From 4b4e9eb862152363a4e671c4b1a2316d46e9e22c Mon Sep 17 00:00:00 2001
From: Tony Bagnall <a.j.bagnall@soton.ac.uk>
Date: Mon, 25 Nov 2024 21:31:57 +0000
Subject: [PATCH] [DOC] speed up notebooks (#2393)

* speed up TSFresh notebook

* deep networks notebook

* rocket notebook

* exclude

* run_examples.sh
---
 .github/utilities/run_examples.sh      |    2 +
 examples/networks/deep_learning.ipynb  |  355 +++---
 examples/transformations/rocket.ipynb  | 1517 ++++++++++++++++++++++--
 examples/transformations/tsfresh.ipynb |  750 +++++++++---
 4 files changed, 2235 insertions(+), 389 deletions(-)

diff --git a/.github/utilities/run_examples.sh b/.github/utilities/run_examples.sh
index 3eeac963a3..fd7376c05b 100755
--- a/.github/utilities/run_examples.sh
+++ b/.github/utilities/run_examples.sh
@@ -9,6 +9,7 @@ excluded=()
 if [ "$1" = true ]; then
   excluded+=(
     "examples/datasets/load_data_from_web.ipynb"
+    "examples/benchmarking/published_results.ipynb"
     "examples/benchmarking/reference_results.ipynb"
     "examples/benchmarking/bakeoff_results.ipynb"
     "examples/benchmarking/regression.ipynb"
@@ -21,6 +22,7 @@ if [ "$1" = true ]; then
     "examples/classification/interval_based.ipynb"
     "examples/classification/shapelet_based.ipynb"
     "examples/classification/convolution_based.ipynb"
+    "examples/similarity_search/code_speed.ipynb"
 
   )
 fi
diff --git a/examples/networks/deep_learning.ipynb b/examples/networks/deep_learning.ipynb
index 9e06feabbb..7cf092f7f3 100644
--- a/examples/networks/deep_learning.ipynb
+++ b/examples/networks/deep_learning.ipynb
@@ -64,8 +64,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-21T11:14:08.477299Z",
-     "start_time": "2024-11-21T11:14:08.433390Z"
+     "end_time": "2024-11-25T16:48:00.794715Z",
+     "start_time": "2024-11-25T16:48:00.780244Z"
     }
    },
    "source": [
@@ -97,7 +97,7 @@
     "from aeon.regression.deep_learning import InceptionTimeRegressor"
    ],
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 12
   },
   {
    "attachments": {},
@@ -116,17 +116,18 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-21T11:12:15.141664Z",
-     "start_time": "2024-11-21T11:12:02.084792Z"
+     "end_time": "2024-11-25T16:48:20.910216Z",
+     "start_time": "2024-11-25T16:48:02.721649Z"
     }
    },
    "source": [
-    "xtrain, ytrain = load_classification(name=\"ArrowHead\", split=\"train\")\n",
-    "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
-    "\n",
-    "inc = InceptionTimeClassifier(n_classifiers=5, use_custom_filters=False, n_epochs=3)\n",
+    "xtrain, ytrain = load_classification(name=\"GunPoint\", split=\"train\")\n",
+    "xtest, ytest = load_classification(name=\"GunPoint\", split=\"test\")\n",
+    "xtrain = xtrain[:10, :, :]\n",
+    "ytrain = ytrain[:10]\n",
+    "inc = InceptionTimeClassifier(n_classifiers=2, use_custom_filters=False, n_epochs=2)\n",
     "inc.fit(X=xtrain, y=ytrain)\n",
-    "ypred = inc.predict(X=xtest)\n",
+    "ypred = inc.predict(X=xtest[0:5, :, :])\n",
     "\n",
     "print(\"Predictions: \", ypred[0:5])\n",
     "print(\"Ground Truth: \", ytest[0:5])"
@@ -136,17 +137,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 72ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 78ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 72ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 74ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 82ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 386ms/step\n",
       "Predictions:  ['2' '2' '2' '2' '2']\n",
-      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
+      "Ground Truth:  ['1' '2' '2' '1' '1']\n"
      ]
     }
    ],
-   "execution_count": 2
+   "execution_count": 13
   },
   {
    "attachments": {},
@@ -170,17 +168,14 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-21T11:12:30.252190Z",
-     "start_time": "2024-11-21T11:12:15.222773Z"
+     "end_time": "2024-11-25T16:48:52.055955Z",
+     "start_time": "2024-11-25T16:48:31.172431Z"
     }
    },
    "source": [
-    "xtrain, ytrain = load_classification(name=\"ArrowHead\", split=\"train\")\n",
-    "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
-    "\n",
-    "inc = InceptionTimeClassifier(n_classifiers=5, use_custom_filters=True, n_epochs=3)\n",
+    "inc = InceptionTimeClassifier(n_classifiers=2, use_custom_filters=True, n_epochs=2)\n",
     "inc.fit(X=xtrain, y=ytrain)\n",
-    "ypred = inc.predict(X=xtest)\n",
+    "ypred = inc.predict(X=xtest[0:5, :, :])\n",
     "\n",
     "print(\"Predictions: \", ypred[0:5])\n",
     "print(\"Ground Truth: \", ytest[0:5])"
