examples/classification/deep_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep learning based time series classification in aeon\n",
    "\n",
    "There are a range of deep learning based classification algorithms in the toolkit.\n",
    "The networks that are common to classification, regression and clustering are in the\n",
    "`networks` module. Our deep learning classifiers are based those used in deep\n",
    "learning bake off [1] and recent experimentation [2]. [3] provides an extensive recent\n",
    "review of related deep learning work.\n",
    "review of related deep learning work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A list of all deep learning classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[('CNNClassifier', aeon.classification.deep_learning._cnn.CNNClassifier),\n ('EncoderClassifier',\n  aeon.classification.deep_learning._encoder.EncoderClassifier),\n ('FCNClassifier', aeon.classification.deep_learning._fcn.FCNClassifier),\n ('InceptionTimeClassifier',\n  aeon.classification.deep_learning._inception_time.InceptionTimeClassifier),\n ('IndividualInceptionClassifier',\n  aeon.classification.deep_learning._inception_time.IndividualInceptionClassifier),\n ('IndividualLITEClassifier',\n  aeon.classification.deep_learning._lite_time.IndividualLITEClassifier),\n ('LITETimeClassifier',\n  aeon.classification.deep_learning._lite_time.LITETimeClassifier),\n ('MLPClassifier', aeon.classification.deep_learning._mlp.MLPClassifier),\n ('ResNetClassifier',\n  aeon.classification.deep_learning._resnet.ResNetClassifier),\n ('TapNetClassifier',\n  aeon.classification.deep_learning._tapnet.TapNetClassifier)]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "from aeon.registry import all_estimators\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "all_estimators(\"classifier\", filter_tags={\"algorithm_type\": \"deeplearning\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "The use case for deep learning classifiers is identical to that of all classifiers.\n",
    "However, you need to have tensorflow installed in your\n",
    "environment. If you have a GPU correctly installed the classifiers should use them,\n",
    "although it is worth checking the output.\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65/65 [==============================] - 0s 726us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "0.9533527696793003"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from aeon.classification.deep_learning import CNNClassifier\n",
    "from aeon.datasets import load_basic_motions  # multivariate dataset\n",
    "from aeon.datasets import load_italy_power_demand  # univariate dataset\n",
    "\n",
    "italy, italy_labels = load_italy_power_demand(split=\"train\")\n",
    "italy_test, italy_test_labels = load_italy_power_demand(split=\"test\")\n",
    "motions, motions_labels = load_basic_motions(split=\"train\")\n",
    "motions_test, motions_test_labels = load_basic_motions(split=\"train\")\n",
    "cnn = CNNClassifier()\n",
    "cnn.fit(italy, italy_labels)\n",
    "y_pred = cnn.predict(italy_test)\n",
    "accuracy_score(italy_test_labels, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Classifier Details\n",
    "\n",
    "The deep learning bake off [1] found that the Residual Network (ResNet) was the best\n",
    "performing architecture for TSC. ResNet has the following network structure.\n",
    "\n",
    "\n",
    "<img src=\"./img/resnet.png\" width=\"600\" alt=\"ROCKET.\">\n",
    "\n",
    "The InceptionTime deep learning algorithm Subsequent to [1],\n",
    "InceptionTime is an ensemble of five SingleInceptionTime deep learning\n",
    "classifiers. Each base classifier shares the same architecture based on\n",
    "Inception modules. Diversity is achieved through randomly intialising weights.\n",
    "A SingleInceptionTimeClassifier has the following structure.\n",
    "\n",
    "<img src=\"./img/inception_module.png\" width=\"600\" alt=\"ROCKET.\">\n",
    "\n",
    "A SingleInceptionTimeClassifier is structured as follows.\n",
    "\n",
    "<img src=\"./img/inception.png\" width=\"600\" alt=\"ROCKET.\">"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Performance on the UCR univariate datasets\n",
    "You can find the dictionary based classifiers as follows. Please note we have not\n",
    "fully evaluated all the deep learners yet, they take a long time to run."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('CNNClassifier', <class 'aeon.classification.deep_learning._cnn.CNNClassifier'>)\n",
      "('EncoderClassifier', <class 'aeon.classification.deep_learning._encoder.EncoderClassifier'>)\n",
      "('FCNClassifier', <class 'aeon.classification.deep_learning._fcn.FCNClassifier'>)\n",
      "('InceptionTimeClassifier', <class 'aeon.classification.deep_learning._inception_time.InceptionTimeClassifier'>)\n",
      "('IndividualInceptionClassifier', <class 'aeon.classification.deep_learning._inception_time.IndividualInceptionClassifier'>)\n",
      "('IndividualLITEClassifier', <class 'aeon.classification.deep_learning._lite_time.IndividualLITEClassifier'>)\n",
      "('LITETimeClassifier', <class 'aeon.classification.deep_learning._lite_time.LITETimeClassifier'>)\n",
      "('MLPClassifier', <class 'aeon.classification.deep_learning._mlp.MLPClassifier'>)\n",
      "('ResNetClassifier', <class 'aeon.classification.deep_learning._resnet.ResNetClassifier'>)\n",
      "('TapNetClassifier', <class 'aeon.classification.deep_learning._tapnet.TapNetClassifier'>)\n"
     ]
    }
   ],
   "source": [
    "from aeon.registry import all_estimators\n",
    "\n",
    "est = all_estimators(\"classifier\", filter_tags={\"algorithm_type\": \"deeplearning\"})\n",
    "for c in est:\n",
    "    print(c)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "(112, 4)"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aeon.benchmarking import get_estimator_results_as_array\n",
    "from aeon.datasets.tsc_datasets import univariate\n",
    "\n",
    "names = [t[0].replace(\"Classifier\", \"\") for t in est]\n",
    "# Not done these yet\n",
    "names.remove(\"Encoder\")\n",
    "names.remove(\"FCN\")\n",
    "names.remove(\"IndividualInception\")\n",
    "names.remove(\"IndividualLITE\")\n",
    "names.remove(\"MLP\")\n",
    "names.remove(\"TapNet\")  # Multivariate only\n",
    "\n",
    "\n",
    "results, present_names = get_estimator_results_as_array(\n",
    "    names, univariate, include_missing=False\n",
    ")\n",
    "results.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 600x230 with 1 Axes>, <Axes: >)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x230 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from aeon.visualisation import plot_boxplot_median, plot_critical_difference\n",
    "\n",
    "plot_critical_difference(results, names)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 1000x600 with 1 Axes>, <Axes: >)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boxplot_median(results, names)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## References\n",
    "\n",
    "[1] Fawaz et al. (2019) \"Deep learning for time series classification: a review\" Data\n",
    " Mining and Knowledge Discovery. 33(4): 917-963\n",
    "\n",
    "[2] Fawaz et al. (2020) \"InceptionTime: finding AlexNet for time series classification.\n",
    "Data Mining and Knowledge Discovery. 34(6): 1936-1962\n",
    "\n",
    "[3] Foumani et al. (2023) \"Deep Learning for Time Series Classification and Extrinsic\n",
    "Regression: A Current Survey\" ArXiv https://arxiv.org/pdf/2302.02515.pdf\n",
    "\n",
    "[4]  https://github.com/MSD-IRIMAS/CF-4-TSC"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
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  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
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  "version": 3
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 "nbformat": 4,
 "nbformat_minor": 2
}