15_tsfresh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b384c3d3-0d12-4afd-9a7a-abafa771a176",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sktime.datasets import load_basic_motions\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X, y = load_basic_motions(return_type='pd-multiindex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ce7108-40b9-4e56-8388-5822d1dd4e8f",
   "metadata": {},
   "source": [
    "## Format data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f687705e-c8a4-4bc1-afee-7b9fb95221b0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>dim_0</th>\n",
       "      <th>dim_1</th>\n",
       "      <th>dim_2</th>\n",
       "      <th>dim_3</th>\n",
       "      <th>dim_4</th>\n",
       "      <th>dim_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>timepoints</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.351565</td>\n",
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       "      <td>0.633883</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.079106</td>\n",
       "      <td>0.394032</td>\n",
       "      <td>0.551444</td>\n",
       "      <td>0.351565</td>\n",
       "      <td>0.023970</td>\n",
       "      <td>0.633883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.903497</td>\n",
       "      <td>-3.666397</td>\n",
       "      <td>-0.282844</td>\n",
       "      <td>-0.095881</td>\n",
       "      <td>-0.319605</td>\n",
       "      <td>0.972131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.116125</td>\n",
       "      <td>-0.656101</td>\n",
       "      <td>0.333118</td>\n",
       "      <td>1.624657</td>\n",
       "      <td>-0.569962</td>\n",
       "      <td>1.209171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.638200</td>\n",
       "      <td>1.405135</td>\n",
       "      <td>0.393875</td>\n",
       "      <td>1.187864</td>\n",
       "      <td>-0.271664</td>\n",
       "      <td>1.739182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dim_0     dim_1     dim_2     dim_3     dim_4     dim_5\n",
       "  timepoints                                                            \n",
       "0 0           0.079106  0.394032  0.551444  0.351565  0.023970  0.633883\n",
       "  1           0.079106  0.394032  0.551444  0.351565  0.023970  0.633883\n",
       "  2          -0.903497 -3.666397 -0.282844 -0.095881 -0.319605  0.972131\n",
       "  3           1.116125 -0.656101  0.333118  1.624657 -0.569962  1.209171\n",
       "  4           1.638200  1.405135  0.393875  1.187864 -0.271664  1.739182"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cd8f1319-5153-42d6-b33c-31dc13fdf71d",
   "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>id</th>\n",
       "      <th>timepoints</th>\n",
       "      <th>dim_0</th>\n",
       "      <th>dim_1</th>\n",
       "      <th>dim_2</th>\n",
       "      <th>dim_3</th>\n",
       "      <th>dim_4</th>\n",
       "      <th>dim_5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.079106</td>\n",
       "      <td>0.394032</td>\n",
       "      <td>0.551444</td>\n",
       "      <td>0.351565</td>\n",
       "      <td>0.023970</td>\n",
       "      <td>0.633883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079106</td>\n",
       "      <td>0.394032</td>\n",
       "      <td>0.551444</td>\n",
       "      <td>0.351565</td>\n",
       "      <td>0.023970</td>\n",
       "      <td>0.633883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.903497</td>\n",
       "      <td>-3.666397</td>\n",
       "      <td>-0.282844</td>\n",
       "      <td>-0.095881</td>\n",
       "      <td>-0.319605</td>\n",
       "      <td>0.972131</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.116125</td>\n",
       "      <td>-0.656101</td>\n",
       "      <td>0.333118</td>\n",
       "      <td>1.624657</td>\n",
       "      <td>-0.569962</td>\n",
       "      <td>1.209171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.638200</td>\n",
       "      <td>1.405135</td>\n",
       "      <td>0.393875</td>\n",
       "      <td>1.187864</td>\n",
       "      <td>-0.271664</td>\n",
       "      <td>1.739182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  timepoints     dim_0     dim_1     dim_2     dim_3     dim_4     dim_5\n",
       "0   0           0  0.079106  0.394032  0.551444  0.351565  0.023970  0.633883\n",
       "1   0           1  0.079106  0.394032  0.551444  0.351565  0.023970  0.633883\n",
       "2   0           2 -0.903497 -3.666397 -0.282844 -0.095881 -0.319605  0.972131\n",
       "3   0           3  1.116125 -0.656101  0.333118  1.624657 -0.569962  1.209171\n",
