{ "cells": [ { "cell_type": "markdown", "source": [ "# Shapelet based time series machine learning\n", "\n", "Shapelets a subsections of times series taken from the train data that are a useful for time series machine learning. They were first proposed ia primitive for machine learning [1][2] and were embedded in a decision tree for classification. The Shapelet Transform Classifier (STC)[3,4] is a pipeline classifier which searches the training data for shapelets, transforms series to vectors of distances to a filtered set of selected shapelets based on information gain, then builds a classifier on the latter.\n", "\n", "Finding shapelets involves selecting and evaluating shapelets. The original shapelet tree and STC performed a full enumeration of all possible shapelets before keeping the best ones. This is computationally inefficient, and modern shapelet based machine learning algorithms randomise the search." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 1, "outputs": [ { "data": { "text/plain": "[('MrSQMClassifier',\n aeon.classification.shapelet_based._mrsqm.MrSQMClassifier),\n ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n ('ShapeletTransformClassifier',\n aeon.classification.shapelet_based._stc.ShapeletTransformClassifier)]" }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import warnings\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score\n", "\n", "from aeon.datasets import load_basic_motions\n", "from aeon.registry import all_estimators\n", "from aeon.transformations.collection.shapelet_based import RandomShapeletTransform\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "all_estimators(\"classifier\", filter_tags={\"algorithm_type\": \"shapelet\"})" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "### Shapelet Transform for Classification\n", "\n", "The `RandomShapeletTransform` transformer takes a set of labelled training time series in the `fit` function, randomly samples `n_shapelet_samples` shapelets, keeping the best `max_shapelets`. The resulting shapelets are used in the `transform` function to create a new tabular dataset, where each row represents a time series instance, and each column stores the distance from a time series to a shapelet. The resulting tabular data can be used by any scikit learn compatible classifier. In this notebook we will explain these terms and describe how the algorithm works. But first we show it in action. We will use the BasicMotions data as an example. This data set contains time series of motion traces for the activities \"running\", \"walking\", \"standing\" and \"badminton\". The learning problem is to predict the activity given the time series. Each time series has six channels: x, y, z position and x, y, z accelerometer of the wrist. Data was recorded on a smart watch." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 2, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Shape of transformed data = (40, 8)\n", " Distance of second series to third shapelet = 1.302772121165026\n", " Shapelets + random forest acc = 0.95\n" ] } ], "source": [ "X, y = load_basic_motions(split=\"train\")\n", "rst = RandomShapeletTransform(n_shapelet_samples=100, max_shapelets=10, random_state=42)\n", "st = rst.fit_transform(X, y)\n", "print(\" Shape of transformed data = \", st.shape)\n", "print(\" Distance of second series to third shapelet = \", st[1][2])\n", "testX, testy = load_basic_motions(split=\"test\")\n", "tr_test = rst.transform(testX)\n", "rf = RandomForestClassifier(random_state=10)\n", "rf.fit(st, y)\n", "preds = rf.predict(tr_test)\n", "print(\" Shapelets + random forest acc = \", accuracy_score(preds, testy))" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "### Visualising Shapelets\n", "The first column of the transformed data represents the distance from the first shapelet to each time series. The shapelets are sorted, so the first shapelet is the one we estimate is the best (using the calculation described below). You can recover the shapelets from the transform. Each shapelet is a 7-tuple, storing the following information:" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 3, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quality = 0.81127812\n", "Length = 39\n", "position = 55\n", "Channel = 0\n", "Origin Instance Index = 11\n", "Class label = running\n", "Shapelet = [-0.85667017 -1.88711152 -0.8751295 