From bf5c7471d148c862f3fc9bc0559fa1541b0796a5 Mon Sep 17 00:00:00 2001
From: Thomas S
Date: Wed, 23 Oct 2024 11:23:35 +0200
Subject: [PATCH] Add html content from sphinx
---
docs/latest/.buildinfo | 4 +
docs/latest/.nojekyll | 0
.../auto_examples_python.zip | Bin 0 -> 11728 bytes
.../plot_01_getting_started.py | 124 ++
.../auto_examples_jupyter.zip | Bin 0 -> 25350 bytes
.../plot_02_basic_usage.zip | Bin 0 -> 24946 bytes
.../plot_01_getting_started.zip | Bin 0 -> 9193 bytes
.../plot_03_cross_validate.ipynb | 65 +
.../plot_02_basic_usage.py | 352 ++++++
.../plot_03_cross_validate.py | 35 +
.../plot_02_basic_usage.ipynb | 623 ++++++++++
.../plot_03_cross_validate.zip | Bin 0 -> 2961 bytes
.../plot_01_getting_started.ipynb | 180 +++
.../sphx_glr_plot_01_getting_started_001.png | Bin 0 -> 11989 bytes
...sphx_glr_plot_01_getting_started_thumb.png | Bin 0 -> 10683 bytes
.../sphx_glr_plot_02_basic_usage_001.png | Bin 0 -> 21824 bytes
.../sphx_glr_plot_02_basic_usage_thumb.png | Bin 0 -> 24044 bytes
.../sphx_glr_plot_03_cross_validate_thumb.png | Bin 0 -> 26794 bytes
docs/latest/_sources/api.rst.txt | 39 +
.../_sources/auto_examples/index.rst.txt | 101 ++
.../plot_01_getting_started.rst.txt | 749 +++++++++++
.../auto_examples/plot_02_basic_usage.rst.txt | 820 ++++++++++++
.../plot_03_cross_validate.rst.txt | 162 +++
.../auto_examples/sg_execution_times.rst.txt | 43 +
.../_sources/generated/skore.Project.rst.txt | 19 +
.../generated/skore.cross_validate.rst.txt | 10 +
.../_sources/generated/skore.load.rst.txt | 10 +
docs/latest/_sources/getting_started.rst.txt | 41 +
docs/latest/_sources/index.rst.txt | 39 +
docs/latest/_sources/install.rst.txt | 21 +
.../_sources/sg_execution_times.rst.txt | 43 +
docs/latest/_sources/user_guide.rst.txt | 20 +
.../user_guide/getting_started.rst.txt | 107 ++
.../_sphinx_design_static/design-tabs.js | 101 ++
.../sphinx-design.min.css | 1 +
docs/latest/_static/basic.css | 914 ++++++++++++++
docs/latest/_static/binder_badge_logo.svg | 1 +
docs/latest/_static/broken_example.png | Bin 0 -> 21404 bytes
docs/latest/_static/css/custom.css | 43 +
docs/latest/_static/design-tabs.js | 101 ++
docs/latest/_static/doctools.js | 149 +++
docs/latest/_static/documentation_options.js | 13 +
docs/latest/_static/file.png | Bin 0 -> 286 bytes
.../latest/_static/jupyterlite_badge_logo.svg | 3 +
docs/latest/_static/language_data.js | 192 +++
docs/latest/_static/minus.png | Bin 0 -> 90 bytes
docs/latest/_static/no_image.png | Bin 0 -> 4315 bytes
docs/latest/_static/plus.png | Bin 0 -> 90 bytes
docs/latest/_static/pygments.css | 152 +++
docs/latest/_static/scripts/bootstrap.js | 3 +
.../_static/scripts/bootstrap.js.LICENSE.txt | 5 +
docs/latest/_static/scripts/bootstrap.js.map | 1 +
.../_static/scripts/pydata-sphinx-theme.js | 2 +
.../scripts/pydata-sphinx-theme.js.map | 1 +
docs/latest/_static/searchtools.js | 632 ++++++++++
docs/latest/_static/seer_extract.png | Bin 0 -> 298012 bytes
docs/latest/_static/seer_home.png | Bin 0 -> 124427 bytes
docs/latest/_static/sg_gallery-binder.css | 11 +
docs/latest/_static/sg_gallery-dataframe.css | 47 +
.../_static/sg_gallery-rendered-html.css | 224 ++++
docs/latest/_static/sg_gallery.css | 367 ++++++
docs/latest/_static/sphinx-design.min.css | 1 +
docs/latest/_static/sphinx_highlight.js | 154 +++
docs/latest/_static/styles/bootstrap.css | 6 +