@@ -190,17 +185,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 94ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 91ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 94ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 97ms/step\n",
-      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 96ms/step\n",
-      "Predictions:  ['0' '0' '0' '0' '0']\n",
-      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 557ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
+      "Predictions:  ['2' '2' '2' '2' '2']\n",
+      "Ground Truth:  ['1' '2' '2' '1' '1']\n"
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 14
   },
   {
    "cell_type": "markdown",
@@ -217,37 +209,36 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-21T11:12:43.183566Z",
-     "start_time": "2024-11-21T11:12:30.257417Z"
+     "end_time": "2024-11-25T16:49:32.976474Z",
+     "start_time": "2024-11-25T16:49:14.568788Z"
     }
    },
    "source": [
-    "xtrain, ytrain = load_regression(name=\"Covid3Month\", split=\"train\")\n",
-    "xtest, ytest = load_regression(name=\"Covid3Month\", split=\"test\")\n",
+    "x_train, y_train = load_regression(name=\"Covid3Month\", split=\"train\")\n",
+    "x_test, y_test = load_regression(name=\"Covid3Month\", split=\"test\")\n",
+    "x_train = x_train[:10, :, :]\n",
+    "y_train = y_train[:10]\n",
     "\n",
-    "inc = InceptionTimeRegressor(n_regressors=5, n_epochs=1, use_custom_filters=False)\n",
-    "inc.fit(X=xtrain, y=ytrain)\n",
-    "ypred = inc.predict(X=xtest)\n",
+    "inc = InceptionTimeRegressor(n_regressors=2, n_epochs=1, use_custom_filters=False)\n",
+    "inc.fit(X=x_train, y=y_train)\n",
+    "ypred = inc.predict(X=x_test[0:5, :, :])\n",
     "\n",
     "print(\"Predictions: \", ypred[0:5])\n",
-    "print(\"Ground Truth: \", ytest[0:5])"
+    "print(\"Ground Truth: \", y_train[0:5])"
    ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 107ms/step\n",
-      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 109ms/step\n",
-      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 112ms/step\n",
-      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 114ms/step\n",
-      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 108ms/step\n",
-      "Predictions:  [-0.4258549  -0.0387525  -0.01732254 -0.60533425 -4.51287463]\n",
-      "Ground Truth:  [0.0118838  0.00379507 0.08298755 0.04510921 0.12783075]\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374ms/step\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step\n",
+      "Predictions:  [ -3.88514614  -0.3810918   -0.2344005   -5.31711912 -37.39011002]\n",
+      "Ground Truth:  [0.         0.07758621 0.         0.         0.15400309]\n"
      ]
     }
    ],
-   "execution_count": 4
+   "execution_count": 15
   },
   {
    "attachments": {},
@@ -274,18 +265,15 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-21T11:14:19.577898Z",
-     "start_time": "2024-11-21T11:14:16.120345Z"
+     "end_time": "2024-11-25T16:49:45.025079Z",
+     "start_time": "2024-11-25T16:49:38.455222Z"
     }
    },
    "source": [
-    "xtrain, _ = load_classification(name=\"ArrowHead\", split=\"train\")\n",
-    "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
-    "\n",
     "aefcn = AEFCNClusterer(\n",
     "    temporal_latent_space=False,\n",
     "    estimator=KMeans(n_clusters=3),\n",
-    "    n_epochs=10,\n",
+    "    n_epochs=3,\n",
     ")\n",
     "\n",
     "aefcn.fit(X=xtrain)\n",
@@ -298,14 +286,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1m2/2\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step\n",
-      "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step \n",
-      "Predictions:  [2 0 2 2 2]\n",
-      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step\n",
+      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "Predictions:  [1 0 1 1 0]\n",
+      "Ground Truth:  ['1' '2' '2' '1' '1']\n"
      ]
     }
    ],
-   "execution_count": 8
+   "execution_count": 16
   },
   {
    "attachments": {},
@@ -332,42 +320,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11/11 [==============================] - 0s 29ms/step\n",
-      "11/11 [==============================] - 0s 29ms/step\n",
-      "['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1']\n",
-      "['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
-      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1']\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:49:51.216960Z",
+     "start_time": "2024-11-25T16:49:47.246635Z"
     }
-   ],
+   },
    "source": [
