Data model

Defining the data model is a crucial step before training one of getML’s Pipeline s. You typically deal with this step after having imported your data and specified the roles of each columns.

When working with getML, the raw data usually comes in the form of relational data. That means the information relevant for a prediction is spread over several tables. The data model is the definition of the relationships between all of them.

Most relational machine learning problems can be represented in the form of a star schema, in which case you can use the StarSchema abstraction. If your data set is a time series, you can use the TimeSeries abstraction.


When defining the data model, we distinguish between a population table and one or more peripheral tables. In the context of this tutorial, we will use the term “table” as a catch-all for DataFrame s and View s.

The population table

The population table is the main table of the analysis. It defines the statistical population of your machine learning problem (hence the name) and contains the target variable(s). A target variable is the variable which is the variable we want to predict. Furthermore, the table usually also contains one or more columns with the role join_key. These are keys used to establish a relationship – also called joins – with one or more peripheral tables.

The following example contains the population table of a customer churn analysis. The target variable is churn – whether a person stops using the services and products of a company. It contains the information whether or not a given customer has churned after a certain reference date. The join key customer_id is used to establish relations with a peripheral table. Additionally, the date the customer joined our fictional company is contained in column date_joined, which we have assigned the role time_stamp.


Peripheral tables

Peripheral tables contain additional information relevant for the prediction of the target variable in the population table. Each of them is related to the latter (or another peripheral table, refer to the snowflake schema) via a join_key.

The following pictures contain two peripheral tables that could be used for our customer churn analysis from the example above. One represents complaints a certain customer made with a certain agent and the other the transactions the customer made using her account.



In getML, Placeholder s are used to construct the DataModel. They are abstract representations of DataFrame s or View s and the relationships among each other, but do not contain any data themselves.

The idea behind the placeholder concept is that they allow constructing an abstract data model without any reference to an actual data set. This data model serves as input for the Pipeline. Later on, the feature_learning algorithms can be trained and applied on any data set that follows this data model.

More information on how to construct placeholders and build a data model can be found in the API documentation for Placeholder and DataModel.


Joins are used to establish relationships between placeholders. In order to join two placeholders, the data frames used to derive them should both have at least one join_key. The joining itself is done using the method.

All columns corresponding to time stamps have to be given the role time_stamp and one of them in both the population and peripheral table is usually passed to the method. This prevents easter eggs by incorporating only those rows of the peripheral table in the join operation for which the time stamp of the corresponding row in the population table is either the same or more recent. This ensures that no information from the future is considered during training.

Data schemata

After having created placeholders for all data frames in an analysis, we are ready to create the actual data schema. A data schema is a certain way of assembling population and peripheral tables.

The star schema

The StarSchema is the simplest way of establishing relations between the population and the peripheral tables. It is sufficient for the majority of data science projects.

In the star schema, the population table is surrounded by any number of peripheral tables, all joined via a certain join key. However, no joins between peripheral tables are allowed.

Because this is a very popular schema in many machine learning problems on relational data, getML contains a special class for these sort of problems: StarSchema.

The population table and two peripheral tables introduced in Tables can be arranged in a star schema like this:


The snowflake schema

In some cases, the star schema is not enough to represent the complexity of a data set. This is where the snowflake schema comes in: In a snowflake schema, peripheral tables can have peripheral tables of their own.

Assume that in the customer churn analysis shown above, there is an additional table containing information about the calls a certain agent made in customer service. It can be joined to the COMPLAINTS table using the key agent_id.


In order to model snowflake schemata, you need to use the DataModel and Container classes.

Time series

Time series can be handled by a self join (self-joining a single table). In addition, some extra parameters and considerations are required when building features based on time stamps.

Self-joining a single table

If you deal with a classical (multivariate) time series and all your data is contained in a single table, all the concepts covered so far still apply. You just have to do a so-called self-join by providing your table as both population and peripheral table and join them.

You can think of the process as working in the following way: Whenever a row in the population table - a single measurement - is taken, it will be combined with all the content of the peripheral table - the same time series - for which the time stamps are smaller than the one in the line we picked.

You can also use TimeSeries, which abstracts away the self-join. In this case, you do not have think about self-joins too much.

Features based on time stamps

The getML engine is able to automatically generate features based on aggregations over time windows. Both the length of the time window and the aggregation itself will be figured out by the feature learning algorithm. The only thing you have to do is to provide the temporal resolution your time series is sampled with in the delta_t parameter in any feature learning algorithm.