notebooks/dwpc.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metagraph and Metapath Generation\n",
    "\n",
    "Here, we use the same syntax as [HetNetPy](https://github.com/hetio/hetnetpy/tree/e1ff1f8296b766dd5a63e5870a8a1a7d82427a80) to create a metagraph and extract metapaths from the KG.\n",
    "\n",
    "Metagraph creation is based on [this notebook](https://github.com/dhimmel/integrate/blob/93feba1765fbcd76fd79e22f25121f5399629148/integrate.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import os.path as osp\n",
    "import numpy as np\n",
    "\n",
    "import hetnetpy.hetnet\n",
    "import hetnetpy.readwrite\n",
    "import hetnetpy.stats\n",
    "\n",
    "from hetnetpy.abbreviation import metaedges_from_metapath\n",
    "from hetnetpy.pathtools import DWPC, paths_between\n",
    "\n",
    "import networkx as nx\n",
    "from networkx import DiGraph, MultiDiGraph, connected_components\n",
    "\n",
    "import re\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "from collections import Counter\n",
    "\n",
    "from itertools import chain\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "KG_DATA_PATH = '../data/kg/splits/'\n",
    "\n",
    "MOA_NET = os.path.join(KG_DATA_PATH, 'MoA-net')\n",
    "\n",
    "MOA_NET_10K = os.path.join(MOA_NET, '10k')\n",
    "\n",
    "PROT_MOA_NET = os.path.join(KG_DATA_PATH, 'MoA-net-protclass')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load in MoA-net-10k, the version of MoA-net which was cut down by the automatic KG trimming feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_graph_from_nxobj(nx_graph_obj, node_mapping_file, edge_mapping_file):\n",
    "    graph = DiGraph()\n",
    "\n",
    "    old_graph = nx.read_graphml(nx_graph_obj,\n",
    "                    node_type=int,\n",
    "                    edge_key_type=int,\n",
    "                    force_multigraph=True)\n",
    "    \n",
    "    for u, v, data in old_graph.edges(data=True):\n",
    "        graph.add_edge(\n",
    "            node_mapping_file[u],\n",
    "            node_mapping_file[v],\n",
    "            polarity=edge_mapping_file[data['type']],\n",
    "        )\n",
    "\n",
    "    connected_components_subgraph = [\n",
    "        component\n",
    "        for component in sorted(\n",
    "            connected_components(\n",
    "                graph.to_undirected()\n",
    "            ),\n",
    "            key=len,\n",
    "            reverse=True\n",
    "        )\n",
    "    ]\n",
    "\n",
    "    final_subgraph = graph.subgraph(connected_components_subgraph[0])\n",
    "\n",
    "    return final_subgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the entity mapper in\n",
    "entity_mapping = json.load(open(os.path.join(MOA_NET, 'MARS/vocab/entity_vocab.json')))\n",
    "entity_mapping = {int(v): k for k, v in entity_mapping.items()}\n",
    "relation_mapping = json.load(open(os.path.join(MOA_NET, 'MARS/vocab/relation_vocab.json')))\n",
    "relation_mapping = {int(v): k for k, v in relation_mapping.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "kg = create_graph_from_nxobj(os.path.join(MOA_NET_10K, 'nx_graph.graphml'),\n",
    "                                 entity_mapping,\n",
    "                                 relation_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_10k = np.load(os.path.join(MOA_NET_10K, 'test-10k.npy'))\n",
    "val_10k = np.load(os.path.join(MOA_NET_10K, 'dev-10k.npy'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop those from the test set which are no longer connected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "gold_standard = {'source': [], 'target': []}\n",
    "test_10k_df = {'source': [], 'target': [], 'relation': []}\n",
    "\n",
    "for source, rel, target in test_10k:\n",
    "    e1 = entity_mapping[source]\n",
    "    e2 = entity_mapping[target]\n",
    "    r = relation_mapping[rel]\n",
    "\n",
    "    if e1 in kg and e2 in kg and nx.has_path(kg, e1, e2) and nx.shortest_path_length(kg, e1, e2) <= 4:\n",
    "        gold_standard['source'].append(e1)\n",
    "        test_10k_df['source'].append(e1)\n",
    "        gold_standard['target'].append(e2)\n",
    "        test_10k_df['target'].append(e2)\n",
    "        test_10k_df['relation'].append(r)\n",
    "\n",
    "gold_standard = pd.DataFrame(gold_standard)\n",
    "test_10k_df = pd.DataFrame(test_10k_df)\n",
    "print(len(gold_standard))\n",
    "print(len(test_10k_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90\n"
     ]
    }
   ],
   "source": [
    "validation_set = {'source': [], 'target': [], 'relation': []}\n",
    "\n",
