graphpattern2vec_process-multithread-Edited2022.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To run experiment and evaluate it:\n",
    "  - We will use **graphpattern2vec_process-multithread-Edited2022** notebook\n",
    "    - All the cells should be run until **Link Prediction**.\n",
    "    - Stop using the notebook, and manually run the metapath2vec to generate walk files.\n",
    "    - Run all rest of cells after **Link Prediction**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphpattern2vec import split_data as sp\n",
    "from graphpattern2vec import biased_walk as BW\n",
    "from graphpattern2vec import biased_walk_multiprocess as BWMP\n",
    "# from graphpattern2vec import check_walks as CW\n",
    "from graphpattern2vec import CheckWalks as CW\n",
    "from graphpattern2vec import link_prediction as LP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data/'\n",
    "main_edge_file = 'df_edges.csv'\n",
    "relation_to_predict = 'hasPathway'\n",
    "train_file = 'df_train.csv'\n",
    "\n",
    "node_file = 'df_node.csv'\n",
    "relation_file = 'df_relation.csv'\n",
    "type_file = 'df_type.csv'\n",
    "test_file = 'df_test.csv'\n",
    "\n",
    "result_path = 'results/'\n",
    "\n",
    "source_types = ['Protein', 'LightKinase', 'DarkKinase']\n",
    "target_types = ['Pathway']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_edges: (6279373, 10)\n",
      "number of total node in data based on the Networkx's Graph 9272\n",
      "df_interest: 68856, count_msp: 5, sum_edges: 59590\n",
      "all removed nodes 8905 in the train 9272(should be equal to nodes in graph).\n",
      "number of candidate edges to remove: 59590\n",
      "total number of edge of interest: (68856, 10)\n",
      "edge_to_index: 137712, index_to_edge: 68856\n",
      "candidates_indices: 59590 == unique 59590\n",
      "The minimum of 34428.0 and 59590 is 34428.\n",
      "selected indices: 34428, df_test_edges: (34428, 10)\n",
      "indices of the rest of interest: 34428, df_interest_rest: (34428, 10)\n",
      "rest of edges except the edge of interest: (6210517, 10)\n",
      "df_train: (6244945, 10)\n",
      "True\n",
      "saving file df_test.csv ...\n",
      "file df_test.csv and size (34428, 10) is saved.\n",
      "saving file df_train.csv ...\n",
      "file df_train.csv and size (6244945, 10) is saved.\n",
      "split is done.\n"
     ]
    }
   ],
   "source": [
    "split_pred = 0.5\n",
    "\n",
    "\n",
    "sp.split_process(data_path,main_edge_file ,relation_to_predict, split_pred, train_file_name = train_file, test_file_name=test_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Walks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Types\n",
    "- 0,Protein\n",
    "- 1,Pathway\n",
    "- 2,LightKinase\n",
    "- 3,DarkKinase\n",
    "\n",
    "- 4,PTM\n",
    "- 5,FunctionalDomain\n",
    "- 6,BiologicalProcess\n",
    "- 7,MolecularFunction\n",
    "- 8,CellularComponent\n",
    "- 9,Chemical\n",
    "- 10,Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'graphpattern2vec.biased_walk_multiprocess' from '/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import imp\n",
    "imp.reload(BWMP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{0: 1, 2: 1, 3: 1},\n",
       " {0: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 10: 1},\n",
       " {0: 1, 2: 1, 3: 1},\n",
       " {1: 1}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_pattern = [{0:1,2:1,3:1}, {0:1, 2:1, 3:1, 4:1,5:1, 6:1, 7:1, 8:1, 9:1, 10:1}, {0:1,2:1,3:1}, {1:1}]\n",
    "graph_pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data ...\n",
      "df_train: (6244945, 5)\n",
      "based on graphpattern df_train_sel: (6244945, 5), set_sel_types: 11\n",
      "h: 1020715, t: 98719\n",
      "nodes: 1064097\n",
      "types of node and their counts:\n",
      "type_to_node: 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Nodes:   4%|▍         | 40380/1064097 [00:00<00:02, 403768.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_to_type: 1064097\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Nodes: 100%|██████████| 1064097/1064097 [00:02<00:00, 379974.05it/s]\n",
      "Edges: 100%|██████████| 6244945/6244945 [00:43<00:00, 144890.03it/s]\n"
     ]
    }
   ],
   "source": [
    "loop_index = []\n",
    "type_loop = {}\n",
    "num_allow_loop = 0\n",
    "num_processes = 0 # zero for number of cores\n",
    "\n",
    "biasedwalk = BWMP.BiasedWalk(graph_pattern, loop_index, type_loop, data_path = data_path, \n",
    "                            train_file = train_file, postfix='', num_processes = num_processes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "prepare allow dict: 100%|██████████| 4/4 [00:58<00:00, 14.55s/it]\n",
      "check backwards: 100%|██████████| 1064097/1064097 [00:13<00:00, 79975.24it/s]\n",
      "calc scores: 100%|██████████| 1064097/1064097 [00:06<00:00, 161718.33it/s]\n",
      "assign scores of neighbours: 100%|██████████| 1064097/1064097 [01:41<00:00, 10457.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "allowed attr prepared.\n",
      "scores attr prepared.\n",
      "nei_scores attr prepared\n",
      "added attributes to the graph\n",
      "source nodes: 1014602, target_nodes: 1974\n",
      "source nodes: 1014602, selected_nodes: 1014602\n",
      "job_number: 6, chunks for thread: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/169100 [00:00<?, ?it/s]/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py:406: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  p_index = np.where(all_d == pnode)\n",