docs/latest/_static/styles/bootstrap.css.map | 1 +
.../_static/styles/pydata-sphinx-theme.css | 2 +
.../styles/pydata-sphinx-theme.css.map | 1 +
docs/latest/_static/styles/theme.css | 2 +
.../vendor/fontawesome/6.5.2/LICENSE.txt | 165 +++
.../vendor/fontawesome/6.5.2/css/all.min.css | 5 +
.../vendor/fontawesome/6.5.2/js/all.min.js | 2 +
.../6.5.2/js/all.min.js.LICENSE.txt | 5 +
.../6.5.2/webfonts/fa-brands-400.ttf | Bin 0 -> 209128 bytes
.../6.5.2/webfonts/fa-brands-400.woff2 | Bin 0 -> 117852 bytes
.../6.5.2/webfonts/fa-regular-400.ttf | Bin 0 -> 67860 bytes
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.../6.5.2/webfonts/fa-v4compatibility.ttf | Bin 0 -> 10832 bytes
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docs/latest/_static/webpack-macros.html | 31 +
docs/latest/api.html | 555 +++++++++
docs/latest/auto_examples/index.html | 524 ++++++++
.../plot_01_getting_started.html | 1104 +++++++++++++++++
.../auto_examples/plot_02_basic_usage.html | 848 +++++++++++++
.../auto_examples/plot_03_cross_validate.html | 593 +++++++++
.../auto_examples/sg_execution_times.html | 496 ++++++++
docs/latest/generated/skore.Project.html | 685 ++++++++++
.../generated/skore.cross_validate.html | 553 +++++++++
docs/latest/generated/skore.load.html | 527 ++++++++
docs/latest/genindex.html | 490 ++++++++
docs/latest/getting_started.html | 541 ++++++++
docs/latest/index.html | 487 ++++++++
docs/latest/install.html | 513 ++++++++
docs/latest/objects.inv | Bin 0 -> 822 bytes
docs/latest/search.html | 441 +++++++
docs/latest/searchindex.js | 1 +
docs/latest/sg_execution_times.html | 496 ++++++++
docs/latest/user_guide.html | 522 ++++++++
docs/latest/user_guide/getting_started.html | 562 +++++++++
100 files changed, 16883 insertions(+)
create mode 100644 docs/latest/.buildinfo
create mode 100644 docs/latest/.nojekyll
create mode 100644 docs/latest/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
create mode 100644 docs/latest/_downloads/3feb988b860c59c9cee8450bd29651e5/plot_01_getting_started.py
create mode 100644 docs/latest/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
create mode 100644 docs/latest/_downloads/8b537b46e9e0a388f6e4838262a7f9fd/plot_02_basic_usage.zip
create mode 100644 docs/latest/_downloads/97b4b84f5410e1134b3cbe5e4682d2a1/plot_01_getting_started.zip
create mode 100644 docs/latest/_downloads/9825adffd8a0e006b517b57261e52107/plot_03_cross_validate.ipynb
create mode 100644 docs/latest/_downloads/aa82e1dec70e24e3e1f1a2f4a1a5d48b/plot_02_basic_usage.py
create mode 100644 docs/latest/_downloads/b53d88bff5e11c5ed50d2a0f66cc5fb1/plot_03_cross_validate.py
create mode 100644 docs/latest/_downloads/c1371f03d806c9b2f4a67995ee052139/plot_02_basic_usage.ipynb
create mode 100644 docs/latest/_downloads/dafe7022153ade7f6466ced5b7bc6a6d/plot_03_cross_validate.zip
create mode 100644 docs/latest/_downloads/e97c5b0c37bdd66afd2a201f006613dd/plot_01_getting_started.ipynb
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create mode 100644 docs/latest/_images/sphx_glr_plot_02_basic_usage_001.png
create mode 100644 docs/latest/_images/sphx_glr_plot_02_basic_usage_thumb.png
create mode 100644 docs/latest/_images/sphx_glr_plot_03_cross_validate_thumb.png
create mode 100644 docs/latest/_sources/api.rst.txt
create mode 100644 docs/latest/_sources/auto_examples/index.rst.txt