-    "xtrain, ytrain = load_classification(name=\"ArrowHead\", split=\"train\")\n",
-    "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
-    "\n",
     "fcn = FCNClassifier(\n",
     "    save_best_model=True,\n",
     "    save_last_model=True,\n",
@@ -392,7 +351,36 @@
     "\n",
     "os.remove(\"./best_fcn.keras\")\n",
     "os.remove(\"./last_fcn.keras\")"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
+      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1']\n",
+      "['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1'\n",
+      " '1' '1' '1' '1' '1' '1']\n"
+     ]
+    }
+   ],
+   "execution_count": 17
   },
   {
    "attachments": {},
@@ -409,24 +397,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:17.048341Z",
+     "start_time": "2024-11-25T16:50:17.030069Z"
+    }
+   },
    "source": [
     "# define self-supervised space dimension\n",
     "\n",
     "n_dim = 16\n",
-    "\n",
-    "# load the data\n",
-    "\n",
-    "xtrain, ytrain = load_classification(name=\"ArrowHead\", split=\"train\")\n",
-    "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
-    "\n",
     "# Flip axis to be handled correctly in tensorflow\n",
     "\n",
     "xtrain = np.transpose(xtrain, axes=(0, 2, 1))\n",
     "xtest = np.transpose(xtest, axes=(0, 2, 1))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 18
   },
   {
    "cell_type": "markdown",
@@ -437,9 +424,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:22.513036Z",
+     "start_time": "2024-11-25T16:50:22.499412Z"
+    }
+   },
    "source": [
     "def triplet_loss_function(alpha):\n",
     "    \"\"\"Create a triplet loss function for triplet-based training.\"\"\"\n",
@@ -466,7 +456,9 @@
     "        return loss\n",
     "\n",
     "    return temp"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 19
   },
   {
    "cell_type": "markdown",
@@ -477,9 +469,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:26.420369Z",
+     "start_time": "2024-11-25T16:50:26.207821Z"
+    }
+   },
    "source": [
     "# Define the triplets input layers\n",
     "\n",
@@ -512,7 +507,9 @@
     ")\n",
     "\n",
     "SSL_model.compile(loss=triplet_loss_function(alpha=1e-5))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 20
   },
   {
    "cell_type": "markdown",
@@ -523,9 +520,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:28.681579Z",
+     "start_time": "2024-11-25T16:50:28.664819Z"
+    }
+   },
    "source": [
     "def triplet_generation(x):\n",
     "    \"\"\"Generate triplet samples (ref, pos, neg) for triplet loss training.\"\"\"\n",
@@ -570,7 +570,9 @@
     "        neg[i] = w1 * nota + w2 * b2 + w2 * c2\n",
     "\n",
     "    return ref, pos, neg"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 21
   },
   {
    "cell_type": "markdown",
@@ -581,12 +583,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:30.987857Z",
+     "start_time": "2024-11-25T16:50:30.983090Z"
+    }
+   },
    "source": [
     "xtrain_ref, xtrain_pos, xtrain_neg = triplet_generation(x=xtrain)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 22
   },
   {
    "cell_type": "markdown",
@@ -597,9 +604,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:35.325071Z",
+     "start_time": "2024-11-25T16:50:35.314592Z"
+    }
+   },
    "source": [
     "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n",
     "    monitor=\"loss\", factor=0.5, patience=50, min_lr=0.0001\n",
@@ -612,7 +622,9 @@
     ")\n",
     "\n",
     "callbacks = [reduce_lr, model_checkpoint]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 23
   },
   {
    "cell_type": "markdown",
@@ -623,25 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:53.529773Z",
+     "start_time": "2024-11-25T16:50:44.026512Z"
     }
-   ],
+   },
    "source": [
     "history = SSL_model.fit(\n",
     "    [xtrain_ref, xtrain_pos, xtrain_neg],\n",
     "    np.zeros(shape=len(xtrain)),\n",
-    "    epochs=20,\n",
+    "    epochs=4,\n",
     "    callbacks=callbacks,\n",
     "    verbose=False,\n",
     ")\n",
@@ -650,7 +654,20 @@
     "plt.plot(history.history[\"loss\"], lw=3, color=\"blue\", label=\"training loss\")\n",
     "plt.legend()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 24
   },
   {
    "cell_type": "markdown",
@@ -661,27 +678,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2/2 [==============================] - 1s 10ms/step\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:50:57.858973Z",
+     "start_time": "2024-11-25T16:50:56.487914Z"
     }
-   ],
+   },
    "source": [
     "plt.cla()\n",
     "plt.clf()\n",