    "for source, rel, target in val_10k:\n",
    "    e1 = entity_mapping[source]\n",
    "    e2 = entity_mapping[target]\n",
    "    r = relation_mapping[rel]\n",
    "\n",
    "    if e1 in kg and e2 in kg and nx.has_path(kg, e1, e2) and nx.shortest_path_length(kg, e1, e2) <= 4:\n",
    "        validation_set['source'].append(e1)\n",
    "        validation_set['target'].append(e2)\n",
    "        validation_set['relation'].append(r)\n",
    "\n",
    "validation_set = pd.DataFrame(validation_set)\n",
    "print(len(validation_set))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Instantiate Metagraph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "kind_to_abbev = {\n",
    "    \n",
    "    # metanodes\n",
    "    'Compound': 'C',\n",
    "    'Gene': 'G',\n",
    "    'Biological Process': 'BP',\n",
    "    \n",
    "    # metaedges\n",
    "    'CuG': 'u',\n",
    "    'CdG': 'd',\n",
    "    'GiG': 'i',\n",
    "    'GpBP': 'p',\n",
    "    'CtBP': 't',\n",
    "}\n",
    "\n",
    "metaedge_tuples = [\n",
    "    ('Compound', 'Gene', 'CuG', 'forward'),\n",
    "    ('Compound', 'Gene', 'CdG', 'forward'),\n",
    "    ('Gene', 'Gene', 'GiG', 'forward'),\n",
    "    ('Gene', 'Biological Process', 'GpBP', 'forward'),\n",
    "    ('Compound', 'Biological Process', 'CtBP', 'forward')\n",
    "]\n",
    "\n",
    "metagraph = hetnetpy.hetnet.MetaGraph.from_edge_tuples(metaedge_tuples, kind_to_abbev)\n",
    "graph = hetnetpy.hetnet.Graph(metagraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map2type(node_name):\n",
    "    if 'ncbigene' in node_name:\n",
    "        return 'Gene'\n",
    "    elif 'GO:' in node_name:\n",
    "        return 'Biological Process'\n",
    "    elif 'pubchem' in node_name:\n",
    "        return 'Compound'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "seen = set()\n",
    "all_bps = set()\n",
    "\n",
    "for u, v, data in kg.edges(data=True):\n",
    "    if '_' in data['polarity']:\n",
    "        continue\n",
    "    if not u in seen:\n",
    "        graph.add_node(kind=map2type(u), identifier=u)\n",
    "        seen.add(u)\n",
    "        if map2type(u) == 'Biological Process':\n",
    "            all_bps.add(u)\n",
    "    if not v in seen:\n",
    "        graph.add_node(kind=map2type(v), identifier=v)\n",
    "        seen.add(v)\n",
    "        if map2type(v) == 'Biological Process':\n",
    "            all_bps.add(v)\n",
    "\n",
    "    src_id = map2type(u), u\n",
    "    trgt_id = map2type(v), v\n",
    "\n",
    "    graph.add_edge(source_id=src_id, target_id=trgt_id, kind=data['polarity'], direction='forward')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metapath Extraction\n",
    "\n",
    "Here, we have functions which can extract metapaths:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract all metapaths up to a certain length:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract metapaths from a given source node type (and, optionally, to a target node type):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Ct>BP, Cd>Gp>BP, Cu>Gp>BP, Ct>BP<tCt>BP, Ct>BP<pGp>BP, Cd>G<dCt>BP, Cd>G<uCt>BP, Cd>G<iGp>BP, Cd>Gi>Gp>BP, Cu>G<dCt>BP, Cu>G<uCt>BP, Cu>G<iGp>BP, Cu>Gi>Gp>BP, Ct>BP<tCd>Gp>BP, Ct>BP<tCu>Gp>BP, Ct>BP<pG<dCt>BP, Ct>BP<pG<uCt>BP, Ct>BP<pG<iGp>BP, Ct>BP<pGi>Gp>BP, Cd>Gp>BP<tCt>BP, Cd>Gp>BP<pGp>BP, Cd>G<dCd>Gp>BP, Cd>G<dCu>Gp>BP, Cd>G<uCd>Gp>BP, Cd>G<uCu>Gp>BP, Cd>G<iG<dCt>BP, Cd>G<iG<uCt>BP, Cd>G<iG<iGp>BP, Cd>G<iGi>Gp>BP, Cd>Gi>G<dCt>BP, Cd>Gi>G<uCt>BP, Cd>Gi>G<iGp>BP, Cd>Gi>Gi>Gp>BP, Cu>Gp>BP<tCt>BP, Cu>Gp>BP<pGp>BP, Cu>G<dCd>Gp>BP, Cu>G<dCu>Gp>BP, Cu>G<uCd>Gp>BP, Cu>G<uCu>Gp>BP, Cu>G<iG<dCt>BP, Cu>G<iG<uCt>BP, Cu>G<iG<iGp>BP, Cu>G<iGi>Gp>BP, Cu>Gi>G<dCt>BP, Cu>Gi>G<uCt>BP, Cu>Gi>G<iGp>BP, Cu>Gi>Gi>Gp>BP]\n"
     ]
    }
   ],
   "source": [
    "tgt_metapaths = metagraph.extract_metapaths(source='Compound', target='Biological Process')\n",
    "print(tgt_metapaths)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not want any with a compound in the middle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "CtoBP_metapaths = []\n",
    "\n",
    "\n",
    "for i in tgt_metapaths:\n",
    "    if (repr(i) != 'Ct>BP') & (Counter(re.split('>|<|[^A-Z]', repr(i)))['C'] == 1):\n",
    "        CtoBP_metapaths.append(i)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many did it prune?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "47\n"
     ]
    }
   ],
   "source": [
    "print(len(CtoBP_metapaths))\n",
    "print(len(tgt_metapaths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics based on Metapaths\n",
    "\n",
