sktime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## weekdays_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/carolina/Desktop/Mestrado', '/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python39.zip', '/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9', '/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/lib-dynload', '', '/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages', './MultiRocket']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Allows importing modules defined inside the project directory:\n",
    "path = os.path.abspath(os.path.curdir)\n",
    "while len(path) > 1 and not path.endswith('Mestrado'):\n",
    "    path = os.path.abspath(os.path.join(path, '..'))\n",
    "if path not in sys.path:\n",
    "    sys.path.append(path)\n",
    "sys.path.append('./MultiRocket')\n",
    "print(sys.path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('weekdays_datasets/df_timeseries.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colunas CSV em weekdays_dataset.zip\n",
    "\n",
    "h00-h23: Faixa de popularidade (0-1) entre meia-noite e onze da noite\n",
    "\n",
    "country: 0 - Brasil, 1 - Estados Unidos\n",
    "\n",
    "city: 0 - Curitiba, 1 - Rio de Janeiro, 2 - São Paulo, 0 - Chicago, 1 - New York, 2 - San Francisco\n",
    "\n",
    "category: 0 - bakeries, 1 - bars, 2 - coffee, 3 - dance clubs, 4 - restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>h00</th>\n",
       "      <th>h01</th>\n",
       "      <th>h02</th>\n",
       "      <th>h03</th>\n",
       "      <th>h04</th>\n",
       "      <th>h05</th>\n",
       "      <th>h06</th>\n",
       "      <th>h07</th>\n",
       "      <th>h08</th>\n",
       "      <th>h09</th>\n",
       "      <th>h10</th>\n",
       "      <th>h11</th>\n",
       "      <th>h12</th>\n",
       "      <th>h13</th>\n",
       "      <th>h14</th>\n",
       "      <th>h15</th>\n",
       "      <th>h16</th>\n",
       "      <th>h17</th>\n",
       "      <th>h18</th>\n",
       "      <th>h19</th>\n",
       "      <th>h20</th>\n",
       "      <th>h21</th>\n",
       "      <th>h22</th>\n",
       "      <th>h23</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009199</td>\n",
       "      <td>0.070899</td>\n",
       "      <td>0.343362</td>\n",
       "      <td>0.637979</td>\n",
       "      <td>0.494100</td>\n",
       "      <td>0.357201</td>\n",
       "      <td>0.156741</td>\n",
       "      <td>0.132883</td>\n",
       "      <td>0.347754</td>\n",
       "      <td>0.522437</td>\n",
       "      <td>0.940441</td>\n",
       "      <td>0.823425</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.208770</td>\n",
       "      <td>0.809270</td>\n",
       "      <td>0.442064</td>\n",
       "      <td>0.396663</td>\n",
       "      <td>0.396663</td>\n",
       "      <td>0.482997</td>\n",
       "      <td>0.320058</td>\n",
       "      <td>0.212006</td>\n",
       "      <td>0.212006</td>\n",
       "      <td>0.212006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110649</td>\n",
       "      <td>0.386266</td>\n",
       "      <td>0.588860</td>\n",
       "      <td>0.854669</td>\n",
       "      <td>0.991342</td>\n",
       "      <td>0.805263</td>\n",
       "      <td>0.483633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080787</td>\n",
       "      <td>0.303251</td>\n",
       "      <td>0.519247</td>\n",
       "      <td>0.688605</td>\n",
       "      <td>0.503356</td>\n",
       "      <td>0.488349</td>\n",
       "      <td>0.488349</td>\n",
       "      <td>0.624601</td>\n",
       "      <td>0.432878</td>\n",
       "      <td>0.551867</td>\n",
       "      <td>0.728864</td>\n",
       "      <td>0.986970</td>\n",
       "      <td>0.917125</td>\n",
       "      <td>0.562874</td>\n",
       "      <td>0.378466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060529</td>\n",
       "      <td>0.172893</td>\n",
       "      <td>0.211369</td>\n",
       "      <td>0.339528</td>\n",
       "      <td>0.415355</td>\n",
       "      <td>0.325870</td>\n",
       "      <td>0.246288</td>\n",
       "      <td>0.263654</td>\n",
       "      <td>0.372387</td>\n",
       "      <td>0.625273</td>\n",
       "      <td>0.878997</td>\n",
       "      <td>0.994609</td>\n",
       "      <td>0.901742</td>\n",
       "      <td>0.667729</td>\n",
       "      <td>0.387283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12699</th>\n",
       "      <td>12699</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255691</td>\n",
       "      <td>0.403481</td>\n",
       "      <td>0.515107</td>\n",
       "      <td>0.428191</td>\n",
       "      <td>0.345968</td>\n",
       "      <td>0.228686</td>\n",
       "      <td>0.430487</td>\n",
       "      <td>0.759348</td>\n",
       "      <td>0.991525</td>\n",
       "      <td>0.870099</td>\n",
       "      <td>0.588773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12700</th>\n",
       "      <td>12700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.140073</td>\n",
       "      <td>0.333913</td>\n",
       "      <td>0.402098</td>\n",
       "      <td>0.182383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.192508</td>\n",
       "      <td>0.564814</td>\n",
       "      <td>0.973335</td>\n",
       "      <td>0.966669</td>\n",
       "      <td>0.548366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12701</th>\n",
       "      <td>12701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009804</td>\n",
       "      <td>0.067227</td>\n",
       "      <td>0.671104</td>\n",
       "      <td>0.355351</td>\n",
       "      <td>0.381766</td>\n",
       "      <td>0.381766</td>\n",
       "      <td>0.571624</td>\n",
       "      <td>0.201609</td>\n",
       "      <td>0.144007</td>\n",
       "      <td>0.334674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12702</th>\n",
       "      <td>12702</td>\n",
       "      <td>0.474252</td>\n",
       "      <td>0.256943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003145</td>\n",
       "      <td>0.039702</td>\n",
       "      <td>0.081856</td>\n",
       "      <td>0.174295</td>\n",
       "      <td>0.341396</td>\n",
       "      <td>0.630530</td>\n",
       "      <td>0.810973</td>\n",
       "      <td>0.971333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897192</td>\n",
       "      <td>0.743494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12703</th>\n",
       "      <td>12703</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.194935</td>\n",
       "      <td>0.321710</td>\n",
       "      <td>0.455875</td>\n",
       "      <td>0.313857</td>\n",
       "      <td>0.252949</td>\n",
       "      <td>0.277011</td>\n",
       "      <td>0.341672</td>\n",
       "      <td>0.531967</td>\n",
       "      <td>0.812802</td>\n",
       "      <td>0.982816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12704 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id       h00       h01  h02  ...       h23  country  city  category\n",
       "0          0  0.000000  0.000000  0.0  ...  0.000000      0.0   0.0       0.0\n",
       "1          1  0.000000  0.000000  0.0  ...  0.000000      0.0   0.0       0.0\n",
       "2          2  0.000000  0.000000  0.0  ...  0.000000      0.0   0.0       0.0\n",
       "3          3  0.000000  0.000000  0.0  ...  0.000000      0.0   0.0       0.0\n",
       "4          4  0.000000  0.000000  0.0  ...  0.000000      0.0   0.0       0.0\n",
       "...      ...       ...       ...  ...  ...       ...      ...   ...       ...\n",
       "12699  12699  0.000000  0.000000  0.0  ...  0.000000      1.0   4.0       4.0\n",
       "12700  12700  0.000000  0.000000  0.0  ...  0.000000      1.0   4.0       4.0\n",
       "12701  12701  0.000000  0.000000  0.0  ...  0.000000      1.0   4.0       4.0\n",
       "12702  12702  0.474252  0.256943  0.0  ...  0.743494      1.0   4.0       4.0\n",
       "12703  12703  0.000000  0.000000  0.0  ...  0.000000      1.0   4.0       4.0\n",
       "\n",
       "[12704 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(816, 24) (273, 24)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def extract_x_y(df, country, city,category=None):\n",
    "    if category is None:\n",
    "        filtered_df = df[(df['country'] == country) & (df['city'] == city)]\n",
    "    else:\n",
    "        filtered_df = df[(df['country'] == country) & (df['city'] == city) & (df['category'] == category)]\n",
    "\n",
    "    df_x = filtered_df.drop(columns=['id','country', 'city', 'category'])\n",
    "\n",
    "    return np.array(df_x),np.array(filtered_df['category'])\n",
    "\n",
    "X, y = extract_x_y(df,0,0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "incluir testes pra (1) tudo/ (2) country somente/ (2) cidades\n",
    "verificar percentagem de treinamento e teste\n",
    "manter mesma quantidade de series\n",
    "prompt chatgpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>h00</th>\n",
       "      <th>h01</th>\n",
       "      <th>h02</th>\n",
       "      <th>h03</th>\n",
       "      <th>h04</th>\n",
       "      <th>h05</th>\n",
       "      <th>h06</th>\n",
       "      <th>h07</th>\n",
       "      <th>h08</th>\n",
       "      <th>...</th>\n",
       "      <th>h17</th>\n",
       "      <th>h18</th>\n",
       "      <th>h19</th>\n",
       "      <th>h20</th>\n",
       "      <th>h21</th>\n",
       "      <th>h22</th>\n",
       "      <th>h23</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896498</td>\n",
       "      <td>0.808148</td>\n",
       "      <td>0.462531</td>\n",
       "      <td>0.010060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003126</td>\n",
       "      <td>0.031889</td>\n",
       "      <td>0.234067</td>\n",
       "      <td>...</td>\n",
       "      <td>0.677492</td>\n",
       "      <td>0.475074</td>\n",
       "      <td>0.385600</td>\n",
       "      <td>0.248011</td>\n",
       "      <td>0.017268</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>0.138568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972788</td>\n",
       "      <td>0.965489</td>\n",
       "      <td>0.831592</td>\n",
       "      <td>0.573811</td>\n",
       "      <td>0.275817</td>\n",
       "      <td>0.002511</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.043055</td>\n",
       "      <td>0.531299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775702</td>\n",
       "      <td>0.628683</td>\n",
       "      <td>0.533388</td>\n",
       "      <td>0.353358</td>\n",
       "      <td>0.008911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.015091</td>\n",
       "      <td>0.179335</td>\n",
       "      <td>0.768777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412289</td>\n",
       "      <td>0.603218</td>\n",
       "      <td>0.448638</td>\n",
       "      <td>0.349549</td>\n",
       "      <td>0.154791</td>\n",
       "      <td>0.030829</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>0.497889</td>\n",
       "      <td>0.302093</td>\n",
       "      <td>0.001882</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.005409</td>\n",
       "      <td>0.075049</td>\n",
       "      <td>0.140844</td>\n",
       "      <td>0.265839</td>\n",
       "      <td>0.581515</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>0.804521</td>\n",
       "      <td>0.541695</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076270</td>\n",
       "      <td>0.183042</td>\n",
       "      <td>0.224729</td>\n",
       "      <td>0.324142</td>\n",
       "      <td>0.479679</td>\n",
       "      <td>0.725018</td>\n",
       "      <td>0.896602</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>0.518485</td>\n",
       "      <td>0.394968</td>\n",
       "      <td>0.019825</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556677</td>\n",
       "      <td>0.665041</td>\n",
       "      <td>0.706486</td>\n",
       "      <td>0.850784</td>\n",
       "      <td>0.827417</td>\n",
       "      <td>0.919327</td>\n",
       "      <td>0.747429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>0.024066</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327476</td>\n",
       "      <td>0.501589</td>\n",
       "      <td>0.829388</td>\n",
       "      <td>0.633860</td>\n",
       "      <td>0.500545</td>\n",
       "      <td>0.039253</td>\n",
       "      <td>0.019268</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>0.042649</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.079399</td>\n",
       "      <td>0.402822</td>\n",
       "      <td>0.847623</td>\n",
       "      <td>0.546787</td>\n",
       "      <td>0.089079</td>\n",
       "      <td>0.064854</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id       h00       h01       h02      h03      h04       h05       h06  \\\n",
       "0    0  0.000000  0.000000  0.000000  0.00000  0.00000  0.000000  0.000000   \n",
       "1    1  0.000000  0.000000  0.000000  0.00000  0.00000  0.000000  0.003126   \n",
       "2    2  0.000000  0.000000  0.000000  0.00000  0.00000  0.000000  0.000174   \n",
       "3    3  0.000000  0.000000  0.000000  0.00000  0.00000  0.000000  0.001596   \n",
       "4    4  0.000000  0.000000  0.000000  0.00000  0.00000  0.000467  0.015091   \n",
       "..  ..       ...       ...       ...      ...      ...       ...       ...   \n",
       "66  66  0.497889  0.302093  0.001882  0.00000  0.00000  0.000000  0.000000   \n",
       "67  67  0.804521  0.541695  0.005939  0.00000  0.00000  0.000000  0.000000   \n",
       "68  68  0.518485  0.394968  0.019825  0.00000  0.00000  0.000000  0.000000   \n",
       "69  69  0.024066  0.000008  0.000000  0.00000  0.00002  0.000000  0.000000   \n",
       "70  70  0.042649  0.000410  0.000041  0.00001  0.00000  0.000000  0.000000   \n",
       "\n",
       "         h07       h08  ...       h17       h18       h19       h20       h21  \\\n",
       "0   0.000000  0.000119  ...  0.896498  0.808148  0.462531  0.010060  0.000000   \n",
       "1   0.031889  0.234067  ...  0.677492  0.475074  0.385600  0.248011  0.017268   \n",
       "2   0.009180  0.138568  ...  0.972788  0.965489  0.831592  0.573811  0.275817   \n",
       "3   0.043055  0.531299  ...  0.775702  0.628683  0.533388  0.353358  0.008911   \n",
       "4   0.179335  0.768777  ...  0.412289  0.603218  0.448638  0.349549  0.154791   \n",
       "..       ...       ...  ...       ...       ...       ...       ...       ...   \n",
       "66  0.000000  0.000000  ...  0.000000  0.000041  0.005409  0.075049  0.140844   \n",
       "67  0.000000  0.000000  ...  0.076270  0.183042  0.224729  0.324142  0.479679   \n",
       "68  0.000000  0.000000  ...  0.556677  0.665041  0.706486  0.850784  0.827417   \n",
       "69  0.000000  0.002157  ...  0.327476  0.501589  0.829388  0.633860  0.500545   \n",
       "70  0.000000  0.000055  ...  0.015850  0.079399  0.402822  0.847623  0.546787   \n",
       "\n",
       "         h22       h23  country  city  category  \n",
       "0   0.000000  0.000000      0.0   0.0       0.0  \n",
       "1   0.000000  0.000000      0.0   0.0       0.0  \n",
       "2   0.002511  0.000106      0.0   0.0       0.0  \n",
       "3   0.000000  0.000000      0.0   0.0       0.0  \n",
       "4   0.030829  0.000874      0.0   0.0       0.0  \n",
       "..       ...       ...      ...   ...       ...  \n",
       "66  0.265839  0.581515      1.0   4.0       3.0  \n",
       "67  0.725018  0.896602      1.0   4.0       3.0  \n",
       "68  0.919327  0.747429      1.0   4.0       3.0  \n",
       "69  0.039253  0.019268      1.0   4.0       4.0  \n",
       "70  0.089079  0.064854      1.0   4.0       4.0  \n",
       "\n",
       "[71 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = pd.read_csv('weekdays_datasets/df_signatures.csv')\n",
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dfs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Xs, ys \u001b[38;5;241m=\u001b[39m extract_x_y(\u001b[43mdfs\u001b[49m,\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m      2\u001b[0m Xs\u001b[38;5;241m.\u001b[39mshape\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dfs' is not defined"
     ]
    }
   ],
   "source": [
    "Xs, ys = extract_x_y(dfs,0,0)\n",
    "Xs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HIVECOTEV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.classification.hybrid import HIVECOTEV2\n",
    "from sktime.classification.sklearn import RotationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicting...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results...\n",
      "0.7509157509157509\n",
      "0.7369878085320226\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hc2_classifier = HIVECOTEV2(\n",
    "    stc_params={\n",
    "        \"estimator\": RotationForest(n_estimators=3),\n",
    "        \"n_shapelet_samples\": 100,\n",
    "        \"max_shapelets\": 10,\n",
    "        \"batch_size\": 20,\n",
    "    },\n",
    "    drcif_params={\"n_estimators\": 2, \"n_intervals\": 2, \"att_subsample_size\": 2},\n",
    "    arsenal_params={\"num_kernels\": 50, \"n_estimators\": 3},\n",
    "    tde_params={\n",
    "        \"n_parameter_samples\": 10,\n",
    "        \"max_ensemble_size\": 3,\n",
    "        \"randomly_selected_params\": 5,\n",
    "    },\n",
    ")\n",
    "hc2_classifier.fit(X_train, y_train)\n",
    "print(\"predicting...\")\n",
    "hc2_pred = hc2_classifier.predict(X_test)\n",
    "print(\"results...\")\n",
    "print(accuracy_score(y_test, hc2_pred))\n",
    "print(f1_score(y_test, hc2_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7308325964960712\n",
      "0.7509157509157509\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(precision_score(y_test, hc2_pred, average='weighted'))\n",
    "print(recall_score(y_test, hc2_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n",
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/transformations/panel/catch22.py:354: FutureWarning: In Catch22._transform_single_feature, the argument case_id is deprecated and will be removed in the future.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy_score': 0.7472527472527473,\n",
       " 'f1_score': 0.7347127129963419,\n",
       " 'precision_score': 0.7311485867556571,\n",
       " 'recall_score': 0.7472527472527473}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classifiers.hivecotev2 import hivecotev2\n",
    "\n",
    "hivecotev2(X_train,y_train,X_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "validating signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc2_pred_sig = hc2_classifier.predict(Xs)\n",
    "print(\"validating signatures...\")\n",
    "accuracy_score(ys, hc2_pred_sig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HIVECOTE2 from aeon package (TODO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73992673992674"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aeon.classification.hybrid import HIVECOTEV2\n",
    "\n",
    "hc2_AEON = HIVECOTEV2(time_limit_in_minutes=0.2)\n",
    "hc2_AEON.fit(X_train, y_train)\n",
    "y_pred_hc2_AEON = hc2_AEON.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test, y_pred_hc2_AEON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.73992673992674\n",
      "0.7292639212057452\n",
      "0.7219879281854993\n",
      "0.73992673992674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred_hc2_AEON))\n",
    "print(f1_score(y_test, y_pred_hc2_AEON, average='weighted'))\n",
    "print(precision_score(y_test, y_pred_hc2_AEON, average='weighted'))\n",
    "print(recall_score(y_test, y_pred_hc2_AEON, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hydra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([816, 1, 24])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tensor = torch.tensor(np.expand_dims(X_train, axis=1), dtype=torch.float32)\n",
    "X_test_tensor = torch.tensor(np.expand_dims(X_test, axis=1), dtype=torch.float32)\n",
    "X_train_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([273, 1, 24])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydra import Hydra, SparseScaler\n",
    "from sklearn.linear_model import RidgeClassifierCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = Hydra(X_train.shape[-1])\n",
    "\n",
    "X_training_transform = transform(X_train_tensor)\n",
    "X_test_transform = transform(X_test_tensor)\n",
    "\n",
    "scaler = SparseScaler()\n",
    "\n",
    "X_training_transform = scaler.fit_transform(X_training_transform)\n",
    "X_test_transform = scaler.transform(X_test_transform)\n",
    "\n",
    "hydra_classifier = RidgeClassifierCV(alphas = np.logspace(-3, 3, 10))\n",
    "hydra_classifier.fit(X_training_transform, y_train)\n",
    "\n",
    "hydra_pred = hydra_classifier.predict(X_test_transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([273, 2048])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_transform.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7728937728937729\n",
      "0.7597487368682977\n",
      "0.756021955320861\n",
      "0.7728937728937729\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, hydra_pred))\n",
    "print(f1_score(y_test, hydra_pred, average='weighted'))\n",
    "print(precision_score(y_test, hydra_pred, average='weighted'))\n",
    "print(recall_score(y_test, hydra_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carolina/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy_score': 0.7728937728937729,\n",
       " 'f1_score': 0.7614005249603597,\n",
       " 'precision_score': 0.7570851806145924,\n",
       " 'recall_score': 0.7728937728937729}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classifiers.hydra import hydra\n",
    "\n",
    "hydra(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hydra_pred_sig = hydra_classifier.predict(transform(torch.tensor(np.expand_dims(Xs, axis=1), dtype=torch.float32)))\n",
    "accuracy_score(ys, hydra_pred_sig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MultiRocket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multirocket.multirocket import MultiRocket\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MultiRocket] Creating MultiRocket with 6250 kernels\n",
      "[MultiRocket] Training with training set of (816, 24)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MultiRocket] Kernels applied!, took 2.023037400000021s\n",
      "[MultiRocket] Transformed Shape (816, 49728)\n",
      "[MultiRocket] Training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 0: 4it [00:00, 11.08it/s]                       \n",
      "epoch: 1: 4it [00:00, 28.76it/s]                       \n",
      "epoch: 2: 4it [00:00, 31.85it/s]                       \n",
      "epoch: 3: 4it [00:00,  7.09it/s]                       \n",
      "epoch: 4: 4it [00:00,  7.77it/s]                       \n",
      "epoch: 5: 4it [00:00, 15.10it/s]                       \n",
      "epoch: 6: 4it [00:00, 12.85it/s]                       \n",
