from sklearn.datasets import make_regression
import pickle
from sklearn.manifold.t_sne import trustworthiness
regression_models = dict(LinearRegression=cuml.LinearRegression(),
Lasso=cuml.Lasso(),
Ridge=cuml.Ridge(),
ElasticNet=cuml.ElasticNet())
solver_models = dict(CD=cuml.CD(), SGD=cuml.SGD(eta0=0.005))
cluster_models = dict(KMeans=cuml.KMeans())
decomposition_models = dict(
PCA=cuml.PCA(),
TruncatedSVD=cuml.TruncatedSVD(),
)
decomposition_models_xfail = dict(
GaussianRandomProjection=cuml.GaussianRandomProjection(),
SparseRandomProjection=cuml.SparseRandomProjection())
neighbor_models = dict(NearestNeighbors=cuml.NearestNeighbors())
dbscan_model = dict(DBSCAN=cuml.DBSCAN())
umap_model = dict(UMAP=cuml.UMAP())
def unit_param(*args, **kwargs):
return pytest.param(*args, **kwargs, marks=pytest.mark.unit)
def custom_TruncatedSVD(*args, **kwargs):
if 'algorithm' in kwargs and kwargs['algorithm'] == 'arpack':
kwargs['algorithm'] = 'full'
return cuml.TruncatedSVD(*args, **kwargs)
lambda fit_intercept=True: cuml.Ridge(fit_intercept=fit_intercept),
"ElasticNet":
lambda fit_intercept=True: cuml.ElasticNet(fit_intercept=fit_intercept)
}
solver_models = {
"CD": lambda: cuml.CD(),
"SGD": lambda: cuml.SGD(eta0=0.005),
"QN": lambda: cuml.QN(loss="softmax")
}
cluster_models = {"KMeans": lambda: cuml.KMeans()}
decomposition_models = {
"PCA": lambda: cuml.PCA(),
"TruncatedSVD": lambda: cuml.TruncatedSVD(),
}
decomposition_models_xfail = {
"GaussianRandomProjection": lambda: cuml.GaussianRandomProjection(),
"SparseRandomProjection": lambda: cuml.SparseRandomProjection()
}
neighbor_models = {"NearestNeighbors": lambda: cuml.NearestNeighbors()}
dbscan_model = {"DBSCAN": lambda: cuml.DBSCAN()}
umap_model = {"UMAP": lambda: cuml.UMAP()}
rf_models = {
"rfc": lambda: cuml.RandomForestClassifier(),