      "  0%|          | 0/169100 [00:00<?, ?it/s]/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py:406: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  p_index = np.where(all_d == pnode)\n",
      "  0%|          | 0/169100 [00:00<?, ?it/s] 5.97it/s]/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py:406: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  p_index = np.where(all_d == pnode)\n",
      "/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py:406: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  p_index = np.where(all_d == pnode)\n",
      "  0%|          | 0/169101 [00:00<?, ?it/s]/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py:406: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  p_index = np.where(all_d == pnode)\n",
      "/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py:406: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  p_index = np.where(all_d == pnode)\n",
      "  0%|          | 556/169100 [04:06<20:46:21,  2.25it/s]\n",
      "Process Process-4:\n",
      "  0%|          | 496/169100 [04:06<23:19:18,  2.01it/s]\n",
      "Process Process-2:\n",
      "  0%|          | 502/169101 [04:06<23:01:47,  2.03it/s]Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n",
      "    self.run()\n",
      "Process Process-3:\n",
      "  0%|          | 524/169101 [04:06<22:02:41,  2.12it/s]\n",
      "Traceback (most recent call last):\n",
      "Process Process-6:\n",
      "  0%|          | 526/169100 [04:07<21:59:44,  2.13it/s]  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n",
      "    self.run()\n",
      "\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Process Process-1:\n",
      "Traceback (most recent call last):\n",
      "  0%|          | 522/169100 [04:06<22:08:07,  2.12it/s]  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 515, in walk\n",
      "    next_node= self.select_next_GP_visited_biased(node, prev_node, i) # normal step\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in select_next_GP_visited_biased\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in <listcomp>\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/coreviews.py\", line 54, in __getitem__\n",
      "    return self._atlas[key]\n",
      "KeyboardInterrupt\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 515, in walk\n",
      "    next_node= self.select_next_GP_visited_biased(node, prev_node, i) # normal step\n",
      "\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in select_next_GP_visited_biased\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 515, in walk\n",
      "    next_node= self.select_next_GP_visited_biased(node, prev_node, i) # normal step\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 515, in walk\n",
      "    next_node= self.select_next_GP_visited_biased(node, prev_node, i) # normal step\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in <listcomp>\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in select_next_GP_visited_biased\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/graph.py\", line 476, in __getitem__\n",
      "    return self.adj[n]\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in select_next_GP_visited_biased\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "Process Process-5:\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in <listcomp>\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/coreviews.py\", line 82, in __getitem__\n",
      "    return AtlasView(self._atlas[name])\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 515, in walk\n",
      "    next_node= self.select_next_GP_visited_biased(node, prev_node, i) # normal step\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in <listcomp>\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/graph.py\", line 476, in __getitem__\n",
      "    return self.adj[n]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/coreviews.py\", line 44, in __init__\n",
      "    def __init__(self, d):\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in select_next_GP_visited_biased\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/coreviews.py\", line 54, in __getitem__\n",
      "    return self._atlas[key]\n",
      "KeyboardInterrupt\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in <listcomp>\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/coreviews.py\", line 81, in __getitem__\n",
      "    def __getitem__(self, name):\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 515, in walk\n",
      "    next_node= self.select_next_GP_visited_biased(node, prev_node, i) # normal step\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in select_next_GP_visited_biased\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "KeyboardInterrupt\n",
      "  File \"/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\", line 416, in <listcomp>\n",
      "    rels = [self.G[node][n]['relation'] for n in nei]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/graph.py\", line 476, in __getitem__\n",
      "    return self.adj[n]\n",
      "  File \"/home/esbg/anaconda3/lib/python3.8/site-packages/networkx/classes/coreviews.py\", line 81, in __getitem__\n",
      "    def __getitem__(self, name):\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-c39f64114f70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0minitials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'v'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'v'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'v'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m walk_file_name = biasedwalk.walk_progress(initials, all_nw = all_nw, all_wl = all_wl, num_source = num_source, \n\u001b[0m\u001b[1;32m     10\u001b[0m                                           \u001b[0mnum_allow_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_allow_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalk_file_prefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwalk_file_prefix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                                           loop_chance = loop_chance)\n",
