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fix: rename _convert_X in TimesNet to avoid BaseClassifier collision #5

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Merged
TonyBagnall merged 5 commits into from
Mar 23, 2026
Merged

fix: rename _convert_X in TimesNet to avoid BaseClassifier collision #5

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30f5174
Fix issue in Timesnet
LinGinQiu Mar 23, 2026
401d4fc
Fix issue in Timesnet
LinGinQiu Mar 23, 2026
c3cd68d
Fix issue in Timesnet
LinGinQiu Mar 23, 2026
7f1dcaf
Fix issue in Timesnet
LinGinQiu Mar 23, 2026
f02b12c
Fix issue in Timesnet
LinGinQiu Mar 23, 2026
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7 changes: 4 additions & 3 deletions multiverse/classification/_timesnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,8 @@ def _resolve_device(self) -> torch.device:
return torch.device(self.device)
return torch.device("cuda" if torch.cuda.is_available() else "cpu")

def _convert_X(self, X: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
@staticmethod
def _preprocess_X(X: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
"""
Convert aeon numpy3D input to TimesNet layout and create a mask.

Expand Down Expand Up @@ -296,7 +297,7 @@ def _fit(self, X: np.ndarray, y):
"X.shape[2] to match seq_len."
)

x_t, mask = self._convert_X(X)
x_t, mask = self._preprocess_X(X)

if self.standardise:
self.scaler_ = _StandardisePerChannel().fit(x_t)
Expand Down Expand Up @@ -432,7 +433,7 @@ def _predict_proba(self, X: np.ndarray) -> np.ndarray:
f"seq_len={self.seq_len_}."
)

x_t, mask = self._convert_X(X)
x_t, mask = self._preprocess_X(X)

if self.scaler_ is not None:
x_t = self.scaler_.transform(x_t)
Expand Down
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