{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "from experiments import ExperimentRunner\n",
    "import params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = ExperimentRunner(params.experiment1, n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running param set: {'data_path_base': '/Users/aziai/Downloads/vtest_new2', 'conv_model_name': 'vgg', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.001, 'use_vggish': False, 'momentum': 0.9}\n",
      "Downloading: \"https://download.pytorch.org/models/vgg11_bn-6002323d.pth\" to /Users/aziai/.cache/torch/checkpoints/vgg11_bn-6002323d.pth\n",
      "100%|██████████| 531503671/531503671 [00:40<00:00, 13079409.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating ALL params\n",
      "Epoch 0/9\n",
      "----------\n",
      "train Loss: 1.0483 F1: 0.2951 Acc: 0.4557\n",
      "val Loss: 0.5709 F1: 0.8189 Acc: 0.7294\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "train Loss: 0.5769 F1: 0.7872 Acc: 0.7468\n",
      "val Loss: 0.3201 F1: 0.9353 Acc: 0.8941\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "train Loss: 0.3647 F1: 0.8247 Acc: 0.7848\n",
      "val Loss: 0.4309 F1: 0.8333 Acc: 0.7647\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "train Loss: 0.2243 F1: 0.8571 Acc: 0.8608\n",
      "val Loss: 0.7989 F1: 0.6796 Acc: 0.6118\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "train Loss: 0.1799 F1: 0.9231 Acc: 0.9241\n",
      "val Loss: 0.8629 F1: 0.7407 Acc: 0.6706\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "ex.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = glob.glob('results/*.csv') # * means all if need specific format then *.csv\n",
    "latest = max(paths, key=os.path.getctime)\n",
    "df = pd.read_csv(latest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch_size</th>\n",
       "      <th>conv_model_name</th>\n",
       "      <th>data_path_base</th>\n",
       "      <th>experiment_uuid</th>\n",
       "      <th>feature_extract</th>\n",
       "      <th>lr</th>\n",
       "      <th>momentum</th>\n",
       "      <th>num_epochs</th>\n",
       "      <th>runner_uuid</th>\n",
       "      <th>use_vggish</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>resnet</td>\n",
       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>resnet</td>\n",
       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
       "      <td>20191402191416</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.9</td>\n",
       "      <td>10</td>\n",
       "      <td>0e57debc92afdd0dc7a209584b4d97860c9dba98f3aed4...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.878049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>resnet</td>\n",
       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
       "      <td>20191402191416</td>\n",
       "      <td>True</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.9</td>\n",
       "      <td>10</td>\n",
       "      <td>8e644bc291a463725bf0bcb11825a196383a4860eeecd7...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.729412</td>\n",
       "      <td>0.824427</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64</td>\n",
       "      <td>resnet</td>\n",
       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
       "      <td>20191402191416</td>\n",
       "      <td>True</td>\n",
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       "      <td>0.9</td>\n",
       "      <td>10</td>\n",
       "      <td>e07e5119b07164f06098d1adba9e4c43ad0344716a0746...</td>\n",
       "      <td>False</td>\n",
       "      <td>0.564706</td>\n",
       "      <td>0.626263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   batch_size conv_model_name                     data_path_base  ...  use_vggish   val_acc    val_f1\n",
       "1          64          resnet  /Users/aziai/Downloads/vtest_new2  ...        True  0.894118  0.935252\n",
       "0          64          resnet  /Users/aziai/Downloads/vtest_new2  ...       False  0.823529  0.878049\n",
       "3          64          resnet  /Users/aziai/Downloads/vtest_new2  ...        True  0.729412  0.824427\n",
       "2          64          resnet  /Users/aziai/Downloads/vtest_new2  ...       False  0.564706  0.626263\n",
       "\n",
       "[4 rows x 12 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='val_f1', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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