_paper_inception_time.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "induced-stock",
   "metadata": {},
   "source": [
    "# InceptionTime Results\n",
    "\n",
    "This notebook makes up the data for the deep learning result section of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "polar-adams",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tsai.all import *\n",
    "from sklearn.metrics import roc_auc_score, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indirect-investor",
   "metadata": {},
   "source": [
    "## Loading and preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "boolean-roberts",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of frames: 10424241\n",
      "Number of frames after dropping non-labelled frames: 865548\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_hdf(\"merged_20210405.h5\")\n",
    "print(f\"Number of frames: {len(df)}\")\n",
    "df = df[(df['behavior'] != \"not_defined\")]\n",
    "print(f\"Number of frames after dropping non-labelled frames: {len(df)}\")\n",
    "\n",
    "df.loc[(df['behavior'] == \"back_preening\") | (df['behavior'] == \"front_preening\"), 'behavior'] = \"preening\"\n",
    "df.loc[(df['behavior'] == \"standing_shaking\") | (df['behavior'] == \"scratch_with_feet\"), 'behavior'] = \"standing\"\n",
    "df.loc[(df['behavior'] == \"body_pump\") | (df['behavior'] == \"feather_clapping\"), 'behavior'] = \"fluffing\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "british-professor",
   "metadata": {},
   "source": [
    "We use the same features as for RandomForest.\n",
    "For windows, label of the window is defined by the modal (most common) value of the window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "romance-commons",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [('head', 'x'), ('head', 'y'), \n",
    "            ('tail', 'x'), ('tail', 'y'),  \n",
    "            \n",
    "            ('left_neck', 'x'), ('left_neck', 'y'), \n",
    "            ('right_neck', 'x'), ('right_neck', 'y'), \n",
    "            \n",
    "            ('left_up_wing', 'x'), ('left_up_wing', 'y'),\n",
    "            ('left_middle_wing', 'x'), ('left_middle_wing', 'y'),\n",
    "            ('left_down_wing', 'x'), ('left_down_wing', 'y'),\n",
    "            \n",
    "            ('right_up_wing', 'x'), ('right_up_wing', 'y'),\n",
    "            ('right_middle_wing', 'x'), ('right_middle_wing', 'y'),\n",
    "            ('right_down_wing', 'x'), ('right_down_wing', 'y'),\n",
    "]\n",
    "def y_func(o): \n",
    "    return scipy.stats.mode(o, axis=1).mode.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smoking-testing",
   "metadata": {},
   "source": [
    "## 16 Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "painted-commitment",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = SlidingWindow(16, get_x=features, get_y='behavior', y_func=y_func)(df)\n",
    "splits = get_splits(y, n_splits=5, shuffle=False, stratify=True, check_splits=True)\n",
    "tfms  = [None, [Categorize()]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "funny-observation",
   "metadata": {},
   "source": [
    "First some manual experiments to find some hyper parameters for the model. For this, we want to use the last split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "derived-matthew",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 1024\n",
    "\n",
    "last_split = splits[-1]\n",
    "\n",
    "tfms  = [None, [Categorize()]] \n",
    "dsets = TSDatasets(x, y, tfms=tfms, splits=last_split, inplace=True)\n",
    "dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=bs, batch_tfms=[TSStandardize()], num_workers=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "urban-induction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum/10: 0.10964782238006592, steepest point: 0.001737800776027143\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.343953</td>\n",
       "      <td>0.513091</td>\n",
       "      <td>0.855672</td>\n",
       "      <td>0.830308</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.238416</td>\n",
       "      <td>0.379724</td>\n",
       "      <td>0.891205</td>\n",
       "      <td>0.879879</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.206470</td>\n",
       "      <td>0.380006</td>\n",
       "      <td>0.896520</td>\n",
       "      <td>0.889418</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.185836</td>\n",
       "      <td>0.393178</td>\n",
       "      <td>0.896815</td>\n",
       "      <td>0.891814</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.174170</td>\n",
       "      <td>0.396790</td>\n",
       "      <td>0.898438</td>\n",
       "      <td>0.889616</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.160151</td>\n",
       "      <td>0.385449</td>\n",
       "      <td>0.900922</td>\n",
       "      <td>0.893046</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.153962</td>\n",
       "      <td>0.381299</td>\n",
       "      <td>0.904140</td>\n",
       "      <td>0.898090</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.154680</td>\n",
       "      <td>0.381764</td>\n",
       "      <td>0.904296</td>\n",
       "      <td>0.897917</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = InceptionTime(dls.vars, dls.c, depth=1, ks=20)\n",
    "f1 = F1Score(average=\"weighted\")\n",
    "learn = Learner(dls, model, metrics=[accuracy, f1])\n",
    "lr_min, lr_steep, lr_valley, lr_slide  = learn.lr_find(suggest_funcs=(minimum, steep, valley, slide))\n",
    "print(f\"Minimum/10: {lr_min}, steepest point: {lr_steep}\")\n",
    "\n",
    "learn.fit_one_cycle(8, lr_max=lr_steep)\n",
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaning-saying",
   "metadata": {},
   "source": [
    "## Cross validation\n",
    "\n",
    "We do cross val using some specific hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "republican-insider",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++ Depth 1 +++++++++++++++++++\n",
      "===================== Fold 0 ============================\n",
      "26.417078971862793\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.304707</td>\n",
       "      <td>0.855269</td>\n",
       "      <td>0.835311</td>\n",
       "      <td>0.970873</td>\n",
       "      <td>0.797333</td>\n",
       "      <td>0.826628</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.228698</td>\n",
       "      <td>0.915977</td>\n",
       "      <td>0.908389</td>\n",
       "      <td>0.816981</td>\n",
       "      <td>0.908294</td>\n",
       "      <td>0.901336</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.190765</td>\n",
       "      <td>0.930563</td>\n",
       "      <td>0.926656</td>\n",
       "      <td>0.957146</td>\n",
       "      <td>0.902101</td>\n",
       "      <td>0.897723</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.171693</td>\n",
       "      <td>0.939703</td>\n",
       "      <td>0.937146</td>\n",
       "      <td>1.115454</td>\n",
       "      <td>0.901558</td>\n",
       "      <td>0.906648</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.154068</td>\n",
       "      <td>0.946135</td>\n",
       "      <td>0.944321</td>\n",
       "      <td>1.058907</td>\n",
       "      <td>0.908762</td>\n",
       "      <td>0.904050</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.139089</td>\n",
       "      <td>0.951874</td>\n",
       "      <td>0.950471</td>\n",
       "      <td>1.247502</td>\n",
       "      <td>0.902505</td>\n",
       "      <td>0.904990</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.132281</td>\n",
       "      <td>0.955668</td>\n",
       "      <td>0.954503</td>\n",
       "      <td>1.213510</td>\n",
       "      <td>0.907918</td>\n",
       "      <td>0.908789</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.128372</td>\n",
       "      <td>0.957836</td>\n",
       "      <td>0.956775</td>\n",
       "      <td>1.238643</td>\n",
       "      <td>0.902032</td>\n",
       "      <td>0.904775</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 1 ============================\n",
      "23.642162799835205\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.283084</td>\n",
       "      <td>0.853833</td>\n",
       "      <td>0.838804</td>\n",
       "      <td>0.481538</td>\n",
       "      <td>0.859925</td>\n",
       "      <td>0.856825</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.213368</td>\n",
       "      <td>0.923253</td>\n",
       "      <td>0.916778</td>\n",
       "      <td>0.620706</td>\n",
       "      <td>0.819747</td>\n",
       "      <td>0.826060</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.179470</td>\n",
       "      <td>0.937711</td>\n",
       "      <td>0.934529</td>\n",
       "      <td>0.594332</td>\n",
       "      <td>0.844628</td>\n",
       "      <td>0.855546</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.158750</td>\n",
       "      <td>0.945425</td>\n",
       "      <td>0.943423</td>\n",
       "      <td>0.600756</td>\n",
       "      <td>0.846898</td>\n",
       "      <td>0.843879</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.141595</td>\n",
       "      <td>0.951225</td>\n",
       "      <td>0.949725</td>\n",
       "      <td>0.773594</td>\n",
       "      <td>0.789957</td>\n",
       "      <td>0.788976</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.128385</td>\n",
       "      <td>0.955969</td>\n",
       "      <td>0.954826</td>\n",
       "      <td>0.504078</td>\n",
       "      <td>0.876146</td>\n",
       "      <td>0.876973</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.123099</td>\n",
       "      <td>0.959387</td>\n",
       "      <td>0.958410</td>\n",
       "      <td>0.512313</td>\n",
       "      <td>0.874887</td>\n",
       "      <td>0.877933</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.120580</td>\n",
       "      <td>0.961748</td>\n",
       "      <td>0.960869</td>\n",
       "      <td>0.530299</td>\n",
       "      <td>0.870808</td>\n",
       "      <td>0.875474</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 2 ============================\n",
      "24.157520294189453\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.269817</td>\n",
       "      <td>0.867017</td>\n",
       "      <td>0.847430</td>\n",
       "      <td>0.547637</td>\n",
       "      <td>0.848561</td>\n",
       "      <td>0.837419</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>0.924569</td>\n",
       "      <td>0.918511</td>\n",
       "      <td>1.018464</td>\n",
       "      <td>0.631549</td>\n",
       "      <td>0.645044</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.176986</td>\n",
       "      <td>0.937288</td>\n",
       "      <td>0.933993</td>\n",
       "      <td>0.892350</td>\n",
       "      <td>0.710143</td>\n",
       "      <td>0.731219</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.156828</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.942534</td>\n",
       "      <td>1.054756</td>\n",
       "      <td>0.687145</td>\n",
       "      <td>0.716092</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.142826</td>\n",
       "      <td>0.950673</td>\n",
       "      <td>0.948976</td>\n",
       "      <td>0.824649</td>\n",
       "      <td>0.807586</td>\n",
       "      <td>0.821756</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.128765</td>\n",
       "      <td>0.955087</td>\n",
       "      <td>0.953651</td>\n",
       "      <td>0.894345</td>\n",
       "      <td>0.790672</td>\n",
       "      <td>0.812234</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.124044</td>\n",
       "      <td>0.958652</td>\n",
       "      <td>0.957489</td>\n",
       "      <td>0.825483</td>\n",
       "      <td>0.819197</td>\n",
       "      <td>0.828971</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.118640</td>\n",
       "      <td>0.960537</td>\n",
       "      <td>0.959421</td>\n",
       "      <td>0.863338</td>\n",
       "      <td>0.802058</td>\n",
       "      <td>0.815905</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 3 ============================\n",
      "24.993677139282227\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.278154</td>\n",
       "      <td>0.859290</td>\n",
       "      <td>0.844378</td>\n",
       "      <td>0.427217</td>\n",
       "      <td>0.868381</td>\n",
       "      <td>0.837761</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.213608</td>\n",
       "      <td>0.924701</td>\n",
       "      <td>0.917325</td>\n",
       "      <td>0.627272</td>\n",
       "      <td>0.792098</td>\n",
       "      <td>0.808373</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.183426</td>\n",
       "      <td>0.937150</td>\n",
       "      <td>0.933068</td>\n",
       "      <td>0.432274</td>\n",
       "      <td>0.879837</td>\n",
       "      <td>0.862705</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.162837</td>\n",
       "      <td>0.945016</td>\n",
       "      <td>0.942354</td>\n",
       "      <td>0.463584</td>\n",
       "      <td>0.859323</td>\n",
       "      <td>0.846875</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.145305</td>\n",
       "      <td>0.950636</td>\n",
       "      <td>0.948673</td>\n",
       "      <td>0.422141</td>\n",
       "      <td>0.882916</td>\n",
       "      <td>0.868249</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.129112</td>\n",
       "      <td>0.955382</td>\n",
       "      <td>0.953791</td>\n",
       "      <td>0.415096</td>\n",
       "      <td>0.884499</td>\n",
       "      <td>0.879560</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.126520</td>\n",
       "      <td>0.959020</td>\n",
       "      <td>0.957662</td>\n",
       "      <td>0.413704</td>\n",
       "      <td>0.885873</td>\n",
       "      <td>0.877624</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.122050</td>\n",
       "      <td>0.960823</td>\n",
       "      <td>0.959569</td>\n",
       "      <td>0.418762</td>\n",
       "      <td>0.885764</td>\n",
       "      <td>0.879810</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 4 ============================\n",
      "24.97601008415222\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.286587</td>\n",
       "      <td>0.854421</td>\n",
       "      <td>0.838435</td>\n",
       "      <td>0.495017</td>\n",
       "      <td>0.859086</td>\n",
       "      <td>0.864721</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.214260</td>\n",
       "      <td>0.921444</td>\n",
       "      <td>0.914178</td>\n",
       "      <td>0.406580</td>\n",
       "      <td>0.892003</td>\n",
       "      <td>0.878870</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.187365</td>\n",
       "      <td>0.934037</td>\n",
       "      <td>0.929934</td>\n",
       "      <td>0.396506</td>\n",
       "      <td>0.894775</td>\n",
       "      <td>0.885014</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.169575</td>\n",
       "      <td>0.942588</td>\n",
       "      <td>0.939912</td>\n",
       "      <td>0.627720</td>\n",
       "      <td>0.817649</td>\n",
       "      <td>0.813536</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.149125</td>\n",
       "      <td>0.948218</td>\n",
       "      <td>0.946197</td>\n",
       "      <td>0.502347</td>\n",
       "      <td>0.877474</td>\n",
       "      <td>0.879377</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.135030</td>\n",
       "      <td>0.953212</td>\n",
       "      <td>0.951581</td>\n",
       "      <td>0.447493</td>\n",
       "      <td>0.895654</td>\n",
       "      <td>0.892776</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.127986</td>\n",
       "      <td>0.956843</td>\n",
       "      <td>0.955453</td>\n",
