![\"Windowing.\"](\"./img/convolution.png\")
\n",
"\n",
"ROCKET computes two features from the resulting feature maps: the maximum value\n",
"(sometimes called a max pooling operation), and the proportion of positive values (or\n",
" ppv). In the above example the first entry of the activation map is the result of a\n",
" dot-product between $T_{1:3} * \\omega = T_{1:3} \\cdot \\omega = 0 + 0 + 3 = 3$. Max\n",
" pooling extracts the maximum from the activation map as feature. The proportion of\n",
" positive values (PPV) is $8 / 11$ in this example.\n",
"\n",
"A large number of random convolutions are generated, and the two features are\n",
"combined to produce a transformed train data set. This is used to train a linear\n",
"classifier. [1] reccomend a RIDGE Regression Classifier using cross-validation to\n",
"train the $L_2$-regularisation parameter $\\alpha$. A logistic regression classifier\n",
"is suggested as a replacement for larger datasets.\n",
"![\"ROCKET.\"](\"./img/rocket2.png\")
\n",
"\n",
"ROCKET employs dilation. Dilation is a form of down sampling, in that it defines\n",
"spaces between time points. Hence, a convolution with dilation $d$ is compared to\n",
"time points $d$ steps apart when calculating the distance.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": " name \\\n0 Arsenal \n1 HydraClassifier \n2 MultiRocketHydraClassifier \n3 RocketClassifier \n\n estimator \n0 | \n | name | \nestimator | \n
|---|---|---|
| 0 | \nArsenal | \n<class 'aeon.classification.convolution_based.... | \n
| 1 | \nHydraClassifier | \n<class 'aeon.classification.convolution_based.... | \n
| 2 | \nMultiRocketHydraClassifier | \n<class 'aeon.classification.convolution_based.... | \n
| 3 | \nRocketClassifier | \n<class 'aeon.classification.convolution_based.... | \n
![\"Rocket](\"./img/rocket.png\")
\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": "0.9689018464528668"
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rocket = RocketClassifier()\n",
"rocket.fit(italy, italy_labels)\n",
"y_pred = rocket.predict(italy_test)\n",
"accuracy_score(italy_test_labels, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
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"outputs": [
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"data": {
"text/plain": "0.9698736637512148"
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"afc = Arsenal()\n",
"afc.fit(italy, italy_labels)\n",
"y_pred = afc.predict(italy_test)\n",
"accuracy_score(italy_test_labels, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"MiniROCKET[2] is a fast version of ROCKET that uses hard coded convolutions and only\n",
" uses PPV. MultiROCKET [3] adds three new pooling operations extracted from each\n",
" kernel: mean of positive values (MPV), mean of indices of positive values (MIPV) and\n",
" longest stretch of positive values (LSPV). MultiRocket generates a total of 50k\n",
" features from 10k kernels and 5 pooling operations. It also extracts features from\n",
" first order differences. The RocketClassifier and Arsenal can be configured to use\n",
" MiniROCKET and MultiROCKET. Simply set with rocket_transform as \"minirocket\" or\n",
" \"multirocket\". Both work on multivariate series: channels are simply randomly\n",
" selected for each convolution."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
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"outputs": [
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"data": {
"text/plain": "0.9718172983479106"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"multi_r = Arsenal(rocket_transform=\"multirocket\")\n",
"multi_r.fit(italy, italy_labels)\n",
"y_pred = multi_r.predict(italy_test)\n",
"accuracy_score(italy_test_labels, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": "1.0"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mini_r = RocketClassifier(rocket_transform=\"minirocket\")\n",
"mini_r.fit(motions, motions_labels)\n",
"y_pred = mini_r.predict(motions_test)\n",
"accuracy_score(motions_test_labels, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"RocketClassifier has three other parameters that may effect performance.\n",
"`num_kernels` (default 10,000) determines the number of convolutions/kernels generated\n",
" and will influence the memory usage. `max_dilations_per_kernel` (default=32) and\n",
"`n_features_per_kernel` (default=4) are used in 'MiniROCKET' and 'MultiROCKET. For\n",
"each candidate convolution, `max_dilations_per_kernel` are assessed and\n",
"`n_features_per_kernel` are retained.\n"
]
},
{
"cell_type": "markdown",
"source": [
"## Performance on the UCR univariate datasets\n",
"\n",
"You can find the convolution based classifiers as follows"
],
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},
{
"cell_type": "code",
"execution_count": 14,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Arsenal',