sample_run.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m Conda ─→ `~/.julia/packages/Conda/CpuvI/deps/build.log`\n",
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m PyCall → `~/.julia/packages/PyCall/0jMpb/deps/build.log`\n"
     ]
    }
   ],
   "source": [
    "# packages needed\n",
    "ENV[\"PYTHON\"]=\"\"\n",
    "using Pkg\n",
    "Pkg.build(\"PyCall\")\n",
    "using Plots\n",
    "pyplot()\n",
    "\n",
    "# for input files\n",
    "using MAT\n",
    "using NumbersFromText\n",
    "using DelimitedFiles\n",
    "using CSV\n",
    "\n",
    "# graphs/linear algebra packages\n",
    "using MatrixNetworks\n",
    "using SparseArrays\n",
    "using LinearAlgebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Recompiling stale cache file /homes/hnassar/.julia/compiled/v1.1/StatsPlots/SiylL.ji for StatsPlots [f3b207a7-027a-5e70-b257-86293d7955fd]\n",
      "└ @ Base loading.jl:1184\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n",
      "┌ Warning: `@nodeps` is deprecated. Simply use e.g. `Widgets.dropdown(x)` instead of `Widgets.@nodeps dropdown(x)`.\n",
      "└ @ Widgets ~/.julia/packages/Widgets/AcUe0/src/utils.jl:63\n"
     ]
    }
   ],
   "source": [
    "# code that I need\n",
    "include(\"all_code/code_needed.jl\")\n",
    "include(\"all_code/methods.jl\")\n",
    "include(\"all_code/global_functions.jl\")\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data\n",
    "M = Int.(readdlm(\"real-world/ChCh-Miner_durgbank-chem-chem.tsv\"))\n",
    "A = sparse(M[:,1],M[:,2],1,maximum(M),maximum(M))\n",
    "A = max.(A,A')\n",
    "A = largest_component(A)[1]\n",
    "A = A-Diagonal(A)\n",
    "dropzeros!(A)\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data\n",
    "tao = 0.7\n",
    "Atrain,Atest = split_train_test(A,tao);\n",
    "cc = scomponents(Atrain)\n",
    "cids = findall(cc.map.==argmax(cc.sizes))\n",
    "\n",
    "Atrain = Atrain[cids,cids]\n",
    "Atest = Atest[cids,cids]\n",
    "\n",
    "n = size(Atrain,1)\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topkref = [25, 10, 5]\n",
      "1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, \"deow\" = \"deow\"\n",
      "1, 1, 2, 1, 3, 1, 4, 1, 5, 1, 6, 1, 7, 1, 8, 1, 9, 1, 10, 1, 11, 1, 12, 1, 13, 1, 14, 1, 15, 1, 16, 1, 17, 1, 18, 1, 19, 1, 20, 1, 21, 1, 22, 1, 23, 1, 24, 1, 25, 1, 26, 1, 27, 1, 28, 1, 29, 1, 30, 1, 31, 1, 32, 1, 33, 1, 34, 1, 35, 1, 36, 1, 37, 1, 38, 1, 39, 1, 40, 1, 41, 1, 42, 1, 43, 1, 44, 1, 45, 1, 46, 1, 47, 1, 48, 1, 49, 1, 50, 1, 51, 1, 52, 1, 53, 1, 54, 1, 55, 1, 56, 1, 57, 1, 58, 1, 59, 1, 60, 1, 61, 1, 62, 1, 63, 1, 64, 1, 65, 1, 66, 1, 67, 1, 68, 1, 69, 1, 70, 1, 71, 1, 72, 1, 73, 1, 74, 1, 75, 1, 76, 1, 77, 1, 78, 1, 79, 1, 80, 1, 81, 1, 82, 1, 83, 1, 84, 1, 85, 1, 86, 1, 87, 1, 88, 1, 89, 1, 90, 1, 91, 1, 92, 1, 93, 1, 94, 1, 95, 1, 96, 1, 97, 1, 98, 1, 99, 1, 100, 1, 101, 1, 102, 1, 103, 1, 104, 1, 105, 1, 106, 1, 107, 1, 108, 1, 109, 1, 110, 1, 111, 1, 112, 1, 113, 1, 114, 1, 115, 1, 116, 1, 117, 1, 118, 1, 119, 1, 120, 1, 121, 1, 122, 1, 123, 1, 124, 1, 125, 1, 126, 1, 127, 1, 128, 1, 129, 1, 130, 1, 131, 1, 132, 1, 133, 1, 134, 1, 135, 1, 136, 1, 137, 1, 138, 1, 139, 1, 140, 1, 141, 1, 142, 1, 143, 1, 144, 1, 145, 1, 146, 1, 147, 1, 148, 1, 149, 1, 150, 1, 151, 1, 152, 1, 153, 1, 154, 1, 155, 1, 156, 1, 157, 1, 158, 1, 159, 1, 160, 1, 161, 1, 162, 1, 163, 1, 164, 1, 165, 1, 166, 1, 167, 1, 168, 1, 169, 1, 170, 1, 171, 1, 172, 1, 173, 1, 174, 1, 175, 