{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo of ROCKET transform\n", "\n", "## Overview\n", "\n", "ROCKET [1] transforms time series using random convolutional kernels (random length, weights, bias, dilation, and padding). ROCKET computes two features from the resulting feature maps: the max, and the proportion of positive values (or ppv). The transformed features are used to train a linear classifier.\n", "\n", "[1] Dempster A, Petitjean F, Webb GI (2019) ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. [arXiv:1910.13051](https://arxiv.org/abs/1910.13051)\n", "\n", "***\n", "\n", "## Contents\n", "\n", "1. Imports\n", "2. Univariate Time Series\n", "3. Multivariate Time Series\n", "4. Pipeline Example\n", "\n", "***\n", "\n", "## 1 Imports\n", "\n", "Import example data, ROCKET, and a classifier (`RidgeClassifierCV` from scikit-learn), as well as NumPy and `make_pipeline` from scikit-learn.\n", "\n", "**Note**: ROCKET compiles (via Numba) on import, which may take a few seconds." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:46.441933Z", "iopub.status.busy": "2020-12-19T14:32:46.441213Z", "iopub.status.idle": "2020-12-19T14:32:46.443225Z", "shell.execute_reply": "2020-12-19T14:32:46.444014Z" } }, "outputs": [], "source": [ "# !pip install --upgrade numba" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:46.448396Z", "iopub.status.busy": "2020-12-19T14:32:46.447602Z", "iopub.status.idle": "2020-12-19T14:32:51.904418Z", "shell.execute_reply": "2020-12-19T14:32:51.905034Z" } }, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.linear_model import RidgeClassifierCV\n", "from sklearn.pipeline import make_pipeline\n", "\n", "from aeon.datasets import load_arrow_head # univariate dataset\n", "from aeon.datasets import load_basic_motions # multivariate dataset\n", "from aeon.transformations.collection.convolution_based import Rocket" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 Univariate Time Series\n", "\n", "We can transform the data using ROCKET and separately fit a classifier, or we can use ROCKET together with a classifier in a pipeline (section 4, below).\n", "\n", "### 2.1 Load the Training Data\n", "For more details on the data set, see the [univariate time series classification\n", "notebook](https://github.com/aeon-toolkit/aeon/tree/main/examples/classification.ipynb)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:51.908710Z", "iopub.status.busy": "2020-12-19T14:32:51.908101Z", "iopub.status.idle": "2020-12-19T14:32:51.918987Z", "shell.execute_reply": "2020-12-19T14:32:51.919508Z" } }, "outputs": [], "source": [ "X_train, y_train = load_arrow_head(split=\"train\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Initialise ROCKET and Transform the Training Data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:51.923023Z", "iopub.status.busy": "2020-12-19T14:32:51.922451Z", "iopub.status.idle": "2020-12-19T14:32:52.164365Z", "shell.execute_reply": "2020-12-19T14:32:52.164864Z" } }, "outputs": [], "source": [ "rocket = Rocket() # by default, ROCKET uses 10,000 kernels\n", "rocket.fit(X_train)\n", "X_train_transform = rocket.transform(X_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Fit a Classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We recommend using `RidgeClassifierCV` from scikit-learn for smaller datasets (fewer than approx. 20K training examples), and using logistic regression trained using stochastic gradient descent for larger datasets." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:52.168847Z", "iopub.status.busy": "2020-12-19T14:32:52.168155Z", "iopub.status.idle": "2020-12-19T14:32:52.284816Z", "shell.execute_reply": "2020-12-19T14:32:52.285506Z" } }, "outputs": [ { "data": { "text/plain": "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n 4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n 2.15443469e+02, 1.00000000e+03]))", "text/html": "
RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n       2.15443469e+02, 1.00000000e+03]))
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" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n", "classifier.fit(X_train_transform, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Load and Transform the Test Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:52.289448Z", "iopub.status.busy": "2020-12-19T14:32:52.288717Z", "iopub.status.idle": "2020-12-19T14:32:53.307829Z", "shell.execute_reply": "2020-12-19T14:32:53.308341Z" } }, "outputs": [], "source": [ "X_test, y_test = load_arrow_head(split=\"test\")\n", "X_test_transform = rocket.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5 Classify the Test Data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:53.312125Z", "iopub.status.busy": "2020-12-19T14:32:53.311628Z", "iopub.status.idle": "2020-12-19T14:32:53.409775Z", "shell.execute_reply": "2020-12-19T14:32:53.410342Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": "0.7771428571428571" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier.score(X_test_transform, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## 3 Multivariate Time Series\n", "\n", "We can use ROCKET in exactly the same way for multivariate time series.