    "First, we need the KG with the test BPs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_paths(\n",
    "    graph,\n",
    "    source,\n",
    "    target,\n",
    "    rule,\n",
    "):\n",
    "    try:\n",
    "        paths = paths_between(\n",
    "            graph,\n",
    "            source=('Compound', source),\n",
    "            target=('Biological Process', target),\n",
    "            metapath=tuple(rule),\n",
    "            duplicates=False,\n",
    "        )\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        paths = []        \n",
    "    \n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 1/100 [00:37<1:01:04, 37.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 / 79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 2/100 [00:38<26:12, 16.05s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 3/100 [00:41<16:11, 10.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 58\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 4/100 [00:45<12:10,  7.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 5/100 [00:54<12:56,  8.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 6/100 [01:00<11:50,  7.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 7/100 [01:01<08:32,  5.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 8/100 [01:07<08:23,  5.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▉         | 9/100 [01:14<08:54,  5.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 65\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 10/100 [01:25<11:23,  7.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|█         | 11/100 [01:28<09:03,  6.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 12/100 [01:29<06:50,  4.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 13/100 [01:39<08:50,  6.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 14/100 [02:12<20:45, 14.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|█▌        | 15/100 [02:19<17:11, 12.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 / 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▌        | 16/100 [02:28<15:30, 11.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 17/100 [02:46<18:20, 13.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 / 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 18/100 [02:54<15:52, 11.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 / 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|█▉        | 19/100 [03:02<14:07, 10.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 20/100 [03:12<13:50, 10.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|██        | 21/100 [03:18<12:11,  9.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 / 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 22/100 [03:23<10:18,  7.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 / 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 23/100 [03:39<13:10, 10.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██▍       | 24/100 [03:41<10:00,  7.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 25/100 [03:52<10:59,  8.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 26/100 [04:07<12:51, 10.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 27/100 [04:12<10:43,  8.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 / 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|██▊       | 28/100 [04:13<07:51,  6.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 29%|██▉       | 29/100 [04:14<05:50,  4.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 30/100 [04:15<04:29,  3.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 31%|███       | 31/100 [04:18<03:53,  3.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|███▏      | 32/100 [04:32<07:39,  6.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 / 79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 33/100 [04:34<05:45,  5.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|███▍      | 34/100 [04:37<04:55,  4.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 / 63\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35%|███▌      | 35/100 [04:41<04:44,  4.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 / 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███▌      | 36/100 [06:03<29:33, 27.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███▋      | 37/100 [06:16<24:33, 23.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 38/100 [06:27<20:12, 19.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 39%|███▉      | 39/100 [06:32<15:36, 15.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 40/100 [06:40<12:55, 12.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|████      | 41/100 [07:28<23:03, 23.