Lookup Tables

Simulations tend to require a large quantity of data to run. A completely reasonable way to look at a simulation is to think of it as a task of getting the right data and the right random numbers in the appropriate place at the appropriate time. To address the first concern, vivarium provides the Lookup Table abstraction to ensure that the right data can be retrieved when it’s needed. In particular, it attempts to wrap different strategies for constructing interpolations or distributions on data such that a user simply needs to request values for a set of simulants when they’re needed. This idea is extended to compositions of of several data-based values by vivarium’s values system.

The Lookup Table

A Lookup Table for a quantity is a callable object that is built from a scalar value or a pandas.DataFrame of data points that describes how the quantity varies with other variable(s). It is called with a pandas.Index as a function parameter which represents a simulated population as discussed in the population concept. When called, the Lookup Table simply returns appropriate values of the quantity for the population it was passed, interpolating if necessary or extrapolating if configured to. This behavior represents the standard interface for asking for data about a population in a simulation.

The lookup table system is built in layers. At the top is the Lookup Table object which is responsible for providing a uniform interface to the user regardless of the underlying implementation. From the user’s perspective, it takes in a data set or scalar value on initialization and then lets them query against that data with a population index.

The next layer is selected at initialization time based on the type of data provided. The Lookup Table picks either a ScalarTable if a single value is provided as the data or a InterpolatedTable if a pandas.DataFrame is provided as the data.


The InterpolatedTable is a misnomer here. It confuses the data handling strategy with the underlying data representation. A better name would be BinnedDataTable to indicate that it wraps data where the continuous parameters are represented by bin edges in the provided data. This would allow us to easily think about and extend the lookup system to wrap data where the continuous parameters are represented by points and to tables where all parameters are categorical.

If the underlying data is a single value, this is the last layer of abstraction. The ScalarTable has only one reasonable strategy which is to broadcast the value over the population index. If we have a pandas.DataFrame and therefore an InterpolatedTable, there are additional layers to the lookup system to allow the user to control the strategy for turning the population index into values based on the data. The InterpolatedTable is then responsible for turning the population index into a set of attributes relevant to the value production based on the structure of the input data and then providing those attributes to the value production strategy.


I’m being careful with language here. We have objects named Interpolation and InterpolatedTable though the operation they perform is actually disaggregation. If we extend the system to work with point estimates for the continuous parameters, then interpolation would appropriately describe what we do. Both are value production strategies based on the structure of the input data.

More information about the value production strategies can be found in here.

Construction Parameters

A lookup table is defined for a set of categorical variables, continuous variables, and the values that depend on those variables. The lookup table calls these variables keys, parameters, and values, respectively.

A categorical variable, such as sex, that a quantity depends on.
A continuous variable, such as age, that a quantity depends on. This data frequently represents bins for which values are defined.
Known values of the quantity of interest, which vary with the keys and parameters.

Along with data about these variables, A lookup table is instantiated with the corresponding column names which are used to query an internal population view when the table itself is called. This means the lookup table only needs to be called with a population index – it gathers the population information it needs itself. It also means the data must be available in the population state table with the same column name.

In the table below is an example of (unrealistic) data that could be used to create a lookup table for a quantity of interest about a population, in this case, Body Mass Index (BMI). We may find ourselves in a situation where we want to know the BMI of a simulant in order to make a treatment decision. If we construct a lookup table with these data, we can cleanly get the information we want and go on implementing our treatment. When called, the lookup table will return values of BMI for the simulants defined by the population index.

Key Parameter Value
sex age_start age_end BMI
Male 0 20 20
Male 20 40 25
Male 40 60 30
Male 60 100 27
Female 0 20 20
Female 20 40 25
Female 40 60 30
Female 60 100 27

Example Usage

The following is an example of creating and calling a lookup table in an interactive setting using the data above. The interface and process are the same when integrating a lookup table into a component, which is primarily how they are used. Assuming you have a valid simulation object named sim and the data from the above table in a pandas.DataFrame named data, you can construct a lookup table in the following way, using the interface from the builder.

  # value_columns implicitly set to remaining columns
> bmi = sim.builder.lookup.build_table(data, key_columns=['sex'], parameter_columns=['age'])
> population = sim.get_population()
> bmi(population.index).head()  # returns BMI values for the population

  0     20.0
  1     20.0
  2     30.0
  3     27.0
  4     25.0
  Name: BMI, dtype: float64


Constructing a lookup table currently requires your data meet specific conditions. These are a consequence of the method the lookup table uses to arrive at the correct data. Specifically, your parameter columns must represent bins and they must overlap.

Estimating Unknown Values


If a lookup table was constructed with a scalar value or values, the lookup call trivially returns the same scalar(s) back for any population passed in. However, if the lookup table was instead created with a pandas.DataFrame of varying data the lookup will perform interpolation which is an important feature. Interpolation is the process of estimating values for unspecified parameters within the bounds of the parameters we have defined in the lookup table. Currently, the most common case arises when the values are binned by the parameters. Then, the interpolation simply finds the correct bin a value belongs to. Please see the interpolation concept note for more in-depth information about the kinds of interpolation performed by the lookup table.


Previously, we discussed interpolation as the process of estimating data within the bounds defined by our lookup table. What would happen if we wanted data outside of this range? Estimating such data is called extrapolation, and it can be performed using a lookup table as well. Extrapolation is a configurable option that, when enabled, allows a lookup data to provide values outside of the range it was created with. This is done by extending the edge points outwards to encompass outside points. This is a dumb but useful strategy and is primarily used to run simulations beyond the time bounds included in the data under the assumption that parameters do not change in the future.

Specifying Options in the Model Configuration

Configuring interpolation and extrapolation in a model specification is straightforward. Currently, the only acceptable value for order is 0. Extrapolation can be turned on and off.

        order: 0
        extrapolate: True