Source code for aydin.regression.support_vector

from sklearn.svm import SVR, LinearSVR

from aydin.regression.base import RegressorBase
from aydin.util.log.log import lprint, lsection

[docs]class SupportVectorRegressor(RegressorBase): """ The Support Vector Regressor is too slow and does not in pour experience perform better than random forests or gradient boosting. """ def __init__(self, linear: bool = True): """Constructs a linear regressor. Parameters ---------- linear : bool Flag to choose between a linear or non-linear SVR (advanced) """ super().__init__() self.linear = linear def __repr__(self): return f"<{self.__class__.__name__}, linear={self.linear}>" def _fit( self, 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). """ if self.linear: model = LinearSVR() # gamma='scale') else: model = SVR(gamma='scale'), y_train) return _SVRModel(model)
class _SVRModel: def __init__(self, model): self.model = model self.loss_history = {'training': [], 'validation': []} def _save_internals(self, path: str): pass def _load_internals(self, path: str): pass def predict(self, x): with lsection("SVR regressor prediction"): lprint(f"Number of data points : {x.shape[0]}") lprint(f"Number of features per data points: {x.shape[-1]}") with lsection("SVR prediction now"): prediction = self.model.predict(x) lprint("SVR regressor predicting done!") return prediction