Creating a custom expectation in version 1.0.0

[ai-learner-00]

I upgraded to version 1.0.0 from 0.18. The code is still valid, but the documentation is missing, e.g. the link mentioned in ColumnMapMetricProvider. Is this still the recommended way to create custom expectations?

import json
from typing import Optional

from great_expectations.expectations.expectation_configuration import (
    ExpectationConfiguration,
) # was great_expectations.core before
from great_expectations.exceptions import InvalidExpectationConfigurationError
from great_expectations.execution_engine import (
    PandasExecutionEngine,
    SparkDFExecutionEngine,
    SqlAlchemyExecutionEngine,
)
from great_expectations.expectations.expectation import ColumnMapExpectation
from great_expectations.expectations.metrics import (
    ColumnMapMetricProvider,
    column_condition_partial,
)


class ColumnValuesEqualThree(ColumnMapMetricProvider):
    condition_metric_name = "column_values.equal_three"

    @column_condition_partial(engine=PandasExecutionEngine)
    def _pandas(cls, column, **kwargs):
        print("TEST", type(column), column)
        return column == 3

    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
    # @column_condition_partial(engine=SqlAlchemyExecutionEngine)
    # def _sqlalchemy(cls, column, _dialect, **kwargs):
    #     raise NotImplementedError

    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
    # @column_condition_partial(engine=SparkDFExecutionEngine)
    # def _spark(cls, column, **kwargs):
    #     raise NotImplementedError


class ExpectColumnValuesToBeEqualThree(ColumnMapExpectation):
    """Expect values in this column to equal 3."""

    examples = [
        {
            "data": {
                "all_threes": [3, 3, 3, 3, 3],
                "some_zeroes": [3, 3, 3, 0, 0],
            },
            "tests": [
                {
                    "title": "basic_positive_test",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {"column": "all_threes"},
                    "out": {"success": True},
                },
                {
                    "title": "basic_negative_test",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {"column": "some_zeroes", "mostly": 0.8},
                    "out": {
                        "success": False,
                    },
                },
            ],
        },
    ]

    map_metric = "column_values.equal_three"

    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
    success_keys = ("mostly",)

    # This dictionary contains default values for any parameters that should have default values
    default_kwarg_values = {}

    def validate_configuration(
        self, configuration: Optional[ExpectationConfiguration] = None
    ) -> None:
        """
        Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
        necessary configuration arguments have been provided for the validation of the expectation.

        Args:
            configuration (OPTIONAL[ExpectationConfiguration]): \
                An optional Expectation Configuration entry that will be used to configure the expectation
        Returns:
            None. Raises InvalidExpectationConfigurationError if the config is not validated successfully
        """

        super().validate_configuration(configuration)
        configuration = configuration or self.configuration

        # # Check other things in configuration.kwargs and raise Exceptions if needed
        # try:
        #     assert (
        #         ...
        #     ), "message"
        #     assert (
        #         ...
        #     ), "message"
        # except AssertionError as e:
        #     raise InvalidExpectationConfigurationError(str(e))

    # This object contains metadata for display in the public Gallery
    # <snippet name="version-0.17.23 docs/docusaurus/versioned_docs/version-0.17.23/guides/expectations/creating_custom_expectations/column_map_expectation_template.py library_metadata">
    library_metadata = {
        "tags": [],  # Tags for this Expectation in the Gallery
        "contributors": [  # Github handles for all contributors to this Expectation.
            "@your_name_here",  # Don't forget to add your github handle here!
        ],
    }


#     </snippet>


ExpectColumnValuesToBeEqualThree(column=?).print_diagnostic_checklist()  # what should be the column value?

There is a new page about customizing an existing expectation class, but I am not sure how I would use it with a custom metric.