Data drift algorithm
In some tests and metrics, Evidently uses the default Data Drift Detection algorithm. It helps detect the distribution drift in the individual features, prediction, or target. This page describes how the default algorithm works.
Evidently compares the distributions of the values in a given column (or columns) of the two datasets. You should pass these datasets as reference and current. Evidently applies several statistical tests and metrics to detect if the distribution has changed significantly. It returns a "drift detected" or "not detected" result for each column.
There is a default logic to choosing the appropriate drift test for each column. It is based on:
- column type: categorical, numerical or text data
- the number of observations in the reference dataset
- the number of unique values in the column (n_unique)
For small data with <= 1000 observations in the reference dataset:
- For binary categorical features (n_unique <= 2): proportion difference test for independent samples based on Z-score.
All tests use a 0.95 confidence level by default.
For larger data with > 1000 observations in the reference dataset:
All metrics use a threshold = 0.1 by default.
You can always modify this drift detection logic. You can select any of the statistical tests available in the library (including PSI, K-L divergence, Jensen-Shannon distance, Wasserstein distance, etc.), specify custom thresholds, or pass a custom test. You can read more about using data drift parameters and available drift detection methods.
For text data:
- Text content drift using a domain classifier. Evidently trains a binary classification model to discriminate between data from reference and current distributions.
The drift score, in this case, is the ROC-AUC score of the domain classifier computed on a validation dataset. The ROC AUC of the created classifier is compared to the ROC AUC of the random classifier at a set percentile (threshold). To ensure the result is statistically meaningful, we repeat the calculation 1000 times with randomly assigned target class probabilities. This produces a distribution with a mean of 0,5. We then take the 95th percentile (default) of this distribution and compare it to the ROC-AUC score of the classifier. If the classifier score is higher, we consider the data drift to be detected. You can also set a different percentile as a parameter.
The method above calculates drift for each column individually.
To detect dataset-level drift, you can set a rule on top of the individual feature results. For example, you can declare dataset drift if at least 50% of all features (columns) drifted or if ⅓ of the most important features drifted. Some of the Evidently tests and presets include such defaults. You can always modify them and set custom parameters.
To evaluate data or prediction drift in the dataset, you need to ensure that the columns you test for drift are not empty. If these columns are empty in either reference or current data, Evidently will not calculate distribution drift and will raise an error.
If some columns contain empty or infinite values (+-np.inf), these values will be filtered out when calculating distribution drift in the corresponding column.
By default, drift tests do not react to changes or increases in the number of empty values. Since the high number of nulls can be an important indicator, we recommend grouping the data drift tests (that check for distribution shift) with data integrity tests (that check for a share of nulls). You can choose from several null-related tests and metrics and set a threshold.
To build up a better intuition for which tests are better in different kinds of use cases, you can read our in-depth blog with experimental code: