flytekitplugins.whylogs

Directory

Classes

flytekitplugins.whylogs.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.WhylogsDatasetProfileTransformer

Transforms whylogs Dataset Profile Views to and from a Schema (typed/untyped)

def WhylogsDatasetProfileTransformer()

Methods

assert_type()

def assert_type(
    t: Type[T],
    v: T,
)

from_binary_idl()

def from_binary_idl(
    binary_idl_object: Binary,
    expected_python_type: Type[T],
) -> Optional[T]

This function primarily handles deserialization for untyped dicts, dataclasses, Pydantic BaseModels, and attribute access.｀

For untyped dict, dataclass, and pydantic basemodel: Life Cycle (Untyped Dict as example): python val -> msgpack bytes -> binary literal scalar -> msgpack bytes -> python val (to_literal) (from_binary_idl)

For attribute access: Life Cycle: python val -> msgpack bytes -> binary literal scalar -> resolved golang value -> binary literal scalar -> msgpack bytes -> python val (to_literal) (propeller attribute access) (from_binary_idl)

from_generic_idl()

def from_generic_idl(
    generic: Struct,
    expected_python_type: Type[T],
) -> Optional[T]

TODO: Support all Flyte Types. This is for dataclass attribute access from input created from the Flyte Console.

Note:

• This can be removed in the future when the Flyte Console support generate Binary IDL Scalar as input.

get_literal_type()

def get_literal_type(
    t: typing.Type[whylogs.core.view.dataset_profile_view.DatasetProfileView],
) -> flytekit.models.types.LiteralType

Converts the python type to a Flyte LiteralType

guess_python_type()

def guess_python_type(
    literal_type: LiteralType,
) -> Type[T]

Converts the Flyte LiteralType to a python object type.

isinstance_generic()

def isinstance_generic(
    obj,
    generic_alias,
)

to_html()

def to_html(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: whylogs.core.view.dataset_profile_view.DatasetProfileView,
    expected_python_type: typing.Type[whylogs.core.view.dataset_profile_view.DatasetProfileView],
) -> str

Converts any python val (dataframe, int, float) to a html string, and it will be wrapped in the HTML div

to_literal()

def to_literal(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: whylogs.core.view.dataset_profile_view.DatasetProfileView,
    python_type: typing.Type[whylogs.core.view.dataset_profile_view.DatasetProfileView],
    expected: flytekit.models.types.LiteralType,
) -> flytekit.models.literals.Literal

Converts a given python_val to a Flyte Literal, assuming the given python_val matches the declared python_type. Implementers should refrain from using type(python_val) instead rely on the passed in python_type. If these do not match (or are not allowed) the Transformer implementer should raise an AssertionError, clearly stating what was the mismatch

to_python_value()

def to_python_value(
    ctx: flytekit.core.context_manager.FlyteContext,
    lv: flytekit.models.literals.Literal,
    expected_python_type: typing.Type[whylogs.core.view.dataset_profile_view.DatasetProfileView],
) -> ~T

Converts the given Literal to a Python Type. If the conversion cannot be done an AssertionError should be raised

Properties

flytekitplugins.whylogs.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