def test_gp_std(get_kernel): from uncoverml.optimise.models import TransformedGPRegressor np.random.seed(10) sklearn_gp = TransformedGPRegressor(kernel=get_kernel(length_scale=1)) sklearn_gp.fit(X=1 + np.random.rand(10, 3), y=1 + np.random.rand(10)) p, v, uq, lq = sklearn_gp.predict_dist(X=1 + np.random.rand(5, 3))
import uncoverml as ls import uncoverml.config import uncoverml.mllog from uncoverml.config import ConfigException from uncoverml.optimise.models import (TransformedGPRegressor, kernels, TransformedSVR) from uncoverml.scripts.uncoverml import _load_data from uncoverml.transforms import target as transforms from uncoverml.optimise.models import transformed_modelmaps as all_modelmaps log = logging.getLogger(__name__) pca = decomposition.PCA() algos = {k: v(ml_score=True) for k, v in all_modelmaps.items()} algos['transformedgp'] = TransformedGPRegressor(n_restarts_optimizer=10, normalize_y=True, ml_score=True) algos['transformedsvr'] = TransformedSVR(verbose=True, max_iter=1000000, ml_score=True) def setup_pipeline(config): if config.optimisation['algorithm'] not in algos: raise ConfigException('optimisation algo must exist in algos dict') steps = [] param_dict = {} if 'featuretransforms' in config.optimisation: config.featuretransform = config.optimisation['featuretransforms'] if 'pca' in config.featuretransform: steps.append(('pca', pca))