add RMSE metric & rmse functional metric

CHANGELOG.md

@@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 ### Added
--
+- `RMSE` metric & `rmse` functional metric
 - 
 -
 -

etna/metrics/__init__.py

@@ -7,6 +7,7 @@
 from etna.metrics.base import Metric
 from etna.metrics.base import MetricAggregationMode
 from etna.metrics.functional_metrics import mape
+from etna.metrics.functional_metrics import rmse
 from etna.metrics.functional_metrics import sign
 from etna.metrics.functional_metrics import smape
 from etna.metrics.intervals_metrics import Coverage
@@ -16,6 +17,7 @@
 from etna.metrics.metrics import MSE
 from etna.metrics.metrics import MSLE
 from etna.metrics.metrics import R2
+from etna.metrics.metrics import RMSE
 from etna.metrics.metrics import SMAPE
 from etna.metrics.metrics import MedAE
 from etna.metrics.metrics import Sign

etna/metrics/functional_metrics.py

@@ -2,6 +2,7 @@
 from typing import Union
 
 import numpy as np
+from sklearn.metrics import mean_squared_error
 
 ArrayLike = List[Union[float, List[float]]]
 
@@ -112,3 +113,34 @@ def sign(y_true: ArrayLike, y_pred: ArrayLike) -> float:
         raise ValueError("Shapes of the labels must be the same")
 
     return np.mean(np.sign(y_true_array - y_pred_array))
+
+
+def rmse(y_true: ArrayLike, y_pred: ArrayLike) -> float:
+    """Root mean squared error metric.
+
+    .. math::
+        RMSE(y\_true, y\_pred) = \\sqrt\\frac{\\sum_{i=0}^{n-1}{(y\_true_i - y\_pred_i)^2}}{n}
+
+    Parameters
+    ----------
+    y_true:
+        array-like of shape (n_samples,) or (n_samples, n_outputs)
+
+        Ground truth (correct) target values.
+
+    y_pred:
+        array-like of shape (n_samples,) or (n_samples, n_outputs)
+
+        Estimated target values.
+
+    Returns
+    -------
+    float
+        A floating point value (the best value is 0.0).
+    """
+    y_true_array, y_pred_array = np.asarray(y_true), np.asarray(y_pred)
+
+    if len(y_true_array.shape) != len(y_pred_array.shape):
+        raise ValueError("Shapes of the labels must be the same")
+
+    return mean_squared_error(y_true=y_true_array, y_pred=y_pred_array, squared=False)

etna/metrics/metrics.py

@@ -4,6 +4,7 @@
 from etna.metrics import mse
 from etna.metrics import msle
 from etna.metrics import r2_score
+from etna.metrics import rmse
 from etna.metrics import sign
 from etna.metrics import smape
 from etna.metrics.base import Metric
@@ -68,6 +69,35 @@ def greater_is_better(self) -> bool:
         return False
 
 
+class RMSE(Metric):
+    """Root mean squared error metric with multi-segment computation support.
+
+    .. math::
+        RMSE(y\_true, y\_pred) = \\sqrt\\frac{\\sum_{i=0}^{n-1}{(y\_true_i - y\_pred_i)^2}}{n}
+
+    Notes
+    -----
+    You can read more about logic of multi-segment metrics in Metric docs.
+    """
+
+    def __init__(self, mode: str = MetricAggregationMode.per_segment, **kwargs):
+        """Init metric.
+
+        Parameters
+        ----------
+        mode: 'macro' or 'per-segment'
+            metrics aggregation mode
+        kwargs:
+            metric's computation arguments
+        """
+        super().__init__(mode=mode, metric_fn=rmse, **kwargs)
+
+    @property
+    def greater_is_better(self) -> bool:
+        """Whether higher metric value is better."""
+        return False
+
+
 class R2(Metric):
     """Coefficient of determination metric with multi-segment computation support.
 
@@ -242,4 +272,4 @@ def greater_is_better(self) -> None:
         return None
 
 
-__all__ = ["MAE", "MSE", "R2", "MSLE", "MAPE", "SMAPE", "MedAE", "Sign"]
+__all__ = ["MAE", "MSE", "RMSE", "R2", "MSLE", "MAPE", "SMAPE", "MedAE", "Sign"]

tests/test_metrics/test_functional_metrics.py

@@ -6,6 +6,7 @@
 from etna.metrics import mse
 from etna.metrics import msle
 from etna.metrics import r2_score
+from etna.metrics import rmse
 from etna.metrics import sign
 from etna.metrics import smape
 
@@ -27,7 +28,7 @@ def y_pred_1d():
 
 @pytest.mark.parametrize(
     "metric, right_metrics_value",
-    ((mae, 1), (mse, 1), (mape, 100), (smape, 66.6666666667), (medae, 1), (r2_score, 0), (sign, -1)),
+    ((mae, 1), (mse, 1), (rmse, 1), (mape, 100), (smape, 66.6666666667), (medae, 1), (r2_score, 0), (sign, -1)),
 )
 def test_all_1d_metrics(metric, right_metrics_value, y_true_1d, y_pred_1d):
     assert round(metric(y_true_1d, y_pred_1d), 10) == right_metrics_value
@@ -52,7 +53,7 @@ def y_pred_2d():
 
 @pytest.mark.parametrize(
     "metric, right_metrics_value",
-    ((mae, 1), (mse, 1), (mape, 100), (smape, 66.6666666667), (medae, 1), (r2_score, 0.0), (sign, -1)),
+    ((mae, 1), (mse, 1), (rmse, 1), (mape, 100), (smape, 66.6666666667), (medae, 1), (r2_score, 0.0), (sign, -1)),
 )
 def test_all_2d_metrics(metric, right_metrics_value, y_true_2d, y_pred_2d):
     assert round(metric(y_true_2d, y_pred_2d), 10) == right_metrics_value

tests/test_metrics/test_metrics.py

@@ -8,6 +8,7 @@
 from etna.metrics import mse
 from etna.metrics import msle
 from etna.metrics import r2_score
+from etna.metrics import rmse
 from etna.metrics import sign
 from etna.metrics import smape
 from etna.metrics.base import MetricAggregationMode
@@ -16,6 +17,7 @@
 from etna.metrics.metrics import MSE
 from etna.metrics.metrics import MSLE
 from etna.metrics.metrics import R2
+from etna.metrics.metrics import RMSE
 from etna.metrics.metrics import SMAPE
 from etna.metrics.metrics import MedAE
 from etna.metrics.metrics import Sign
@@ -28,6 +30,7 @@
     (
         (MAE, "MAE", {}, ""),
         (MSE, "MSE", {}, ""),
+        (RMSE, "RMSE", {}, ""),
         (MedAE, "MedAE", {}, ""),
         (MSLE, "MSLE", {}, ""),
         (MAPE, "MAPE", {}, ""),
@@ -50,7 +53,7 @@ def test_repr(metric_class, metric_class_repr, metric_params, param_repr):
 
 @pytest.mark.parametrize(
     "metric_class",
-    (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign),
+    (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign),
 )
 def test_name_class_name(metric_class):
     """Check metrics name property without changing its during inheritance"""
@@ -74,7 +77,7 @@ def test_name_repr(metric_class):
     assert metric_name == true_name
 
 
-@pytest.mark.parametrize("metric_class", (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign))
+@pytest.mark.parametrize("metric_class", (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign))
 def test_metrics_macro(metric_class, train_test_dfs):
     """Check metrics interface in 'macro' mode"""
     forecast_df, true_df = train_test_dfs
@@ -83,7 +86,7 @@ def test_metrics_macro(metric_class, train_test_dfs):
     assert isinstance(value, float)
 
 
-@pytest.mark.parametrize("metric_class", (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
+@pytest.mark.parametrize("metric_class", (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
 def test_metrics_per_segment(metric_class, train_test_dfs):
     """Check metrics interface in 'per-segment' mode"""
     forecast_df, true_df = train_test_dfs
@@ -94,14 +97,14 @@ def test_metrics_per_segment(metric_class, train_test_dfs):
         assert segment in value
 
 
-@pytest.mark.parametrize("metric_class", (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
+@pytest.mark.parametrize("metric_class", (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
 def test_metrics_invalid_aggregation(metric_class):
     """Check metrics behavior in case of invalid aggregation mode"""
     with pytest.raises(NotImplementedError):
         _ = metric_class(mode="a")
 
 
-@pytest.mark.parametrize("metric_class", (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
+@pytest.mark.parametrize("metric_class", (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
 def test_invalid_timestamps(metric_class, two_dfs_with_different_timestamps):
     """Check metrics behavior in case of invalid timeranges"""
     forecast_df, true_df = two_dfs_with_different_timestamps
@@ -110,7 +113,7 @@ def test_invalid_timestamps(metric_class, two_dfs_with_different_timestamps):
         _ = metric(y_true=true_df, y_pred=forecast_df)
 
 
-@pytest.mark.parametrize("metric_class", (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
+@pytest.mark.parametrize("metric_class", (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
 def test_invalid_segments(metric_class, two_dfs_with_different_segments_sets):
     """Check metrics behavior in case of invalid segments sets"""
     forecast_df, true_df = two_dfs_with_different_segments_sets
@@ -119,7 +122,7 @@ def test_invalid_segments(metric_class, two_dfs_with_different_segments_sets):
         _ = metric(y_true=true_df, y_pred=forecast_df)
 
 
-@pytest.mark.parametrize("metric_class", (MAE, MSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
+@pytest.mark.parametrize("metric_class", (MAE, MSE, RMSE, MedAE, MSLE, MAPE, SMAPE, R2, Sign, DummyMetric))
 def test_invalid_segments_target(metric_class, train_test_dfs):
     """Check metrics behavior in case of no target column in segment"""
     forecast_df, true_df = train_test_dfs
@@ -134,6 +137,7 @@ def test_invalid_segments_target(metric_class, train_test_dfs):
     (
         (MAE, mae),
         (MSE, mse),
+        (RMSE, rmse),
         (MedAE, medae),
         (MSLE, msle),
         (MAPE, mape),
@@ -164,6 +168,7 @@ def test_metrics_values(metric_class, metric_fn, train_test_dfs):
     (
         (MAE(), False),
         (MSE(), False),
+        (RMSE(), False),
         (MedAE(), False),
         (MSLE(), False),
         (MAPE(), False),