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Adding Desirability functions as objectives (#497)
* initial commit of numerical objectives. Needs to be adjusted, tested etc. * moved torch functions to torch_tools.py * removed torch dependencies from data-model * added validators and tests for desirability data-models * after hooks * added test for "get_objective_callable" * after hooks * added tutorial notebook desirability_objectives.ipynb * after hooks * added to AnyRealObjective * after hooks * changed validators to model validators * added type: Literals to objectives * after hooks * after hooks * debugged new validators * after hooks * fixed tests * after hooks * got rid of using desirability base class as actual usable class * - moved clip to abstract class - desirability base class inherits from _SeriesNumpyCallable - deleted pyright_output.txt * added abstractmehtod decorator * after hooks * changed data model specs for tests * changed bounds defs in specs to lists * debugged invalid specs definition * after hooks * moved helper class with __call__ method to abstract desirability class * after hooks
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,221 @@ | ||
| from abc import abstractmethod | ||
| from typing import Literal, Optional, Union | ||
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| import numpy as np | ||
| import pandas as pd | ||
| import pydantic | ||
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| from bofire.data_models.objectives.identity import IdentityObjective | ||
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| class DesirabilityObjective(IdentityObjective): | ||
| """Abstract class for desirability objectives. Works as Identity Objective""" | ||
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| type: Literal["DesirabilityObjective"] = "DesirabilityObjective" # type: ignore | ||
| clip: bool = True | ||
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| @pydantic.model_validator(mode="after") | ||
| def validate_clip(self): | ||
| if self.clip: | ||
| return self | ||
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| log_shapes = { | ||
| key: val | ||
| for (key, val) in self.__dict__.items() | ||
| if key.startswith("log_shape_factor") | ||
| } | ||
| for key, log_shape_ in log_shapes.items(): | ||
| if log_shape_ != 0: | ||
| raise ValueError( | ||
| f"Log shape factor {key} must be zero if clip is False." | ||
| ) | ||
| return self | ||
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| def __call__( | ||
| self, x: Union[pd.Series, np.ndarray], x_adapt | ||
| ) -> Union[pd.Series, np.ndarray]: | ||
| """Wrapper function for to call numpy and torch functions with series or numpy arrays. matches __call__ | ||
| signature of objectives.""" | ||
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| convert_to_series = False | ||
| if isinstance(x, pd.Series): | ||
| convert_to_series = True | ||
| name = x.name | ||
| x = x.values | ||
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| y = self.call_numpy(x) | ||
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| if convert_to_series: | ||
| return pd.Series(y, name=name) | ||
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| return y | ||
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| @abstractmethod | ||
| def call_numpy(self, x: np.ndarray) -> np.ndarray: | ||
| raise NotImplementedError() | ||
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| class IncreasingDesirabilityObjective(DesirabilityObjective): | ||
| """An objective returning a reward the scaled identity, but trimmed at the bounds: | ||
| d = ((x - lower_bound) / (upper_bound - lower_bound))^t | ||
| if clip is True, the reward is zero for x < lower_bound and one for x > upper_bound. | ||
| where: | ||
| t = exp(log_shape_factor) | ||
| Note, that with clipping the reward is always between zero and one. | ||
| Attributes: | ||
| clip (bool): Whether to clip the values below/above the lower/upper bound, by | ||
| default True. | ||
| log_shape_factor (float): Logarithm of the shape factor: | ||
| Whether the interpolation between the lower bound and the upper is linear (=0), | ||
| convex (>0) or concave (<0) , by default 0.0. | ||
| w (float): relative weight, by default = 1. | ||
| bounds (tuple[float]): lower and upper bound of the desirability. Below | ||
| bounds[0] the desirability is =0 (if clip=True) or <0 (if clip=False). Above | ||
| bounds[1] the desirability is =1 (if clip=True) or >1 (if clip=False). | ||
| Defaults to (0, 1). | ||
| """ | ||
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| type: Literal["IncreasingDesirabilityObjective"] = "IncreasingDesirabilityObjective" # type: ignore | ||
| log_shape_factor: float = 0.0 | ||
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| def call_numpy( | ||
| self, | ||
| x: np.ndarray, | ||
| x_adapt: Optional[Union[pd.Series, np.ndarray]] = None, | ||
| ) -> np.ndarray: | ||
| y = np.zeros(x.shape) | ||
| if self.clip: | ||
| y[x < self.lower_bound] = 0.0 | ||
| y[x > self.upper_bound] = 1.0 | ||
| between = (x >= self.lower_bound) & (x <= self.upper_bound) | ||
| else: | ||
| between = np.full(x.shape, True) | ||
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| t = np.exp(self.log_shape_factor) | ||
|