      "epoch: 7: 4it [00:00, 13.48it/s]                       \n",
      "epoch: 8: 4it [00:00, 17.47it/s]                       \n",
      "epoch: 9: 4it [00:00, 24.70it/s]                       \n",
      "epoch: 10: 4it [00:00, 34.22it/s]                       \n",
      "epoch: 11: 4it [00:00, 32.30it/s]                       \n",
      "epoch: 12: 4it [00:00, 32.52it/s]                       \n",
      "epoch: 13: 4it [00:00, 33.45it/s]                       \n",
      "epoch: 14: 4it [00:00,  9.66it/s]                       \n",
      "epoch: 15: 4it [00:00, 34.04it/s]                       \n",
      "epoch: 16: 4it [00:00, 28.54it/s]                       \n",
      "epoch: 17: 4it [00:00, 30.88it/s]                       \n",
      "epoch: 18: 4it [00:00, 13.55it/s]                       \n",
      "epoch: 19: 4it [00:00, 26.32it/s]                       \n",
      "epoch: 20: 4it [00:00, 33.95it/s]                       \n",
      "epoch: 21: 4it [00:00, 34.50it/s]                       \n",
      "epoch: 22: 4it [00:00, 33.65it/s]                       \n",
      "epoch: 23: 4it [00:00,  9.00it/s]                       \n",
      "epoch: 24: 4it [00:00, 25.51it/s]                       \n",
      "epoch: 25: 4it [00:00, 31.65it/s]                       \n",
      "epoch: 26: 4it [00:00, 17.94it/s]                       \n",
      "epoch: 27: 4it [00:00, 15.54it/s]                       \n",
      "epoch: 28: 4it [00:00, 31.03it/s]                       \n",
      "epoch: 29: 4it [00:00, 32.32it/s]                       \n",
      "epoch: 30: 4it [00:00, 34.05it/s]                       \n",
      "epoch: 31: 4it [00:00, 34.01it/s]                       \n",
      "epoch: 32: 4it [00:00, 32.00it/s]                       \n",
      "epoch: 33: 4it [00:00, 34.18it/s]                       \n",
      "epoch: 34: 4it [00:00, 33.98it/s]                       \n",
      "epoch: 35: 4it [00:00, 34.06it/s]                       \n",
      "epoch: 36: 4it [00:00, 33.10it/s]                       \n",
      "epoch: 37: 4it [00:00, 33.62it/s]                       \n",
      "epoch: 38: 4it [00:00, 28.10it/s]                       \n",
      "epoch: 39: 4it [00:00, 29.96it/s]                       \n",
      "epoch: 40: 4it [00:00, 33.60it/s]                       \n",
      "epoch: 41: 4it [00:00, 34.18it/s]                       \n",
      "epoch: 42: 4it [00:00, 15.01it/s]                       \n",
      "epoch: 43: 4it [00:00, 10.49it/s]                       \n",
      "epoch: 44: 4it [00:00, 29.71it/s]                       \n",
      "epoch: 45: 4it [00:00, 34.20it/s]                       \n",
      "epoch: 46: 4it [00:00, 35.33it/s]                       \n",
      "epoch: 47: 4it [00:00, 34.91it/s]                       \n",
      "epoch: 48: 4it [00:00, 28.72it/s]                       \n",
      "epoch: 49: 4it [00:00, 24.93it/s]                       \n",
      "epoch: 50: 4it [00:00, 35.36it/s]                       \n",
      "epoch: 51: 4it [00:00, 33.02it/s]                       \n",
      "epoch: 52: 4it [00:00, 12.09it/s]                       \n",
      "epoch: 53: 4it [00:00, 30.55it/s]                       \n",
      "epoch: 54: 4it [00:00, 34.16it/s]                       \n",
      "epoch: 55: 4it [00:00, 33.23it/s]                       \n",
      "epoch: 56: 4it [00:00, 15.29it/s]                       \n",
      "epoch: 57: 4it [00:00, 17.93it/s]                       \n",
      "epoch: 58: 4it [00:00, 31.56it/s]                       \n",
      "epoch: 59: 4it [00:00, 33.93it/s]                       \n",
      "epoch: 60: 4it [00:00, 33.93it/s]                       \n",
      "epoch: 61: 4it [00:00, 14.75it/s]                       \n",
      "epoch: 62: 4it [00:00, 34.13it/s]                       \n",
      "epoch: 63: 4it [00:00, 33.03it/s]                       \n",
      "epoch: 64: 4it [00:00, 33.39it/s]                       \n",
      "epoch: 65: 4it [00:00, 13.14it/s]                       \n",
      "epoch: 66: 4it [00:00, 31.26it/s]                       \n",
      "epoch: 67: 4it [00:00, 33.37it/s]                       \n",
      "epoch: 68: 4it [00:00, 34.45it/s]                       \n",
      "epoch: 69: 4it [00:00, 35.22it/s]                       \n",
      "epoch: 70: 4it [00:00, 33.94it/s]                       \n",
      "epoch: 71: 4it [00:00, 34.25it/s]                       \n",
      "epoch: 72: 4it [00:00, 34.47it/s]                       \n",
      "epoch: 73: 4it [00:00, 13.23it/s]                       \n",
      "epoch: 74: 4it [00:00, 30.26it/s]                       \n",
      "epoch: 75: 4it [00:00, 34.26it/s]                       \n",
      "epoch: 76: 4it [00:00,  7.64it/s]                       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<Stopped at Epoch 77>\n",
      "[MultiRocket] Training done!, took 19.228s\n",
      "[MultiRocket] Predicting\n",
      "Kernels applied!, took 0.114s. Transformed shape: (273, 49728).\n",
      "[MultiRocket] Predicting completed, took 0.228s\n"
     ]
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "y_train = encoder.fit_transform(y_train)\n",
    "y_test = encoder.transform(y_test)\n",
    "\n",
    "# returns ntc format, remove the last dimension\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1]))\n",
    "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
    "\n",
    "nb_classes = len(np.unique(np.concatenate((y_train, y_test), axis=0)))\n",
    "\n",
    "mrocket_classifier = MultiRocket(\n",
    "    num_features=50000,\n",
    "    classifier=\"logistic\",\n",
    "    verbose=2,\n",
    ")\n",
    "yhat_train = mrocket_classifier.fit(\n",
    "    X_train, y_train,\n",
    "    predict_on_train=False\n",
    ")\n",
    "mrocket_pred = mrocket_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7545787545787546\n",
      "0.7503978646972748\n",
      "0.7512523983952555\n",
      "0.7545787545787546\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, mrocket_pred))\n",
    "print(f1_score(y_test, mrocket_pred, average='weighted'))\n",
    "print(precision_score(y_test, mrocket_pred, average='weighted'))\n",
    "print(recall_score(y_test, mrocket_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MultiRocket] Predicting\n",
      "Kernels applied!, took 0.014s. Transformed shape: (15, 49728).\n",
      "[MultiRocket] Predicting completed, took 0.011s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4666666666666667"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrocket_pred_sig = mrocket_classifier.predict(Xs)\n",
    "accuracy_score(ys, mrocket_pred_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MultiRocket] Creating MultiRocket with 6250 kernels\n",
      "[MultiRocket] Training with training set of (816, 24)\n",
      "[MultiRocket] Kernels applied!, took 1.0347758209973108s\n",
      "[MultiRocket] Transformed Shape (816, 49728)\n",
      "[MultiRocket] Training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 0: 4it [00:00,  9.20it/s]                       \n",
      "epoch: 1: 4it [00:00, 28.06it/s]                       \n",
      "epoch: 2: 4it [00:00, 23.79it/s]                       \n",
      "epoch: 3: 4it [00:00, 27.22it/s]                       \n",
      "epoch: 4: 4it [00:00, 27.83it/s]                       \n",
      "epoch: 5: 4it [00:00, 24.36it/s]                       \n",
      "epoch: 6: 4it [00:00, 28.31it/s]                       \n",
      "epoch: 7: 4it [00:00, 27.11it/s]                       \n",
      "epoch: 8: 4it [00:00, 25.26it/s]                       \n",
      "epoch: 9: 4it [00:00, 28.96it/s]                       \n",
      "epoch: 10: 4it [00:00, 27.39it/s]                       \n",
      "epoch: 11: 4it [00:00, 28.04it/s]                       \n",
      "epoch: 12: 4it [00:00, 26.96it/s]                       \n",
      "epoch: 13: 4it [00:00, 26.63it/s]                       \n",
      "epoch: 14: 4it [00:00, 27.16it/s]                       \n",
      "epoch: 15: 4it [00:00, 27.82it/s]                       \n",
      "epoch: 16: 4it [00:00, 25.02it/s]                       \n",
      "epoch: 17: 4it [00:00, 26.79it/s]                       \n",
      "epoch: 18: 4it [00:00, 27.18it/s]                       \n",
      "epoch: 19: 4it [00:00, 28.25it/s]                       \n",
      "epoch: 20: 4it [00:00, 28.45it/s]                       \n",
      "epoch: 21: 4it [00:00, 27.26it/s]                       \n",
      "epoch: 22: 4it [00:00, 27.93it/s]                       \n",
      "epoch: 23: 4it [00:00, 28.61it/s]                       \n",
      "epoch: 24: 4it [00:00, 26.59it/s]                       \n",
      "epoch: 25: 4it [00:00, 22.30it/s]                       \n",
      "epoch: 26: 4it [00:00, 29.18it/s]                       \n",
      "epoch: 27: 4it [00:00, 23.56it/s]                       \n",
      "epoch: 28: 4it [00:00, 28.81it/s]                       \n",
      "epoch: 29: 4it [00:00, 27.68it/s]                       \n",
      "epoch: 30: 4it [00:00, 23.88it/s]                       \n",
      "epoch: 31: 4it [00:00, 31.65it/s]                       \n",
      "epoch: 32: 4it [00:00, 24.06it/s]                       \n",
      "epoch: 33: 4it [00:00, 32.25it/s]                       \n",
      "epoch: 34: 4it [00:00, 31.33it/s]                       \n",
      "epoch: 35: 4it [00:00, 24.44it/s]                       \n",
      "epoch: 36: 4it [00:00, 30.76it/s]                       \n",
      "epoch: 37: 4it [00:00, 31.20it/s]                       \n",
      "epoch: 38: 4it [00:00, 31.42it/s]                       \n",
      "epoch: 39: 4it [00:00, 31.31it/s]                       \n",
      "epoch: 40: 4it [00:00, 31.04it/s]                       \n",
      "epoch: 41: 4it [00:00, 30.51it/s]                       \n",
      "epoch: 42: 4it [00:00, 23.17it/s]                       \n",
      "epoch: 43: 4it [00:00, 31.72it/s]                       \n",
      "epoch: 44: 4it [00:00, 30.92it/s]                       \n",
      "epoch: 45: 4it [00:00, 32.41it/s]                       \n",
      "epoch: 46: 4it [00:00, 31.90it/s]                       \n",
      "epoch: 47: 4it [00:00, 31.34it/s]                       \n",
      "epoch: 48: 4it [00:00, 31.66it/s]                       \n",
      "epoch: 49: 4it [00:00, 31.40it/s]                       \n",
      "epoch: 50: 4it [00:00, 30.19it/s]                       \n",
      "epoch: 51: 4it [00:00, 23.43it/s]                       \n",
      "epoch: 52: 4it [00:00, 31.73it/s]                       \n",
      "epoch: 53: 4it [00:00, 10.98it/s]                       \n",
      "epoch: 54: 4it [00:00, 23.15it/s]                       \n",
      "epoch: 55: 4it [00:00, 32.30it/s]                       \n",
      "epoch: 56: 4it [00:00, 30.98it/s]                       \n",
      "epoch: 57: 4it [00:00, 31.12it/s]                       \n",
      "epoch: 58: 4it [00:00, 29.20it/s]                       \n",
      "epoch: 59: 4it [00:00, 32.78it/s]                       \n",
      "epoch: 60: 4it [00:00, 30.83it/s]                       \n",
      "epoch: 61: 4it [00:00, 25.02it/s]                       \n",
      "epoch: 62: 4it [00:00, 32.39it/s]                       \n",
      "epoch: 63: 4it [00:00, 29.04it/s]                       \n",
      "epoch: 64: 4it [00:00, 31.57it/s]                       \n",
      "epoch: 65: 4it [00:00, 22.97it/s]                       \n",
      "epoch: 66: 4it [00:00, 33.18it/s]                       \n",
      "epoch: 67: 4it [00:00, 31.92it/s]                       \n",
      "epoch: 68: 4it [00:00, 32.11it/s]                       \n",
      "epoch: 69: 4it [00:00, 32.01it/s]                       \n",
      "epoch: 70: 4it [00:00, 24.12it/s]                       \n",
      "epoch: 71: 4it [00:00, 32.35it/s]                       \n",
      "epoch: 72: 4it [00:00, 23.00it/s]                       \n",
      "epoch: 73: 4it [00:00, 31.45it/s]                       \n",
      "epoch: 74: 4it [00:00, 32.74it/s]                       \n",
      "epoch: 75: 4it [00:00, 32.11it/s]                       \n",
      "epoch: 76: 4it [00:00, 30.26it/s]                       \n",
      "epoch: 77: 4it [00:00, 33.29it/s]                       \n",
      "epoch: 78: 4it [00:00, 24.37it/s]                       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<Stopped at Epoch 79>\n",
      "[MultiRocket] Training done!, took 16.660s\n",
      "[MultiRocket] Predicting\n",
      "Kernels applied!, took 0.121s. Transformed shape: (273, 49728).\n",
      "[MultiRocket] Predicting completed, took 0.176s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy_score': 0.73992673992674,\n",
       " 'f1_score': 0.7310353991620838,\n",
       " 'precision_score': 0.7239555631680323,\n",
       " 'recall_score': 0.73992673992674}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classifiers.multirocket import multirocket\n",
    "\n",
    "multirocket(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.registry import all_estimators\n",
    "\n",
    "all_estimators(\"classifier\", as_dataframe=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance based: classification is based on some time series specific distance measure between series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance based classifiers use a distance function to measure the similarity between time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 Proximity Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rank 1 Proximity Forest\n",
    "from sktime.classification.distance_based import ProximityForest\n",
    "\n",
    "clf_pf = ProximityForest(\n",
    "    n_estimators=2, max_depth=2, n_stump_evaluations=1\n",
    ") \n",
    "clf_pf.fit(X_train, y_train) \n",
    "y_pred_pf = clf_pf.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred_pf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(accuracy_score(y_test, \u001b[43my_pred_pf\u001b[49m))\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(f1_score(y_test, y_pred_pf, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweighted\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(precision_score(y_test, y_pred_pf, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweighted\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_pred_pf' is not defined"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred_pf))\n",
    "print(f1_score(y_test, y_pred_pf, average='weighted'))\n",
    "print(precision_score(y_test, y_pred_pf, average='weighted'))\n",
    "print(recall_score(y_test, y_pred_pf, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "PicklingError",
     "evalue": "Could not pickle object as excessively deep recursion required.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:1262\u001b[0m, in \u001b[0;36mPickler.dump\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m   1261\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1263\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/serialize.py:197\u001b[0m, in \u001b[0;36mNumbaPickler.reducer_override\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    196\u001b[0m     _no_pickle(obj)  \u001b[38;5;66;03m# noreturn\u001b[39;00m\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreducer_override\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:1347\u001b[0m, in \u001b[0;36mPickler.reducer_override\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, types\u001b[38;5;241m.\u001b[39mFunctionType):\n\u001b[0;32m-> 1347\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_function_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1348\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1349\u001b[0m     \u001b[38;5;66;03m# fallback to save_global, including the Pickler's\u001b[39;00m\n\u001b[1;32m   1350\u001b[0m     \u001b[38;5;66;03m# dispatch_table\u001b[39;00m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:1225\u001b[0m, in \u001b[0;36mPickler._function_reduce\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m   1224\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1225\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dynamic_function_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:1210\u001b[0m, in \u001b[0;36mPickler._dynamic_function_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m   1209\u001b[0m newargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_function_getnewargs(func)\n\u001b[0;32m-> 1210\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43m_function_getstate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (_make_function, newargs, state, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, _function_setstate)\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:721\u001b[0m, in \u001b[0;36m_function_getstate\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m    710\u001b[0m slotstate \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    711\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m\"\u001b[39m: func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,\n\u001b[1;32m    712\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__qualname__\u001b[39m\u001b[38;5;124m\"\u001b[39m: func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    718\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__closure__\u001b[39m\u001b[38;5;124m\"\u001b[39m: func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__closure__\u001b[39m,\n\u001b[1;32m    719\u001b[0m }\n\u001b[0;32m--> 721\u001b[0m f_globals_ref \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_code_globals\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__code__\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    722\u001b[0m f_globals \u001b[38;5;241m=\u001b[39m {k: func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__globals__\u001b[39m[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m f_globals_ref \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__globals__\u001b[39m}\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:320\u001b[0m, in \u001b[0;36m_extract_code_globals\u001b[0;34m(co)\u001b[0m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Find all globals names read or written to by codeblock co.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 320\u001b[0m out_names \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_code_globals_cache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mco\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    322\u001b[0m     \u001b[38;5;66;03m# We use a dict with None values instead of a set to get a\u001b[39;00m\n\u001b[1;32m    323\u001b[0m     \u001b[38;5;66;03m# deterministic order and avoid introducing non-deterministic pickle\u001b[39;00m\n\u001b[1;32m    324\u001b[0m     \u001b[38;5;66;03m# bytes as a results.\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/weakref.py:442\u001b[0m, in \u001b[0;36mWeakKeyDictionary.get\u001b[0;34m(self, key, default)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, default\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 442\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdefault\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mPicklingError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mclassifiers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mproximity_forest\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m proximity_forest\n\u001b[0;32m----> 3\u001b[0m \u001b[43mproximity_forest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/classifiers/proximity_forest.py:12\u001b[0m, in \u001b[0;36mproximity_forest\u001b[0;34m(X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mproximity_forest\u001b[39m (X_train, y_train, X_test, y_test):\n\u001b[1;32m      9\u001b[0m     clf_pf \u001b[38;5;241m=\u001b[39m ProximityForest(\n\u001b[1;32m     10\u001b[0m         n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, n_stump_evaluations\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     11\u001b[0m     ) \n\u001b[0;32m---> 12\u001b[0m     \u001b[43mclf_pf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m \n\u001b[1;32m     13\u001b[0m     pf_pred \u001b[38;5;241m=\u001b[39m clf_pf\u001b[38;5;241m.\u001b[39mpredict(X_test) \n\u001b[1;32m     15\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     16\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy_score\u001b[39m\u001b[38;5;124m'\u001b[39m: accuracy_score(y_test, pf_pred), \n\u001b[1;32m     17\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf1_score\u001b[39m\u001b[38;5;124m'\u001b[39m: f1_score(y_test, pf_pred, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweighted\u001b[39m\u001b[38;5;124m'\u001b[39m), \n\u001b[1;32m     18\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprecision_score\u001b[39m\u001b[38;5;124m'\u001b[39m: precision_score(y_test, pf_pred, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweighted\u001b[39m\u001b[38;5;124m'\u001b[39m), \n\u001b[1;32m     19\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrecall_score\u001b[39m\u001b[38;5;124m'\u001b[39m: recall_score(y_test, pf_pred, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweighted\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m     20\u001b[0m     }\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/base.py:262\u001b[0m, in \u001b[0;36mBaseClassifier.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    258\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself.n_jobs must be set if capability:multithreading is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m# pass coerced and checked data to inner _fit\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_time_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m)) \u001b[38;5;241m-\u001b[39m start\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# this should happen last: fitted state is set to True\u001b[39;00m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1466\u001b[0m, in \u001b[0;36mProximityForest._fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1459\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrees \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[1;32m   1460\u001b[0m         delayed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_tree)(\n\u001b[1;32m   1461\u001b[0m             X, y, index, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_random_object\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1462\u001b[0m         )\n\u001b[1;32m   1463\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1464\u001b[0m     )\n\u001b[1;32m   1465\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1466\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrees \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   1467\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_tree(\n\u001b[1;32m   1468\u001b[0m             X, y, index, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_random_object\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1469\u001b[0m         )\n\u001b[1;32m   1470\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1471\u001b[0m     ]\n\u001b[1;32m   1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1467\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1459\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrees \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[1;32m   1460\u001b[0m         delayed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_tree)(\n\u001b[1;32m   1461\u001b[0m             X, y, index, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_random_object\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1462\u001b[0m         )\n\u001b[1;32m   1463\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1464\u001b[0m     )\n\u001b[1;32m   1465\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1466\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrees \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m-> 1467\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_tree\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1468\u001b[0m \u001b[43m            \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_random_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_estimators\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1469\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1470\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators)\n\u001b[1;32m   1471\u001b[0m     ]\n\u001b[1;32m   1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1435\u001b[0m, in \u001b[0;36mProximityForest._fit_tree\u001b[0;34m(self, X, y, index, random_state)\u001b[0m\n\u001b[1;32m   1425\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtree \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(index) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m building\u001b[39m\u001b[38;5;124m\"\u001b[39m)  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m   1426\u001b[0m tree \u001b[38;5;241m=\u001b[39m ProximityTree(\n\u001b[1;32m   1427\u001b[0m     random_state\u001b[38;5;241m=\u001b[39mrandom_state,\n\u001b[1;32m   1428\u001b[0m     verbosity\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbosity,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1433\u001b[0m     n_stump_evaluations\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_stump_evaluations,\n\u001b[1;32m   1434\u001b[0m )\n\u001b[0;32m-> 1435\u001b[0m \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tree\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/base.py:262\u001b[0m, in \u001b[0;36mBaseClassifier.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    258\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself.n_jobs must be set if capability:multithreading is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m# pass coerced and checked data to inner _fit\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_time_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m)) \u001b[38;5;241m-\u001b[39m start\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# this should happen last: fitted state is set to True\u001b[39;00m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1117\u001b[0m, in \u001b[0;36mProximityTree._fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1115\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbranches[index] \u001b[38;5;241m=\u001b[39m sub_tree\n\u001b[1;32m   1116\u001b[0m             sub_X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstump\u001b[38;5;241m.\u001b[39mX_branches[index]\n\u001b[0;32m-> 1117\u001b[0m             \u001b[43msub_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43msub_X\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msub_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/base.py:262\u001b[0m, in \u001b[0;36mBaseClassifier.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    258\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself.n_jobs must be set if capability:multithreading is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m# pass coerced and checked data to inner _fit\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_time_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m)) \u001b[38;5;241m-\u001b[39m start\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# this should happen last: fitted state is set to True\u001b[39;00m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1117\u001b[0m, in \u001b[0;36mProximityTree._fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1115\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbranches[index] \u001b[38;5;241m=\u001b[39m sub_tree\n\u001b[1;32m   1116\u001b[0m             sub_X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstump\u001b[38;5;241m.\u001b[39mX_branches[index]\n\u001b[0;32m-> 1117\u001b[0m             \u001b[43msub_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43msub_X\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msub_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "    \u001b[0;31m[... skipping similar frames: BaseClassifier.fit at line 262 (1456 times), ProximityTree._fit at line 1117 (1455 times)]\u001b[0m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1117\u001b[0m, in \u001b[0;36mProximityTree._fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1115\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbranches[index] \u001b[38;5;241m=\u001b[39m sub_tree\n\u001b[1;32m   1116\u001b[0m             sub_X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstump\u001b[38;5;241m.\u001b[39mX_branches[index]\n\u001b[0;32m-> 1117\u001b[0m             \u001b[43msub_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43msub_X\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msub_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/base.py:262\u001b[0m, in \u001b[0;36mBaseClassifier.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    258\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself.n_jobs must be set if capability:multithreading is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m# pass coerced and checked data to inner _fit\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_time_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m)) \u001b[38;5;241m-\u001b[39m start\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# this should happen last: fitted state is set to True\u001b[39;00m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1098\u001b[0m, in \u001b[0;36mProximityTree._fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_random_object \u001b[38;5;241m=\u001b[39m check_random_state(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_state)\n\u001b[1;32m   1096\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my \u001b[38;5;241m=\u001b[39m y\n\u001b[0;32m-> 1098\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstump \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_stump\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1099\u001b[0m n_branches \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstump\u001b[38;5;241m.\u001b[39my_exemplar)\n\u001b[1;32m   1100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbranches \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mNone\u001b[39;00m] \u001b[38;5;241m*\u001b[39m n_branches\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:1208\u001b[0m, in \u001b[0;36mProximityTree.find_stump\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1201\u001b[0m     stump \u001b[38;5;241m=\u001b[39m ProximityStump(\n\u001b[1;32m   1202\u001b[0m         random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_state,\n\u001b[1;32m   1203\u001b[0m         distance_measure\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistance_measure,\n\u001b[1;32m   1204\u001b[0m         verbosity\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbosity,\n\u001b[1;32m   1205\u001b[0m         n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs,\n\u001b[1;32m   1206\u001b[0m     )\n\u001b[1;32m   1207\u001b[0m     stump\u001b[38;5;241m.\u001b[39mfit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my)\n\u001b[0;32m-> 1208\u001b[0m     \u001b[43mstump\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1209\u001b[0m     stumps\u001b[38;5;241m.\u001b[39mappend(stump)\n\u001b[1;32m   1210\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _max(stumps, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_random_object, \u001b[38;5;28;01mlambda\u001b[39;00m stump: stump\u001b[38;5;241m.\u001b[39mentropy)\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:889\u001b[0m, in \u001b[0;36mProximityStump.grow\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Grow the stump, creating branches for each exemplar.\"\"\"\u001b[39;00m\n\u001b[1;32m    888\u001b[0m n_exemplars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_exemplar)\n\u001b[0;32m--> 889\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_closest_exemplar_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    890\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_branches \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mNone\u001b[39;00m] \u001b[38;5;241m*\u001b[39m n_exemplars\n\u001b[1;32m    891\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_branches \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mNone\u001b[39;00m] \u001b[38;5;241m*\u001b[39m n_exemplars\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:878\u001b[0m, in \u001b[0;36mProximityStump.find_closest_exemplar_indices\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    866\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Find the closest exemplar index for each instance in a dataframe.\u001b[39;00m\n\u001b[1;32m    867\u001b[0m \n\u001b[1;32m    868\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;124;03m    reflecting the index of the closest exemplar\u001b[39;00m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    877\u001b[0m n_instances \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 878\u001b[0m distances \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistance_to_exemplars\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    879\u001b[0m indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m    880\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_instances):\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:834\u001b[0m, in \u001b[0;36mProximityStump.distance_to_exemplars\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    827\u001b[0m     distances \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[1;32m    828\u001b[0m         delayed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_to_exemplars_inst)(\n\u001b[1;32m    829\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_exemplar, X\u001b[38;5;241m.\u001b[39miloc[index, :], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_measure()\n\u001b[1;32m    830\u001b[0m         )\n\u001b[1;32m    831\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    832\u001b[0m     )\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 834\u001b[0m     distances \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    835\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_to_exemplars_inst(\n\u001b[1;32m    836\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_exemplar, X\u001b[38;5;241m.\u001b[39miloc[index, :], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_measure()\n\u001b[1;32m    837\u001b[0m         )\n\u001b[1;32m    838\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    839\u001b[0m     ]\n\u001b[1;32m    840\u001b[0m distances \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan_to_num(np\u001b[38;5;241m.\u001b[39mvstack(np\u001b[38;5;241m.\u001b[39marray(distances)))\n\u001b[1;32m    841\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m distances\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:835\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    827\u001b[0m     distances \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[1;32m    828\u001b[0m         delayed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_to_exemplars_inst)(\n\u001b[1;32m    829\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_exemplar, X\u001b[38;5;241m.\u001b[39miloc[index, :], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_measure()\n\u001b[1;32m    830\u001b[0m         )\n\u001b[1;32m    831\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    832\u001b[0m     )\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    834\u001b[0m     distances \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 835\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_distance_to_exemplars_inst\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    836\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_exemplar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_distance_measure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    837\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    838\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    839\u001b[0m     ]\n\u001b[1;32m    840\u001b[0m distances \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan_to_num(np\u001b[38;5;241m.\u001b[39mvstack(np\u001b[38;5;241m.\u001b[39marray(distances)))\n\u001b[1;32m    841\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m distances\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:763\u001b[0m, in \u001b[0;36mProximityStump._distance_to_exemplars_inst\u001b[0;34m(exemplars, instance, distance_measure)\u001b[0m\n\u001b[1;32m    761\u001b[0m     distance \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    762\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 763\u001b[0m     distance \u001b[38;5;241m=\u001b[39m \u001b[43mdistance_measure\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexemplar\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# , min_distance)\u001b[39;00m\n\u001b[1;32m    764\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m distance \u001b[38;5;241m<\u001b[39m min_distance:\n\u001b[1;32m    765\u001b[0m     min_distance \u001b[38;5;241m=\u001b[39m distance\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:148\u001b[0m, in \u001b[0;36mdistance_predefined_params.<locals>.distance\u001b[0;34m(instance_a, instance_b)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdistance\u001b[39m(instance_a, instance_b):\n\u001b[0;32m--> 148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdistance_measure\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance_a\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minstance_b\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/classification/distance_based/_proximity_forest.py:174\u001b[0m, in \u001b[0;36mnumba_wrapper.<locals>.distance\u001b[0;34m(instance_a, instance_b, **params)\u001b[0m\n\u001b[1;32m    172\u001b[0m instance_a \u001b[38;5;241m=\u001b[39m convert(instance_a, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnested_univ\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpyflat\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    173\u001b[0m instance_b \u001b[38;5;241m=\u001b[39m convert(instance_b, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnested_univ\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpyflat\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdistance_measure\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance_a\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minstance_b\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/distances/_distance.py:834\u001b[0m, in \u001b[0;36mmsm_distance\u001b[0;34m(x, y, c, window, itakura_max_slope, bounding_matrix, **kwargs)\u001b[0m\n\u001b[1;32m    826\u001b[0m format_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    827\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m\"\u001b[39m: c,\n\u001b[1;32m    828\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwindow\u001b[39m\u001b[38;5;124m\"\u001b[39m: window,\n\u001b[1;32m    829\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mitakura_max_slope\u001b[39m\u001b[38;5;124m\"\u001b[39m: itakura_max_slope,\n\u001b[1;32m    830\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbounding_matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m: bounding_matrix,\n\u001b[1;32m    831\u001b[0m }\n\u001b[1;32m    832\u001b[0m format_kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs}\n\u001b[0;32m--> 834\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdistance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmsm\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/sktime/distances/_distance.py:1969\u001b[0m, in \u001b[0;36mdistance\u001b[0;34m(x, y, metric, **kwargs)\u001b[0m\n\u001b[1;32m   1963\u001b[0m _y \u001b[38;5;241m=\u001b[39m to_numba_timeseries(y)\n\u001b[1;32m   1965\u001b[0m _metric_callable \u001b[38;5;241m=\u001b[39m _resolve_metric_to_factory(\n\u001b[1;32m   1966\u001b[0m     metric, _x, _y, _METRIC_INFOS, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m   1967\u001b[0m )\n\u001b[0;32m-> 1969\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_metric_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_y\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/dispatcher.py:487\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m    485\u001b[0m             e\u001b[38;5;241m.\u001b[39mpatch_message(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip(), help_msg)))\n\u001b[1;32m    486\u001b[0m     \u001b[38;5;66;03m# ignore the FULL_TRACEBACKS config, this needs reporting!\u001b[39;00m\n\u001b[0;32m--> 487\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    488\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    489\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_types_active_call \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/dispatcher.py:420\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m    418\u001b[0m return_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    419\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 420\u001b[0m     return_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margtypes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mForceLiteralArg \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;66;03m# Received request for compiler re-entry with the list of arguments\u001b[39;00m\n\u001b[1;32m    423\u001b[0m     \u001b[38;5;66;03m# indicated by e.requested_args.\u001b[39;00m\n\u001b[1;32m    424\u001b[0m     \u001b[38;5;66;03m# First, check if any of these args are already Literal-ized\u001b[39;00m\n\u001b[1;32m    425\u001b[0m     already_lit_pos \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m e\u001b[38;5;241m.\u001b[39mrequested_args\n\u001b[1;32m    426\u001b[0m                        \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args[i], types\u001b[38;5;241m.\u001b[39mLiteral)]\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/dispatcher.py:938\u001b[0m, in \u001b[0;36mDispatcher.compile\u001b[0;34m(self, sig)\u001b[0m\n\u001b[1;32m    936\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m existing\u001b[38;5;241m.\u001b[39mentry_point\n\u001b[1;32m    937\u001b[0m \u001b[38;5;66;03m# Try to load from disk cache\u001b[39;00m\n\u001b[0;32m--> 938\u001b[0m cres \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_overload\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargetctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    939\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cres \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    940\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_hits[sig] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/caching.py:635\u001b[0m, in \u001b[0;36mCache.load_overload\u001b[0;34m(self, sig, target_context)\u001b[0m\n\u001b[1;32m    633\u001b[0m target_context\u001b[38;5;241m.\u001b[39mrefresh()\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_guard_against_spurious_io_errors():\n\u001b[0;32m--> 635\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_overload\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_context\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/caching.py:641\u001b[0m, in \u001b[0;36mCache._load_overload\u001b[0;34m(self, sig, target_context)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enabled:\n\u001b[1;32m    640\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 641\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_index_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcodegen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    642\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_file\u001b[38;5;241m.\u001b[39mload(key)\n\u001b[1;32m    643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/caching.py:690\u001b[0m, in \u001b[0;36mCache._index_key\u001b[0;34m(self, sig, codegen)\u001b[0m\n\u001b[1;32m    687\u001b[0m     cvars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m([x\u001b[38;5;241m.\u001b[39mcell_contents \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_py_func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__closure__\u001b[39m])\n\u001b[1;32m    688\u001b[0m     \u001b[38;5;66;03m# Note: cloudpickle serializes a function differently depending\u001b[39;00m\n\u001b[1;32m    689\u001b[0m     \u001b[38;5;66;03m#       on how the process is launched; e.g. multiprocessing.Process\u001b[39;00m\n\u001b[0;32m--> 690\u001b[0m     cvarbytes \u001b[38;5;241m=\u001b[39m \u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcvars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    691\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    692\u001b[0m     cvarbytes \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/core/serialize.py:58\u001b[0m, in \u001b[0;36mdumps\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m io\u001b[38;5;241m.\u001b[39mBytesIO() \u001b[38;5;28;01mas\u001b[39;00m buf:\n\u001b[1;32m     57\u001b[0m     p \u001b[38;5;241m=\u001b[39m pickler(buf, protocol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[0;32m---> 58\u001b[0m     \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m     pickled \u001b[38;5;241m=\u001b[39m buf\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pickled\n",
      "File \u001b[0;32m~/Desktop/Mestrado/.venv/lib/python3.9/site-packages/numba/cloudpickle/cloudpickle.py:1266\u001b[0m, in \u001b[0;36mPickler.dump\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m   1264\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(e\u001b[38;5;241m.\u001b[39margs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecursion\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m e\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m   1265\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not pickle object as excessively deep recursion required.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1266\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mPicklingError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1267\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1268\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "\u001b[0;31mPicklingError\u001b[0m: Could not pickle object as excessively deep recursion required."
     ]
    }
   ],
   "source": [
    "from classifiers.proximity_forest import proximity_forest\n",
    "\n",
    "proximity_forest(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 Elastic Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aeon.classification.distance_based import ElasticEnsemble\n",
    "\n",
    "clf_ee = ElasticEnsemble(\n",
    "    proportion_of_param_options=0.1,\n",
    "    proportion_train_for_test=0.1,\n",
    "    distance_measures = [\"dtw\",\"ddtw\"],\n",
    "    majority_vote=True,\n",
    ")\n",
    "clf_ee.fit(X_train, y_train)\n",
    "\n",
    "y_pred_ee = clf_ee.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6886446886446886"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_ee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6923982611156061"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_ee, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7024074952945478"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_ee, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6886446886446886"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_ee, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature based: global features are extracted and passed to a standard clas- sifier in a simple pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 FreshPrince"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6886446886446886\n",
      "0.6923982611156061\n",
      "0.7024074952945478\n",
      "0.6886446886446886\n",
      "0.6556776556776557\n",
      "0.6423006240971285\n",
      "0.6371016172935642\n",
      "0.6556776556776557\n",
      "0.6446886446886447\n",
      "0.6306001902914513\n",
      "0.6280928853212004\n",
      "0.6446886446886447\n",
      "0.7582417582417582\n",
      "0.7552166709643271\n",
      "0.7541231844637417\n",
      "0.7582417582417582\n",
      "Dataset:  PopularTimesGoogle\n",
      "run  1\n",
      "training time: 22.75474308899993\n",
      "testing time: 0.7761297550005111\n",
      "accuracy:  0.7545787545787546\n",
      "run  2\n",
      "training time: 21.24686804099929\n",
      "testing time: 0.7585352460000649\n",
      "accuracy:  0.7582417582417582\n",
      "run  3\n",
      "training time: 22.352855350000027\n",
      "testing time: 0.7720358349997696\n",
      "accuracy:  0.7582417582417582\n",
      "run  4\n",
      "training time: 22.640391404000184\n",
      "testing time: 0.7551690289992621\n",
      "accuracy:  0.7582417582417582\n",
      "run  5\n",
      "training time: 21.893718408000495\n",
      "testing time: 0.7501649320001889\n",
      "accuracy:  0.7582417582417582\n",
      "run  6\n",
      "training time: 21.792980574000467\n",
      "testing time: 0.7490942350004843\n",
      "accuracy:  0.7619047619047619\n",
      "run  7\n",
      "training time: 23.54628316199978\n",
      "testing time: 0.7977542720000201\n",
      "accuracy:  0.7582417582417582\n",
      "run  8\n",
      "training time: 21.175825567000174\n",
      "testing time: 0.7205124250003792\n",
      "accuracy:  0.7655677655677655\n",
      "run  9\n",
      "training time: 20.968102308999732\n",
      "testing time: 0.6939237379992846\n",
      "accuracy:  0.7582417582417582\n",
      "run  10\n",
      "training time: 20.53585908900004\n",
      "testing time: 0.6974072960001649\n",
      "accuracy:  0.7619047619047619\n",
      "avg accuracy for 10 runs:  0.7593406593406593\n",
      "\n",
      "\n",
      "0.7765567765567766\n",
      "0.7765567765567766\n",
      "0.7608716588308426\n",
      "0.7595043845043845\n",
      "0.7765567765567766\n",
      "0.7838827838827839\n",
      "0.7737765691262555\n",
      "0.7693735150907346\n",
      "0.7838827838827839\n",
      "0.673992673992674\n",
      "0.6683676492764691\n",
      "0.6692609971696973\n",
      "0.673992673992674\n",
      "Best solution: (array([0.01362603, 0.01088563, 0.01076658, 0.00566182, 0.01357704,\n",
      "       0.19008531, 0.39125168, 0.48478115, 0.4647077 , 0.44011196,\n",
      "       0.46602354, 0.47079004, 0.48122378, 0.5386247 , 0.67896835,\n",
      "       0.75474742, 0.67783663, 0.45983365, 0.25521616, 0.21586405,\n",
      "       0.06883266, 0.01615042, 0.00264618, 0.00108969]), array([ 1.        , -0.        , -0.00988973]), 0)\n",
      "Convolution result: [ 4.  7. 10. 13.  7.]\n",
      "0.6\n",
      "0.4666666666666666\n",
      "0.673992673992674\n",
      "0.6683676492764691\n",
      "0.6692609971696973\n",
      "0.673992673992674\n",
      "0.673992673992674\n",
      "0.6683676492764691\n",
      "0.6692609971696973\n",
      "0.673992673992674\n",
      "0.6886446886446886\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 184ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 191ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 62ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step\n",
      "Dataset:  PopularTimesGoogle\n",
      "run  1\n",
      "training time: 33.27957716299716\n",
      "testing time: 1.1056609709994518\n",
      "accuracy:  0.7655677655677655\n",
      "run  2\n",
      "training time: 43.62527375699574\n",
      "testing time: 1.092076013002952\n",
      "accuracy:  0.7619047619047619\n",
      "run  3\n",
      "training time: 36.314891079004155\n",
      "testing time: 1.065815364003356\n",
      "accuracy:  0.7582417582417582\n",
      "run  4\n",
      "training time: 41.400920495005266\n",
      "testing time: 2.5893784330037306\n",
      "accuracy:  0.7619047619047619\n",
      "run  5\n",
      "training time: 37.215806476997386\n",
      "testing time: 1.1762132059957366\n",
      "accuracy:  0.7619047619047619\n",
      "run  6\n",
      "training time: 40.63293838399841\n",
      "testing time: 0.8191470599995228\n",
      "accuracy:  0.7545787545787546\n",
      "run  7\n",
      "training time: 25.499977438994392\n",
      "testing time: 1.0259408819983946\n",
      "accuracy:  0.7619047619047619\n",
      "run  8\n",
      "training time: 25.34941106599581\n",
      "testing time: 0.7967165840018424\n",
      "accuracy:  0.7619047619047619\n",
      "run  9\n",
      "training time: 23.45715410199773\n",
      "testing time: 0.7839317769976333\n",
      "accuracy:  0.7619047619047619\n",
      "run  10\n",
      "training time: 24.319403223002155\n",
      "testing time: 0.93545080399781\n",
      "accuracy:  0.7545787545787546\n",
      "avg accuracy for 10 runs:  0.7604395604395604\n",
      "\n",
      "\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 189ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 171ms/step\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step\n"
     ]
    }
   ],
   "source": [
    "from sktime.classification.feature_based import FreshPRINCE\n",
    "#from sktime._contrib.vector_classifiers._rotation_forest import RotationForest\n",
    "\n",
    "clf_freshPRINCE = FreshPRINCE(\n",
    "    default_fc_parameters=\"minimal\",\n",
    "    n_estimators=5,\n",
    ")\n",
    "clf_freshPRINCE.fit(X_train, y_train)\n",
    "FreshPRINCE(...)\n",
    "y_pred_freshPRINCE = clf_freshPRINCE.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6556776556776557"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_freshPRINCE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6423006240971285"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_freshPRINCE, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6371016172935642"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_freshPRINCE, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6556776556776557"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_freshPRINCE, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 TSFResh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.classification.feature_based import TSFreshClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clfTSFresh = TSFreshClassifier(\n",
    "    default_fc_parameters=\"minimal\",\n",
    "    estimator=RandomForestClassifier(n_estimators=5),\n",
    ")\n",
    "clfTSFresh.fit(X_train, y_train)\n",
    "TSFreshClassifier(...)\n",
    "y_pred_TSFresh = clfTSFresh.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6446886446886447"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_TSFresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6306001902914513"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_TSFresh, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6280928853212004"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_TSFresh, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6446886446886447"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_TSFresh, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interval based: features are derived from selected phase dependent intervals in an ensemble of pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interval based classifiers adopt a random forest ensemble model, where each base classifier is a pipeline of transformation and a tree classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 DrCIF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Diverse Representation Canonical Interval Forest (DrCIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7582417582417582"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aeon.classification.compose import ChannelEnsembleClassifier\n",
    "from aeon.classification.interval_based import DrCIFClassifier\n",
    "\n",
    "from aeon.classification.convolution_based import RocketClassifier\n",
    "\n",
    "\n",
    "cls_DrCIF = ChannelEnsembleClassifier(\n",
    "    estimators=[\n",
    "        (\"DrCIF0\", DrCIFClassifier(n_estimators=5, n_intervals=2), [0]),\n",
    "        #(\"ROCKET3\", RocketClassifier(num_kernels=1000), [3, 4]),\n",
    "    ]\n",
    ")\n",
    "\n",
    "cls_DrCIF.fit(X_train, y_train)\n",
    "y_pred_DrCIF = cls_DrCIF.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test, y_pred_DrCIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7552166709643271"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_DrCIF, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7541231844637417"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_DrCIF, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7582417582417582"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_DrCIF, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 rSTSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nestor Cabello, Elham Naghizade, Jianzhong Qi, Lars Kulik\n",
    "# Fast, accurate and explainable time series classification through randomization.\n",
    "# Data Min Know Disc (2023)\n",
    "\n",
    "from rSTSF_functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Parameters that r-STSF uses\n",
    "\n",
    "# Statistics or aggregation functions\n",
    "# Note: np.percentile and np.quantile are just used as identifiers for \n",
    "# count mean-crossings (cmc) and count of values above mean (cam) statistics. See function getIntervalFeature(...)\n",
    "agg_fns = [np.mean, np.std, np.polyfit, np.median, np.min, np.max, iqr, np.percentile, np.quantile]\n",
    "repr_types = [1,2,3,4] # 1: Raw series, 2: Periodogram, 3: First-order Difference, 4: Autoregressive\n",
    "d = 50 # Number of sets of candidate discriminatory interval features to compute\n",
    "r = 500 # Number of trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rSTSF = rstsf(agg_fns=agg_fns, repr_types=repr_types[:1], d=1, r=2)\n",
    "clf_rSTSF.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_names = [\"PopularTimesGoogle\"]\n",
    "\n",
    "nruns = 10\n",
    "\n",
    "accuracies = np.zeros((len(dset_names),nruns))\n",
    "training_times = []\n",
    "testing_times = []\n",
    "\n",
    "cont_dsets = 0\n",
    "for dset_name in dset_names:\n",
    "    print(\"Dataset: \", dset_name)\n",
    "\n",
    "    inner_training_time = []\n",
    "    inner_testing_time = []\n",
    "    \n",
    "    for nrun in range(nruns):\n",
    "        print('run ',str(nrun+1))\n",
    "        timeA = time.perf_counter()\n",
    "        \n",
    "        clf_rstsf = rstsf()\n",
    "        clf_rstsf.fit(X_train, y_train)\n",
    "\n",
    "        current_training_time = time.perf_counter()-timeA\n",
    "        inner_training_time.append(current_training_time)\n",
    "        print(f\"training time: {current_training_time}\")\n",
    "\n",
    "        timeA = time.perf_counter()\n",
    "        \n",
    "        y_pred_rSTSF = clf_rstsf.predict(X_test)\n",
    "\n",
    "\n",
    "        current_testing_time = time.perf_counter()-timeA\n",
    "        inner_testing_time.append(current_testing_time)\n",
    "        print(f\"testing time: {current_testing_time}\")\n",
    "\n",
    "        accu = np.sum(y_pred_rSTSF==y_test)/len(y_test)\n",
    "        print('accuracy: ', accu)\n",
    "        accuracies[cont_dsets,nrun] = accu\n",
    "\n",
    "    avg_accuracy_this_dataset = np.mean(accuracies[cont_dsets,:])\n",
    "    print('avg accuracy for ' + str(nruns) + ' runs: ' , avg_accuracy_this_dataset)\n",
    "    \n",
    "    training_times.append(np.mean(inner_training_time))\n",
    "    testing_times.append(np.mean(inner_testing_time))\n",
    "\n",
    "    cont_dsets+=1\n",
    "    print(\"\\n\")\n",
    "\n",
    "\n",
    "## comment/uncomment the lines below according to the number of runs\n",
    "columns = {'Dataset':dset_names,\n",
    "           'run1':accuracies[:,0],\n",
    "               'run2':accuracies[:,1],\n",
    "               'run3':accuracies[:,2],\n",
    "               'run4':accuracies[:,3],\n",
    "               'run5':accuracies[:,4],\n",
    "               'run6':accuracies[:,5],\n",
    "               'run7':accuracies[:,6],\n",
    "               'run8':accuracies[:,7],\n",
    "               'run9':accuracies[:,8],\n",
    "               'run10':accuracies[:,9],\n",
    "           'avgAccu':np.mean(accuracies,axis=1),\n",
    "           'avgTrainTime':np.array(training_times),'avgTestTime':np.array(testing_times)}\n",
    "dfResults = pd.DataFrame(columns)\n",
    "dfResults = dfResults[['Dataset',\n",
    "                       'run1',\n",
    "                           'run2',\n",
    "                           'run3',\n",
    "                           'run4',\n",
    "                           'run5',\n",
    "                           'run6',\n",
    "                           'run7',\n",
    "                           'run8',\n",
    "                           'run9',\n",
    "                           'run10',\n",
    "                       'avgAccu','avgTrainTime','avgTestTime'\n",
    "                      ]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7545787545787546"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7545787545787546"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_rSTSF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7459006835714709"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_rSTSF, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74230853764795"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_rSTSF, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7545787545787546"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_rSTSF, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shapelet based: phase independent discriminatory subseries form the basis for classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shapelets are subseries from the training data that are independent of the phase and can be used to discriminate between classes of time series based on their presence or absence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 Random Dilated Shapelet Transform (RDST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7692307692307693"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aeon.classification.shapelet_based import RDSTClassifier\n",
    "\n",
    "rdst_AEON = RDSTClassifier()\n",
    "rdst_AEON.fit(X_train, y_train)\n",
    "y_pred_rdst_AEON = rdst_AEON.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test, y_pred_rdst_AEON)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.750923837956141"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_rdst_AEON, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.739900074382833"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_rdst_AEON, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7692307692307693"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_rdst_AEON, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 MrSQM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple Representations Sequence Miner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mrsqm import MrSQMClassifier, MrSQMTransformer\n",
    "from sklearn import metrics\n",
    "from scipy.interpolate import interp1d\n",
    "import timeit\n",
    "\n",
    "from sklearn.linear_model import RidgeClassifierCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train\n",
    "clf_MrSQMC = MrSQMClassifier(nsax=0, nsfa=5)\n",
    "clf_MrSQMC.fit(X_train,y_train)\n",
    "\n",
    "y_pred_MrSQMC = clf_MrSQMC.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use MrSQM Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfm = MrSQMTransformer()\n",
    "X_train_transform = tfm.fit_transform(X_train,y_train)\n",
    "X_test_transform = tfm.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use ridgecv classifier\n",
    "ridge = RidgeClassifierCV(alphas = np.logspace(-3, 3, 10)).fit(X_train_transform,y_train)\n",
    "y_pred_ridgecvt = ridge.predict(X_test_transform)\n",
    "print(metrics.accuracy_score(y_test, y_pred_ridgecvt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7765567765567766"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_ridgecvt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7608716588308426"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_ridgecvt, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7595043845043845"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_ridgecvt, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7765567765567766"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_ridgecvt, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dictionary based: histograms of counts of repeating patterns are the fea- tures for a classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to shapelet based algorithms, dictionary approaches extract phase- independent subseries. However, instead of measuring the distance to a sub- series, each window is converted into a short sequence of discrete symbols, commonly known as a word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 Weasel-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from aeon.datasets import load_arrow_head\n",
    "from weasel.classification.dictionary_based import WEASEL_V2\n",
    "\n",
    "clf_WEASEL_V2 = WEASEL_V2(random_state=1379, n_jobs=4)\n",
    "clf_WEASEL_V2.fit(X_train,y_train)\n",
    "y_pred_WEASEL_V2 = clf_WEASEL_V2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7838827838827839"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_WEASEL_V2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7737765691262555"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_WEASEL_V2, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7693735150907346"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_WEASEL_V2, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7838827838827839"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_WEASEL_V2, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 Temporal Dictonary Ensemble (TDE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "combines the best improvements introduced in WEASEL, SpatialBOSS and cBOSS and also includes several novel features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.classification.dictionary_based import TemporalDictionaryEnsemble\n",
    "\n",
    "clf_TDE = TemporalDictionaryEnsemble(\n",
    "    n_parameter_samples=10,\n",
    "    max_ensemble_size=3,\n",
    "    randomly_selected_params=5,\n",
    ") \n",
    "clf_TDE.fit(X_train, y_train) \n",
    "y_pred_TDE = clf_TDE.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.673992673992674"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_TDE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6683676492764691"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_TDE, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6692609971696973"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_TDE, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.673992673992674"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_TDE, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution based: convolutions and pooling operations create the feature space for classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hydra MultiRocket\n",
    "MultiRocket\n",
    "Hydra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep learning based: neural network based classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep learning has been the most active area of TSC research since the bake off in terms of the number of publications. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 InceptionTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sktime.classification.deep_learning.inceptiontime import InceptionTimeClassifier\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d {color: black;background-color: white;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d pre{padding: 0;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-toggleable {background-color: white;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-estimator:hover {background-color: #d4ebff;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-item {z-index: 1;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-parallel-item:only-child::after {width: 0;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-ecdd4237-fd98-404b-a74d-96b406f14e8d div.sk-text-repr-fallback {display: none;}</style><div id='sk-ecdd4237-fd98-404b-a74d-96b406f14e8d' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>InceptionTimeClassifier(n_epochs=10000)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('ba4a0089-39ca-4a85-93a3-fc13262e7586') type=\"checkbox\" checked><label for=UUID('ba4a0089-39ca-4a85-93a3-fc13262e7586') class='sk-toggleable__label sk-toggleable__label-arrow'>InceptionTimeClassifier</label><div class=\"sk-toggleable__content\"><pre>InceptionTimeClassifier(n_epochs=10000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "InceptionTimeClassifier(n_epochs=10000)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network = InceptionTimeClassifier(n_epochs=10000, verbose=False)\n",
    "network.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32234432234432236"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_Inception = network.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32234432234432236"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_Inception)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2848183686883983"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_Inception, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7009681169705695"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_Inception, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32234432234432236"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_Inception, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 ResNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.classification.deep_learning.resnet import ResNetClassifier\n",
    "clf_resnet = ResNetClassifier(n_epochs=10000) \n",
    "clf_resnet.fit(X_train, y_train)\n",
    "y_pred_resnet = clf_resnet.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2271062271062271"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_resnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1050650184187391"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred_resnet, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5068448963971353"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred_resnet, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2271062271062271"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_resnet, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hybrid approaches combine two or more of the above approaches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 1 HIVE-COTE version 2 (HC2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 Time Series Combination of Heterogeneous and Integrated Embeddings Forest TS-CHIEF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "não encontrei o código/biblioteca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genetic Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "N = 1\n",
    "categ = 0\n",
    "# X_ini = np.random.rand(N*24)\n",
    "X_cat, y_cat = extract_x_y(df,0,0,categ)\n",
    "X_avg = X_cat.mean(axis=0)\n",
    "X_avg = np.pad(X_avg, ((2,2)), mode='constant', constant_values=0)\n",
    "\n",
    "def fitness(X):\n",
    "    X_np = np.array(X)\n",
    "    X_sig = transform(torch.tensor(np.expand_dims(\n",
    "        X_np.reshape(N,24),\n",
    "        axis=1\n",
    "    ), dtype=torch.float32))\n",
    "\n",
    "    violated_constraints = np.maximum(0, X_np - 1.0) + np.maximum(0, 0.0 - X_np)\n",
    "    penalty = sum(violated_constraints)\n",
    "    distance = np.mean([np.min([np.linalg.norm(X_avg[x:x+24] - row) for x in range(5)]) for row in X_np.reshape(N, 24)])\n",
    "\n",
    "    y = hydra_classifier.predict(X_sig)\n",
    "    \n",
    "    return accuracy_score([categ]*N, y), - penalty, - distance\n",
    "\n",
    "# return accuracy_score([0,0,0,0,0,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5], y)\n",
    "# return accuracy_score([0,1,2,3,4,5], y)\n",
    "# res = minimize(fitness, X_ini, method = 'Nelder-Mead', options={'maxiter':100, 'disp':True,'fatol':0})\n",
    "# res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitness(X_ini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pygad\n",
    "\n",
    "num_generations = 10000\n",
    "num_parents_mating = 10\n",
    "sol_per_pop = 50\n",
    "num_genes = N*24\n",
    "\n",
    "ga_instance = pygad.GA(num_generations=num_generations,\n",
    "                       num_parents_mating=num_parents_mating,\n",
    "                       fitness_func=lambda ga, sol, i: fitness(sol),\n",
    "                       sol_per_pop=sol_per_pop,\n",
    "                       num_genes=num_genes,\n",
    "                       init_range_low=0.0,\n",
    "                       init_range_high=1.0,\n",
    "                       random_mutation_min_val=-0.1,\n",
    "                       random_mutation_max_val=0.1)\n",
    "\n",
    "ga_instance.run()\n",
    "\n",
    "print(\"Best solution:\", ga_instance.best_solution())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.01362603, 0.01088563, 0.01076658, 0.00566182, 0.01357704,\n",
       "        0.19008531, 0.39125168, 0.48478115, 0.4647077 , 0.44011196,\n",
       "        0.46602354, 0.47079004, 0.48122378, 0.5386247 , 0.67896835,\n",
       "        0.75474742, 0.67783663, 0.45983365, 0.25521616, 0.21586405,\n",
       "        0.06883266, 0.01615042, 0.00264618, 0.00108969]),\n",
       " array([ 1.        , -0.        , -0.00988973]),\n",
       " 0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_instance.best_solution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.43259577e-02, 1.24309025e-02, 1.07665806e-02, 9.46031175e-03,\n",
       "        1.35770362e-02, 1.90085311e-01, 3.91303728e-01, 4.84781150e-01,\n",
       "        4.64707697e-01, 4.36796487e-01, 4.64545095e-01, 4.70756179e-01,\n",
       "        4.78358985e-01, 5.41338703e-01, 6.83916835e-01, 7.54747422e-01,\n",
       "        6.77836629e-01, 4.59833647e-01, 2.53343983e-01, 2.17507729e-01,\n",
       "        6.37047521e-02, 1.52418963e-02, 5.31287624e-04, 1.29650917e-03]),\n",
