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Add RMSE as cross validation metric

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This commit is contained in:
Ben Letham 2018-05-04 10:24:15 -07:00
parent 9a5752f019
commit 7179ae3a38
2 changed files with 10 additions and 3 deletions
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11
python/fbprophet/diagnostics.py
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@ -202,6 +202,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.05):
Computes a suite of performance metrics on the output of cross-validation. Computes a suite of performance metrics on the output of cross-validation.
By default the following metrics are included: By default the following metrics are included:
'mse': mean squared error 'mse': mean squared error
'rmse': root mean squared error
'mae': mean absolute error 'mae': mean absolute error
'mape': mean percent error 'mape': mean percent error
'coverage': coverage of the upper and lower intervals 'coverage': coverage of the upper and lower intervals
@ -226,7 +227,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.05):
---------- ----------
df: The dataframe returned by cross_validation. df: The dataframe returned by cross_validation.
metrics: A list of performance metrics to compute. If not provided, will metrics: A list of performance metrics to compute. If not provided, will
use ['mse', 'mae', 'mape', 'coverage']. use ['mse', 'mae', 'mape', 'coverage', 'rmse'].
rolling_window: Proportion of data to use in each rolling window for rolling_window: Proportion of data to use in each rolling window for
computing the metrics. computing the metrics.
@ -234,7 +235,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.05):
------- -------
Dataframe with a column for each metric, and column 'horizon' Dataframe with a column for each metric, and column 'horizon'
""" """
valid_metrics = ['mse', 'mae', 'mape', 'coverage'] valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'coverage']
if metrics is None: if metrics is None:
metrics = valid_metrics metrics = valid_metrics
if len(set(metrics)) != len(metrics): if len(set(metrics)) != len(metrics):
@ -277,6 +278,12 @@ def mse(df, w):
return rolling_mean(se.values, w) return rolling_mean(se.values, w)
def rmse(df, w):
"""Root mean squared error
"""
return np.sqrt(mse(df, w))
def mae(df, w): def mae(df, w):
"""Mean absolute error """Mean absolute error
""" """

2
python/fbprophet/tests/test_diagnostics.py
View file

@ -145,7 +145,7 @@ class TestDiagnostics(TestCase):
df_none = diagnostics.performance_metrics(df_cv, rolling_window=0) df_none = diagnostics.performance_metrics(df_cv, rolling_window=0)
self.assertEqual( self.assertEqual(
set(df_none.columns), set(df_none.columns),
{'horizon', 'coverage', 'mae', 'mape', 'mse'}, {'horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'},
) )
self.assertEqual(df_none.shape[0], 14) self.assertEqual(df_none.shape[0], 14)
# Aggregation level 0.2 # Aggregation level 0.2