@@ -711,16 +713,41 @@
     "\n",
     "plt.legend()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 931ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 25
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T16:51:00.107975Z",
+     "start_time": "2024-11-25T16:51:00.093491Z"
+    }
+   },
    "source": [
     "os.remove(\"./best_ssl_model.keras\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 26
   },
   {
    "attachments": {},
diff --git a/examples/transformations/rocket.ipynb b/examples/transformations/rocket.ipynb
index 3e1ed7db1d..c992fc1952 100644
--- a/examples/transformations/rocket.ipynb
+++ b/examples/transformations/rocket.ipynb
@@ -32,41 +32,49 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:32.515016Z",
+     "start_time": "2024-11-25T17:01:32.504509Z"
     }
    },
-   "outputs": [],
    "source": [
     "# !pip install --upgrade numba"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 33
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:33.167188Z",
+     "start_time": "2024-11-25T17:01:33.161609Z"
     }
    },
-   "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.datasets import load_gunpoint  # univariate dataset\n",
     "from aeon.transformations.collection.convolution_based import Rocket"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 34
   },
   {
    "cell_type": "markdown",
@@ -83,19 +91,34 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:34.603321Z",
+     "start_time": "2024-11-25T17:01:34.573759Z"
     }
    },
-   "outputs": [],
    "source": [
-    "X_train, y_train = load_arrow_head(split=\"train\")"
-   ]
+    "X_train, y_train = load_gunpoint(split=\"train\")\n",
+    "X_train = X_train[:5, :, :]\n",
+    "y_train = y_train[:5]\n",
+    "print(X_train.shape)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5, 1, 150)\n"
+     ]
+    }
+   ],
+   "execution_count": 35
   },
   {
    "cell_type": "markdown",
@@ -106,21 +129,34 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:35.852821Z",
+     "start_time": "2024-11-25T17:01:35.837832Z"
     }
    },
-   "outputs": [],
    "source": [
-    "rocket = Rocket()  # by default, ROCKET uses 10,000 kernels\n",
+    "rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels\n",
     "rocket.fit(X_train)\n",
-    "X_train_transform = rocket.transform(X_train)"
-   ]
+    "X_train_transform = rocket.transform(X_train)\n",
+    "print(X_train_transform.shape)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5, 200)\n"
+     ]
+    }
+   ],
+   "execution_count": 36
   },
   {
    "cell_type": "markdown",
@@ -138,30 +174,448 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:38.060428Z",
+     "start_time": "2024-11-25T17:01:38.038775Z"
     }
    },
+   "source": [
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "classifier.fit(X_train_transform, y_train)"
+   ],
    "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": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifierCV</label><div class=\"sk-toggleable__content\"><pre>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]))</pre></div></div></div></div></div>"
+      "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": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>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]))</pre></div> </div></div></div></div>"
+      ]
      },
-     "execution_count": 5,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "classifier.fit(X_train_transform, y_train)"
-   ]
+   "execution_count": 37
   },
   {
    "cell_type": "markdown",
@@ -172,20 +626,24 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:39.178929Z",
+     "start_time": "2024-11-25T17:01:39.136007Z"
     }
    },
-   "outputs": [],
    "source": [
-    "X_test, y_test = load_arrow_head(split=\"test\")\n",
+    "X_test, y_test = load_gunpoint(split=\"test\")\n",
     "X_test_transform = rocket.transform(X_test)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 38
   },
   {
    "cell_type": "markdown",
@@ -196,7 +654,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.312125Z",
@@ -204,21 +661,28 @@
      "iopub.status.idle": "2020-12-19T14:32:53.409775Z",
      "shell.execute_reply": "2020-12-19T14:32:53.410342Z"
     },
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:40.547350Z",
+     "start_time": "2024-11-25T17:01:40.533334Z"
+    }
    },
+   "source": [
+    "classifier.score(X_test_transform, y_test)"
+   ],
    "outputs": [
     {
      "data": {
-      "text/plain": "0.7771428571428571"
+      "text/plain": [
+       "0.64"
+      ]
      },
