Fix dependencies (numpy + ConfigSpace) and docs (#1122)

* fix(dependency): np.NaN -> np.nan

New(er) numpy does not support np.NaN anymore

* Update CHANGELOG.md

* fix(build): restrict package versions

Numpy and ConfigSpace's API have changed. Do not incorporate changes yet.

* Update CHANGELOG.md

* Fix broken link in docs

CHANGELOG.md

@@ -1,3 +1,9 @@
+# 2.1.1
+
+## Dependencies
+- Update numpy NaN (#1122) and restrict numpy and ConfigSpace versions
+
+
 # 2.1.0
 
 ## Improvements

docs/3_getting_started.rst

@@ -27,7 +27,7 @@ ranges and default values.
         "species": ["mouse", "cat", "dog"],   # Categorical
     })
 
-Please see the documentation of `ConfigSpace <https://automl.github.io/ConfigSpace/main/>`_ for more details.
+Please see the documentation of `ConfigSpace <https://automl.github.io/ConfigSpace/latest/>`_ for more details.
 
 
 Target Function

setup.py

@@ -57,11 +57,11 @@ def read_file(filepath: str) -> str:
     include_package_data=True,
     python_requires=">=3.8",
     install_requires=[
-        "numpy>=1.23.3",
+        "numpy>=1.23.3,<2.0.0",
         "scipy>=1.9.2",
         "psutil",
         "pynisher>=1.0.0",
-        "ConfigSpace>=0.6.1",
+        "ConfigSpace>=0.6.1,<1.0.0",
         "joblib",
         "scikit-learn>=1.1.2",
         "pyrfr>=0.9.0",

smac/model/random_forest/random_forest.py

@@ -213,7 +213,7 @@ def _predict(
                 third_dimension = max(max_num_leaf_data, third_dimension)
 
             # Transform list of 2d arrays into a 3d array
-            preds_as_array = np.zeros((X.shape[0], self._rf_opts.num_trees, third_dimension)) * np.NaN
+            preds_as_array = np.zeros((X.shape[0], self._rf_opts.num_trees, third_dimension)) * np.nan
             for i, preds_per_tree in enumerate(all_preds):
                 for j, pred in enumerate(preds_per_tree):
                     preds_as_array[i, j, : len(pred)] = pred

smac/runhistory/encoder/abstract_encoder.py

@@ -80,9 +80,9 @@ def __init__(
             )
 
         # Learned statistics
-        self._min_y = np.array([np.NaN] * self._n_objectives)
-        self._max_y = np.array([np.NaN] * self._n_objectives)
-        self._percentile = np.array([np.NaN] * self._n_objectives)
+        self._min_y = np.array([np.nan] * self._n_objectives)
+        self._max_y = np.array([np.nan] * self._n_objectives)
+        self._percentile = np.array([np.nan] * self._n_objectives)
         self._multi_objective_algorithm: AbstractMultiObjectiveAlgorithm | None = None
         self._runhistory: RunHistory | None = None