1, 176, 1, 177, 1, 178, 1, 179, 1, 180, 1, 181, 1, 182, 1, 183, 1, 184, 1, 185, 1, 186, 1, 187, 1, 188, 1, 189, 1, 190, 1, 191, 1, 192, 1, 193, 1, 194, 1, 195, 1, 196, 1, 197, 1, 198, 1, 199, 1, 200, 1, 201, 1, 202, 1, 203, 1, 204, 1, 205, 1, 206, 1, 207, 1, 208, 1, 209, 1, 210, 1, 211, 1, 212, 1, 213, 1, 214, 1, 215, 1, 216, 1, 217, 1, 218, 1, 219, 1, 220, 1, 221, 1, 222, 1, 223, 1, 224, 1, 225, 1, 226, 1, 227, 1, 228, 1, 229, 1, 230, 1, 231, 1, 232, 1, 233, 1, 234, 1, 235, 1, 236, 1, 237, 1, 238, 1, 239, 1, 240, 1, 241, 1, 242, 1, 243, 1, 244, 1, 245, 1, 246, 1, 247, 1, 248, 1, 249, 1, 250, 1, 251, 1, 252, 1, 253, 1, 254, 1, 255, 1, 256, 1, 257, 1, 258, 1, 259, 1, 260, 1, 261, 1, 262, 1, 263, 1, 264, 1, 265, 1, 266, 1, 267, 1, 268, 1, 269, 1, 270, 1, 271, 1, 272, 1, 273, 1, 274, 1, 275, 1, 276, 1, 277, 1, 278, 1, 279, 1, 280, 1, 281, 1, 282, 1, 283, 1, 284, 1, 285, 1, 286, 1, 287, 1, 288, 1, 289, 1, 290, 1, 291, 1, 292, 1, 293, 1, 294, 1, 295, 1, 296, 1, 297, 1, 298, 1, 299, 1, 300, 1, 301, 1, 302, 1, 303, 1, 304, 1, 305, 1, 306, 1, 307, 1, 308, 1, 309, 1, 310, 1, 311, 1, 312, 1, 313, 1, 314, 1, 315, 1, 316, 1, 317, 1, 318, 1, 319, 1, 320, 1, 321, 1, 322, 1, 323, 1, 324, 1, 325, 1, 326, 1, 327, 1, 328, 1, 329, 1, 330, 1, 331, 1, 332, 1, 333, 1, 334, 1, 335, 1, 336, 1, 337, 1, 338, 1, 339, 1, 340, 1, 341, 1, 342, 1, 343, 1, 344, 1, 345, 1, 346, 1, 347, 1, 348, 1, 349, 1, 350, 1, 351, 1, 352, 1, 353, 1, 354, 1, 355, 1, 356, 1, 357, 1, 358, 1, 359, 1, 360, 1, 361, 1, 362, 1, 363, 1, 364, 1, 365, 1, 366, 1, 367, 1, 368, 1, 369, 1, 370, 1, 371, 1, 372, 1, 373, 1, 374, 1, 375, 1, 376, 1, 377, 1, 378, 1, 379, 1, 380, 1, 381, 1, 382, 1, 383, 1, 384, 1, 385, 1, 386, 1, 387, 1, 388, 1, 389, 1, 390, 1, 391, 1, 392, 1, 393, 1, 394, 1, 395, 1, 396, 1, 397, 1, 398, 1, 399, 1, 400, 1, 401, 1, 402, 1, 403, 1, 404, 1, 405, 1, 406, 1, 407, 1, 408, 1, 409, 1, 410, 1, 411, 1, 412, 1, 413, 1, 414, 1, 415, 1, 416, 1, 417, 1, 418, 1, 419, 1, 420, 1, 421, 1, 422, 1, 423, 1, 424, 1, 425, 1, 426, 1, 427, 1, 428, 1, 429, 1, 430, 1, 431, 1, 432, 1, 433, 1, 434, 1, 435, 1, 436, 1, 437, 1, 438, 1, 439, 1, 440, 1, 441, 1, 442, 1, 443, 1, 444, 1, 445, 1, 446, 1, 447, 1, 448, 1, 449, 1, 450, 1, 451, 1, 452, 1, 453, 1, 454, 1, 455, 1, 456, 1, 457, 1, 458, 1, 459, 1, 460, 1, 461, 1, 462, 1, 463, 1, 464, 1, 465, 1, 466, 1, 467, 1, 468, 1, 469, 1, 470, 1, 471, 1, 472, 1, 473, 1, 474, 1, 475, 1, 476, 1, 477, 1, 478, 1, 479, 1, 480, 1, 481, 1, 482, 1, 483, 1, 484, 1, 485, 1, 486, 1, 487, 1, 488, 1, 489, 1, 490, 1, 491, 1, 492, 1, 493, 1, 494, 1, 495, 1, 496, 1, 497, 1, 498, 1, 499, 1, 500, 1, "
     ]
    }
   ],
   "source": [
    "method_used = \"BMV\"\n",
    "topk = [25,10,5];\n",
    "AUC1,AUC2,AUC3,CWEDGES1 = general_pairseed_different_evaluation_varytopk(\n",
    "        500,#Edges,#500,\n",
    "        0.85,\n",
    "        topk,\n",
    "        Atrain,\n",
    "        Atest,\n",
    "        method_used, #BMV, MRR, recall_topk\n",
    "        true #\"AND\" (true) or \"OR\" (false) experiment\n",
    "    );\n",
    "AUCW,cWedgesW = general_wedges(A,500,5,true,\"any\",\"all\",\"BMV\")\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyplot()\n",
    "include(\"all_code/plot_values_script.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "savefig(\"CC-BioSnap.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.1.0",
   "language": "julia",
   "name": "julia-1.1"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}