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 / 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 42/100 [08:03<26:10, 27.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|████▎     | 43/100 [08:18<22:16, 23.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 / 79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|████▍     | 44/100 [08:19<15:38, 16.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|████▌     | 45/100 [08:30<13:40, 14.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|████▌     | 46/100 [08:33<10:14, 11.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 / 60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████▋     | 47/100 [09:54<28:22, 32.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|████▊     | 48/100 [09:55<19:50, 22.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 49%|████▉     | 49/100 [09:56<13:56, 16.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 50/100 [09:57<09:53, 11.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 51%|█████     | 51/100 [10:12<10:18, 12.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 / 77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|█████▏    | 52/100 [10:13<07:23,  9.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████▎    | 53/100 [10:28<08:27, 10.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 54%|█████▍    | 54/100 [10:29<06:06,  7.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|█████▌    | 55/100 [10:33<05:06,  6.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 / 62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|█████▌    | 56/100 [10:34<03:46,  5.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|█████▋    | 57/100 [10:44<04:33,  6.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 / 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 58/100 [11:07<08:08, 11.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 59%|█████▉    | 59/100 [11:23<08:42, 12.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 60/100 [11:41<09:33, 14.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 / 77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 61%|██████    | 61/100 [11:48<07:51, 12.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 62/100 [12:00<07:46, 12.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████▎   | 63/100 [12:04<05:54,  9.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 / 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|██████▍   | 64/100 [12:05<04:14,  7.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 65%|██████▌   | 65/100 [12:06<03:06,  5.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████▌   | 66/100 [12:08<02:30,  4.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 67/100 [12:10<02:01,  3.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 / 55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████▊   | 68/100 [12:13<01:50,  3.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 69%|██████▉   | 69/100 [12:23<02:49,  5.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 70/100 [12:25<02:06,  4.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|███████   | 71/100 [13:47<13:18, 27.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 72/100 [13:55<10:05, 21.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 / 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 73/100 [14:01<07:42, 17.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 74%|███████▍  | 74/100 [14:07<05:55, 13.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████▌  | 75/100 [14:16<05:08, 12.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|███████▌  | 76/100 [14:17<03:37,  9.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 77/100 [14:27<03:32,  9.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|███████▊  | 78/100 [14:35<03:12,  8.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 / 71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 79%|███████▉  | 79/100 [15:09<05:44, 16.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 80/100 [15:56<08:33, 25.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 / 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 81%|████████  | 81/100 [15:58<05:48, 18.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|████████▏ | 82/100 [15:59<03:58, 13.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 83/100 [16:12<03:46, 13.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████▍ | 84/100 [16:30<03:55, 14.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 / 77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|████████▌ | 85/100 [16:32<02:39, 10.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▌ | 86/100 [16:33<01:49,  7.