# Data model¶

Defining the data model is a crucial step before training one of getML’s pipelines. 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 relations between all of them. If you are not sure what relational data is, check out our blog post on this topic.

If your data set is a simple time series, you can use MultirelTimeSeries or RelboostTimeSeries, which abstract away the data model (and therefore you won’t have to deal with this).

## Tables¶

When defining the data model, we distinguish between a population table and one or more peripheral tables.

### The population table¶

The population table is the main table of the analysis. It must contain at least one column with the role target, which is the variable we want to predict. Furthermore, the table must also contain one or more columns with the role join_key. These are keys used to establish a relation - also called joins - with one or more peripheral table. In data warehousing, the population table is sometimes called the fact table.

The following example shows 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 column with role join_key. In data warehousing, the peripheral tables are sometimes called dimension tables.

The following pictures show 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.

## Placeholders¶

In getML, Placeholder are used to construct the data model. They are abstract representations of Data Frames and their relations among each other, but do not contain any data.

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 feature learning algorithms. Later on, these algorithms can be trained and applied on any data set that follows this data model.

More information on how to construct placeholders can be found in the API documentation for Placeholder.

## Joins¶

Joins are used to establish relations between placeholders. In order to join two placeholders, the data frames used to derive them must both have at least one column of role join_key. The joining itself is done using the getml.data.Placeholder.join() 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 must be provided in the getml.data.Placeholder.join() 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 star schema is 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.

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.

## 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 MultirelTimeSeries or RelboostTimeSeries, which abstract 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.