from functools import partial
from etna.metrics.base import Metric
from etna.metrics.base import MetricAggregationMode
from etna.metrics.functional_metrics import mae
from etna.metrics.functional_metrics import mape
from etna.metrics.functional_metrics import max_deviation
from etna.metrics.functional_metrics import medae
from etna.metrics.functional_metrics import mse
from etna.metrics.functional_metrics import msle
from etna.metrics.functional_metrics import r2_score
from etna.metrics.functional_metrics import rmse
from etna.metrics.functional_metrics import sign
from etna.metrics.functional_metrics import smape
from etna.metrics.functional_metrics import wape
[docs]class MAE(Metric):
"""Mean absolute error metric with multi-segment computation support.
.. math::
MAE(y\_true, y\_pred) = \\frac{\\sum_{i=0}^{n-1}{\\mid y\_true_i - y\_pred_i \\mid}}{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
"""
mae_per_output = partial(mae, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=mae_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class MSE(Metric):
"""Mean squared error metric with multi-segment computation support.
.. math::
MSE(y\_true, y\_pred) = \\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
"""
mse_per_output = partial(mse, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=mse_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]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
"""
rmse_per_output = partial(rmse, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=rmse_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class R2(Metric):
"""Coefficient of determination metric with multi-segment computation support.
.. math::
R^2(y\_true, y\_pred) = 1 - \\frac{\\sum_{i=0}^{n-1}{(y\_true_i - y\_pred_i)^2}}{\\sum_{i=0}^{n-1}{(y\_true_i - \\overline{y\_true})^2}}
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
"""
r2_per_output = partial(r2_score, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=r2_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return True
[docs]class MAPE(Metric):
"""Mean absolute percentage error metric with multi-segment computation support.
.. math::
MAPE(y\_true, y\_pred) = \\frac{1}{n}\\cdot\\frac{\\sum_{i=0}^{n-1}{\\mid y\_true_i - y\_pred_i\\mid}}{\\mid y\_true_i \\mid + \epsilon}
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
"""
mape_per_output = partial(mape, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=mape_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class SMAPE(Metric):
"""Symmetric mean absolute percentage error metric with multi-segment computation support.
.. math::
SMAPE(y\_true, y\_pred) = \\frac{2 \\cdot 100 \\%}{n}\\cdot\\frac{\\sum_{i=0}^{n-1}{\\mid y\_true_i - y\_pred_i\\mid}}{\\mid y\_true_i \\mid + \\mid y\_pred_i \\mid}
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
"""
smape_per_output = partial(smape, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=smape_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class MedAE(Metric):
"""Median absolute error metric with multi-segment computation support.
.. math::
MedAE(y\_true, y\_pred) = median(\\mid y\_true_1 - y\_pred_1 \\mid, \\cdots, \\mid y\_true_n - y\_pred_n \\mid)
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
"""
medae_per_output = partial(medae, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=medae_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class MSLE(Metric):
"""Mean squared logarithmic error metric with multi-segment computation support.
.. math::
MSLE(y\_true, y\_pred) = \\frac{1}{n}\\cdot\\sum_{i=0}^{n - 1}{(ln(1 + y\_true_i) - ln(1 + y\_pred_i))^2}
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
"""
msle_per_output = partial(msle, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=msle_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class Sign(Metric):
"""Sign error metric with multi-segment computation support.
.. math::
Sign(y\_true, y\_pred) = \\frac{1}{n}\\cdot\\sum_{i=0}^{n - 1}{sign(y\_true_i - y\_pred_i)}
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
"""
sign_per_output = partial(sign, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=sign_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> None:
"""Whether higher metric value is better."""
return None
[docs]class MaxDeviation(Metric):
"""Max Deviation metric with multi-segment computation support (maximum deviation value of cumulative sums).
.. math::
MaxDeviation(y\_true, y\_pred) = \\max_{j} | y_j |, where \\, y_j = \\sum_{i=1}^{j}{y\_pred_i - y\_true_i}
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
"""
max_deviation_per_output = partial(max_deviation, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=max_deviation_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
[docs]class WAPE(Metric):
"""Weighted average percentage Error metric with multi-segment computation support.
.. math::
WAPE(y\_true, y\_pred) = \\frac{\\sum_{i=0}^{n} |y\_true_i - y\_pred_i|}{\\sum_{i=0}^{n}|y\\_true_i|}
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
"""
wape_per_output = partial(wape, multioutput="raw_values")
super().__init__(mode=mode, metric_fn=wape_per_output, metric_fn_signature="matrix_to_array", **kwargs)
@property
def greater_is_better(self) -> bool:
"""Whether higher metric value is better."""
return False
__all__ = ["MAE", "MSE", "RMSE", "R2", "MSLE", "MAPE", "SMAPE", "MedAE", "Sign", "MaxDeviation", "WAPE"]