Getting Started
Once Falkon is installed, getting started is easy. The basic setup to use the Falkon estimator only requires few lines of code:
import torch
from sklearn.datasets import load_boston
from falkon import Falkon, kernels
X, Y = load_boston(return_X_y=True)
X = torch.from_numpy(X)
Y = torch.from_numpy(Y).reshape(-1, 1)
kernel = kernels.GaussianKernel(sigma=1.0)
model = Falkon(
kernel=kernel,
penalty=1e-6,
M=100,
)
model.fit(X, Y)
predictions = model.predict(X)
Passing Options
A number of different options exist for both the Falkon and LogisticFalkon
estimators (see falkon.FalkonOptions).
All options can be passed to the estimator through the FalkonOptions
class, like so:
from falkon import FalkonOptions, Falkon, kernels
# Options to: increase the amount of output information; avoid using the KeOps library
options = FalkonOptions(debug=True, keops_active="no")
kernel = kernels.GaussianKernel(sigma=1.0)
model = Falkon(kernel=kernel,
penalty=1e-6,
M=100,
maxiter=10, # Set the maximum number of conjugate gradient iterations to 10
options=options)
More Examples
For more detailed examples, have a look at the example notebooks.