Create a custom test suite
TL;DR: You can create a custom Test Suite from 50+ individual Tests. You can use auto-generated test conditions or pass your own.
To design a Test Suite, first define which Tests to include. You can use Presets as a starting point to explore available types of analysis. Note that there are additional Tests that are not included in the Presets.
Reference: The complete list of Tests is available in the All tests table. To see interactive examples, refer to the Example notebooks.
There are two types of tests: dataset-level and column-level.
You can apply some of the Tests on the dataset level. For example, to evaluate data drift for the whole dataset.
Create a
TestSuite object and specify the tests to include:data_drift_suite = TestSuite(tests=[
TestShareOfDriftedColumns(),
TestNumberOfDriftedColumns(),
])
To run the Tests and get the visual report:
data_drift_suite.run(
reference_data=ref,
current_data=curr,
column_mapping=ColumnMapping(),
)
data_drift_suite
You can apply some Tests to the individual columns. For example, to check if a specific feature or model prediction stays within the range.
To create a custom Test Suite with column-level tests:
feature_suite = TestSuite(tests=[
TestColumnShareOfMissingValues(column_name='hours-per-week'),
TestColumnDrift(column_name='education'),
TestMeanInNSigmas(column_name='hours-per-week')
])
To run the Tests and get the visual report:
feature_suite.run(reference_data=ref, current_data=curr)
feature_suite
You can freely combine column-level and dataset-level Tests in a single Test Suite. You can also include Presets and individual Tests in the same list.
Here is an example:
my_data_quality_tests = TestSuite(tests=[
DataQualityTestPreset(),
TestColumnAllConstantValues(column_name='education'),
TestNumberOfDriftedColumns()
])
my_data_quality_tests.run(reference_data=ref,current_data=curr)
my_data_quality_tests
Note: If you want to generate multiple column-level Tests, you can use test generator helper function.
Some test have required parameters.
Example 1. If you want to test a quantile value, you need to pass the quantile:
column_tests_suite = TestSuite(tests=[
TestColumnQuantile(column_name='mean perimeter', quantile=0.25),
])
Some tests have optional parameters. They change how the metrics are calculated. If you do not specify it, Evidently will apply a default.
Example 2: To override the default drift detection method, pass the chosen statistical test as a parameter:
dataset_suite = TestSuite(tests=[
TestShareOfDriftedColumns(stattest=psi),
])
Example 3: To change the decision threshold for probabilistic classification, pass it as a parameter:
model_tests = TestSuite(tests=[
TestPrecisionScore(probas_threshold=0.8),
TestRecallScore(probas_threshold=0.8)
])
To define when the test should return "pass" or "fail," you must set a condition. For example, you can set the lower boundary for the expected model precision. If the condition is violated, the test fails.
There are several ways to define the conditions for each test in a test suite.
If you do not define a Test condition manually, Evidently will use the defaults.
- Based on the reference dataset. If you pass the reference, Evidently will auto-generate test conditions. For example,
TestShareOfOutRangeValuestest fails if over 10% of values are out of range. Evidently will automatically derive the value range for each feature. 10% is an encoded heuristic. - Based on heuristics. Some tests can work even without a reference dataset. In this case, Evidently will use heuristics and dummy models. For example,
TestAccuracyScore()fails if the model quality is worse than the quality of a dummy model. Evidently will automatically create a dummy model.
You can also set a custom condition for each test to encode your expectations. You can use the standard parameters:
Condition parameter name | Explanation | Usage Example |
|---|---|---|
eq: val | test_result == val | TestColumnValueMin(column_name=”num_feature”, eq=5) |
not_eq: val | test_result != val | TestColumnValueMin(column_name=”num_feature”, not_eq=0) |
gt: val | test_result > val | TestColumnValueMin(column_name=”num_feature”, gt=5) |
gte: val | test_result >= val | TestColumnValueMin(column_name=”num_feature”, gte=5) |
lt: val | test_result < val | TestColumnValueMin(column_name=”num_feature”, lt=5) |
lte: val | test_result <= val | TestColumnValueMin(column_name=”num_feature”, lte=5) |
is_in: list | test_result == one of the values from list | TestColumnValueMin(column_name=”num_feature”, is_in=[3,5,7]) |
not_in: list | test_result != any of the values from list | TestColumnValueMin(column_name=”num_feature”, not_in=[-1,0,1]) |
Example 1. You can set the upper or lower boundaries of a specific value by defining
gt (greater than) and lt (less than):feature_level_tests = TestSuite(tests=[
TestMeanInNSigmas(column_name='hours-per-week', n_sigmas=3),
TestShareOfOutRangeValues(column_name='hours-per-week', lte=0),
TestNumberOfOutListValues(column_name='education', lt=0),
TestColumnShareOfMissingValues(column_name='education', lt=0.2),
])
Example 2. You can specify both the Test condition (expected precision and recall) and metric parameters (decision threshold) for the classification model quality test:
model_tests = TestSuite(tests=[
TestPrecisionScore(probas_threshold=0.8, gt=0.99),
TestRecallScore(probas_threshold=0.8, gt=0.99)
])
If you want to set an upper and/or lower limit to the value, you can use approx instead of calculating the value itself. You can set the relative or absolute range.
approx(value, relative=None, absolute=None)
To apply
approx, you need to first import this component:from evidently.tests.utils import approx
Example 1. Here is how you can set the upper boundary as 5+10%:
lte=approx(5, relative=0.1)
Example 2. Here is how you can set the boundary as 5 +/-10%:
eq=approx(5, relative=0.1)
Last modified 10d ago