Reverted test_population

experiment/test_population.py

@@ -8,65 +8,62 @@
 
 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)
-    print("feature_names:", feature_names)
+    algorithm = importlib.__import__(f'methods.{ml}.regressor',globals(),
+                                        locals(),
+                                    ['est','hyper_params','complexity'])
 
-    # 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
+    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)
+
     # 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))