notebooks/Visualize Model Results (Move Classifier).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e97cc741",
   "metadata": {},
   "source": [
    "# BASALT 2022 Visualize Model Results\n",
    "\n",
    "## Background\n",
    "I am working on potential solutions for https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition. The goal of this notebook is to visualize the results from MoveClassifier.py.\n",
    "\n",
    "## Code\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23650ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib\n",
    "import json\n",
    "import math\n",
    "from random import randint\n",
    "from IPython.display import Image "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e37bfb",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e696764d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112 114 0 2\n"
     ]
    }
   ],
   "source": [
    "resultPath = \"../model_tests/Move Classifier/\"\n",
    "\n",
    "results = []\n",
    "for path in os.listdir(resultPath):\n",
    "    if not '.DS_Store' in path:\n",
    "        try:\n",
    "            \n",
    "            args = dict(zip(['feature', 'balance', 'batchsize', 'IW', 'IH', 'subset'], path.split(\"-\")))\n",
    "            args['balance'] = 1 if args['balance'][3:]==\"True\" else 0\n",
    "            args['batchsize'] = int(args['batchsize'][2:])\n",
    "            args['IW'] = int(args['IW'][2:])\n",
    "            args['IH'] = int(args['IH'][2:])\n",
    "            args['subset'] = float(args['subset'][3:])\n",
    "            \n",
    "            with open(resultPath + path + \"/time.json\") as json_file:\n",
    "                time = json.load(json_file)\n",
    "                args.update(time)\n",
    "            \n",
    "            df = pd.read_csv(resultPath + path + \"/predictions.csv\")\n",
    "            predictions = {\"TPImages\": [], \"TNImages\": [], \"FPImages\": [], \"FNImages\": [],\n",
    "                          \"TPPredictions\": [], \"TNPredictions\": [], \"FPPredictions\": [], \"FNPredictions\": []}\n",
    "            SSE = 0\n",
    "            for index, row in df.iterrows():\n",
    "                if row['results'] == 'TP':\n",
    "                    predictions['TPImages'].append(row['image'])\n",
    "                    predictions['TPPredictions'].append(row['prediction'])\n",
    "                elif row['results'] == 'TN':\n",
    "                    predictions['TNImages'].append(row['image'])\n",
    "                    predictions['TNPredictions'].append(row['prediction'])\n",
    "                elif row['results'] == 'FP':\n",
    "                    predictions['FPImages'].append(row['image'])\n",
    "                    predictions['FPPredictions'].append(row['prediction'])\n",
    "                elif row['results'] == 'FN':\n",
    "                    predictions['FNImages'].append(row['image'])\n",
    "                    predictions['FNPredictions'].append(row['prediction'])\n",
    "                else:\n",
    "                    \"This should never happen.\"\n",
    "                    \n",
    "                SSE += (row['prediction'] - row['true'])**2\n",
    "            \n",
    "            print(len(predictions['TPImages']), len(predictions['TNImages']), len(predictions['FPImages']), len(predictions['FNImages']))\n",
    "            \n",
    "            recall = len(predictions['TPImages'])/(len(predictions['TPImages'])+len(predictions['FNImages']))      \n",
    "            precision = len(predictions['TPImages'])/(len(predictions['TPImages'])+len(predictions['FPImages']))\n",
    "            predictions.update({\"RMSE\": math.sqrt(SSE), \"recall\": recall, \"precision\": precision,\n",
    "                               \"TP\": len(predictions['TPImages']), \"TN\": len(predictions['TNImages']), \n",
    "                               \"FP\": len(predictions['FPImages']), \"FN\": len(predictions['FNImages'])})\n",
    "            predictions.update(args)\n",
    "            results.append(predictions)\n",
    "        except Exception as e:\n",
    "            print(path, e)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5240e552",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>feature</th>\n",
       "      <th>balance</th>\n",
       "      <th>batchsize</th>\n",
       "      <th>IW</th>\n",
       "      <th>IH</th>\n",
       "      <th>subset</th>\n",
       "      <th>trainDataTime</th>\n",
       "      <th>trainTime</th>\n",
       "      <th>timePerEpoch</th>\n",
       "      <th>testDataTime</th>\n",
       "      <th>predictTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.197737</td>\n",
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       "      <td>112</td>\n",
       "      <td>114</td>\n",
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       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       RMSE    recall  precision   TP   TN  FP  FN    feature  balance  \\\n",