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| y[between] = np.power( | ||
| (x[between] - self.lower_bound) / (self.upper_bound - self.lower_bound), t | ||
| ) | ||
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| return y | ||
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| class DecreasingDesirabilityObjective(DesirabilityObjective): | ||
| """An objective returning a reward the negative, shifted scaled identity, but trimmed at the bounds: | ||
| d = ((upper_bound - x) / (upper_bound - lower_bound))^t | ||
| where: | ||
| t = exp(log_shape_factor) | ||
| Note, that with clipping the reward is always between zero and one. | ||
| Attributes: | ||
| clip (bool): Whether to clip the values below/above the lower/upper bound, by | ||
| default True. | ||
| log_shape_factor (float): Logarithm of the shape factor: | ||
| Whether the interpolation between the lower bound and the upper is linear (=0), | ||
| convex (>0) or concave (<0) , by default 0.0. | ||
| w (float): relative weight, by default = 1. | ||
| bounds (tuple[float]): lower and upper bound of the desirability. Below | ||
| bounds[0] the desirability is =1 (if clip=True) or >1 (if clip=False). Above | ||
| bounds[1] the desirability is =0 (if clip=True) or <0 (if clip=False). | ||
| Defaults to (0, 1). | ||
| """ | ||
|
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| type: Literal["DecreasingDesirabilityObjective"] = "DecreasingDesirabilityObjective" # type: ignore | ||
| log_shape_factor: float = 0.0 | ||
|
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| def call_numpy( | ||
| self, | ||
| x: np.ndarray, | ||
| x_adapt: Optional[Union[pd.Series, np.ndarray]] = None, | ||
| ) -> np.ndarray: | ||
| y = np.zeros(x.shape) | ||
| if self.clip: | ||
| y[x < self.lower_bound] = 1.0 | ||
| y[x > self.upper_bound] = 0.0 | ||
| between = (x >= self.lower_bound) & (x <= self.upper_bound) | ||
| else: | ||
| between = np.full(x.shape, True) | ||
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| t = np.exp(self.log_shape_factor) | ||
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| y[between] = np.power( | ||
| (self.upper_bound - x[between]) / (self.upper_bound - self.lower_bound), t | ||
| ) | ||
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| return y | ||
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| class PeakDesirabilityObjective(DesirabilityObjective): | ||
| """ | ||
| A piecewise (linear or convex/concave) objective that increases from the lower bound | ||
| to the peak position and decreases from the peak position to the upper bound. | ||
| Attributes: | ||
| clip (bool): Whether to clip the values below/above the lower/upper bound, by | ||
| default True. | ||
| log_shape_factor (float): Logarithm of the shape factor for the increasing part: | ||
| Whether the interpolation between the lower bound and the peak is linear (=0), | ||
| convex (>1) or concave (<1) , by default 0.0. | ||
| log_shape_factor_decreasing (float): Logarithm of the shape factor for the | ||
| decreasing part. Whether the interpolation between the peak and the upper | ||
| bound is linear (=0), convex (>0) or concave (<0), by default 0.0. | ||
| peak_position (float): Position of the peak, by default 0.5. | ||
| w (float): relative weight: desirability, when x=peak_position, by default = 1. | ||
| bounds (tuple[float]): lower and upper bound of the desirability. Below | ||
| bounds[0] the desirability is =0 (if clip=True) or <0 (if clip=False). Above | ||
| bounds[1] the desirability is =0 (if clip=True) or <0 (if clip=False). | ||
| Defaults to (0, 1). | ||
| """ | ||
|
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| type: Literal["PeakDesirabilityObjective"] = "PeakDesirabilityObjective" # type: ignore | ||
| log_shape_factor: float = 0.0 | ||
| log_shape_factor_decreasing: float = 0.0 # often named log_t | ||
| peak_position: float = 0.5 # often named T | ||
|
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| def call_numpy( | ||
| self, | ||
| x: np.ndarray, | ||
| x_adapt: Optional[Union[pd.Series, np.ndarray]] = None, | ||
| ) -> np.ndarray: | ||
| y = np.zeros(x.shape) | ||
| if self.clip: | ||
| Incr = (x >= self.lower_bound) & (x <= self.peak_position) | ||
| Decr = (x <= self.upper_bound) & (x > self.peak_position) | ||
| else: | ||
| Incr, Decr = x <= self.peak_position, x > self.peak_position | ||
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| s: float = np.exp(self.log_shape_factor) | ||
| t: float = np.exp(self.log_shape_factor_decreasing) | ||
| y[Incr] = np.power( | ||
| np.divide( | ||
| (x[Incr] - self.lower_bound), (self.peak_position - self.lower_bound) | ||
| ), | ||
| s, | ||
| ) | ||
| y[Decr] = np.power( | ||
| np.divide( | ||
| (x[Decr] - self.upper_bound), (self.peak_position - self.upper_bound) | ||
| ), | ||
| t, | ||
| ) | ||
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| return y * self.w | ||
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| @pydantic.model_validator(mode="after") | ||
| def validate_peak_position(self): | ||
| bounds = self.bounds | ||
| if self.peak_position < bounds[0] or self.peak_position > bounds[1]: | ||
| raise ValueError( | ||
| f"Peak position must be within bounds {bounds}, got {self.peak_position}" | ||
| ) | ||
| return self | ||
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