-     "execution_count": 7,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "classifier.score(X_test_transform, y_test)"
-   ]
+   "execution_count": 39
   },
   {
    "cell_type": "markdown",
@@ -235,19 +699,23 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:41.782580Z",
+     "start_time": "2024-11-25T17:01:41.767897Z"
     }
    },
-   "outputs": [],
    "source": [
     "X_train, y_train = load_basic_motions(split=\"train\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 40
   },
   {
    "cell_type": "markdown",
@@ -258,21 +726,37 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:42.949980Z",
+     "start_time": "2024-11-25T17:01:42.918211Z"
     }
    },
-   "outputs": [],
    "source": [
-    "rocket = Rocket()\n",
+    "rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels\n",
     "rocket.fit(X_train)\n",
-    "X_train_transform = rocket.transform(X_train)"
-   ]
+    "X_train_transform = rocket.transform(X_train)\n",
+    "X_train_transform.shape"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40, 200)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 41
   },
   {
    "cell_type": "markdown",
@@ -283,30 +767,448 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:44.038154Z",
+     "start_time": "2024-11-25T17:01:44.002549Z"
     }
    },
+   "source": [
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "classifier.fit(X_train_transform, y_train)"
+   ],
    "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": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifierCV</label><div class=\"sk-toggleable__content\"><pre>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]))</pre></div></div></div></div></div>"
+      "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": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>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]))</pre></div> </div></div></div></div>"
+      ]
      },
-     "execution_count": 10,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "classifier.fit(X_train_transform, y_train)"
-   ]
+   "execution_count": 42
   },
   {
    "cell_type": "markdown",
@@ -317,20 +1219,24 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:45.150937Z",
+     "start_time": "2024-11-25T17:01:45.106121Z"
     }
    },
-   "outputs": [],
    "source": [
     "X_test, y_test = load_basic_motions(split=\"test\")\n",
     "X_test_transform = rocket.transform(X_test)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 43
   },
   {
    "cell_type": "markdown",
@@ -341,7 +1247,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:55.910253Z",
@@ -349,21 +1254,28 @@
      "iopub.status.idle": "2020-12-19T14:32:56.008364Z",
      "shell.execute_reply": "2020-12-19T14:32:56.008931Z"
     },
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:46.229312Z",
+     "start_time": "2024-11-25T17:01:46.215072Z"
+    }
    },
+   "source": [
+    "classifier.score(X_test_transform, y_test)"
+   ],
    "outputs": [
     {
      "data": {
-      "text/plain": "0.975"
+      "text/plain": [
+       "0.975"
+      ]
      },
-     "execution_count": 12,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "classifier.score(X_test_transform, y_test)"
-   ]
+   "execution_count": 44
   },
   {
    "cell_type": "markdown",
@@ -380,21 +1292,25 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:47.349648Z",
+     "start_time": "2024-11-25T17:01:47.345129Z"
     }
    },
-   "outputs": [],
    "source": [
     "rocket_pipeline = make_pipeline(\n",
     "    Rocket(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 45
   },
   {
    "cell_type": "markdown",
@@ -405,33 +1321,457 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:49.740497Z",
+     "start_time": "2024-11-25T17:01:48.459632Z"
     }
    },
+   "source": [
+    "# 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)"
+   ],
    "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": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;rocket&#x27;, Rocket()),\n                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;rocket&#x27;, Rocket()),\n                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Rocket</label><div class=\"sk-toggleable__content\"><pre>Rocket()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifierCV</label><div class=\"sk-toggleable__content\"><pre>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]))</pre></div></div></div></div></div></div></div>"
+      "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": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;rocket&#x27;, Rocket()),\n",
+       "                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;rocket&#x27;, Rocket()),\n",
