def my_test():
regmet = RegressionMetrics()
assert regmet.count == 0
assert regmet.sum_diff == 0.0
assert regmet.sum2_diff == 0.0
assert regmet.sum_abs_diff == 0.0
assert regmet.mean_squared_error() is None
assert regmet.mean_absolute_error() is None
assert regmet.root_mean_squared_error() is None
def compute_regression_metrics(
self,
predictions: List[Union[float, int]],
targets: List[Union[float, int]],
target_field: str = None,
prediction_field: str = None,
):
regression_metrics = RegressionMetrics(
target_field=target_field, prediction_field=prediction_field)
regression_metrics.add(predictions, targets)
if self.regression_metrics:
self.regression_metrics = self.regression_metrics.merge(
regression_metrics)
else:
self.regression_metrics = regression_metrics
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_merging():
regmet_sum = RegressionMetrics()
regmet = RegressionMetrics(prediction_field="predictions",
target_field="targets")
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())
regmet_sum.add(df["predictions"].to_list(), df["targets"].to_list())
regmet_2 = RegressionMetrics(prediction_field="predictions",
target_field="targets")
df_2 = pd.read_parquet(
os.path.join(
os.path.join(TEST_DATA_PATH, "metrics", "2021-02-13.parquet")))
regmet_2.add(df_2["predictions"].to_list(), df_2["targets"].to_list())
regmet_sum.add(df_2["predictions"].to_list(), df_2["targets"].to_list())
merged_reg_metr = regmet.merge(regmet_2)
assert merged_reg_metr.count == regmet_sum.count
assert merged_reg_metr.mean_squared_error() == pytest.approx(
regmet_sum.mean_squared_error(), 0.001)
assert merged_reg_metr.root_mean_squared_error() == pytest.approx(
regmet_sum.root_mean_squared_error(), 0.001)
assert merged_reg_metr.mean_absolute_error() == pytest.approx(
regmet_sum.mean_absolute_error(), 0.001)
def test_empty_protobuf_should_return_none():
empty_message = RegressionMetricsMessage()
assert RegressionMetrics.from_protobuf(empty_message) is None
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 test_model_metrics_init():
reg_met = RegressionMetrics()
conf_ma = ConfusionMatrix()
with pytest.raises(NotImplementedError):
metrics = ModelMetrics(confusion_matrix=conf_ma, regression_metrics=reg_met)