Adapt xgboost and sklearn iris examples

gmontamat · Sep 17, 2024 · 902422f · 902422f
1 parent 5a84791
commit 902422f
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Showing 3 changed files with 7 additions and 7 deletions.
diff --git a/examples/scikit_iris.py b/examples/scikit_iris.py
@@ -13,7 +13,7 @@
 
 from gentun.algorithms import Tournament
 from gentun.genes import RandomChoice
-from gentun.models.sklearn import SklearnCV
+from gentun.models.sklearn import Sklearn
 from gentun.populations import Population
 
 
@@ -47,12 +47,12 @@ def parse_iris(file_name: str) -> Tuple[np.ndarray, np.ndarray]:
         "sklearn_model": RandomForestClassifier,
         "sklearn_metric": f1_score,
         "metric_kwargs": {"average": "macro"},
-        "kfold": 5,
+        "folds": 5,
     }
 
     # Fetch training data
     x_train, y_train = parse_iris("iris.data")
     # Run genetic algorithm on a population of 10 for 10 generations
-    population = Population(genes, SklearnCV, 10, x_train, y_train, **kwargs)
+    population = Population(genes, Sklearn, 10, x_train, y_train, **kwargs)
     algorithm = Tournament(population)
     algorithm.run(10)
diff --git a/examples/xgboost_grid_iris.py b/examples/xgboost_grid_iris.py
@@ -11,7 +11,7 @@
 
 from gentun.algorithms import Tournament
 from gentun.genes import RandomChoice, RandomLogUniform
-from gentun.models.xgboost import XGBoostCV
+from gentun.models.xgboost import XGBoost
 from gentun.populations import Grid
 
 
@@ -56,6 +56,6 @@ def parse_iris(file_name: str) -> Tuple[np.ndarray, np.ndarray]:
     # Fetch training data
     x_train, y_train = parse_iris("iris.data")
     # Run genetic algorithm on a grid population for 1 generation
-    population = Grid(genes, XGBoostCV, gene_samples, x_train, y_train, **kwargs)
+    population = Grid(genes, XGBoost, gene_samples, x_train, y_train, **kwargs)
     algorithm = Tournament(population)
     algorithm.run(1, maximize=False)
diff --git a/examples/xgboost_iris.py b/examples/xgboost_iris.py
@@ -11,7 +11,7 @@
 
 from gentun.algorithms import Tournament
 from gentun.genes import RandomChoice, RandomLogUniform, RandomUniform
-from gentun.models.xgboost import XGBoostCV
+from gentun.models.xgboost import XGBoost
 from gentun.populations import Population
 
 
@@ -63,6 +63,6 @@ def parse_iris(file_name: str) -> Tuple[np.ndarray, np.ndarray]:
     # Fetch training data
     x_train, y_train = parse_iris("iris.data")
     # Run genetic algorithm on a population of 50 for 100 generations
-    population = Population(genes, XGBoostCV, 50, x_train, y_train, **kwargs)
+    population = Population(genes, XGBoost, 50, x_train, y_train, **kwargs)
     algorithm = Tournament(population)
     algorithm.run(100, maximize=False)