       " array([ 1.        , -0.        , -0.00655205]),\n",
       " 0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_instance.num_generations = 9000\n",
    "ga_instance.run()\n",
    "ga_instance.best_solution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 1.        , -0.        , -0.00655205]),\n",
       " array([ 1.        , -0.        , -0.08766666]),\n",
       " array([ 1.        , -0.        , -0.08621751]),\n",
       " array([ 1.        , -0.06796937, -0.0760524 ]),\n",
       " array([ 1.        , -0.        , -0.08963265]),\n",
       " array([ 1.        , -0.        , -0.11340518]),\n",
       " array([ 1.        , -0.03802613, -0.07233255]),\n",
       " array([ 1.       , -0.       , -0.1155542]),\n",
       " array([ 1.        , -0.03473367, -0.06800981]),\n",
       " array([ 1.       , -0.       , -0.1320281]),\n",
       " array([ 1.        , -0.        , -0.02605693]),\n",
       " array([ 1.       , -0.       , -0.0389118]),\n",
       " array([ 1.        , -0.02726243, -0.08887823]),\n",
       " array([ 1.        , -0.00633809, -0.06437736]),\n",
       " array([ 1.        , -0.        , -0.10426858]),\n",
       " array([ 1.        , -0.03830265, -0.15442425]),\n",
       " array([ 1.       , -0.       , -0.1094665]),\n",
       " array([ 1.        , -0.01994966, -0.06578777]),\n",
       " array([ 1.        , -0.00552953, -0.06605018]),\n",
       " array([ 1.        , -0.        , -0.08322107]),\n",
       " array([ 1.        , -0.        , -0.06300445]),\n",
       " array([ 1.        , -0.02226271, -0.06589464]),\n",
       " array([ 1.        , -0.        , -0.11561357]),\n",
       " array([ 1.        , -0.07444046, -0.11946127]),\n",
       " array([ 1.        , -0.01547816, -0.05702605]),\n",
       " array([ 1.        , -0.0408705 , -0.12013956]),\n",
       " array([ 1.        , -0.00788057, -0.13148227]),\n",
       " array([ 1.        , -0.07364811, -0.10484635]),\n",
       " array([ 1.        , -0.03473367, -0.12344583]),\n",
       " array([ 1.        , -0.06565448, -0.12954151]),\n",
       " array([ 1.        , -0.05327772, -0.07784581]),\n",
       " array([ 1.        , -0.        , -0.10342145]),\n",
       " array([ 1.        , -0.02726243, -0.07890525]),\n",
       " array([ 1.        , -0.06906199, -0.08522858]),\n",
       " array([ 1.        , -0.        , -0.06940437]),\n",
       " array([ 1.        , -0.        , -0.11965716]),\n",
       " array([ 1.        , -0.14038225, -0.12844325]),\n",
       " array([ 1.        , -0.        , -0.11197971]),\n",
       " array([ 1.        , -0.03473367, -0.11427548]),\n",
       " array([ 1.        , -0.07267162, -0.11243898]),\n",
       " array([ 1.        , -0.        , -0.08526715]),\n",
       " array([ 1.        , -0.        , -0.12528681]),\n",
       " array([ 1.        , -0.02726243, -0.06547027]),\n",
       " array([ 1.        , -0.00633809, -0.11048305]),\n",
       " array([ 1.        , -0.        , -0.07320122]),\n",
       " array([ 1.        , -0.        , -0.07728546]),\n",
       " array([ 1.        , -0.        , -0.12096082]),\n",
       " array([ 1.        , -0.        , -0.10206835]),\n",
       " array([ 1.        , -0.03473367, -0.08122262]),\n",
       " array([ 1.        , -0.00728037, -0.11982801])]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(ga_instance.last_generation_fitness,key=lambda x: x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_instance.generations_completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAHNCAYAAAAUgN/UAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABcdElEQVR4nO3deVxUVeMG8GeGfRGQHRQQcU9cwEQ0BZFCLXPL3Vxy630l19zSRK1eLHMrLdsUzdTM0lxKf4a74i7uoBKIG+AGiMoic35/GLcZZ0BggLnI8/185hNz7rn3nnMvOE/n3HtHIYQQICIiIiIAgNLQDSAiIiKSE4YjIiIiIjUMR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIqIyplAoEBwcbOhmVFo8fmRoDEdUqSUlJUGhUGi8TE1N4eHhgf79++PMmTNlsp+EhARMmDABzZo1Q/Xq1WFiYgInJye0a9cOc+bMwdWrV4tcf9WqVVL7jh07Vmi9WbNmafTFyMgIdnZ2qFevHnr16oUVK1bg4cOHZdKn4tK37y+i4OBgKBQKQzej0qlVq5bW36v6a9OmTYWuO2TIECgUCiQlJVVYe6nqMjZ0A4jKgo+PDwYOHAgAyMrKwuHDh7F27Vr89ttviI6ORps2bUq97QULFmDKlCl48uQJWrVqhYEDB8LGxgb37t3D8ePHMWvWLHz88cc4ePAgXn75ZZ3b+OGHH6BQKCCEwPLlywutV6Bnz55o3LgxACAzMxNJSUnYs2cPNmzYgJkzZ+LHH3+skP+zLou+V0UXL16EpaWloZshS0ZGRpgxY4bOZQ0aNADA40cyIIgqscTERAFAhIWFaS2bPn26ACCCgoJKvf1ly5YJAMLb21scO3ZMZ534+HjRp08f8ddff+lcfunSJQFAvPnmm6JevXrC1tZWPHr0SGfdiIgIAUCsXbtWa1l2draYO3euUCqVwsrKSpw+fbrU/SqOsuj7iyooKEjwn8+S8/LyEmZmZqVad/DgwQKASExMLNtGEenAv26q1IoKRykpKQKAsLS0FEIIMWDAAAFAHDlyROe2PvzwQwFArFmzRgghxL1794SNjY0wMzMTcXFxz21LXl6ezvKpU6cKAGLDhg3io48+EgDEqlWrdNYtKhwVmDVrlgAgOnXq9Nw2lVZZ9P3vv/8Ww4YNEx4eHsLU1FS4urqKwYMHi6SkJK26BSE2JSVFDBo0SDg4OAhzc3MREBAgdu/erXOfmZmZYubMmaJRo0bC3Nxc2Nraitdee03s379fq25BmHn8+LGYPn26qF27tjA2NhYRERFCiKchb9KkSaJ58+bC3t5emJmZibp164opU6aIBw8eaLVV12vw4MFa/XnW7du3xdixY0WtWrWEqampcHJyEr169RJnz57VqlsQBv7++2+xePFiUb9+fWFqaio8PT3FrFmzRH5+vs7joi4pKUkoFArRvn17nctzc3OFg4ODqFmzprS99PR08eGHH4qGDRsKKysrUa1aNeHj4yMGDRqk89yVRHHD0bPHz8vLS+cxV69T3r9DN2/eFGPGjBF16tSR6jZo0ECMGjVKpKenS/XK8/hRxWE4okqtOOHIyspKCCHEvn37BAAxYsQIrbpPnjwRNWvWFA4ODiI7O1sIIcS3334rAIiBAweWun1PnjwRbm5uonr16iI7O1v6sCpsNKs44ejBgwfC0tJSKJVKjX+Uy5K+fT98+LCwtbUVxsbGolu3bmLSpEmiV69ewtjYWDg7O4uEhASN+gBE06ZNRZ06dYS/v78YN26c6N+/vzAyMhKmpqZa4eHu3bvipZdeEgBEmzZtxLhx48Q777wjHBwchLGxsdi4caNG/YJw1LlzZ1GjRg0xbNgwMXHiRBEVFSWEECIyMlLY29uLnj17ivHjx4uxY8eKgIAAAUC0atVK5ObmStuKiIiQPqwjIiKkl/o+dYWjtLQ04ePjIwCI4OBgMXXqVNGnTx9hZGQkLC0ttT6QC8JRz549haOjoxgyZIgYM2aM8PT0FADEBx98UKxzERQUJJRKpbh27ZrWsk2bNgkAYsqUKUIIIVQqldTvNm3aiPHjx4uJEyeKt956S9jZ2YmdO3cWa5+FKW04WrhwoWjatKkAIMaOHSsd8xUrVmisU16/Qw8fPhTe3t5CoVCIsLAwMWnSJDF27Fjx5ptvCktLS3H58uUKOX5UcRiOqFIrKhzNnDlTAND4v+ZGjRqJatWqiaysLI26W7duFQDEuHHjpLKhQ4cKAOKHH34odfs2b94sAIhRo0ZJZe3atRMKhUL6B1VdccKREEK0bdtWABDR0dGlbltR9Ol7bm6uqFWrlqhWrZo4efKkxrL9+/cLIyMj8cYbb2iUF4wE/Pe//9UYEfn++++1jp8QQvTv318AEN99951GeWpqqvDw8BBOTk7i8ePHUnlBOGrWrJm4e/euVpuvX78ucnJytMpnz54tAIjVq1drlD9vWk1XOCo4ptOmTdMo37ZtmwAg6tSpo9H3gnDk7e0tbt68KZXfvn1b2NnZiWrVquls87MKjuGnn36qtaxnz54CgDh37pwQQogzZ84IAKJbt25adbOzs7VG0UrKy8tLGBkZaYTKgpf677yu4/e8abXy/B0q+DtW//ehwIMHD6T/oSrv40cVh+GIKrWCcOTj4yP9I/v+++9L4cHc3FwcOnRIqr948WIBQHz//fca2+nWrZsAIM6fPy+VderUSQAQf/75p9Z+T506pfWP+7OjFUII0bVrVwFAHDx4UCor+Mf62Q9JIYofjvr06SMAiJ9//rnIeqWlT99/++03AUDMmTNH57Z79OghlEqlyMjIkMoKRvie/fDIy8sTxsbGws/PTyq7ffu2MDIyEiEhITq3/8UXXwgAYsuWLVJZQZj5/fffi9X/Anfv3hUAxJAhQzTKSxqOcnJyhLm5uXBwcBAPHz7Uqv/qq68KAGLfvn1SWUEYWL58uVb9gmVnzpx5bh/S09OFubm58PX11Si/f/++MDMzE82aNZPKCj7c+/Xr99ztlkZh02MARNeuXaV6pQ1H5fU7VBCOdP3Nqivv40cVh3er0QshISEBs2fPBgCYmJjAxcUF/fv3x9SpU+Hr6yvVGzRoEKZOnYrvvvsOw4YNAwCkpqZi69ataN26NRo1alSs/cXGxkr7KzB48GB069ZNep+SkoJt27ahTp06aN26tVTeq1cvvPfee1i5ciU++ugjGBkZlbbbz5WUlISoqCiNMjs7O4wbN67U23xe3w8fPgwAiI+Px6xZs7TWT0lJgUqlwqVLl9CiRQupvF69erC2ttaoa2xsDBcXF6Snp0tlx44dQ35+PnJycnRu//LlywCAuLg4vPHGGxrLWrZsqbNPQgisWLECUVFROHfuHDIyMqBSqaTlN2/e1LleccXFxSE7Oxvt27fXeRdW+/btsXPnTsTGxqJt27Yay/z9/bXq16xZEwA0jkthbG1t8eabb2L9+vU4ffo0mjZtCgD45ZdfkJOTg7fffluq27BhQzRp0gRr167F9evX0a1bNwQHB6NZs2ZQKsvmyS9mZmbIzs4uk209q7x+h9q1awc3NzfMnTsXp0+fxhtvvIGgoCA0bNhQ45EOFXH8qGIwHNELISwsDNu3b39uPTs7O/Tu3RsrV67EuXPn0LhxY0RFReHJkycYMWKERl0XFxcAuj8YhwwZgiFDhgB4GgYCAwO16qxcuRJPnjzR+PABABsbG3Tt2hXr1q3D9u3b8frrrxe3m5KCNjk5ORVZLykpSSvIeHl5PTcc6dP3e/fuAQB++umnIvfx7POabGxsdNYzNjZGfn6+1vYPHjyIgwcPFnv7wL/9etaYMWOwZMkSeHh44M0334SbmxvMzMwAALNnz0ZOTk6RfXmezMzMIvfv5uamUU+druNibPz0n27141KUt99+G+vXr8fq1aulcPTjjz/CyMgI/fv319jurl27MGvWLPz666+YOHEigKe/Z+Hh4Zg+fXq5hnl9ldfvkK2tLQ4fPoyZM2diy5Yt+OOPPwAAHh4emDp1Kv773/9K+6nMx4/+xShLVc67774LAPjuu+8APH0GkY2NDXr37q1Rr2C0Z/fu3aXaz/LlywEAERERWg+7W7dunbTvksrKysKJEydgZGQEPz+/IusGBwdDPJ0+l17FeYiePn0v+IDasmWL1r7VX0FBQSXetvr2J06cWOT2IyIitNbV9eDGtLQ0LF26FE2aNEFcXByioqIQGRmJWbNmSb8r+ipoc2pqqs7lKSkpGvXKWseOHeHk5IS1a9dCpVIhKSkJBw4cQGhoKFxdXTXqOjg44Msvv8SNGzdw4cIFLFmyBPb29oiIiMBnn31WLu2raKX5HfL09ERUVBRu376NU6dO4dNPP4VKpcLo0aOxdu1aqV5VOH5VAcMRVTmtWrVCkyZNsHr1avzf//0fLl++jAEDBmhNd7z11luoVq0afvnlF2mYvbj279+PS5cuwcfHB8OGDdP5cnJywtatW5GWllaibc+fPx+PHj1Cp06dYGtrW6J1i0ufvgcEBAAAYmJiyqNpePnll6FQKMps+3///TeEEAgNDdX6Hdi/f7/OdQr+77+4IzcNGjSAubk5jh07hkePHmkt37NnDwCgWbNmxW94CRgbG6Nv3764ceMGdu/ejZ9++glCCOnBqbooFAo0bNgQo0ePxs6dOwEAmzdvLpf2FUdJj3lR9PkdUiqVaNasGSZPniyFIl3HRW7Hj0qG4YiqpFGjRuHevXsYOnQoAGhNqQFA9erVMW/ePOTk5KBTp044ceKEzm3puu6jYERo+vTp+P7773W+hg8fjry8PKxatapYbc7JycFnn32GOXPmwNraGpGRkcXsbcnp0/euXbvC09MTCxYswL59+7SW5+Xl4cCBA6Vum6urK3r37o1Dhw5h3rx5EEJo1Tly5IjOEKKLl5cXAODQoUMa1xldv34d06ZN07mOvb09AODatWvF2oepqSn69euHO3fuaJ237du3Y8eOHahTp45eT3J/noLp3R9//BE//vgjrKys0L17d406SUlJOkcWC0a8zM3NpbK8vDzExcUhISGh3NqsrqTHvCgl/R06f/68zlG/Z49LSY4fyRuvOaIqaeDAgZg8eTJu3rwJf39/NG/eXGe9UaNGISsrC1OmTEGLFi0QGBgIf39/2NjY4O7du4iLi8O+fftgYmIijZhkZmbil19+gZWVFXr16lVoG4YMGYLIyEj88MMPeP/99zWWbdiwAXFxcQCeTqMlJiZi3759uHPnDjw8PLB69Wrp60XKS2n6Djy94HbDhg3o1KkTgoKCEBISAl9fXygUCly9ehX79++Hg4OD1L/S+OqrrxAfH4/Jkyfjxx9/RGBgIOzs7HDt2jUcP34cly9fxq1bt4r1FRRubm7o2bMnfv31V7Ro0QIdOnSQLtLv0KGDzg//kJAQbNiwAT179kSnTp1gbm6Opk2bokuXLoXu59NPP8XevXvx8ccf49ChQwgICEBSUhJ++eUXWFpaYsWKFeV60e7LL7+M+vXrY82aNcjLy8Pbb78NKysrjTqxsbHo0aMHWrZsiUaNGsHV1RU3btzApk2boFQqMX78eKnujRs30LBhQ3h5eVXI952FhITg888/x8iRI9GzZ09YWVnBy8tL65q+4irJ79DOnTsxadIktGnTBvXq1YODgwP+/vtvbN68Gebm5hg9ejSAkh0/krmKuCWOqLwU9Zyj5xk4cKAAIJYtW/bcupcvXxbjxo0TTZo0ETY2NsLY2Fg4ODiIV155RURERGg8+fabb77RemJyYdq0aaNxq3/BrfwFL6VSKWxsbESdOnXEW2+9JVasWKHzVvDyVJK+q7t+/boYO3asqFu3rjAzMxM2NjaiYcOGYvjw4VrPZ0IRX/Pi5eUlvLy8tMofPXokPvvsM+Hv7y+srKyEhYWF8Pb2Ft26dROrVq3SeGr38269f/DggZg4caKoVauW9HTsjz76SOTm5upsW15enpg8ebLw9PQUxsbGJXpC9pgxY4SXl5cwMTERjo6O4q233iryCdm6bl0v+D0p7MnPhfn444+l360dO3ZoLb927ZqYOnWqaNWqlXB2dpaeyN2jRw8RExOjUbfgb0/XuSlMaR8CWeCzzz4TdevWFSYmJoU+Ibuw/erzO3ThwgUxduxY0bx5c+Hg4CDMzMxE7dq1xeDBgzUe/1GS40fyphBCx3giURXg6+uLxMRE3Lx5s9wuhCUiosqH1xxRlfTnn3/i3LlzGDBgAIMRERFp4MgRVSlff/01rl27hu+//x4PHjzAhQsX4O3tbehmERGRjDAcUZVSq1YtXL9+HfXr18enn36q9QRlIiIihiMiIiIiNbzmiIiIiEgNwxERERGRGj4EshRUKhVu3ryJatWq6fyuJiIiIpIfIQQePHgAd3f3Ih+6ynBUCjdv3oSHh4ehm0FERESlcO3aNdSsWbPQ5QxHpVCtWjUATw8un5FDRERUOWRmZsLDw0P6HC8Mw1EpFEyl2djYMBwRERFVMs+7JIYXZBMRERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIiNQwHBERERGpYTgiIiIiUlPpw9G+ffvQpUsXuLu7Q6FQYNOmTc9dZ8+ePfDz84OZmRnq1KmDqKiocm8nERERVQ6V/rvVHj58iKZNm+Kdd95Bjx49nls/MTERr7/+Ot5991389NNPiI6OxvDhw+Hm5oawsLAKaLFu91U3kS0yDbZ/IiIiuammdIK1wqHC91vpw1GnTp3QqVOnYtdftmwZvL29MX/+fABAw4YNceDAASxcuNCg4ei3nAgcfrLOYPsnIiKSm95mkXjVNLzC91vpp9VKKiYmBqGhoRplYWFhiImJKXSdnJwcZGZmaryIiIjoxVTlwlFKSgpcXFw0ylxcXJCZmYnHjx/rXCcyMhK2trbSy8PDoyKaSkRERAZQ5cJRaUybNg0ZGRnS69q1a4ZuEhEREZWTSn/NUUm5uroiNTVVoyw1NRU2NjawsLDQuY6ZmRnMzMzKtV3vmH+Ld/Btue6DiIiInq/KhaPAwED88ccfGmU7d+5EYGCggVr0lEKhMOj+iYiI6KlKP62WlZWF2NhYxMbGAnh6q35sbCySk5MBPJ0SGzRokFT/3Xffxd9//43JkycjLi4OX331FdavX4/x48cbovlEREQkM5U+HB0/fhzNmzdH8+bNAQATJkxA8+bNMXPmTADArVu3pKAEAN7e3ti2bRt27tyJpk2bYv78+fj+++8Nehs/ERERyYdCCCEM3YjKJjMzE7a2tsjIyICNjY2hm0NERETFUNzP70o/ckRERERUlhiOiIiIiNQwHBERERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIiNQwHBERERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIiNQwHBERERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIiNQwHBERERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIiNQwHBERERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIiNQwHBERERGpYTgiIiIiUsNwRERERKSG4YiIiIhIDcMRERERkZoXIhwtXboUtWrVgrm5OQICAnD06NFC60ZFRUGhUGi8zM3NK7C1REREJGeVPhz9/PPPmDBhAiIiInDy5Ek0bdoUYWFhSEtLK3QdGxsb3Lp1S3pdvXq1AltMREREclbpw9GCBQswYsQIDB06FI0aNcKyZctgaWmJ5cuXF7qOQqGAq6ur9HJxcanAFhMREZGcVepwlJubixMnTiA0NFQqUyqVCA0NRUxMTKHrZWVlwcvLCx4eHujatSvOnz9fEc0lIiKiSqBSh6M7d+4gPz9fa+THxcUFKSkpOtepX78+li9fjt9//x2rV6+GSqVC69atcf369UL3k5OTg8zMTI0XERERvZgqdTgqjcDAQAwaNAjNmjVDUFAQfvvtNzg5OeGbb74pdJ3IyEjY2tpKLw8PjwpsMREREVWkSh2OHB0dYWRkhNTUVI3y1NRUuLq6FmsbJiYmaN68Oa5cuVJonWnTpiEjI0N6Xbt2Ta92ExERkXxV6nBkamoKf39/REdHS2UqlQrR0dEIDAws1jby8/Nx9uxZuLm5FVrHzMwMNjY2Gi8iIiJ6MRkbugH6mjBhAgYPHowWLVqgZcuWWLRoER4+fIihQ4cCAAYNGoQaNWogMjISADBnzhy0atUKderUQXp6OubNm4erV69i+PDhhuwGERERyUSlD0d9+vTB7du3MXPmTKSkpKBZs2bYvn27dJF2cnIylMp/B8ju37+PESNGICUlBdWrV4e/vz8OHTqERo0aGaoLREREJCMKIYQwdCMqm8zMTNja2iIjI4NTbERERJVEcT+/K/U1R0RERERljeGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLzQoSjpUuXolatWjA3N0dAQACOHj1aZP1ffvkFDRo0gLm5OXx9ffHHH39UUEuJiIhI7ip9OPr5558xYcIERERE4OTJk2jatCnCwsKQlpams/6hQ4fQr18/DBs2DKdOnUK3bt3QrVs3nDt3roJbTkRERHKkEEIIQzdCHwEBAXj55ZexZMkSAIBKpYKHhwfee+89TJ06Vat+nz598PDhQ2zdulUqa9WqFZo1a4Zly5YVa5+ZmZmwtbVFRkYGbGxsyqQfn++Ix5LdVzTKbC1MUMPOoky2T0REVCo6YoICosj3uuob40mJdz24bR108/cu8XqFKe7nt3GZ7dEAcnNzceLECUybNk0qUyqVCA0NRUxMjM51YmJiMGHCBI2ysLAwbNq0qdD95OTkICcnR3qfmZmpX8N1+O3kda2yjMd5yHicV+b7kjtjPIEFctX+2AQU//xUUPbvfzV//neZdpnuev8sKyhUL9NVr5B2FN6mwvf5b/nz65ohD7WUKcgTxrrrKrTbo6tdhbeh8H4V1U83xT0ooIKAUqsfxdlnYfsrbB0lVDBX5MEUuv8uivoHWlHokqLW0b2s8G2Vrg2FreOuuAMBBVT/rKnrXKn/XPjywvZV+Lkq7jaKOncl3YbGckVhx/752zZDbhF91vVel6LrFCcclHSd4rWj7NulLORYy8HhuImA/8wK32+lDkd37txBfn4+XFxcNMpdXFwQFxenc52UlBSd9VNSUgrdT2RkJGbPnq1/g4twMyO73LZtiWzUUdyAEgJKqKCAgBICfsrLEACMoYISKhhBBaVCBSXE05+hgpviHqrjAbJhihqKO3hJeRU3hT2UhQYG3QGhqKChHn6qKR6X23EgIiIqjkodjirKtGnTNEabMjMz4eHhYcAWPaWASvq5puI23jHaDidFBoyQj05Gx3BV5Qwvpe5rr/ThrrhX5tskIiKSi0odjhwdHWFkZITU1FSN8tTUVLi6uupcx9XVtUT1AcDMzAxmZmb6N7iEereoiUAfB0Dkwyt5E6yzrsL59kEY5efALOcuTPOKnt4rj2BERERUUXycrQ2y30odjkxNTeHv74/o6Gh069YNwNMLsqOjoxEeHq5zncDAQERHR2PcuHFS2c6dOxEYGFgBLS6Zz3o0BlZ0Bq4dNnRTSC//TBoqFIW/L2qZ+vu8R4B9bcCsmuby522/NO+LrKO2XAhA9QSw8yrmNvDM+5Ls959jZWz+9KXLs23UXGjYdYpcr5ByoXq6jk0NHevrOmalKS+sfbrOe1ntszzK//mv0gRQPvPxptVfHb/L5blcZ1EFt8HQy5VGz/m70eZkUb1E9ctKpQ5HADBhwgQMHjwYLVq0QMuWLbFo0SI8fPgQQ4cOBQAMGjQINWrUQGRkJABg7NixCAoKwvz58/H6669j3bp1OH78OL799ltDdkNLB+UJYE7/st+waTVAoXz6C6pQArlZTz9karUFlMp/lhn980tc8F8l8Pg+4Or79B+cvEdADX/ApOBOOvV/QAv7+Z96Oj/0dNQzswbMbZ9fr9Blxa1XRu0teF/CP3wiIpKfSh+O+vTpg9u3b2PmzJlISUlBs2bNsH37dumi6+TkZCiV/z7OqXXr1lizZg1mzJiBDz74AHXr1sWmTZvQuHFjQ3VBS7TpRPgob5V+AzVbAj7tgSc5gLUzUL0W4FgfcKxTZm0kIiJ6UVX65xwZQnk856jW1G0AgHeM/sRMkx+Lrtz2faCaK1CzBWBiqbZAAVT3Aowr/vooIiIiuasSzzl6ERUajIwtgKlXGXyIiIjKWaX/+pAXRUgDZ1RHIXef9f8FmJHCYERERFQBGI5kYmqnBqhtcl/HgmtAvdcqvkFERERVFMORTNRzqYavQ021F5iXzTVNREREVDwMRzLibMnbwImIiAyN4UhOHt7RfF+zpWHaQUREVIUxHMnJo2fCkVN9w7SDiIioCmM4kpOHtzXfWzkZph1ERERVGMORnDx+5m41S3vDtIOIiKgKYziSk/wnmu+N+FwjIiKiisZwJCciX/O90sgw7SAiIqrCGI7kRMVwREREZGgMR3KiemZaTcmvviMiIqpoDEdy8uy0moIjR0RERBVN76EJlUoFpVIzY8XExGDr1q0wNzfH0KFDUbNmTX13UzVwWo2IiMjg9Bo5Gj9+PCwtLZGeni6VbdiwAW3btkVkZCQiIiLg5+eH69ev69vOqoHhiIiIyOD0Cke7d+9GSEgI7OzspLKZM2fC1tYWq1atwmeffYb79+/j888/17edVcOz1xxxWo2IiKjC6TWtdu3aNQQFBUnvExMTERcXh4iICAwcOBAAsH//fmzfvl2/VlYVWrfy84JsIiKiiqbXyNHDhw9hZWUlvd+7dy8UCgU6deoklTVq1IjTasXFaTUiIiKD0yscubu7Iz4+Xnq/fft2WFtbw9/fXyrLzMyEmRmf9Fwsz4YjTqsRERFVOL3mbYKCgrB27VosWbIE5ubm+O2339CtWzcYGf37oZ6QkMC71YqLT8gmIiIyOL1GjqZPnw4LCwuMHTsWI0eOhJmZGWbNmiUtf/DgAfbt24c2bdro286qQeshkAxHREREFU2vkaM6dergwoUL+PXXXwEAXbp0gZeXl7T88uXLGDVqFPr3769fK6sKrWuOeEE2ERFRRdP709fNzQ3h4eE6l/n5+cHPz0/fXVQdvOaIiIjI4MplaCIpKQk7d+6Eubk5unfvDmtr6/LYzYuH1xwREREZnF7XHP3vf/+Dt7c37t+/L5Xt2bMHjRs3xrvvvoshQ4bAz88P9+7d07uhVQJv5SciIjI4vcLRpk2bUKtWLVSvXl0qmzJlClQqFWbPno3//Oc/uHLlChYtWqRvO6sGPiGbiIjI4PQKR0lJSWjUqJH0/ubNmzh27BhGjx6NGTNmYMmSJejQoQM2btyod0OrBD4hm4iIyOD0CkeZmZka36u2b98+KBQKdOnSRSrz8/NDcnKyPrupGoQAhEqzjNNqREREFU6vcOTi4oKrV69K73fu3AkzMzMEBARIZdnZ2VAoFPrspmp49nojgCNHREREBqDXp+/LL7+M33//HVu3boW5uTl+/vlntG/fXuPrQhITE+Hu7q53Q194z15vBAAKvbIrERERlYJen74ffPABnjx5gq5duyIsLAzZ2dn44IMPpOU5OTnYt2+fxkgSFeLZ640ATqsREREZgF4jR35+fjh8+DB+/PFHAEDv3r3RsmVLafmpU6fQvn17PiG7ODitRkREJAt6f/o2bdoUTZs21bmsVatWvFOtuDitRkREJAtlNjSRlZWFS5cu4eHDh2jbtm1ZbbZq43OOiIiIKpzeQxNJSUno2rUrqlevjpdffhnt27eXlh08eBCNGjXCnj179N3Ni+/Z2/gBjhwREREZgF6fvsnJyWjVqhX++OMPdO3aFYGBgRBCSMsDAgJw584drF27Vu+GvvAYjoiIiGRBr0/fiIgI3L9/H3v37sWGDRvw6quvaiw3NjZG27ZtcfDgQb0aWSXoDEd8PhQREVFF0ysc7dixA927d0fr1q0LrePl5YUbN27os5uqgSNHREREsqDXp++9e/dQq1atIusIIZCTk6PPbqoGhiMiIiJZ0PvrQy5fvlxknbNnz8LT01Of3VQNDEdERESyoNen76uvvoqtW7fizJkzOpfv378fu3btQufOnfXZTdWgdiG7hOGIiIiowun16TtjxgxYWFigXbt2+OSTT3DlyhUAwJ9//okPP/wQHTt2hKOjIyZNmlQmjX2hceSIiIhIFvR6CGStWrWwY8cO9O3bFx9++CEUCgWEEHjjjTcghICnpyc2bNgANze3smrvi4t3qxEREcmC3k/IDggIwOXLl7FlyxYcOXIE9+7dg42NDQICAtC1a1eYmpqWRTtffJxWIyIikoUy+foQY2NjdO/eHd27dy+LzVVNnFYjIiKSBX76ygXDERERkSzoPXKUm5uLTZs24dixY0hPT0d+fr5WHYVCgR9++EHfXb3YeM0RERGRLOgVjq5evYpXX30VCQkJGt+p9qzyCkf37t3De++9hy1btkCpVKJnz55YvHgxrK2tC10nODgYe/fu1SgbNWoUli1bVubtK5lnjh9HjYiIiAxCr3A0fvx4XLlyBW+//Tbeeecd1KxZE8bGZXIZU7EMGDAAt27dws6dO5GXl4ehQ4di5MiRWLNmTZHrjRgxAnPmzJHeW1palndTn+/ZkSOGIyIiIoPQK8ns2rULHTp0wMqVK8uqPcV28eJFbN++HceOHUOLFi0AAF9++SU6d+6Mzz//HO7u7oWua2lpCVdX14pqavEwHBEREcmCXp/AKpUKzZs3L6u2lEhMTAzs7OykYAQAoaGhUCqVOHLkSJHr/vTTT3B0dETjxo0xbdo0PHr0qMj6OTk5yMzM1HiVOYYjIiIiWdBr5CggIAAXL14sq7aUSEpKCpydnTXKjI2NYW9vj5SUlELX69+/P7y8vODu7o4zZ85gypQpiI+Px2+//VboOpGRkZg9e3aZtV0nrQuyeTE2ERGRIeg1PDF37lzs2rULGzZsKKv2YOrUqVAoFEW+4uLiSr39kSNHIiwsDL6+vhgwYABWrVqFjRs3IiEhodB1pk2bhoyMDOl17dq1Uu+/UM9ez86RIyIiIoPQa+Ro27ZtaN++Pfr06YOgoCD4+fnBxsZGq55CocCHH35YrG1OnDgRQ4YMKbJO7dq14erqirS0NI3yJ0+e4N69eyW6niggIAAAcOXKFfj4+OisY2ZmBjMzs2Jvs3QKv9uPiIiIKo5e4WjWrFnSz3v27MGePXt01itJOHJycoKTk9Nz6wUGBiI9PR0nTpyAv78/gKcXiKtUKinwFEdsbCwAGP773559FAKfcURERGQQeoWj3bt3l1U7Sqxhw4bo2LEjRowYgWXLliEvLw/h4eHo27evdKfajRs30KFDB6xatQotW7ZEQkIC1qxZg86dO8PBwQFnzpzB+PHj0a5dOzRp0sRgfdGN4YiIiMgQ9ApHQUFBZdWOUvnpp58QHh6ODh06SA+B/OKLL6TleXl5iI+Pl+5GMzU1xV9//YVFixbh4cOH8PDwQM+ePTFjxgxDdUENp9WIiIjkQK9wNGfOHAQHB6Ndu3aF1tm/fz92796NmTNn6rMrnezt7Yt84GOtWrU0ntzt4eGh9XRs2eC0GhERkSzodUvUrFmzCr3OqMC+ffvK/zb4FxLDERERkSGU+/3iubm5MDIyKu/dvAA4rUZERCQHeocjRRHTP7m5udi/f7/WwxpJB61pNcM0g4iIqKor8TVHtWvX1ni/cOFCrFixQqtefn4+7ty5g+zsbIwYMaL0LayymI6IiIgMocThSKVSSaNFCoUCQgiNi54LmJiY4KWXXkJISEixn3FUtXFajYiISA5KHI6SkpKkn5VKJcaPH18ud6JVObxbjYiISBb0upU/MTERdnZ2ZdSUqk7ry9UM0goiIqKqTq9w5OXlVVbtICIiIpKFEoWjOXPmQKFQYPTo0bC3t8ecOXOKtV5JvlutyuK0GhERkSwohK6rqQuhVCqhUChw8eJF1KtXD0pl8Z4EoFAokJ+fX+pGyk1mZiZsbW2RkZEBGxubstlo4j5gZZd/31s6ApMTymbbREREVOzP7xKNHO3evRsZGRnw9PSU3hMRERG9SEoUjoKCgmBkZIRZs2bhww8/lL549siRIzhy5AjGjBlTLo2sEjitRkREJAslfkK2rucabd++HePHjy+zRlVNvFuNiIhIDsr9u9WolDhyREREZBAMR3JR/OviiYiIqBwxHMkGp9WIiIjkgOFIrjitRkREZBClekL26tWrcfjwYen9lStXAACdO3fWWV+hUGDbtm2l2VXVwWk1IiIiWShVOLpy5YoUiNRt375dZ30FR0GKgdNqREREclDicJSYmFge7aBnMVASEREZRInDEb9stpxwVo2IiEgWeEG2bHBajYiISA4YjuSCXx9CREQkCwxHRERERGoYjmSD02pERERywHAkF1rTaoZpBhERUVXHcERERESkhuFINjitRkREJAcMR3LBu9WIiIhkgeGIiIiISA3DkWxwWo2IiEgOGI7kgtNqREREssBwRERERKSG4Ug2OK1GREQkBwxHcsFpNSIiIllgOJINjhwRERHJAcMRERERkRqGI7ngtBoREZEsMBzJBqfViIiI5IDhiIiIiEgNw5FccFqNiIhIFhiOZIPTakRERHLAcERERESkhuFILjitRkREJAsMR7LFcERERGQIDEdEREREahiO5ILTakRERLJQqcPRJ598gtatW8PS0hJ2dnbFWkcIgZkzZ8LNzQ0WFhYIDQ3F5cuXy7ehxcK71YiIiOSgUoej3Nxc9OrVC//5z3+Kvc5nn32GL774AsuWLcORI0dgZWWFsLAwZGdnl2NLiYiIqLIwNnQD9DF79mwAQFRUVLHqCyGwaNEizJgxA127dgUArFq1Ci4uLti0aRP69u1bXk0tTuM033PgiIiIyCAq9chRSSUmJiIlJQWhoaFSma2tLQICAhATE2PAlgGcViMiIpKHSj1yVFIpKSkAABcXF41yFxcXaZkuOTk5yMnJkd5nZmaWfeOeHTkiIiIig5DdyNHUqVOhUCiKfMXFxVVomyIjI2Frayu9PDw8yn+nvFuNiIjIIGQ3cjRx4kQMGTKkyDq1a9cu1bZdXV0BAKmpqXBzc5PKU1NT0axZs0LXmzZtGiZMmCC9z8zMLIeAxGk1IiIiOZBdOHJycoKTk1O5bNvb2xuurq6Ijo6WwlBmZiaOHDlS5B1vZmZmMDMzK5c2SficIyIiIlmQ3bRaSSQnJyM2NhbJycnIz89HbGwsYmNjkZWVJdVp0KABNm7cCABQKBQYN24cPv74Y2zevBlnz57FoEGD4O7ujm7duhmoF0RERCQnshs5KomZM2di5cqV0vvmzZsDAHbv3o3g4GAAQHx8PDIyMqQ6kydPxsOHDzFy5Eikp6fjlVdewfbt22Fubl6hbdfGaTUiIiI5UAjB26RKKjMzE7a2tsjIyICNjU3ZbPTkj8Dm8H/f1/AHRuwqm20TERFRsT+/K/W0GhEREVFZYziSDU6rERERyQHDkVzwbjUiIiJZYDgiIiIiUsNwJBucViMiIpIDhiO54LQaERGRLDAcEREREalhOJINTqsRERHJAcORXHBajYiISBYYjmSDDyonIiKSA4Yj2eLIERERkSEwHMkFp9WIiIhkgeGIiIiISA3DkWxx5IiIiMgQGI7kgtNqREREssBwJBu8W42IiEgOGI6IiIiI1DAcyQWn1YiIiGSB4Ug2+PUhREREcsBwRERERKSG4UguOK1GREQkCwxHssFpNSIiIjlgOJKLZ0eOiIiIyCAYjuSK02pEREQGwXAkG5xWIyIikgOGI7ngtBoREZEsMBzJFafViIiIDILhSDY4rUZERCQHDEdywWk1IiIiWWA4kitOqxERERkEw5FscFqNiIhIDhiO5ILTakRERLLAcCRXnFYjIiIyCIYj2eC0GhERkRwwHMkFp9WIiIhkgeFIrjitRkREZBAMR7LBaTUiIiI5MDZ0A+gfWtmI4YiIyJDy8/ORl5dn6GZQCZiYmMDIyEjv7TAcyQavOSIikgMhBFJSUpCenm7oplAp2NnZwdXVFQo9BhkYjoiIiNQUBCNnZ2dYWlrq9SFLFUcIgUePHiEtLQ0A4ObmVuptMRzJxbN3q/GPkYiowuXn50vByMHBwdDNoRKysLAAAKSlpcHZ2bnUU2y8IFs2OK1GRGRoBdcYWVpaGrglVFoF506f68UYjmSLI0dERIbCqbTKqyzOHcORXHBajYiISBYYjmSD02pERERywHAkWxw5IiIiMgTerSYXnFYjIpIdlVDhobhrsP1bKRygVFTOcYy8vDyYmJgYuhmlwnAkG5xWIyKSm4fiLiY8rG2w/S+w+hvVFE7Fqrt9+3Z8/PHHOHfuHIyMjBAYGIjFixfDx8cHAHD9+nVMmjQJO3bsQE5ODho2bIilS5ciICAAALBlyxbMmTMHZ8+ehbW1Ndq2bYuNGzcCeHqR88aNG9GtWzdpf3Z2dli0aBGGDBmCpKQkeHt7Y926dfjqq69w5MgRLFu2DF26dEF4eDj27duH+/fvw8fHBx988AH69esnbUelUuHzzz/Ht99+i2vXrsHFxQWjRo3C9OnTERISgkaNGmHJkiVS/du3b6NGjRr4888/0aFDB30PsU6VM47+45NPPkHr1q1haWkJOzu7Yq0zZMgQKBQKjVfHjh3Lt6GlwpEjIiIqvocPH2LChAk4fvw4oqOjoVQq0b17d6hUKmRlZSEoKAg3btzA5s2bcfr0aUyePBkqlQoAsG3bNnTv3h2dO3fGqVOnEB0djZYtW5a4DVOnTsXYsWNx8eJFhIWFITs7G/7+/ti2bRvOnTuHkSNH4u2338bRo0eldaZNm4a5c+fiww8/xIULF7BmzRq4uLgAAIYPH441a9YgJydHqr969WrUqFEDISEheh6xwlXqkaPc3Fz06tULgYGB+OGHH4q9XseOHbFixQrpvZmZWXk0r2Q4rUZERHro2bOnxvvly5fDyckJFy5cwKFDh3D79m0cO3YM9vb2AIA6depIdT/55BP07dsXs2fPlsqaNm1a4jaMGzcOPXr00Ch7//33pZ/fe+897NixA+vXr0fLli3x4MEDLF68GEuWLMHgwYMBAD4+PnjllVcAAD169EB4eDh+//139O7dGwAQFRUlDXSUl0odjgpOYlRUVInWMzMzg6urazm0SB+cViMiotK7fPkyZs6ciSNHjuDOnTvSqFBycjJiY2PRvHlzKRg9KzY2FiNGjNC7DS1atNB4n5+fj//9739Yv349bty4gdzcXOTk5EgParx48SJycnIKnR4zNzfH22+/jeXLl6N37944efIkzp07h82bN+vd1qJU6nBUWnv27IGzszOqV6+OkJAQfPzxx0U+Jj4nJ0djSC8zM7MCWsmRIyIiQ7NSOGCB1d8G3X9xdenSBV5eXvjuu+/g7u4OlUqFxo0bIzc3V/pajcI8b7lCoYB4ZoZD1xOoraysNN7PmzcPixcvxqJFi+Dr6wsrKyuMGzcOubm5xdov8HRqrVmzZrh+/TpWrFiBkJAQeHl5PXc9fVS5cNSxY0f06NED3t7eSEhIwAcffIBOnTohJiam0O9giYyM1BhqLBecViMikh2lQlnsC6IN6e7du4iPj8d3332Htm3bAgAOHDggLW/SpAm+//573Lt3T+foUZMmTRAdHY2hQ4fq3L6TkxNu3bolvb98+TIePXr03HYdPHgQXbt2xcCBAwE8vfj60qVLaNSoEQCgbt26sLCwQHR0NIYPH65zG76+vmjRogW+++47rFmzRuPi7PIiuwuyp06dqnXB9LOvuLi4Um+/b9++ePPNN+Hr64tu3bph69atOHbsGPbs2VPoOtOmTUNGRob0unbtWqn3XzhOqxERUelUr14dDg4O+Pbbb3HlyhXs2rULEyZMkJb369cPrq6u6NatGw4ePIi///4bv/76K2JiYgAAERERWLt2LSIiInDx4kWcPXsWn376qbR+SEgIlixZglOnTuH48eN49913i3Wbft26dbFz504cOnQIFy9exKhRo5CamiotNzc3x5QpUzB58mSsWrUKCQkJOHz4sNZ1xMOHD8fcuXMhhED37t31PVzPJbtwNHHiRFy8eLHIV+3aZXdbZe3ateHo6IgrV64UWsfMzAw2NjYarzL37MgRp9WIiKiYlEol1q1bhxMnTqBx48YYP3485s2bJy03NTXF//3f/8HZ2RmdO3eGr68v5s6dK82YBAcH45dffsHmzZvRrFkzhISEaNxRNn/+fHh4eKBt27bo378/3n///WJ9Oe+MGTPg5+eHsLAwBAcHSwFN3YcffoiJEydi5syZaNiwIfr06YO0tDSNOv369YOxsTH69esHc3NzPY5U8SjEs5OIlVBUVBTGjRuH9PT0Eq97/fp1eHp6YtOmTXjzzTeLtU5mZiZsbW2RkZFRdkFpZwRwcNG/75v0AXp8WzbbJiKiYsnOzkZiYiK8vb0r5EOYiicpKQk+Pj44duwY/Pz8iqxb1Dks7ue37EaOSqLgCvzk5GTk5+cjNjYWsbGxyMrKkuo0aNBAeohVVlYWJk2ahMOHDyMpKQnR0dHo2rUr6tSpg7CwMEN14x+VPqMSERGVqby8PKSkpGDGjBlo1arVc4NRWanUF2TPnDkTK1eulN43b94cALB7924EBwcDAOLj45GRkQEAMDIywpkzZ7By5Uqkp6fD3d0dr732Gj766CPDP+uI02pEREQaDh48iPbt26NevXrYsGFDhe23UoejqKio5z7jSH3W0MLCAjt27CjnVpUR3q1GRERVXHBwsNYjBCpCpZ5We7Fw5IiIiEgOGI7kovJfF09ERPRCYDiSK06rERERGQTDkWwxHBERERkCw5FccFqNiIhIFhiO5IoDR0REVAkpFAps2rTJ0M3QC8ORbPBuNSIierEkJSVBoVAgNjbW0E0pEYYjueC0GhERkSwwHMnGM+GId6sRERmcSiVwNyvHYC+Vqvj/4xwcHIzw8HCEh4fD1tYWjo6O+PDDD6WHKObk5OD9999HjRo1YGVlhYCAAOzZs0daPyoqCnZ2dtixYwcaNmwIa2trdOzYEbdu3ZLqHDt2DK+++iocHR1ha2uLoKAgnDx5stA2eXt7A3j6DRYKhQLBwcHYt28fTExMkJKSolF33LhxaNu2bbH7W54q9ROyX2wMR0REhnb/US78P/7LYPs/MSMUDtbF/3qrlStXYtiwYTh69CiOHz+OkSNHwtPTEyNGjEB4eDguXLiAdevWwd3dHRs3bkTHjh1x9uxZ1K1bFwDw6NEjfP755/jxxx+hVCoxcOBAvP/++/jpp58AAA8ePMDgwYPx5ZdfQgiB+fPno3Pnzrh8+TKqVaum1Z6jR4+iZcuW+Ouvv/DSSy/B1NQU9vb2qF27Nn788UdMmjQJwNPvUPvpp5/w2WeflcFR0x/DkVxwWo2IiPTk4eGBhQsXQqFQoH79+jh79iwWLlyIsLAwrFixAsnJyXB3dwcAvP/++9i+fTtWrFiB//3vfwCehpRly5bBx8cHABAeHo45c+ZI2w8JCdHY37fffgs7Ozvs3bsXb7zxhlZ7nJycAAAODg5wdXWVyocNG4YVK1ZI4WjLli3Izs5G7969y/BolB6n1WSD02pERKSfVq1aQaH2+REYGIjLly/j7NmzyM/PR7169WBtbS299u7di4SEBKm+paWlFIwAwM3NDWlpadL71NRUjBgxAnXr1oWtrS1sbGyQlZWF5OTkErVzyJAhuHLlCg4fPgzg6ZRe7969YWVlVdqulymOHMkWwxEREZWNrKwsGBkZ4cSJEzAyMtJYZm1tLf1sYmKisUyhUGh88evgwYNx9+5dLF68GF5eXjAzM0NgYCByc3NL1B5nZ2d06dIFK1asgLe3N/7880+N658MjeFILjitRkQkO9UtTXFiRqhB918SR44c0Xh/+PBh1K1bF82bN0d+fj7S0tL0uuj54MGD+Oqrr9C5c2cAwLVr13Dnzp1C65uaPm1/fn6+1rLhw4ejX79+qFmzJnx8fNCmTZtSt6usMRzJBqfViIjkRqlUlOiCaENLTk7GhAkTMGrUKJw8eRJffvkl5s+fj3r16mHAgAEYNGgQ5s+fj+bNm+P27duIjo5GkyZN8Prrrxdr+3Xr1sWPP/6IFi1aIDMzE5MmTYKFhUWh9Z2dnWFhYYHt27ejZs2aMDc3h62tLQAgLCwMNjY2+PjjjzWua5IDXnMkWwxHRERUMoMGDcLjx4/RsmVLjB49GmPHjsXIkSMBACtWrMCgQYMwceJE1K9fH926dcOxY8fg6elZ7O3/8MMPuH//Pvz8/PD2229jzJgxcHZ2LrS+sbExvvjiC3zzzTdwd3dH165dpWVKpRJDhgxBfn4+Bg0aVPpOlwOFEJzPKanMzEzY2toiIyMDNjY2ZbPRrROA4z/8+77FMOCNBWWzbSIiKpbs7GwkJibC29sb5ubmhm5OiQQHB6NZs2ZYtGiRoZtSbMOGDcPt27exefPmMttmUeewuJ/fnFaTDU6rERFR1ZCRkYGzZ89izZo1ZRqMygrDkWwxHBER0Yupa9euOHr0KN599128+uqrhm6OFoYjuXh2dpMjR0REVAJyuhX+eeTeVl6QLRu89IuIiEgOGI5kiyNHREREhsBwJBecViMiIpIFhiPZ4LQaERGRHDAcyYXW46Y4ckRERGQIDEdyxWk1IiIig2A4kg1OqxERUdkKDg7GuHHjAAC1atWqVE/PNiSGI7nQykYcOSIiorJz7Ngx6XvWnqeqByk+BFKuOK1GRERlyMnJydBNqDQ4ciQbnFYjIpIdlQp4eMdwL5Wq2E19+PAhBg0aBGtra7i5uWH+/Pkay9VHg4QQmDVrFjw9PWFmZgZ3d3eMGTMGwNOpuKtXr2L8+PFQKBRQ/PM/63fv3kW/fv1Qo0YNWFpawtfXF2vXrtXYR3BwMMaMGYPJkyfD3t4erq6umDVrlkad9PR0jBo1Ci4uLjA3N0fjxo2xdetWafmBAwfQtm1bWFhYwMPDA2PGjMHDhw+LfRzKAkeO5ELrbjUiIjK4x/eAeT6G2/+kBMDKsXhVJ03C3r178fvvv8PZ2RkffPABTp48iWbNmmnV/fXXX7Fw4UKsW7cOL730ElJSUnD69GkAwG+//YamTZti5MiRGDFihLROdnY2/P39MWXKFNjY2GDbtm14++234ePjg5YtW0r1Vq5ciQkTJuDIkSOIiYnBkCFD0KZNG7z66qtQqVTo1KkTHjx4gNWrV8PHxwcXLlyAkZERACAhIQEdO3bExx9/jOXLl+P27dsIDw9HeHg4VqxYoceBLBmGI7nitBoRERVTVlYWfvjhB6xevRodOnQA8DSk1KxZU2f95ORkuLq6IjQ0FCYmJvD09JQCjr29PYyMjFCtWjW4urpK69SoUQPvv/++9P69997Djh07sH79eo1w1KRJE0RERAAA6tatiyVLliA6Ohqvvvoq/vrrLxw9ehQXL15EvXr1AAC1a9eW1o2MjMSAAQOki8jr1q2LL774AkFBQfj6669hbm5eBkfr+TitJhscOSIiotJJSEhAbm4uAgICpDJ7e3vUr19fZ/1evXrh8ePHqF27NkaMGIGNGzfiyZMnRe4jPz8fH330EXx9fWFvbw9ra2vs2LEDycnJGvWaNGmi8d7NzQ1paWkAgNjYWNSsWVMKRs86ffo0oqKiYG1tLb3CwsKgUqmQmJj43ONQVjhyJBd8CCQREVUQDw8PxMfH46+//sLOnTvx3//+F/PmzcPevXthYmKic5158+Zh8eLFWLRoEXx9fWFlZYVx48YhNzdXo96z6ysUCqj+uXbKwsKiyHZlZWVh1KhR0vVP6jw9PUvSRb0wHMkVp9WIiAzPwv7pdT+G3H8x+Pj4wMTEBEeOHJFCxP3793Hp0iUEBQXp3rSFBbp06YIuXbpg9OjRaNCgAc6ePQs/Pz+YmpoiPz9fo/7BgwfRtWtXDBw4EACgUqlw6dIlNGrUqNjdadKkCa5fv45Lly7pHD3y8/PDhQsXUKdOnWJvszwwHMkGp9WIiGRHqSz2BdGGZG1tjWHDhmHSpElwcHCAs7Mzpk+fDqVS99UzUVFRyM/PR0BAACwtLbF69WpYWFjAy8sLwNM72/bt24e+ffvCzMwMjo6OqFu3LjZs2IBDhw6hevXqWLBgAVJTU0sUjoKCgtCuXTv07NkTCxYsQJ06dRAXFweFQoGOHTtiypQpaNWqFcLDwzF8+HBYWVnhwoUL2LlzJ5YsWVImx6o4eM2RXHBajYiI9DBv3jy0bdsWXbp0QWhoKF555RX4+/vrrGtnZ4fvvvsObdq0QZMmTfDXX39hy5YtcHBwAADMmTMHSUlJ8PHxkZ6PNGPGDPj5+SEsLAzBwcFwdXVFt27dStzOX3/9FS+//DL69euHRo0aYfLkydIoVZMmTbB3715cunQJbdu2RfPmzTFz5ky4u7uX7qCUkkII3kNeUpmZmbC1tUVGRgZsbGzKZqO/jgDOrv/3/SvjgdBZZbNtIiIqluzsbCQmJsLb27vC7oyislXUOSzu5zen1eSi53dAj2+f/sy8SkREZDAMR3JScBE2L8YmIiIyGF5zRERERKSG4YiIiIhIDcMRERERkRqGIyIiomfwRu7KqyzOHcMRERHRPwq++uLRo0cGbgmVVsG5K+xrUIqDd6sRERH9w8jICHZ2dtIXpVpaWkLBO4grBSEEHj16hLS0NNjZ2cHIyKjU22I4IiIiUuPq6goAUkCiysXOzk46h6XFcERERKRGoVDAzc0Nzs7OyMvLM3RzqARMTEz0GjEqUGnDUVJSEj766CPs2rULKSkpcHd3x8CBAzF9+nSYmpoWul52djYmTpyIdevWIScnB2FhYfjqq6/g4uJSga0nIiK5MzIyKpMPWqp8Ku0F2XFxcVCpVPjmm29w/vx5LFy4EMuWLcMHH3xQ5Hrjx4/Hli1b8Msvv2Dv3r24efMmevToUUGtJiIiIrl7ob54dt68efj666/x999/61yekZEBJycnrFmzBm+99RaApyGrYcOGiImJQatWrYq1n3L54lkiIiIqV8X9/K60I0e6ZGRkwN7evtDlJ06cQF5eHkJDQ6WyBg0awNPTEzExMYWul5OTg8zMTI0XERERvZgq7TVHz7py5Qq+/PJLfP7554XWSUlJgampKezs7DTKXVxckJKSUuh6kZGRmD17tlY5QxIREVHlUfC5/bxJM9mFo6lTp+LTTz8tss7FixfRoEED6f2NGzfQsWNH9OrVCyNGjCjzNk2bNg0TJkzQ2F+jRo3g4eFR5vsiIiKi8vXgwQPY2toWulx24WjixIkYMmRIkXVq164t/Xzz5k20b98erVu3xrffflvkeq6ursjNzUV6errG6FFqamqRz0QwMzODmZmZ9N7a2hrXrl1DtWrVyvThYJmZmfDw8MC1a9eqxLVMVam/7OuLqyr1l319cVWV/goh8ODBA7i7uxdZT3bhyMnJCU5OTsWqe+PGDbRv3x7+/v5YsWIFlMqiL6Hy9/eHiYkJoqOj0bNnTwBAfHw8kpOTERgYWOw2KpVK1KxZs9j1S8rGxuaF/uV8VlXqL/v64qpK/WVfX1xVob9FjRgVqLQXZN+4cQPBwcHw9PTE559/jtu3byMlJUXj2qEbN26gQYMGOHr0KICnB2TYsGGYMGECdu/ejRMnTmDo0KEIDAws9p1qRERE9GKT3chRce3cuRNXrlzBlStXtEZxCi60ysvLQ3x8vMYXCC5cuBBKpRI9e/bUeAgkEREREVCJw9GQIUOee21SrVq1tK5INzc3x9KlS7F06dJybF3pmJmZISIiQuP6phdZVeov+/riqkr9ZV9fXFWtv8/zQj0EkoiIiEhflfaaIyIiIqLywHBEREREpIbhiIiIiEgNwxERERGRGoYjGVm6dClq1aoFc3NzBAQESM9nkqvIyEi8/PLLqFatGpydndGtWzfEx8dr1AkODoZCodB4vfvuuxp1kpOT8frrr8PS0hLOzs6YNGkSnjx5olFnz5498PPzg5mZGerUqYOoqKjy7p6WWbNmafVF/WtssrOzMXr0aDg4OMDa2ho9e/ZEamqqxjYqS19r1aql1VeFQoHRo0cDqNzndd++fejSpQvc3d2hUCiwadMmjeVCCMycORNubm6wsLBAaGgoLl++rFHn3r17GDBgAGxsbGBnZ4dhw4YhKytLo86ZM2fQtm1bmJubw8PDA5999plWW3755Rc0aNAA5ubm8PX1xR9//FGh/c3Ly8OUKVPg6+sLKysruLu7Y9CgQbh586bGNnT9PsydO1d2/X3euR0yZIhWPzp27KhRp7Kc2+f1Vdffr0KhwLx586Q6leW8GoQgWVi3bp0wNTUVy5cvF+fPnxcjRowQdnZ2IjU11dBNK1RYWJhYsWKFOHfunIiNjRWdO3cWnp6eIisrS6oTFBQkRowYIW7duiW9MjIypOVPnjwRjRs3FqGhoeLUqVPijz/