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 87/100 [16:42<01:47,  8.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|████████▊ | 88/100 [16:45<01:18,  6.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 89%|████████▉ | 89/100 [16:57<01:32,  8.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 / 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 90/100 [17:12<01:43, 10.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 / 79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 91%|█████████ | 91/100 [17:15<01:12,  8.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 92/100 [17:18<00:53,  6.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 61\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 93/100 [17:54<01:47, 15.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 94/100 [18:02<01:19, 13.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 67\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▌| 95/100 [18:10<00:58, 11.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 / 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 96/100 [18:12<00:34,  8.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 97/100 [18:47<00:49, 16.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 / 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 98/100 [18:51<00:25, 12.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 / 62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 99/100 [19:00<00:11, 11.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 / 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [19:24<00:00, 11.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 / 78\n",
      "Hits at 10: 0.78\n",
      "Hits at 3: 0.56\n",
      "Hits at 1: 0.37\n",
      "MRR: 0.5077486538860714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hits_at_10 = []\n",
    "hits_at_3 = []\n",
    "hits_at_1 = []\n",
    "mrr = []\n",
    "\n",
    "for i, row in tqdm(test_10k_df.iterrows(), total=len(test_10k_df)):\n",
    "    \n",
    "    source = row['source']\n",
    "    target = row['target']\n",
    "\n",
    "    rank = {}\n",
    "    \n",
    "    for bp in all_bps:\n",
    "        \n",
    "        all_paths = []\n",
    "    \n",
    "        # All metapaths going from compound to biological process\n",
    "        for rule in CtoBP_metapaths:\n",
    "                    \n",
    "            paths = get_paths(\n",
    "                graph=graph,\n",
    "                source=source,\n",
    "                target=bp,\n",
    "                rule=rule,\n",
    "            )\n",
    "            \n",
    "            if not paths:\n",
    "                continue\n",
    "                \n",
    "            all_paths.append(paths)\n",
    "                    \n",
    "        # flatten paths\n",
    "        all_paths = list(chain(*all_paths))\n",
    "        \n",
    "        if not all_paths:\n",
    "            continue\n",
    "    \n",
    "        score = DWPC(all_paths, damping_exponent=0.4)\n",
    "                \n",
    "        rank[bp] = score\n",
    "        \n",
    "    # Sort rank by score\n",
    "    ranked_bp_predictions = [\n",
    "        go\n",
    "        for go, _ in sorted(rank.items(), key=lambda x: x[1], reverse=True)\n",
    "    ]\n",
    "            \n",
    "    # Hits at 10\n",
    "    if target in ranked_bp_predictions[:10]:\n",
    "        hits_at_10.append(1) \n",
    "    else:\n",
    "        hits_at_10.append(0)\n",
    "    \n",
    "    # Hits at 3\n",
    "    if target in ranked_bp_predictions[:3]:\n",
    "        hits_at_3.append(1)\n",
    "    else:\n",
    "        hits_at_3.append(0)\n",
    "    \n",
    "    # Hits at 1\n",
    "    if target in ranked_bp_predictions[:1]:\n",
    "        hits_at_1.append(1)\n",
    "    else:\n",
    "        hits_at_1.append(0)\n",
    "        \n",
    "    # MRR\n",
    "    if target in ranked_bp_predictions:\n",
    "        print(f\"{ranked_bp_predictions.index(target)} / {len(ranked_bp_predictions)}\")\n",
    "        mrr.append(\n",
    "            1.0 / (ranked_bp_predictions.index(target) + 1)\n",
    "        )\n",
    "    else:\n",
    "        mrr.append(0)\n",
    "\n",
    "        \n",
    "print(f'Hits at 10: {sum(hits_at_10) / len(hits_at_10)}')\n",
    "print(f'Hits at 3: {sum(hits_at_3) / len(hits_at_3)}')\n",
    "print(f'Hits at 1: {sum(hits_at_1) / len(hits_at_1)}')\n",
    "print(f'MRR: {sum(mrr) / len(mrr)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nesy_venv",
   "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": "3.10.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}