analyze_results.ipynb

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   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "logs_dir = './logs'\n",
    "\n",
    "llama_1 = {'name': 'llama-1', 'path': logs_dir + '/llama/llama-1/*/*.json'}\n",
    "llama_2 = {'name': 'llama-2', 'path': logs_dir + '/llama/llama-2/*/*.json'}\n",
    "\n",
    "mistral = {'name': 'mistral', 'path': logs_dir + '/mistral/*/*.json'}\n",
    "\n",
    "phi_1 = {'name': 'phi-1', 'path': logs_dir + '/phi/phi-1/*/*.json'}\n",
    "phi_1_5 = {'name': 'phi-1_5', 'path': logs_dir + '/phi/phi-1-5/*/*.json'}\n",
    "phi_2 = {'name': 'phi-2', 'path': logs_dir + '/phi/phi-2/*/*.json'}\n",
    "\n",
    "models = [llama_1, llama_2, mistral, phi_1, phi_1_5, phi_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['target_0', 'target_1', 'target_2', 'target_3'])\n"
     ]
    }
   ],
   "source": [
    "with open ('logs/phi/phi-1-5/6gim80lk/evaluations.json', 'r') as file:\n",
    "    data = json.load(file)\n",
    "    print(data['evaluations'][0]['stats'].keys())"
   ]
  },
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   "execution_count": 62,
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   "source": [
    "dfs = {}\n",
    "\n",
    "for model in models:\n",
    "    eval_scores = [0] * 10\n",
    "    move_scores = [0] * 10\n",
    "    coin_scores = [0] * 10\n",
    "    partial_coin_scores = [0] * 10\n",
    "    steps = [0] * 10\n",
    "\n",
    "    for i, file in enumerate(glob.glob(model['path'])):\n",
    "\n",
    "        with open(file, 'r') as eval_file:\n",
    "            data = json.load(eval_file)\n",
    "            \n",
    "            for j, eval in enumerate(data[\"evaluations\"]):\n",
    "                eval_scores[j] += eval['eval_score']\n",
    "                steps[j] = eval[\"step\"]\n",
    "                stats = eval['stats']\n",
    "\n",
    "                move, coin, partial = 0, 0 ,0\n",
    "                for key in stats.keys():\n",
    "                    move += stats[key]['move_percentage']\n",
    "                    coin += stats[key]['coins_percentage']\n",
    "                    partial += stats[key]['partial_coins']\n",
    "                \n",
    "                move_scores[j] += move / len(stats.keys())\n",
    "                coin_scores[j] += coin / len(stats.keys())\n",
    "                partial_coin_scores[j] += partial / len(stats.keys())\n",
    "\n",
    "    eval_scores = [s / len(eval_scores) for s in eval_scores]\n",
    "    move_scores = [s / len(eval_scores) for s in move_scores]\n",
    "    coin_scores = [s / len(eval_scores) for s in coin_scores]\n",
    "    partial_coin_scores = [s / len(eval_scores) for s in partial_coin_scores]\n",
    "\n",
    "    dfs[model['name']] = pd.DataFrame({'models': [model['name']] * 10, \n",
    "                                       'steps': steps, \n",
    "                                       'eval_scores': eval_scores,\n",
    "                                       'move_scores': move_scores,\n",
    "                                       'coin_scores': coin_scores,\n",
    "                                       'partial_coin_scores': partial_coin_scores\n",
    "                                       })"
   ]
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   "execution_count": 63,
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   "source": [
    "df = pd.concat(dfs.values())"
   ]
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