def test_load_parquet():
mean_absolute_error = 85.94534216005789
mean_squared_error = 11474.89611670205
root_mean_squared_error = 107.12094154133472
regmet = RegressionMetrics()
df = pd.read_parquet(
os.path.join(
os.path.join(TEST_DATA_PATH, "metrics", "2021-02-12.parquet")))
regmet.add(df["predictions"].to_list(), df["targets"].to_list())
assert regmet.count == len(df["predictions"].to_list())
assert regmet.mean_squared_error() == pytest.approx(
mean_squared_error, 0.01)
assert regmet.mean_absolute_error() == pytest.approx(
mean_absolute_error, 0.01)
assert regmet.root_mean_squared_error() == pytest.approx(
root_mean_squared_error, 0.01)
msg = regmet.to_protobuf()
new_regmet = RegressionMetrics.from_protobuf(msg)
assert regmet.count == new_regmet.count
assert regmet.mean_squared_error() == new_regmet.mean_squared_error()
assert regmet.root_mean_squared_error(
) == new_regmet.root_mean_squared_error()
assert regmet.mean_absolute_error() == new_regmet.mean_absolute_error()
def from_protobuf(
cls,
message,
):
return ModelMetrics(
confusion_matrix=ConfusionMatrix.from_protobuf(
message.scoreMatrix),
regression_metrics=RegressionMetrics.from_protobuf(
message.regressionMetrics),
model_type=message.modelType,
)
def test_empty_protobuf_should_return_none():
empty_message = RegressionMetricsMessage()
assert RegressionMetrics.from_protobuf(empty_message) is None