+EI6OjmLatGlSnb///ltYWlqKCRMmiAsXLogvv/xSGBkZie3bt1dofyMiIsRLL72k0Zfbt29Ly999913h4eEhoqOjxfHjx0WrVq1E69atK2Vf09LSNPq5c+dOAUDs3r1bCFG5z+sff/whpk+fLn777TcBQGzcuFFj+dy5c4Wtra3YtGmTOH36tHjzzTeFt7e3ePz4sVSnY8eOomnTpuLw4cNi//79ok6dOqJfv37S8oyMDOHi4iIGDBggzp07J9auXSssLCzEN998I9U5ePCgMDIyEp999pm4cOGCmDFjhjAxMRFnz56tsP6mp6eL0NBQ8fPPP4u4uDgRExMjWrZsKfz9/TW24eXlJebMmaNxvtX/zuXS3+ed28GDB4uOHTtq9OPevXsadSrLuX1eX9X7eOvWLbF8+XKhUChEQkKCVKeynFdDYDiSiZYtW4rRo0dL7/Pz84W7u7uIjIw0YKtKJi0tTQAQe/fulcqCgoLE2LFjC13njz/+EEqlUqSkpEhlX3/9tbCxsRE5OTlCCCEmT54sXnrpJY31+vTpI8LCwsq2A88REREhmjZtqnNZenq6MDExEb/88otUdvHiRQFAxMTECCEqV1+fNXbsWOHj4yNUKpUQ4sU5r89+qKhUKuHq6irmzZsnlaWnpwszMzOxdu1aIYQQFy5cEADEsWPHpDp//vmnUCgU4saNG0IIIb766itRvXp1qa9CCDFlyhRRv3596X3v3r3F66+/rtGegIAAMWrUqDLtozpdH6LPOnr0qAAgrl69KpV5eXmJhQsXFrqOHPtbWDjq2rVroetU1nNbnPPatWtXERISolFWGc9rReG0mgzk5ubixIkTCA0NlcqUSiVCQ0MRExNjwJaVTEZGBgDA3t5eo/ynn36Co6MjGjdujGnTpmk8sTwmJga+vr5wcXGRysLCwpCZmYnz589LddSPTUEdQxyby5cvw93dHbVr18aAAQOQnJwMADhx4gTy8vI02tmgQQN4enpK7axsfS2Qm5uL1atX45133tH4ouUX6bwWSExMREpKika7bG1tERAQoHEe7ezs0KJFC6lOaGgolEoljhw5ItVp164dTE1NpTphYWGIj4/H/fv3pTpy6z/w9O9YoVBofDk3AMydOxcODg5o3rw55s2bpzFFWpn6u2fPHjg7O6N+/fr4z3/+g7t370rLXtRzm5qaim3btmHYsGFay16U81rWKu0Tsl8kd+7cQX5+vsYHCQC4uLggLi7OQK0qGZVKhXHjxqFNmzZo3LixVN6/f394eXnB3d0dZ86cwZQpUxAfH4/ffvsNAJCSkqKz3wXLiqqTmZmJx48fw8LCojy7JgkICEBUVBTq16+PW7duYfbs2Wjbti3OnTuHlJQUmJqaan2guLi4PLcfBcuKqlPRfVW3adMmpKenazyR/kU6r+oK2qarXertdnZ21lhubGwMe3t7jTre3t5a2yhYVr169UL7r/79kBUtOzsbU6ZMQb9+/TS+fHTMmDHw8/ODvb09Dh06hGnTpuHWrVtYsGABgMrT344dO6JHjx7w9vZGQkICPvjgA3Tq1AkxMTEwMjJ6Yc/typUrUa1aNfTo0UOj/EU5r+WB4YjKxOjRo3Hu3DkcOHBAo3zkyJHSz76+vnBzc0OHDh2QkJAAHx+fim6mXjp16iT93KRJEwQEBMDLywvr1683yAd5Rfnhhx/QqVMnuLu7S2Uv0nmlp/Ly8tC7d28IIfD1119rLJswYYL0c5MmTWBqaopRo0YhMjKyUn3dRN++faWffX190aRJE/j4+GDPnj3o0KGDAVtWvpYvX44BAwbA3Nxco/xFOa/lgdNqMuDo6AgjIyOtO5tSU1Ph6upqoFYVX3h4OLZu3Yrdu3drfQnwswICAgAAV65cAQC4urrq7HfBsqLq2NjYGDSU2NnZoV69erhy5QpcXV2Rm5uL9PR0jTrq57Ay9vXq1av466+/MHz48CLrvSjntaBtRf0turq6Ii0tTWP5kydPcO/evTI514b4my8IRlevXsXOnTs1Ro10CQgIwJMnT5CUlASg8vW3QO3ateHo6Kjxe/uindv9+/cjPj7+uX/DwItzXssCw5EMmJqawt/fH9HR0VKZSqVCdHQ0AgMDDdiyogkhEB4ejo0bN2LXrl1aw6+6xMbGAgDc3NwAAIGBgTh79qzGP0gF/zg3atRIqqN+bArqGPrYZGVlISEhAW5ubvD394eJiYlGO+Pj45GcnCy1szL2dcWKFXB2dsbrr79eZL0X5bx6e3vD1dVVo12ZmZk4cuSIxnlMT0/HiRMnpDq7du2CSqWSQmJgYCD27duHvLw8qc7OnTtRv359VK9eXaojh/4XBKPLly/jr7/+goODw3PXiY2NhVKplKagKlN/1V2/fh13797V+L19kc4t8HTk19/fH02bNn1u3RflvJYJQ18RTk+tW7dOmJmZiaioKHHhwgUxcuRIYWdnp3G3j9z85z//Eba2tmLPnj0at4I+evRICCHElStXxJw5c8Tx48dFYmKi+P3330Xt2rVFu3btpG0U3PL92muvidjYWLF9+3bh5OSk85bvSZMmiYsXL4qlS5ca5Pb2iRMnij179ojExERx8OBBERoaKhwdHUVaWpoQ4umt/J6enmLXrl3i+PHjIjAwUAQGBlbKvgrx9I5JT09PMWXKFI3yyn5eHzx4IE6dOiVOnTolAIgFCxaIU6dOSXdnzZ07V9jZ2Ynff/9dnDlzRnTt2lXnrfzNmzcXR44cEQcOHBB169bVuN07PT1duLi4iLffflucO3dOrFu3TlhaWmrdAm1sbCw+//xzcfHiRREREVEut0AX1d/c3Fzx5ptvipo1a4rY2FiNv+OCO5QOHTokFi5cKGJjY0VCQoJYvXq1cHJyEoMGDZJdf4vq64MHD8T7778vYmJiRGJiovjrr7+En5+fqFu3rsjOzpa2UVnO7fN+j4V4eiu+paWl+Prrr7XWr0zn1RAYjmTkyy+/FJ6ensLU1FS0bNlSHD582NBNKhIAna8VK1YIIYRITk4W7dq1E/b29sLMzEzUqVNHTJo0SeN5OEIIkZSUJDp16iQsLCyEo6OjmDhxosjLy9Oos3v3btGsWTNhamoqateuLe2jIvXp00e4ubkJU1NTUaNGDdGnTx9x5coVafnjx4/Ff//7X1G9enVhaWkpunfvLm7duqWxjcrSVyGE2LFjhwAg4uPjNcor+3ndvXu3zt/bwYMHCyGe3s7/4YcfChcXF2FmZiY6dOigdQzu3r0r+vXrJ6ytrYWNjY0YOnSoePDggUad06dPi1deeUWYmZmJGjVqiLlz52q1Zf369aJevXrC1NRUvPTSS2Lbtm0V2t/ExMRC/44Lnml14sQJERAQIGxtbYW5ublo2LCh+N///qcRKOTS36L6+ujRI/Haa68JJycnYWJiIry8vMSIESO0/ge0spzb5/0eCyHEN998IywsLER6errW+pXpvBqCQgghynVoioiIiKgS4TVHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIgNISkqCQqHAkCFDDN0UInoGwxERlZnY2Fi8++67aNSoEWxsbGBqagpXV1e8+uqrmD9/Pm7fvm3oJlaoWrVqoVatWoZuBhGVEJ+QTUR6U6lUmDx5MubPnw8jIyO0a9cOTZo0gZWVFdLS0hATE4Pz58/DysoK8fHxqFGjhqGbXCEKglHBt5yry8vLQ0JCAmxtbaUvPiUieTA2dAOIqPKbPn065s+fDz8/P/z888+oU6eOVp2TJ09iypQpePz4sQFaKD8mJiZo0KCBoZtBRDpwWo2I9HLp0iXMmzcPTk5O2L59u85gBAB+fn7YuXOn1jTTmTNn0LdvX7i5ucHU1BReXl547733cPfuXY166tfoXLlyBd27d0f16tVhZWWF0NBQnD59Wud+09LSMH78eNSpUwdmZmZwdHREz549ce7cOa26BdNg6enpCA8Ph4eHB4yNjREVFQUAOHHiBMLDw9G4cWPY2trCwsICvr6+mDt3LvLy8rTaevXqVVy9ehUKhUJ6zZo1S6s/z7p69SqGDRuGGjVqwNTUFDVr1sSwYcOQnJysVTc4OBgKhQJ5eXmYNWsWatWqBTMzM9SrVw9fffWVzmNCREXjyBER6WXlypXIz8/HqFGj4OTk9Nz6xsb//rOzefNm9O7dG0qlEl27doWHhwcuXLiAJUuWYMeOHThy5AiqV6+usX5SUhJatWqFl156Ce+88w4SEhLw+++/o3379rh48SJcXFykugkJCQgODsb169fx2muvoVu3bkhLS8Ovv/6KHTt2IDo6GgEBARrbz8nJQUhICLKysvDmm2/C2NhY2uZ3332HLVu2oF27dujcuTMePXqEPXv2YNq0aTh27Bh+/fVXAICdnR0iIiKwaNEiAMC4ceOk7QcHBxd5fC5duoRXXnkFt2/fRpcuXfDSSy/h3LlzWL58ObZs2YIDBw6gXr16Wuv169cPR48eRadOnWBkZIT169dj9OjRMDExwYgRI557XohIjSAi0kP79u0FABEdHV2i9e7cuSNsbGxEjRo1RFJSksaytWvXCgAiPDxcKktMTBQABAAxd+5cjfozZswQAERkZKRGeevWrYWRkZHYvn27Rnl8fLyoVq2a8PX11Sj38vISAERYWJh49OiRVpuvXr0qnjx5olGmUqnEO++8IwCIAwcOaG3Py8tLZ/8L+jN48GCN8oLj+c0332iUL126VAAQISEhGuVBQUECgAgICBAZGRlSeVxcnDA2Nhb169fXuX8iKhzDERHppWHDhgKAuHjxotay3bt3i4iICI3X7t27hRBCLFiwQAAQq1at0rldPz8/4ejoKL0vCBPe3t4iPz9fo27Bsh49ekhlJ0+eFADEO++8o3P7EyZMEADE2bNnpbKCcHT69Oli918IIU6cOCEAiFmzZmmUlzQcXb16VQAQjRo1EiqVSqN+fn6+aNCggQAgkpOTpfKCcLRr1y6tfRQsy8zMLFF/iKo6TqsRUbnZs2cPZs+erVUeHByMw4cPAwCOHDmChIQErTrZ2dm4c+cO7ty5A0dHR6m8WbNmUCo1L5esWbMmACA9PV0qK9h+amqqdJ2Puri4OOm/jRs3lsrNzc3h6+ursz+5ublYsmQJ1q1bh7i4OGRlZUGo3fB78+ZNnesVV2xsLAAgKCgICoVCY5lSqUS7du0QFxeH2NhYeHh4aCz39/fX2p76calWrZpebSOqShiOiEgvLi4uuHjxIm7evKl199WsWbOkYLJu3Tr069dPWnbv3j0AwNKlS4vc/sOHDzXCkY2NjVadguuY8vPztba/bds2bNu2rcjtq3N2dtYKJgXeeustbNmyBfXq1UOfPn3g7OwMExMTpKenY/HixcjJySmyL8+TmZkJABrXTakruOW/oJ664h4XIno+hiMi0kvr1q2xZ88e7N69GyEhIcVer+DD/OzZsxojN2WlYPtffvklwsPDi71eYcHo2LFj2LJlC8LCwrBt2zYYGRlJyw4fPozFixfr12D82+bU1FSdy1NSUjTqEVH54K38RKSXwYMHQ6lU4ttvv8WdO3eKvV7BXWIxMTHl0q6y3n7B1N/rr7+uEYwAYP/+/TrXMTIyKtGoTbNmzQAA+/bt05iuAwAhBPbt26dRj4jKB8MREemlXr16mDx5MtLS0tCpUydcuXJFZz3164EAYOjQoahWrRqmT5+O8+fPa9V/9OiRdN1QabRs2RIBAQFYu3Ytfv75Z63lKpUKe/fuLfb2vLy8AAAHDhzQKD9//jwiIyN1rmNvb487d+4gOzu7WPvw9PRE+/btcf78eSxfvlxj2bfffouLFy8iJCRE63ojIipbnFYjIr198sknyM3NxYIFC9CgQQO0a9cOTZs2haWlJdLS0nDmzBkcPXoU1tbW0qiHk5MT1q5di169eqFp06bo2LEjGjRogJycHCQlJWHv3r1o3bo1tm/fXup2rV27Fu3bt0ffvn2xaNEi+Pn5wcLCAsnJyYiJicHt27eLHVxatmyJli1bYv369bh16xZatWqF5ORkbN68Ga+//jo2bNigtU5ISAiOHz+OTp06oW3btjA1NUW7du3Qrl27Qvfz9ddf45VXXsGIESOwZcsWNGrUCOfPn8fmzZvh5OSEr7/+utTHg4iKycB3yxHRC+TkyZNi5MiRokGDBsLa2lqYmJgIFxcXERISIubNmydSU1O11omLixPDhg0TXl5ewtTUVFSvXl34+vqKMWPGiKNHj0r1CnsuUAEAIigoSKv83r17YsaMGaJx48bCwsJCWFtbi7p164r+/fuL3377TaNuUbfeCyFEWlqaeOedd4S7u7swNzcXvr6+YunSpeLvv//W2bYHDx6IESNGCDc3N2FkZCQAiIiIiOf2JykpSQwdOlS4ubkJY2Nj4ebmJoYOHar1PCgh/r1dX5fBgwcLACIxMbHQPhGRNn7xLBEREZEaXnNEREREpIbhiIiIiEgNwxERERGRGoYjIiIiIjUMR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIiIiIjUMR0RERERq/h+5/CQMFmA4dAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x130f76700>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = ga_instance.plot_fitness(label=['accuracy','penalty','distance'])\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.        , -0.00655205],\n",
       "       [ 1.        , -0.        , -0.08766666],\n",
       "       [ 1.        , -0.        , -0.08621751],\n",
       "       [ 1.        , -0.06796937, -0.0760524 ],\n",
       "       [ 1.        , -0.        , -0.08963265],\n",
       "       [ 1.        , -0.        , -0.11340518],\n",
       "       [ 1.        , -0.03802613, -0.07233255],\n",
       "       [ 1.        , -0.        , -0.1155542 ],\n",
       "       [ 1.        , -0.03473367, -0.06800981],\n",
       "       [ 1.        , -0.        , -0.1320281 ],\n",
       "       [ 1.        , -0.        , -0.02605693],\n",
       "       [ 1.        , -0.        , -0.0389118 ],\n",
       "       [ 1.        , -0.02726243, -0.08887823],\n",
       "       [ 1.        , -0.00633809, -0.06437736],\n",
       "       [ 1.        , -0.        , -0.10426858],\n",
       "       [ 1.        , -0.03830265, -0.15442425],\n",
       "       [ 1.        , -0.        , -0.1094665 ],\n",
       "       [ 1.        , -0.01994966, -0.06578777],\n",
       "       [ 1.        , -0.00552953, -0.06605018],\n",
       "       [ 1.        , -0.        , -0.08322107],\n",
       "       [ 1.        , -0.        , -0.06300445],\n",
       "       [ 1.        , -0.02226271, -0.06589464],\n",
       "       [ 1.        , -0.        , -0.11561357],\n",
       "       [ 1.        , -0.07444046, -0.11946127],\n",
       "       [ 1.        , -0.01547816, -0.05702605],\n",
       "       [ 1.        , -0.0408705 , -0.12013956],\n",
       "       [ 1.        , -0.00788057, -0.13148227],\n",
       "       [ 1.        , -0.07364811, -0.10484635],\n",
       "       [ 1.        , -0.03473367, -0.12344583],\n",
       "       [ 1.        , -0.06565448, -0.12954151],\n",
       "       [ 1.        , -0.05327772, -0.07784581],\n",
       "       [ 1.        , -0.        , -0.10342145],\n",
       "       [ 1.        , -0.02726243, -0.07890525],\n",
       "       [ 1.        , -0.06906199, -0.08522858],\n",
       "       [ 1.        , -0.        , -0.06940437],\n",
       "       [ 1.        , -0.        , -0.11965716],\n",
       "       [ 1.        , -0.14038225, -0.12844325],\n",
       "       [ 1.        , -0.        , -0.11197971],\n",
       "       [ 1.        , -0.03473367, -0.11427548],\n",
       "       [ 1.        , -0.07267162, -0.11243898],\n",
       "       [ 1.        , -0.        , -0.08526715],\n",
       "       [ 1.        , -0.        , -0.12528681],\n",
       "       [ 1.        , -0.02726243, -0.06547027],\n",
       "       [ 1.        , -0.00633809, -0.11048305],\n",
       "       [ 1.        , -0.        , -0.07320122],\n",
       "       [ 1.        , -0.        , -0.07728546],\n",
       "       [ 1.        , -0.        , -0.12096082],\n",
       "       [ 1.        , -0.        , -0.10206835],\n",
       "       [ 1.        , -0.03473367, -0.08122262],\n",
       "       [ 1.        , -0.00728037, -0.11982801]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_instance.last_generation_fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 750x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "x = range(24)\n",
    "y,fit,idx = ga_instance.best_solution()\n",
    "\n",
    "plt.title(\"Line graph\")\n",
    "for y_ in np.array(y).reshape(N,24):\n",
    "    plt.plot(x, y_)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_instance.save('ga_1sig_ng100_npm10_spp50_categ0_10000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRANSFORMERS\n",
    "DTW VS TRANSFORMERS TIME SERIES\n",
    "MÉTRICAS (f1-score)\n",
    "\n",
    "OVERLEAF\n",
    "\n",
    "ESTUDO (RELATÓRIO)\n",
    "- CLASSIFICADORES (NOVAS TÉCNICAS)\n",
    "- DEEP LEARNING (SUMARIZAÇÃO)\n",
    "- OUTRAS TÉCNICAS (SUMARIZAÇÃO / LEVANTAMENTO)\n",
    "- TESTES-PILOTO \n",
    "- USO DAS ASSINATURAS (parâmetros dos classificadores (talvez))\n",
    "\n",
    "- aplicar em outros datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#teste\n",
    "import numpy as np\n",
    "\n",
    "# Define input signal and kernel\n",
    "signal = np.array([0, 0, 1, 2, 3, 4, 5, 0, 0])\n",
    "kernel = np.array([0, 0.5, 1, 1.5, 0])\n",
    "\n",
    "# Perform 1D convolution\n",
    "result = np.correlate(signal, kernel, mode='valid')\n",
    "\n",
    "print(\"Convolution result:\", result)\n",
    "result.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#teste\n",
    "two_d = np.array([\n",
    "    [1,2],\n",
    "    [3,4]\n",
    "])\n",
    "\n",
    "np.mean([np.correlate(\n",
    "    np.array([1,2,3,4]),\n",
    "    row\n",
    ").max() for row in two_d])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reunião 16/02\n",
    "\n",
    "*verificar se é bom ou não o uso de algoritmos genéticos\n",
    "\n",
    "*comparar com a geração simples de signature pela média do dataframe\n",
    "\n",
    "*ver os transformers\n",
    "\n",
    "*ver se tem mais classificadores interessantes\n",
    "\n",
    "*M2 do Leonardo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_filtrado = df[(df['country'] == 0) & (df['city'] == 0) & (df['category'] == 1)]\n",
    "df_teste = df_filtrado.drop(columns=['id','country', 'city', 'category'])\n",
    "\n",
    "column_means = np.mean(df_teste, axis=0)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, 25), column_means)\n",
    "\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Mean')\n",
    "plt.title('Mean of p=0 c=0 cat=0')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_avg(country, city, cat):\n",
    "    df_filtrado = df[(df['country'] == country) & (df['city'] == city) & (df['category'] == cat)]\n",
    "    df_teste = df_filtrado.drop(columns=['id','country', 'city', 'category'])\n",
    "    column_means = np.mean(df_teste, axis=0)\n",
    "    return column_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_new = np.array([\n",
    "    extract_avg(0,0,0),\n",
    "    extract_avg(0,0,1),\n",
    "    extract_avg(0,0,2),\n",
    "    extract_avg(0,0,3),\n",
    "    extract_avg(0,0,4)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 1, 24])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_tensor_new = torch.tensor(np.expand_dims(X_test_new, axis=1), dtype=torch.float32)\n",
    "X_test_tensor_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_new = [0,1,2,3,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Hydra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydra import Hydra, SparseScaler\n",
    "from sklearn.linear_model import RidgeClassifierCV\n",
    "\n",
    "X_test_transform_new = transform(X_test_tensor_new)\n",
    "\n",
    "X_test_transform_new = scaler.transform(X_test_transform_new)\n",
    "\n",
    "hydra_pred_new = hydra_classifier.predict(X_test_transform_new)\n",
    "\n",
    "print(accuracy_score(y_test_new, hydra_pred_new))\n",
    "print(f1_score(y_test_new, hydra_pred_new, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incluir k-fold = 5\n",
    "(desvio padrão e intervalo de confiança)\n",
    "ssh / conta "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grafos\n",
    "verificar possibilidades de grafos"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}