Deprecate LogSpaceObjectives and add LogTransform (#263)

summit/strategies/base.py

@@ -1,3 +1,4 @@
+import warnings
 from summit.domain import *
 from summit.utils.dataset import DataSet
 
@@ -6,7 +7,7 @@
 
 
 from abc import ABC, abstractmethod, abstractclassmethod
-from typing import Type, Tuple
+from typing import List, Type, Tuple
 import json
 
 __all__ = [
@@ -367,7 +368,7 @@ def un_transform(self, ds, **kwargs):
         return new_ds
 
     def to_dict(self, **kwargs):
-        """ Output a dictionary representation of the transform"""
+        """Output a dictionary representation of the transform"""
         return dict(
             transform_domain=self.transform_domain.to_dict(),
             name=self.__class__.__name__,
@@ -514,7 +515,7 @@ def transform_inputs_outputs(self, ds, **kwargs):
         return inputs, outputs
 
     def to_dict(self):
-        """ Output a dictionary representation of the transform"""
+        """Output a dictionary representation of the transform"""
         transform_params = dict(expression=self.expression, maximize=self.maximize)
         d = super().to_dict(**transform_params)
         return d
@@ -558,22 +559,23 @@ class LogSpaceObjectives(Transform):
 
     def __init__(self, domain: Domain):
         super().__init__(domain)
-        objectives = [
-            (i, v)
-            for i, v in enumerate(self.transform_domain.variables)
-            if v.is_objective
-        ]
 
-        # Check that the domain has objectives
-        num_objectives = len(objectives)
-        if num_objectives == 0:
-            raise ValueError(
-                f"The domain must have objectives. Currently has {num_objectives} objectives."
-            )
+        warnings.warn(
+            "This class will be deprecated in a future version of summit.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+        self.to_transform = [v.name for v in self.transform_domain.output_variables]
 
-        # Rename objectives in new domain
-        for i, v in objectives:
+        # Rename variables and set new bounds in transformeed domain
+        for name in self.to_transform:
+            v = self.transform_domain[name]
+            if v is None:
+                raise ValueError(f"Variable {name} not found in domain.")
             v.name = "log_" + v.name
+            v._lower_bound = np.log(v.bounds[0])
+            v._upper_bound = np.log(v.bounds[1])
 
     def transform_inputs_outputs(self, ds, **kwargs):
         """Transform of data into inputs and outptus for a strategy
@@ -594,15 +596,70 @@ def transform_inputs_outputs(self, ds, **kwargs):
         inputs, outputs
             Datasets with the input and output datasets
         """
-        inputs, outputs = super().transform_inputs_outputs(ds, **kwargs)
-        if (outputs.any() < 0).any():
-            raise ValueError("Cannot complete log transform for values less than zero.")
-        outputs = outputs.apply(np.log)
-        columns = [v.name for v in self.transform_domain.variables if v.is_objective]
-        outputs = DataSet(outputs.data_to_numpy(), columns=columns)
-        return inputs, outputs
+        for name in self.to_transform:
+            ds.loc[:, ("log_" + name, "DATA")] = np.log(ds[name].astype(float).values)
+        return super().transform_inputs_outputs(ds, **kwargs)
 
-    def un_transform(self, ds, **kwargs):
+    def un_transform(self, ds: DataSet, **kwargs):
+        """Untransform objectives from log space
+
+        Parameters
+        ----------
+        ds: `DataSet`
+            Dataset with columns corresponding to the inputs and objectives of the domain.
+        copy: bool, optional
+            Copy the dataset internally. Defaults to True.
+        transform_descriptors: bool, optional
+            Transform the descriptors into continuous variables. Default True.
+        """
+        for name in self.to_transform:
+            if not "log_" + name in ds.data_columns:
+                continue
+            ds.loc[:, (name, "DATA")] = np.exp(ds["log_" + name].astype(float).values)
+        ds = super().un_transform(ds, **kwargs)
+        return ds
+
+
+class LogTransform(Transform):
+    def __init__(self, domain: Domain, to_transform: List[str]):
+        super().__init__(domain)
+
+        self.to_transform = to_transform
+
+        # Rename variables and set new bounds in transformeed domain
+        for name in self.to_transform:
+            v = self.transform_domain[name]
+            if v is None:
+                raise ValueError(f"Variable {name} not found in domain.")
+            v.name = "log_" + v.name
+            v._lower_bound = np.log(v.bounds[0])
+            v._upper_bound = np.log(v.bounds[1])
+            # v.bounds = (np.log(v.bounds[0]), np.log(v.bounds[1]))
+
+    def transform_inputs_outputs(self, ds, **kwargs):
+        """Transform of data into inputs and outptus for a strategy
+
+        This will do a log transform on the objectives (outputs).
+
+        Parameters
+        ----------
+        ds: `DataSet`
+            Dataset with columns corresponding to the inputs and objectives of the domain.
+        copy: bool, optional
+            Copy the dataset internally. Defaults to True.
+        transform_descriptors: bool, optional
+            Transform the descriptors into continuous variables. Default True.
+
+        Returns
+        -------
+        inputs, outputs
+            Datasets with the input and output datasets
+        """
+        for name in self.to_transform:
+            ds.loc[:, ("log_" + name, "DATA")] = np.log(ds[name].astype(float).values)
+        return super().transform_inputs_outputs(ds, **kwargs)
+
+    def un_transform(self, ds: DataSet, **kwargs):
         """Untransform objectives from log space
 
         Parameters
@@ -614,10 +671,11 @@ def un_transform(self, ds, **kwargs):
         transform_descriptors: bool, optional
             Transform the descriptors into continuous variables. Default True.
         """
+        for name in self.to_transform:
+            if not "log_" + name in ds.data_columns:
+                continue
+            ds.loc[:, (name, "DATA")] = np.exp(ds["log_" + name].astype(float).values)
         ds = super().un_transform(ds, **kwargs)
-        for v in self.domain.variables:
-            if v.is_objective and ds.get("log_" + v.name):
-                ds[v.name] = np.exp(ds["log_" + v.name])
         return ds
 
 
@@ -1195,7 +1253,7 @@ def _closest_point_index(design_point, candidate_matrix):
 
 
 def _design_distances(design_point, candidate_matrix):
-    """ Return the distances between a design_point and all candidates"""
+    """Return the distances between a design_point and all candidates"""
     diff = design_point - candidate_matrix
     squared = np.power(diff, 2)
     summed = np.sum(squared, axis=1)