+       "                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Rocket</label><div class=\"sk-toggleable__content fitted\"><pre>Rocket()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>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]))</pre></div> </div></div></div></div></div></div>"
+      ]
      },
-     "execution_count": 14,
+     "execution_count": 46,
      "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)"
-   ]
+   "execution_count": 46
   },
   {
    "cell_type": "markdown",
@@ -442,30 +1782,41 @@
   },
   {
    "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"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:50.960464Z",
+     "start_time": "2024-11-25T17:01:49.763086Z"
     }
    },
+   "source": [
+    "rocket_pipeline.score(X_test, y_test)"
+   ],
    "outputs": [
     {
      "data": {
-      "text/plain": "0.7885714285714286"
+      "text/plain": [
+       "0.975"
+      ]
      },
-     "execution_count": 15,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "X_test, y_test = load_arrow_head(split=\"test\")\n",
-    "\n",
-    "rocket_pipeline.score(X_test, y_test)"
-   ]
+   "execution_count": 47
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {
diff --git a/examples/transformations/tsfresh.ipynb b/examples/transformations/tsfresh.ipynb
index da00e2f48e..d1d37d1761 100644
--- a/examples/transformations/tsfresh.ipynb
+++ b/examples/transformations/tsfresh.ipynb
@@ -17,39 +17,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:39.713903Z",
      "iopub.status.busy": "2020-12-19T14:30:39.713342Z",
      "iopub.status.idle": "2020-12-19T14:30:39.715128Z",
      "shell.execute_reply": "2020-12-19T14:30:39.715641Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:05.457198Z",
+     "start_time": "2024-11-25T14:07:05.449815Z"
     }
    },
-   "outputs": [],
    "source": [
     "# !pip install --upgrade tsfresh"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:39.719083Z",
      "iopub.status.busy": "2020-12-19T14:30:39.718586Z",
      "iopub.status.idle": "2020-12-19T14:30:40.743724Z",
      "shell.execute_reply": "2020-12-19T14:30:40.744213Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:07.829632Z",
+     "start_time": "2024-11-25T14:07:06.056664Z"
     }
    },
-   "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.pipeline import make_pipeline\n",
     "\n",
     "from aeon.datasets import load_arrow_head, load_basic_motions\n",
     "from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -59,46 +67,43 @@
     "\n",
     "We use the ArrowHead data from the [UCR TSC archive](https://timeseriesclassification.com).\n",
     "as an example dataset. See\n",
-    "[dataset notebook](https://github.com/aeon-toolkit/aeon/blob/main/examples/datasets\n",
-    "/provided_data.ipynb) for more details."
+    "[dataset notebook](https://github.com/aeon-toolkit/aeon/blob/main/examples/datasets/provided_data.ipynb) for more details. We only use the first few cases for examples to speed up the \n",
+    "notebook. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:40.748159Z",
      "iopub.status.busy": "2020-12-19T14:30:40.747656Z",
      "iopub.status.idle": "2020-12-19T14:30:40.795200Z",
      "shell.execute_reply": "2020-12-19T14:30:40.795889Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:09.120656Z",
+     "start_time": "2024-11-25T14:07:09.090118Z"
     }
    },
-   "outputs": [],
    "source": [
-    "X_train, y_train = load_arrow_head(split=\"train\")\n",
-    "X_test, y_test = load_arrow_head(split=\"test\")\n",
+    "X, y = load_arrow_head()\n",
+    "n_cases = 24\n",
+    "X_train = X[:n_cases, :, :]\n",
+    "y_train = y[:n_cases]\n",
+    "X_test = X[n_cases : 2 * n_cases, :, :]\n",
+    "y_test = y[n_cases : 2 * n_cases]\n",
     "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2020-12-19T14:30:40.808841Z",
-     "iopub.status.busy": "2020-12-19T14:30:40.808198Z",
-     "iopub.status.idle": "2020-12-19T14:30:40.816155Z",
-     "shell.execute_reply": "2020-12-19T14:30:40.816682Z"
-    },
-    "jupyter": {
-     "outputs_hidden": false
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(24, 1, 251) (24,) (24, 1, 251) (24,)\n"
+     ]
     }
-   },
-   "outputs": [],
-   "source": [
-    "X_train[0]"
-   ]
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -114,22 +119,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:40.829452Z",
      "iopub.status.busy": "2020-12-19T14:30:40.828907Z",
      "iopub.status.idle": "2020-12-19T14:30:53.049755Z",
