Update tests folder

thieu1995 · Nov 2, 2024 · 60e1fdb · 60e1fdb
1 parent 23e95c2
commit 60e1fdb
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Showing 4 changed files with 12 additions and 8 deletions.
diff --git a/tests/test_MhaMlpClassifier.py b/tests/test_MhaMlpClassifier.py
@@ -15,8 +15,8 @@ def test_MhaMlpClassifier_class():
     y = np.random.randint(0, 2, size=100)
 
     opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-    model = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
-                             obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
+    model = MhaMlpClassifier(hidden_layers=(100,), act_names="ELU", dropout_rates=None, act_output=None,
+                             optim="BaseGA", optim_paras=opt_paras, obj_name="F1S", seed=42, verbose=True)
     model.fit(X, y)
     pred = model.predict(X)
     assert MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES == model.SUPPORTED_CLS_OBJECTIVES

diff --git a/tests/test_MhaMlpRegressor.py b/tests/test_MhaMlpRegressor.py
@@ -16,8 +16,8 @@ def test_MhaMlpRegressor_class():
     y = 2 * X + 1 + noise
 
     opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-    model = MhaMlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
-                            obj_name="MSE", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
+    model = MhaMlpRegressor(hidden_layers=(30, 15,), act_names="ELU", dropout_rates=0.2, act_output=None,
+                            optim="BaseGA", optim_paras=opt_paras, obj_name="MSE", seed=42, verbose=True)
     model.fit(X, y)
 
     pred = model.predict(X)

diff --git a/tests/test_MlpClassifier.py b/tests/test_MlpClassifier.py
@@ -14,8 +14,10 @@ def test_MlpClassifier_class():
     X = np.random.rand(100, 6)
     y = np.random.randint(0, 2, size=100)
 
-    model = MlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="NLLL",
-                          max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
+    model = MlpClassifier(hidden_layers=(30,), act_names="ReLU", dropout_rates=None, act_output=None,
+                          epochs=10, batch_size=16, optim="Adam", optim_paras=None,
+                          early_stopping=True, n_patience=10, epsilon=0.001, valid_rate=0.1,
+                          seed=42, verbose=True)
     model.fit(X, y)
     pred = model.predict(X)
     assert MlpClassifier.SUPPORTED_CLS_METRICS == model.SUPPORTED_CLS_METRICS

diff --git a/tests/test_MlpRegressor.py b/tests/test_MlpRegressor.py
@@ -15,8 +15,10 @@ def test_MlpRegressor_class():
     noise = np.random.normal(loc=0.0, scale=0.1, size=(100, 5))
     y = 2 * X + 1 + noise
 
-    model = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
-                         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
+    model = MlpRegressor(hidden_layers=(30,), act_names="Tanh", dropout_rates=None, act_output=None,
+                         epochs=10, batch_size=16, optim="Adam", optim_paras=None,
+                         early_stopping=True, n_patience=10, epsilon=0.001, valid_rate=0.1,
+                         seed=42, verbose=True)
     model.fit(X, y)
 
     pred = model.predict(X)