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Fix Issue #2577 - Zero Division Error in diagnostics.performance_metrics() causing failed assertion #2578

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merged 4 commits into from
May 18, 2024
Merged

Fix Issue #2577 - Zero Division Error in diagnostics.performance_metrics() causing failed assertion #2578

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03d5bfa
Add fix for zero division in performance_metrics().
ThomasChia May 1, 2024
fb07236
Update metrics test to handle zero values.
ThomasChia May 4, 2024
c860bbf
Add back skip MAPE test.
ThomasChia May 18, 2024
dfe529a
Add test calls for skip MAPE test.
ThomasChia May 18, 2024
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1 change: 1 addition & 0 deletions python/prophet/diagnostics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -630,6 +630,7 @@ def smape(df, w):
Dataframe with columns horizon and smape.
"""
sape = np.abs(df['y'] - df['yhat']) / ((np.abs(df['y']) + np.abs(df['yhat'])) / 2)
sape = sape.fillna(0)
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are there tests covering these methods?

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There was a test looking at the mape metric underneath the wider test_performance_metrics() method, so I have extended that to test for zero values across all other metrics.

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Great nice one

if w < 0:
return pd.DataFrame({'horizon': df['horizon'], 'smape': sape})
return rolling_mean_by_h(
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8 changes: 4 additions & 4 deletions python/prophet/tests/test_diagnostics.py
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Expand Up @@ -234,13 +234,13 @@ def test_performance_metrics(self, ts_short, backend):
metrics=["coverage", "mse"],
)
assert set(df_horizon.columns) == {"coverage", "mse", "horizon"}
# Skip MAPE
df_cv.loc[0, "y"] = 0.0
# Handle zero y and yhat and skip MAPE
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You can leave the MAPE skip test as-is and do the test for 0.0 y and 0.0 yhat afterwards.

I guess mdape doesn't have the same problem because it uses the rolling_median function?

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Updated!

Yep, rolling_median doesn't get rid of the np.nan values, they're still included with the groupby.

df_cv["y"] = 0.0
df_cv["yhat"] = 0.0
df_horizon = diagnostics.performance_metrics(
df_cv,
metrics=["coverage", "mape"],
)
assert set(df_horizon.columns) == {"coverage", "horizon"}
assert set(df_horizon.columns) == {"coverage", "horizon", "mae", "mdape", "mse", "rmse", "smape"}
df_horizon = diagnostics.performance_metrics(
df_cv,
metrics=["mape"],
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