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.. _getting_started: | ||
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Getting started | ||
=============== | ||
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.. currentmodule:: skore | ||
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From your shell, initialize a skore project, here named ``project.skore``, that | ||
will be in your current working directory: | ||
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.. 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: | ||
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.. 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: | ||
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.. code:: console | ||
python -m skore launch project.skore | ||
This will automatically open a browser at the UI's location: | ||
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#. 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. | ||
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.. 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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.. You can adapt this file completely to your liking, but it should at least | ||
contain the root `toctree` directive. | ||
skore | ||
===== | ||
Welcome to ``skore`` | ||
==================== | ||
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Gradient-boosting survival analysis | ||
----------------------------------- | ||
#OwnYourDataScience | ||
------------------- | ||
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.. container:: index-features | ||
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* survival and competing risks | ||
* track and visualize your objects from a user-friendly dashboard | ||
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* scikit-learn compatible | ||
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* scalable gradient boosting | ||
With ``skore``, data scientists can: | ||
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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. | ||
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.. 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! | ||
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**Competing risks settings** | ||
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Predicting which event will happen first, and when, from data where some | ||
events have not yet been observed: | ||
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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. | ||
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.. 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. | ||
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The theory behind the model is described in `this paper | ||
<https://arxiv.org/abs/2406.14085>`_. | ||
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- 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. | ||
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.. 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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.. currentmodule:: skore | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:caption: Contents: | ||
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api | ||
install | ||
getting_started | ||
auto_examples/index | ||
user_guide | ||
api |
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.. _install: | ||
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Install | ||
======= | ||
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.. currentmodule:: skore | ||
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First of all, we recommend using a `virtual environment (venv) <https://docs.python.org/3/tutorial/venv.html>`_. | ||
You need ``python>=3.9``. | ||
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Then, you can install ``skore`` by using `pip``: | ||
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.. code:: console | ||
pip install -U skore | ||
You can check ``skore``'s latest version on `PyPI <https://pypi.org/project/skore/>`_. | ||
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🚨 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>`_. |
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Examples | ||
======== | ||
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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``. |
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""" | ||
========================== | ||
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/* | ||
""" | ||
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# %% | ||
# 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" | ||
# ``` | ||
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# %% | ||
import sklearn | ||
import matplotlib.pyplot as plt | ||
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# %% | ||
sklearn.__version__ | ||
plt.plot([0, 1, 2, 3], [0, 1, 2, 3]) | ||
plt.show() |
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