05_clean_classification_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "310b67e7-995b-41d7-8af3-d4e5ae9d3e58",
   "metadata": {},
   "source": [
    "<font size=\"+3\"><mark>Classification benchmark / Clean the results</mark></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6397cf7-a453-433c-bb01-671f91be13b6",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6746e88-e5ff-438f-a34a-a2aa77525968",
   "metadata": {},
   "source": [
    "## README"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f638a5c6-a54b-4ab0-b7cc-987a28c9c7c1",
   "metadata": {},
   "source": [
    "_Associated GitHub repository: https://github.com/sylvaincom/astride._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f575ad89-f039-40f5-9d23-dc0474ed3de9",
   "metadata": {},
   "source": [
    "## Configuration parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4571f76f-d793-469c-973b-89df5bc8f16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "METHOD_ABBREVIATES = [\"sax\", \"saxtslearn\", \"1dsax\", \"astride\", \"fastride\"]\n",
    "DATE_EXP = \"2023_02_08\"\n",
    "\n",
    "IS_EXPORT_DF = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11ced15-ac91-44bc-82b5-8c98f6c2aece",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a147606b-ab4f-493d-bd7f-926196206ee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.utils import Bunch\n",
    "import ast\n",
    "\n",
    "from pathlib import Path\n",
    "import pprint\n",
    "\n",
    "from src.utils import concatenate_df, create_path\n",
    "from src.utils_clean_classification_results import (\n",
    "    clean_acc_method_alldatasets,\n",
    ")\n",
    "from src.metadata import D_REPLACE_METHOD_NAMES;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fda9f730-deae-464b-aac6-675fde92ed8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "pp = pprint.PrettyPrinter()\n",
    "cwd = Path.cwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b3cd52-f76d-4173-8d1e-34e321f35a1b",
   "metadata": {},
   "source": [
    "# Load the classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01288808-94c2-4ef7-adab-ecd6d25dad68",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_df_acc_allmethods = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f63f8d1b-edc7-40e4-9794-d518bcd22a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "for METHOD_ABBREVIATE in METHOD_ABBREVIATES:\n",
    "    path = Path(cwd / \"results\" / DATE_EXP)\n",
    "    l_csvfiles_acc_method_alldatasets = list(path.rglob(f\"acc/df_acc_{METHOD_ABBREVIATE}_*.csv\"))\n",
    "    d_df_acc_allmethods[METHOD_ABBREVIATE] = concatenate_df(l_csvfiles_acc_method_alldatasets).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57f995db-1d95-4d24-9545-8f6a29382077",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_segmentation__mean_or_slope</th>\n",
       "      <th>param_segmentation__n_segments</th>\n",
       "      <th>param_segmentation__pen_factor</th>\n",
       "      <th>param_segmentation__uniform_or_adaptive</th>\n",
       "      <th>param_segmentation__univariate_or_multivariate</th>\n",
       "      <th>param_segmentfeature__features_names</th>\n",
       "      <th>param_symbolicsignaldistance__distance</th>\n",
       "      <th>param_symbolicsignaldistance__n_samples</th>\n",
       "      <th>param_symbolicsignaldistance__weighted_bool</th>\n",
       "      <th>param_symbolization__features_scaling</th>\n",
       "      <th>param_symbolization__lookup_table_type</th>\n",
       "      <th>param_symbolization__n_regime_lengths</th>\n",
       "      <th>param_symbolization__n_symbols</th>\n",
       "      <th>param_symbolization__reconstruct_bool</th>\n",
       "      <th>param_symbolization__seglen_bins_method</th>\n",
       "      <th>param_symbolization__symb_cluster_method</th>\n",
       "      <th>param_symbolization__symb_method</th>\n",
       "      <th>param_symbolization__symb_quantif_method</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>NonInvasiveFetalECGThorax2</td>\n",
       "      <td>935.847596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>933.834386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mean</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>{'segmentation__mean_or_slope': 'mean', 'segme...</td>\n",
       "      <td>0.714504</td>\n",
       "      <td>0.714504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>BirdChicken</td>\n",
       "      <td>0.219375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.195752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mean</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>{'segmentation__mean_or_slope': 'mean', 'segme...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>ECGFiveDays</td>\n",
       "      <td>0.291909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.599017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mean</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>{'segmentation__mean_or_slope': 'mean', 'segme...</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>NonInvasiveFetalECGThorax2</td>\n",
       "      <td>823.953697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>831.718551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mean</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>{'segmentation__mean_or_slope': 'mean', 'segme...</td>\n",
       "      <td>0.317048</td>\n",
       "      <td>0.317048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>PigArtPressure</td>\n",
       "      <td>2.252927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.158841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mean</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>{'segmentation__mean_or_slope': 'mean', 'segme...</td>\n",
       "      <td>0.110577</td>\n",
       "      <td>0.110577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         dataset  mean_fit_time  std_fit_time  \\\n",
       "1090  NonInvasiveFetalECGThorax2     935.847596           0.0   \n",
       "125                  BirdChicken       0.219375           0.0   \n",
       "367                  ECGFiveDays       0.291909           0.0   \n",
       "1088  NonInvasiveFetalECGThorax2     823.953697           0.0   \n",
       "1195              PigArtPressure       2.252927           0.0   \n",
       "\n",
       "      mean_score_time  std_score_time param_segmentation__mean_or_slope  \\\n",
       "1090       933.834386             0.0                              mean   \n",
       "125          0.195752             0.0                              mean   \n",
       "367          8.599017             0.0                              mean   \n",
       "1088       831.718551             0.0                              mean   \n",
       "1195         2.158841             0.0                              mean   \n",
       "\n",
       "      param_segmentation__n_segments  param_segmentation__pen_factor  \\\n",
       "1090                              15                             NaN   \n",
       "125                               10                             NaN   \n",
       "367                               10                             NaN   \n",
       "1088                              15                             NaN   \n",
       "1195                              20                             NaN   \n",
       "\n",
       "     param_segmentation__uniform_or_adaptive  \\\n",
       "1090                                adaptive   \n",
       "125                                 adaptive   \n",
       "367                                 adaptive   \n",
       "1088                                adaptive   \n",
       "1195                                adaptive   \n",
       "\n",
       "     param_segmentation__univariate_or_multivariate  \\\n",
       "1090                                   multivariate   \n",
       "125                                    multivariate   \n",
       "367                                    multivariate   \n",
       "1088                                   multivariate   \n",
       "1195                                   multivariate   \n",
       "\n",
       "     param_segmentfeature__features_names  \\\n",
       "1090                             ['mean']   \n",
       "125                              ['mean']   \n",
       "367                              ['mean']   \n",
       "1088                             ['mean']   \n",
       "1195                             ['mean']   \n",
       "\n",
       "     param_symbolicsignaldistance__distance  \\\n",
       "1090                                    lev   \n",
       "125                                     lev   \n",
       "367                                     lev   \n",
       "1088                                    lev   \n",
       "1195                                    lev   \n",
       "\n",
       "      param_symbolicsignaldistance__n_samples  \\\n",
       "1090                                      NaN   \n",
       "125                                       NaN   \n",
       "367                                       NaN   \n",
       "1088                                      NaN   \n",
       "1195                                      NaN   \n",
       "\n",
       "      param_symbolicsignaldistance__weighted_bool  \\\n",
       "1090                                         True   \n",
       "125                                          True   \n",
       "367                                          True   \n",
       "1088                                         True   \n",
       "1195                                         True   \n",
       "\n",
       "      param_symbolization__features_scaling  \\\n",
       "1090                                    NaN   \n",
       "125                                     NaN   \n",
       "367                                     NaN   \n",
       "1088                                    NaN   \n",
       "1195                                    NaN   \n",
       "\n",
       "     param_symbolization__lookup_table_type  \\\n",
       "1090                                    mof   \n",
       "125                                     mof   \n",
       "367                                     mof   \n",
       "1088                                    mof   \n",
       "1195                                    mof   \n",
       "\n",
       "     param_symbolization__n_regime_lengths  param_symbolization__n_symbols  \\\n",
