flytekitplugins.whylogs.renderer

Directory

Classes

flytekitplugins.whylogs.renderer.WhylogsConstraintsRenderer

Creates a whylogs’ Constraints report from a Constraints object. Currently our API requires the user to have a profiled DataFrame in place to be able to use it. Then the report will render a nice HTML that will let users check which constraints passed or failed their logic. An example constraints object definition can be written as follows:

.. code-block:: python

profile_view = why.log(df).view()
builder = ConstraintsBuilder(profile_view)
num_constraint = MetricConstraint(
                    name=f'numbers between {min_value} and {max_value} only',
                    condition=lambda x: x.min > min_value and x.max < max_value,
                    metric_selector=MetricsSelector(
                                            metric_name='distribution',
                                            column_name='sepal_length'
                                            )
                )

builder.add_constraint(num_constraint)
constraints = builder.build()

Each Constraints object (builder.build() in the former example) can have as many constraints as desired. If you want to learn more, check out our docs and examples at https://whylogs.readthedocs.io/

Methods

to_html()

def to_html(
    constraints: whylogs.core.constraints.metric_constraints.Constraints,
) -> str

flytekitplugins.whylogs.renderer.WhylogsSummaryDriftRenderer

Creates a whylogs’ Summary Drift report from two pandas DataFrames. One of them is the reference and the other one is the target data, meaning that this is what the report will compare it against.

Methods

to_html()

def to_html(
    reference_data: pandas.core.frame.DataFrame,
    target_data: pandas.core.frame.DataFrame,
) -> str

This static method will profile the input data and then generate an HTML report with the Summary Drift calculations for all the dataframe’s columns