score function takes just one argument

Author: SimonBlanke

src/hyperactive/base/_experiment.py

@@ -1,4 +1,5 @@
 """Base class for experiment."""
+
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
 import numpy as np
@@ -19,7 +20,7 @@ class BaseExperiment(BaseObject):
  def __init__(self):
  super().__init__()
 
- def __call__(self, **kwargs):
+ def __call__(self, kwargs):
  """Score parameters, with kwargs call."""
  score, _ = self.score(kwargs)
  return score

src/hyperactive/experiment/integrations/sklearn_cv.py

@@ -1,4 +1,5 @@
 """Experiment adapter for sklearn cross-validation experiments."""
+
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
 from sklearn import clone
@@ -8,6 +9,7 @@
 
 from hyperactive.base import BaseExperiment
 
+
 class SklearnCvExperiment(BaseExperiment):
  """Experiment adapter for sklearn cross-validation experiments.
 
@@ -71,8 +73,6 @@ class SklearnCvExperiment(BaseExperiment):
  >>> params = {"C": 1.0, "kernel": "linear"}
  >>> score, add_info = sklearn_exp.score(params)
 
- Quick call without metadata return or dictionary:
- >>> score = sklearn_exp(C=1.0, kernel="linear")
  """
 
  def __init__(self, estimator, X, y, scoring=None, cv=None):

src/hyperactive/experiment/toy/_ackley.py

@@ -1,4 +1,5 @@
 """Ackley function, common benchmark for optimization algorithms."""
+
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
 import numpy as np
@@ -41,8 +42,6 @@ class Ackley(BaseExperiment):
  >>> params = {"x0": 1, "x1": 2}
  >>> score, add_info = ackley.score(params)
 
- Quick call without metadata return or dictionary:
- >>> score = ackley(x0=1, x1=2)
  """ # noqa: E501
 
  _tags = {

src/hyperactive/experiment/toy/_parabola.py

@@ -1,4 +1,5 @@
 """2D parabola function, common benchmark for optimization algorithms."""
+
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
 from hyperactive.base import BaseExperiment
@@ -35,8 +36,6 @@ class Parabola(BaseExperiment):
  >>> params = {"x": 1, "y": 2}
  >>> score, add_info = parabola.score(params)
 
- Quick call without metadata return or dictionary:
- >>> score = parabola(x=1, y=2)
  """
 
  _tags = {

src/hyperactive/experiment/toy/_sphere.py

@@ -1,4 +1,5 @@
 """Sphere function, a common benchmark for optimization algorithms."""
+
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
 import numpy as np
@@ -38,12 +39,6 @@ class Sphere(BaseExperiment):
  >>> params = {"x0": 1, "x1": 2, "x2": 3}
  >>> score, add_info = sphere.score(params)
 
- Quick call without metadata return or dictionary:
- >>> score = sphere(x0=1, x1=2, x2=3)
-
- Different number of dimensions changes the parameter names:
- >>> sphere4D = Sphere(const=0, n_dim=4)
- >>> score4D = sphere4D(x0=1, x1=2, x2=3, x3=4)
  """
 
  _tags = {
@@ -63,7 +58,7 @@ def _paramnames(self):
 
  def _score(self, params):
  params_vec = np.array([params[f"x{i}"] for i in range(self.n_dim)])
- return np.sum(params_vec ** 2) + self.const, {}
+ return np.sum(params_vec**2) + self.const, {}
 
  @classmethod
  def get_test_params(cls, parameter_set="default"):