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Sklearn covtype fixes #26

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Sklearn covtype fixes #26

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584ee19
Launching sklearn RF fit with multi-thread support
vinaydes May 27, 2021
f879c7b
Updating covtype dataset preprocessing to work with cuML
vinaydes May 27, 2021
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3 changes: 2 additions & 1 deletion algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ def __exit__(self, exc_type, exc_value, traceback):

# learning parameters shared by all algorithms, using the xgboost convention
shared_params = {"max_depth": 8, "learning_rate": 0.1,
"reg_lambda": 1}
"reg_lambda": 1, "max_samples" : 0.5}

class CumlRfAlgorithm(Algorithm):
def configure(self, data, args):
Expand Down Expand Up @@ -222,6 +222,7 @@ def configure(self, data, args):
del params["learning_rate"]
params["n_estimators"] = args.ntrees
params.update(args.extra)
params.update({"n_jobs" : -1})
return params

def fit(self, data, args):
Expand Down
4 changes: 4 additions & 0 deletions datasets.py
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Expand Up @@ -295,7 +295,11 @@ def prepare_covtype(dataset_folder, nrows): # pylint: disable=unused-argument
if nrows is not None:
X = X[0:nrows]
y = y[0:nrows]
# Labele range in covtype start from 1, making it start from 0
y = y - 1

X = np.float32(X)
y = np.int32(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=77,
test_size=0.2,
)
Expand Down