master-thesis/classification/classifier/hyp-metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "747ddcf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/requests/__init__.py:104: RequestsDependencyWarning: urllib3 (1.26.13) or chardet (5.1.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33me1527193\u001b[0m (\u001b[33mflower-classification\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import wandb\n",
    "import torch\n",
    "wandb.login()\n",
    "\n",
    "from evaluation.helpers import set_size\n",
    "\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76cc2ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = wandb.Api()\n",
    "\n",
    "# Project is specified by <entity/project-name>\n",
    "runs = api.runs(\"flower-classification/pytorch-sweeps-demo\")\n",
    "\n",
    "summary_list, config_list, name_list = [], [], []\n",
    "for run in runs: \n",
    "    # .summary contains the output keys/values for metrics like accuracy.\n",
    "    #  We call ._json_dict to omit large files \n",
    "    summary_list.append(run.summary._json_dict)\n",
    "\n",
    "    # .config contains the hyperparameters.\n",
    "    #  We remove special values that start with _.\n",
    "    config_list.append(\n",
    "        {k: v for k,v in run.config.items()\n",
    "          if not k.startswith('_')})\n",
    "\n",
    "    # .name is the human-readable name of the run.\n",
    "    name_list.append(run.name)\n",
    "\n",
    "runs_df = pd.DataFrame({\n",
    "    \"summary\": summary_list,\n",
    "    \"config\": config_list,\n",
    "    \"name\": name_list\n",
    "    })\n",
    "\n",
    "runs_df.to_csv(\"hyp-metrics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "353f9082",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>name</th>\n",
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       "      <td>1</td>\n",
       "      <td>radiant-sweep-25</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.685185</td>\n",
       "      <td>0.645458</td>\n",
       "      <td>0.712531</td>\n",
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       "      <td>9</td>\n",
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       "      <td>0.900</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.0003</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>blooming-sweep-24</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.348129</td>\n",
       "      <td>0.998771</td>\n",
       "      <td>1039</td>\n",
       "      <td>9</td>\n",
       "      <td>1.680692e+09</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.999</td>\n",
       "      <td>sgd</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>visionary-sweep-23</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.760870</td>\n",
       "      <td>0.555318</td>\n",
       "      <td>0.835381</td>\n",
       "      <td>529</td>\n",
       "      <td>9</td>\n",
       "      <td>1.680692e+09</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
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       "      <td>0.900</td>\n",
       "      <td>sgd</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0003</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ancient-sweep-22</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>1.560271</td>\n",
       "      <td>0.557740</td>\n",
       "      <td>410</td>\n",
       "      <td>1</td>\n",
       "      <td>1.680692e+09</td>\n",
       "      <td>0.707692</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
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       "      <td>0.990</td>\n",
       "      <td>adam</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0100</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>133</td>\n",
       "      <td>different-sweep-5</td>\n",
       "      <td>0.822222</td>\n",
       "      <td>0.945946</td>\n",
       "      <td>0.493642</td>\n",
       "      <td>0.821867</td>\n",
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       "      <td>9</td>\n",
       "      <td>1.678732e+09</td>\n",
       "      <td>0.813953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506896</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0001</td>\n",
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       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>134</td>\n",
       "      <td>wise-sweep-4</td>\n",
       "      <td>0.855556</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.548264</td>\n",
       "      <td>0.812039</td>\n",
       "      <td>1159</td>\n",
       "      <td>9</td>\n",
       "      <td>1.678731e+09</td>\n",
       "      <td>0.835443</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sgd</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>135</td>\n",
       "      <td>misty-sweep-3</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.241948</td>\n",
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       "      <td>9</td>\n",
