def train_nusvr(x_train: np.ndarray, y_train: np.ndarray,
**kwargs) -> (NuSVR, dict):
mdl = NuSVR(**kwargs)
mdl.degree = 2
should_grid_search = False
param_grid = {}
if 'C' not in kwargs:
should_grid_search = True
param_grid['C'] = [0.1, 1, 10, 100, 250]
if 'gamma' not in kwargs:
should_grid_search = True
param_grid['gamma'] = [1e-5, 1e-4, 1e-3, 0.01, 0.1, 0.25]
if 'kernel' not in kwargs:
should_grid_search = True
param_grid['kernel'] = ['rbf', 'linear', 'poly']
if 'nu' not in kwargs:
should_grid_search = True
param_grid['nu'] = [0.001, 0.1, 0.25, 0.5, 1]
if should_grid_search:
param_grid['degree'] = [2]
logger.info(f'Performing grid search. Using param_grid: {param_grid} '
f'This may take a while...')
gs = GridSearchCV(estimator=mdl, param_grid=param_grid)
gs.fit(x_train, y_train)
mdl = gs.best_estimator_
mdl.fit(x_train, y_train)
return mdl, get_model_params(model_name='nusvr', model=mdl)
