Show raw data in Reports

How to change data aggregation in plots.

Pre-requisites:

• You know how to generate Reports with default parameters.

• You know how to pass custom parameters for Reports or Metrics.

Code example

You can refer to an example How-to-notebook:

evidently/how_to_use_aggregated_data_option.ipynb at main · evidentlyai/evidentlyGitHub

Default

Starting from version 0.3.2, all visualizations in Reports are aggregated by default. This helps reduce the size of the resulting HTML.

For example, you can create a custom Report:

report = Report(metrics=[
    RegressionPredictedVsActualScatter(),
    RegressionPredictedVsActualPlot()
])
report.run(reference_data=housing_ref, current_data=housing_cur)
report

Here is how the Scatter Plot in this Report will look:

RegressionPredictedVsActualScatter()

This does not affect Test Suites. All visualizations in Test Suites are already aggregated.

Non-aggregated plots for Reports

If you want to see non-aggregated plots, you can set the raw_data parameter as True in the render options.

You can set it on the Report level:

report = Report(
    metrics=[
      RegressionPredictedVsActualScatter(),
      RegressionPredictedVsActualPlot()
    ],
    options={"render": {"raw_data": True}}
  )
report.run(reference_data=housing_ref, current_data=housing_cur)
report

All plots in the Report will be non-aggregated. Here is how the Scatter Plot in this Report will look:

RegressionPredictedVsActualScatter()

Consider the data size. We recommend setting this option for smaller datasets or when you apply sampling. With non-aggregated plots, the HTML will contain all the data on individual data points. They may take significant time to load and be large in size.

Raw data is not available on Spark. If you run the computations using Spark, the raw data option is not available.

Non-aggregated plots for Metrics

If you want to generate non-aggregated plots only for some visualizations, you can pass the option to the chosen Metrics:

report = Report(
    metrics=[
      RegressionPredictedVsActualScatter(options={"render": {"raw_data": True}}),
      RegressionPredictedVsActualPlot()
    ],
  )
report.run(reference_data=housing_ref, current_data=housing_cur)
report