create mode 100644 docs/latest/_sources/auto_examples/plot_01_getting_started.rst.txt
create mode 100644 docs/latest/_sources/auto_examples/plot_02_basic_usage.rst.txt
create mode 100644 docs/latest/_sources/auto_examples/plot_03_cross_validate.rst.txt
create mode 100644 docs/latest/_sources/auto_examples/sg_execution_times.rst.txt
create mode 100644 docs/latest/_sources/generated/skore.Project.rst.txt
create mode 100644 docs/latest/_sources/generated/skore.cross_validate.rst.txt
create mode 100644 docs/latest/_sources/generated/skore.load.rst.txt
create mode 100644 docs/latest/_sources/getting_started.rst.txt
create mode 100644 docs/latest/_sources/index.rst.txt
create mode 100644 docs/latest/_sources/install.rst.txt
create mode 100644 docs/latest/_sources/sg_execution_times.rst.txt
create mode 100644 docs/latest/_sources/user_guide.rst.txt
create mode 100644 docs/latest/_sources/user_guide/getting_started.rst.txt
create mode 100644 docs/latest/_sphinx_design_static/design-tabs.js
create mode 100644 docs/latest/_sphinx_design_static/sphinx-design.min.css
create mode 100644 docs/latest/_static/basic.css
create mode 100644 docs/latest/_static/binder_badge_logo.svg
create mode 100644 docs/latest/_static/broken_example.png
create mode 100644 docs/latest/_static/css/custom.css
create mode 100644 docs/latest/_static/design-tabs.js
create mode 100644 docs/latest/_static/doctools.js
create mode 100644 docs/latest/_static/documentation_options.js
create mode 100644 docs/latest/_static/file.png
create mode 100644 docs/latest/_static/jupyterlite_badge_logo.svg
create mode 100644 docs/latest/_static/language_data.js
create mode 100644 docs/latest/_static/minus.png
create mode 100644 docs/latest/_static/no_image.png
create mode 100644 docs/latest/_static/plus.png
create mode 100644 docs/latest/_static/pygments.css
create mode 100644 docs/latest/_static/scripts/bootstrap.js
create mode 100644 docs/latest/_static/scripts/bootstrap.js.LICENSE.txt
create mode 100644 docs/latest/_static/scripts/bootstrap.js.map
create mode 100644 docs/latest/_static/scripts/pydata-sphinx-theme.js
create mode 100644 docs/latest/_static/scripts/pydata-sphinx-theme.js.map
create mode 100644 docs/latest/_static/searchtools.js
create mode 100644 docs/latest/_static/seer_extract.png
create mode 100644 docs/latest/_static/seer_home.png
create mode 100644 docs/latest/_static/sg_gallery-binder.css
create mode 100644 docs/latest/_static/sg_gallery-dataframe.css
create mode 100644 docs/latest/_static/sg_gallery-rendered-html.css
create mode 100644 docs/latest/_static/sg_gallery.css
create mode 100644 docs/latest/_static/sphinx-design.min.css
create mode 100644 docs/latest/_static/sphinx_highlight.js
create mode 100644 docs/latest/_static/styles/bootstrap.css
create mode 100644 docs/latest/_static/styles/bootstrap.css.map
create mode 100644 docs/latest/_static/styles/pydata-sphinx-theme.css
create mode 100644 docs/latest/_static/styles/pydata-sphinx-theme.css.map
create mode 100644 docs/latest/_static/styles/theme.css
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/LICENSE.txt
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/css/all.min.css
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/js/all.min.js
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/js/all.min.js.LICENSE.txt
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.ttf
