[ENH] experiments: uniform call signature and terminology (#178)

In `BaseExperiment` descendants, makes the following consistent:

* `__call__` signature is now `__call__(params)`, instead of
`__call__(**params)` - for consistency with `evaluate` and the expected
function/callable interface
* the second return of `score` and `evaluate` is now consistently called
"metadata" (instead of `add_info`), as referred to in the docstrings

Minor additional changes:

* removed some unused imports

Author: fkiraly

examples/optuna/gp_sampler_example.py

@@ -14,10 +14,8 @@
 - Can handle constraints and noisy observations
 """
 
-import numpy as np
 from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
-from sklearn.model_selection import cross_val_score
 
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.optuna import GPOptimizer

examples/optuna/grid_sampler_example.py

@@ -14,10 +14,8 @@
 - Interpretable and deterministic results
 """
 
-import numpy as np
 from sklearn.datasets import load_iris
 from sklearn.neighbors import KNeighborsClassifier
-from sklearn.model_selection import cross_val_score
 
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.optuna import GridOptimizer

examples/optuna/nsga_ii_sampler_example.py

@@ -33,7 +33,7 @@ def __init__(self, X, y):
         self.X = X
         self.y = y
 
-    def __call__(self, **params):
+    def __call__(self, params):
         # Create model with parameters
         model = RandomForestClassifier(random_state=42, **params)
 

examples/optuna/nsga_iii_sampler_example.py

@@ -34,7 +34,7 @@ def __init__(self, X, y):
         self.X = X
         self.y = y
 
-    def __call__(self, **params):
+    def __call__(self, params):
         # Create model with parameters
         model = DecisionTreeClassifier(random_state=42, **params)
 

examples/optuna/qmc_sampler_example.py

@@ -19,10 +19,8 @@
 - Baseline optimization comparisons
 """
 
-import numpy as np
 from sklearn.datasets import load_wine
 from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import cross_val_score
 
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.optuna import QMCOptimizer

examples/optuna/random_sampler_example.py

@@ -15,10 +15,8 @@
 - Good when objective function is noisy
 """
 
-import numpy as np
 from sklearn.datasets import load_digits
 from sklearn.svm import SVC
-from sklearn.model_selection import cross_val_score
 
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.optuna import RandomOptimizer

examples/optuna/tpe_sampler_example.py

@@ -13,10 +13,8 @@
 - Default choice for most hyperparameter optimization tasks
 """
 
-import numpy as np
 from sklearn.datasets import load_wine
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import cross_val_score
 
 from hyperactive.experiment.integrations import SklearnCvExperiment
 from hyperactive.opt.optuna import TPEOptimizer

src/hyperactive/base/_experiment.py

@@ -21,9 +21,9 @@ class BaseExperiment(BaseObject):
     def __init__(self):
         super().__init__()
 
-    def __call__(self, **kwargs):
-        """Score parameters, with kwargs call. Same as score call."""
-        score, _ = self.score(kwargs)
+    def __call__(self, params):
+        """Score parameters. Same as score call, returns only only a first element."""
+        score, _ = self.score(params)
         return score
 
     @property

src/hyperactive/experiment/bench/_ackley.py

@@ -39,10 +39,10 @@ class Ackley(BaseExperiment):
     >>> from hyperactive.experiment.bench import Ackley
     >>> ackley = Ackley(a=20)
     >>> params = {"x0": 1, "x1": 2}
-    >>> score, add_info = ackley.score(params)
+    >>> score, metadata = ackley.score(params)
 
     Quick call without metadata return or dictionary:
-    >>> score = ackley(x0=1, x1=2)
+    >>> score = ackley({"x0": 1, "x1": 2})
     """  # noqa: E501
 
     _tags = {

src/hyperactive/experiment/bench/_parabola.py

@@ -33,10 +33,10 @@ class Parabola(BaseExperiment):
     >>> from hyperactive.experiment.bench import Parabola
     >>> parabola = Parabola(a=1.0, b=0.0, c=0.0)
     >>> params = {"x": 1, "y": 2}
-    >>> score, add_info = parabola.score(params)
+    >>> score, metadata = parabola.score(params)
 
     Quick call without metadata return or dictionary:
-    >>> score = parabola(x=1, y=2)
+    >>> score = parabola({"x": 1, "y": 2})
     """
 
     _tags = {