Bump GeneticEngine

algorithms/geneticengine/environment.yml

@@ -8,4 +8,4 @@ dependencies:
   - pip=24.2
   - dill=0.3.9
   - pip:
-      - geneticengine @ git+https://github.com/alcides/GeneticEngine.git@c7b7a770c9d408cf6aeea1fdaec79696b4595434
+      - geneticengine @ git+https://github.com/alcides/GeneticEngine.git@5e7c4ec8807b8299f92e0347443714e16f22e40f

experiment/methods/geneticengine/metadata.yml

@@ -0,0 +1,10 @@
+authors: # the participants
+  - Alcides Fonseca, Guilherme Espada, Leon Ingelse, Eduardo Madeira
+email: me@alcidesfonseca.com, gjespada@fc.ul.pt, leoningelse@gmail.com, jmadeira@lasige.di.fc.ul.pt
+name: Genetic Engine
+description:
+  GeneticEngine is an extensible framework for implementing evolutionary algorithms. Users can write their own class structure and select the algorithm they want to run. Each algorithm will generate random trees following the class specification.
+  Currently, GeneticEngine supports several representations (tree-based, grammatical evolution, structured grammatical evolution, stack-based), several algorithms (hill-climbing, Genetic Programming, 1+1) and several operators (Tournament, Lexicase, different mutations and crossovers).
+  A distinctive feature of this framework is that it supports dependent types that semantically constraint the tree generation.
+
+url: "https://github.com/alcides/GeneticEngine/"

experiment/test_population.py

@@ -8,62 +8,65 @@
 
 root_dir = d(abspath(__file__))
 sys.path.append(root_dir)
-print('appended',root_dir,'to sys.path')
+print("appended", root_dir, "to sys.path")
 
 import importlib
 from read_file import read_file
 
-if 'OMP_NUM_THREADS' not in os.environ.keys():
-    os.environ['OMP_NUM_THREADS'] = '1'
-if 'OPENBLAS_NUM_THREADS' not in os.environ.keys():
-    os.environ['OPENBLAS_NUM_THREADS'] = '1'
-if 'MKL_NUM_THREADS' not in os.environ.keys():
-    os.environ['MKL_NUM_THREADS'] = '1'
+if "OMP_NUM_THREADS" not in os.environ.keys():
+    os.environ["OMP_NUM_THREADS"] = "1"
+if "OPENBLAS_NUM_THREADS" not in os.environ.keys():
+    os.environ["OPENBLAS_NUM_THREADS"] = "1"
+if "MKL_NUM_THREADS" not in os.environ.keys():
+    os.environ["MKL_NUM_THREADS"] = "1"
 
 
 def test_population(ml):
     """Sympy compatibility of model string"""
 
-    dataset = 'test/192_vineyard_small.tsv.gz'
+    dataset = "test/192_vineyard_small.tsv.gz"
     random_state = 42
 
-    algorithm = importlib.__import__(f'methods.{ml}.regressor',globals(),
-                                        locals(),
-                                    ['est','hyper_params','complexity'])
-
-    features, labels, feature_names =  read_file(
-        dataset, 
-        use_dataframe=True
+    algorithm = importlib.__import__(
+        f"methods.{ml}.regressor",
+        globals(),
+        locals(),
+        ["est", "hyper_params", "complexity"],
     )
-    print('feature_names:',feature_names)
+
+    features, labels, feature_names = read_file(dataset, use_dataframe=True)
+    print("feature_names:", feature_names)
 
     # generate train/test split
-    X_train, X_test, y_train, y_test = train_test_split(features, labels,
-                                                    train_size=0.75,
-                                                    test_size=0.25,
-                                                    random_state=random_state)
-
+    X_train, X_test, y_train, y_test = train_test_split(
+        features, labels, train_size=0.75, test_size=0.25, random_state=random_state
+    )
+
     # Few samples to try to make it quick
     sample_idx = np.random.choice(np.arange(len(X_train)), size=10)
-    
+
     y_train = y_train[sample_idx]
     X_train = X_train.iloc[sample_idx]
 
     algorithm.est.fit(X_train.values, y_train)
 
-    if 'get_population' not in dir(algorithm):
+    if "get_population" not in dir(algorithm):
         algorithm.get_population = lambda est: [est]
-    if 'get_best_solution' not in dir(algorithm):
+    if "get_best_solution" not in dir(algorithm):
         algorithm.get_best_solution = lambda est: est
 
     population = algorithm.get_population(algorithm.est)
+    print(population, "pop", algorithm.est.get_population())
     best_model = algorithm.get_best_solution(algorithm.est)
     print(algorithm.model(best_model, X_train))
     print(algorithm.est.predict(X_train.values))
 
     # assert that population has at least 1 and no more than 100 individuals
-    assert 1 <= len(population) <= 100, "Population size is not within the expected range"
-
+    assert (
+        1 <= len(population) <= 100
+    ), "Population size is not within the expected range"
+
     for p in population:
+        print(population, algorithm.est, type(algorithm.est))
         print(algorithm.model(p, X_train))
         print(p.predict(X_train.values))