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.ttf
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.ttf
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-v4compatibility.ttf
create mode 100644 docs/latest/_static/vendor/fontawesome/6.5.2/webfonts/fa-v4compatibility.woff2
create mode 100644 docs/latest/_static/webpack-macros.html
create mode 100644 docs/latest/api.html
create mode 100644 docs/latest/auto_examples/index.html
create mode 100644 docs/latest/auto_examples/plot_01_getting_started.html
create mode 100644 docs/latest/auto_examples/plot_02_basic_usage.html
create mode 100644 docs/latest/auto_examples/plot_03_cross_validate.html
create mode 100644 docs/latest/auto_examples/sg_execution_times.html
create mode 100644 docs/latest/generated/skore.Project.html
create mode 100644 docs/latest/generated/skore.cross_validate.html
create mode 100644 docs/latest/generated/skore.load.html
create mode 100644 docs/latest/genindex.html
create mode 100644 docs/latest/getting_started.html
create mode 100644 docs/latest/index.html
create mode 100644 docs/latest/install.html
create mode 100644 docs/latest/objects.inv
create mode 100644 docs/latest/search.html
create mode 100644 docs/latest/searchindex.js
create mode 100644 docs/latest/sg_execution_times.html
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create mode 100644 docs/latest/user_guide/getting_started.html
diff --git a/docs/latest/.buildinfo b/docs/latest/.buildinfo
new file mode 100644
index 000000000..ba4af92a8
--- /dev/null
+++ b/docs/latest/.buildinfo
@@ -0,0 +1,4 @@
+# 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
diff --git a/docs/latest/.nojekyll b/docs/latest/.nojekyll
new file mode 100644
index 000000000..e69de29bb
diff --git a/docs/latest/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/docs/latest/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
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diff --git a/docs/latest/_downloads/3feb988b860c59c9cee8450bd29651e5/plot_01_getting_started.py b/docs/latest/_downloads/3feb988b860c59c9cee8450bd29651e5/plot_01_getting_started.py
new file mode 100644
index 000000000..ebfb42e17
--- /dev/null
+++ b/docs/latest/_downloads/3feb988b860c59c9cee8450bd29651e5/plot_01_getting_started.py
@@ -0,0 +1,124 @@
+"""
+=================================
+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
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z_lEnAhkjw0p*+FSDfEM9z;idarATLVBsyt2g@2(_`0GDD``dqh@aPf!{OE>GL6%E3
z{sKN5Pf(@Vc)(7*1Je>cetAf>FSg=>oAXRBRNSw(3*k70Ja)hMvRSPv@CKPjl&URu
zYrm1IRBi4ju4^Ue#MTzv$7>J1^YXYsQo9i}{ygCoE4pTUDSJ3lpgt>cSJF_}}PIh^P@YN-KJbF?{|;v%e3
zFzGz!rN#X`U{ju35w_aM!9|X0#K+;Q2HB=?3O8KaqOwYf=Ed(d|`=?(2o+IAup
zk*gif27GtwaaK^@!xFEPhT5K@xt7}8QMU+9KXgkAm8z>9)CQNnX9GnTuJli|NO|;{
z_9^YQ5@}5Et*rO&pXmG>Zl6+hefLD%@G%S1fMs`ADQr!4;~`-kPndwl9sh?;-!|
zM8FoV%eq@^J4@oWUR~)S-)XyawC_7#uX?nsDeB|Hhf?^P>#V;))}UUxwZ=o4qHfkV
z%^Tin_BKix|hYL6l7?hCHtO81L;$+}#AxxFpGTAE90+
z8`mD$4u{(D(Ko>(Te-SJFOqHZ*eg}{n|r0JgD)5^Ro&6o4NhE5zsQkk{PjThZ*B2g
zhHLM%@$Wtogth5Skv+du4w46MnhvJbc#yW&=Yy$&qf`F6G9kRt-#bIXkilyd_X&V^B9E^P
zHXkmjQ;EHCc1pIn`-s%T`titHHX$W3eku#RnKF~3H2(h#NYfZ^Y#qpdeB)Ob{B$&p
ze?~m7Wq3?q{E#JHonrctOnfek61gqKgeIF|OcQ4kFM9cMNUy4~9-mZi{SVu*P|nqC
zj9IGOU$nWNK9WDY35CZghIl|*8yzza%T;HL-@-27;|xuc0f<>-i6tu
ziE=sfqyAxzF`9=60<37VSXy}_Y^nCpl{ZB8;Hh-UH70;&mI-REH>#u(D{p<1gY5U2u?|hd)-^0JZ
LTitle
bold, italic, etc.
