draft of getting_started notebook.

Author: benHeid

.binder/Dockerfile

@@ -0,0 +1,22 @@
+# This Dockerfile is used to build sktime when launching binder.
+# Find out more at: https://mybinder.readthedocs.io/en/latest/index.html
+
+FROM  jupyter/scipy-notebook:python-3.11.6
+# Set up user to avoid running as root
+ARG NB_USER
+ARG NB_UID
+ENV USER ${NB_USER}
+ENV HOME /home/${NB_USER}
+
+# Binder will automatically clone the repo, but we need to make sure the
+# contents of our repo are in the ${HOME} directory
+COPY . ${HOME}
+USER root
+RUN chown -R ${NB_UID} ${HOME}
+
+# Switch user and directory
+USER ${USER}
+WORKDIR ${HOME}
+
+# Install extra requirements and sktime based on master branch
+RUN pip install --upgrade pip --no-cache-dir && pip install .[binder]

examples/getting_started.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Welcome to tsbootstrap\n",
+    "\n",
+    "`tsbootstrap` is a `sklearn` and `sktime` compatible library of bootstrapping algorithm for time series.\n",
+    "This notebook explains how you can start with tsbootstrap. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting started\n",
+    "`tsbootsrap` is following the [`sklearn` design principle](https://scikit-learn.org/stable/developers/develop.html). This means that there are mainly three steps of interaction of a bootstrapper:\n",
+    "1. Initiate the bootrapper\n",
+    "2. Fitting the bootstrapper\n",
+    "3. Transforming the data using the fitted bootstrapper"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, we use the moving block bootstrapper. Therefore, we instantiate the class. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tsbootstrap import MovingBlockBootstrap, MovingBlockBootstrapConfig\n",
+    "\n",
+    "bootstrapper = MovingBlockBootstrap(block_length=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the instantiation, we can fit the bootsrapper. Therefore, we load the airline passenger dataset using the dataset loading functionalities of sktime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sktime.datasets import load_airline\n",
+    "\n",
+    "y = load_airline()\n",
+    "\n",
+    "bootstrapper.fit(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After fitting the bootstrapper, we can use the bootstrapper to transform the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bootstrapper.transform(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of calling first `fit` and `transform` afterwards, we can also use the shortcut: `fit_transform`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bootstrapper.fit_transform(y)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pyproject.toml

@@ -226,3 +226,8 @@ docs = [
   "sphinx-version-warning",
   "tabulate",
 ]
+
+binder = [
+  "jupyter",
+  "sktime",
+]