      "shell.execute_reply": "2020-12-19T14:30:53.050249Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:16.339473Z",
+     "start_time": "2024-11-25T14:07:11.573523Z"
     }
    },
-   "outputs": [],
    "source": [
     "t = TSFresh()\n",
     "Xt = t.fit_transform(X_train)\n",
-    "Xt.shape\n",
-    "Xt2 = t.transform(X_test)"
-   ]
+    "Xt2 = t.transform(X_test)\n",
+    "print(f\"Train shape = {Xt.shape} test shape = {Xt2.shape}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape = (24, 777) test shape = (24, 777)\n"
+     ]
+    }
+   ],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -143,8 +160,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
    "source": [
     "t = TSFreshRelevant()\n",
     "t.fit(X_train, y_train)\n",
@@ -152,8 +167,25 @@
     "Xt.shape"
    ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:32.455607Z",
+     "start_time": "2024-11-25T14:07:26.124172Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(24, 75)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -166,16 +198,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:53.062147Z",
      "iopub.status.busy": "2020-12-19T14:30:53.061631Z",
      "iopub.status.idle": "2020-12-19T14:31:09.307275Z",
      "shell.execute_reply": "2020-12-19T14:31:09.307781Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:41.090159Z",
+     "start_time": "2024-11-25T14:07:36.403997Z"
     }
    },
-   "outputs": [],
    "source": [
     "classifier = make_pipeline(\n",
     "    TSFresh(default_fc_parameters=\"efficient\", show_warnings=False),\n",
@@ -183,7 +217,20 @@
     ")\n",
     "classifier.fit(X_train, y_train)\n",
     "classifier.score(X_test, y_test)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.625"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -197,26 +244,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[7 1 4 4 0 4 4 5 2 1 5 7 4 4 5 0 0 6 6 0 6 4 5 6 3 6 7 1 6 4 4 1 5 0 4 4 7\n",
-      " 6 6 2 1 0 0 4 6 5 4 6 4 6 6 0 4 6 1 1 4 1 4 1 4 0 1 4 1 5 4 7 4 7 6 4 6 1\n",
-      " 6 4 6 7 4 6 6 1 6 1 4 7 6 4 6 0 4 6 4 6 6 4 0 3 4 6 4 1 0 0 4 4 6 1 0 7 4\n",
-      " 6 0 4 4 0 1 6 6 0 2 0 6 0 3 6 5 7 6 4 4 3 6 6 6 1 7 4 6 6 4 4 6 6 0 4 6 4\n",
-      " 5 0 4 4 6 4 6 1 5 6 6 0 6 0 3 4 4 6 1 5 3 7 6 6 6 7 4]\n"
-     ]
-    }
-   ],
    "source": [
     "from aeon.classification.feature_based import TSFreshClassifier\n",
     "from aeon.clustering.feature_based import TSFreshClusterer\n",
     "\n",
-    "cls = TSFreshClassifier()\n",
-    "clst = TSFreshClusterer()\n",
+    "cls = TSFreshClassifier(relevant_feature_extractor=False)\n",
+    "clst = TSFreshClusterer(n_clusters=2)\n",
     "\n",
     "cls.fit(X_train, y_train)\n",
     "cls.score(X_test, y_test)\n",
@@ -225,8 +258,24 @@
     "print(clst.predict(X_test))"
    ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:08:02.405107Z",
+     "start_time": "2024-11-25T14:07:50.878523Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['0' '1' '0' '1' '1' '2' '0' '1' '1' '0' '1' '1' '0' '2' '0' '0' '0' '2'\n",
+      " '2' '1' '0' '0' '0' '0']\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]\n"
+     ]
+    }
+   ],
+   "execution_count": 7
   },
   {
    "cell_type": "markdown",
@@ -242,29 +291,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "source": [
+    "from aeon.classification.sklearn import RotationForestClassifier\n",
+    "\n",
+    "cls = TSFreshClassifier(estimator=RotationForestClassifier(n_estimators=5))\n",
+    "cls.fit(X_train, y_train)\n",
+    "cls.score(X_test, y_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:08:13.304452Z",
+     "start_time": "2024-11-25T14:08:06.677532Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "0.5771428571428572"
+      "text/plain": [
+       "0.5833333333333334"
+      ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from aeon.classification.sklearn import RotationForestClassifier\n",
-    "\n",
-    "cls = TSFreshClassifier(\n",
-    "    relevant_feature_extractor=False, estimator=RotationForestClassifier(n_estimators=5)\n",
-    ")  #\n",
-    "cls.fit(X_train, y_train)\n",
-    "cls.score(X_test, y_test)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   "execution_count": 8
   },
   {
    "cell_type": "markdown",
@@ -279,20 +332,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 0 1 1 1 1 1 0 2 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1\n",
-      " 1 1 2 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1\n",
-      " 1 1 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
-      " 1 1 1 1 1 0 1 1 1 2 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
