text text-anchor=\"middle\" x=\"228.5708\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">6 (berhenti)</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;6 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>3&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M167.9005,-343.7616C178.8238,-331.5615 193.3591,-315.3273 205.464,-301.8076\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"208.2017,-303.9969 212.2646,-294.2121 202.9865,-299.3276 208.2017,-303.9969\"/>\n",
       "<text text-anchor=\"middle\" x=\"214.6226\" y=\"-314.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">xcomp</text>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>12</title>\n",
       "<text text-anchor=\"middle\" x=\"340.5708\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">12 (mengantuk)</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;12 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>3&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M195.9665,-343.9678C209.3999,-338.3635 224.147,-332.0622 237.5708,-326 256.7,-317.3612 277.5726,-307.3422 295.4958,-298.5513\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"297.15,-301.6381 304.5747,-294.0801 294.0573,-295.3584 297.15,-301.6381\"/>\n",
       "<text text-anchor=\"middle\" x=\"282.1226\" y=\"-314.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>2</title>\n",
       "<text text-anchor=\"middle\" x=\"43.5708\" y=\"-185.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">2 (Mahathir)</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M45.1467,-257.7616C44.8815,-246.3597 44.5344,-231.4342 44.2335,-218.494\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"47.726,-218.128 43.9943,-208.2121 40.7279,-218.2908 47.726,-218.128\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.5156\" y=\"-228.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>5</title>\n",
       "<text text-anchor=\"middle\" x=\"142.5708\" y=\"-185.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">5 (supaya)</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;5 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>6&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M210.3324,-257.7616C198.0183,-245.4475 181.5943,-229.0235 168.0001,-215.4293\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"170.3289,-212.8083 160.7829,-208.2121 165.3791,-217.7581 170.3289,-212.8083\"/>\n",
       "<text text-anchor=\"middle\" x=\"203.6157\" y=\"-228.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">case</text>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>7</title>\n",
       "<text text-anchor=\"middle\" x=\"236.5708\" y=\"-185.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">7 (berehat)</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;7 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>6&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M230.2674,-257.7616C231.328,-246.3597 232.7165,-231.4342 233.9202,-218.494\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"237.4353,-218.4933 234.8767,-208.2121 230.4654,-217.8449 237.4353,-218.4933\"/>\n",
       "<text text-anchor=\"middle\" x=\"242.5156\" y=\"-228.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obj</text>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>11</title>\n",
       "<text text-anchor=\"middle\" x=\"340.5708\" y=\"-185.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">11 (sekiranya)</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;11 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>12&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M340.5708,-257.7616C340.5708,-246.3597 340.5708,-231.4342 340.5708,-218.494\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"344.0709,-218.2121 340.5708,-208.2121 337.0709,-218.2121 344.0709,-218.2121\"/>\n",
       "<text text-anchor=\"middle\" x=\"354.9536\" y=\"-228.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mark</text>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>14</title>\n",
       "<text text-anchor=\"middle\" x=\"455.5708\" y=\"-185.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">14 (memandu)</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;14 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>12&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M364.6786,-257.9716C381.6989,-245.2433 404.6929,-228.0478 423.2945,-214.1371\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"425.5825,-216.7965 431.4948,-208.0047 421.3903,-211.1906 425.5825,-216.7965\"/>\n",
       "<text text-anchor=\"middle\" x=\"419.7295\" y=\"-228.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advcl</text>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<text text-anchor=\"middle\" x=\"236.5708\" y=\"-99.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">9 (tidur)</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>7&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M236.5708,-171.7616C236.5708,-160.3597 236.5708,-145.4342 236.5708,-132.494\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"240.0709,-132.2121 236.5708,-122.2121 233.0709,-132.2121 240.0709,-132.2121\"/>\n",
       "<text text-anchor=\"middle\" x=\"248.6226\" y=\"-142.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>8</title>\n",
       "<text text-anchor=\"middle\" x=\"191.5708\" y=\"-13.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">8 (dan)</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;8 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M227.0275,-85.7616C220.8823,-74.0176 212.7812,-58.5355 205.8739,-45.3349\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"208.8378,-43.4497 201.1004,-36.2121 202.6355,-46.6951 208.8378,-43.4497\"/>\n",
       "<text text-anchor=\"middle\" x=\"223.7847\" y=\"-56.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>10</title>\n",
       "<text text-anchor=\"middle\" x=\"282.5708\" y=\"-13.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">10 (sebentar)</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M246.3262,-85.7616C252.6079,-74.0176 260.889,-58.5355 267.9498,-45.3349\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"271.1991,-46.6808 272.8294,-36.2121 265.0266,-43.3792 271.1991,-46.6808\"/>\n",
       "<text text-anchor=\"middle\" x=\"281.2295\" y=\"-56.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">ccomp</text>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>13</title>\n",
       "<text text-anchor=\"middle\" x=\"241.5708\" y=\"-443.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">13 (ketika)</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;13 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>13&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M280.1366,-455.8058C290.3505,-455.3597 298.0864,-452.7578 298.0864,-448 298.0864,-445.0264 295.0646,-442.8949 290.3046,-441.6055\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"290.5229,-438.1024 280.1366,-440.1942 289.5604,-445.0359 290.5229,-438.1024\"/>\n",
