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Fix StringDtype vs object dtype mismatch in Data._safecast_df #4795

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Fix StringDtype vs object dtype mismatch in Data._safecast_df #4795

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b5cbb5a
Fix read-only numpy arrays in cross-validation (#4794)
mgarrard Jan 22, 2026
94da5d0
Fix StringDtype vs object dtype mismatch in Data._safecast_df (#4795)
mgarrard Jan 22, 2026
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9 changes: 5 additions & 4 deletions ax/adapter/cross_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -240,12 +240,13 @@ def _efficient_loo_cross_validate(
# This is a known limitation for fully Bayesian models and models with
# non-Gaussian posteriors (such as PFNs).
# Shape: n x 1 x m
loo_means = posterior.mixture_mean.detach().cpu().numpy()
loo_vars = posterior.mixture_variance.detach().cpu().numpy()
# Use .copy() to ensure arrays are writeable (numpy returns read-only views)
loo_means = posterior.mixture_mean.detach().cpu().numpy().copy()
loo_vars = posterior.mixture_variance.detach().cpu().numpy().copy()
else:
# Shape: n x 1 x m
loo_means = posterior.mean.detach().cpu().numpy()
loo_vars = posterior.variance.detach().cpu().numpy()
loo_means = posterior.mean.detach().cpu().numpy().copy()
loo_vars = posterior.variance.detach().cpu().numpy().copy()

# Squeeze out the q dimension: n x 1 x m -> n x m
loo_means = loo_means.squeeze(1)
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10 changes: 7 additions & 3 deletions ax/core/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,9 +195,13 @@ def _safecast_df(cls: type[TData], df: pd.DataFrame) -> pd.DataFrame:
if col in df.columns.values and coltype is not Any:
# Pandas timestamp handlng is weird
dtype = "datetime64[ns]" if coltype is pd.Timestamp else coltype
if (dtype != dtypes[col]) and not (
coltype is int and df.loc[:, col].isnull().any()
):
current_dtype = dtypes[col]
# Handle StringDtype -> object conversion (pandas 2.0+ compatibility)
needs_cast = (
isinstance(current_dtype, pd.StringDtype)
or (dtype != current_dtype)
) and not (coltype is int and df.loc[:, col].isnull().any())
if needs_cast:
df[col] = df[col].astype(dtype)
df.reset_index(inplace=True, drop=True)
return df
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