-      " 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1]\n"
-     ]
-    }
-   ],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "\n",
@@ -301,8 +340,22 @@
     "print(clst.predict(X_test))"
    ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:08:38.025066Z",
+     "start_time": "2024-11-25T14:08:33.300907Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 2 0 0 1]\n"
+     ]
+    }
+   ],
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
@@ -316,39 +369,442 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "TSFreshRegressor()",
-      "text/html": "<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TSFreshRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TSFreshRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TSFreshRegressor()</pre></div> </div></div></div></div>"
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "TSFreshRegressor()",
-      "text/html": "<style>#sk-container-id-2 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-2 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n  padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TSFreshRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TSFreshRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TSFreshRegressor()</pre></div> </div></div></div></div>"
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "from aeon.regression.feature_based import TSFreshRegressor\n",
     "\n",
-    "reg = TSFreshRegressor()\n",
+    "reg = TSFreshRegressor(relevant_feature_extractor=False)\n",
     "from aeon.datasets import load_covid_3month\n",
     "\n",
-    "X, y = load_covid_3month()\n",
+    "X, y = load_covid_3month(split=\"train\")\n",
     "reg.fit(X, y)"
    ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:09:11.745540Z",
+     "start_time": "2024-11-25T14:08:56.573376Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TSFreshRegressor(relevant_feature_extractor=False)"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TSFreshRegressor(relevant_feature_extractor=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TSFreshRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TSFreshRegressor(relevant_feature_extractor=False)</pre></div> </div></div></div></div>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 10
   },
   {
    "cell_type": "markdown",
@@ -363,14 +819,13 @@
    "source": [
     "## TSFresh with multivariate time series data\n",
     "\n",
-    "All three estimators can be used with multivariate time series. The estimators\n",
-    "calculate the features on each channel independently then concatenate the results.\n",
-    "The full transform creates `777*n_channels` features."
+    "``TSFresh`` transformers and all three estimators can be used with multivariate time \n",
+    "series. The transform calculates the features on each channel independently then \n",
+    "concatenate the results. The full transform creates `777*n_channels` features."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:31:09.311742Z",
@@ -378,8 +833,17 @@
      "iopub.status.idle": "2020-12-19T14:31:09.380791Z",
      "shell.execute_reply": "2020-12-19T14:31:09.381304Z"
     },
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:11:57.583864Z",
+     "start_time": "2024-11-25T14:11:57.545946Z"
+    }
    },
+   "source": [
+    "X_train, y_train = load_basic_motions(split=\"train\")\n",
+    "X_test, y_test = load_basic_motions(split=\"test\")\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -389,16 +853,10 @@
      ]
     }
    ],
-   "source": [
-    "X_train, y_train = load_basic_motions(split=\"train\")\n",
-    "X_test, y_test = load_basic_motions(split=\"test\")\n",
-    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
-   ]
+   "execution_count": 14
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
    "source": [
     "tsfresh = TSFresh()\n",
     "X = tsfresh.fit_transform(X_train, y_train)\n",
@@ -408,24 +866,32 @@
     "collapsed": false,
     "pycharm": {
      "is_executing": true
+    },
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:12:19.453228Z",
+     "start_time": "2024-11-25T14:11:58.795027Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40, 4662)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 15
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": null,
    "outputs": [],
-   "source": [
-    "cls = TSFreshClassifier()\n",
-    "clst = TSFreshClusterer(estimator=KMeans(n_clusters=4))\n",
-    "cls.fit(X_train, y_train)\n",
-    "cls.score(X_test, y_test)\n",
-    "clst.fit(X_train)\n",
-    "print(cls.predict(X_test))"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {