Add html content from sphinx

Author: thomass-dev

docs/latest/.buildinfo

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+# Sphinx build info version 1
+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 064d3c6f5318632d4336e957c486b0de
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/latest/.nojekyll

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+"""
+=================================
+1) Getting started with ``skore``
+=================================
+
+This example runs the :ref:`getting_started` guide.
+
+``skore`` UI
+------------
+
+This section provides a quick start to the ``skore`` UI, an open-source package that aims to enable data scientists to:
+
+#. Store objects of different types from their Python code: python lists, ``scikit-learn`` fitted pipelines, ``plotly`` figures, and more.
+#. Track and visualize these stored objects on a user-friendly dashboard.
+#. Export the dashboard to a HTML file.
+
+Initialize a Project and launch the UI
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+From your shell, initialize a skore project, here named ``my_project_gs``, that
+will be in your current working directory:
+"""
+
+# %%
+import subprocess
+
+# remove the project if it already exists
+subprocess.run("rm -rf my_project_gs.skore".split())
+
+# create the project
+subprocess.run("python3 -m skore create my_project_gs".split())
+
+# %%
+# This will create a ``skore`` project directory named ``my_project_gs`` in the
+# current directory.
+#
+# From your shell (in the same directory), start the UI locally:
+#
+# .. code:: console
+#
+#     python -m skore launch "my_project_gs"
+#
+# This will automatically open a browser at the UI's location.
+#
+# Now that the project file exists, we can load it in our notebook so that we can
+# read from and write to it:
+
+# %%
+from skore import load
+
+my_project_gs = load("my_project_gs.skore")
+
+# %%
+# Storing some items
+# ^^^^^^^^^^^^^^^^^^
+#
+# Storing an integer:
+
+# %%
+my_project_gs.put("my_int", 3)
+
+# %%
+# Here, the name of my stored item is ``my_int`` and the integer value is 3.
+
+# %%
+my_project_gs.get("my_int")
+
+# %%
+# For a ``pandas`` data frame:
+
+# %%
+import numpy as np
+import pandas as pd
+
+my_df = pd.DataFrame(np.random.randn(3, 3))
+
+my_project_gs.put("my_df", my_df)
+
+# %%
+my_project_gs.get("my_df")
+
+# %%
+# For a ``matplotlib`` figure:
+
+# %%
+import matplotlib.pyplot as plt
+
+x = [0, 1, 2, 3, 4, 5]
+fig, ax = plt.subplots(figsize=(5, 3), layout="constrained")
+_ = ax.plot(x)
+
+my_project_gs.put("my_figure", fig)
+
+# %%
+# For a ``scikit-learn`` fitted pipeline:
+
+# %%
+from sklearn.datasets import load_diabetes
+from sklearn.linear_model import Lasso
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+diabetes = load_diabetes()
+X = diabetes.data[:150]
+y = diabetes.target[:150]
+my_pipeline = Pipeline(
+    [("standard_scaler", StandardScaler()), ("lasso", Lasso(alpha=2))]
+)
+my_pipeline.fit(X, y)
+
+my_project_gs.put("my_fitted_pipeline", my_pipeline)
+
+# %%
+my_project_gs.get("my_fitted_pipeline")
+
+# %%
+# Back to the dashboard
+# ^^^^^^^^^^^^^^^^^^^^^
+#
+# #. On the top left, create a new ``View``.
+# #. 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.
+#
+# .. image:: https://raw.githubusercontent.com/sylvaincom/sylvaincom.github.io/master/files/probabl/skore/2024_10_14_skore_demo.gif
+#    :alt: Getting started with ``skore`` demo

docs/latest/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# 3) Using ``skore``'s cross validate\n\nThis example illustrates the use of :func:`~skore.cross_validate`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import subprocess\n\n# remove the project if it already exists\nsubprocess.run(\"rm -rf my_project_cv.skore\".split())\n\n# create the project\nsubprocess.run(\"python3 -m skore create my_project_cv\".split())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skore import load\n\nmy_project_gs = load(\"my_project_cv.skore\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn import datasets, linear_model\nfrom skore.cross_validate import cross_validate\n\ndiabetes = datasets.load_diabetes()\nX = diabetes.data[:150]\ny = diabetes.target[:150]\nlasso = linear_model.Lasso()\n\ncv_results = cross_validate(lasso, X, y, cv=3, project=my_project_gs)\n\nmy_project_gs.get_item(\"cross_validation\").plot"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}