       "      <td>1.678731e+09</td>\n",
       "      <td>0.849315</td>\n",
       "      <td>...</td>\n",
       "      <td>1.758836</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>136</td>\n",
       "      <td>unique-sweep-2</td>\n",
       "      <td>0.811111</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.479234</td>\n",
       "      <td>0.832924</td>\n",
       "      <td>1159</td>\n",
       "      <td>9</td>\n",
       "      <td>1.678730e+09</td>\n",
       "      <td>0.753623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.455120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0003</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>137</td>\n",
       "      <td>polar-sweep-1</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.544247</td>\n",
       "      <td>0.990172</td>\n",
       "      <td>2289</td>\n",
       "      <td>9</td>\n",
       "      <td>1.678730e+09</td>\n",
       "      <td>0.883721</td>\n",
       "      <td>...</td>\n",
       "      <td>2.532007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sgd</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                name  test/epoch_acc  test/precision  \\\n",
       "0             0      fiery-sweep-26        0.733333        0.828571   \n",
       "1             1    radiant-sweep-25        0.722222        0.685185   \n",
       "2             2   blooming-sweep-24        0.888889        0.935484   \n",
       "3             3  visionary-sweep-23        0.800000        0.760870   \n",
       "4             4    ancient-sweep-22        0.577778        0.589744   \n",
       "..          ...                 ...             ...             ...   \n",
       "133         133   different-sweep-5        0.822222        0.945946   \n",
       "134         134        wise-sweep-4        0.855556        0.825000   \n",
       "135         135       misty-sweep-3        0.877778        0.939394   \n",
       "136         136      unique-sweep-2        0.811111        0.838710   \n",
       "137         137       polar-sweep-1        0.888889        0.904762   \n",
       "\n",
       "     test/epoch_loss  train/epoch_acc  _step  epoch    _timestamp  \\\n",
       "0           0.566462         0.823096   2059      9  1.680693e+09   \n",
       "1           0.645458         0.712531   1039      9  1.680693e+09   \n",
       "2           0.348129         0.998771   1039      9  1.680692e+09   \n",
       "3           0.555318         0.835381    529      9  1.680692e+09   \n",
       "4           1.560271         0.557740    410      1  1.680692e+09   \n",
       "..               ...              ...    ...    ...           ...   \n",
       "133         0.493642         0.821867   1159      9  1.678732e+09   \n",
       "134         0.548264         0.812039   1159      9  1.678731e+09   \n",
       "135         0.241948         0.996314   2289      9  1.678731e+09   \n",
       "136         0.479234         0.832924   1159      9  1.678730e+09   \n",
       "137         0.544247         0.990172   2289      9  1.678730e+09   \n",
       "\n",
       "     test/f1-score  ... test/batch_loss           eps  gamma  epochs  \\\n",
       "0         0.707317  ...             NaN  1.000000e-01    0.1      10   \n",
       "1         0.747475  ...             NaN  1.000000e+00    0.5      10   \n",
       "2         0.852941  ...             NaN  1.000000e-08    0.5      10   \n",
       "3         0.795455  ...             NaN  1.000000e+00    0.1      10   \n",
       "4         0.707692  ...             NaN  1.000000e-08    0.5      10   \n",
       "..             ...  ...             ...           ...    ...     ...   \n",
       "133       0.813953  ...        0.506896           NaN    0.5      10   \n",
       "134       0.835443  ...        0.515937           NaN    0.5      10   \n",
       "135       0.849315  ...        1.758836           NaN    0.5      10   \n",
       "136       0.753623  ...        0.455120           NaN    0.1      10   \n",
       "137       0.883721  ...        2.532007           NaN    0.5      10   \n",
       "\n",
       "     beta_one  beta_two  optimizer  step_size  batch_size  learning_rate  \n",
       "0        0.99     0.900       adam          3           4         0.0003  \n",
       "1        0.99     0.900       adam          2           8         0.0003  \n",
       "2        0.90     0.999        sgd          5           8         0.0030  \n",
       "3        0.90     0.900        sgd          2          16         0.0003  \n",
       "4        0.90     0.990       adam          7           4         0.0100  \n",
       "..        ...       ...        ...        ...         ...            ...  \n",
       "133       NaN       NaN        sgd          3           8         0.0001  \n",
       "134       NaN       NaN        sgd          2           8         0.0001  \n",