       "4   0           4  1.638200  1.405135  0.393875  1.187864 -0.271664  1.739182"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.reset_index(names=['id', 'timepoints'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a222e3-8ee8-46d7-92d8-a4d8d959c12e",
   "metadata": {},
   "source": [
    "## Compute features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "06f987d9-c0bc-41ea-92cf-83b4c8778419",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████████████████████████████████████████████████████████| 30/30 [00:07<00:00,  3.87it/s]\n"
     ]
    }
   ],
   "source": [
    "from tsfresh import extract_features, extract_relevant_features, select_features\n",
    "\n",
    "X_feat = extract_features(X, column_id='id', column_sort='timepoints')\n",
    "\n",
    "X_feat = X_feat.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9af6a5b6-2c57-4574-a7e1-890715720aad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 3501) (80,)\n"
     ]
    }
   ],
   "source": [
    "print(X_feat.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cb12b900-512d-4bea-b9a1-56ab3d06abdf",
   "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>dim_4__variance_larger_than_standard_deviation</th>\n",
       "      <th>dim_4__has_duplicate_max</th>\n",
       "      <th>dim_4__has_duplicate_min</th>\n",
       "      <th>dim_4__has_duplicate</th>\n",
       "      <th>dim_4__sum_values</th>\n",
       "      <th>dim_4__abs_energy</th>\n",
       "      <th>dim_4__mean_abs_change</th>\n",
       "      <th>dim_4__mean_change</th>\n",
       "      <th>dim_4__mean_second_derivative_central</th>\n",
       "      <th>dim_4__median</th>\n",
       "      <th>...</th>\n",
       "      <th>dim_3__fourier_entropy__bins_3</th>\n",
       "      <th>dim_3__fourier_entropy__bins_5</th>\n",
       "      <th>dim_3__fourier_entropy__bins_10</th>\n",
       "      <th>dim_3__fourier_entropy__bins_100</th>\n",
       "      <th>dim_3__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>dim_3__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>dim_3__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>dim_3__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>dim_3__permutation_entropy__dimension_7__tau_1</th>\n",
       "      <th>dim_3__mean_n_absolute_max__number_of_maxima_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.211833</td>\n",
       "      <td>1.553383</td>\n",
       "      <td>0.067714</td>\n",
       "      <td>-0.000350</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>-0.010653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.482064</td>\n",
       "      <td>0.798569</td>\n",
       "      <td>1.306161</td>\n",
       "      <td>3.009338</td>\n",
       "      <td>1.703819</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>3.829694</td>\n",
       "      <td>4.317736</td>\n",
       "      <td>4.493485</td>\n",
       "      <td>0.804719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.013321</td>\n",
       "      <td>1.326601</td>\n",
       "      <td>0.054370</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192626</td>\n",
       "      <td>0.192626</td>\n",
       "      <td>0.192626</td>\n",
       "      <td>0.602410</td>\n",
       "      <td>1.597731</td>\n",
       "      <td>2.744861</td>\n",
       "      <td>3.693810</td>\n",
       "      <td>4.294787</td>\n",
       "      <td>4.484304</td>\n",
       "      <td>2.687343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.720537</td>\n",
       "      <td>2.960324</td>\n",
       "      <td>0.084609</td>\n",
       "      <td>-0.000081</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329286</td>\n",
       "      <td>0.413917</td>\n",
       "      <td>0.545824</td>\n",
       "      <td>1.783263</td>\n",
       "      <td>1.716592</td>\n",
       "      <td>2.724103</td>\n",
       "      <td>3.640944</td>\n",
       "      <td>4.178235</td>\n",
       "      <td>4.419746</td>\n",
       "      <td>0.603825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.295639</td>\n",
       "      <td>1.891844</td>\n",
       "      <td>0.075193</td>\n",
       "      <td>-0.001426</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>0.003995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261160</td>\n",
       "      <td>0.329286</td>\n",
       "      <td>0.329286</td>\n",
       "      <td>1.572172</td>\n",
       "      <td>1.714921</td>\n",
       "      <td>2.910144</td>\n",
       "      <td>3.837495</td>\n",
       "      <td>4.320396</td>\n",
       "      <td>4.469556</td>\n",
       "      <td>0.526587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.972129</td>\n",
