Tutorial

To get started with flexcv, we take you through a couple of quick and basic code examples. You will learn how to set up flexcv with a linear model and how the interaction with the interface class works in practical use. At the end of this section you will be familiar with the basic concepts of flexcv and will be able to use it for your own projects.

Linear Model

First we will use a LinearModel on a randomly generated regression dataset. Because Linear Models do not have any hyperparameters, we naturally don't need an inner cross validation loop.

# import the class interface, data generator and model
from flexcv import CrossValidation
from flexcv.synthesizer import generate_regression
from flexcv.models import LinearModel

# make sample data
X, y, group, random_slopes = generate_regression(10, 100, n_slopes=1, noise_level=9.1e-2, random_seed=42)

# instantiate our cross validation class
cv = CrossValidation()

# now we can use method chaining to set up our configuration perform the cross validation
results = (
    cv
    .set_data(X, y, group, dataset_name="ExampleData")
    .set_splits(method_outer_split="GroupKFold", method_inner_split="KFold")
    .add_model(LinearModel)
    .set_splits(break_cross_val=True)
    .perform()
    .get_results()
)

# results has a summary property which returns a dataframe
# we can simply call the pandas method "to_excel"
results.summary.to_excel("my_cv_results.xlsx")