Sample models · Feyn Documentation

by: Kevin Broløs & Chris Cave
(Feyn version 3.4.0 or newer)

The QLattice is a statistical source of models. We can sample models from the QLattice by using the QLattice.sample_models() function. Here we go through a small example using it.

Example

import feyn
from feyn.datasets import make_classification

train, test = make_classification()

ql = feyn.QLattice()

models = ql.sample_models(
    input_names=train.columns,
    output_name='y',
    max_complexity=10
)

This returns a list of Models from the inputs to the output that have been sampled from the QLattice. There are many of them and they are not in any specific order.

Parameters of `sample_models`

input_names

The parameter input_names can take an iterable of strings. For example:

A list of strings.
pd.DataFrame.columns
Dict.keys

This is used to define the possible inputs of a Model.

You can as a convenience pass in a Dict or pd.DataFrame, as it will iterate over the column names.

output_name

This is the name of the output as a string.

kind

The kind parameter can take one of classification, regression or auto to specify the sampled Models. The default is auto, however it relies on an stype being defined for the output column to work. If kind is auto and no stype is given, it defaults to regression.

The difference between the two is that the final mathematical transform of a classification is a logisitic function:

$\frac{1}{1 + e^{-x}}$

which takes any value and maps it to a value between 0 and 1.

max_complexity

The max_complexity parameter controls the complexity of the model. Low values of 4 or 5 will yield very simple models while values above 10 yield highly complex models. This is the number of edges in the graph representation of the Model.