def test_convert_input_dtype(from_dtype, to_dtype, input_type, num_rows,
num_cols, order):
if from_dtype == np.float16 and input_type != 'numpy':
pytest.xfail("float16 not yet supported by numba/cuDF.from_gpu_matrix")
if from_dtype in [np.uint8, np.uint16, np.uint32, np.uint64]:
if input_type == 'cudf':
pytest.xfail("unsigned int types not yet supported by \
cuDF")
elif not has_cupy():
pytest.xfail("unsigned int types not yet supported by \
cuDF and cuPy is not installed.")
input_data, real_data = get_input(input_type,
num_rows,
num_cols,
from_dtype,
out_dtype=to_dtype,
order=order)
if input_type == 'cupy' and input_data is None:
pytest.skip('cupy not installed')
converted_data = convert_dtype(input_data, to_dtype=to_dtype)
if input_type == 'numpy':
np.testing.assert_equal(converted_data, real_data)
elif input_type == 'cudf':
np.testing.assert_equal(converted_data.as_matrix(), real_data)
elif input_type == 'pandas':
np.testing.assert_equal(converted_data.to_numpy(), real_data)
else:
np.testing.assert_equal(converted_data.copy_to_host(), real_data)
def _treelite_fil_accuracy_score(y_true, y_pred):
"""Function to get correct accuracy for FIL (returns class index)"""
# convert the input if necessary
y_pred1 = (y_pred.copy_to_host()
if cuda.devicearray.is_cuda_ndarray(y_pred) else y_pred)
y_true1 = (y_true.copy_to_host()
if cuda.devicearray.is_cuda_ndarray(y_true) else y_true)
y_pred_binary = input_utils.convert_dtype(y_pred1 > 0.5, np.int32)
return cuml.metrics.accuracy_score(y_true1, y_pred_binary)
def _treelite_fil_accuracy_score(y_true, y_pred):
"""Function to get correct accuracy for FIL (returns class index)"""
y_pred_binary = input_utils.convert_dtype(y_pred > 0.5, np.int32)
if isinstance(y_true, np.ndarray):
return cuml.metrics.accuracy_score(y_true, y_pred_binary)
elif cuda.devicearray.is_cuda_ndarray(y_true):
y_true_np = y_true.copy_to_host()
return cuml.metrics.accuracy_score(y_true_np, y_pred_binary)
elif isinstance(y_true, cudf.Series):
return cuml.metrics.accuracy_score(y_true, y_pred_binary)
elif isinstance(y_true, pd.Series):
return cuml.metrics.accuracy_score(y_true, y_pred_binary)
else:
raise TypeError("Received unsupported input type")
