Quick start

by: Kevin Broløs & Tom Jelen
(Feyn version 3.0 or newer)

A Feyn tour around the block

Welcome to this quick guided tour. We'll get you set up and taking advantage of a QLattice in no time.

Installation

Feyn is available as Python3.7+ package through pip. You can install it with the following command:

richard@feyn:~$ pip3 install feyn

Once installed, go to your preferred Python environment and follow along with this example.

Instantiate a QLattice

If you're using a community QLattice then get started with it by:

import feyn

ql = feyn.QLattice()

If you have paid for a license to use the QLattice then here's a guide on how to set it up instead of using the community version.

Auto run

The quickest way to get started is to use the auto_run function on the QLattice. Below you can see how to use auto_run function in a regression or a classification problem.


Regression

We can make a regression problem using feyn.datasets.make_regression. Then we use the auto_run function to find models for the dataset.

from feyn.datasets import make_regression

train, test = make_regression()
models = ql.auto_run(train, output_name = 'y')

This returns a list of fitted models that are the best the QLattice has sampled, sorted by ascending loss.

Evaluate

The model with the lowest loss is models[0]. We can evaluate that model with the plot function and with plot_regression.

best = models[0]
best.plot(train, test)
best.plot_regression(test)

Classification

We can make a classification problem using feyn.datasets.make_classification. Then we use the auto_run function to find models for the dataset. We use the kind parameter to tell the auto_run function we want classifier models.

from feyn.datasets import make_classification

train, test = make_classification()
models = ql.auto_run(train, output_name = 'y', kind = 'classification')

This returns a list of fitted models that are the best the QLattice has sampled, sorted by ascending loss.

Evaluate

The model with the lowest loss is models[0]. We can evaluate that model with the plot function and it's ROC curve.

best = models[0]
best.plot(train, test)
best.plot_roc_curve(test)

Of course we've got way more in store for you, so take a dive off the deep end of the pool with the rest of our documentation.

Using Feyn →