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Update vanilla.py #255

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36 changes: 36 additions & 0 deletions modnet/models/vanilla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1383,6 +1383,24 @@ class OR only return the most probable class.
if self._scale_impute is not None:
x = self._scale_impute.transform(x)

# Clip the scaled features to safe bounds
if self._scaler is not None:
if isinstance(self._scaler, MinMaxScaler):
# For MinMaxScaler, use 5x the feature range as bounds
lower_bound = 5.0 * self._scaler.feature_range[0]
upper_bound = 5.0 * self._scaler.feature_range[1]
elif isinstance(self._scaler, StandardScaler):
# For StandardScaler, use 10 standard deviations as bounds
lower_bound = -10.0
upper_bound = 10.0
else:
# For other scalers, use default wide bounds
lower_bound = -np.inf
upper_bound = np.inf

# Clip the features
x = np.clip(x, lower_bound, upper_bound)

p = np.array(self.model.predict(x))

if len(p.shape) == 2:
Expand Down Expand Up @@ -1445,6 +1463,24 @@ def evaluate(self, test_data: MODData) -> pd.DataFrame:
if self._scale_impute is not None:
x = self._scale_impute.transform(x)

# Clip the scaled features to safe bounds
if self._scaler is not None:
if isinstance(self._scaler, MinMaxScaler):
# For MinMaxScaler, use 5x the feature range as bounds
lower_bound = 5.0 * self._scaler.feature_range[0]
upper_bound = 5.0 * self._scaler.feature_range[1]
elif isinstance(self._scaler, StandardScaler):
# For StandardScaler, use 10 standard deviations as bounds
lower_bound = -10.0
upper_bound = 10.0
else:
# For other scalers, use default wide bounds
lower_bound = -np.inf
upper_bound = np.inf

# Clip the features
x = np.clip(x, lower_bound, upper_bound)

y_pred = np.array(self.model.predict(x))
if len(y_pred.shape) == 2:
y_pred = np.array([y_pred])
Expand Down