", media_type="text/html"
+ ),
+)
+
+# %%
+# Note that the media type is only used for the UI, and not in this notebook at hand:
+
+# %%
+my_project_gs.get("my_string_3")
+
+# %%
+# You can also conveniently use Python f-strings:
+
+# %%
+x = 2
+y = [1, 2, 3, 4]
+my_project_gs.put(
+ "my_string_4", f"The value of `x` is {x} and the value of `y` is {y}."
+)
+
+# %%
+# Storing many kinds of data
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# %%
+# Python list:
+
+# %%
+my_list = [1, 2, 3, 4]
+my_project_gs.put("my_list", my_list)
+
+# %%
+# Python dictionary:
+
+# %%
+my_dict = {
+ "company": "probabl",
+ "year": 2023,
+}
+my_project_gs.put("my_dict", my_dict)
+
+# %%
+# ``numpy`` array:
+
+# %%
+my_arr = np.random.randn(3, 3)
+my_project_gs.put("my_arr", my_arr)
+
+# %%
+# ``pandas`` data frame:
+
+# %%
+my_df = pd.DataFrame(np.random.randn(3, 3))
+my_project_gs.put("my_df", my_df)
+
+# %%
+# Data visualization
+# ^^^^^^^^^^^^^^^^^^
+#
+# Note that, in the dashboard, the interactivity of plots is supported, for example for ``altair`` and ``plotly``.
+
+# %%
+# ``matplotlib`` figures:
+
+# %%
+x = np.linspace(0, 2, 100)
+
+fig, ax = plt.subplots(figsize=(5, 2.7), layout="constrained")
+ax.plot(x, x, label="linear")
+ax.plot(x, x**2, label="quadratic")
+ax.plot(x, x**3, label="cubic")
+ax.set_xlabel("x label")
+ax.set_ylabel("y label")
+ax.set_title("Simple Plot")
+ax.legend()
+plt.show()
+
+my_project_gs.put("my_figure", fig)
+
+# %%
+# ``altair`` charts:
+
+# %%
+num_points = 100
+df_plot = pd.DataFrame(
+ {"x": np.random.randn(num_points), "y": np.random.randn(num_points)}
+)
+
+my_altair_chart = (
+ alt.Chart(df_plot)
+ .mark_circle()
+ .encode(x="x", y="y", tooltip=["x", "y"])
+ .interactive()
+ .properties(title="My title")
+)
+my_altair_chart.show()
+
+my_project_gs.put("my_altair_chart", my_altair_chart)
+
+# %%
+# Plotly figures:
+#
+# NOTE: Some users reported the following error when running the Plotly cells:
+# ```
+# ValueError: Mime type rendering requires nbformat>=4.2.0 but it is not installed
+# ```
+# This is a Plotly issue which is documented `here `_; to solve it, we recommend installing nbformat in your environment, e.g. with
+#
+# .. code:: console
+#
+# pip install --upgrade nbformat
+
+# %%
+df = px.data.iris()
+fig = px.scatter(
+ df, x=df.sepal_length, y=df.sepal_width, color=df.species, size=df.petal_length
+)
+fig.show()
+my_project_gs.put("my_plotly_fig", fig)
+
+# %%
+# Animated ``plotly`` figures:
+
+# %%
+df = px.data.gapminder()
+my_anim_plotly_fig = px.scatter(
+ df,
+ x="gdpPercap",
+ y="lifeExp",
+ animation_frame="year",
+ animation_group="country",
+ size="pop",
+ color="continent",
+ hover_name="country",
+ log_x=True,
+ size_max=55,
+ range_x=[100, 100000],
+ range_y=[25, 90],
+)
+my_anim_plotly_fig.show()
+my_project_gs.put("my_anim_plotly_fig", my_anim_plotly_fig)
+
+# %%
+# PIL images:
+
+# %%
+my_pil_image = PIL.Image.new("RGB", (100, 100), color="red")
+with io.BytesIO() as output:
+ my_pil_image.save(output, format="png")
+
+my_project_gs.put("my_pil_image", my_pil_image)
+
+# %%
+# Scikit-learn models and pipelines
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# As ``skore`` is developed by `Probabl `_, the spin-off of scikit-learn, ``skore`` treats scikit-learn models and pipelines as first-class citizens.
