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ScalerBasis

Source: Distributed_Design_Optimizer/subsystem/tools/ScalerBasis.py

Base scaler implementation for normalizing values to a scaled range.

Classes

ScalerBasis

Inherits from: ScalerInterface

Base implementation for scalers that normalize values to a scaled range.

Provides common initialization, transform, inverse_transform, and bound-violation warning logic. Subclasses set the scaled bounds and display name, and may add extra validation.

Methods

init(self, lower_scaler_bound_unscaled: float, upper_scaler_bound_unscaled: float, lower_scaler_bound_scaled: float, upper_scaler_bound_scaled: float) → None

Initialize the scaler.

Args:

lower_scaler_bound_unscaled: The lower bound of the unscaled data range.
upper_scaler_bound_unscaled: The upper bound of the unscaled data range.
lower_scaler_bound_scaled: The lower bound of the scaled feature range.
upper_scaler_bound_scaled: The upper bound of the scaled feature range.

Raises:

ValueError: If lower_scaler_bound_unscaled is not strictly less than upper_scaler_bound_unscaled.

transform(self, value_unscaled: float) → float

Scale value_unscaled according to feature_range.

Args:

value_unscaled: The value to transform.

Returns:

The scaled value.

Warns: UserWarning: If the input value is outside the fitted range (once per bound).

inverse_transform(self, value_scaled: float) → float

Undo the scaling of value_scaled according to feature_range.

Args:

value_scaled: The scaled value to inverse transform.

Returns:

The original unscaled value.

Warns: UserWarning: If the input value is outside the feature_range (once per bound).

get_scale(self) → float

Return the constant affine scale factor d(scaled)/d(unscaled) of this scaler.

Since the transform is affine (scaled = unscaled * scale + offset), this factor is a constant equal to self._scale. Use it to map a derivative taken w.r.t. an unscaled quantity into scaled space, e.g. to convert d(response_unscaled) into d(response_scaled). The inverse mapping (into scaled design-variable space) is obtained at the call-site as 1.0 / get_scale().

Returns:

The derivative d(scaled)/d(unscaled).

get_bound_violation_warnings(self) → List[str]

Get warning messages for observed values outside the fitted range.

Returns:

List of warning message strings. Empty list if no violations.

get_bound_utilization_report(self) → tuple[str, bool, bool]

Get a compact one-line bound utilization summary for this scaler.

Returns:

A tuple of (summary_line, has_violation, has_conservative).
summary_line is a compact string. has_violation / has_conservative
indicate whether advisory footnotes should be printed.
Returns ('No values observed.', False, False) if nothing was seen.