scikit-optimize · holgern · Feb 28, 2020 · Feb 25, 2020 · Feb 26, 2020 · Feb 28, 2020
diff --git a/doc/whats_new/v0.8.rst b/doc/whats_new/v0.8.rst
@@ -17,6 +17,12 @@ Version 0.8.0
 - |Enhancement| Improve sampler and add grid sampler
   :pr:`851` by :user:`Holger Nahrstaedt <holgern>`
 
+:mod:`skopt.space`
+------------------
+- |Enhancement| Add `is_constant`  property to dimension and
+  `n_constant_dimensions` property to Space
+  :pr:`883` by :user:`Holger Nahrstaedt <holgern>`
+
 :mod:`skopt.utils`
 ------------------
 - |Fix| Fix Optimizer for full categorical spaces

diff --git a/examples/plots/partial-dependence-plot-with-categorical.py b/examples/plots/partial-dependence-plot-with-categorical.py
@@ -94,6 +94,3 @@ def objective(params):
 
 _ = plot_objective(result, n_points=10, sample_source=[15, 4, 7, 15, 'b', 'entropy', 'e'],
                    minimum=[15, 4, 7, 15, 'b', 'entropy', 'e'])
-
-
-
diff --git a/examples/plots/partial-dependence-plot.py b/examples/plots/partial-dependence-plot.py
@@ -19,7 +19,6 @@
 np.random.seed(123)
 import matplotlib.pyplot as plt
 
-
 #############################################################################
 # Objective function
 # ==================
@@ -33,7 +32,6 @@ def funny_func(x):
         s += (x[i] * i) ** 2
     return s
 
-
 #############################################################################
 # Optimisation using decision trees
 # =================================

diff --git a/skopt/space/space.py b/skopt/space/space.py
@@ -166,6 +166,10 @@ def transformed_size(self):
     def bounds(self):
         raise NotImplementedError
 
+    @property
+    def is_constant(self):
+        raise NotImplementedError
+
     @property
     def transformed_bounds(self):
         raise NotImplementedError
@@ -202,6 +206,7 @@ class Real(Dimension):
 
     prior : "uniform" or "log-uniform", default="uniform"
         Distribution to use when sampling random points for this dimension.
+
         - If `"uniform"`, points are sampled uniformly between the lower
           and upper bounds.
         - If `"log-uniform"`, points are sampled uniformly between
@@ -226,6 +231,7 @@ class Real(Dimension):
     dtype : str or dtype, default=np.float
         float type which will be used in inverse_transform,
         can be float.
+
     """
     def __init__(self, low, high, prior="uniform", base=10, transform=None,
                  name=None, dtype=np.float):
@@ -329,6 +335,10 @@ def inverse_transform(self, Xt):
     def bounds(self):
         return (self.low, self.high)
 
+    @property
+    def is_constant(self):
+        return self.low == self.high
+
     def __contains__(self, point):
         if isinstance(point, list):
             point = np.array(point)
@@ -375,6 +385,7 @@ class Integer(Dimension):
     prior : "uniform" or "log-uniform", default="uniform"
         Distribution to use when sampling random integers for
         this dimension.
+
         - If `"uniform"`, intgers are sampled uniformly between the lower
           and upper bounds.
         - If `"log-uniform"`, intgers are sampled uniformly between
@@ -383,6 +394,7 @@ class Integer(Dimension):
 
     base : int
         The logarithmic base to use for a log-uniform prior.
+
         - Default 10, otherwise commonly 2.
 
     transform : "identity", "normalize", optional
@@ -401,6 +413,7 @@ class Integer(Dimension):
         can be int, np.int16, np.uint32, np.int32, np.int64 (default).
         When set to int, `inverse_transform` returns a list instead of
         a numpy array
+
     """
     def __init__(self, low, high, prior="uniform", base=10, transform=None,
                  name=None, dtype=np.int64):
@@ -504,6 +517,10 @@ def inverse_transform(self, Xt):
     def bounds(self):
         return (self.low, self.high)
 
+    @property
+    def is_constant(self):
+        return self.low == self.high
+
     def __contains__(self, point):
         if isinstance(point, list):
             point = np.array(point)
@@ -548,7 +565,7 @@ class Categorical(Dimension):
     transform : "onehot", "string", "identity", "label", default="onehot"
         - "identity", the transformed space is the same as the original
           space.
-        -  "string",  the transformed space is a string encoded
+        - "string",  the transformed space is a string encoded
           representation of the original space.
         - "label", the transformed space is a label encoded
           representation (integer) of the original space.
@@ -557,6 +574,7 @@ class Categorical(Dimension):
 
     name : str or None
         Name associated with dimension, e.g., "colors".
+
     """
     def __init__(self, categories, prior=None, transform=None, name=None):
         self.categories = tuple(categories)
@@ -669,6 +687,10 @@ def transformed_size(self):
     def bounds(self):
         return self.categories
 
+    @property
+    def is_constant(self):
+        return len(self.categories) <= 1
+
     def __contains__(self, point):
         return point in self.categories
 
@@ -752,25 +774,28 @@ def from_yaml(cls, yml_path, namespace=None):
         yml_path : str
             Full path to yaml configuration file, example YaML below:
             Space:
-              - Integer:
-                  low: -5
-                  high: 5
-              - Categorical:
-                  categories:
-                  - a
-                  - b
-              - Real:
-                  low: 1.0
-                  high: 5.0
-                  prior: log-uniform
+
+            - Integer:
+              low: -5
+              high: 5
+            - Categorical:
+              categories:
+              - a
+              - b
+            - Real:
+              low: 1.0
+              high: 5.0
+              prior: log-uniform
+
         namespace : str, default=None
            Namespace within configuration file to use, will use first
-             namespace if not provided
+           namespace if not provided
 
         Returns
         -------
         space : Space
            Instantiated Space object
+
         """
         with open(yml_path, 'rb') as f:
             config = yaml.safe_load(f)
@@ -1018,6 +1043,17 @@ def is_partly_categorical(self):
         """Space contains any categorical dimensions"""
         return any([isinstance(dim, Categorical) for dim in self.dimensions])
 
+    @property
+    def n_constant_dimensions(self):
+        """Returns the number of constant dimensions which have zero degree of
+        freedom, e.g. an Integer dimensions with (0., 0.) as bounds.
+        """
+        n = 0
+        for dim in self.dimensions:
+            if dim.is_constant:
+                n += 1
+        return n
+
     def distance(self, point_a, point_b):
         """Compute distance between two points in this space.
 

diff --git a/skopt/tests/test_space.py b/skopt/tests/test_space.py
@@ -366,6 +366,17 @@ def test_space_from_space():
     assert_equal(space, space2)
 
 
+@pytest.mark.fast_test
+def test_constant_property():
+    space = Space([(0.0, 1.0), (1,),
+                   ("a", "b", "c"), (1.0, 5.0, "log-uniform"), ("e",)])
+    assert space.n_constant_dimensions == 2
+    for i in [1, 4]:
+        assert space.dimensions[i].is_constant
+    for i in [0, 2, 3]:
+        assert not space.dimensions[i].is_constant
+
+
 @pytest.mark.fast_test
 def test_set_get_transformer():
     # can you pass a Space instance to the Space constructor?
Original file line number	Diff line number	Diff line change
Expand Up		@@ -94,6 +94,3 @@ def objective(params):

		_ = plot_objective(result, n_points=10, sample_source=[15, 4, 7, 15, 'b', 'entropy', 'e'],
		minimum=[15, 4, 7, 15, 'b', 'entropy', 'e'])