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)