+#
+# First of all, you can store a scikit-learn model:
+
+# %%
+my_model = Lasso(alpha=2)
+my_project_gs.put("my_model", my_model)
+
+# %%
+# You can also store ``scikit-learn`` pipelines:
+
+# %%
+my_pipeline = Pipeline(
+ [("standard_scaler", StandardScaler()), ("lasso", Lasso(alpha=2))]
+)
+my_project_gs.put("my_pipeline", my_pipeline)
+
+# %%
+# Moreover, you can store fitted ``scikit-learn`` pipelines:
+
+# %%
+diabetes = load_diabetes()
+X = diabetes.data[:150]
+y = diabetes.target[:150]
+my_pipeline.fit(X, y)
+
+my_project_gs.put("my_fitted_pipeline", my_pipeline)
+
+# %%
+# *Stay tuned for some new features!*
+
+# %%
+# Manipulating the skore UI
+# ^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# The following is just some ``skore`` strings that we generate in order to provide more context on the obtained report.
+
+# %%
+my_project_gs.put(
+ "my_comment_1",
+ "Welcome to skore!
skore
allows data scientists to create tracking and visualizations from their Python code. This HTML document is actually a skore report generated using the 01_basic_usage.ipynb
example notebook then exported (into HTML)!
",
+)
+
+# %%
+my_project_gs.put(
+ "my_comment_2",
+ "
Integers
",
+)
+
+# %%
+my_project_gs.put("my_comment_3", "Strings
")
+
+# %%
+my_project_gs.put(
+ "my_comment_4",
+ "Many kinds of data",
+)
+
+# %%
+my_project_gs.put(
+ "my_comment_5",
+ "
Plots",
+)
+
+# %%
+my_project_gs.put("my_comment_6", "
Scikit-learn models and pipelines")
diff --git a/docs/latest/_downloads/b53d88bff5e11c5ed50d2a0f66cc5fb1/plot_03_cross_validate.py b/docs/latest/_downloads/b53d88bff5e11c5ed50d2a0f66cc5fb1/plot_03_cross_validate.py
new file mode 100644
index 000000000..d4bf45a11
--- /dev/null
+++ b/docs/latest/_downloads/b53d88bff5e11c5ed50d2a0f66cc5fb1/plot_03_cross_validate.py
@@ -0,0 +1,35 @@
+"""
+===================================
+3) Using ``skore``'s cross validate
+===================================
+
+This example illustrates the use of :func:`~skore.cross_validate`.
