Merge pull request #2 from fmfn/master

update from upstream

Author: bwheelz36

README.md

@@ -40,6 +40,8 @@ to learn how to make the package more flexible, how to deal with categorical par
 - Check out this
 [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/visualization.ipynb)
 with a step by step visualization of how this method works.
+- To understand how to use bayesian optimization when additional constraints are present, see the
+[constrained optimization notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/constraints.ipynb).
 - Explore this [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation_vs_exploration.ipynb)
 exemplifying the balance between exploration and exploitation and how to
 control it.

bayes_opt/bayesian_optimization.py

@@ -2,7 +2,7 @@
 
 from bayes_opt.constraint import ConstraintModel
 
-from .target_space import TargetSpace, ConstrainedTargetSpace
+from .target_space import TargetSpace
 from .event import Events, DEFAULT_EVENTS
 from .logger import _get_default_logger
 from .util import UtilityFunction, acq_max, ensure_rng
@@ -129,7 +129,7 @@ def __init__(self,
             # Data structure containing the function to be optimized, the
             # bounds of its domain, and a record of the evaluations we have
             # done so far
-            self._space = TargetSpace(f, pbounds, random_state)
+            self._space = TargetSpace(f, pbounds, random_state=random_state)
             self.is_constrained = False
         else:
             constraint_ = ConstraintModel(
@@ -138,11 +138,11 @@ def __init__(self,
                 constraint.ub,
                 random_state=random_state
             )
-            self._space = ConstrainedTargetSpace(
+            self._space = TargetSpace(
                 f,
-                constraint_,
                 pbounds,
-                random_state
+                constraint=constraint_,
+                random_state=random_state
             )
             self.is_constrained = True
 
@@ -175,9 +175,9 @@ def max(self):
     def res(self):
         return self._space.res()
 
-    def register(self, params, target):
+    def register(self, params, target, constraint_value=None):
         """Expect observation with known target"""
-        self._space.register(params, target)
+        self._space.register(params, target, constraint_value)
         self.dispatch(Events.OPTIMIZATION_STEP)
 
     def probe(self, params, lazy=True):
@@ -234,7 +234,7 @@ def _prime_queue(self, init_points):
 
     def _prime_subscriptions(self):
         if not any([len(subs) for subs in self._events.values()]):
-            _logger = _get_default_logger(self._verbose)
+            _logger = _get_default_logger(self._verbose, self.is_constrained)
             self.subscribe(Events.OPTIMIZATION_START, _logger)
             self.subscribe(Events.OPTIMIZATION_STEP, _logger)
             self.subscribe(Events.OPTIMIZATION_END, _logger)

bayes_opt/constraint.py

@@ -40,17 +40,13 @@ class ConstraintModel():
     def __init__(self, fun, lb, ub, random_state=None):
         self.fun = fun
 
-        if isinstance(lb, float):
-            self._lb = np.array([lb])
-        else:
-            self._lb = lb
-        
-        if isinstance(ub, float):
-            self._ub = np.array([ub])
-        else:
-            self._ub = ub
-        
-        
+        self._lb = np.atleast_1d(lb)        
+        self._ub = np.atleast_1d(ub)
+
+        if np.any(self._lb >= self._ub):
+            msg = "Lower bounds must be less than upper bounds."
+            raise ValueError(msg)
+
         basis = lambda: GaussianProcessRegressor(
             kernel=Matern(nu=2.5),
             alpha=1e-6,

bayes_opt/logger.py

@@ -6,16 +6,17 @@
 from .event import Events
 from .util import Colours
 
-def _get_default_logger(verbose):
-    return ScreenLogger(verbose=verbose)
+def _get_default_logger(verbose, is_constrained):
+    return ScreenLogger(verbose=verbose, is_constrained=is_constrained)
 
 
 class ScreenLogger(_Tracker):
     _default_cell_size = 9
     _default_precision = 4
 
-    def __init__(self, verbose=2):
+    def __init__(self, verbose=2, is_constrained=False):
         self._verbose = verbose
+        self._is_constrained = is_constrained
         self._header_length = None
         super(ScreenLogger, self).__init__()
 
@@ -27,6 +28,10 @@ def verbose(self):
     def verbose(self, v):
         self._verbose = v
 
+    @property
+    def is_constrained(self):
+        return self._is_constrained
+
     def _format_number(self, x):
         if isinstance(x, int):
                 s = "{x:<{s}}".format(
@@ -47,6 +52,20 @@ def _format_number(self, x):
                 return s[:self._default_cell_size - 3] + "..."
         return s
 
+    def _format_bool(self, x):
+        if 5 > self._default_cell_size:
+            if x == True:
+                x_ = 'T'
+            elif x == False:
+                x_ = 'F'
+        else:
+            x_ = str(x)
+        s = "{x:<{s}}".format(
+            x=x_,
+            s=self._default_cell_size,
+        )
+        return s
+
     def _format_key(self, key):
         s = "{key:^{s}}".format(
             key=key,
@@ -62,6 +81,9 @@ def _step(self, instance, colour=Colours.black):
 
         cells.append(self._format_number(self._iterations + 1))
         cells.append(self._format_number(res["target"]))
+        if self._is_constrained:
+            cells.append(self._format_bool(res["allowed"]))
+
 
         for key in instance.space.keys:
             cells.append(self._format_number(res["params"][key]))
@@ -72,6 +94,10 @@ def _header(self, instance):
         cells = []
         cells.append(self._format_key("iter"))
         cells.append(self._format_key("target"))
+
+        if self._is_constrained:
+            cells.append(self._format_key("allowed"))
+
         for key in instance.space.keys:
             cells.append(self._format_key(key))