       "1090                          divide_exact                              16   \n",
       "125                           divide_exact                               9   \n",
       "367                           divide_exact                              25   \n",
       "1088                          divide_exact                               4   \n",
       "1195                          divide_exact                              25   \n",
       "\n",
       "      param_symbolization__reconstruct_bool  \\\n",
       "1090                                   True   \n",
       "125                                    True   \n",
       "367                                    True   \n",
       "1088                                   True   \n",
       "1195                                   True   \n",
       "\n",
       "      param_symbolization__seglen_bins_method  \\\n",
       "1090                                      NaN   \n",
       "125                                       NaN   \n",
       "367                                       NaN   \n",
       "1088                                      NaN   \n",
       "1195                                      NaN   \n",
       "\n",
       "      param_symbolization__symb_cluster_method  \\\n",
       "1090                                       NaN   \n",
       "125                                        NaN   \n",
       "367                                        NaN   \n",
       "1088                                       NaN   \n",
       "1195                                       NaN   \n",
       "\n",
       "     param_symbolization__symb_method  \\\n",
       "1090                          quantif   \n",
       "125                           quantif   \n",
       "367                           quantif   \n",
       "1088                          quantif   \n",
       "1195                          quantif   \n",
       "\n",
       "     param_symbolization__symb_quantif_method  \\\n",
       "1090                                quantiles   \n",
       "125                                 quantiles   \n",
       "367                                 quantiles   \n",
       "1088                                quantiles   \n",
       "1195                                quantiles   \n",
       "\n",
       "                                                 params  split0_test_score  \\\n",
       "1090  {'segmentation__mean_or_slope': 'mean', 'segme...           0.714504   \n",
       "125   {'segmentation__mean_or_slope': 'mean', 'segme...           0.600000   \n",
       "367   {'segmentation__mean_or_slope': 'mean', 'segme...           0.714286   \n",
       "1088  {'segmentation__mean_or_slope': 'mean', 'segme...           0.317048   \n",
       "1195  {'segmentation__mean_or_slope': 'mean', 'segme...           0.110577   \n",
       "\n",
       "      mean_test_score  std_test_score  rank_test_score  \n",
       "1090         0.714504             0.0                6  \n",
       "125          0.600000             0.0               15  \n",
       "367          0.714286             0.0               13  \n",
       "1088         0.317048             0.0               18  \n",
       "1195         0.110577             0.0                4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_df_acc_allmethods[\"astride\"].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71eca33-2faa-4b42-a202-a048886a59e0",
   "metadata": {},
   "source": [
    "# Clean the classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a98cfba2-4739-4ec6-a5cb-dfba2fbd87e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sax\n",
      "saxtslearn\n",
      "1dsax\n",
      "astride\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/swc/Documents/PhD/code/astride/src/utils_clean_classification_results.py:209: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_acc_method_alldatasets[col] = None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fastride\n"
     ]
    }
   ],
   "source": [
    "d_df_acc_allmethods_alldatasets_clean = dict()\n",
    "for METHOD_ABBREVIATE in METHOD_ABBREVIATES:\n",
    "    print(METHOD_ABBREVIATE)\n",
    "    df_acc_method_alldatasets_clean, _ = clean_acc_method_alldatasets(\n",
    "        df_acc_method_alldatasets=d_df_acc_allmethods[METHOD_ABBREVIATE],\n",
    "        method_abbreviate=METHOD_ABBREVIATE\n",
    "    )\n",
    "    d_df_acc_allmethods_alldatasets_clean[METHOD_ABBREVIATE] = df_acc_method_alldatasets_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "655f36a6-12fd-4472-a307-a02593259a22",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>dataset</th>\n",
       "      <th>mean_or_slope</th>\n",
       "      <th>n_segments</th>\n",
       "      <th>pen_factor</th>\n",
       "      <th>uniform_or_adaptive</th>\n",
       "      <th>univariate_or_multivariate</th>\n",
       "      <th>features_names</th>\n",
       "      <th>distance</th>\n",
       "      <th>weighted_bool</th>\n",
       "      <th>features_scaling</th>\n",
       "      <th>lookup_table_type</th>\n",
       "      <th>n_regime_lengths</th>\n",
       "      <th>n_symbols</th>\n",
       "      <th>reconstruct_bool</th>\n",
       "      <th>seglen_bins_method</th>\n",