       "      <td>0.434929</td>\n",
       "      <td>0.902788</td>\n",
       "      <td>0.900850</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.125973</td>\n",
       "      <td>0.958797</td>\n",
       "      <td>0.957476</td>\n",
       "      <td>0.441799</td>\n",
       "      <td>0.899923</td>\n",
       "      <td>0.895368</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++ Depth 2 +++++++++++++++++++\n",
      "===================== Fold 0 ============================\n",
      "28.280982494354248\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.214673</td>\n",
       "      <td>0.888711</td>\n",
       "      <td>0.876781</td>\n",
       "      <td>0.777584</td>\n",
       "      <td>0.905145</td>\n",
       "      <td>0.897422</td>\n",
       "      <td>00:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.150375</td>\n",
       "      <td>0.940934</td>\n",
       "      <td>0.938704</td>\n",
       "      <td>0.773704</td>\n",
       "      <td>0.901708</td>\n",
       "      <td>0.898060</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.106318</td>\n",
       "      <td>0.958817</td>\n",
       "      <td>0.958148</td>\n",
       "      <td>0.846438</td>\n",
       "      <td>0.909004</td>\n",
       "      <td>0.906124</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.081446</td>\n",
       "      <td>0.969818</td>\n",
       "      <td>0.969520</td>\n",
       "      <td>1.063450</td>\n",
       "      <td>0.775081</td>\n",
       "      <td>0.824233</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.056991</td>\n",
       "      <td>0.978120</td>\n",
       "      <td>0.977992</td>\n",
       "      <td>0.985192</td>\n",
       "      <td>0.907127</td>\n",
       "      <td>0.908458</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>0.984496</td>\n",
       "      <td>0.984432</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.899848</td>\n",
       "      <td>0.901508</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.033924</td>\n",
       "      <td>0.989067</td>\n",
       "      <td>0.989027</td>\n",
       "      <td>1.185078</td>\n",
       "      <td>0.911009</td>\n",
       "      <td>0.912295</td>\n",
       "      <td>00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.029505</td>\n",
       "      <td>0.991282</td>\n",
       "      <td>0.991253</td>\n",
       "      <td>1.237082</td>\n",
       "      <td>0.907034</td>\n",
       "      <td>0.910447</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 1 ============================\n",
      "22.575355052947998\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.204375</td>\n",
       "      <td>0.897465</td>\n",
       "      <td>0.887791</td>\n",
       "      <td>0.611048</td>\n",
       "      <td>0.807587</td>\n",
       "      <td>0.822730</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.139142</td>\n",
       "      <td>0.946308</td>\n",
       "      <td>0.944709</td>\n",
       "      <td>0.808725</td>\n",
       "      <td>0.752893</td>\n",
       "      <td>0.753671</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.101598</td>\n",
       "      <td>0.961632</td>\n",
       "      <td>0.961097</td>\n",
       "      <td>0.657228</td>\n",
       "      <td>0.830244</td>\n",
       "      <td>0.843255</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.078765</td>\n",
       "      <td>0.970744</td>\n",
       "      <td>0.970469</td>\n",
       "      <td>1.115589</td>\n",
       "      <td>0.697621</td>\n",
       "      <td>0.729413</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.059270</td>\n",
       "      <td>0.978269</td>\n",
       "      <td>0.978131</td>\n",
       "      <td>0.630296</td>\n",
       "      <td>0.875961</td>\n",
       "      <td>0.880320</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.042646</td>\n",
       "      <td>0.984265</td>\n",
       "      <td>0.984190</td>\n",
       "      <td>0.758196</td>\n",
       "      <td>0.866851</td>\n",
       "      <td>0.874088</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.034372</td>\n",
       "      <td>0.988800</td>\n",
       "      <td>0.988755</td>\n",
       "      <td>0.820711</td>\n",
       "      <td>0.846136</td>\n",
       "      <td>0.861419</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.029620</td>\n",
       "      <td>0.991055</td>\n",
       "      <td>0.991023</td>\n",
       "      <td>0.831886</td>\n",
       "      <td>0.854431</td>\n",
       "      <td>0.866172</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 2 ============================\n",
      "23.50901961326599\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.196895</td>\n",
       "      <td>0.901512</td>\n",
       "      <td>0.892003</td>\n",
       "      <td>0.785820</td>\n",
       "      <td>0.718652</td>\n",
       "      <td>0.730803</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.141316</td>\n",
       "      <td>0.944889</td>\n",
       "      <td>0.942918</td>\n",
       "      <td>1.032583</td>\n",
       "      <td>0.638118</td>\n",
       "      <td>0.655439</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.101186</td>\n",
       "      <td>0.961241</td>\n",
       "      <td>0.960547</td>\n",
       "      <td>1.560324</td>\n",
       "      <td>0.622243</td>\n",
       "      <td>0.626787</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.081655</td>\n",
       "      <td>0.970729</td>\n",
       "      <td>0.970403</td>\n",
       "      <td>1.377758</td>\n",
       "      <td>0.643403</td>\n",
       "      <td>0.654510</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.059623</td>\n",
       "      <td>0.977861</td>\n",
       "      <td>0.977684</td>\n",
       "      <td>1.470071</td>\n",
       "      <td>0.650740</td>\n",
       "      <td>0.674543</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.044974</td>\n",
       "      <td>0.983695</td>\n",
       "      <td>0.983587</td>\n",
       "      <td>1.466740</td>\n",
       "      <td>0.667764</td>\n",
       "      <td>0.684064</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.988147</td>\n",
       "      <td>0.988085</td>\n",
       "      <td>1.017717</td>\n",
       "      <td>0.788228</td>\n",
       "      <td>0.802299</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.031840</td>\n",
       "      <td>0.990399</td>\n",
       "      <td>0.990351</td>\n",
       "      <td>1.109084</td>\n",
       "      <td>0.746132</td>\n",
       "      <td>0.762557</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 3 ============================\n",
      "25.103209972381592\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.191492</td>\n",
       "      <td>0.896721</td>\n",
       "      <td>0.887387</td>\n",
       "      <td>0.406853</td>\n",
       "      <td>0.868173</td>\n",
       "      <td>0.857056</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.137092</td>\n",
       "      <td>0.946033</td>\n",
       "      <td>0.943962</td>\n",
       "      <td>0.679923</td>\n",
       "      <td>0.774889</td>\n",
       "      <td>0.798551</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.106264</td>\n",
       "      <td>0.960943</td>\n",
       "      <td>0.960156</td>\n",
       "      <td>0.463631</td>\n",
       "      <td>0.868543</td>\n",
       "      <td>0.862231</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.080091</td>\n",
       "      <td>0.971102</td>\n",
       "      <td>0.970717</td>\n",
       "      <td>0.474448</td>\n",
       "      <td>0.872512</td>\n",
       "      <td>0.871942</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.059827</td>\n",
       "      <td>0.978412</td>\n",
       "      <td>0.978224</td>\n",
       "      <td>0.531057</td>\n",
       "      <td>0.860895</td>\n",
       "      <td>0.870311</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.044297</td>\n",
       "      <td>0.984041</td>\n",
       "      <td>0.983937</td>\n",
       "      <td>0.551143</td>\n",
       "      <td>0.870345</td>\n",
       "      <td>0.874041</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.036473</td>\n",
       "      <td>0.988368</td>\n",
       "      <td>0.988304</td>\n",
       "      <td>0.555849</td>\n",
       "      <td>0.875175</td>\n",
       "      <td>0.876978</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.032136</td>\n",
       "      <td>0.990385</td>\n",
       "      <td>0.990341</td>\n",
       "      <td>0.557665</td>\n",
       "      <td>0.878352</td>\n",
       "      <td>0.879607</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 4 ============================\n",
      "26.213767290115356\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.207296</td>\n",
       "      <td>0.892464</td>\n",
       "      <td>0.882136</td>\n",
       "      <td>0.438009</td>\n",
       "      <td>0.887404</td>\n",
       "      <td>0.876687</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.141469</td>\n",
       "      <td>0.943030</td>\n",
       "      <td>0.940947</td>\n",
       "      <td>0.486835</td>\n",
       "      <td>0.871922</td>\n",
       "      <td>0.866551</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.103194</td>\n",
       "      <td>0.959697</td>\n",
       "      <td>0.959049</td>\n",
       "      <td>0.441095</td>\n",
       "      <td>0.904088</td>\n",
       "      <td>0.893614</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.083783</td>\n",
       "      <td>0.969813</td>\n",
       "      <td>0.969517</td>\n",
       "      <td>0.565275</td>\n",
       "      <td>0.857827</td>\n",
       "      <td>0.860667</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.059262</td>\n",
       "      <td>0.977907</td>\n",
       "      <td>0.977766</td>\n",
       "      <td>0.510437</td>\n",
       "      <td>0.897687</td>\n",
       "      <td>0.898462</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.043112</td>\n",
       "      <td>0.984173</td>\n",
       "      <td>0.984099</td>\n",
       "      <td>0.598552</td>\n",
       "      <td>0.901968</td>\n",
       "      <td>0.900968</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.034694</td>\n",
       "      <td>0.988492</td>\n",
       "      <td>0.988450</td>\n",
       "      <td>0.616500</td>\n",
       "      <td>0.904544</td>\n",
       "      <td>0.902290</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.031696</td>\n",
       "      <td>0.990931</td>\n",
       "      <td>0.990901</td>\n",
       "      <td>0.627563</td>\n",
       "      <td>0.902447</td>\n",
       "      <td>0.901433</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++ Depth 3 +++++++++++++++++++\n",
      "===================== Fold 0 ============================\n",
      "33.53912353515625\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.177486</td>\n",
       "      <td>0.893186</td>\n",
       "      <td>0.889025</td>\n",
       "      <td>0.566857</td>\n",
       "      <td>0.910408</td>\n",
       "      <td>0.903800</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.122506</td>\n",
       "      <td>0.950572</td>\n",
       "      <td>0.949616</td>\n",
       "      <td>0.873713</td>\n",
       "      <td>0.800332</td>\n",
       "      <td>0.836103</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.078584</td>\n",
       "      <td>0.967571</td>\n",
       "      <td>0.967260</td>\n",
       "      <td>0.786778</td>\n",
       "      <td>0.899681</td>\n",
       "      <td>0.903434</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058628</td>\n",
       "      <td>0.978348</td>\n",
       "      <td>0.978232</td>\n",
       "      <td>1.320253</td>\n",
       "      <td>0.698395</td>\n",
       "      <td>0.753933</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.035276</td>\n",
       "      <td>0.986123</td>\n",
       "      <td>0.986069</td>\n",
       "      <td>1.204454</td>\n",
       "      <td>0.890946</td>\n",
       "      <td>0.895013</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.022114</td>\n",
       "      <td>0.991537</td>\n",
       "      <td>0.991516</td>\n",
       "      <td>1.208405</td>\n",
       "      <td>0.835899</td>\n",
       "      <td>0.861665</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.013750</td>\n",
       "      <td>0.995181</td>\n",
       "      <td>0.995171</td>\n",
       "      <td>1.172634</td>\n",
       "      <td>0.867388</td>\n",
       "      <td>0.882538</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.010792</td>\n",
       "      <td>0.996794</td>\n",
       "      <td>0.996789</td>\n",
       "      <td>1.196855</td>\n",
       "      <td>0.871317</td>\n",
       "      <td>0.885488</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 1 ============================\n",
      "27.151046991348267\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.167538</td>\n",
       "      <td>0.912479</td>\n",
       "      <td>0.907026</td>\n",
       "      <td>0.710893</td>\n",
       "      <td>0.775884</td>\n",
       "      <td>0.797440</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.116204</td>\n",
       "      <td>0.954000</td>\n",
       "      <td>0.953099</td>\n",
       "      <td>1.006016</td>\n",
       "      <td>0.664335</td>\n",
       "      <td>0.667026</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.080976</td>\n",
       "      <td>0.968366</td>\n",
       "      <td>0.968039</td>\n",
       "      <td>0.717130</td>\n",
       "      <td>0.836691</td>\n",
       "      <td>0.848276</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058048</td>\n",
       "      <td>0.978120</td>\n",
       "      <td>0.977974</td>\n",
       "      <td>0.768417</td>\n",
       "      <td>0.824669</td>\n",
       "      <td>0.843657</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.036711</td>\n",
       "      <td>0.985119</td>\n",
       "      <td>0.985051</td>\n",
       "      <td>0.866157</td>\n",
       "      <td>0.815415</td>\n",
       "      <td>0.836474</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.024297</td>\n",
       "      <td>0.990662</td>\n",
       "      <td>0.990635</td>\n",
       "      <td>1.363126</td>\n",
       "      <td>0.752852</td>\n",
       "      <td>0.802735</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.015377</td>\n",
       "      <td>0.994334</td>\n",
       "      <td>0.994322</td>\n",
       "      <td>1.073077</td>\n",
       "      <td>0.797501</td>\n",
       "      <td>0.830320</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.011851</td>\n",
       "      <td>0.996322</td>\n",
       "      <td>0.996315</td>\n",
       "      <td>1.130703</td>\n",
       "      <td>0.796646</td>\n",
       "      <td>0.830331</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 2 ============================\n",
      "27.0572566986084\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.160794</td>\n",
       "      <td>0.908033</td>\n",
       "      <td>0.902181</td>\n",
       "      <td>0.829594</td>\n",
       "      <td>0.737086</td>\n",
       "      <td>0.764221</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.118421</td>\n",
       "      <td>0.953918</td>\n",
       "      <td>0.952998</td>\n",
       "      <td>1.312741</td>\n",
       "      <td>0.638175</td>\n",
       "      <td>0.639724</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.079244</td>\n",
       "      <td>0.969165</td>\n",
       "      <td>0.968852</td>\n",
       "      <td>1.477020</td>\n",
       "      <td>0.594480</td>\n",
       "      <td>0.614791</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.055970</td>\n",
       "      <td>0.978649</td>\n",
       "      <td>0.978508</td>\n",
       "      <td>1.073803</td>\n",
       "      <td>0.707711</td>\n",