       "0  1.197737  0.982456        1.0  112  114   0   2  isGuiOpen        1   \n",
       "\n",
       "   batchsize   IW  IH  subset  trainDataTime   trainTime  timePerEpoch  \\\n",
       "0         32  160  90   0.005       0.921871  259.807622     25.980762   \n",
       "\n",
       "   testDataTime  predictTime  \n",
       "0      9.185453     0.559052  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(results).drop([\"TPImages\", \"TNImages\", \"FPImages\", \"FNImages\",\n",
    "                          \"TPPredictions\", \"TNPredictions\", \"FPPredictions\", \"FNPredictions\"], axis=1)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2aa43f1",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>balance</th>\n",
       "      <th>batchsize</th>\n",
       "      <th>IW</th>\n",
       "      <th>IH</th>\n",
       "      <th>subset</th>\n",
       "      <th>trainDataTime</th>\n",
       "      <th>trainTime</th>\n",
       "      <th>timePerEpoch</th>\n",
       "      <th>testDataTime</th>\n",
       "      <th>predictTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.197737</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.197737</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.197737</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.197737</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.197737</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.197737</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.921871</td>\n",
       "      <td>259.807622</td>\n",
       "      <td>25.980762</td>\n",
       "      <td>9.185453</td>\n",
       "      <td>0.559052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           RMSE    recall  precision     TP     TN   FP   FN  balance  \\\n",
       "count  1.000000  1.000000        1.0    1.0    1.0  1.0  1.0      1.0   \n",
       "mean   1.197737  0.982456        1.0  112.0  114.0  0.0  2.0      1.0   \n",
       "std         NaN       NaN        NaN    NaN    NaN  NaN  NaN      NaN   \n",
       "min    1.197737  0.982456        1.0  112.0  114.0  0.0  2.0      1.0   \n",
       "25%    1.197737  0.982456        1.0  112.0  114.0  0.0  2.0      1.0   \n",
       "50%    1.197737  0.982456        1.0  112.0  114.0  0.0  2.0      1.0   \n",
       "75%    1.197737  0.982456        1.0  112.0  114.0  0.0  2.0      1.0   \n",
       "max    1.197737  0.982456        1.0  112.0  114.0  0.0  2.0      1.0   \n",
       "\n",
       "       batchsize     IW    IH  subset  trainDataTime   trainTime  \\\n",
       "count        1.0    1.0   1.0   1.000       1.000000    1.000000   \n",
       "mean        32.0  160.0  90.0   0.005       0.921871  259.807622   \n",
       "std          NaN    NaN   NaN     NaN            NaN         NaN   \n",
       "min         32.0  160.0  90.0   0.005       0.921871  259.807622   \n",
       "25%         32.0  160.0  90.0   0.005       0.921871  259.807622   \n",
       "50%         32.0  160.0  90.0   0.005       0.921871  259.807622   \n",
       "75%         32.0  160.0  90.0   0.005       0.921871  259.807622   \n",
       "max         32.0  160.0  90.0   0.005       0.921871  259.807622   \n",
       "\n",
       "       timePerEpoch  testDataTime  predictTime  \n",
       "count      1.000000      1.000000     1.000000  \n",
       "mean      25.980762      9.185453     0.559052  \n",
       "std             NaN           NaN          NaN  \n",
       "min       25.980762      9.185453     0.559052  \n",
       "25%       25.980762      9.185453     0.559052  \n",
       "50%       25.980762      9.185453     0.559052  \n",
       "75%       25.980762      9.185453     0.559052  \n",
       "max       25.980762      9.185453     0.559052  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02ee1965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1 entries, 0 to 0\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   RMSE           1 non-null      float64\n",
      " 1   recall         1 non-null      float64\n",
      " 2   precision      1 non-null      float64\n",
      " 3   TP             1 non-null      int64  \n",
      " 4   TN             1 non-null      int64  \n",
      " 5   FP             1 non-null      int64  \n",
      " 6   FN             1 non-null      int64  \n",
      " 7   feature        1 non-null      object \n",
      " 8   balance        1 non-null      int64  \n",
      " 9   batchsize      1 non-null      int64  \n",
      " 10  IW             1 non-null      int64  \n",
      " 11  IH             1 non-null      int64  \n",
      " 12  subset         1 non-null      float64\n",
      " 13  trainDataTime  1 non-null      float64\n",
      " 14  trainTime      1 non-null      float64\n",
      " 15  timePerEpoch   1 non-null      float64\n",
      " 16  testDataTime   1 non-null      float64\n",