      "\u001b[0;32m/hdd/BigIronOnHisHip/r2v/newRegpatterin/RegPattern2Vec_project-main/notebooks/graphpattern2vec/biased_walk_multiprocess.py\u001b[0m in \u001b[0;36mwalk_progress\u001b[0;34m(self, initials, all_nw, all_wl, num_source, num_allow_loop, walk_file_prefix, loop_chance)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mwalk_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m                     \u001b[0moutfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rlock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/multiprocessing/connection.py\u001b[0m in \u001b[0;36mrecv_bytes\u001b[0;34m(self, maxlength)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmaxlength\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmaxlength\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"negative maxlength\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaxlength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbuf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bad_message_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_recv_bytes\u001b[0;34m(self, maxsize)\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    415\u001b[0m         \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstruct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"!i\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_recv\u001b[0;34m(self, size, read)\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0mremaining\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m             \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "all_nw = 40\n",
    "all_wl = 40\n",
    "\n",
    "walk_file_prefix = 'walk_test_REL_simplified_sep_probs'\n",
    "loop_chance = 0\n",
    "num_source = 0\n",
    "\n",
    "initials = {0:'v', 1:'a', 2:'v', 3:'v',4:'f',5:'f', 6:'f', 7:'f', 8:'f', 9:'f', 10:'f'}\n",
    "walk_file_name = biasedwalk.walk_progress(initials, all_nw = all_nw, all_wl = all_wl, num_source = num_source, \n",
    "                                          num_allow_loop = num_allow_loop, walk_file_prefix = walk_file_prefix, \n",
    "                                          loop_chance = loop_chance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "walk_file_name\n",
    "#emb_walk_final_100_40_0_3_bwV4_s128_w7_n5_mc5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_embedding_file = 'GP2VEC_emb_walk_file_40_40_0_0_bwV4_s256_w7_n5_mc5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imp\n",
    "imp.reload(LP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[source_types, target_types]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp = LP.LinkPrediction(data_path, train_file, node_file, relation_file, type_file\n",
    "                , list_embedding_file ,test_file, result_path, \n",
    "                    [source_types, target_types])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lp.draw_pca_of_all_nodes() #this code runs the pca not need but you can still run it if you want to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ver_name = list_embedding_file[19:] + '.png'\n",
    "\n",
    "scoring = ('accuracy', 'f1', 'roc_auc') # accuracy, f1_micro, roc_auc, roc_auc_ovr\n",
    "\n",
    "lp.train_model_cross_validation('newton-cg', 'ovr', C=1, penalty='l2', cv = 10, ver = ver_name, scoring=scoring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__load_test_set| df_test_set: (34428, 10)\n",
      "__load_test_set| unique heads: 6301, unique tails: 1791\n",
      "6413\n",
      "5153\n",
      "5153\n",
      "5153\n",
      "2259\n",
      "2259\n",
      "2259\n",
      "__get_test_set_emb| test set emb list : positive 34421 with 7 errors, negative: 34276, with 114 errors.\n",
      "{False, True}\n",
      "__prepare_test_set_emb| list of test set prepared 68697\n",
      "save_list_of_test_embs| lst: 68697 to save.\n",
      "save_list_of_test_embs| data/test_set_embeddings.csv is saved.\n",
      "__test| 68697 and 68697\n",
      "__test| 256 and 1\n",
      "__AUC_ROC| roc_auc_lr: 0.9576201564877593\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kochut/graphpattern2vec_project/graphpattern2vec/link_prediction.py:953: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.axes().set_aspect('equal')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cutt off is calculated to be 0.9724078496865195\n",
      "[[30708  3568]\n",
      " [ 3583 30838]]\n",
      "The test set, precision: 0.8962971574725339, recall: 0.895906568664478, f1 score: 0.8961018205064872\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90     34276\n",
      "           1       0.90      0.90      0.90     34421\n",
      "\n",
      "    accuracy                           0.90     68697\n",
      "   macro avg       0.90      0.90      0.90     68697\n",
      "weighted avg       0.90      0.90      0.90     68697\n",
      "\n",
      "y_all: 68697, lst_pred_prob: 68697, \n",
      "for_tjur_rs_test is saved!\n"
     ]
    }
   ],
   "source": [
    "ver_name = list_embedding_file[19:] + '_test_set.png'\n",
    "precision, recall, f1_score, model_auc_roc = lp.evaluate_test_set(ver_name, model = 'Logit')"
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}