Neptune-bokeh Integration

This integration lets you log interactive charts generated in bokeh, like confusion matrix or distribution, in Neptune.

bokeh neptune.ai integration

Follow these steps:

  1. Create an experiment:

    import neptune

neptune.init(api_token='ANONYMOUS',project_qualified_name='shared/showroom')
neptune.create_experiment()

  2. Create and log bokeh charts into Neptune:

    from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.unemployment import data as unemployment
from bokeh.sampledata.us_counties import data as counties

palette = tuple(reversed(palette))

counties = {
    code: county for code, county in counties.items() if county["state"] == "tx"
}

county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]

county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)

data=dict(
    x=county_xs,
    y=county_ys,
    name=county_names,
    rate=county_rates,
)

TOOLS = "pan,wheel_zoom,reset,hover,save"

p = figure(
    title="Texas Unemployment, 2009", tools=TOOLS,
    x_axis_location=None, y_axis_location=None,
    tooltips=[
        ("Name", "@name"), ("Unemployment rate", "@rate%"), ("(Long, Lat)", "($x, $y)")
    ])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"

p.patches('x', 'y', source=data,
          fill_color={'field': 'rate', 'transform': color_mapper},
          fill_alpha=0.7, line_color="white", line_width=0.5)

    from neptunecontrib.api import log_chart

log_chart(name='bokeh_figure', chart=p)

  3. Explore the results in the Neptune dashboard:

Check out this experiment in the app.

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