+"""
+
+# %%
+import subprocess
+
+# remove the project if it already exists
+subprocess.run("rm -rf my_project_cv.skore".split())
+
+# create the project
+subprocess.run("python3 -m skore create my_project_cv".split())
+
+
+# %%
+from skore import load
+
+my_project_gs = load("my_project_cv.skore")
+
+# %%
+from sklearn import datasets, linear_model
+from skore.cross_validate import cross_validate
+
+diabetes = datasets.load_diabetes()
+X = diabetes.data[:150]
+y = diabetes.target[:150]
+lasso = linear_model.Lasso()
+
+cv_results = cross_validate(lasso, X, y, cv=3, project=my_project_gs)
+
+my_project_gs.get_item("cross_validation").plot
diff --git a/docs/latest/_downloads/c1371f03d806c9b2f4a67995ee052139/plot_02_basic_usage.ipynb b/docs/latest/_downloads/c1371f03d806c9b2f4a67995ee052139/plot_02_basic_usage.ipynb
new file mode 100644
index 000000000..a64b85564
--- /dev/null
+++ b/docs/latest/_downloads/c1371f03d806c9b2f4a67995ee052139/plot_02_basic_usage.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n# 2) Basic usage of ``skore``\n\nThis example complements the `Getting started with skore` example.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## ``skore`` UI\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import altair as alt\nimport io\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport PIL\n\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.linear_model import Lasso\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom skore import load\nfrom skore.item import MediaItem"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Initialize a Project and launch the UI\n\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_bu.skore\".split())\n\n# create the project\nsubprocess.run(\"python3 -m skore create my_project_bu\".split())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from skore import load\n\nmy_project_gs = load(\"my_project_gs.skore\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Storing an integer\n\nNow, let us store our first object, for example an integer:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\"my_int\", 3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, the name of my object is ``my_int`` and the integer value is 3.\n\nYou can read it from the project:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.get(\"my_int\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Careful; like in a traditional Python dictionary, the `put` method will *overwrite* past data if you use a key which already exists!\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\"my_int\", 30_000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us check the updated value:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.get(\"my_int\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By using the `delete_item` method, you can also delete an object so that your `skore` UI does not become cluttered:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\"my_int_2\", 10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.delete_item(\"my_int_2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can use `my_project_gs.list_item_keys` to display all the keys in your project:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.list_item_keys()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Storing a string\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We just stored a integer, now let us store some text using strings!\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\"my_string\", \"Hello world!\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.get(\"my_string\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "``my_project_gs.get`` infers the type of the inserted object by default. For example, strings are assumed to be in Markdown format. Hence, you can customize the display of your text:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\n \"my_string_2\",\n (\n \"\"\"Hello world!, **bold**, *italic*, `code`\n\n```python\ndef my_func(x):\n return x+2\n```\n\"\"\"\n ),\n)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Moreover, you can also explicitly tell `skore` the media type of an object, for example in HTML:\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note: we use ``put_item`` instead of ``put``:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put_item(\n \"my_string_3\",\n MediaItem.factory(\n \"
Title
bold, italic, etc.\", media_type=\"text/html\"\n ),\n)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the media type is only used for the UI, and not in this notebook at hand:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.get(\"my_string_3\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also conveniently use Python f-strings:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = 2\ny = [1, 2, 3, 4]\nmy_project_gs.put(\n \"my_string_4\", f\"The value of `x` is {x} and the value of `y` is {y}.