scripts/StatisticalResults/generate_statistics_molecules_graph_regression_ZINC_100k.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_dir = \"../../out/molecules_graph_regression/results\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = []\n",
    "dataset = []\n",
    "model = []\n",
    "layer = []\n",
    "params = []\n",
    "acc_test = []\n",
    "acc_train = []\n",
    "convergence = []\n",
    "total_time = []\n",
    "epoch_time = []\n",
    "\n",
    "for filename in os.listdir(f_dir):\n",
    "\n",
    "    if filename[-4:] == \".txt\":\n",
    "        file.append( filename )\n",
    "        \n",
    "        with open(os.path.join(f_dir, filename), \"r\") as f:\n",
    "            lines = f.readlines()\n",
    "\n",
    "            for line in lines:\n",
    "                #print('h1c',line)\n",
    "\n",
    "                if line[:9] == \"Dataset: \":\n",
    "                    dataset.append( line[9:-2] )\n",
    "\n",
    "                if line[:7] == \"Model: \":\n",
    "                    model.append( line[7:-1] )\n",
    "\n",
    "                if line[:17] == \"net_params={'L': \":\n",
    "                    layer.append( line[17:18] )\n",
    "                    \n",
    "                if line[:18] == \"Total Parameters: \":\n",
    "                    params.append( line[18:-1] )\n",
    "\n",
    "                if line[:10] == \"TEST MAE: \":\n",
    "                    acc_test.append( float(line[10:-1]) )\n",
    "                \n",
    "                if line[:11] == \"TRAIN MAE: \":\n",
    "                    acc_train.append( float(line[11:-1]) )\n",
    "                \n",
    "                if line[4:31] == \"Convergence Time (Epochs): \":\n",
    "                    convergence.append( float(line[31:-1]) )\n",
    "\n",
    "                if line[:18] == \"Total Time Taken: \":\n",
    "                    total_time.append( float(line[18:-4]) )\n",
    "\n",
    "                if line[:24] == 'Average Time Per Epoch: ':\n",
    "                    epoch_time.append( float(line[24:-2]) )\n",
    "                    \n",
    "                    \n",
    "        \n",
    "        \n",
    "# print('file',file)\n",
    "# print('dataset',dataset)\n",
    "# print('model',model)\n",
    "# print('layer',layer)\n",
    "# print('params',params)\n",
    "# print('acc_test',acc_test)\n",
    "# print('acc_train',acc_train)\n",
    "# print('convergence',convergence)\n",
    "# print('total_time',total_time)\n",
    "# print('epoch_time',epoch_time)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "list_datasets = ['ZINC','MNIST','CIFAR10','SBM_PATTERN','SBM_CLUSTER','TSP','COLLAB','CSL', 'AQSOL']\n",
    "#print('list_datasets',list_datasets)\n",
    "\n",
    "list_gnns = ['MLP','GCN','GraphSage','GatedGCN','GAT','MoNet','GIN','3WLGNN','RingGNN']\n",
    "#print('list_gnns',list_gnns)\n",
    "\n",
    "\n",
    "#list_datasets = ['SBM_CLUSTER']\n",
    "for data in list_datasets:\n",
    "    #print(data)\n",
    "    \n",
    "    for gnn in list_gnns:\n",
    "        #print('gnn:',gnn)\n",
    "        \n",
    "        acc_test_one_gnn = []\n",
    "        acc_train_one_gnn = []\n",
    "        convergence_one_gnn = []\n",
    "        total_time_one_gnn = []\n",
    "        epoch_time_one_gnn = []\n",
    "        nb_seeds = 0\n",
    "            \n",
    "        for i in range(len(file)):\n",
    "            \n",
    "            if data==dataset[i] and gnn==model[i]:\n",
    "                params_one_gnn = params[i]\n",
    "                acc_test_one_gnn.append(acc_test[i])\n",
    "                acc_train_one_gnn.append(acc_train[i])\n",
    "                convergence_one_gnn.append(convergence[i])\n",
    "                total_time_one_gnn.append(total_time[i])\n",
    "                epoch_time_one_gnn.append(epoch_time[i])\n",
    "                L = layer[i]\n",
    "                nb_seeds = nb_seeds + 1\n",
    "                \n",
    "                \n",
    "        \n",
    "        if len(acc_test_one_gnn)>0:\n",
    "            latex_str = f\"{data} & {nb_seeds} & {gnn} & {L} & {params_one_gnn} & {np.mean(acc_test_one_gnn):.3f}$\\pm${np.std(acc_test_one_gnn):.3f} & {np.mean(acc_train_one_gnn):.3f}$\\pm${np.std(acc_train_one_gnn):.3f} & {np.mean(convergence_one_gnn):.2f} & {np.mean(epoch_time_one_gnn):.2f}s/{np.mean(total_time_one_gnn):.2f}hr\"\n",
    "            print(\"\\nDataset & #Seeds & Model & L & Param & Acc_test & Acc_train & Speed & Epoch/Time\\n{}\".format(latex_str,nb_seeds))\n",
    "\n",
    "        \n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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