       "      <td>0.222901</td>\n",
       "      <td>0.029432</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.010653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192626</td>\n",
       "      <td>0.356468</td>\n",
       "      <td>0.451359</td>\n",
       "      <td>2.044414</td>\n",
       "      <td>1.701245</td>\n",
       "      <td>2.791571</td>\n",
       "      <td>3.680559</td>\n",
       "      <td>4.230909</td>\n",
       "      <td>4.449241</td>\n",
       "      <td>0.170456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dim_4__variance_larger_than_standard_deviation  dim_4__has_duplicate_max  \\\n",
       "0                                             0.0                       0.0   \n",
       "1                                             0.0                       0.0   \n",
       "2                                             0.0                       0.0   \n",
       "3                                             0.0                       1.0   \n",
       "4                                             0.0                       0.0   \n",
       "\n",
       "   dim_4__has_duplicate_min  dim_4__has_duplicate  dim_4__sum_values  \\\n",
       "0                       0.0                   1.0          -1.211833   \n",
       "1                       0.0                   1.0          -0.013321   \n",
       "2                       0.0                   1.0          -1.720537   \n",
       "3                       0.0                   1.0          -0.295639   \n",
       "4                       0.0                   1.0           0.972129   \n",
       "\n",
       "   dim_4__abs_energy  dim_4__mean_abs_change  dim_4__mean_change  \\\n",
       "0           1.553383                0.067714           -0.000350   \n",
       "1           1.326601                0.054370            0.001264   \n",
       "2           2.960324                0.084609           -0.000081   \n",
       "3           1.891844                0.075193           -0.001426   \n",
       "4           0.222901                0.029432           -0.000054   \n",
       "\n",
       "   dim_4__mean_second_derivative_central  dim_4__median  ...  \\\n",
       "0                               0.000014      -0.010653  ...   \n",
       "1                               0.000000       0.000000  ...   \n",
       "2                               0.000000      -0.015980  ...   \n",
       "3                              -0.000054       0.003995  ...   \n",
       "4                               0.000217       0.010653  ...   \n",
       "\n",
       "   dim_3__fourier_entropy__bins_3  dim_3__fourier_entropy__bins_5  \\\n",
       "0                        0.482064                        0.798569   \n",
       "1                        0.192626                        0.192626   \n",
       "2                        0.329286                        0.413917   \n",
       "3                        0.261160                        0.329286   \n",
       "4                        0.192626                        0.356468   \n",
       "\n",
       "   dim_3__fourier_entropy__bins_10  dim_3__fourier_entropy__bins_100  \\\n",
       "0                         1.306161                          3.009338   \n",
       "1                         0.192626                          0.602410   \n",
       "2                         0.545824                          1.783263   \n",
       "3                         0.329286                          1.572172   \n",
       "4                         0.451359                          2.044414   \n",
       "\n",
       "   dim_3__permutation_entropy__dimension_3__tau_1  \\\n",
       "0                                        1.703819   \n",
       "1                                        1.597731   \n",
       "2                                        1.716592   \n",
       "3                                        1.714921   \n",
       "4                                        1.701245   \n",
       "\n",
       "   dim_3__permutation_entropy__dimension_4__tau_1  \\\n",
       "0                                        2.883721   \n",
       "1                                        2.744861   \n",
       "2                                        2.724103   \n",
       "3                                        2.910144   \n",
       "4                                        2.791571   \n",
       "\n",
       "   dim_3__permutation_entropy__dimension_5__tau_1  \\\n",
       "0                                        3.829694   \n",
       "1                                        3.693810   \n",
       "2                                        3.640944   \n",
