writing and updating some doc

Author: sylvaincom

doc/api.rst

@@ -1,11 +1,10 @@
 API
 ===
 
-This page lists all the public functions and classes of the `skore`
-package:
-
 .. currentmodule:: skore
 
+This page lists all the public functions and classes of the ``skore``
+package.
 
 Project
 -------

doc/getting_started.rst

@@ -0,0 +1,36 @@
+.. _getting_started:
+
+Getting started
+===============
+
+.. currentmodule:: skore
+
+From your shell, initialize a skore project, here named ``project.skore``, that
+will be in your current working directory:
+
+.. code:: console
+
+    python -m skore create "project.skore"
+
+Then, from your Python code (in the same directory), load the project and store
+an integer for example:
+
+.. code-block:: python
+
+    from skore import load
+    project = load("project.skore")
+    project.put("my_int", 3)
+
+Finally, from your shell (in the same directory), start the UI locally:
+
+.. code:: console
+
+    python -m skore launch project.skore
+
+This will automatically open a browser at the UI's location:
+
+#. 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

doc/index.rst

@@ -1,59 +1,44 @@
 .. You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-skore
-=====
+Welcome to ``skore``
+====================
 
-Gradient-boosting survival analysis
------------------------------------
+#OwnYourDataScience
+-------------------
 
 .. container:: index-features
 
-   * survival and competing risks
+   * track and visualize your objects from a user-friendly dashboard
 
    * scikit-learn compatible
 
-   * scalable gradient boosting
+With ``skore``, data scientists can:
 
-A scalable **time-to-event and competing risk prediction model**
-implemented in Python.
+#. 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.
 
-.. container:: index-box sd-card
+These are only the first features: skore is a work in progress and aims to be
+an end-to-end library for data scientists.
+Stay tuned!
 
-   **Competing risks settings**
-
-   Predicting which event will happen first, and when, from data where some
-   events have not yet been observed:
-
-The model is **a gradient-boosting variant**, that offers prediction for
-survival and competing risks settings, fully compatible with
-`scikit-learn <https://scikit-learn.org>`_. It can be used with
-scikit-learn tools such as pipelines, column transformers,
-cross-validation, hyper-parameter search tools, etc.
-
-.. This package will also offer neural network based estimators by leveraging
-   `PyTorch <https://pytorch.org>`_ and `skorch
-   <https://skorch.readthedocs.io/>`_.
-
-This library puts a focus on predictive accuracy rather than on inference.
-Quantifying the statistical association or causal effect of covariates with/on
-the cumulated event incidence or instantaneous hazard rate is not in the scope
-of this library at this time.
-
-The theory behind the model is described in `this paper
-<https://arxiv.org/abs/2406.14085>`_.
-
-- License: MIT
-- GitHub repository: https://github.com/soda-inria/hazardous
-- Changelog: https://github.com/soda-inria/hazardous/blob/main/CHANGES.rst
+- License: BSD
+- GitHub repository: https://github.com/probabl-ai/skore
+- Discord: http://discord.probabl.ai
 - Status: under development, API is subject to change without notice.
 
+.. 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
+
 .. currentmodule:: skore
 
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
-   api
+   install
+   getting_started
    auto_examples/index
    user_guide
+   api

doc/install.rst

@@ -0,0 +1,21 @@
+.. _install:
+
+Install
+=======
+
+.. currentmodule:: skore
+
+First of all, we recommend using a `virtual environment (venv) <https://docs.python.org/3/tutorial/venv.html>`_.
+You need ``python>=3.9``.
+
+Then, you can install ``skore`` by using `pip``:
+
+.. code:: console
+
+    pip install -U skore
+
+You can check ``skore``'s latest version on `PyPI <https://pypi.org/project/skore/>`_.
+
+🚨 For Windows users, the encoding must be set to
+`UTF-8 <https://docs.python.org/3/using/windows.html#utf-8-mode>`_:
+see `PYTHONUTF8 <https://docs.python.org/3/using/cmdline.html#envvar-PYTHONUTF8>`_.

doc/sg_execution_times.rst

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:00.681** total execution time for 2 files **from all galleries**:
+**00:00.637** total execution time for 2 files **from all galleries**:
 
 .. container::
 
@@ -32,9 +32,9 @@ Computation times
    * - Example
      - Time
      - Mem (MB)
-   * - :ref:`sphx_glr_auto_examples_plot_demo.py` (``../examples/plot_demo.py``)
-     - 00:00.652
+   * - :ref:`sphx_glr_auto_examples_plot_2_basic_usage.py` (``../examples/plot_2_basic_usage.py``)
+     - 00:00.610
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_demo_2.py` (``../examples/plot_demo_2.py``)
-     - 00:00.029
+   * - :ref:`sphx_glr_auto_examples_plot_1_getting_started.py` (``../examples/plot_1_getting_started.py``)
+     - 00:00.027
      - 0.0

doc/user_guide.rst

@@ -5,4 +5,5 @@ User guide
 
 .. currentmodule:: skore
 
-*The user guide is incoming.*
+*The User Guide is currently work in progress and will be released soon, thank
+you for your patience!*

examples/README.rst

@@ -1,5 +1,4 @@
 Examples
 ========
 
-Below is a gallery of examples on how to use `skore` for predictive
-competitive risk analysis problems.
+Below is a gallery of examples on how to use ``skore``.

examples/plot_1_getting_started.py

@@ -0,0 +1,33 @@
+"""
+==========================
+Getting started with skore
+==========================
+
+`This is work in progress.`
+
+This guide provides a quick start to ``skore``, an open-source package that aims to enable data scientists to:
+
+1. Store objects of different types from their Python code: python lists, `scikit-learn` fitted pipelines, `plotly` figures, and more.
+2. Track and visualize these stored objects on a user-friendly dashboard.
+3. Export the dashboard to a HTML file.
+
+.. prompt:: bash
+
+    twine upload dist/*
+"""
+
+# %%
+# From your shell, initialize a skore project, here named project.skore, that will be in your current working directory:
+# ```
+# python -m skore create "project.skore"
+# ```
+
+
+# %%
+import sklearn
+import matplotlib.pyplot as plt
+
+# %%
+sklearn.__version__
+plt.plot([0, 1, 2, 3], [0, 1, 2, 3])
+plt.show()

examples/plot_demo.py renamed to examples/plot_2_basic_usage.py

@@ -1,9 +1,9 @@
 """
-======
-blabla
-======
+====================
+Basic usage of skore
+====================
 
-blabla
+This is work in progress.
 """
 
 # %%