\n", "\n", "### 3.1 Load the Training Data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:53.413597Z", "iopub.status.busy": "2020-12-19T14:32:53.412786Z", "iopub.status.idle": "2020-12-19T14:32:53.775638Z", "shell.execute_reply": "2020-12-19T14:32:53.776690Z" } }, "outputs": [], "source": [ "X_train, y_train = load_basic_motions(split=\"train\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Initialise ROCKET and Transform the Training Data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:53.794896Z", "iopub.status.busy": "2020-12-19T14:32:53.794345Z", "iopub.status.idle": "2020-12-19T14:32:54.613570Z", "shell.execute_reply": "2020-12-19T14:32:54.614198Z" } }, "outputs": [], "source": [ "rocket = Rocket()\n", "rocket.fit(X_train)\n", "X_train_transform = rocket.transform(X_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 Fit a Classifier" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:54.618359Z", "iopub.status.busy": "2020-12-19T14:32:54.617890Z", "iopub.status.idle": "2020-12-19T14:32:54.836560Z", "shell.execute_reply": "2020-12-19T14:32:54.837249Z" } }, "outputs": [ { "data": { "text/plain": "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n 4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n 2.15443469e+02, 1.00000000e+03]))", "text/html": "
RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n       2.15443469e+02, 1.00000000e+03]))
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" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n", "classifier.fit(X_train_transform, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 Load and Transform the Test Data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:54.841004Z", "iopub.status.busy": "2020-12-19T14:32:54.840351Z", "iopub.status.idle": "2020-12-19T14:32:55.906455Z", "shell.execute_reply": "2020-12-19T14:32:55.907064Z" } }, "outputs": [], "source": [ "X_test, y_test = load_basic_motions(split=\"test\")\n", "X_test_transform = rocket.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.5 Classify the Test Data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:55.910253Z", "iopub.status.busy": "2020-12-19T14:32:55.909743Z", "iopub.status.idle": "2020-12-19T14:32:56.008364Z", "shell.execute_reply": "2020-12-19T14:32:56.008931Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": "0.975" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier.score(X_test_transform, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## 4 Pipeline Example\n", "\n", "We can use ROCKET together with `RidgeClassifierCV` (or another classifier) in a pipeline. We can then use the pipeline like a self-contained classifier, with a single call to `fit`, and without having to separately transform the data, etc.\n", "\n", "### 4.1 Initialise the Pipeline" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:56.012465Z", "iopub.status.busy": "2020-12-19T14:32:56.011939Z", "iopub.status.idle": "2020-12-19T14:32:56.013801Z", "shell.execute_reply": "2020-12-19T14:32:56.014399Z" } }, "outputs": [], "source": [ "rocket_pipeline = make_pipeline(\n", " Rocket(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Load and Fit the Training Data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:56.017692Z", "iopub.status.busy": "2020-12-19T14:32:56.017166Z", "iopub.status.idle": "2020-12-19T14:32:56.420648Z", "shell.execute_reply": "2020-12-19T14:32:56.421247Z" } }, "outputs": [ { "data": { "text/plain": "Pipeline(steps=[('rocket', Rocket()),\n ('ridgeclassifiercv',\n RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n 4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n 2.15443469e+02, 1.00000000e+03])))])", "text/html": "
Pipeline(steps=[('rocket', Rocket()),\n                ('ridgeclassifiercv',\n                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n       2.15443469e+02, 1.00000000e+03])))])
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" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train, y_train = load_arrow_head(split=\"train\")\n", "\n", "# it is necessary to pass y_train to the pipeline\n", "# y_train is not used for the transform, but it is used by the classifier\n", "rocket_pipeline.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Load and Classify the Test Data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2020-12-19T14:32:56.425026Z", "iopub.status.busy": "2020-12-19T14:32:56.424348Z", "iopub.status.idle": "2020-12-19T14:32:57.602704Z", "shell.execute_reply": "2020-12-19T14:32:57.603291Z" } }, "outputs": [ { "data": { "text/plain": "0.7885714285714286" }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test, y_test = load_arrow_head(split=\"test\", return_X_y=True)\n", "\n", "rocket_pipeline.score(X_test, y_test)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 4 }