       "3                                        3.837495   \n",
       "4                                        3.680559   \n",
       "\n",
       "   dim_3__permutation_entropy__dimension_6__tau_1  \\\n",
       "0                                        4.317736   \n",
       "1                                        4.294787   \n",
       "2                                        4.178235   \n",
       "3                                        4.320396   \n",
       "4                                        4.230909   \n",
       "\n",
       "   dim_3__permutation_entropy__dimension_7__tau_1  \\\n",
       "0                                        4.493485   \n",
       "1                                        4.484304   \n",
       "2                                        4.419746   \n",
       "3                                        4.469556   \n",
       "4                                        4.449241   \n",
       "\n",
       "   dim_3__mean_n_absolute_max__number_of_maxima_7  \n",
       "0                                        0.804719  \n",
       "1                                        2.687343  \n",
       "2                                        0.603825  \n",
       "3                                        0.526587  \n",
       "4                                        0.170456  \n",
       "\n",
       "[5 rows x 3501 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785c8542-6f68-4d1f-9a48-d9a80f44ca06",
   "metadata": {},
   "source": [
    "## Train model using all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6e729a01-1ad1-49fe-b2df-0a3dae3eb128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_feat, y, test_size=0.5, shuffle=False)\n",
    "\n",
    "X_train.shape\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "preds = knn.predict(X_test)\n",
    "\n",
    "knn_baseline_accuracy = round(accuracy_score(y_test, preds)*100,2)\n",
    "\n",
    "print(knn_baseline_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61324d2f-22e7-4b3f-a2d3-e4ef326f9c9c",
   "metadata": {},
   "source": [
    "## Filter features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "53d83a67-305d-4e05-9d8e-dd54ab19581e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of relevant features for class badminton: 703/3501\n",
      "Number of relevant features for class running: 425/3501\n",
      "Number of relevant features for class standing: 968/3501\n",
      "Number of relevant features for class walking: 51/3501\n"
     ]
    }
   ],
   "source": [
    "relevant_features = set()\n",
    "\n",
    "for label in np.unique(y):\n",
    "    y_train_binary = y_train == label\n",
    "    X_train_filtered = select_features(X_train, y_train_binary)\n",
    "    \n",
    "    print(f\"Relevant features for class {label}: {X_train_filtered.shape[1]}/{X_train.shape[1]}\")\n",
    "    \n",
    "    relevant_features = relevant_features.union(set(X_train_filtered.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6de0b550-0780-411d-831c-7df144930291",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_X_train = X_train[list(relevant_features)]\n",
    "filtered_X_test = X_test[list(relevant_features)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bc7fde4b-cf71-4f90-b2c6-005372455388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "knn.fit(filtered_X_train, y_train)\n",
    "\n",
    "preds = knn.predict(filtered_X_test)\n",
    "\n",
    "knn_selected_accuracy = round(accuracy_score(y_test, preds)*100,2)\n",
    "\n",
    "print(knn_selected_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9a4476-6780-4c4f-95c4-1089c057ab0c",
   "metadata": {},
   "source": [
    "## Another way of filtering features for multiclass scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "71cba02f-74b5-480d-ac34-699fee7921a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.5\n"
     ]
    }
   ],
   "source": [
    "# The parameter n_significant specifies the number of classes for which a feature\n",
    "# must be significant to be kept\n",
    "\n",
    "X_train_filtered_multi = select_features(X_train, y_train, multiclass=True, n_significant=3)\n",
    "X_test_filtered_multi = X_test[X_train_filtered_multi.columns]\n",
    "\n",
    "knn.fit(X_train_filtered_multi, y_train)\n",
    "\n",
    "preds = knn.predict(X_test_filtered_multi)\n",
    "\n",
    "knn_selected_accuracy = round(accuracy_score(y_test, preds)*100,2)\n",
    "\n",
    "print(knn_selected_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adee9411-b26d-429c-b484-1b9f230a2668",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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