How to compute statistics on feature data#

Introduction#

In this guide you will learn how to configure, compute and visualize statistics for the features registered with Hopsworks.

Hopsworks groups statistics in four categories:

Descriptive: These are the basic statistics Hopsworks computes. They include an approximate count of the distinctive values and the completeness (i.e. the percentage of non null values). For numerical features Hopsworks also computes the minimum, maximum, mean, standard deviation and the sum of each feature. Enabled by default.
Histograms: Hopsworks computes the distribution of the values of a feature. Exact histograms are computed as long as the number of distinct values is less than 20. If a feature has a numerical data type (e.g. integer, float, double, ...) and has more than 20 unique values, then the values are bucketed in 20 buckets and the histogram represents the distribution of values in those buckets. By default histograms are disabled.
Correlation: If enabled, Hopsworks computes the Pearson correlation between features of numerical data type within a feature group. By default correlation is disabled.
Exact Statistics: Exact statistics are an enhancement of the descriptive statistics that provide an exact count of distinctive values, entropy, uniqueness and distinctiveness of the value of a feature. These statistics are more expensive to compute as they take into consideration all the values and they don't use approximations. By default they are disabled.

When statistics are enabled, they are computed every time new data is written into the offline storage of a feature group. Statistics are then displayed on the Hopsworks UI and users can track how data has changed over time.

Prerequisites#

Before you begin this guide we suggest you read the Feature Group concept page to understand what a feature group is and how it fits in the ML pipeline. We also suggest you familiarize with the APIs to create a feature group.

Enable statistics when creating a feature group#

As mentioned above, by default only descriptive statistics are enabled when creating a feature group. To enable histograms, correlations or exact statistics the statistics_config configuration parameter can be provided in the create statement.

The statistics_config parameter takes a dictionary with the keys: enabled, correlations, histograms and exact_uniqueness and, as values, a boolean to describe whether or not to compute the specific class of statistics.

Additionally it is possible to restrict the statistics computation to only a subset of columns. This is configurable by adding a columns key to the statistics_config parameter. The key should contain the list of columns for which to compute statistics. By default the value is empty list [] and the statistics are computed for all columns in the feature group.

Python

fg = feature_store.create_feature_group(name="weather",
    version=1,
    description="Weather Features",
    online_enabled=True,
    primary_key=['location_id'],
    partition_key=['day'],
    event_time='event_time',
    statistics_config={
        "enabled": True,
        "histograms": True,
        "correlations": True,
        "exact_uniqueness": False,
        "columns": []
    }
)

Enable statistics after creating a feature group#

It is possible users to change the statistics configuration after a feature group was created. Either to add or remove a class of statistics, or to change the set of features for which to compute statistics.

Python

fg.statistics_config = {
        "enabled": True,
        "histograms": False,
        "correlations": False,
        "exact_uniqueness": False 
        "columns": ['location_id', 'min_temp', 'max_temp']
    }

fg.update_statistics_config()

Explicitly compute statistics#

As mentioned above, the statistics are computed every time new data is written into the offline storage of a feature group. By invoking the compute_statistics method, users can trigger explicitly the statistics computation for the data available in a feature group.

This is useful when a feature group is receiving frequent updates. Users can schedule periodic statistics computation that take into consideration several data commits.

By default, the compute_statistics method computes statistics on the most recent version of the data available in a feature group. Users can provide a specific time using the wallclock_time parameter, to compute the statistics for a previous version of the data.

Hopsworks can compute statistics of external feature groups. As external feature groups are read only from an Hopsworks perspective, statistics computation can be triggered using the compute_statistics method.

Python

fg.compute_statistics(wallclock_time='20220611 20:00')

Inspect statistics#

You can also create a new feature group through the UI.