       "      <td>0.722880</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.035433</td>\n",
       "      <td>0.985466</td>\n",
       "      <td>0.985395</td>\n",
       "      <td>1.218394</td>\n",
       "      <td>0.682108</td>\n",
       "      <td>0.700931</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.024372</td>\n",
       "      <td>0.990944</td>\n",
       "      <td>0.990910</td>\n",
       "      <td>0.888653</td>\n",
       "      <td>0.830740</td>\n",
       "      <td>0.838265</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.015499</td>\n",
       "      <td>0.994661</td>\n",
       "      <td>0.994648</td>\n",
       "      <td>1.018739</td>\n",
       "      <td>0.817649</td>\n",
       "      <td>0.828275</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.012211</td>\n",
       "      <td>0.996269</td>\n",
       "      <td>0.996260</td>\n",
       "      <td>1.071853</td>\n",
       "      <td>0.798378</td>\n",
       "      <td>0.810844</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 3 ============================\n",
      "27.393736124038696\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.162428</td>\n",
       "      <td>0.906171</td>\n",
       "      <td>0.901327</td>\n",
       "      <td>0.442169</td>\n",
       "      <td>0.872731</td>\n",
       "      <td>0.855501</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.112389</td>\n",
       "      <td>0.955403</td>\n",
       "      <td>0.954373</td>\n",
       "      <td>0.613564</td>\n",
       "      <td>0.800267</td>\n",
       "      <td>0.819587</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.080717</td>\n",
       "      <td>0.969490</td>\n",
       "      <td>0.969158</td>\n",
       "      <td>0.639237</td>\n",
       "      <td>0.848948</td>\n",
       "      <td>0.849545</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.055678</td>\n",
       "      <td>0.978962</td>\n",
       "      <td>0.978821</td>\n",
       "      <td>0.540066</td>\n",
       "      <td>0.864170</td>\n",
       "      <td>0.869814</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.036127</td>\n",
       "      <td>0.985970</td>\n",
       "      <td>0.985913</td>\n",
       "      <td>0.605884</td>\n",
       "      <td>0.865568</td>\n",
       "      <td>0.868249</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.023352</td>\n",
       "      <td>0.991150</td>\n",
       "      <td>0.991125</td>\n",
       "      <td>0.611118</td>\n",
       "      <td>0.883909</td>\n",
       "      <td>0.881180</td>\n",
       "      <td>00:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.015538</td>\n",
       "      <td>0.994740</td>\n",
       "      <td>0.994729</td>\n",
       "      <td>0.647497</td>\n",
       "      <td>0.881339</td>\n",
       "      <td>0.881957</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.012304</td>\n",
       "      <td>0.996400</td>\n",
       "      <td>0.996395</td>\n",
       "      <td>0.650985</td>\n",
       "      <td>0.884961</td>\n",
       "      <td>0.884599</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 4 ============================\n",
      "27.727115631103516\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.171464</td>\n",
       "      <td>0.907305</td>\n",
       "      <td>0.899832</td>\n",
       "      <td>0.392468</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.887435</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.119841</td>\n",
       "      <td>0.952570</td>\n",
       "      <td>0.951552</td>\n",
       "      <td>0.589141</td>\n",
       "      <td>0.827181</td>\n",
       "      <td>0.841763</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.083298</td>\n",
       "      <td>0.967418</td>\n",
       "      <td>0.967082</td>\n",
       "      <td>0.644397</td>\n",
       "      <td>0.821566</td>\n",
       "      <td>0.840475</td>\n",
       "      <td>09:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>0.977792</td>\n",
       "      <td>0.977649</td>\n",
       "      <td>0.513843</td>\n",
       "      <td>0.882263</td>\n",
       "      <td>0.883613</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.985259</td>\n",
       "      <td>0.985199</td>\n",
       "      <td>0.491047</td>\n",
       "      <td>0.902314</td>\n",
       "      <td>0.902133</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.023670</td>\n",
       "      <td>0.990915</td>\n",
       "      <td>0.990891</td>\n",
       "      <td>0.575316</td>\n",
       "      <td>0.899599</td>\n",
       "      <td>0.898063</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>0.994593</td>\n",
       "      <td>0.994583</td>\n",
       "      <td>0.604317</td>\n",
       "      <td>0.898288</td>\n",
       "      <td>0.897543</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.996387</td>\n",
       "      <td>0.996382</td>\n",
       "      <td>0.613745</td>\n",
       "      <td>0.898039</td>\n",
       "      <td>0.898334</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++ Depth 4 +++++++++++++++++++\n",
      "===================== Fold 0 ============================\n",
      "22.92965030670166\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.163615</td>\n",
       "      <td>0.911321</td>\n",
       "      <td>0.905870</td>\n",
       "      <td>0.572872</td>\n",
       "      <td>0.893436</td>\n",
       "      <td>0.902110</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.111816</td>\n",
       "      <td>0.955055</td>\n",
       "      <td>0.954567</td>\n",
       "      <td>0.988681</td>\n",
       "      <td>0.758202</td>\n",
       "      <td>0.762875</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.070283</td>\n",
       "      <td>0.971178</td>\n",
       "      <td>0.971060</td>\n",
       "      <td>0.754071</td>\n",
       "      <td>0.893973</td>\n",
       "      <td>0.898661</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.050263</td>\n",
       "      <td>0.980899</td>\n",
       "      <td>0.980845</td>\n",
       "      <td>0.993015</td>\n",
       "      <td>0.844894</td>\n",
       "      <td>0.875411</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.030122</td>\n",
       "      <td>0.988158</td>\n",
       "      <td>0.988137</td>\n",
       "      <td>1.031426</td>\n",
       "      <td>0.898577</td>\n",
       "      <td>0.904085</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.017995</td>\n",
       "      <td>0.992978</td>\n",
       "      <td>0.992966</td>\n",
       "      <td>1.110208</td>\n",
       "      <td>0.880536</td>\n",
       "      <td>0.891637</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.010485</td>\n",
       "      <td>0.996124</td>\n",
       "      <td>0.996120</td>\n",
       "      <td>1.199185</td>\n",
       "      <td>0.896509</td>\n",
       "      <td>0.903200</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.007555</td>\n",
       "      <td>0.997681</td>\n",
       "      <td>0.997680</td>\n",
       "      <td>1.216542</td>\n",
       "      <td>0.902234</td>\n",
       "      <td>0.907043</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 1 ============================\n",
      "24.46892261505127\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.916693</td>\n",
       "      <td>0.911278</td>\n",
       "      <td>0.489541</td>\n",
       "      <td>0.862703</td>\n",
       "      <td>0.865777</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.109686</td>\n",
       "      <td>0.957158</td>\n",
       "      <td>0.956507</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>0.842179</td>\n",
       "      <td>0.855568</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.070874</td>\n",
       "      <td>0.971099</td>\n",
       "      <td>0.970889</td>\n",
       "      <td>0.561524</td>\n",
       "      <td>0.868446</td>\n",
       "      <td>0.875391</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.050107</td>\n",
       "      <td>0.980924</td>\n",
       "      <td>0.980848</td>\n",
       "      <td>0.713018</td>\n",
       "      <td>0.861964</td>\n",
       "      <td>0.867433</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.030785</td>\n",
       "      <td>0.987766</td>\n",
       "      <td>0.987728</td>\n",
       "      <td>0.863494</td>\n",
       "      <td>0.845575</td>\n",
       "      <td>0.857600</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.018920</td>\n",
       "      <td>0.992638</td>\n",
       "      <td>0.992626</td>\n",
       "      <td>0.847500</td>\n",
       "      <td>0.864708</td>\n",
       "      <td>0.873912</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.995874</td>\n",
       "      <td>0.995868</td>\n",
       "      <td>0.973297</td>\n",
       "      <td>0.851468</td>\n",
       "      <td>0.866302</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.007677</td>\n",
       "      <td>0.997466</td>\n",
       "      <td>0.997463</td>\n",
       "      <td>0.977726</td>\n",
       "      <td>0.860497</td>\n",
       "      <td>0.871074</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 2 ============================\n",
      "27.280725479125977\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.157119</td>\n",
       "      <td>0.916748</td>\n",
       "      <td>0.912090</td>\n",
       "      <td>0.620016</td>\n",
       "      <td>0.828284</td>\n",
       "      <td>0.827903</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.109433</td>\n",
       "      <td>0.956166</td>\n",
       "      <td>0.955420</td>\n",
       "      <td>0.820398</td>\n",
       "      <td>0.746196</td>\n",
       "      <td>0.782399</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.071549</td>\n",
       "      <td>0.970674</td>\n",
       "      <td>0.970380</td>\n",
       "      <td>0.991589</td>\n",
       "      <td>0.714400</td>\n",
       "      <td>0.742797</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.050101</td>\n",
       "      <td>0.980593</td>\n",
       "      <td>0.980460</td>\n",
       "      <td>0.867005</td>\n",
       "      <td>0.763082</td>\n",
       "      <td>0.786876</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.032324</td>\n",
       "      <td>0.987254</td>\n",
       "      <td>0.987197</td>\n",
       "      <td>1.461425</td>\n",
       "      <td>0.649071</td>\n",
       "      <td>0.668965</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.019653</td>\n",
       "      <td>0.992255</td>\n",
       "      <td>0.992231</td>\n",
       "      <td>1.692133</td>\n",
       "      <td>0.652693</td>\n",
       "      <td>0.658011</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.011855</td>\n",
       "      <td>0.995526</td>\n",
       "      <td>0.995517</td>\n",
       "      <td>1.388440</td>\n",
       "      <td>0.694569</td>\n",
       "      <td>0.707148</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.008595</td>\n",
       "      <td>0.997234</td>\n",
       "      <td>0.997230</td>\n",
       "      <td>1.480987</td>\n",
       "      <td>0.682270</td>\n",
       "      <td>0.693561</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 3 ============================\n",
      "27.20937418937683\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.150489</td>\n",
       "      <td>0.920812</td>\n",
       "      <td>0.914215</td>\n",
       "      <td>0.416914</td>\n",
       "      <td>0.866302</td>\n",
       "      <td>0.867040</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.104002</td>\n",
       "      <td>0.958537</td>\n",
       "      <td>0.957784</td>\n",
       "      <td>0.929880</td>\n",
       "      <td>0.723366</td>\n",
       "      <td>0.773357</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.071576</td>\n",
       "      <td>0.971989</td>\n",
       "      <td>0.971753</td>\n",
       "      <td>0.555682</td>\n",
       "      <td>0.869600</td>\n",
       "      <td>0.862422</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.047221</td>\n",
       "      <td>0.981437</td>\n",
       "      <td>0.981333</td>\n",
       "      <td>0.569127</td>\n",
       "      <td>0.870559</td>\n",
       "      <td>0.872163</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.030974</td>\n",
       "      <td>0.987737</td>\n",
       "      <td>0.987692</td>\n",
       "      <td>0.675692</td>\n",
       "      <td>0.864921</td>\n",
       "      <td>0.871762</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.017858</td>\n",
       "      <td>0.992534</td>\n",
       "      <td>0.992517</td>\n",
       "      <td>0.694472</td>\n",
       "      <td>0.885238</td>\n",
       "      <td>0.884110</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.011804</td>\n",
       "      <td>0.995860</td>\n",
       "      <td>0.995854</td>\n",
       "      <td>0.756409</td>\n",
       "      <td>0.880212</td>\n",
       "      <td>0.881186</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.008218</td>\n",
       "      <td>0.997368</td>\n",
       "      <td>0.997365</td>\n",
       "      <td>0.768104</td>\n",
       "      <td>0.886208</td>\n",
       "      <td>0.883872</td>\n",
       "      <td>00:27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 4 ============================\n",
      "32.73758006095886\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.158234</td>\n",
       "      <td>0.916989</td>\n",
       "      <td>0.911982</td>\n",
       "      <td>0.549256</td>\n",
       "      <td>0.824501</td>\n",
       "      <td>0.839765</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.105660</td>\n",
       "      <td>0.957064</td>\n",
       "      <td>0.956412</td>\n",
       "      <td>0.357030</td>\n",
       "      <td>0.910974</td>\n",
       "      <td>0.909736</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.067668</td>\n",
       "      <td>0.971983</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>0.539498</td>\n",
       "      <td>0.878739</td>\n",
       "      <td>0.880986</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.048417</td>\n",
       "      <td>0.981201</td>\n",
       "      <td>0.981122</td>\n",
       "      <td>0.525039</td>\n",
       "      <td>0.899819</td>\n",
       "      <td>0.896738</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.031687</td>\n",
       "      <td>0.987982</td>\n",
       "      <td>0.987951</td>\n",
       "      <td>0.580271</td>\n",
       "      <td>0.892742</td>\n",
       "      <td>0.895093</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.018065</td>\n",
       "      <td>0.992736</td>\n",
       "      <td>0.992724</td>\n",
       "      <td>0.664367</td>\n",
       "      <td>0.897935</td>\n",
       "      <td>0.896522</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.010603</td>\n",
       "      <td>0.995851</td>\n",
       "      <td>0.995847</td>\n",
       "      <td>0.695569</td>\n",
       "      <td>0.893961</td>\n",
       "      <td>0.895470</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.997434</td>\n",
       "      <td>0.997433</td>\n",
       "      <td>0.716593</td>\n",
       "      <td>0.895711</td>\n",
       "      <td>0.897394</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++ Depth 5 +++++++++++++++++++\n",
      "===================== Fold 0 ============================\n",
      "29.87711787223816\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.154954</td>\n",
       "      <td>0.916442</td>\n",
       "      <td>0.910999</td>\n",
       "      <td>0.534977</td>\n",
       "      <td>0.890160</td>\n",
       "      <td>0.890196</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.104016</td>\n",
       "      <td>0.956713</td>\n",
       "      <td>0.956307</td>\n",
       "      <td>0.471935</td>\n",
       "      <td>0.907364</td>\n",
       "      <td>0.911074</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.064986</td>\n",
       "      <td>0.972216</td>\n",
       "      <td>0.972100</td>\n",
       "      <td>0.582583</td>\n",
       "      <td>0.908097</td>\n",
       "      <td>0.906844</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.047097</td>\n",
       "      <td>0.982392</td>\n",
       "      <td>0.982349</td>\n",
       "      <td>1.095216</td>\n",
       "      <td>0.754429</td>\n",
       "      <td>0.799744</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.027076</td>\n",
       "      <td>0.989064</td>\n",
       "      <td>0.989046</td>\n",
       "      <td>0.666932</td>\n",
       "      <td>0.900992</td>\n",
       "      <td>0.902184</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.016073</td>\n",
       "      <td>0.993875</td>\n",
       "      <td>0.993868</td>\n",
       "      <td>0.875699</td>\n",
       "      <td>0.868798</td>\n",
       "      <td>0.881122</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009115</td>\n",
       "      <td>0.996670</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.801095</td>\n",
       "      <td>0.899588</td>\n",
       "      <td>0.903535</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.005812</td>\n",
       "      <td>0.998015</td>\n",
       "      <td>0.998014</td>\n",
       "      <td>0.809639</td>\n",
       "      <td>0.896486</td>\n",
       "      <td>0.901951</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 1 ============================\n",
      "27.772966384887695\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.147366</td>\n",
       "      <td>0.924377</td>\n",
       "      <td>0.920081</td>\n",
       "      <td>0.528673</td>\n",
       "      <td>0.853177</td>\n",
       "      <td>0.846624</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.104466</td>\n",
       "      <td>0.958716</td>\n",
       "      <td>0.958168</td>\n",
       "      <td>0.886726</td>\n",
       "      <td>0.677777</td>\n",
       "      <td>0.675118</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.067702</td>\n",
       "      <td>0.973202</td>\n",
       "      <td>0.973031</td>\n",
       "      <td>0.690601</td>\n",
       "      <td>0.811146</td>\n",
       "      <td>0.822786</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.047373</td>\n",
       "      <td>0.982029</td>\n",
       "      <td>0.981959</td>\n",
       "      <td>0.731519</td>\n",
       "      <td>0.849405</td>\n",
       "      <td>0.862379</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.028741</td>\n",
       "      <td>0.988553</td>\n",
       "      <td>0.988524</td>\n",
       "      <td>0.804074</td>\n",
       "      <td>0.847649</td>\n",
       "      <td>0.864033</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.017826</td>\n",
       "      <td>0.993161</td>\n",
       "      <td>0.993150</td>\n",
       "      <td>0.891361</td>\n",
       "      <td>0.850064</td>\n",
       "      <td>0.864022</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.996217</td>\n",
       "      <td>0.996212</td>\n",
       "      <td>0.973169</td>\n",
       "      <td>0.850838</td>\n",
       "      <td>0.865778</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.006791</td>\n",
       "      <td>0.997735</td>\n",
       "      <td>0.997733</td>\n",
       "      <td>1.014119</td>\n",
       "      <td>0.849995</td>\n",
       "      <td>0.864406</td>\n",
       "      <td>00:29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 2 ============================\n",
      "55.39413523674011\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.145955</td>\n",
       "      <td>0.923389</td>\n",
       "      <td>0.919095</td>\n",
       "      <td>1.214411</td>\n",
       "      <td>0.647482</td>\n",
       "      <td>0.661226</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.104704</td>\n",
       "      <td>0.958921</td>\n",
       "      <td>0.958322</td>\n",
       "      <td>1.007696</td>\n",
       "      <td>0.699103</td>\n",
       "      <td>0.719203</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.065714</td>\n",
       "      <td>0.973250</td>\n",
       "      <td>0.973022</td>\n",
       "      <td>0.697688</td>\n",
       "      <td>0.836124</td>\n",
       "      <td>0.849446</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.045580</td>\n",
       "      <td>0.982221</td>\n",
       "      <td>0.982125</td>\n",
       "      <td>1.149999</td>\n",
       "      <td>0.688538</td>\n",
       "      <td>0.700980</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.029124</td>\n",
       "      <td>0.988416</td>\n",
       "      <td>0.988373</td>\n",
       "      <td>1.783395</td>\n",
       "      <td>0.640070</td>\n",
       "      <td>0.651566</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.018199</td>\n",
       "      <td>0.992946</td>\n",
       "      <td>0.992927</td>\n",
       "      <td>1.456964</td>\n",
       "      <td>0.694777</td>\n",
       "      <td>0.708240</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.011004</td>\n",
       "      <td>0.996121</td>\n",
       "      <td>0.996114</td>\n",
       "      <td>1.596873</td>\n",
       "      <td>0.702275</td>\n",
       "      <td>0.716676</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.007198</td>\n",
       "      <td>0.997689</td>\n",
       "      <td>0.997686</td>\n",
       "      <td>1.724535</td>\n",
       "      <td>0.697729</td>\n",
       "      <td>0.712027</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 3 ============================\n",
      "28.047295331954956\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.147241</td>\n",
       "      <td>0.923535</td>\n",
       "      <td>0.918810</td>\n",
       "      <td>0.474812</td>\n",
       "      <td>0.864545</td>\n",
       "      <td>0.846376</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.100862</td>\n",
       "      <td>0.959421</td>\n",
       "      <td>0.958774</td>\n",
       "      <td>0.876665</td>\n",
       "      <td>0.739495</td>\n",
       "      <td>0.778145</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.066539</td>\n",
       "      <td>0.974014</td>\n",
       "      <td>0.973830</td>\n",
       "      <td>0.510421</td>\n",
       "      <td>0.871143</td>\n",
       "      <td>0.864011</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.045474</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.982837</td>\n",
       "      <td>0.547611</td>\n",
       "      <td>0.880975</td>\n",
       "      <td>0.880005</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.027312</td>\n",
       "      <td>0.988989</td>\n",
       "      <td>0.988966</td>\n",
       "      <td>0.660348</td>\n",
       "      <td>0.875851</td>\n",
       "      <td>0.877766</td>\n",
       "      <td>14:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.016299</td>\n",
       "      <td>0.993518</td>\n",
       "      <td>0.993510</td>\n",
       "      <td>0.829849</td>\n",
       "      <td>0.852154</td>\n",
       "      <td>0.861038</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009743</td>\n",
       "      <td>0.996422</td>\n",
       "      <td>0.996419</td>\n",
       "      <td>0.805200</td>\n",
       "      <td>0.879103</td>\n",
       "      <td>0.882323</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.007199</td>\n",
       "      <td>0.997772</td>\n",
       "      <td>0.997771</td>\n",
       "      <td>0.800797</td>\n",
       "      <td>0.881079</td>\n",
       "      <td>0.883272</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 4 ============================\n",
      "23.06434988975525\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.154954</td>\n",
       "      <td>0.917641</td>\n",
       "      <td>0.913815</td>\n",
       "      <td>0.495630</td>\n",
       "      <td>0.849092</td>\n",
       "      <td>0.862528</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.105285</td>\n",
       "      <td>0.957397</td>\n",
       "      <td>0.956771</td>\n",
       "      <td>0.411898</td>\n",
       "      <td>0.899362</td>\n",
       "      <td>0.892287</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.065906</td>\n",
       "      <td>0.972162</td>\n",
       "      <td>0.971978</td>\n",
       "      <td>0.525049</td>\n",
       "      <td>0.867295</td>\n",
       "      <td>0.870906</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.046307</td>\n",
       "      <td>0.982092</td>\n",
       "      <td>0.982024</td>\n",
       "      <td>0.572858</td>\n",
       "      <td>0.886653</td>\n",
       "      <td>0.886224</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.029325</td>\n",
       "      <td>0.988564</td>\n",
       "      <td>0.988537</td>\n",
       "      <td>0.611474</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>0.890055</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.016572</td>\n",
       "      <td>0.993223</td>\n",
       "      <td>0.993213</td>\n",
       "      <td>0.640712</td>\n",
       "      <td>0.900795</td>\n",
       "      <td>0.900907</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009730</td>\n",
       "      <td>0.996310</td>\n",
       "      <td>0.996307</td>\n",
       "      <td>0.722807</td>\n",
       "      <td>0.890281</td>\n",
       "      <td>0.892668</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.997796</td>\n",
       "      <td>0.997794</td>\n",
       "      <td>0.732122</td>\n",
       "      <td>0.890322</td>\n",
       "      <td>0.892386</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++ Depth 6 +++++++++++++++++++\n",
      "===================== Fold 0 ============================\n",
      "24.46183490753174\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.154617</td>\n",
       "      <td>0.919236</td>\n",
       "      <td>0.914460</td>\n",
       "      <td>0.616339</td>\n",
       "      <td>0.893049</td>\n",
       "      <td>0.895230</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.108613</td>\n",
       "      <td>0.956456</td>\n",
       "      <td>0.955974</td>\n",
       "      <td>0.756392</td>\n",
       "      <td>0.848776</td>\n",
       "      <td>0.850161</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.067145</td>\n",
       "      <td>0.971804</td>\n",
       "      <td>0.971649</td>\n",
       "      <td>0.853231</td>\n",
       "      <td>0.898750</td>\n",
       "      <td>0.902572</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.045200</td>\n",
       "      <td>0.982470</td>\n",
       "      <td>0.982407</td>\n",
       "      <td>1.089351</td>\n",
       "      <td>0.800158</td>\n",
       "      <td>0.840204</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.026405</td>\n",
       "      <td>0.989239</td>\n",
       "      <td>0.989215</td>\n",
       "      <td>1.039581</td>\n",
       "      <td>0.892800</td>\n",
       "      <td>0.898011</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.015762</td>\n",
       "      <td>0.993859</td>\n",
       "      <td>0.993849</td>\n",
       "      <td>1.140615</td>\n",
       "      <td>0.876209</td>\n",
       "      <td>0.887038</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>0.996643</td>\n",
       "      <td>0.996639</td>\n",
       "      <td>1.116417</td>\n",
       "      <td>0.899034</td>\n",
       "      <td>0.904698</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>0.998096</td>\n",
       "      <td>0.998095</td>\n",
       "      <td>1.184075</td>\n",
       "      <td>0.892951</td>\n",
       "      <td>0.899577</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 1 ============================\n",
      "27.173120498657227\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.146501</td>\n",
       "      <td>0.924838</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.793019</td>\n",
       "      <td>0.759496</td>\n",
       "      <td>0.791047</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.103321</td>\n",
       "      <td>0.958420</td>\n",
       "      <td>0.957876</td>\n",
       "      <td>1.330719</td>\n",
       "      <td>0.668702</td>\n",
       "      <td>0.701115</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.066469</td>\n",
       "      <td>0.972697</td>\n",
       "      <td>0.972527</td>\n",
       "      <td>0.647591</td>\n",
       "      <td>0.851421</td>\n",
       "      <td>0.856458</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.047321</td>\n",
       "      <td>0.982147</td>\n",
       "      <td>0.982069</td>\n",
       "      <td>0.806428</td>\n",
       "      <td>0.831388</td>\n",
       "      <td>0.852018</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.027866</td>\n",
       "      <td>0.988777</td>\n",
       "      <td>0.988744</td>\n",
       "      <td>0.928955</td>\n",
       "      <td>0.820672</td>\n",
       "      <td>0.846973</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.016416</td>\n",
       "      <td>0.993453</td>\n",
       "      <td>0.993440</td>\n",
       "      <td>0.940391</td>\n",
       "      <td>0.834432</td>\n",
       "      <td>0.855942</td>\n",
       "      <td>00:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.008744</td>\n",
       "      <td>0.996446</td>\n",
       "      <td>0.996441</td>\n",
       "      <td>1.135912</td>\n",
       "      <td>0.812694</td>\n",
       "      <td>0.842797</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.006194</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>0.997873</td>\n",
       "      <td>1.142398</td>\n",
       "      <td>0.818528</td>\n",
       "      <td>0.845503</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 2 ============================\n",
      "29.501326322555542\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.144978</td>\n",
       "      <td>0.925859</td>\n",
       "      <td>0.922130</td>\n",
       "      <td>0.598729</td>\n",
       "      <td>0.823750</td>\n",
       "      <td>0.833727</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.108022</td>\n",
       "      <td>0.959019</td>\n",
       "      <td>0.958425</td>\n",
       "      <td>1.089865</td>\n",
       "      <td>0.675239</td>\n",
       "      <td>0.692337</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.064073</td>\n",
       "      <td>0.973126</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>1.447939</td>\n",
       "      <td>0.628234</td>\n",
       "      <td>0.652471</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.044948</td>\n",
       "      <td>0.982392</td>\n",
       "      <td>0.982310</td>\n",
       "      <td>0.860511</td>\n",
       "      <td>0.773873</td>\n",
       "      <td>0.789819</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.027881</td>\n",
       "      <td>0.988709</td>\n",
       "      <td>0.988674</td>\n",
       "      <td>1.077474</td>\n",
       "      <td>0.801237</td>\n",
       "      <td>0.822536</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.017181</td>\n",
       "      <td>0.993297</td>\n",
       "      <td>0.993282</td>\n",
       "      <td>1.128763</td>\n",
       "      <td>0.790828</td>\n",
       "      <td>0.807297</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.996417</td>\n",
       "      <td>0.996412</td>\n",
       "      <td>1.186561</td>\n",
       "      <td>0.806523</td>\n",
       "      <td>0.825578</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.006931</td>\n",
       "      <td>0.997892</td>\n",
       "      <td>0.997890</td>\n",
       "      <td>1.302093</td>\n",
       "      <td>0.778055</td>\n",
       "      <td>0.797268</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 3 ============================\n",
      "34.84472417831421\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.142625</td>\n",
       "      <td>0.924965</td>\n",
