Commit 307b3c37 authored by Amir MOHAMMADI's avatar Amir MOHAMMADI
Browse files

Simplify the landing page

parent 2517925d
Pipeline #61780 passed with stage
in 12 minutes and 29 seconds
......@@ -43,6 +43,9 @@ Each row will contain exactly **one** sample (e.g. one image) and
each column will represent one attribute of samples (e.g. path to data or other
metadata).
An Example
----------
Below is an example of creating the iris database. The ``.csv`` files are
distributed with this package have the following format::
......@@ -63,7 +66,6 @@ As you can see there is only protocol called ``default`` and two groups
>>> import pkg_resources
>>> import bob.pipelines as mario
>>> import os
>>> dataset_protocols_path = pkg_resources.resource_filename(
... 'bob.pipelines', 'tests/data/iris_database')
>>> database = mario.FileListDatabase(
......@@ -77,6 +79,10 @@ As you can see there is only protocol called ``default`` and two groups
As you can see, all attributes are strings. Furthermore, we may want to
*transform* our samples further before using them.
Transforming Samples
--------------------
:any:`bob.pipelines.FileListDatabase` accepts a transformer that will be applied
to all samples:
......@@ -112,9 +118,14 @@ to all samples:
load the data from disk in this transformer and return delayed samples.
Now our samples are ready to be used and we can run a simple experiment with
them. Here, we want to train an Linear Discriminant Analysis (LDA) on the data.
Before that, we want to normalize the range of our data and convert the
``target`` labels to integers.
them.
Running An Experiment
---------------------
Here, we want to train an Linear Discriminant Analysis (LDA) on the data. Before
that, we want to normalize the range of our data and convert the ``target``
labels to integers.
.. doctest:: csv_iris_database
......
......@@ -8,34 +8,22 @@
Easily boost your :doc:`Scikit Learn Pipelines <modules/generated/sklearn.pipeline.Pipeline>` with powerful features, such as:
.. figure:: img/dask.png
:width: 40%
:align: center
Scale them with Dask
.. figure:: img/metadata.png
:width: 40%
:align: center
Wrap datapoints with metadata and pass them to the `estimator.fit` and `estimator.transform` methods
.. figure:: img/checkpoint.png
:width: 40%
:align: center
Checkpoint datapoints after each step of your pipeline
* Scaling experiments on dask_.
* Wrapping data-points with metadata and passing them to the `estimator.fit` and `estimator.transform` methods.
* Checkpointing data-points after each step of your pipeline.
* Expressing database protocol as csv files and using them easily.
.. warning::
Before any investigation of this package is capable of, check the scikit learn :ref:`user guide <scikit-learn:pipeline>`. Several :ref:`tutorials <scikit-learn:tutorial_menu>` are available online.
.. warning::
If you want to implement your own scikit-learn estimator, please, check it out this :doc:`link <scikit-learn:developers/develop>`
Before any investigation of this package is capable of, check the scikit
learn :ref:`user guide <scikit-learn:pipeline>`. Several :ref:`tutorials
<scikit-learn:tutorial_menu>` are available online.
.. warning::
If you want to implement your own scikit-learn estimator, please, check out
this :doc:`link <scikit-learn:developers/develop>`
User Guide
==========
......@@ -49,3 +37,5 @@ User Guide
datasets
xarray
py_api
.. include:: links.rst
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