      " 17  predictTime    1 non-null      float64\n",
      "dtypes: float64(9), int64(8), object(1)\n",
      "memory usage: 272.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3ae8f46",
   "metadata": {},
   "source": [
    "### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b1d14ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.197736600924766 | Recall: 0.9824561403508771 | Precision: 1.0\n",
      "True Positives: 112\n",
      "True Negatives: 114\n",
      "False Positives: 0\n",
      "False Negatives: 2\n",
      "Train Time Total: 259.80762219429016 | AVG Time Per Epoch: 25.980762219429018 | Epochs Ran: 10\n"
     ]
    }
   ],
   "source": [
    "feature = \"isGuiOpen\"\n",
    "batchsize = 32\n",
    "shrink = .25\n",
    "IW, IH = (int(640*shrink), int(360*shrink))\n",
    "subset = 0.005\n",
    "balance = 1\n",
    "sample = {}\n",
    "for item in results:\n",
    "    if item[\"feature\"] == feature and item[\"batchsize\"] == batchsize and item[\"IW\"] == IW and item[\"IH\"] == IH and item[\"subset\"] == subset and item[\"balance\"] == balance:\n",
    "        sample = item\n",
    "print(\"RMSE:\", sample[\"RMSE\"], \"| Recall:\", sample[\"recall\"], \"| Precision:\", sample[\"precision\"])\n",
    "print(\"True Positives:\", sample[\"TP\"])\n",
    "print(\"True Negatives:\", sample[\"TN\"])\n",
    "print(\"False Positives:\", sample[\"FP\"])\n",
    "print(\"False Negatives:\", sample[\"FN\"])\n",
    "print(\"Train Time Total:\", sample[\"trainTime\"], \"| AVG Time Per Epoch:\", sample[\"timePerEpoch\"], \"| Epochs Ran:\", (int(sample[\"trainTime\"]/sample[\"timePerEpoch\"])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ebe93297",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../assets/datasets/BASALT Contractor Dataset/MineRLBasaltBuildVillageHouse-v0/Player347-1f668d2c7ba4-20220707-001339.mp4\n",
      "0.9987944\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot embed the 'mp4' image format",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(sample[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTPImages\u001b[39m\u001b[38;5;124m\"\u001b[39m][index])\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(sample[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTPPredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m][index])\n\u001b[0;32m----> 5\u001b[0m display(\u001b[43mImage\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTPImages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/IPython/core/display.py:948\u001b[0m, in \u001b[0;36mImage.__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata, alt)\u001b[0m\n\u001b[1;32m    945\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed \u001b[38;5;241m=\u001b[39m embed \u001b[38;5;28;01mif\u001b[39;00m embed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (url \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    947\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ACCEPTABLE_EMBEDDINGS:\n\u001b[0;32m--> 948\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot embed the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m image format\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat))\n\u001b[1;32m    949\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed:\n\u001b[1;32m    950\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mimetype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_MIMETYPES\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat)\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot embed the 'mp4' image format"
     ]
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    index = randint(0, len(sample[\"TPImages\"]))\n",
    "    print(sample[\"TPImages\"][index])\n",
    "    print(sample[\"TPPredictions\"][index])\n",
    "    display(Image(filename=sample[\"TPImages\"][index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f478dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultPath = \"../model_tests/Move Classifier/\"\n",
    "\n",
    "\n",
    "for path in os.listdir(resultPath):\n",
    "    results = []\n",
    "    if not '.DS_Store' in path:\n",
    "        try:\n",
    "            df = pd.read_csv(resultPath + path + \"/predictions.csv\")\n",
    "            for index, row in df.iterrows():\n",
    "                results.append({\"image\": row['image'].split(\"'\")[3], \n",
    "                                \"prediction\": row['prediction'], \n",
    "                                \"true\": row['true'], \n",
    "                                \"results\": row['results']})\n",
    "            df2 = pd.DataFrame.from_dict(results)\n",
    "            df2.to_csv(resultPath + path + \"/predictions2.csv\", index=False)\n",
    "        except:\n",
    "            print(resultPath + path)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a91cf29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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