       "      <td>0.920656</td>\n",
       "      <td>0.583615</td>\n",
       "      <td>0.810758</td>\n",
       "      <td>0.831834</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.101199</td>\n",
       "      <td>0.960071</td>\n",
       "      <td>0.959389</td>\n",
       "      <td>0.419263</td>\n",
       "      <td>0.875764</td>\n",
       "      <td>0.879900</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.070925</td>\n",
       "      <td>0.973457</td>\n",
       "      <td>0.973246</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>0.888086</td>\n",
       "      <td>0.881300</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.042737</td>\n",
       "      <td>0.983154</td>\n",
       "      <td>0.983085</td>\n",
       "      <td>0.543390</td>\n",
       "      <td>0.876428</td>\n",
       "      <td>0.877218</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.028202</td>\n",
       "      <td>0.989207</td>\n",
       "      <td>0.989180</td>\n",
       "      <td>0.590008</td>\n",
       "      <td>0.886769</td>\n",
       "      <td>0.885594</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.016059</td>\n",
       "      <td>0.993596</td>\n",
       "      <td>0.993586</td>\n",
       "      <td>0.604570</td>\n",
       "      <td>0.891673</td>\n",
       "      <td>0.887665</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009461</td>\n",
       "      <td>0.996633</td>\n",
       "      <td>0.996629</td>\n",
       "      <td>0.706040</td>\n",
       "      <td>0.886902</td>\n",
       "      <td>0.888589</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.006570</td>\n",
       "      <td>0.997874</td>\n",
       "      <td>0.997872</td>\n",
       "      <td>0.704415</td>\n",
       "      <td>0.892586</td>\n",
       "      <td>0.891990</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== Fold 4 ============================\n",
      "42.716442823410034\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.151449</td>\n",
       "      <td>0.920256</td>\n",
       "      <td>0.916128</td>\n",
       "      <td>0.414251</td>\n",
       "      <td>0.886855</td>\n",
       "      <td>0.881678</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.104076</td>\n",
       "      <td>0.958173</td>\n",
       "      <td>0.957531</td>\n",
       "      <td>0.421873</td>\n",
       "      <td>0.895428</td>\n",
       "      <td>0.891572</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.064179</td>\n",
       "      <td>0.972903</td>\n",
       "      <td>0.972720</td>\n",
       "      <td>0.530073</td>\n",
       "      <td>0.866296</td>\n",
       "      <td>0.867031</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.046132</td>\n",
       "      <td>0.982374</td>\n",
       "      <td>0.982306</td>\n",
       "      <td>0.556717</td>\n",
       "      <td>0.881997</td>\n",
       "      <td>0.887017</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.029807</td>\n",
       "      <td>0.988985</td>\n",
       "      <td>0.988959</td>\n",
       "      <td>0.517637</td>\n",
       "      <td>0.900830</td>\n",
       "      <td>0.900778</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.016173</td>\n",
       "      <td>0.993590</td>\n",
       "      <td>0.993581</td>\n",
       "      <td>0.608442</td>\n",
       "      <td>0.897046</td>\n",
       "      <td>0.898402</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.009058</td>\n",
       "      <td>0.996530</td>\n",
       "      <td>0.996528</td>\n",
       "      <td>0.678355</td>\n",
       "      <td>0.891824</td>\n",
       "      <td>0.893738</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.006233</td>\n",
       "      <td>0.997904</td>\n",
       "      <td>0.997903</td>\n",
       "      <td>0.691109</td>\n",
       "      <td>0.894643</td>\n",
       "      <td>0.896369</td>\n",
       "      <td>00:34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "def save_split(x, y, i, splits, bs):\n",
    "    tfms  = [None, [Categorize()]]\n",
    "    dsets = TSDatasets(x, y, tfms=tfms, splits=splits[i], inplace=True)\n",
    "    dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=bs, batch_tfms=[TSStandardize()], num_workers=0)\n",
    "    \n",
    "    torch.save(dls, f\"dls_{bs}_{i}.pkl\")\n",
    "    \n",
    "def train_and_evaluate_it(i, bs, lr, epochs, depth):\n",
    "\n",
    "    start = time.time()\n",
    "    dls = torch.load(f\"dls_{bs}_{i}.pkl\")\n",
    "    end = time.time()\n",
    "    print(end - start)\n",
    "    \n",
    "    model = InceptionTime(dls.vars, dls.c, depth=depth, ks=20)\n",
    "    f1 = F1Score(average=\"weighted\")\n",
    "    learn = Learner(dls, model, metrics=[accuracy, f1])\n",
    "    learn.recorder.train_metrics = True\n",
    "\n",
    "    learn.fit_one_cycle(epochs, lr_max=lr)\n",
    "    \n",
    "    return learn\n",
    "\n",
    "def cross_val_score(bs, lr, epochs, depth):\n",
    "    scores = []\n",
    "    for i in range(5):\n",
    "        print(f'===================== Fold {i} ============================')\n",
    "        model = train_and_evaluate_it(i, bs,  lr, epochs, depth)\n",
    "        scores.append(model.final_record)\n",
    "    \n",
    "    return scores\n",
    "\n",
    "scores = []\n",
    "\n",
    "for i in range(5):\n",
    "    save_split(x, y, i, splits, bs)\n",
    "for d in range(1, 7):\n",
    "    print(f'+++++++++++++++++++ Depth {d} +++++++++++++++++++')\n",
    "    s = cross_val_score(1024, 0.005, 8, d)\n",
    "    scores.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "funky-claim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(#6) [0.12837247550487518,0.9578359127044678,0.9567747345333031,1.2386432886123657,0.9020317196846008,0.9047745144061481],\n",
       "  (#6) [0.12058044970035553,0.961747944355011,0.9608687331886886,0.5302988290786743,0.8708082437515259,0.8754736869902846],\n",
       "  (#6) [0.11864025890827179,0.960537314414978,0.9594211207328552,0.863337516784668,0.802057683467865,0.8159046189903827],\n",
       "  (#6) [0.12204963713884354,0.9608233571052551,0.959569179767864,0.4187622368335724,0.8857636451721191,0.8798099260027318],\n",
       "  (#6) [0.1259729564189911,0.9587965607643127,0.9574755561697311,0.4417993426322937,0.8999226093292236,0.8953680229479866]],\n",
       " [(#6) [0.029505493119359016,0.9912817478179932,0.9912531216511251,1.2370824813842773,0.9070343971252441,0.9104470685854175],\n",
       "  (#6) [0.0296198558062315,0.991054892539978,0.9910227807894677,0.8318864703178406,0.8544310927391052,0.8661721577493994],\n",
       "  (#6) [0.03183978423476219,0.9903990626335144,0.9903513480434717,1.1090840101242065,0.7461324334144592,0.7625570892712235],\n",
       "  (#6) [0.03213615342974663,0.9903846383094788,0.9903413924397285,0.5576651096343994,0.8783519864082336,0.8796065199466306],\n",
       "  (#6) [0.03169581666588783,0.9909306764602661,0.9909005524807292,0.6275634765625,0.9024470448493958,0.901432866823992]],\n",
       " [(#6) [0.010791890323162079,0.9967944025993347,0.996789128221103,1.196855068206787,0.8713166117668152,0.8854880498281269],\n",
       "  (#6) [0.01185095775872469,0.996321976184845,0.9963149007229679,1.1307034492492676,0.796645998954773,0.8303308563120471],\n",
       "  (#6) [0.01221100240945816,0.9962685704231262,0.9962599751878549,1.071852684020996,0.7983778715133667,0.8108438978294449],\n",
       "  (#6) [0.012303701601922512,0.996399998664856,0.9963945656755302,0.6509847640991211,0.8849606513977051,0.8845986359085644],\n",
       "  (#6) [0.01260362472385168,0.9963870048522949,0.9963818862713164,0.6137449145317078,0.8980393409729004,0.8983338238273132]],\n",
       " [(#6) [0.007555489428341389,0.9976813793182373,0.9976795632794596,1.2165422439575195,0.902233898639679,0.9070426583529863],\n",
       "  (#6) [0.007677064742892981,0.9974661469459534,0.997463301136152,0.977726399898529,0.8604966998100281,0.8710740659034387],\n",
       "  (#6) [0.008594638668000698,0.9972335696220398,0.9972296301856596,1.4809865951538086,0.682269811630249,0.6935609949530102],\n",
       "  (#6) [0.008218169212341309,0.9973679184913635,0.9973651533787807,0.7681039571762085,0.8862084746360779,0.8838722119704543],\n",
       "  (#6) [0.007968340069055557,0.9974343776702881,0.9974325336365771,0.7165929079055786,0.8957113027572632,0.8973940694591924]],\n",
       " [(#6) [0.005812251474708319,0.9980151057243347,0.9980142856974764,0.8096386790275574,0.8964859843254089,0.9019512971764261],\n",
       "  (#6) [0.0067913648672401905,0.9977348446846008,0.9977333518922344,1.0141186714172363,0.8499945402145386,0.8644055859740291],\n",
       "  (#6) [0.007198370061814785,0.9976885914802551,0.997685940595781,1.724534511566162,0.6977285742759705,0.7120274555382917],\n",
       "  (#6) [0.007198619190603495,0.9977723956108093,0.9977712608018384,0.800796627998352,0.8810786604881287,0.8832724690041157],\n",
       "  (#6) [0.00664880545809865,0.9977955222129822,0.9977943239262886,0.7321220636367798,0.8903215527534485,0.8923862025630208]],\n",
       " [(#6) [0.0057172635570168495,0.9980959892272949,0.9980950740918391,1.1840749979019165,0.8929505944252014,0.8995768653544457],\n",
       "  (#6) [0.006193771958351135,0.9978749752044678,0.997873019751927,1.1423976421356201,0.818528413772583,0.8455031169459464],\n",
       "  (#6) [0.006930513773113489,0.9978923201560974,0.9978901082285467,1.3020930290222168,0.7780550718307495,0.797268026981365],\n",
       "  (#6) [0.0065697659738361835,0.9978735446929932,0.9978717560012726,0.7044151425361633,0.8925860524177551,0.8919903083513876],\n",
       "  (#6) [0.006232985761016607,0.9979038834571838,0.9979027405285412,0.6911087036132812,0.8946425914764404,0.8963689858114068]]]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "short-waste",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"it_1024_d6_crossval.npy\", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26c431bb-bea7-4304-8b01-c47950c95faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.load(\"it_1024_d6_crossval.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "approximate-assurance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2: train f1\n",
    "# 5: valid f1\n",
    "\n",
    "t_mean = []\n",
    "t_std = []\n",
    "v_mean = []\n",
    "v_std = []\n",
    "\n",
    "for s in scores:\n",
    "    train = []\n",
    "    valid = []\n",
    "    \n",
    "    for fold in s:\n",
    "        train.append(fold[2])\n",
    "        valid.append(fold[5])\n",
    "    \n",
    "    t_mean.append(np.mean(train))\n",
    "    t_std.append(np.std(train))\n",
    "    \n",
    "    v_mean.append(np.mean(valid))\n",
    "    v_std.append(np.std(valid))\n",
    "    \n",
    "t_mean = np.array(t_mean)\n",
    "t_std = np.array(t_std)\n",
    "\n",
    "v_mean = np.array(v_mean)\n",
    "v_std = np.array(v_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2fe28fe-76f5-44f0-a109-ad802adbe630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87426615 0.86404314 0.86191905 0.8505888  0.8508086  0.86614146]\n",
      "[0.03103002 0.05309894 0.03464789 0.07945179 0.07048866 0.03967186]\n"
     ]
    }
   ],
   "source": [
    "print(v_mean)\n",
    "print(v_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "oriental-underground",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z2XrodLlyGVvtyNvt72h4s8Z4rrxz1B6Gs4fPma6ynjdg2ZtXZS1ccV4VkXuYhji1unDnMu0scCS8JSdnw8dHVeArWF2BwasMql49uLibQMXIGNgMnPvzNW9AsxXejMsf5Dc/xPRiAmfCm2nYN14lZj4EWLh5VWV9bhuRpzB9P3MkxKWn5xZxi/LH4EVEDivofBF7vWum+7Raw4UHxn35XXhgCF2cV0VEnoPBi4iKnGF4Wun0sILphQfWwpskgfOqiMijMHgRUYlV0AsPiIhKKr6bEREREbkJgxcRERGRm3CosQzYu/cqnntumTxsExTkDY1GCY1GBY1GCW9vldnX5s9bPuflZftY3pqGiIjINgavMiA5OQs3bqTJX1+5klJkdalUCiuBzFqgM3zt5ZVf8DM/R96yXl5KXqFGREQeg8GrDFAoJISH+yIzU4vsbC1yc60vhOkKxhW+3bVWiiRBDm+mPXEajQre3pZhzvC8/aDn5WW7J9DbW8WJ3kREJZzhcygH6em5yMjIRXp6LtLTc3D+/H0IAXTsWB2PP16xWNrG4FUGdOnyKP74YxCuXUvBjRupqFOnHHJydMjKMgSx7Gyd/K/hOZ3F89bLWe7LyrJ+jE5XNKsEC4EHdejyL+wiSqVkI9AZ/jUuwAnAZJV/81sqGW+DZO0uALbuEmB6rLX7TOa9ube1uvLuz3sua+3I237rt4fKf795XYX7XhifYxAmKj1yc3VyQHoYlnKRkZEjbzuyLyMjN9/PhOBgb3TvXtdNr8wcg1cZpFAYPrC8vd3349dq9fmEPPvBLytLi5wcx0Ke8RxFRacT8n9uKn5K5cPf57wPYy+lt7fSZhnLspblNRoGPCJrcnJ0cs9S3iBkCEg5FtvGcqYBKj09p0hHY/JKS8txW115MXiRWxjubecFPz/31Ge8h5+1oJeTYzvk2Qt+pl9bO4c73zToIZ1OyG/eRUmtVlgJaLZDnWW5/MvyAhUqakIY7ttr/OMxLS3HSkCy3Gerl0mrLRnve8bbfxkeXvD1Nfzr56d+sG34OjMzFyEhPmjVKqL42lpsNRMVIUkyDgeqAGjcUqdOp5dv1CzEw1XXgYfbQhj/hfy1aVnDfjx4/uG26X7TY/Put6wLZnWan8uyDXn3W2tv3ros22/9WFd+L4z3Z8zJMfR8ZmVpzR7Z2Vq4+h64hns/5iA1tWj/UjbOWbTV82Y9uOXfo2f68PLiCv+exBiWTMORMRSZByTbvUym85yKauqHs7y8lCahSC2HJV9fNfz9veR9eUOUcZ/ptpeXY/cyO3v2HgIDNahcOaCIX51tDF5ELqJUKuDjo4CPj5P3xSGXM/Z4mgcyy4BmGtQsn7de3rRsUQxpG8N7Skq2y89tyjSs2Qp0xnmLeUOarXtoOhp2nTneWllnQrWjx5eE12QMV6YhyRiuSkpY0miUcjgyBB/zwGTe42QMTl5WeqPUUKsLcOPXUoDBi4hKHdMez6CgoqtHr7cW8Ow/jEPU+QfAh+Vyclwf8IwXpSQnF23Ao+Kn0ShNepDyhqKHvUn2ApOxHOc6Fh6DFxFRAbnrQhWdTm81sNkKas6EOtNHSZmvQ4CPj8rKPCXzIGQ+JGc5n8nPTw0fHzXDUgnD4FUG3biRCrVa+WDCuwJqteFfTuolKpmUSgV8fRXw9S3aYWytVm8lqOmsDo1ZW7jY2lrG1p9zbH6Ze+ooTL2urcM4jOfjo+L7cSnG4FVGKBQSvLyUCAjQICtLi7S0XGi1ujxlFFCpJKjVSjmM5Q1ofDMgKr1UKgX8/b3g7+9V3E0hKrUYvMqI8uX9kJGRa/YXs/HKsNxcHXJzDSvO5+bqHqy5pbMaziRJglr9MJCZbjOcERER2cfgVUYEB3ujceOK0GoNSx7k5urkq6dyc/Um2zr5sn/APJwZgpkxnIkHC+flQqfTmw1FGMOZscfMWu8Z5xwQEVFZxOBVxjwMPbbnihh7vmwFs5wc6+FMq9U9CGZ6OaTl5BgW6tNqLcNZ3h6zvL1nDGdERFTalJrgdenSJfzvf/9DXFwcrly5Ao1Gg0cffRR9+vTByJEj4evrW+g6Ll68iLlz52LLli04d+4c0tPTERAQgLp16+Kpp57Ca6+9hvLly7vg1RQvY+ixtx6V6WKh1oKZtXCm0+Ud1nQ8nJn2mOUNaOS5jL8XxkVTDb2nht8vw9f29xnvm2m6kCiHuonIlHHZl7yfS8VFErZWffMg69atQ79+/ZCSkmJ1f2RkJOLi4lCrVq0C1/H9999j+PDhyMzMtFkmNDQUsbGx6NKlS4HrSUlJQVBQEJKTkxEYGFjg85QExnBmK5jl5uqh05lfvv5wnpm1uWeGXjVr4SxvQMsb1si18gYm03CUNzDZ22fv7efhjbEVUCgMF4golYoH/xr26XR6eW0s47nUastV3Pk7QFT6GafSGO/ra7xtXG7uw7nKSqUC4eG+KF/eH489VsGl9Tv6+e3xwevQoUOIjo5GZmYm/P39MXHiRHTo0AGZmZmIjY3F/PnzARjCV0JCAgICnL9NwO7du9G2bVvo9XooFAoMGDAAPXr0QOXKlXH58mUsWbIE69atAwD4+Pjg2LFjqFmzZoFeT2kKXo7Q6fR2g1lOjs5qODP2mFkb3rQXzmwNbyqVksOXhnsy9wamh4+8gcnRffbqUColed0p4y1V8i5Uqtcb9qtUCotb7zh6ixEiKlmshSvTzwrDe74SGo0SXl4P//XyUkGlMryvVKjgj4gI137Glpng1bZtW8THx0OlUuGPP/5Aq1atzPbPnDkTEyZMAABMmTIFU6dOdbqObt26IS4uDgDw9ddf4/XXX7coM27cOMyaNQsAMHLkSHz11VdO1wOUveDlCNNuYmvBzDjx35Sxd8zYW5Z3eNORcJa390ylUhRbODMGJmtDc8ZQ5Mi+/AKTMdBYC0XO7LNXh7GcUqmAUmn5ryP7jHXo9QKZmYZbqmRmah/8mysPJ+RdVT47WydfqatQKOR7IZrepLosBHCiks70fT87WyuHK8O9cA3/vw13qDAEqofhSmn2/9i4yLGPj9qiJ9zVykTw2rdvH1q0aAEAGD58OL755huLMnq9Hg0bNsSJEycQHByM27dvQ612bhHC0NBQ3L9/H2FhYUhMTLRaJjk5GcHBwQCApk2b4sCBA869mAcYvArG+J/UGMys9aLlF86sDW/m/e9hbW0z096zvOHM9KbS1kKRrX155zIVNDAZhugUDu+zV4cxZFkLRY7uc8eNmYUQyMrSmoUxw73uDD9/00VCjSu45+Zq5deZ98bT1u5VSESu4cjwoEpl2XOl0Rj+KDayNsXAx0fl1vtBOvr57dGT61evXi1vDxo0yGoZhUKB/v37Y+LEiUhKSsL27dvRtWtXp+rJyckBANSoUcNmmaCgIJQrVw6JiYlyeXIfR27dotcLu8HMWjgzXhBgOrxp/Do9PddmODMem9/fNXmDjyGkKKFUAgqF2srwm+LBPscCE4BCBSV3BiZXkSQJPj5qi4tDsrO1ZkEsIyNXfnPX6YTZau0ZGblISsqCEAKSJMlv+qZv6pzET+QYIYT8nuvI8GBAgAYajVLuzVIqJblM3v+Hnvj/0aOD165duwAAfn5+aNasmc1y7dq1k7d3797tdPCqU6cODh48iAsXLtgsk5KSIveG1alTx6nzk3soFA9vnGyLMZzZm3dm+pcYYBrOHvagGetzZWAqyPCcJwWmomb82QcHe8vP5ebqLMJYdrah90uvF/IQh7GHLDU1g5P4iWwwjjzkDVe2hgf9/b2sDg8qlQqr4aq0TAXw6OB14sQJAECtWrWgUtl+KXXr1rU4xhmvvfYaXn31Vdy9exfffPMNXnvtNYsyH330kVl58kwPw5ntMqZ/vdmadyZJsNuz5EiIoqJnmMOnRGDgwx+4Tqe3CGNZWVoIYejBzMnRm80bu3s3U57Eb+0Dg5P4qbQxDg/mnXtlbXjQ11eN4GBvm8ODPj6WAcudw4PFwWODV1ZWltzDFBERYbdsSEgI/Pz8kJ6ejitXrjhd1+DBg7Fr1y4sXboUI0eOxIEDB9C9e3dUqlQJly9fxvfffy8Pe7777rvo3Lmzw+fOzs5Gdna2/LWtJTGo5DAOPfEDtXRSKi3vVyiEsAhjppP4c3PNw1hycjbu3s0AYOjlzDs8Ulr+cqfSy9bwoOnVwoa7lBh6qwIDNSbzsOwPD/r4qMv0mnseG7xSU1PlbX9//3zLG4NXWlqa03UplUosWbIEMTEx+Pjjj7FgwQIsWLDArEyHDh0wadIkp0IXAEyfPh0ffPCB020iIveRJAm+vmqLe51mZ+vMgphxIWDAcPGGaRhLT8/F/fuZ8vlMP4yMV1VyaJjczfnhQY3JJHdFmRkedCWPDV5ZWVnytpeXl52SBpoHY0f2FkC158SJE1i6dCmOHj1qdf+ePXuwcOFC1KtXD1WqVHH4vBMnTsSbb74pf52SkoKqVasWqI1E5D6mV0CGhvrIzxvvxGAMYhkZucjJMQzB6PXCZGmLh71jxg84Ly/z5S04b4xcRavVWw1Xzg4PGn9Hy9rwoCt5bPDy9n44QdaRqwiNw3k+Pj75lLQUHx+PmJgYJCcno1q1avjPf/6DLl26IDQ0FLdu3cLatWvx/vvvIzY2Fn/88Qd+//13NGjQwKFzazQaORQSkeczDkObTuLXavUWYSwryzCJ39rir9Ym8Zv2kPFDjqwxDg/mnXtV0OHBvGtfleXhQVfy2OBlugK9I8OH6enpABwbljSVnZ2Nl156CcnJyahYsSL27t2LihUryvsjIiLw+uuvo127doiKisL169cxYMAAJCQkOFUPEZVeKpUCgYEas0n8pou/Phyu1MqByzCf5mEYu38/S770Xqm0nDfm5cUhnbLC2vBgdrYWubkP73drnFvo5aV0anjQx4e/S0XNY4OXt7c3wsLCcPfuXVy9etVu2fv378vBy9lhvN9++w3Xrl0DALzxxhtmoctUgwYN0K9fPyxYsAAHDhzA33//jccff9ypuoio7FAoJPj5ecHPz3wSv3EdMdMwZn3xVy1SU3Nw797DeWOcX1O6ODM86OfnZbaKu+kQNYcHSxaPDV4AUL9+fcTHx+Ps2bPQarU2l5Q4efKkvF2vXj2n6jBdfqJp06Z2yzZr1kyedH/y5EkGLyJyiunir2FhD5/PzrYMY8YPX61WmPWMpadbLv7K4SL3M961QgjLbSEg3/PUuA3kXcXd+eFBa+GKF22UPB4dvFq3bo34+Hikp6fjwIED8u2D8tq5c6e8HR0d7VQdpmFOq9XaLZubm2v1OCKiwjAu/hoS8nCOam6uziKM5V381bR3LCUlu0wt/upo2DHeqsu4ba2svRBlq46CcGZ4MO/6Vxwe9BwenQ569uyJ6dOnAwAWLVpkNXjp9XosXboUABAcHIwOHTo4VYfpbYLi4+PRrVs3m2VNA5692wsRERWWWq1EUJASQUEPJ/HrdHqzIJZ38VfTSfzZ2TqzxV9VKoVJr1jhF38tjrBT2OBjvO+pQiFBkoz3KH34vCQZhogN92SF3JP08E4UxvIwOY9kcp68dRjPCYvQZG140MdHXeoCclnk0TfJBoC2bdsiPj4eKpUKf/zxB1q1amW2f+bMmZgwYQIAYMqUKZg6darZ/h07dshhbMCAAVi8eLHZ/qSkJFSpUgUZGRkICAjA7t270ahRI4t2bNy4Ed26dYNer0eVKlVw+fJlKBTO/wfhTbKJyJWMk/hNF4A1Xfw1J0cnBzFjKNNqDcOYCoUC3t6G1f1NA5GtsGMargry0WIt+BgDjum2MeCYBh9rAcf0edOAYytcFaS9xvMY6zKGsII8p1RKXNPNg5WJm2QDwOzZsxEdHY3MzEx07doVkyZNQocOHZCZmYnY2FjMmzcPABAZGYlx48Y5ff7g4GC88847mDx5MlJTU/Gvf/0Lb7zxBrp06YKQkBDcunULa9aswfz58+W/HD/55JMChS4iIlezN4k/72r8lpP4DWHMuA7Zw7AgQaUqeM+OrXDlLNNjTQNNYZ5zNDQZ207kLI/v8QKAdevWoV+/fjZvtxMZGYm4uDjUqlXLYl9+PV6A4U3qzTffxOzZs+3+FadWq/Hxxx9j/PjxBXshYI8XERUf4+Kvpo+8N4XPq6DBpbABicGHSpoy0+MFADExMThy5Ahmz56NuLg4XL16FV5eXqhVqxZeeOEFjBo1Cr6+vgU+vyRJ+Pzzz+XlInbt2oVLly4hIyMD/v7+qFWrFtq1a4fhw4cjMjLSha+MiMh9bC3+mpursxqaGHyInFcqerxKE/Z4EREReR5HP785EYmIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNyEwYuIiIjITRi8iIiIiNykyIKXXq9HYmIiLl++DJ1OV1TVEBEREXkMlwYvnU6HhQsXok2bNvD19UWFChVQs2ZNnDp1yqzc+vXrMWHCBEybNs2V1RMRERGVaCpXnej27dvo2bMn/vrrLwgh7JatXr06unfvDkmS8Oyzz6Jx48auagYRERFRieWSHi+dToeYmBjs3bsXkiShT58++Oqrr2yWb9iwIVq0aAEAWLVqlSuaQERERFTiuSR4LVmyBPv374darUZcXBxiY2Px+uuv2z2me/fuEEJg165drmgCERERUYnnkuD1888/Q5IkDB8+HE8++aRDxzRp0gQALOZ/EREREZVWLgleR44cAWDoxXJU+fLlAQB37951RROIiIiISjyXBK+kpCQAQFhYmMPHGJeYUCqVrmgCERERUYnnkuAVGhoKALhy5YrDx5w5cwYAEB4e7oomEBEREZV4LgleDRo0AADs37/f4WOWLVsGSZLQvHlzVzSBiIiIqMRzSfDq2bMnhBD46quvcP/+/XzLr1ixAuvWrQMAPP/8865oAi5duoRx48ahbt268PPzQ2hoKJo3b46ZM2ciIyPDJXUYbdmyBQMHDkStWrXg5+eHoKAgREZGonfv3pg7dy7S0tJcWh8RERGVDpLIb7VTB2RnZ6NOnTq4cuUKmjZtiiVLlqB+/fpQKBSQJAlHjx5F/fr1cfv2bcyePRszZ86ETqdDw4YNcfjwYUiSVKj6161bh379+iElJcXq/sjISMTFxaFWrVqFquf+/fsYNGgQ1qxZY7fcoUOHCrwobEpKCoKCgpCcnIzAwMACnYOIiIjcy9HPb5esXK/RaLBmzRq0b98eBw4cQKNGjVCnTh15f79+/ZCWlobz589DCAEhBMLCwvDrr78WOnQdOnQIL774IjIzM+Hv74+JEyeiQ4cOyMzMRGxsLObPn4/Tp0/j2WefRUJCAgICAgpUT3JyMrp06YIDBw4AAHr16oXevXvj0UcfhVKpxJUrV7Bz5078+uuvhXo9REREVIoJFzpz5oz417/+JSRJkh8KhUIoFAqz51q0aCHOnTvnkjrbtGkjAAiVSiX+/PNPi/3//e9/BQABQEyZMqXA9bzyyisCgNBoNGLNmjU2y+n1epGbm1vgepKTkwUAkZycXOBzEBERkXs5+vntkqHGvHbt2oW1a9ciISEBt2/fhk6nQ1hYGJo0aYLu3bujS5cuLqln37598q2Hhg8fjm+++caijF6vR8OGDXHixAkEBwfj9u3bUKvVTr+eNm3aAABmzpyJ8ePHF77xNnCokYiIyPO4dajx8uXLAAB/f3+EhoaidevWaN26tStObdfq1avl7UGDBlkto1Ao0L9/f0ycOBFJSUnYvn07unbt6lQ9xvtOBgUFYdSoUQVuLxEREZVtLrmqsXr16qhRowZiY2NdcTqHGe/z6Ofnh2bNmtks165dO3l79+7dTtWRk5MjT6bv0qULvL29ARgWgL1y5QouXryIrKwsZ5tOREREZZBLgpePjw8AuH1NrhMnTgAAatWqBZXKdudd3bp1LY5x1N9//y0Hq0aNGiElJQVjxoxBuXLl8Mgjj6BGjRoICgpCly5dsGPHDudfBBEREZUZLgleVapUAfDwNkDukJWVhcTERABARESE3bIhISHw8/MD4Nzq+gBw/PhxeVuv1yMqKgqzZ8+Wb5MEGHrFtmzZgo4dO2LGjBlOnT87OxspKSlmDyIiIiqdXBK8jHOmjEN/7pCamipv+/v751veGLycXdz03r178vaMGTNw5swZPPXUU9i3bx+ysrJw+/ZtzJ07F0FBQRBC4J133sl3nS9T06dPR1BQkPyoWrWqU+0jIiIiz+GS4DV69Gj4+Pjg008/xbVr11xxynyZzqvy8vLKt7xGowEAZGZmOlVPenq6WZ1dunTB+vXr0bx5c2g0GoSHh+O1117D+vXroVAYvp0TJ06EoxeLTpw4EcnJyfLD2R45IiIi8hwuCV61a9fGTz/9hIyMDLRs2RI//fQTcnJyXHFqm4yT3AE4VFd2djaAh/PRClIPYOj1UiqVFuVat26N5557DoBhHtnRo0cdOr9Go0FgYKDZg4iIiEonlywn0bFjRwBAeHg4Lly4gFdeeQVDhgxB7dq1ERISYjWoGEmShK1btzpdp+kK9I4MHxp7rhwZlrRVT3h4OJo0aWKz7JNPPokVK1YAMNww/LHHHnOqLiIiIirdXBK8duzYYXbrHyEEsrOzcezYMZvHSJIEIUSBbxnk7e2NsLAw3L17F1evXrVb9v79+3LwcnYOlWn5/Cbxm5a9c+eOU/UQERFR6eeS4NW2bdtC33OxIOrXr4/4+HicPXsWWq3W5pISJ0+elLfr1avnVB0NGjSQt/O7atN0v73lLYiIiKhsclmPV3Fo3bo14uPjkZ6ejgMHDsi3D8pr586d8nZ0dLRTdVSrVg2PPPIILl++jIsXL9rtpTt37py8bVxig4iIiMjIJZPri0vPnj3l7UWLFlkto9frsXTpUgBAcHAwOnTo4HQ9zz//PADDfZjszUdbuXKlvO2OWyYRERGRZ/Ho4PXEE0/IN69euHAh9uzZY1Hms88+k1erHz16tMUNso3z0yRJwsCBA63WM2bMGPnqxjfffNPqIqc//PCD3PP37LPPcj0uIiIislBkE5GEEDh//ry8AGloaChq1qzp8rlgs2fPRnR0NDIzM9G1a1dMmjQJHTp0QGZmJmJjYzFv3jwAQGRkJMaNG1egOh555BF8+OGHmDBhAo4ePYonnngCb7/9Nh577DGkpKRg5cqVmDt3LgAgMDAQn3/+ucteHxEREZUeLg9emzZtwldffYUdO3YgIyPDbJ+vry86dOiAUaNGyavdF1aTJk2wbNky9OvXDykpKZg0aZJFmcjISMTFxZktDeGst956C/fu3cOMGTNw6tQpDB482KJM+fLlsXr1atSuXbvA9RAREVHp5bKhxpycHPTt2xfPPPMMNmzYgPT0dAghzB7p6emIi4vD008/jb59+7pskdWYmBgcOXIEY8eORWRkJHx9fREcHIyoqCjMmDEDhw4dQq1atQpdz/Tp07F792688sorqF69OjQaDYKCgtC8eXN89NFHOH36NFq1auWCV0RERESlkSQcvbdNPnr37o1Vq1ZBCAGVSoUuXbqgRYsWqFixIgDg5s2b2LdvHzZv3ozc3FxIkoTnn38ey5cvd0X1pUZKSgqCgoKQnJzMVeyJiIg8hKOf3y4ZaoyLi8PKlSshSRI6dOiA7777DtWqVbNa9vLlyxg8eDC2bduGX3/9FRs2bMAzzzzjimYQERERlWguGWpcvHgxAODxxx/Hb7/9ZjN0AYaJ6hs3bkTjxo0B2F4GgoiIiKi0cUnw2rt3LyRJwrhx4yyWa7BGrVZj/PjxEEJg7969rmgCERERUYnnkuBlvC9h/fr1HT6mbt26AIDExERXNIGIiIioxHNJ8PLz8wMA3L171+Fj7t+/D8CwxAQRERFRWeCS4FWnTh0AwLJlyxw+xljWeCwRERFRaeeS4NW9e3cIIbBo0SJ5or0933//Pb777jtIkmR2v0UiIiKi0swl63ilp6cjMjISN2/eBAA8/fTTGDx4MFq0aIHy5ctDkiTcunULf/31F7777jts3LgRQghUqVIFp06d4nCjCa7jRURE5Hkc/fx22QKqhw4dQufOnXH//v1878cohEBISAi2bduGxx9/3BXVlxoMXkRERJ7H0c9vl90yqEmTJjh69Cief/55KBQKi9sFGR8KhQK9e/fGkSNHGLqIiIioTHHpTbIrV66MX375BTdu3MCOHTtw7Ngx3Lt3DwAQGhqKhg0bon379qhUqZIrqyUiIiLyCC4NXkaVKlXCSy+9VBSnJiIiIvJYLhtqJCIiIiL7XNbjdfnyZQBAhQoVoNFo7JbNysrC7du3ARju3UhERERUFrikx+v3339HjRo10KhRI2RkZORbPiMjAw0aNEDNmjWxY8cOVzSBiIiIqMRzSfD65ZdfIIRAz549ERISkm/50NBQPP/889Dr9U6tdk9ERETkyVwSvPbs2QNJktC1a1eHj3nyySflY4mIiIjKApcEr4sXLwIAIiMjHT6mVq1aAIALFy64oglEREREJZ5LgpdWqwUAKJVKh48xls3KynJFE4iIiIhKPJcEr3LlygEAzp8/7/AxxrKhoaGuaAIRERFRieeS4NW4cWMAcGqifGxsLACgYcOGrmgCERERUYnnkuDVo0cPCCGwcuVK/PLLL/mWX758OVauXAlJktCzZ09XNIGIiIioxHNJ8BowYACqV68OIQT69u2L8ePH48qVKxblrly5gjfffBMvv/wyJElC1apVMXToUFc0gYiIiKjEk4QQwhUnOnz4MNq2bYu0tDRIkgTAsCq98YbYN27ckFe3F0LA398fO3fuRJMmTVxRfamRkpKCoKAgJCcnIzAwsLibQ0RERA5w9PPbZfdqbNy4Mf766y80adIEQggIIXDp0iX89ddf+Ouvv3Dp0iX5+WbNmmHfvn0MXURERFSmuOxejQBQr149HDhwAJs3b8b69etx6NAhJCYmAjBc+di0aVPExMSgU6dOrqyWiIiIyCO4bKiRXINDjURERJ7H7UON+bl79y7u3bvnruqIiIiISpwiDV63bt3Cq6++inLlyqF8+fIIDw9HSEgIBg4cKE+0JyIiIiornA5eV69eReXKlVG5cmXMnTvXZrnz58+jWbNmWLhwIe7duydPrE9OTsb333+PJk2a4PDhw4VpOxEREZFHcTp4/fbbb7h58ybu3buHPn362Cz373//G9evX4dxClnVqlXRokULBAQEQAiB+/fv46WXXpLv80hERERU2jkdvPbs2QMA6NChA8LCwqyWWb9+PRISEiBJEkJDQ/Hbb7/h0qVL2LNnD27evIlBgwYBAE6fPo1ff/21EM0nIiIi8hxOB6+jR49CkiR06dLFZpkff/xR3v7ss8/QtWtX+WsfHx8sWLAAjRo1AgCsWbPG2SYQEREReSSng9fFixcBAI8//rjNMjt27AAABAUFoW/fvhb7JUnC4MGDIYTA33//7WwTiIiIiDyS08ErJSUFgGFBVGsuXryIW7duQZIktG3bFmq12mo546r1169fd7YJRERERB7J6eBlvA9jTk6O1f379u2Tt6OiomyeJzg4GACQnp7ubBOIiIiIPJLTwcs4of706dNW9//555/ydvPmzW2eJzU1FQDg7e3tbBOIiIiIPJLTwcs4t8va1YhCCKxduxYAoFKpEB0dbfM8ly5dAgBUqFDB2SYQEREReSSng1f37t0hhMCaNWvw/fffm+379NNPcfHiRUiShM6dO8Pf39/meYzLUtSpU8fZJhARERF5JKeD1yuvvIKqVasCAAYOHIgWLVrg5ZdfRtOmTfHOO+/I5d58802b5xBCYPXq1ZAkCS1btixAs4mIiIg8j9PBy9fXF7GxsfD394cQAgkJCYiNjcXff/8tr1I/ePBgdOrUyeY5NmzYgGvXrgEAOnfuXMCmm7t06RLGjRuHunXrws/PD6GhoWjevDlmzpyJjIwMl9SRV0ZGBmrWrAlJkiBJEqpXr14k9RAREVHpUKCbZLdq1QoJCQl4/vnn4e3tLd+HsVq1avj0008xb948u8d/9NFHAICKFSu6pMdr3bp1eOyxxzBr1iycOnUKGRkZuH//PhISEjBhwgQ0adIEZ8+eLXQ9eU2ePBkXLlxw+XmJiIiodJKEsZuqgPR6Pe7cuQMvLy+EhIQ4dIxxCQmVSgWNRlOY6nHo0CFER0cjMzMT/v7+mDhxIjp06IDMzEzExsZi/vz5AIDIyEgkJCQgICCgUPWZ1tu8eXOo1Wqo1WqkpqaiWrVq8gKzBZWSkoKgoCAkJycjMDDQJW0lIiKiouXo53eBerzMTqBQoEKFCg6HLgDw8/ODn59foUMXAIwePRqZmZlQqVT4/fffMWnSJLRq1QodO3bEvHnz8N///heAYfmLzz77rND1AYBOp8OwYcOg0+kwadIkhIaGuuS8REREVLoVOngVp3379iE+Ph4AMGTIELRq1cqizLhx41CvXj0AwOzZs5Gbm1voemfPno0DBw6gTp06ePvttwt9PiIiIiobPDp4rV69Wt4eNGiQ1TIKhQL9+/cHACQlJWH79u2FqvPSpUuYPHkyAOCbb76Bl5dXoc5HREREZYdHB69du3YBMAxdNmvWzGa5du3aydu7d+8uVJ2vv/460tPT8corr6B9+/aFOhcRERGVLR4dvE6cOAEAqFWrFlQqlc1ydevWtTimIGJjY7FhwwaEhIS4bL4YERERlR2200oJl5WVhcTERABARESE3bIhISHw8/NDeno6rly5UqD67t+/jzFjxgAAPvnkE4SHhxfoPHllZ2cjOztb/jolJcUl5yUiIqKSx2N7vIw32QZg99ZERn5+fgCAtLS0AtX31ltv4datW2jVqhWGDRtWoHNYM336dAQFBckP410BiIiIqPTx2OCVlZUlbzsywd24dEVmZqbTdf3xxx/47rvvoFKp8M0330CSJKfPYcvEiRORnJwsPwraI0dEREQln8cONXp7e8vbOTk5+ZY3Duf5+Pg4VU92djZeffVVCCEwevRoPPbYY841NB8ajcYl65kRERFRyeexPV6mK9A7MnxoXC3fkWFJU9OmTcOpU6dQtWpVfPDBB841koiIiMiER/d4hYWF4e7du7h69ardsvfv35eDl7NzqGbMmAHAcDPvdevWWS1jPHd6ejpiY2MBAOXLl0fHjh2dqouIiIhKN48NXgBQv359xMfH4+zZs9BqtTaXlDh58qS8bVzF3lHGYcxFixZh0aJFdssmJibipZdeAmBYO4zBi4iIiEx57FAjALRu3RqAoafpwIEDNsvt3LlT3o6Oji7ydhERERFZ49HBq2fPnvK2rd4ovV6PpUuXAgCCg4PRoUMHp+oQQuT7qFatGgCgWrVq8nM7duwo0GsiIiKi0sujg9cTTzyBNm3aAAAWLlyIPXv2WJT57LPP5NXqR48eDbVabbZ/x44dkCQJkiRh4MCBRd5mIiIiKrs8eo4XAMyePRvR0dHIzMxE165dMWnSJHTo0AGZmZmIjY3FvHnzAACRkZEYN25cMbeWiIiIyjKPD15NmjTBsmXL0K9fP6SkpGDSpEkWZSIjIxEXF2e2BAURERGRu3n0UKNRTEwMjhw5grFjxyIyMhK+vr4IDg5GVFQUZsyYgUOHDqFWrVrF3UwiIiIq4yQhhCjuRtBDKSkpCAoKQnJyMgIDA4u7OUREROQARz+/S0WPFxEREZEnYPAiIiIichMGLyIiIiI38firGsk6nU4HnU4HTuEjIiJ7JEmCUqmEUqks7qaUCQxepYher0dqaiokSYJCoYBKpYIkScXdLCIiKsH0ej1ycnKg1+shhEBAQAAUCg6IFRUGr1JCq9UiNTUVQUFB/A9DREQFotfrkZycjICAAKhUjAhFgZ/QpUR6ejqCg4MZuoiIqMAUCgWCg4ORnp5e3E0ptfgpXQro9Xr5fpNERESFYfw84RzhosHgVQpotVqLm38TEREVlFqtRm5ubnE3o1Ri8CoFhBAcYiQiIpdRKBTs8Soi/LQmIiIichMGLyIiIiI3YfAiIiIichMGLyIiIiI3YfAiIiIichMGLyIiIiI3YfAiKoUuXrwoL4K4ePHiIqunffv2kCQJ7du3L7I6iIhKEwYvIgeZhpnCPIiIqOxi8CIiIiJyE956nMhBVapUwdGjR23ub9SoEQAgKioKixYtclezrKpevbpbVp3esWNHkddBRFSaMHgROUitVqNhw4b5lvPz83OoHBERlT0caiQiIiJyEwYvIjfIe/XfmTNnMGrUKNSuXRu+vr6QJAkXL16Uy9+4cQNz5sxB7969Ubt2bfj5+UGj0aBKlSro0aMHli1bBr1eb7O+/K5qnDp1qtlk/6ysLMycORNNmzZFQEAAAgIC8MQTT+Crr76CVqt1+HXl14bNmzcjJiYGFStWhEajQY0aNTBixAhcvXo13+/h3bt3MWHCBNSpUwc+Pj6oUKECunTpglWrVgEAFi9eLNdn+r101qpVq9CzZ09ERERAo9EgICAANWvWRJs2bfD+++9j3759do/fvXs3hg4dijp16iAwMBBeXl6IiIhAt27d8PXXXyMpKcnmsevWrUPv3r3lusPCwtCqVSt88sknSEtLs3lc3teenZ2NL774Ai1btkS5cuUgSRKmTp1qcdz27dsxYMAA1KxZE76+vggMDESjRo3w1ltv4fr1645+y4jIGYJKlOTkZAFAJCcnO3xMVlaWyMrKKsJWkSMACACiXbt2FvvatWsn71u9erXw8/OTyxsfFy5cEEIIodVqhUKhsNif99GlSxeRmppqtS0XLlyQyy1atMhi/5QpU+T9N2/eFI0bN7ZZT0xMjNDpdFbrMX1d+bXhnXfesVlHeHi4OH78uM3v7ZEjR0SFChVsHv/qq6+KRYsWWXwvnaHVasULL7yQ7/e9WbNmVo/PyMgQL730Ur7HT5kyxeLYzMxM0atXL7vHVa5cWRw6dMhq3aavff/+/VZ/nqb1ZmZmin//+9926/Pz8xNr1651+vtIpQM/V5zn6Oc353gRudHly5fRr18/+Pr64v3330ebNm2gVCqxf/9++Pv7A4A8Kb5jx454+umn0ahRI4SHhyM1NRXnz5/H/PnzsWfPHmzevBkjR47EkiVLCtWm5557DsePH8f//d//ISYmBqGhoTh16hQ++ugjnDhxAuvWrcP8+fMxfPjwAtcxf/58/Pnnn2jXrh2GDx+OyMhIJCUlYenSpVi6dCnu3LmDwYMHY8+ePRbHJiUl4amnnsKtW7cAAK+88gr69u2L8PBwnD17FrNnz8a8efPw999/F7h9ADB37lz88ssvAIDWrVtj6NChePTRR+Hn54e7d+/iyJEj+O2335CcnGxxrF6vR48ePbB582YAQO3atfH6668jKioKvr6+uHHjBv78808sX77cat0DBgyQe+4ef/xxjBs3DvXq1cO9e/cQGxuLxYsX4/r16+jUqROOHDmCKlWq2HwdQ4YMwdGjR9G/f3+8+OKLqFixIi5fvgyNRgPA8PvVu3dvxMXFAQBiYmLQp08f1KxZEwqFAvv27cNnn32Gy5cvo3fv3ti9ezeioqIK/o0lInNuiYHkMPZ4eS440OOFBz0Xly5dsnkevV4vzpw5Y7euyZMnCwBCkiRx+vRpi/3O9Hip1Wqxfft2izJ3796Ve5kee+wxq+1wtMcLgBg2bJjQ6/UW5YYOHSqXOXjwoMX+MWPGyPu/+OILi/1arVb06NHDau+hM9q0aSMAiBYtWojc3Fyb5e7evWvx3OzZs+W6e/XqZfP/o06nE1evXjV7bv369fKxnTp1EtnZ2RbHzZs3Ty7Tp08fi/2mPV4AxIIFC2y233gutVotNm7caLXMvXv3RIMGDQQAER0dbfNcVHrxc8V57PEi236IAtJvFncripZfRaBfQnG3wqpPPvkEjzzyiM39kiShVq1ads8xefJkzJkzB4mJiVi7di3GjRtX4Pa88cYbVudohYaGYtCgQfjkk09w9OhRJCcnIygoqEB1VKpUCV9++aXVBWTHjx+PBQsWAADi4+PRpEkTeV92drY8P6x58+YYPXq0xfFKpRLffvstNm3ahKysrAK1DwBu3jT8n/jXv/4Flcr2W2NoaKjZ13q9HjNnzgQAREREYOnSpXLvUl4KhcKit+rrr78GYLhqdtGiRfDy8rI4btiwYVi+fDm2bNmClStX4saNG6hUqZLVOjp27IghQ4ZY3SeEwIwZMwAA//d//4ennnrKarmQkBDMnDkTzzzzDHbv3o0zZ86gdu3aVssSkXMYvMqi9JtA2rXibkWZ5OXlhRdeeMGpY/R6PW7evInU1FTk5ubKz0dERCAxMbHQQ2wvv/yyzX3NmjUDYPjAvnDhAho3blygOnr37m0zjNSpUwf+/v5IS0vD+fPnzfYlJCTIk9H79etn8/wVKlTAk08+iTVr1hSofYAhHJ45cwbr1q3DpEmTUK5cOYeOO3z4sHxxwLBhw+QhY0dotVrs3LkTANC1a1dUrVrVZtlhw4Zhy5Yt0Gq12LFjB1566SWr5ez9PI8fP45z584BMPxM7Gnbtq28vWfPHgYvIhdh8CqL/CoWdwuKXgl9jbVr14a3t3e+5YQQ+PHHH7Fw4UL89ddfyMzMtFk2MTGxUG2qW7euzX2mvTupqalFUgdg6GFJS0uzqOPYsWPytjEE2hIVFVWo4DVgwAD88ccfOHv2LGrVqoXnnnsOXbp0QZs2bRAREWHzuEOHDsnbbdq0carO8+fPIyMjAwDQokULu2VN95t+X/J67LHHbO5LSHjYC9yqVStHmyn3BhJR4TF4lUUldAiuLAgJCcm3TFZWFp577jls3LjRoXPaC2WO8PX1tblPoXi44oxOpyuSOkzryVvH/fv35e3w8HC758hvf34GDx6Mc+fO4b///S+Sk5OxaNEi+Q4Ejz76KHr06IGRI0eiZs2aZseZBl9bw3+23Lt3T94uX7683bIVKz78Y8L0uLzs/Y7dvn3bidY9ZAyHRFR4DF5EbqRUKvMtM23aNDl0tWvXDiNHjkTTpk1RsWJF+Pj4yCGlbdu2iI+Pd8utgcqKadOm4dVXX8WPP/6IrVu3Yu/evcjIyMC5c+cwa9YsfPnll/jf//6H1157zeV1u+oG6vZ+x0yD7bp161C9enWHzplfKCQixzF4EZUgQgh5onmbNm2wbds2s14nU/Z6PUoL096bO3fuIDIy0mbZO3fuuKTOatWqYdKkSZg0aRJyc3Oxf/9+LF++HN9++y2ysrLw+uuvo0WLFvJFAKZzwW7cuJHvsKop06Fc43IZtpgO9+Wd4O+osLAweTs4OJi3tiIqBly5nqgEuXfvnvwB+8ILL9gMXWlpaTh16pQ7m1YsGjRoIG8fOHDAblnT+Uuuolar8a9//QtffPEFfvrpJwCGcLxixQq5TNOmTeXtP/74w6nzG1eMB4C//vrLblnTFfMLGphMrxjdvXt3gc5BRIXD4EVUgpjenic9Pd1muQULFti9lU9pERUVJS9h8cMPP9gsd+vWLWzatKlI29KpUyd523Re1+OPPy5fjbhgwQK7t/bJS6VSoV27dgAMt1Oyd+skY0+oSqWyuvyHI5o2bSpfKDBv3rxCLb9BRAXD4EVUgoSHhyM4OBgA8PPPPyM7O9uizP79+/H++++7uWXFw9vbG/379wdgeN2zZ8+2KKPX6zF8+PBCh4gffvjBbpj9/fff5e0aNWrI2wqFAm+99RYA4OrVq+jfvz9ycnKsnkOv11vcA3HkyJEAgJycHAwZMsRsyRCj7777Tq7/ueeec3oSv2lbJ02aBMBwRWX//v2t/o4ZpaSk4KuvvipQXURkHYMXUQmiUCjkdZiOHDmC1q1b4+eff0ZCQgK2bt2KcePGoW3btvD29rY736k0mTp1qnxF35gxY9C/f39s2rQJBw8exPLly9GmTRusWbMGTzzxhHxMQSaqv/LKK4iIiMDrr7+OH374AXv27MGhQ4fw22+/Ydy4cXIA9Pf3t1gra+TIkejSpQsAw022GzVqhNmzZ2P37t04dOgQNm7ciClTpqBu3bqYN2+e2bHPPvusvLbb77//jpYtW+LHH3/EgQMHsGXLFgwdOhRDhw4FYJjbNWvWLKdfm6nXXnsNvXr1AgD88ssvaNCgAWbOnImdO3fi8OHD+OOPPzBv3jz07dsXlStXtnpzbSIqOE6uJyphpk2bht27d+Pw4cNISEhA3759zfaHhobi119/xeTJk3H69OliaqX7hIaG4rfffkOXLl1w584dfP/99/j+++/NygwcOBBt2rSR50E5slaaNbdu3cLcuXMxd+5cq/uDgoIQGxtrsdCpQqHA6tWrMWDAAKxYsQKnT5/GmDFjHK536dKl0Gq1WLVqFQ4ePGh1sdjKlSsjLi7O7n0aHSFJEpYtW4bRo0fjm2++wblz5zBhwgSb5XlFI5FrsceLqIQJCgrC7t278dFHH6FRo0bw9vaGv78/6tWrh/Hjx+Pvv/82W1W8LHj88cdx/PhxjBs3DrVr14ZGo0G5cuXQoUMH/PTTT1i0aBFSUlLk8gW5tdGxY8cwY8YMxMTEoH79+ggLC4NSqURwcDBatmyJKVOm4NSpUzZvs+Pr64tffvkF27ZtwyuvvIIaNWrAx8cHXl5eqFq1KmJiYvDtt99avb2Tt7c3Vq5cibVr1+K5555D5cqV4eXlhZCQELRo0QLTp0/HqVOnCnzngLzUajXmzJmDv//+G2+88QYaNWqEoKAgKJVKBAUFoXHjxhgyZAhWrFiBEydOuKROIjKQBBcBKlFSUlIQFBSE5ORkBAYGOnSMcY6GrVuyEJUFQ4cOxcKFCxEREYErV64Ud3OIPBo/V5zn6Od3qenxunTpEsaNG4e6devCz88PoaGhaN68OWbOnFnoVZczMjKwcuVKjBgxAs2bN0dISAjUajXCwsLQqlUrTJ06lbfUICpGmZmZ8u2CWrZsWcytISKyrVT0eK1btw79+vUzG2owFRkZibi4ONSqVcvpcx85cgTR0dH5XiIeGBiIefPm4cUXX3S6DlPs8SKydO7cOdSsWdPqpHmdTochQ4ZgyZIlAAzvB926dXN3E4lKFX6uOM/Rz2+Pn1x/6NAhvPjii8jMzIS/vz8mTpyIDh06IDMzE7GxsZg/fz5Onz6NZ599FgkJCQgICHDq/CkpKXLoio6ORrdu3RAVFYWwsDDcuXMHK1euxPz585GSkoKXX34ZgYGBePrpp4vipRKVWR999BH27duHf//732jRogXKly+PzMxMHDlyBPPnz8fBgwcBAJ07d8azzz5bzK0lIrLN44PX6NGjkZmZCZVKhd9//x2tWrWS93Xs2BG1a9fGhAkTcPr0aXz22WdOXxqtUCjQp08fTJkyBfXr17fY37VrVzz99NPo1asXdDod3njjDZw5c8Zl910jIoMTJ05gypQpNvdHR0cjNjaW//eIqETz6KHGffv2oUWLFgCA4cOH45tvvrEoo9fr0bBhQ5w4cQLBwcG4ffs21Gq1y9vSu3dv/PrrrwAMtzYxvY2IMzjUSGTp1KlT+PXXX7FlyxZcvHgRd+7cQW5uLsLCwhAVFYUXX3wR//73v23eYomInMPPFeeVicn1q1evlrcHDRpktYxCoZAXPkxKSsL27duLpC0dOnSQt8+dO1ckdRCVVXXq1MGkSZOwbds2nD9/HqmpqcjKysK1a9ewZs0a9O3bl6GLiDyCR79T7dq1CwDg5+eHZs2a2SxnvBcaUHQ3hjW97YZSqSySOoiIiMizeXTwMi7sV6tWLahUtqer1a1b1+IYV9u5c6e8Xa9evSKpg4iIiDybx06uz8rKQmJiIgAgIiLCbtmQkBD4+fkhPT29SBZW/PvvvxEXFwcAaNSokVPBKzs726y3zNaSGEREROT5PLbHKzU1Vd729/fPt7yfnx8A5Lsel7Oys7MxdOhQ6HQ6AIb77Dlj+vTpCAoKkh957wFHREREpYfHBq+srCx528vLK9/yxiszMjMzXdqOUaNGISEhAQAwYMAAxMTEOHX8xIkTkZycLD94qxMiIqLSy2OHGr29veXtnJycfMsbh/N8fHxc1obp06djwYIFAIDmzZvj66+/dvocGo2Gl+sSERGVER7b42W6Ar0jw4fp6ekAHBuWdMS3336LSZMmATBM3t+wYYM8nElERERkjccGL29vb4SFhQEArl69arfs/fv35eDlijlUP//8M15//XUAQLVq1bB582aUK1eu0OclIiKi0s1jgxcA+RY+Z8+ehVartVnu5MmT8nZhl3pYu3Yt+vfvD71ej0qVKmHr1q35XlVJREREBHh48GrdujUAwzDigQMHbJYzXWMrOjq6wPVt3boVffr0gVarRVhYGDZv3oxHH320wOcjIiKissWjg1fPnj3l7UWLFlkto9frsXTpUgBAcHCw2a19nPHnn3+iR48eyM7ORlBQEDZt2oQGDRoU6FxERERUNnl08HriiSfQpk0bAMDChQuxZ88eizKfffaZvFr96NGjLW6QvWPHDkiSBEmSMHDgQKv1HD58GM8++yzS09Ph5+eHuLg4u7coIiIiIrLGY5eTMJo9ezaio6ORmZmJrl27YtKkSejQoQMyMzMRGxuLefPmAQAiIyMxbtw4p89/7tw5PPnkk0hKSgIA/Oc//0FQUBCOHTtm85jy5cujfPnyBXo9REREVHp5dI8XADRp0gTLli1DYGAg0tLSMGnSJLRq1QodO3Y0C11xcXFmS1A4Kj4+Hrdv35a/Hjt2LBo1amT3MWfOHJe9PiJyn4EDB0KSJFSvXt3q/urVq9vtHXfExYsX5V72xYsXF/g8rpLfayYi1/L44AUAMTExOHLkCMaOHYvIyEj4+voiODgYUVFRmDFjBg4dOoRatWoVdzOplMrJycHPP/+M/v37o27duggLC4NarUa5cuXQrFkzjBgxAlu2bIFery/uphIRUTHz+KFGo2rVqmHWrFmYNWuWU8e1b98eQgib+wcOHFiov26pdFu5ciXGjRuHixcvWuy7e/cu7t69i4MHD+Kbb75BZGQkZs2ahWeffdb9DaUyZerUqfjggw8AwO77GxG5X6kJXkTu9tFHH2Hy5Mny1126dEH37t1Rv359BAcH4969ezh16hTWrVuHzZs34/Tp03j33XcZvDyYtYDt6RYvXlwihjyJygoGL6ICWLRokRy6ypcvj+XLl6Ndu3YW5Tp37oyRI0fi2LFjGDt2LO7cuePuphIRUQnC4EXkpGvXrmHUqFEAAD8/P+zcuRN169a1e0zDhg2xadMm/PTTT+5oIhERlVClYnI9kTt9/vnnyMjIAAB8+OGH+YYuI4VCgX79+pk9Z+0Kt5UrV+KZZ55B5cqVoVKp0L59e4tzrVu3Dr1790ZERAQ0Gg3CwsLQqlUrfPLJJ/neND4pKQnTpk1Dq1atEBISArVajfDwcNSvXx+9evXC3LlzcevWLavHbtu2DS+99BJq1KgBHx8f+Pr6olq1amjZsiXGjx+Pbdu2OfS9MDV48GBIkgQfHx+kpqbmW75OnTqQJAlPPPGE2fN6vR7btm3D+PHjER0djXLlykGtViM4OBiNGzfG+PHjcfnyZafbZ8qRqxp1Oh3mzJmDFi1aIDAwEEFBQWjatCk+/fRTZGdnO1TP3r178d5776F9+/aoWLEivLy8EBgYiPr162PEiBE4fvy41eMWL14MSZLk+V0A5N8v04fpkKmjVzUePXoUr776KmrXrg1fX18EBASgQYMGGDt2rN0hWGu/45s3b0ZMTAwqVqwIjUaDGjVqYMSIEfned9cRp0+fxhtvvIGGDRsiICAAXl5eqFy5Mho3bozBgwdj2bJldn8Od+7cwYcffojo6GiUL18earUaISEhaNGiBSZMmIAjR47Yfa1jx45FgwYNEBAQAF9fX9SuXRvDhw/H0aNH7bbb+D2aOnUqAMP/tRdeeAFVq1aFWq22+vO5efMm3n33XURFRSE0NBQajQZVq1ZFnz59sGXLFoe+X1QMBJUoycnJAoBITk52+JisrCyRlZVVhK0iI71eL8qVKycACD8/P5GSklKo8124cEEAEADEd999J1555RX5a+OjXbt2cvnMzEzRq1cvizKmj8qVK4tDhw5Zre/48eOicuXKdo8HIL788kuLY8eMGZPvcWFhYU5/D7Zs2SIfv3jxYrtl9+/fL5f94osvzPZNmTIl3/b5+vqKlStX2jz/gAEDBABRrVo1q/urVasmAIgBAwZY3Z+amiratGljs/6mTZuKgwcPyl8vWrTI4hyLFi3K93UolUrx9ddfF+hYAOLChQsOv2YhhPj444+FQqGweT6NRiOWLFli9VjT3/FFixaJd955x+Z5wsPDxfHjx222Iz/Lly8XXl5e+b7+o0ePWj3+hx9+EH5+fnaPtfV9WrJkidBoNHZ/Zh9//LHNthvLTZkyRUyaNCnfeh1p65AhQ0Rubm6Bvpf8XHGeo5/fHGokcsI///yDxMREAECbNm0KtDacLV988QWOHDmCNm3aYMSIEYiMjERSUpJZb8KAAQOwatUqAMDjjz+OcePGoV69erh37x5iY2OxePFiXL9+HZ06dcKRI0dQpUoVszpeeeUVXL9+HWq1GsOGDcPTTz+NihUrQq/X4+rVq9i7d698flPr16/HF198AQB47LHHMGLECNSrVw9BQUFISkrCP//8gy1btmDfvn1Ov+4OHTqgcuXKuH79On788UcMGDDAZlnjUK1SqcS///1vs31arRaVKlVCr1690KpVK9SsWRPe3t64cuUK/vzzT8yZMwdpaWno27cvDh48iHr16jnd1vz069cP8fHxAAx31hg7dixq166NW7duYfHixfjll18wfPhwu+fQarUICQlBjx490LZtW9SuXRt+fn64fv06Dh48iP/9739ITEzEqFGjULduXXTs2FE+tmfPnoiKisKcOXMwd+5cALDa05L398KeOXPmYNKkSQCA8PBwvP3224iOjoZOp8OWLVswc+ZMpKenY+DAgShXrhyeeeYZm+eaP38+/vzzT7Rr1w7Dhw+Xf8eXLl2KpUuX4s6dOxg8eLDVu5Dk59atWxg0aBBycnJQvnx5jBo1Ci1btkS5cuWQmZmJs2fPYufOnVi9erXV47///nv0798fAODt7W32/yMtLQ1HjhzB2rVrcebMGYtj4+LiMHDgQAgh4O/vj3HjxqFz585QqVT4888/MX36dCQmJmLSpEkIDg7GiBEjbL6OlStX4ujRo2jUqBHGjh2Lhg0bIjMzE4cPH5bLLF++HK+88gqEEKhZsyZGjRqF+vXrIzw8HBcvXsTChQuxYcMGLFy4EIGBgU5f7U9FzC0xkBzGHq+S7YcffpD/mnz33XcLfT7T3gAAon///kKv11stu379erlcp06dRHZ2tkWZefPmyWX69Oljtu/cuXN2e7SM9Hq9uHfvntlzxp64atWqidTUVJvH3r17197LtenNN9+UewVu3rxptYxOp5N767p27Wqx/8KFCyInJ8dmHVeuXBFVqlQRAES/fv2slilMj5fpz+eZZ56x2tPwwQcfmP28rfV4Xb16VaSnp9t8HUlJSeKxxx4TAETr1q2tljHt/cuPvdd8+/Zt4evrKwBDT+rly5ctyhw8eFDuealSpYrFzyDv7/iwYcOs/o4PHTpULnPw4MF8253XwoUL8+3REkKIjIwMkZGRYfbc9evX5ddZvnx5u8fn/R7k5OTIv5f+/v5We5svXrwoKlWqJPe63rlzx6KM6feoU6dONt/T79y5I4KCggQAMXjwYJs9WsZeM4VCIU6ePGnz9djCzxXnsceLbIqKmoebN+3PA/J0FSv6IyHhVZef9+7du/K2q28LFRwcjK+++gqSJFnd//XXXwMA1Go1Fi1aBC8vL4syw4YNw/Lly7FlyxasXLkSN27cQKVKlQAY5oMYtW3b1mY7JElCSEiI2XPGY5s2bQp/f3+bx4aGhtrcZ8/LL7+MWbNmQafTITY2FqNHj7Yos337dly/fl0un1d+c5QiIiLw1ltvYcyYMVi7di2EEDa/1wVhvGOFRqPB/PnzoVJZvr2+9957+OWXX+zeciy/3qigoCB8+OGH6NmzJ3bt2oW7d+8iLCyscI23YdGiRfJ8xlmzZqFq1aoWZZo0aYKJEyfivffew7Vr17B69Wq88MILVs9XqVIlfPnll1a/7+PHj8eCBQsAGO4Y0qRJE6faavwdDQkJQcOGDW2W8/HxsXjuyy+/lF/nvHnz7B6f93uwatUq+ffyvffeQ+PGjS2OqVatGmbOnIl+/fohIyMDixYtwltvvWX1/AqFAgsWLIBGo7G6f+7cuUhOTkaVKlUwZ84cq79nAPDBBx9gyZIluHbtGpYuXYpp06bZfE3kXgxeZdDNm2m4di3/ScxkyXTyt5+fn0vPHRMTY3PoUqvVYufOnQCArl27Wv0ANBo2bBi2bNkCrVaLHTt24KWXXgIAOYABhknYzgw/GI/9448/cO7cOTz66KMOH+uIpk2bom7dujh58iR++uknq8HLOMzo4+ODXr165XvOlJQU3L17FxkZGfIior6+vvK+CxcuoGbNmi5pv06nw44dOwAYfj6VK1e2Wk6hUGDAgAE2P3StSU9Px507d5Ceni6/DrVaLe//+++/zYYbXck4QTs4OBjPPfeczXJDhw7Fe++9Jx9jK3j17t3bZqCoU6cO/P39kZaWhvPnzzvdVuPv6P3797FmzRr06NHD4WPXr18PAKhZsya6d+/uVL3G75EkSRg8eLDNci+88AJGjhyJ5ORkbNmyxebvQHR0tN0/ItauXQsA6Natm83vJQCoVCq0atUKK1asKNDQLRUdBq8yqGJF2z0WpUVRvUbTYJSenu7Scz/22GM2950/f17+i7xFixZ2z2O637RnpUaNGmjTpg3i4+Px+eefY9OmTXj++efRvn17tGzZUg4l1vTv3x9Lly7F3bt30bBhQ/To0QNPPvkk2rRpY/d2XLdv3za716kpPz8/1KhRQ/765Zdfxvvvv499+/bh7NmzZufNzs7GypUrAQDdu3e3GVAvXbqETz/9FOvWrcOlS5dstgsAEhMTXRa8zp07J/98mjdvbrds3qsxbbVt1qxZ+PXXX3HmzBm7q88b5xwWBePvT9OmTc3CXl4VKlRA9erVcfHiRbu9efldARwSEoK0tDSHrm7Nq3v37ggODkZSUhJ69eqF9u3bIyYmBm3btkXjxo2hVCqtHpebmyu3uXXr1k73ghqPrVGjBsLDw22W8/LyQpMmTbBjxw673yN77wM6nU6e6/Xtt9/i22+/daiNpr3dVPwYvMqgohiCKytMh3RsLblQUHmH90zdu3dP3s5viLNixYpWjwOAn3/+GS+88AL27NmD48eP4/jx4/joo4+gVqvRsmVL9O3bFwMHDoS3t7fZcZ06dcJXX32Ft956C5mZmVi2bBmWLVsGwDA01q1bN4wYMQKPP/642XFz5swxW9rAVLt27eReIgDo27cv3n//fQDAjz/+iClTpsj74uLikJSUBMD6MCMAbNy4Eb1795YDUH4yMzMdKucIZ34+FSpUsLv/wIEDePLJJ82Gte1x5evIy/i6HBlWr1ixIi5evGjxO2fKXrgHDD2CgCFgOCssLAxr167FSy+9hGvXrmH79u3Yvn07ACAwMBCdOnXC4MGD0a1bN7Pj7t27Jwdb015hRzn7PTI9xpr83ge0Wq2TLYTD/yfIPbiOF5ETTIPFwYMHXXpuW3+R51WYeUlVqlTBn3/+iS1btuD1119HgwYNIEkScnNzER8fjxEjRqBhw4Y4ffq0xbEjR47ExYsX8fnnn+OZZ55BUFAQAMOCst9++y2aNGkiDzcVRM2aNdGqVSsAsFho1vh1WFgYnnrqKYtjExMT0bdvX2RkZMDf3x9Tp07Fnj17cPv2bWRnZ0MIASEEtm7dKh9jrxepMArz88nJyUGfPn1w9+5dqNVqvPnmm9i5cydu3LiBrKws+XWcO3dOPqaoXocpV86FK0pt2rTB2bNn8cMPP6Bv376IiIgAYBhaXrVqFWJiYvDUU08VSRBx1ffI3vuAaSAdOnQojh496tDj999/d0nbyDXY40XkhAYNGqBcuXJITExEfHw8UlJSEBgYWOT1mk5az6+nzXRYwdZk906dOqFTp04ADBcMbNmyBfPmzcO2bdtw7tw5vPjiizh06JDFceXLl8eYMWMwZswY6PV6HD58GKtWrcJXX30lL8zavHlzeX7N1KlT5QUhHfHyyy9jz549OH36NBISEhAVFYWUlBTExcUBMMyTsTbktWLFCrlHbNWqVejcubPV89vraSgM016K/H4+9vZv27ZNnt80Z84cDB061Gq5onodeYWGhuLGjRsO9e4af+8KeoGFq3h7e+Pll1+We0YvXLiAuLg4fPnllzh9+jQ2bdqEd999F59//jkAQ3sVCgX0ej1u3LjhdH3G1+uO75HpcUIIuxcBUMnFHi8iJ0iSJK8zlZ6eLl+FVdRq1qwpD9P89ddfdsuarqXlyBtzWFgYXnzxRWzdulWeWHz48GGr6xWZUigUaNq0KT766COznqTly5fnW6ctffr0ka/SMvZy/frrr8jKygJge5jxn3/+AWD4YLIVugAgISGhwG2z59FHH5Wvltu/f7/dsvb2G18HALz44os2y+X3OlzV+2L8/Tl48KDdIa7bt2/Lc+pKWhioUaMGRo0ahf3798s9YKa/o2q1Wm5zfHy80z2IxmMvXLhg916subm58h8zBf0eeXl5oUGDBgCA3bt3F+gcVPwYvIicNHbsWDkETZ48GSdPnnToOL1ejx9//LFAdapUKvkm3Js3b7Z7axVjGLR1uyF7jL1ggHOTtps2bSr3+hRmsnd4eDi6du0KAIiNjYVer5cDWLVq1RAdHW31OGMoyMrKgl6vt1omIyMD33//fYHbZo/p9/r333+32XOi1+uxZMkSm+cxDTe2Lt7Q6/WYP3++3faYztFz9DZF1hhDbFJSknxxgzULFy6UA4u94FucAgMD5Qsf8v6OxsTEADCEpzVr1jh1XuPrFUJg0aJFNsutWLECycnJZscUhPGPo5MnT2LTpk0FPg8VHwYvIidVqVIFX331FQDDh2O7du3kpR5sOX78OJ566inMnDmzwPWOHDkSgGEe0JAhQ5Cbm2tR5rvvvpPnczz33HNmk4UPHz5stvp1XkIIs0vjTS9pX7Zsmd1J3AkJCbh//z4AmF2pWBDGXq0bN27gp59+kidI9+3b12ZPTu3atQEYwpW1HjedToehQ4fK6y0VBeNq5NnZ2Rg+fLjVCeLTp0+3e88+4+sAIN/XMK+JEyfmO7/Q9OduOh/MWYMGDZL/yBg3bhyuXbtmUebvv//Gxx9/DMDwf6Nnz54Frq8wNm3aZHeoMDk5We4Nzvs7OmrUKHl5mOHDh9u96jDvHz09e/aUlw+ZNm2a1Z/vlStXMH78eACGCwwGDRrkwCuybvTo0fJaeoMGDTLrJbUmLi7O7v0lqRgU0QKuVEBcud5zfPjhh2arTXft2lV8/fXXYtu2beLgwYNiy5YtYs6cOeLZZ58VSqVSABCPP/642Tny3scuPy+88IJcvmnTpuKHH34QCQkJYvPmzWLIkCFCkiQBQISGhoqrV6+aHWu8j1/z5s3Fhx9+KNavXy8SEhLEnj17xE8//SS6dOkin7tHjx5mx1arVk0EBweLAQMGiIULF4r4+Hhx8OBBsXnzZjFlyhQRGhoqrzy/f//+Qn1f09LS5JXQg4OD5TYdO3bM5jFXrlyR75Pn7e0t3n77bbFlyxaxf/9+sXjxYtGsWTMBQERHR8vn2759u8V5CnuvxpiYGPn8LVq0ELGxseLAgQNi48aN4sUXXxQARFRUlM2feVpamihfvrz8vRw+fLj47bffREJCgoiNjRWdOnWyeB3Wfm/OnDlj9nu5c+dOcfr0aXHmzBlx5swZs9XO83vNX3/9tXyuChUqiM8//1z89ddfYvfu3eKDDz4Q/v7+AoCQJEnExcVZHO/M73h+3197BgwYINRqtXjmmWfEF198IbZs2SIOHjwodu7cKb7++mtRr149uR2ff/65xfFLly6V9/v4+Ij/+7//Exs3bhSHDh0S8fHxYu7cueLpp58WNWvWtDh2/fr18v+9gIAA8eGHH4rdu3eLvXv3ilmzZsk/UwBizpw5Vttv3D9lypR8X+uvv/4q1+ft7S1ee+01sWbNGnHgwAGxd+9esWLFCjFhwgRRs2ZNAUCsW7fO2W8nP1cKwNHPbwavEobBy7P8+uuvonr16mYBzNajQYMGYtOmTWbHOxu8CnOTbEdvoPyvf/1LJCYmmh1r/EC099BoNA69Bke8/PLLZufOG1it+e677+zeyPnFF180uyF3UQSvlJQUs1CU99GkSRNx4MABuz/z3377TXh7e9s8R/v27cWxY8fy/b3p06ePzXM4e5PsadOmuewm2fYUNng58vv92muvCZ1OZ/UcixcvFj4+PnaPt/V9Wrx4sctuku2ItWvXyn/w2HsoFAqxbds2h85pip8rzmPw8lAMXp4nOztb/Pjjj6Jfv36iTp06IiQkRKhUKhEaGiqaNm0qXn/9dbFt2zar96dzNngZrV27Vjz33HOicuXKwsvLS4SEhIgWLVqI6dOn27yXYlZWltiwYYMYO3asaN26tahRo4bw9fUVXl5eIiIiQnTv3l38+OOPVj+Uzp8/L2bPni2ef/550ahRIxEeHi5UKpUIDAwUTZo0EePHjxfnzp1zuP352bBhg9mHx3//+1+Hjtu9e7fo2bOnCA8PF2q1WlSqVEk89dRTYtmyZUIIIbZv316kwUsIIXJzc8WXX34pmjdvLvz9/UVAQIBo3LixmD59usjMzHToZ37s2DHRr18/UblyZaFWq0V4eLho166dmDdvntDpdA6dIycnR/z3v/8VTzzxhAgKCjILTs4GLyGE+Pvvv8WwYcPEo48+Knx8fISfn5+oV6+eGD16tNn58nJX8Lp375744YcfxODBg0VUVJSoUqWK8PLyEj4+PiIyMlIMGDBAxMfH53ue69evi3fffVc0a9ZMBAcHC6VSKUJCQkTLli3FpEmTxIkTJ2wee+HCBTF69GhRr1494efnJ3x8fMSjjz4qhg0bJo4cOWK3XmeDlxCGz4tPP/1UdOzYUVSoUEGo1Wrh4+MjatSoIbp16yZmzZpl9f6ajuDnivMc/fyWhHDDIjDksJSUFAQFBSE5OdnhZQqMk2ft3T6CiIjIUfxccZ6jn9+cXE9ERETkJgxeRERERG7C4EVERETkJgxeRERERG7C4EVERETkJgxeRERERG7C4EVERETkJgxeRERERG7C4EVERETkJgxepYAkSdDr9cXdDCIiKiX0ej0kSSruZpRKDF6lgEqlglarLe5mEBFRKaHVaqFSqYq7GaUSg1cpoFAo2ONFREQuo9froVAwIhQFfldLCYVCwV4vIiIqNK1Wy9BVhPidLSX8/f2RlpbG8EVERAWm1WqRlpYGf3//4m5KqcUB3FJCkiQEBQUhLS1N7iJWqVT8q4WIiOzS6/XQarXyZ0dQUBAn1hchBq9SRJIkBAQEAHj4H0kIUcytIiKikkypVEKj0fAPdTdh8CqlFAoFvLy8irsZREREZILxloiIiMhNGLyIiIiI3ITBi4iIiMhNSkXwunTpEsaNG4e6devCz88PoaGhaN68OWbOnImMjAyX1bNx40b06tULERER0Gg0iIiIQK9evbBx40aX1UFERESllyQ8/LK3devWoV+/fkhJSbG6PzIyEnFxcahVq1aB69Dr9Xj11VexcOFCm2WGDh2Kb7/9ttBXhaSkpCAoKAjJyckIDAws1LmIiIjIPRz9/PboHq9Dhw7hxRdfREpKCvz9/TFt2jT8+eef2Lp1K4YNGwYAOH36NJ599lmkpqYWuJ53331XDl1NmjTBzz//jH379uHnn39GkyZNAAALFizAe++9V/gXRURERKWWR/d4tW3bFvHx8VCpVPjjjz/QqlUrs/0zZ87EhAkTAABTpkzB1KlTna7j9OnTaNCgAbRaLaKiovDHH3/Ax8dH3p+RkYF27dohISEBKpUKJ06cKFTvGnu8iIiIPE+p7/Hat28f4uPjAQBDhgyxCF0AMG7cONSrVw8AMHv2bOTm5jpdzxdffCHfhufLL780C10A4Ovriy+//BKA4VYLn3/+udN1EBERUdngscFr9erV8vagQYOsllEoFOjfvz8AICkpCdu3b3eqDiEE1qxZAwCoW7cuWrZsabVcy5YtUadOHQDAmjVruFo8ERERWeWxwWvXrl0AAD8/PzRr1sxmuXbt2snbu3fvdqqOCxcu4Pr16xbnsVfPtWvXcPHiRafqISIiorLBY4PXiRMnAAC1atWCSmX7zkd169a1OMZRx48ft3oeV9dDREREZYNH3qsxKysLiYmJAICIiAi7ZUNCQuDn54f09HRcuXLFqXquXr0qb+dXT9WqVeVtZ+rJzs5Gdna2/HVycjIA2Fweg4iIiEoe4+d2ftONPDJ4mS4N4e/vn295Y/BKS0srsnr8/PzkbWfqmT59Oj744AOL502DHBEREXmG1NRUBAUF2dzvkcErKytL3vby8sq3vEajAQBkZmYWWT3GOpytZ+LEiXjzzTflr/V6Pe7du4ewsDBIkuREa6kkSElJQdWqVXHlyhUuB0KlAn+nqTQpyt9nIQRSU1NRuXJlu+U8Mnh5e3vL2zk5OfmWNw7l5V0KwpX1mA4XOlOPRqMxC20AEBwc7PDxVDIFBgbyQ4pKFf5OU2lSVL/P9nq6jDxycn1AQIC87ciwXnp6OgDHhiULWo+xjoLUQ0RERGWDRwYvb29vhIWFATCfAG/N/fv35VDk7Lwp0wn1+dVjOqGe87OIiIjIGo8MXgBQv359AMDZs2flleWtOXnypLxtXMXe2TrynsfV9VDpodFoMGXKFIvhYyJPxd9pKk1Kwu+zxwav1q1bAzAM8R04cMBmuZ07d8rb0dHRTtVRo0YNeZKc6Xms+eOPPwAAVapUQfXq1Z2qh0oPjUaDqVOn8kOKSg3+TlNpUhJ+nz02ePXs2VPeXrRokdUyer0eS5cuBWCYsN6hQwen6pAkCT169ABg6NHau3ev1XJ79+6Ve7x69OjBqxGJiIjIKo8NXk888QTatGkDAFi4cCH27NljUeazzz6TV5EfPXo01Gq12f4dO3ZAkiRIkoSBAwdarWfMmDFQKpUAgDfeeMNiqYjMzEy88cYbAACVSoUxY8YU5mURERFRKeaxwQsAZs+eDR8fH2i1WnTt2hXTp0/H3r17sX37dgwfPhwTJkwAAERGRmLcuHEFqiMyMhJvvfUWACAhIQHR0dFYtmwZEhISsGzZMkRHRyMhIQEA8NZbb6F27dqueXFERERU6kgiv7XtS7h169ahX79+Nm+xExkZibi4ONSqVcti344dO+ThxwEDBmDx4sVWz6HX6zFs2DB89913NtsxZMgQzJs3DwqFR2dZIiIiKkIenxJiYmJw5MgRjB07FpGRkfD19UVwcDCioqIwY8YMHDp0yGrocoZCocDChQsRFxeHHj16oHLlyvDy8kLlypXRo0cPbNiwAQsWLGDoIiIiIrs8vseLiIiIyFOwi4aIiIjITRi8iAooISEBH374Ibp27YqIiAhoNBr4+/sjMjISgwYNwq5du4q7iUQu8fbbb8tXgEuShB07dhR3k4icdvnyZUyZMgVRUVEIDw+Ht7c3qlatijZt2mDy5Mk4duyYW9rBoUaiAmjbti3i4+PzLde/f3/Mnz8fXl5ebmgVkesdPnwYzZs3N7tDyPbt29G+ffviaxSRk7788ktMnDjR7L7KeY0ePRpffPFFkbdFVeQ1EJVC169fBwBUrlwZL7zwAtq0aYNHHnkEOp0Oe/bswWeffYZr165h6dKlyM3NxU8//VTMLSZynl6vx6uvvgqtVovy5cvj9u3bxd0kIqf95z//wfvvvw/AsNLBsGHD0Lx5cwQFBeHu3bs4dOgQVq1a5bYL5NjjRVQA3bp1Q//+/fH888/LC+yaSkxMRHR0NE6fPg3AcMuptm3buruZRIXyxRdfYOzYsahbty569eqF6dOnA2CPF3mOrVu3onPnzgAMIxALFiywWEzdKCcnxy2jE5zjRVQA69evR58+fayGLgAoV64cPvvsM/nrFStWuKtpRC5x+fJluZfgm2++4XA5eRy9Xo8RI0YAAB5//HEsXLjQZugC4LbfcQYvoiJiem/Qc+fOFWNLiJw3cuRIpKWlYcCAAWjXrl1xN4fIab///jvOnDkDwHCBiEpVMmZXMXgRFZHs7Gx521bPGFFJtHz5cqxfvx6hoaH49NNPi7s5RAXyyy+/AAAkSUK3bt3k5+/du4czZ87g3r17xdIuBi+iIrJz5055u169esXYEiLHJSUlYfTo0QCAGTNmoFy5csXcIqKC2bt3LwCgevXqCAgIwE8//YRGjRohLCwMkZGRCAsLQ506dfDpp5+a/aFc1Bi8iIqAXq/HJ598In/dp0+fYmwNkeMmTJiAmzdvIjo6GkOGDCnu5hAViF6vx8mTJwEY5tyOHj0aL7/8ssVaXadPn8Zbb72Fjh07IikpyS1tY/AiKgKff/459u3bBwB47rnn0KxZs2JuEVH+4uPjsWDBAqhUKnzzzTeQJKm4m0RUIMnJydDr9QCAo0eP4n//+x8qVaqEH374Affu3UNGRgZ27tyJli1bAgD+/PNPDB482C1tY/AicrGdO3finXfeAQCUL18ec+fOLeYWEeUvJycHr776KoQQGDt2LBo2bFjcTSIqMNOFUrOysuDr64vt27fj5ZdfRkhICHx8fNC2bVts27YNjz/+OABg1apV+Ouvv4q8bQxeRC70zz//oFevXtBqtfD29sYvv/yC8uXLF3eziPL18ccf4+TJk3jkkUcwZcqU4m4OUaF4e3ubfT106FDUqVPHopyPjw+mTZsmf71s2bIibxuDF5GLXLhwAV27dsX9+/ehVCoRGxvLRVPJI5w8eVJeHPXLL7+En59fMbeIqHACAgLMvu7atavNsp06dZKXmti/f3+RtgvgLYOIXOL69evo3Lkzrl+/DkmS8N1336FHjx7F3Swih3z++efIyclBzZo1kZGRgdjYWIsyppOSt23bhps3bwIAYmJiGNSoxNFoNAgPD8edO3cAAFWrVrVZ1tvbG+XKlcPNmzfl8kWJwYuokBITE9GlSxecP38egKHHoH///sXcKiLHGS+lP3/+PF566aV8y3/00Ufy9oULFxi8qERq0KABduzYAQDQ6XR2yxr3u2ORVQ41EhVCcnIynnzySRw/fhwA8Mknn2DkyJHF3CoiIjKd6mH8w9ialJQUJCYmAgCqVKlS5O1i8CIqoIyMDDz77LM4ePAgAODdd9/F22+/XcytInLe4sWLIYSw+zCdcL99+3b5+erVqxdfw4nseP755+XtVatW2Sy3atUqCCEAAG3atCnydjF4ERVATk4OevXqhd27dwMARo8ejf/85z/F3CoiIjJ67LHH8PTTTwMAfv75Z2zdutWizM2bN/Hee+8BMNwke9CgQUXeLs7xIiqAl156Cb///jsAoGPHjhgyZIjFisimvLy8EBkZ6a7mERERgC+++AJ79uxBUlISunXrhjFjxuCZZ56Bj48P9u3bh+nTp+Pq1asADHMX3THUKAlj/xoROczZFb2rVauGixcvFk1jiNxg6tSp+OCDDwAYhhrbt29fvA0ictCuXbvQu3dv3Lp1y+p+SZLw7rvvml00UpTY40VERESlVuvWrfHPP//gyy+/xOrVq3HhwgXk5OSgUqVKaN++Pd544w00adLEbe1hjxcRERGRm3ByPREREZGbMHgRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGbMHgREZVCAwcOhCRJqF69enE3hYhMqIq7AURErrRjxw506NDB4nmlUonAwEAEBQWhatWqaNasGVq3bo2YmBh4eXkVQ0uJqCxijxcRlQk6nQ7379/HxYsXER8fjy+++AK9e/dGREQE/vOf/0Cr1RZ3E/O1ePFiSJIESZJw8eLF4m4OERUAe7yIqNQaMWIEXn/9dfnrtLQ03L9/H0eOHMHWrVuxZcsW3LlzB++//z7WrVuH9evXIzw8vBhbTESlHYMXEZVa5cuXR8OGDS2ef/rpp/H222/j+PHj6NevHw4dOoR9+/ahV69e2LZtG4ceiajIcKiRiMqs+vXrY/fu3WjSpAkAYPfu3fj666+LuVVEVJoxeBFRmebj44Pvv/8ekiQBAD799FPk5uZaLXvz5k28++67iIqKQmhoKDQaDapWrYo+ffpgy5YtNuu4ePGiPDdr8eLFAIBffvkFnTt3Rvny5eHj44O6deti4sSJSEpKsjh+x44dkCQJgwYNkp+rUaOGfE7jY8eOHTbbkJSUhMmTJ6NBgwbw8/NDcHAw2rZtix9//DH/bxIRuQyDFxGVeQ0aNECXLl0AANevX8f+/fstyvz444+oVasWPv74Yxw4cAD3799HTk4Orl69il9++QVdunTB0KFDHZqkP2TIEPTp0wdbt27FnTt3kJWVhVOnTuGTTz5BgwYNcPLkSZe+vlOnTqFJkyb46KOPcPz4cWRkZCA5ORnx8fHo168fRo0a5dL6iMg2Bi8iIgCdO3eWt+Pj4832LV++HK+88grS09NRs2ZNzJo1C7/99hsOHDiAX3/9Fc888wwAYOHChZgwYYLdeubMmYPvvvsOTzzxBH7++WckJCRgw4YN6NOnDwBD8HvyySeRmpoqH9O8eXMcPXoU//nPf+TnNm3ahKNHj5o9mjdvblFfRkYGYmJicPfuXbz33nvYsWMHEhISMH/+fERERAAAvv76a2zatMnJ7xgRFYggIipFtm/fLgAIAGLKlCkOH7dlyxb5uMGDB8vP37lzRwQFBcnP5+bmWj1+0qRJAoBQKBTi5MmTZvsuXLggnxuAeOaZZ6ye58MPP5TLvPXWWxb7Fy1aJO+/cOGC3dczYMAAuWxQUJA4duyYRZkzZ84Ib29vAUB0797d7vmIyDXY40VEBCAsLEzevn//vrw9d+5cJCcno0qVKpgzZw5UKusXg3/wwQeoUqUK9Ho9li5darMejUaD+fPnWz3Pu+++K1+FuXDhQuTk5BT05Zj56KOP0KBBA4vna9WqhZ49ewIAdu3a5ZK6iMg+Bi8iIgD+/v7ytukw39q1awEA3bp1g0ajsXm8SqVCq1atAAB79uyxWa5r166oXLmy1X0KhQIDBgwAANy7dw8HDx50/AXYIEkS+vbta3N/s2bN5PqsTewnItdi8CIignnYCgwMBGBY7f7w4cMAgG+//dbiKsK8jxUrVgAwXP1oi7V5WKaeeOIJefvo0aMFfTmycuXKmfXm5RUaGipvm34PiKhoMHgREQFITEyUt41h5N69ewW6lVBGRobNfeXLl7d7bIUKFeTte/fuOV13Xr6+vnb3KxQPPwZ0Ol2h6yMi+7hyPRERgEOHDsnbderUAWAeRIYOHYrRo0c7dC57K98b1wsjorKJwYuICMDmzZvl7datWwMwH4YTQli9/ZCzbt265fB+0/qJqHTgUCMRlXnHjh3D1q1bAQBVq1ZFVFQUAEPPlfFqwN27d7ukLmuLs9ranzfosbeMyPMxeBFRmZaZmYn+/ftDCAEAGD9+vNlSD927dwcAnDx50iWLjP7++++4ceOG1X16vR5LliwBAISEhKBp06Zm+729veXt7OzsQreFiNyPwYuIyqzjx4+jdevW8vyudu3aYcSIEWZlRo8eLS81MWjQIPzzzz92zxkXF4cjR47Y3J+dnY3hw4dbncj+ySefyFcyDh482GL5ikqVKsnb586ds9sOIiqZOMeLiEqt27dv49ixY/LX6enpuH//Po4cOYKtW7di8+bNck9Xy5YtsWLFCqjVarNzVKhQAUuWLEHv3r1x48YNREVFYeDAgXj66acRERGB3NxcXL16Ffv27cOKFStw/vx5rFu3Do899pjVNkVFRWHdunWIjo7G2LFjUbt2bdy+fRtLlixBbGwsACAiIgLvv/++xbFNmjSBt7c3srKy8P7770OtVqNatWrylYlVqlSBj4+PS753RFREinnlfCIilzK9ZZAjj/DwcDFt2jSbtwIyWrt2rQgNDc33fAqFQmzbts3sWNNbBi1atEgMHDjQ5vGVKlUS//zzj812TJgwweax27dvl8sZbxlUrVo1u6/LmdsQEVHhsceLiMoEhUKBgIAABAUFoVq1amjWrBnatGmDbt262V3+wSgmJgYXLlzA/PnzsWHDBvzzzz+4d+8eVCoVKlasiAYNGqBjx47o3bs3qlatavdcixYtQteuXTFv3jwcPXoUaWlpqFatGnr27Il33nkHISEhNo/95JNPULt2bSxduhT//PMPkpOTuf4WkQeRhHjQz05EREXi4sWLqFGjBgBD6Bo4cGDxNoiIig0n1xMRERG5CYMXERERkZsweBERERG5CYMXERERkZsweBERERG5Ca9qJCIiInIT9ngRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGbMHgRERERuQmDFxEREZGb/D9fRwgwfrb7xgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_range = list(range(1, 7))\n",
