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| # Sphinx documentation | ||
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| doc/auto_examples/ | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "0", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Getting started with `skore`\n", | ||
| "\n", | ||
| "This guide provides a quick start to `skore`, an open-source package that aims at enable data scientist to:\n", | ||
| "1. Store objects of different types from their Python code: python lists, `scikit-learn` fitted pipelines, `plotly` figures, and more.\n", | ||
| "2. **Track** and **visualize** these stored objects on a user-friendly dashboard.\n", | ||
| "3. Export the dashboard to a HTML file." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "1", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Initialize a Project and launch the UI\n", | ||
| "\n", | ||
| "From your shell, initialize a `skore` project, here named `project.skore`, that will be in your current working directory:\n", | ||
| "```bash\n", | ||
| "python -m skore create \"project.skore\"\n", | ||
| "```\n", | ||
| "This will create a skore project directory named `project.skore` in the current directory.\n", | ||
| "\n", | ||
| "From your shell (in the same directory), start the UI locally:\n", | ||
| "```bash\n", | ||
| "python -m skore launch \"project.skore\"\n", | ||
| "```\n", | ||
| "This will automatically open a browser at the UI's location.\n", | ||
| "\n", | ||
| "Now that the project file exists, we can load it in our notebook so that we can read from and write to it:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from skore import load\n", | ||
| "\n", | ||
| "project = load(\"project.skore\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "3", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Storing some items" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "4", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Storing an integer:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "5", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "project.put(\"my_int\", 3)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "6", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Here, the name of my stored item is `my_int` and the integer value is 3." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "7", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "For a `pandas` data frame:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "my_df = pd.DataFrame(np.random.randn(3, 3))\n", | ||
| "project.put(\"my_df\", my_df)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "for a `matplotlib` figure:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "10", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import matplotlib.pyplot as plt\n", | ||
| "\n", | ||
| "x = [0, 1, 2, 3, 4, 5]\n", | ||
| "fig, ax = plt.subplots(figsize=(5, 3), layout=\"constrained\")\n", | ||
| "ax.plot(x)\n", | ||
| "project.put(\"my_figure\", fig)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "11", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "For a `scikit-learn` fitted pipeline:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "12", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from sklearn.datasets import load_diabetes\n", | ||
| "from sklearn.linear_model import Lasso\n", | ||
| "from sklearn.pipeline import Pipeline\n", | ||
| "from sklearn.preprocessing import StandardScaler\n", | ||
| "\n", | ||
| "diabetes = load_diabetes()\n", | ||
| "X = diabetes.data[:150]\n", | ||
| "y = diabetes.target[:150]\n", | ||
| "my_pipeline = Pipeline(\n", | ||
| " [(\"standard_scaler\", StandardScaler()), (\"lasso\", Lasso(alpha=2))]\n", | ||
| ")\n", | ||
| "my_pipeline.fit(X, y)\n", | ||
| "project.put(\"my_fitted_pipeline\", my_pipeline)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "13", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Back to the dashboard\n", | ||
| "\n", | ||
| "1. On the top left, create a new `View`.\n", | ||
| "2. From the `Elements` section on the bottom left, you can add stored items to this view, either by double-cliking on them or by doing drag-and-drop." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "14", | ||
| "metadata": {}, | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "jupytext": { | ||
| "formats": "ipynb,py:percent" | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": ".venv", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.12.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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