Merge pull request #99 from stratakis/scikit-learn-1.8.0

Fix compatibility with scikit-learn 1.8+

Author: firefly-cpp

README.md

@@ -172,8 +172,7 @@ In the modifier version of NiaAML optimization process there are two types of op
 
 ```python
 self._params = dict(
-    n_estimators = ParameterDefinition(MinMax(min=10, max=111), np.uint),
-    algorithm = ParameterDefinition(['SAMME', 'SAMME.R'])
+    n_estimators = ParameterDefinition(MinMax(min=10, max=111), np.uint)
 )
 ```
 

docs/getting_started.rst

@@ -181,8 +181,7 @@ In NiaAML there are two types of optimization. Goal of the first type is to find
 .. code:: python
 
     self._params = dict(
-        n_estimators = ParameterDefinition(MinMax(min=10, max=111), np.uint),
-        algorithm = ParameterDefinition(['SAMME', 'SAMME.R'])
+        n_estimators = ParameterDefinition(MinMax(min=10, max=111), np.uint)
     )
 
 An individual in the second type of optimization is a real-valued vector that has a size equal to the sum of number of keys in all three dictionaries (classifier's _params, feature transformation algorithm's _params and feature selection algorithm's _params) and a value of each dimension is in range [0.0, 1.0]. The second type of optimization maps real values from the individual's vector to those parameter definitions in the dictionaries. Each parameter's value can be defined as a range or array of values. In the first case, a value from vector is mapped from one iterval to another and in the second case, a value from vector falls into one of the bins that represent an index of the array that holds possible parameter's values.

examples/classifier.py

@@ -19,7 +19,7 @@
 classifier = AdaBoost()
 
 # set parameters of the classifier
-classifier.set_parameters(n_estimators=50, algorithm="SAMME")
+classifier.set_parameters(n_estimators=50)
 
 # fit classifier to the data
 classifier.fit(data_reader.get_x(), data_reader.get_y())

niaaml/classifiers/ada_boost.py

@@ -51,9 +51,8 @@ def __init__(self, **kwargs):
 
         self._params = dict(
             n_estimators=ParameterDefinition(MinMax(min=10, max=111), np.uint),
-            algorithm=ParameterDefinition(["SAMME"]),
         )
-        self.__ada_boost = AdaBoostClassifier(algorithm='SAMME')
+        self.__ada_boost = AdaBoostClassifier()
 
     def set_parameters(self, **kwargs):
         r"""Set the parameters/arguments of the algorithm."""