Parameters provide a useful way of passing values at runtime, rather than hard coding them directly in a script. Parameters and their values are defined in the same way as for flows; that is, as entries in the parameters table of a flow, or as parameters on the command line. The Stream class implements a set of functions defined by the ParameterProvider object as shown in the following table. Session provides a getParameters() call which returns an object that defines those functions.

Table 1. Functions defined by the ParameterProvider object
Method Return type Description
p.parameterIterator() Iterator Returns an iterator of parameter names for this object.
p.getParameterDefinition( parameterName) ParameterDefinition Returns the parameter definition for the parameter with the specified name, or None if no such parameter exists in this provider. The result may be a snapshot of the definition at the time the method was called and need not reflect any subsequent modifications made to the parameter through this provider.
p.getParameterLabel(parameterName) string Returns the label of the named parameter, or None if no such parameter exists.
p.setParameterLabel(parameterName, label) Not applicable Sets the label of the named parameter.
p.getParameterStorage( parameterName) ParameterStorage Returns the storage of the named parameter, or None if no such parameter exists.
p.setParameterStorage( parameterName, storage) Not applicable Sets the storage of the named parameter.
p.getParameterType(parameterName) ParameterType Returns the type of the named parameter, or None if no such parameter exists.
p.setParameterType(parameterName, type) Not applicable Sets the type of the named parameter.
p.getParameterValue(parameterName) Object Returns the value of the named parameter, or None if no such parameter exists.
p.setParameterValue(parameterName, value) Not applicable Sets the value of the named parameter.

In the following example, the script aggregates some Telco data to find which region has the lowest average income data. A flow parameter is then set with this region. That flow parameter is then used in a Select node to exclude that region from the data, before a churn model is built on the remainder.

The example is artificial because the script generates the Select node itself and could therefore have generated the correct value directly into the Select node expression. However, flows are typically pre-built, so setting parameters in this way provides a useful example.

The first part of this example script creates the flow parameter that will contain the region with the lowest average income. The script also creates the nodes in the aggregation branch and the model building branch, and connects them together.

import modeler.api

stream =

# Initialize a flow parameter
stream.setParameterStorage("LowestRegion", modeler.api.ParameterStorage.INTEGER)

# First create the aggregation branch to compute the average income per region
sourcenode = stream.findByID("idGXVBG5FBZH")

aggregatenode ="aggregate", "Aggregate", 294, 142)
aggregatenode.setPropertyValue("keys", ["region"])
aggregatenode.setKeyedPropertyValue("aggregates", "income", ["Mean"])

tablenode ="table", "Table", 462, 142), aggregatenode), tablenode)

selectnode = stream.createAt("select", "Select", 210, 232)
selectnode.setPropertyValue("mode", "Discard")
# Reference the flow parameter in the selection
selectnode.setPropertyValue("condition", "'region' = '$P-LowestRegion'")

typenode = stream.createAt("type", "Type", 366, 232)
typenode.setKeyedPropertyValue("direction", "Drug", "Target")

c50node = stream.createAt("c50", "C5.0", 534, 232), selectnode), typenode), c50node)

The example script creates the following flow.

Figure 1. Flow that results from the example script
Flow that results from the example script