test_acc10_maxpool.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import keras\n",
    "from keras.models import load_model\n",
    "import numpy as np\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = './models/try_maxpool_w_adam.h5'\n",
    "\n",
    "df = pd.read_csv('./data/csv/test_acc10_300.csv')\n",
    "model = load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lig_id</th>\n",
       "      <th>pro_id</th>\n",
       "      <th>dest</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2701</td>\n",
       "      <td>2701</td>\n",
       "      <td>./processed_data/test_acc10/2701_pro_2701_lig.npy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2701</td>\n",
       "      <td>2702</td>\n",
       "      <td>./processed_data/test_acc10/2702_pro_2701_lig.npy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2701</td>\n",
       "      <td>2703</td>\n",
       "      <td>./processed_data/test_acc10/2703_pro_2701_lig.npy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2701</td>\n",
       "      <td>2704</td>\n",
       "      <td>./processed_data/test_acc10/2704_pro_2701_lig.npy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2701</td>\n",
       "      <td>2705</td>\n",
       "      <td>./processed_data/test_acc10/2705_pro_2701_lig.npy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lig_id  pro_id                                               dest  score\n",
       "0    2701    2701  ./processed_data/test_acc10/2701_pro_2701_lig.npy      1\n",
       "1    2701    2702  ./processed_data/test_acc10/2702_pro_2701_lig.npy      0\n",
       "2    2701    2703  ./processed_data/test_acc10/2703_pro_2701_lig.npy      0\n",
       "3    2701    2704  ./processed_data/test_acc10/2704_pro_2701_lig.npy      0\n",
       "4    2701    2705  ./processed_data/test_acc10/2705_pro_2701_lig.npy      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by ligand\n",
    "# for each group, compute top10 accuracy manually\n",
    "\n",
    "# bad code, copy from baseline!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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