Add docstrings and typing to unittests

tests/test_dehb.py

@@ -1,21 +1,56 @@
 import pytest
+import typing
 import ConfigSpace
 import numpy as np
 import time
 from dehb.optimizers.dehb import DEHB
 
 def create_toy_searchspace():
+    """Creates a toy searchspace with a single hyperparameter.
+
+    Can be used in order to instantiate a DEHB instance for simple unittests not
+    requiring a proper configuration space for optimization.
+
+
+    Returns:
+        ConfigurationSpace: Toy searchspace
+    """
     cs = ConfigSpace.ConfigurationSpace()
-    cs.add_hyperparameter(ConfigSpace.UniformFloatHyperparameter("x0", lower=3, upper=10, log=False))
+    cs.add_hyperparameter(
+        ConfigSpace.UniformFloatHyperparameter("x0", lower=3, upper=10, log=False))
     return cs
 
-def create_toy_optimizer(configspace, min_budget, max_budget, eta, objective_function):
+def create_toy_optimizer(configspace: ConfigSpace.ConfigurationSpace, min_budget: float,
+                         max_budget: float, eta: int,
+                         objective_function: typing.Callable):
+    """Creates a DEHB instance.
+
+    Args:
+        configspace (ConfigurationSpace): Searchspace to use
+        min_budget (float): Minimum budget for DEHB
+        max_budget (float): Maximum budget for DEHB
+        eta (int): Eta parameter of DEHB
+        objective_function (Callable): Function to optimize
+
+    Returns:
+        _type_: _description_
+    """
     dim = len(configspace.get_hyperparameters())
-    return DEHB(f=objective_function, cs=configspace, dimensions=dim, min_budget=min_budget,
-         max_budget=max_budget, eta=eta, n_workers=1)
+    return DEHB(f=objective_function, cs=configspace, dimensions=dim,
+                min_budget=min_budget,
+                max_budget=max_budget, eta=eta, n_workers=1)
+
 
+def objective_function(x: ConfigSpace.Configuration, budget: float, **kwargs):
+    """Toy objective function.
 
-def objective_function(x, budget, **kwargs):
+    Args:
+        x (ConfigSpace.Configuration): Configuration to evaluate
+        budget (float): Budget to evaluate x on
+
+    Returns:
+        dict: Result dictionary
+    """
     y = np.random.uniform()
     cost = 5
     result = {
@@ -25,7 +60,14 @@ def objective_function(x, budget, **kwargs):
     return result
 
 class TestBudgetExhaustion():
+    """Class that bundles all Budget exhaustion tests.
+
+    These tests include budget exhaustion tests for runtime, number of function
+    evaluations and number of brackets to run.
+    """
     def test_runtime_exhaustion(self):
+        """Test for runtime budget exhaustion.
+        """
         cs = create_toy_searchspace()
         dehb = create_toy_optimizer(configspace=cs, min_budget=3, max_budget=27, eta=3,
                                         objective_function=objective_function)
@@ -35,6 +77,8 @@ def test_runtime_exhaustion(self):
         assert dehb._is_run_budget_exhausted(total_cost=1), "Run budget should be exhausted"
 
     def test_fevals_exhaustion(self):
+        """Test for function evaluations budget exhaustion.
+        """
         cs = create_toy_searchspace()
         dehb = create_toy_optimizer(configspace=cs, min_budget=3, max_budget=27, eta=3,
                                     objective_function=objective_function)
@@ -44,6 +88,8 @@ def test_fevals_exhaustion(self):
         assert dehb._is_run_budget_exhausted(fevals=1), "Run budget should be exhausted"
 
     def test_brackets_exhaustion(self):
+        """Test for bracket budget exhaustion.
+        """
         cs = create_toy_searchspace()
         dehb = create_toy_optimizer(configspace=cs, min_budget=3, max_budget=27, eta=3,
                                         objective_function=objective_function)