def train_cuml_regressor(data, targets, depth=25, trees=100):
"""Train cuML regression model"""
model = cuRFR(
max_depth=depth, n_estimators=trees, random_state=0
)
return model.fit(data, targets)
def _construct_rf(
n_estimators,
random_state,
**kwargs
):
return cuRFR(
n_estimators=n_estimators,
random_state=random_state,
**kwargs)
def _func_build_rf(
n,
n_estimators,
max_depth,
handle,
max_features,
n_bins,
split_algo,
split_criterion,
bootstrap,
bootstrap_features,
verbose,
min_rows_per_node,
rows_sample,
max_leaves,
n_streams,
accuracy_metric,
quantile_per_tree,
r,
):
return cuRFR(
n_estimators=n_estimators,
max_depth=max_depth,
handle=handle,
max_features=max_features,
n_bins=n_bins,
split_algo=split_algo,
split_criterion=split_criterion,
bootstrap=bootstrap,
bootstrap_features=bootstrap_features,
verbose=verbose,
min_rows_per_node=min_rows_per_node,
rows_sample=rows_sample,
max_leaves=max_leaves,
n_streams=n_streams,
accuracy_metric=accuracy_metric,
quantile_per_tree=quantile_per_tree,
)
def _construct_rf(n_estimators, seed, **kwargs):
return cuRFR(n_estimators=n_estimators, seed=seed, **kwargs)