class NuSVCImpl(): def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight='balanced', verbose=False, max_iter=(- 1), decision_function_shape='ovr', random_state=None): self._hyperparams = { 'nu': nu, 'kernel': kernel, 'degree': degree, 'gamma': gamma, 'coef0': coef0, 'shrinking': shrinking, 'probability': probability, 'tol': tol, 'cache_size': cache_size, 'class_weight': class_weight, 'verbose': verbose, 'max_iter': max_iter, 'decision_function_shape': decision_function_shape, 'random_state': random_state} def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self def predict(self, X): return self._sklearn_model.predict(X) def predict_proba(self, X): return self._sklearn_model.predict_proba(X)
class CreateNuSVC(CreateLinearSVC): def fit(self, data, args): self.model = NuSVC(probability=True) with Timer() as t: self.model.fit(data.X_train, data.y_train) return t.interval