       "<text text-anchor=\"middle\" x=\"312.4692\" y=\"-443.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mark</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x10e0c8a90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.to_graphvis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function malaya._utils._parse_dependency.DependencyGraph.__init__.<locals>.<lambda>()>,\n",
       "            {0: {'address': 0,\n",
       "              'word': None,\n",
       "              'lemma': None,\n",
       "              'ctag': 'TOP',\n",
       "              'tag': 'TOP',\n",
       "              'feats': None,\n",
       "              'head': None,\n",
       "              'deps': defaultdict(list, {'root': [3]}),\n",
       "              'rel': None},\n",
       "             1: {'address': 1,\n",
       "              'word': 'Dr',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 3,\n",
       "              'deps': defaultdict(list, {'flat': [2]}),\n",
       "              'rel': 'nsubj'},\n",
       "             3: {'address': 3,\n",
       "              'word': 'menasihati',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 0,\n",
       "              'deps': defaultdict(list,\n",
       "                          {'nsubj': [1],\n",
       "                           'obj': [4],\n",
       "                           'xcomp': [6],\n",
       "                           'amod': [12]}),\n",
       "              'rel': 'root'},\n",
       "             2: {'address': 2,\n",
       "              'word': 'Mahathir',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 1,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'flat'},\n",
       "             4: {'address': 4,\n",
       "              'word': 'mereka',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 3,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'obj'},\n",
       "             5: {'address': 5,\n",
       "              'word': 'supaya',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 6,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'case'},\n",
       "             6: {'address': 6,\n",
       "              'word': 'berhenti',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 3,\n",
       "              'deps': defaultdict(list, {'case': [5], 'obj': [7]}),\n",
       "              'rel': 'xcomp'},\n",
       "             7: {'address': 7,\n",
       "              'word': 'berehat',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 6,\n",
       "              'deps': defaultdict(list, {'conj': [9]}),\n",
       "              'rel': 'obj'},\n",
       "             8: {'address': 8,\n",
       "              'word': 'dan',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 9,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'cc'},\n",
       "             9: {'address': 9,\n",
       "              'word': 'tidur',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 7,\n",
       "              'deps': defaultdict(list, {'cc': [8], 'ccomp': [10]}),\n",
       "              'rel': 'conj'},\n",
       "             10: {'address': 10,\n",
       "              'word': 'sebentar',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 9,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'ccomp'},\n",
       "             11: {'address': 11,\n",
       "              'word': 'sekiranya',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 12,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'mark'},\n",
       "             12: {'address': 12,\n",
       "              'word': 'mengantuk',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 3,\n",
       "              'deps': defaultdict(list, {'mark': [11], 'advcl': [14]}),\n",
       "              'rel': 'amod'},\n",
       "             13: {'address': 13,\n",
       "              'word': 'ketika',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 13,\n",
       "              'deps': defaultdict(list, {'mark': [13]}),\n",
       "              'rel': 'mark'},\n",
       "             14: {'address': 14,\n",
       "              'word': 'memandu',\n",
       "              'lemma': '_',\n",
       "              'ctag': '_',\n",
       "              'tag': '_',\n",
       "              'feats': '_',\n",
       "              'head': 12,\n",
       "              'deps': defaultdict(list, {}),\n",
       "              'rel': 'advcl'}})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flat the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('menasihati', '_'), 'nsubj', ('Dr', '_')),\n",
       " (('Dr', '_'), 'flat', ('Mahathir', '_')),\n",
       " (('menasihati', '_'), 'obj', ('mereka', '_')),\n",
       " (('menasihati', '_'), 'xcomp', ('berhenti', '_')),\n",
       " (('berhenti', '_'), 'case', ('supaya', '_')),\n",
       " (('berhenti', '_'), 'obj', ('berehat', '_')),\n",
       " (('berehat', '_'), 'conj', ('tidur', '_')),\n",
       " (('tidur', '_'), 'cc', ('dan', '_')),\n",
       " (('tidur', '_'), 'ccomp', ('sebentar', '_')),\n",
       " (('menasihati', '_'), 'amod', ('mengantuk', '_')),\n",
       " (('mengantuk', '_'), 'mark', ('sekiranya', '_')),\n",
       " (('mengantuk', '_'), 'advcl', ('memandu', '_'))]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(graph.triples())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the graph contains cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.contains_cycle()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate networkx\n",
    "\n",
    "Make sure you already installed networkx, `pip install networkx`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.multidigraph.MultiDiGraph at 0x1e8b294a8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digraph = graph.to_networkx()\n",
    "digraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "nx.draw_networkx(digraph)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OutMultiEdgeDataView([(1, 3), (2, 1), (4, 3), (5, 6), (6, 3), (7, 6), (8, 9), (9, 7), (10, 9), (11, 12), (12, 3), (13, 13), (14, 12)])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digraph.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digraph.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Dr',\n",
       " 2: 'Mahathir',\n",
       " 3: 'menasihati',\n",
       " 4: 'mereka',\n",
       " 5: 'supaya',\n",
       " 6: 'berhenti',\n",
       " 7: 'berehat',\n",
       " 8: 'dan',\n",
       " 9: 'tidur',\n",
       " 10: 'sebentar',\n",
       " 11: 'sekiranya',\n",
       " 12: 'mengantuk',\n",
       " 13: 'ketika',\n",
       " 14: 'memandu'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = {i:graph.get_by_address(i)['word'] for i in digraph.nodes()}\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "nx.draw_networkx(digraph,labels=labels)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}