       "135       NaN       NaN        sgd          3           4         0.0030  \n",
       "136       NaN       NaN        sgd          3           8         0.0003  \n",
       "137       NaN       NaN        sgd          7           4         0.0030  \n",
       "\n",
       "[138 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('hyp-metrics.csv',\n",
    "                 delimiter=',')\n",
    "df['summary'] = df['summary'].map(eval)\n",
    "df['config'] = df['config'].map(eval)\n",
    "df = df.join(pd.json_normalize(df['summary'])).drop('summary', axis='columns')\n",
    "df = df.join(pd.json_normalize(df['config'])).drop('config', axis='columns')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4679b2f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of Series.sort_values will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0100    21\n",
       "0.1000    21\n",
       "0.0003    23\n",
       "0.0010    23\n",
       "0.0001    23\n",
       "0.0030    27\n",
       "Name: learning_rate, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['learning_rate'].value_counts().sort_values(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b1a54fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Style the plots (with grid this time)\n",
    "width = 418\n",
    "sns.set_theme(style='whitegrid',\n",
    "              rc={'text.usetex': True, 'font.family': 'serif', 'axes.labelsize': 10,\n",
    "                  'font.size': 10, 'legend.fontsize': 8,\n",
    "                  'xtick.labelsize': 8, 'ytick.labelsize': 8})\n",
    "\n",
    "fig_save_dir = '../../thesis/graphics/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00efa25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 578.387x357.463 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_prepared = df.rename(columns={'learning_rate': 'learning rate', 'batch_size': 'batch size'})\n",
    "fig, ax = plt.subplots(1, 1, figsize=set_size(width, subplots=(1,1)))\n",
    "sns.scatterplot(x=\"learning rate\", y=\"test/f1-score\",\n",
    "                style=\"optimizer\", hue=\"batch size\",\n",
    "                palette=sns.cubehelix_palette(5, light=0.8, gamma=1.2),\n",
    "                sizes=(5, 30), linewidth=0, s=15,\n",
    "                data=df_prepared, ax=ax)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xticks([0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1])\n",
    "ax.set_xticklabels(labels = ['0.0001', '0.0003', '0.001', '0.003', '0.01', '0.1'])\n",
    "fig.tight_layout()\n",
    "fig.savefig(fig_save_dir + 'classifier-hyp-metrics.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44e275ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_dict = {\n",
    "    'optimizer': {\n",
    "        'values': ['adam', 'sgd']\n",
    "    },\n",
    "}\n",
    "\n",
    "parameters_dict.update({\n",
    "    'batch_size': {\n",
    "        'values': [4, 8, 16, 32, 64]},\n",
    "    'learning_rate': {\n",
    "        'values': [0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1]},\n",
    "    'step_size': {\n",
    "        'values': [2, 3, 5, 7]},\n",
    "    'gamma': {\n",
    "        'values': [0.1, 0.5]},\n",
    "    'beta_one': {\n",
    "        'values': [0.9, 0.99]},\n",
    "    'beta_two': {\n",
    "        'values': [0.5, 0.9, 0.99, 0.999]},\n",
    "    'eps': {\n",
    "        'values': [1e-08, 0.1, 1]}\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d3c2860",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = pd.DataFrame.from_dict(parameters_dict)\n",
    "params = params.transpose()\n",
    "params['values_string'] = [', '.join(map(str, l)) for l in params['values']]\n",
    "params['values'] = params['values_string']\n",
    "params = params.drop(['values_string'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acc3a77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>optimizer</th>\n",
       "      <td>adam, sgd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batch_size</th>\n",
       "      <td>4, 8, 16, 32, 64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning_rate</th>\n",
       "      <td>0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>step_size</th>\n",
       "      <td>2, 3, 5, 7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>0.1, 0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_one</th>\n",
       "      <td>0.9, 0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_two</th>\n",
       "      <td>0.5, 0.9, 0.99, 0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eps</th>\n",
       "      <td>1e-08, 0.1, 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                values\n",
       "optimizer                                    adam, sgd\n",
       "batch_size                            4, 8, 16, 32, 64\n",
       "learning_rate  0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1\n",
       "step_size                                   2, 3, 5, 7\n",
