Add tests folder

thieu1995 · Aug 15, 2023 · 72de14c · 72de14c
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diff --git a/tests/__init__.py b/tests/__init__.py
@@ -0,0 +1,5 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 15:45, 15/08/2023 ----------%
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
diff --git a/tests/test_MhaElmClassifier.py b/tests/test_MhaElmClassifier.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 15:45, 15/08/2023 ----------%
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+import numpy as np
+from intelelm import MhaElmClassifier
+
+np.random.seed(41)
+
+
+def test_MhaElmClassifier_class():
+    X = np.random.rand(100, 6)
+    y = np.random.randint(0, 2, size=100)
+
+    opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
+    model = MhaElmClassifier(hidden_size=10, act_name="elu", obj_name="AS", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=False)
+    model.fit(X, y)
+    pred = model.predict(X)
+    assert MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES == model.SUPPORTED_CLS_OBJECTIVES
+    assert pred[0] in (0, 1)
diff --git a/tests/test_MhaElmRegressor.py b/tests/test_MhaElmRegressor.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 15:45, 15/08/2023 ----------%
+#       Email: nguyenthieu2102@gmail.com            %                                                    
+#       Github: https://github.com/thieu1995        %                         
+# --------------------------------------------------%
+
+import numpy as np
+from intelelm import MhaElmRegressor
+
+np.random.seed(42)
+
+
+def test_MhaElmRegressor_class():
+    X = np.random.uniform(low=0.0, high=1.0, size=(100, 5))
+    noise = np.random.normal(loc=0.0, scale=0.1, size=(100, 5))
+    y = 2 * X + 1 + noise
+
+    opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
+    model = MhaElmRegressor(hidden_size=10, act_name="elu", obj_name="RMSE", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=False)
+    model.fit(X, y)
+
+    pred = model.predict(X)
+    assert MhaElmRegressor.SUPPORTED_REG_OBJECTIVES == model.SUPPORTED_REG_OBJECTIVES
+    assert len(pred) == X.shape[0]