How to add support for the auto-initializing framework to a custom Expectation

Prerequisites

• Completion of the Quickstart.
• A configured and functional development environment.
• A custom Expectation.

Steps

1. Determine if the auto-initializing framework is appropriate to include in your Expectation

Auto-initializing Expectations automate parameter estimation for Expectations, but not all parameters require this kind of estimation. If your expectation only takes in a Domain (such as the name of a column) then it will not benefit from being configured to work in the auto-initializing framework. In general, the auto-initializing Expectation framework benefits those Expectations that have numeric ranges which are intended to be descriptive of the data found in a Batch or Batches. Existing examples of these would be Expectations such as ExpectColumnMeanToBeBetween, ExpectColumnMaxToBeBetween, or ExpectColumnSumToBeBetween.

2. Build a Custom Profiler for your Expectation

In order to automate the estimation of parameters, auto-initializing Expectations utilize a Custom Profiler. You will need to create an appropriate configuration for the Profiler that your Expectation will use. The easiest way to do this is to modify an existing Profiler configuration.

You can find existing Profiler configurations in the source code for any Expectation that works within the auto-initializing framework. For this example, we will look at the existing configuration for the ExpectColumnMeanToBeBetween Expectation. You can view the source code for this Expectation on our GitHub.

2a. Modifying variables

Key-value pairs defined in the variables portion of a Profiler Configuration will be shared across all of its Rules and Rule components. This helps you define and keep track of values without having to input them multiple times. In our example, the variables are:

• strict_min: Used by expect_column_mean_to_be_between Expectation. Recognized values are True or False.
• strict_max: Used by expect_column_mean_to_be_between Expectation. Recognized values are True or False.
• false_positive_rate: Used by NumericMetricRangeMultiBatchParameterBuilder. Typically, this will be a float 0 <= 1.0.
• quantile_statistic_interpolation_method: Used by NumericMetricRangeMultiBatchParameterBuilder, which is used when estimating quantile values (not relevant in our case). Recognized values include auto, nearest, and linear.
• estimator: Used by NumericMetricRangeMultiBatchParameterBuilder. Recognized values include oneshot, bootstrap, and kde.
• n_resamples: Used by NumericMetricRangeMultiBatchParameterBuilder. Integer values are expected.
• include_estimator_samples_histogram_in_details: Used by NumericMetricRangeMultiBatchParameterBuilder. Recognized values are True or False.
• truncate_values: A value used by the NumericMetricRangeMultiBatchParameterBuilder to specify the [lower_bound, upper_bound] interval, where either boundary is numeric or None. In our case the value is an empty dictionary, and an equivalent configuration would have been truncate_values : { lower_bound: None, upper_bound: None }.
• round_decimals : Used by NumericMetricRangeMultiBatchParameterBuilder, and determines how many digits after the decimal point to output (in our case 2).

2b. Modifying the domain_builder

The DomainBuilder configuration requries a class_name and module_name. In this example, we will be using the ColumnDomainBuilder which outputs the column of interest (for example: trip_distance in the NYC taxi data) which is then accessed by the ExpectationConfigurationBuilder using the variable $domain.domain_kwargs.column.

• class_name: is the name of the DomainBuilder class that is to be used. Additional Domain Builders are:

  • ColumnDomainBuilder: This DomainBuilder outputs column Domains, which are required by ColumnAggregateExpectations like (expect_column_median_to_be_between).
  • MultiColumnDomainBuilder: This DomainBuilder outputs multicolumn Domains by taking in a column list in the include_column_names parameter.
  • ColumnPairDomainBuilder: This DomainBuilder outputs columnpair domains by taking in a column pair list in the include_column_names parameter.
  • TableDomainBuilder: This DomainBuilder outputs table Domains, which is required by Expectations that act on tables, like (expect_table_row_count_to_equal, or expect_table_columns_to_match_set).
  • MapMetricColumnDomainBuilder: This DomainBuilder allows you to choose columns based on Map Metrics, which give a yes/no answer for individual values or rows.
  • CategoricalColumnDomainBuilder: This DomainBuilder allows you to choose columns based on their cardinality (number of unique values).
    note

    CategoricalColumnDomainBuilder will take in various cardinality_limit_mode values for cardinality. For a full listing of valid modes, along with the associated values, please refer to the CardinalityLimitMode enum in the source code on our GitHub.

• module_name: is great_expectations.rule_based_profiler.domain_builder, which is common for all DomainBuilders.

