def test_dataset_profile_metrics():
x1 = DatasetProfile(name="test", model_profile=ModelProfile())
assert x1.tags["name"] == "test"
targets_1 = ["cat", "dog", "pig"]
predictions_1 = ["cat", "dog", "dog"]
scores_1 = [0.1, 0.2, 0.4]
x1.track_metrics(predictions_1, targets_1, scores_1)
assert x1.model_profile.metrics.confusion_matrix.labels is not None
def test_track_metrics():
import pandas as pd
mean_absolute_error = 85.94534216005789
mean_squared_error = 11474.89611670205
root_mean_squared_error = 107.12094154133472
x1 = DatasetProfile(name="test")
df = pd.read_parquet(
os.path.join(
os.path.join(TEST_DATA_PATH, "metrics", "2021-02-12.parquet")))
x1.track_metrics(df["predictions"].to_list(), df["targets"].to_list())
regression_metrics = x1.model_profile.metrics.regression_metrics
assert regression_metrics is not None
assert regression_metrics.count == len(df["predictions"].to_list())
assert regression_metrics.mean_squared_error() == pytest.approx(
mean_squared_error, 0.01)
assert regression_metrics.mean_absolute_error() == pytest.approx(
mean_absolute_error, 0.01)
assert regression_metrics.root_mean_squared_error() == pytest.approx(
root_mean_squared_error, 0.01)