\"\n)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Storing many kinds of data\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Python list:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_list = [1, 2, 3, 4]\nmy_project_gs.put(\"my_list\", my_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Python dictionary:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_dict = {\n \"company\": \"probabl\",\n \"year\": 2023,\n}\nmy_project_gs.put(\"my_dict\", my_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "``numpy`` array:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_arr = np.random.randn(3, 3)\nmy_project_gs.put(\"my_arr\", my_arr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "``pandas`` data frame:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_df = pd.DataFrame(np.random.randn(3, 3))\nmy_project_gs.put(\"my_df\", my_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data visualization\n\nNote that, in the dashboard, the interactivity of plots is supported, for example for ``altair`` and ``plotly``.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "``matplotlib`` figures:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "x = np.linspace(0, 2, 100)\n\nfig, ax = plt.subplots(figsize=(5, 2.7), layout=\"constrained\")\nax.plot(x, x, label=\"linear\")\nax.plot(x, x**2, label=\"quadratic\")\nax.plot(x, x**3, label=\"cubic\")\nax.set_xlabel(\"x label\")\nax.set_ylabel(\"y label\")\nax.set_title(\"Simple Plot\")\nax.legend()\nplt.show()\n\nmy_project_gs.put(\"my_figure\", fig)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "``altair`` charts:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "num_points = 100\ndf_plot = pd.DataFrame(\n {\"x\": np.random.randn(num_points), \"y\": np.random.randn(num_points)}\n)\n\nmy_altair_chart = (\n alt.Chart(df_plot)\n .mark_circle()\n .encode(x=\"x\", y=\"y\", tooltip=[\"x\", \"y\"])\n .interactive()\n .properties(title=\"My title\")\n)\nmy_altair_chart.show()\n\nmy_project_gs.put(\"my_altair_chart\", my_altair_chart)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plotly figures:\n\n#### NOTE: Some users reported the following error when running the Plotly cells:\nValueError: Mime type rendering requires nbformat>=4.2.0 but it is not installed\n```\nThis is a Plotly issue which is documented [here](https://github.com/plotly/plotly.py/issues/3285); to solve it, we recommend installing nbformat in your environment, e.g. with\n\n.. code:: console\n\n pip install --upgrade nbformat\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "df = px.data.iris()\nfig = px.scatter(\n df, x=df.sepal_length, y=df.sepal_width, color=df.species, size=df.petal_length\n)\nfig.show()\nmy_project_gs.put(\"my_plotly_fig\", fig)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Animated ``plotly`` figures:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "df = px.data.gapminder()\nmy_anim_plotly_fig = px.scatter(\n df,\n x=\"gdpPercap\",\n y=\"lifeExp\",\n animation_frame=\"year\",\n animation_group=\"country\",\n size=\"pop\",\n color=\"continent\",\n hover_name=\"country\",\n log_x=True,\n size_max=55,\n range_x=[100, 100000],\n range_y=[25, 90],\n)\nmy_anim_plotly_fig.show()\nmy_project_gs.put(\"my_anim_plotly_fig\", my_anim_plotly_fig)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "PIL images:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_pil_image = PIL.Image.new(\"RGB\", (100, 100), color=\"red\")\nwith io.BytesIO() as output:\n my_pil_image.save(output, format=\"png\")\n\nmy_project_gs.put(\"my_pil_image\", my_pil_image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Scikit-learn models and pipelines\n\nAs ``skore`` is developed by [Probabl](https://probabl.ai), the spin-off of scikit-learn, ``skore`` treats scikit-learn models and pipelines as first-class citizens.\n\nFirst of all, you can store a scikit-learn model:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_model = Lasso(alpha=2)\nmy_project_gs.put(\"my_model\", my_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also store ``scikit-learn`` pipelines:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_pipeline = Pipeline(\n [(\"standard_scaler\", StandardScaler()), (\"lasso\", Lasso(alpha=2))]\n)\nmy_project_gs.put(\"my_pipeline\", my_pipeline)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Moreover, you can store fitted ``scikit-learn`` pipelines:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "diabetes = load_diabetes()\nX = diabetes.data[:150]\ny = diabetes.target[:150]\nmy_pipeline.fit(X, y)\n\nmy_project_gs.put(\"my_fitted_pipeline\", my_pipeline)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Stay tuned for some new features!*\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Manipulating the skore UI\n\nThe following is just some ``skore`` strings that we generate in order to provide more context on the obtained report.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\n \"my_comment_1\",\n \"Welcome to skore!
skore
allows data scientists to create tracking and visualizations from their Python code. This HTML document is actually a skore report generated using the 01_basic_usage.ipynb
example notebook then exported (into HTML)!
\",\n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\n \"my_comment_2\",\n \"
Integers\",\n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\"my_comment_3\", \"
Strings\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\n \"my_comment_4\",\n \"
Many kinds of data\",\n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\n \"my_comment_5\",\n \"
Plots\",\n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "my_project_gs.put(\"my_comment_6\", \"