2c. Modifying the ParameterBuilder

Our example contains a configuration for one ParamterBuilder, a NumericMetricRangeMultiBatchParameterBuilder. You can find the other types of ParameterBuilder by browsing the source code in our GitHub. For the NumericMetricRangeMultiBatchParameterBuilder the configuration key-value pairs consist of:

• name: an arbitrary name assigned to this ParameterBuilder configuration.
• class_name: the name of the class that corresponds to the ParameterBuilder defined by this configuration.
• module_name: great_expectations.rule_based_profiler.parameter_builder which is the same for all ParameterBuilders.
• json_serialize: Boolean value that determines whether to convert computed value to JSON prior to saving result.
• estimator: choice of the estimation algorithm: "oneshot" (one observation), "bootstrap" (default), or "kde" (kernel density estimation). Value is pulled from $variables.estimator, which is set to "bootstrap" in our configuration.
• quantile_statistic_interpolation_method: Applicable for the "bootstrap" sampling method. Determines the value of interpolation "method" to np.quantile() statistic, which is used for confidence intervals. Value is pulled from $variables.quantile_statistic_interpolation_method, which is set to "auto" in our configuration.
• enforce_numeric_metric: used in MetricConfiguration to ensure that metric computations return numeric values. Set to True.
• n_resamples: Applicable for the "bootstrap" and "kde" sampling methods -- if omitted (default), then 9999 is used. Value is pulled from $variables.n_resamples, which is set to 9999 in our configuration.
• round_decimals: User-configured non-negative integer indicating the number of decimals of the rounding precision of the computed parameter values (i.e., min_value, max_value) prior to packaging them on output. If omitted, then no rounding is performed, unless the computed value is already an integer. Value is pulled from $variables.round_decimals which is 2 in our configuration.
• reduce_scalar_metric: If True (default), then reduces computation of 1-dimensional metric to scalar value. This value is set to True.
• include_estimator_samples_histogram_in_details: For the "bootstrap" sampling method -- if True, then add 10-bin histogram of bootstraps to "details"; otherwise, omit this information (default). Value pulled from $variables.include_estimator_samples_histogram_in_details, which is False in our configuration.
• truncate_values: User-configured directive for whether or not to allow the computed parameter values (i.e.,lower_bound, upper_bound) to take on values outside the specified bounds when packaged on output. Value pulled from $variables.truncate_values, which is None in our configuration.
• false_positive_rate: User-configured fraction between 0 and 1 expressing desired false positive rate for identifying unexpected values as judged by the upper- and lower- quantiles of the observed metric data. Value pulled from $variables.false_positive_rate and is 0.05 in our configuration.
• replace_nan_with_zero: If False, then if the computed metric gives NaN, then exception is raised; otherwise, if True (default), then if the computed metric gives NaN, then it is converted to the 0.0 (float) value. Set to True in our configuration.
• metric_domain_kwargs: Domain values for ParameteBuilder. Pulled from $domain.domain_kwargs, and is empty in our configuration.

2d. Modifing the expectation_configuration_builders

Our Configuration contains 1 ExpectationConfigurationBuilder, for the expect_column_mean_to_be_between Expectation type.

The ExpectationConfigurationBuilder configuration requires a expectation_type, class_name and module_name:

• expectation_type: expect_column_mean_to_be_between
• class_name: DefaultExpectationConfigurationBuilder
• module_name: great_expectations.rule_based_profiler.expectation_configuration_builder which is common for all ExpectationConfigurationBuilders

Also included are:

• validation_parameter_builder_configs: Which are a list of ValidationParameterBuilder configurations, and our configuration case contains the ParameterBuilder described in the previous section.

Next are the parameters that are specific to the expect_column_mean_to_be_between Expectation.

• column: Pulled from DomainBuilder using the parameter$domain.domain_kwargs.column
• min_value: Pulled from the ParameterBuilder using $parameter.mean_range_estimator.value[0]
• max_value: Pulled from the ParameterBuilder using $parameter.mean_range_estimator.value[1]
• strict_min: Pulled from `$variables.strict_min, which is False.
• strict_max: Pulled from `$variables.strict_max, which is False.

Last is meta which contains details from our parameter_builder.

3. Assign your configuration to the default_profiler_config class attribute of your Expectation

Once you have modified the necessary parts of the Profiler configuration to suit your purposes you will need to assign it to the default_profiler_config class attribute of your Expectation. If you initially copied the Profiler configuration that you modified from another Expectation that was already set up to work with the auto-initializing framework then you can refer to that Expectation for an example of this.

4. Test your Expectation with auto=True

After assigning your Profiler configuration to the default_profiler_config attribute of your Expectation, your Expectation should be able to work in the auto-initializing framework. Test your expectation with the parameter auto=True.