    "\n",
    "plt.style.use(['default', 'seaborn-paper'])\n",
    "matplotlib.rcParams['axes.labelsize'] = 20\n",
    "matplotlib.rcParams['xtick.labelsize'] = 20\n",
    "matplotlib.rcParams['ytick.labelsize'] = 20\n",
    "matplotlib.rcParams['legend.fontsize'] = 20\n",
    "\n",
    "# plt.title(\"Validation Curve for InceptionTime\")\n",
    "plt.xlabel(r\"Depth\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "lw = 2\n",
    "plt.plot(param_range, t_mean, label=\"Training score\",\n",
    "             color=\"darkorange\", lw=lw)\n",
    "plt.fill_between(param_range, t_mean - t_std,\n",
    "                 t_mean + t_std, alpha=0.2,\n",
    "                 color=\"darkorange\", lw=lw)\n",
    "plt.plot(param_range, v_mean, label=\"Cross-validation score\",\n",
    "             color=\"navy\", lw=lw)\n",
    "plt.fill_between(param_range, v_mean - v_std,\n",
    "                 v_mean + v_std, alpha=0.2,\n",
    "                 color=\"navy\", lw=lw)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"figures/cross-val-depth-inc.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focused-mills",
   "metadata": {},
   "source": [
    "## Confusion Matrix\n",
    "\n",
    "The confusion matrix is calculated on the last fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "narrow-occasion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.286239</td>\n",
       "      <td>0.861236</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>0.424946</td>\n",
       "      <td>0.882760</td>\n",
       "      <td>0.877482</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.224144</td>\n",
       "      <td>0.920507</td>\n",
       "      <td>0.913013</td>\n",
       "      <td>0.456487</td>\n",
       "      <td>0.877688</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.186763</td>\n",
       "      <td>0.932681</td>\n",
       "      <td>0.928232</td>\n",
       "      <td>0.537745</td>\n",
       "      <td>0.869092</td>\n",
       "      <td>0.852679</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.171980</td>\n",
       "      <td>0.941080</td>\n",
       "      <td>0.938127</td>\n",
       "      <td>0.439834</td>\n",
       "      <td>0.900847</td>\n",
       "      <td>0.900717</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.150214</td>\n",
       "      <td>0.947479</td>\n",
       "      <td>0.945346</td>\n",
       "      <td>0.464644</td>\n",
       "      <td>0.899743</td>\n",
       "      <td>0.891045</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.138140</td>\n",
       "      <td>0.952517</td>\n",
       "      <td>0.950857</td>\n",
       "      <td>0.441435</td>\n",
       "      <td>0.901511</td>\n",
       "      <td>0.896504</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.132577</td>\n",
       "      <td>0.956310</td>\n",
       "      <td>0.954942</td>\n",
       "      <td>0.438284</td>\n",
       "      <td>0.902805</td>\n",
       "      <td>0.897963</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.126768</td>\n",
       "      <td>0.958464</td>\n",
       "      <td>0.957223</td>\n",
       "      <td>0.439663</td>\n",
       "      <td>0.903533</td>\n",
       "      <td>0.899222</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls = torch.load(f\"dls_1024_4.pkl\")\n",
    "\n",
    "model = InceptionTime(dls.vars, dls.c, depth=1, ks=20)\n",
    "f1 = F1Score(average=\"weighted\")\n",
    "learn = Learner(dls, model, metrics=[accuracy, f1])\n",
    "learn.recorder.train_metrics = True\n",
    "\n",
    "learn.fit_one_cycle(8, lr_max=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "arctic-subcommittee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(['default', 'seaborn-paper'])\n",
    "matplotlib.rcParams['axes.labelsize'] = 12\n",
    "matplotlib.rcParams['xtick.labelsize'] = 12\n",
    "matplotlib.rcParams['ytick.labelsize'] = 12\n",
    "matplotlib.rcParams['legend.fontsize'] = 12\n",
    "\n",
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "interp.plot_confusion_matrix(figsize=(10, 10), cmap='Blues')\n",
    "plt.xticks(rotation=0) \n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title(\"\")\n",
    "plt.savefig(\"figures/conf-inc-abs.svg\")\n",
    "interp.plot_confusion_matrix(normalize=True, figsize=(10, 10), cmap='Blues')\n",
    "plt.xticks(rotation=0) \n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title(\"\")\n",
    "plt.savefig(\"figures/conf-inc-rel.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mobile-component",
   "metadata": {},
   "source": [
    "## Custom Prediction Activation Function\n",
    "\n",
    "We want to encode domain logic into the activation function used for prediction.\n",
    "E.g. when head_shake is confused with standing, we want to give precedence to head_shake.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "277ebbdb-60a6-49cb-b756-e9fcea437dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_f1_score</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>valid_f1_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.285060</td>\n",
       "      <td>0.857000</td>\n",
       "      <td>0.839152</td>\n",
       "      <td>0.408622</td>\n",
       "      <td>0.884735</td>\n",
       "      <td>0.868768</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.218602</td>\n",
       "      <td>0.921423</td>\n",
       "      <td>0.913896</td>\n",
       "      <td>0.373492</td>\n",
       "      <td>0.898773</td>\n",
       "      <td>0.885654</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.189831</td>\n",
       "      <td>0.933926</td>\n",
       "      <td>0.929512</td>\n",
       "      <td>0.462468</td>\n",
       "      <td>0.885325</td>\n",
       "      <td>0.872089</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.166294</td>\n",
       "      <td>0.942402</td>\n",
       "      <td>0.939521</td>\n",
       "      <td>0.498003</td>\n",
       "      <td>0.874892</td>\n",
       "      <td>0.864066</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.145779</td>\n",
       "      <td>0.949031</td>\n",
       "      <td>0.946942</td>\n",
       "      <td>0.444677</td>\n",
       "      <td>0.893984</td>\n",
       "      <td>0.890044</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.135388</td>\n",
       "      <td>0.953943</td>\n",
       "      <td>0.952301</td>\n",
       "      <td>0.422333</td>\n",
       "      <td>0.901557</td>\n",
       "      <td>0.898739</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.125797</td>\n",
       "      <td>0.957278</td>\n",
       "      <td>0.955893</td>\n",
       "      <td>0.430774</td>\n",
       "      <td>0.901505</td>\n",
       "      <td>0.897162</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.127138</td>\n",
       "      <td>0.959374</td>\n",
       "      <td>0.958097</td>\n",
       "      <td>0.431746</td>\n",
       "      <td>0.901615</td>\n",
       "      <td>0.897344</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from tsai.all import *\n",
    "from sklearn.metrics import roc_auc_score, classification_report\n",
    "\n",
    "dls = torch.load(f\"dls_1024_4.pkl\")\n",
    "\n",
    "model = InceptionTime(dls.vars, dls.c, depth=1, ks=20)\n",
    "f1 = F1Score(average=\"weighted\")\n",
    "learn = Learner(dls, model, metrics=[accuracy, f1])\n",
    "learn.recorder.train_metrics = True\n",
    "\n",
    "learn.fit_one_cycle(8, lr_max=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sticky-drama",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probas, targets, preds = learn.get_preds(dl=learn.dls.valid, with_decoded=True, act=None, save_preds=None, save_targs=None, reorder=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fcddd31-748a-4bd2-babe-9165e93635a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98     66299\n",
      "           1       0.84      0.64      0.73     11791\n",
      "           2       0.46      0.35      0.40      1589\n",
      "           3       0.67      0.66      0.66      8787\n",
      "           4       0.90      0.96      0.93     76499\n",
      "           5       0.60      0.54      0.57      1755\n",
      "           6       0.55      0.43      0.48      6386\n",
      "\n",
      "    accuracy                           0.90    173106\n",
      "   macro avg       0.71      0.65      0.68    173106\n",
      "weighted avg       0.90      0.90      0.90    173106\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(targets, (preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "motivated-abortion",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_preds = []\n",
    "for i, x in enumerate(probas):\n",
    "    argmax = np.argmax(x)\n",
    "    ind = np.argpartition(x, -2)[-2:]\n",
    "    \n",
    "    stand_proba = x[4]\n",
    "    head_proba = x[2]\n",
    "    preening_proba = x[3]\n",
    "    if (all(x in ind for x in [2, 4]) and head_proba > 0.):\n",
    "        new_preds.append(2)\n",
    "    elif (all(x in ind for x in [3, 4]) and preening_proba > 0.1): \n",
    "        new_preds.append(3)\n",
    "    else:\n",
    "        new_preds.append(argmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "driving-purchase",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98     65770\n",
      "           1       0.64      0.84      0.73      9093\n",
      "           2       0.63      0.08      0.14     13036\n",
      "           3       0.78      0.57      0.66     12113\n",
      "           4       0.79      0.91      0.85     66527\n",
      "           5       0.54      0.60      0.57      1575\n",
      "           6       0.43      0.55      0.48      4992\n",
      "\n",
      "    accuracy                           0.84    173106\n",
      "   macro avg       0.69      0.65      0.63    173106\n",
      "weighted avg       0.83      0.84      0.81    173106\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(new_preds, targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "honest-dylan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98     65770\n",
      "           1       0.64      0.84      0.73      9093\n",
      "           2       0.35      0.46      0.40      1194\n",
      "           3       0.66      0.67      0.66      8573\n",
      "           4       0.96      0.90      0.93     81909\n",
      "           5       0.54      0.60      0.57      1575\n",
      "           6       0.43      0.55      0.48      4992\n",
      "\n",
      "    accuracy                           0.90    173106\n",
      "   macro avg       0.65      0.71      0.68    173106\n",
      "weighted avg       0.91      0.90      0.91    173106\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "controversial-union",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10884\n",
      "tensor(0)\n",
      "tensor(0.2511)\n",
      "10885\n",
      "tensor(4)\n",
      "tensor(0.2635)\n",
      "43352\n",
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      "tensor(0.2614)\n",
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      "tensor(0.2910)\n",
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      "tensor(0.2534)\n",
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      "tensor(0.2866)\n",
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      "tensor(0.2321)\n",
      "62500\n",
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      "tensor(0.2495)\n",
      "62501\n",
      "tensor(6)\n",
      "tensor(0.2632)\n",
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      "tensor(0.2801)\n",
      "83627\n",
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      "tensor(0.2953)\n",
      "85180\n",
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      "tensor(0.2871)\n",
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      "tensor(0.2601)\n",
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      "tensor(0.2632)\n",
      "86906\n",
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      "tensor(0.2432)\n",
      "86907\n",
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      "tensor(0.2471)\n",
      "88341\n",
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      "88342\n",
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      "tensor(0.2617)\n",
      "88716\n",
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      "tensor(0.2979)\n",
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      "tensor(0.2496)\n",
      "88718\n",
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      "tensor(0.2308)\n",
      "88719\n",
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      "tensor(0.2513)\n",
      "88720\n",
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      "tensor(0.2844)\n",
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      "tensor(0.2994)\n",
      "123524\n",
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      "123538\n",
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      "tensor(0.2882)\n",
      "125065\n",
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      "tensor(0.2368)\n",
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      "tensor(0.2999)\n",
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      "tensor(0.2971)\n",
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      "tensor(0.2828)\n",
      "165525\n",
      "tensor(1)\n",
      "tensor(0.2931)\n",
      "166188\n",
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      "tensor(0.2913)\n",
      "166280\n",
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      "tensor(0.2801)\n",
      "166281\n",
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      "tensor(0.2645)\n",
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      "tensor(0.2837)\n",
      "166959\n",
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      "tensor(0.2633)\n",
      "172527\n",
      "tensor(1)\n",
      "tensor(0.2935)\n",
      "172528\n",
      "tensor(3)\n",
      "tensor(0.2539)\n",
      "172593\n",
      "tensor(0)\n",
      "tensor(0.2581)\n",
      "172976\n",
      "tensor(4)\n",
      "tensor(0.2706)\n",
      "172982\n",
      "tensor(2)\n",
      "tensor(0.2874)\n",
      "172984\n",
      "tensor(4)\n",
      "tensor(0.2550)\n",
      "172985\n",
      "tensor(4)\n",
      "tensor(0.2897)\n",
      "172991\n",
      "tensor(4)\n",
      "tensor(0.2680)\n",
      "172992\n",
      "tensor(6)\n",
      "tensor(0.2260)\n",
      "172993\n",
      "tensor(3)\n",
      "tensor(0.2695)\n",
      "172999\n",
      "tensor(3)\n",
      "tensor(0.2964)\n",
      "173000\n",
      "tensor(3)\n",
      "tensor(0.2883)\n",
      "173001\n",
      "tensor(3)\n",
      "tensor(0.2948)\n",
      "173081\n",
      "tensor(3)\n",
      "tensor(0.2924)\n",
      "173084\n",
      "tensor(3)\n",
      "tensor(0.2896)\n",
      "173085\n",
      "tensor(3)\n",
      "tensor(0.2761)\n",
      "173086\n",
      "tensor(3)\n",
      "tensor(0.2833)\n",
      "173087\n",
      "tensor(3)\n",
      "tensor(0.2871)\n",
      "173088\n",
      "tensor(3)\n",
      "tensor(0.2762)\n",
      "173089\n",
      "tensor(5)\n",
      "tensor(0.2973)\n",
      "173104\n",
      "tensor(3)\n",
      "tensor(0.2960)\n"
     ]
    }
   ],
   "source": [
    "for i, x in enumerate(probas):\n",
    "    max_val = torch.max(x)\n",
    "    if max_val < 0.3:\n",
    "        print(i)\n",
    "        print(torch.argmax(x))\n",
    "        print(torch.max(x))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}