Model signal · Feyn Documentation

by: Kevin Broløs and Chris Cave
(Feyn version 3.0 or newer)

The signal of a Model can be seen using the plot_signal function on the Model. This provides a graph visualisation of the Model that colours the nodes of the graph with its signal capture amount at each point.

Example

import feyn

from sklearn.datasets import load_diabetes
import pandas as pd

from feyn.tools import split

# Load diabetes dataset into a pandas dataframe
dataset = load_diabetes()
df_diabetes = pd.DataFrame(dataset.data, columns=dataset.feature_names)
df_diabetes['response'] = dataset.target

# Train/test split
train, test = split(df_diabetes, ratio=[0.6, 0.4])

# Instantiate a QLattice
ql = feyn.QLattice()

models = ql.auto_run(
    data=train,
    output_name='response'
)
# Select the best Model
best = models[0]
best.plot_signal(train)

Colours on a graph

The nodes in the graph returned from the plot_signal function are coloured and have values above them.

The values show the paths in the graph that are important or unnecessary in the Model. If the value of the node has increased significantly (about 0.05) compared to its inputs then those inputs are important. Otherwise the node is likely to be unnecessary.

If there is redundancy then run a simulation with lower max_complexity and with a criterion. You will get a Model with less unnecessary paths without sacrificing much on performance.

Repeating this process with many iterations enables you to decide the Model with the correct balance of interpreability and performance for your dataset.

The values are the Pearson's correlation coefficient of the activation values of the node and the output variable. The colours correspond to the correlation coefficient, where -1 is represented by red, 0 by white and 1 by green.

Parameters

data

This should be the data the model has trained on.

corr_func

Takes a correlation function amongst ['pearson', 'spearman', 'mutual_information'] to compute the correlations at each interaction in the model.

filename

Use to specify a path to a file to save the plot to (as SVG).

best.plot_signal(train, filename="feyn-signal-plot.svg")

Location in `Feyn`

This function can also be found in feyn.plots module.

from feyn.plots import plot_model_signal

plot_model_signal(best, train)