Skip to content

tuto_streamlit.rst

Latest commit

 

History

History
76 lines (40 loc) · 3.07 KB

tuto_streamlit.rst

File metadata and controls

76 lines (40 loc) · 3.07 KB
Raw

HiPlot component for Streamlit

Streamlit is an open-source app framework. It enables data scientists and machine learning engineers to create beautiful, performant apps in pure Python.

Getting started

You'll need both Streamlit (>=0.63 for components support) and HiPlot (hiplot-mm >= 0.0.4)

>>> pip install -U streamlit "hiplot-mm>=0.0.4"

Displaying an :class:`hiplot.Experiment`

Displaying an HiPlot experiment in Streamlit is very similar to how you would do it in a Jupyter notebook, except that you should call :meth:`hiplot.Experiment.to_streamlit` before calling :meth:`hiplot.Experiment.display`.

Note

:meth:`hiplot.Experiment.to_streamlit` has a key parameter, that can be used to assign your component a fixed identity if you want to change its arguments over time and not have it be re-created.

If you remove the key argument in the example below, then the component will be re-created whenever any slider changes, and lose its current configuration/state.

.. literalinclude:: ../examples/demo_streamlit.py
    :start-after: DEMO_STREAMLIT_BEGIN
../assets/streamlit.png

HiPlot component return values

HiPlot is highly interactive, and there are multiple values/information that can be returned, depending on what the user provides for the parameter ret in :meth:`hiplot.Experiment.to_streamlit`

  • ret="filtered_uids": returns a list of uid for filtered datapoints. Filtered datapoints change when the user clicks on Keep or Exclude buttons.
  • ret="selected_uids": returns a list of uid for selected datapoints. Selected datapoints correspond to currently visible points (for example when slicing in the parallel plot) - it's a subset of filtered datapoints.
  • ret="brush_extents": returns information about current brush extents in the parallel plot
  • or a list containing several values above. In that case, HiPlot will return a list with the return values
xp.to_streamlit(key="hip1").display()  # Does not return anything
filtered_uids = xp.to_streamlit(ret="filtered_uids", key="hip2").display()
filtered_uids, selected_uids = xp.to_streamlit(ret=["filtered_uids", "selected_uids"], key="hip3").display()

Improving performance with streamlit caching (EXPERIMENTAL)

Generating / displaying a large HiPlot Experiment / component can take a significant amount of time and bandwidth. Several things can speed up streamlit iterations:

  • A data compression mode, which represents the underlying data more efficiently if most of the rows have the same columns
  • Using streamlit's caching to store a frozen copy of the experiment
.. literalinclude:: ../examples/demo_streamlit_cache.py
    :start-after: DEMO_STREAMLIT_BEGIN