0.80633757 1.10838333 0.69810992\n", " 0.85713394 1.23190921 0.01801365 -1.29683966 -1.94694259 -0.37487726\n", " -0.37487726 1.39471462 0.74922685 0.74922685 0.22343376 0.22343376\n", " -0.7730703 -1.37591995 -0.80376393 1.32758071 0.99778845 0.6013481\n", " 0.83711118 0.93684593 0.93684593 -1.30429475 -1.64522057 -0.56312308\n", " 0.96855713 0.56796251 0.35714242 0.62066541 0.65135287 -0.80531237\n", " -1.49170075 -1.18512797 0.69685753]\n" ] } ], "source": [ "running_shapelet = rst.shapelets[0]\n", "print(\"Quality = \", running_shapelet[0])\n", "print(\"Length = \", running_shapelet[1])\n", "print(\"position = \", running_shapelet[2])\n", "print(\"Channel = \", running_shapelet[3])\n", "print(\"Origin Instance Index = \", running_shapelet[4])\n", "print(\"Class label = \", running_shapelet[5])\n", "print(\"Shapelet = \", running_shapelet[6])" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "We can directly extract shapelets and inspect them. These are the the two shapelets that are best at discriminating badminton and running against other activities. All shapelets are normalised to provide scale invariance." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 4, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Badminton shapelet from channel 0 (x-dimension) (0.65194393, 74, 7, 1, 1, 'standing', array([-5.27667376, -0.94911454, 0.90433173, 1.26316864, 2.34760078,\n", " 1.84408 , 0.9192852 , 0.9192852 , -1.29868372, -1.29868372,\n", " -1.5476774 , -1.03000413, 0.27593674, -0.70184658, 0.37460295,\n", " 1.27398121, 1.02881837, 0.64543662, -0.0669839 , -0.54373096,\n", " -0.55716134, -0.56605101, -0.08611633, 0.31270572, 0.25642625,\n", " 0.5512744 , 0.78929504, 0.73385326, 0.73385326, -0.26777726,\n", " -0.63967737, -0.63967737, -0.5539071 , -0.5539071 , 0.3867047 ,\n", " 0.3867047 , 0.88832979, 0.85074214, 0.46901267, 0.0925433 ,\n", " -0.34444436, -0.72498936, -0.83763127, -0.53034818, -0.05869122,\n", " 0.46600593, 1.02537238, 0.81800526, 0.51709059, 0.17497366,\n", " -0.31072836, -0.64876695, -0.89102368, -0.60834799, -0.0627886 ,\n", " 0.42532723, 0.95696668, 0.91077086, 0.77491818, 0.14283377,\n", " 0.14283377, -1.08722874, -1.08722874, -0.65706914, -0.65706914,\n", " 0.28210933, 0.74159654, 0.8064869 , 0.8064869 , 0.19889294,\n", " -0.16601048, -0.78706337, -0.76364317, -0.63789726]))\n" ] }, { "data": { "text/plain": "" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "badminton_shapelet = rst.shapelets[4]\n", "print(\" Badminton shapelet from channel 0 (x-dimension)\", badminton_shapelet)\n", "plt.title(\"Best shapelets for running and badminton\")\n", "plt.plot(badminton_shapelet[6], label=\"Badminton\")\n", "plt.plot(running_shapelet[6], label=\"Running\")\n", "plt.legend()" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "Both shapelets are in the x-axis, so represent side to side motion. Badminton is characterised by sa single large peak in one direction, capturing the drawing of the hand back and quickly hittig the shuttlcock. Running is chaaracterised by a longer repetition of side to side motions, with a sharper peak representing bringing the arm forward accross the body in a running motion." ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "## Performance on the UCR univariate datasets\n", "\n", "You can find the interval based classifiers as follows." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 5, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MrSQMClassifier\n", "RDSTClassifier\n", "ShapeletTransformClassifier\n" ] } ], "source": [ "from aeon.registry import all_estimators\n", "\n", "est = [\"MrSQMClassifier\", \"RDSTClassifier\", \"ShapeletTransformClassifier\"]\n", "for c in est:\n", " print(c)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 6, "outputs": [ { "data": { "text/plain": "(112, 3)" }, "execution_count": 6, "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.replace(\"Classifier\", \"\") for t in est]\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": 7, "outputs": [ { "data": { "text/plain": "(
, )" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", "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": 8, "outputs": [ { "data": { "text/plain": "(
, )" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", "image/png": 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" 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