Random Forest Regressor

class aydin.regression.random_forest.RandomForestRegressor(num_leaves: int = 1024, max_num_estimators: int = 2048, max_bin: int = 512, learning_rate: float = 0.0001, loss: str = 'l1', patience: int = 32, verbosity: int = 100)[source]

Random Forrest Regressor (uses the LGBM library).

fit(x_train, y_train, x_valid=None, y_valid=None, regressor_callback=None)

Fits function y=f(x) given training pairs (x_train, y_train). Stops when performance stops improving on the test dataset: (x_test, y_test). The target y_train can have multiple ‘channels’. This will cause multiple regressors to be instanciated internally to be able to predict these channels from the input features.

Parameters
x_train

x training values

y_train

y training values

x_valid

x validation values

y_valid

y validation values

regressor_callback

regressor callback

static load(path: str)

Returns an ‘all-batteries-included’ regressor from a given path (folder).

Parameters
path

path to load from.

Returns
thawedobject
predict(x, models_to_use=None)

Predicts y given x by applying the learned function f: y=f(x) If the regressor is trained on multiple ouput channels, this will return the corresponding number of channels…

Parameters
x

x values

models_to_use
Returns
numpy.typing.ArrayLike

inferred y values

recommended_max_num_datapoints() int

Recommended maximum number of datapoints

Returns
int
save(path: str)

Saves an ‘all-batteries-included’ regressor at a given path (folder).

Parameters
path

path to save to

Returns
frozenEncoded JSON object
stop_fit()

Stops training (can be called by another thread)