12_summaryclassifier_starter.ipynb

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    "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_train, y_train = load_basic_motions(split='train', return_type='numpy3D')\n",
    "X_test, y_test = load_basic_motions(split='test', return_type='numpy3D')"
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    "series_indices = [0, 10, 20, 30]\n",
    "categories = ['standing', 'running', 'walking', 'badminton']\n",
    "features = [\"accel_1\", \"accel_2\", \"accel_3\", \"gyro_1\", \"gyro_2\", \"gyro_3\"]\n",
    "\n",
    "selected_series = X_train[series_indices]\n",
    "\n",
    "fig, axes = plt.subplots(4, 1, figsize=(10, 18))\n",
    "\n",
    "for i in range(4):  \n",
    "    for j in range(selected_series.shape[1]):\n",
    "        axes[i].plot(selected_series[i, j], label=features[j])\n",
    "\n",
    "    axes[i].set_title(f\"Category: {categories[i]}\")\n",
    "    axes[i].set_xlabel(\"Time Steps\")\n",
    "    axes[i].set_ylabel(\"Values\")\n",
    "    axes[i].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
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    "## Summary classifier"
   ]
  },
  {
   "cell_type": "code",
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   "id": "680d625f-be81-4c0b-9517-095917f21850",
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   "source": [
    "from sktime.classification.feature_based import SummaryClassifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Empty list to store accuracy for each model\n",
    "accuracies = []\n",
    "\n",
    "# Initialize all models\n",
    "\n",
    "# Create a list of classifiers\n",
    "\n",
    "\n",
    "# Use each algorithm in the SummaryClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fac922b5-c589-4b65-810d-d3069d88ba35",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['KNN', 'AdaBoost', 'Random Forest', 'SVC']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x, accuracies)\n",
    "ax.set_xlabel('Model')\n",
    "ax.set_ylabel('Accuracy (%)')\n",
    "\n",
    "for i, v in enumerate(accuracies):\n",
    "    ax.text(x=i, y=v+1, s=str(v), ha='center')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
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   "execution_count": null,
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   "source": []
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