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Update performance_metrics() in diagnostics.py (#1710)

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* Update performance_metrics() in diagnostics.py

Include smape as a valid metric.
Add support for monthly horizons.

* Update performance_metrics() docstring

* Update performance_metrics() docstring

* Include smape in test_diagnostics.py

* sMAPE code formatting improvement
This commit is contained in:
Pawel Kranzberg 2020-11-07 06:29:32 +01:00 committed by GitHub
parent 0a33f381ba
commit 97bb057de4
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2 changed files with 14 additions and 7 deletions
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19
python/fbprophet/diagnostics.py
View file

@ -301,7 +301,7 @@ def prophet_copy(m, cutoff=None):
return m2
def performance_metrics(df, metrics=None, rolling_window=0.1):
def performance_metrics(df, metrics=None, rolling_window=0.1, monthly=False):
"""Compute performance metrics from cross-validation results.
Computes a suite of performance metrics on the output of cross-validation.
@ -311,6 +311,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
'mae': mean absolute error
'mape': mean absolute percent error
'mdape': median absolute percent error
'smape': symmetric mean absolute percentage error
'coverage': coverage of the upper and lower intervals
A subset of these can be specified by passing a list of names as the
@ -337,15 +338,17 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
----------
df: The dataframe returned by cross_validation.
metrics: A list of performance metrics to compute. If not provided, will
use ['mse', 'rmse', 'mae', 'mape', 'mdape', 'coverage'].
use ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'].
rolling_window: Proportion of data to use in each rolling window for
computing the metrics. Should be in [0, 1] to average
computing the metrics. Should be in [0, 1] to average.
monthly: monthly=True will compute horizons as numbers of calendar months
from the cutoff date, starting from 0 for the cutoff month.
Returns
-------
Dataframe with a column for each metric, and column 'horizon'
"""
valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'mdape', 'coverage']
valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage']
if metrics is None:
metrics = valid_metrics
if ('yhat_lower' not in df or 'yhat_upper' not in df) and ('coverage' in metrics):
@ -357,7 +360,10 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
'Valid values for metrics are: {}'.format(valid_metrics)
)
df_m = df.copy()
df_m['horizon'] = df_m['ds'] - df_m['cutoff']
if monthly:
df_m['horizon'] = df_m['ds'].dt.to_period('M').astype(int) - df_m['cutoff'].dt.to_period('M').astype(int)
else:
df_m['horizon'] = df_m['ds'] - df_m['cutoff']
df_m.sort_values('horizon', inplace=True)
if 'mape' in metrics and df_m['y'].abs().min() < 1e-8:
logger.info('Skipping MAPE because y close to 0')
@ -590,6 +596,7 @@ def mdape(df, w):
def smape(df, w):
"""Symmetric mean absolute percentage error
based on Chen and Yang (2004) formula
Parameters
----------
@ -600,7 +607,7 @@ def smape(df, w):
-------
Dataframe with columns horizon and smape.
"""
sape = np.abs(df['yhat']-df['y']) / ((np.abs(df['y']) + np.abs(df['yhat'])) /2)
sape = np.abs(df['y'] - df['yhat']) / ((np.abs(df['y']) + np.abs(df['yhat'])) / 2)
if w < 0:
return pd.DataFrame({'horizon': df['horizon'], 'smape': sape})
return rolling_mean_by_h(

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

@ -190,7 +190,7 @@ class TestDiagnostics(TestCase):
df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)
self.assertEqual(
set(df_none.columns),
{'horizon', 'coverage', 'mae', 'mape', 'mdape', 'mse', 'rmse'},
{'horizon', 'coverage', 'mae', 'mape', 'mdape', 'mse', 'rmse', 'smape'},
)
self.assertEqual(df_none.shape[0], 16)
# Aggregation level 0