       "gamma                                         0.1, 0.5\n",
       "beta_one                                     0.9, 0.99\n",
       "beta_two                         0.5, 0.9, 0.99, 0.999\n",
       "eps                                      1e-08, 0.1, 1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73a26951",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>step_size</th>\n",
       "      <th>gamma</th>\n",
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       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>adam</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>adam</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>adam</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>adam</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>adam</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>sgd</td>\n",
       "      <td>64</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>sgd</td>\n",
       "      <td>64</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>sgd</td>\n",
       "      <td>64</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>sgd</td>\n",
       "      <td>64</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>sgd</td>\n",
       "      <td>64</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11520 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       optimizer batch_size learning_rate step_size gamma beta_one beta_two  \\\n",
       "values      adam          4        0.0001         2   0.1      0.9      0.5   \n",
       "values      adam          4        0.0001         2   0.1      0.9      0.5   \n",
       "values      adam          4        0.0001         2   0.1      0.9      0.5   \n",
       "values      adam          4        0.0001         2   0.1      0.9      0.9   \n",
       "values      adam          4        0.0001         2   0.1      0.9      0.9   \n",
       "...          ...        ...           ...       ...   ...      ...      ...   \n",
       "values       sgd         64           0.1         7   0.5     0.99     0.99   \n",
       "values       sgd         64           0.1         7   0.5     0.99     0.99   \n",
       "values       sgd         64           0.1         7   0.5     0.99    0.999   \n",
       "values       sgd         64           0.1         7   0.5     0.99    0.999   \n",
       "values       sgd         64           0.1         7   0.5     0.99    0.999   \n",
       "\n",
       "        eps  \n",
       "values  0.0  \n",
       "values  0.1  \n",
       "values    1  \n",
       "values  0.0  \n",
       "values  0.1  \n",
       "...     ...  \n",
       "values  0.1  \n",
       "values    1  \n",
       "values  0.0  \n",
       "values  0.1  \n",
       "values    1  \n",
       "\n",
       "[11520 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(parameters_dict).explode('optimizer').explode('batch_size').explode('learning_rate').explode('step_size').explode('gamma').explode('beta_one').explode('beta_two').explode('eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d01bf18",
   "metadata": {},
   "source": [
    "# F1-score stratified 10-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bb567230",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_scores_test = pd.read_csv('f1-scores-folds.csv', delimiter=',')\n",
    "f_scores_test['epoch'] = np.resize(np.arange(25), 10*25)\n",
    "f_scores_test['fold'] = np.repeat(np.arange(10), 25)\n",
    "f_scores_test = pd.melt(f_scores_test[['epoch', 'fold', 'StratifiedKFold-ROC - test/f1-score']], ['epoch', 'fold'])\n",
    "\n",
    "f_scores_train = pd.read_csv('f1-scores-folds-train.csv', delimiter=',')\n",
    "f_scores_train['epoch'] = np.resize(np.arange(25), 10*25)\n",
    "f_scores_train['fold'] = np.repeat(np.arange(10), 25)\n",
    "f_scores_train = pd.melt(f_scores_train[['epoch', 'fold', 'StratifiedKFold-ROC - train/f1-score']], ['epoch', 'fold'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "493e415e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 578.387x714.925 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=set_size(width, subplots=(2,1)), sharex=True)\n",
    "sns.lineplot(x=\"epoch\", y=\"value\",\n",
    "                hue='fold',\n",
    "                palette=sns.cubehelix_palette(10, light=0.8, gamma=1.2),\n",
    "                linewidth=1,\n",
    "                data=f_scores_train, ax=ax[0])\n",
    "\n",
    "sns.lineplot(x=\"epoch\", y=\"value\",\n",
    "                hue='fold',\n",
    "                palette=sns.cubehelix_palette(10, light=0.8, gamma=1.2),\n",
    "                linewidth=1,\n",
    "                data=f_scores_test, ax=ax[1])\n",
    "ax[0].set_ylabel('train/f1-score')\n",
    "ax[1].set_ylabel('test/f1-score')\n",
    "fig.tight_layout()\n",
    "fig.savefig(fig_save_dir + 'classifier-hyp-folds-f1.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c642d40",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}