       "      <th>symb_cluster_method</th>\n",
       "      <th>symb_method</th>\n",
       "      <th>symb_quantif_method</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>n_samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>ASTRIDE</td>\n",
       "      <td>InsectWingbeatSound</td>\n",
       "      <td>mean</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>0.460101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>ASTRIDE</td>\n",
       "      <td>OliveOil</td>\n",
       "      <td>mean</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>ASTRIDE</td>\n",
       "      <td>PowerCons</td>\n",
       "      <td>mean</td>\n",
       "      <td>20</td>\n",
       "      <td>None</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>0.911111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>ASTRIDE</td>\n",
       "      <td>Rock</td>\n",
       "      <td>mean</td>\n",
       "      <td>20</td>\n",
       "      <td>None</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>ASTRIDE</td>\n",
       "      <td>RefrigerationDevices</td>\n",
       "      <td>mean</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>['mean']</td>\n",
       "      <td>lev</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>mof</td>\n",
       "      <td>divide_exact</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>quantif</td>\n",
       "      <td>quantiles</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       method               dataset mean_or_slope  n_segments pen_factor  \\\n",
       "903   ASTRIDE   InsectWingbeatSound          mean           5       None   \n",
       "1120  ASTRIDE              OliveOil          mean           5       None   \n",
       "1255  ASTRIDE             PowerCons          mean          20       None   \n",
       "1294  ASTRIDE                  Rock          mean          20       None   \n",
       "1262  ASTRIDE  RefrigerationDevices          mean           5       None   \n",
       "\n",
       "     uniform_or_adaptive univariate_or_multivariate features_names distance  \\\n",
       "903             adaptive               multivariate       ['mean']      lev   \n",
       "1120            adaptive               multivariate       ['mean']      lev   \n",
       "1255            adaptive               multivariate       ['mean']      lev   \n",
       "1294            adaptive               multivariate       ['mean']      lev   \n",
       "1262            adaptive               multivariate       ['mean']      lev   \n",
       "\n",
       "      weighted_bool features_scaling lookup_table_type n_regime_lengths  \\\n",
       "903            True             None               mof     divide_exact   \n",
       "1120           True             None               mof     divide_exact   \n",
       "1255           True             None               mof     divide_exact   \n",
       "1294           True             None               mof     divide_exact   \n",
       "1262           True             None               mof     divide_exact   \n",
       "\n",
       "      n_symbols  reconstruct_bool seglen_bins_method symb_cluster_method  \\\n",
       "903          25              True               None                None   \n",
       "1120          4              True               None                None   \n",
       "1255         25              True               None                None   \n",
       "1294         16              True               None                None   \n",
       "1262         16              True               None                None   \n",
       "\n",
       "     symb_method symb_quantif_method  mean_test_score  std_test_score  \\\n",
       "903      quantif           quantiles         0.460101             0.0   \n",
       "1120     quantif           quantiles         0.466667             0.0   \n",
       "1255     quantif           quantiles         0.911111             0.0   \n",
       "1294     quantif           quantiles         0.700000             0.0   \n",
       "1262     quantif           quantiles         0.426667             0.0   \n",
       "\n",
       "      n_samples  \n",
       "903         256  \n",
       "1120        570  \n",
       "1255        144  \n",
       "1294       2844  \n",
       "1262        720  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_df_acc_allmethods_alldatasets_clean[\"astride\"].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae90f7d-1d56-4381-ba4d-c052c797d6e5",
   "metadata": {},
   "source": [
    "# Export the cleaned classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f332c151-0240-4157-8027-57d2bdd8ad84",
   "metadata": {},
   "outputs": [],
   "source": [
    "if IS_EXPORT_DF:\n",
    "    folder = path / \"acc_clean\"\n",
    "    create_path(folder) \n",
    "    for METHOD_ABBREVIATE in METHOD_ABBREVIATES:\n",
    "        d_df_acc_allmethods_alldatasets_clean[METHOD_ABBREVIATE].to_csv(\n",
    "            folder / f'df_acc_{METHOD_ABBREVIATE}_alldatasets_clean.csv', index=False\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb4fcd5-e32c-4b0e-a4f4-751d09b4963b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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