Merge 3806f3d into f3fe324

tests/test_ann.py

@@ -1,5 +1,6 @@
 import numpy as np
 import torch.nn as nn
+from torch import Tensor, from_numpy
 
 from unittest import TestCase
 from ezyrb import ANN
@@ -22,6 +23,32 @@ class TestANN(TestCase):
     def test_constructor_empty(self):
         ann = ANN([10, 5], nn.Tanh(), 20000)
 
+    def test_constrctor_loss_none(self):
+        ann = ANN([10, 5], nn.Tanh(), 20000, loss=None)
+        assert isinstance(ann.loss, nn.MSELoss)
+
+    def test_constructor_single_function(self):
+        passed_func = nn.Tanh()
+        ann = ANN([10, 5], passed_func, 20000)
+
+        assert isinstance(ann.function, list)
+        for func in ann.function:
+            assert func == passed_func
+
+    def test_constructor_layers(self):
+        ann = ANN([10, 5], nn.Tanh(), 20000)
+        assert ann.layers == [10, 5]
+
+    def test_constructor_stop_training(self):
+        ann = ANN([10, 5], nn.Tanh(), 20000)
+        assert isinstance(ann.stop_training, list)
+        assert ann.stop_training == [20000]
+
+    def test_constructor_fields_initialized(self):
+        ann = ANN([10, 5], nn.Tanh(), 20000)
+        assert ann.loss_trend == []
+        assert ann.model is None
+
     def test_fit_mono(self):
         x, y = get_xy()
         ann = ANN([10, 5], nn.Tanh(), [20000, 1e-5])
@@ -33,7 +60,7 @@ def test_fit_01(self):
         ann = ANN([10, 5], nn.Tanh(), [20000, 1e-8])
         ann.fit(x, y)
         assert isinstance(ann.model, nn.Sequential)
-        
+
     def test_fit_02(self):
         x, y = get_xy()
         ann = ANN([10, 5, 2], [nn.Tanh(), nn.Sigmoid(), nn.Tanh()], [20000, 1e-8])
@@ -55,11 +82,56 @@ def test_predict_02(self):
         ann.fit(x, y)
         test_y = ann.predict(x)
         np.testing.assert_array_almost_equal(y, test_y, decimal=3)
-        
+
     def test_predict_03(self):
         np.random.seed(1)
         x, y = get_xy()
         ann = ANN([10, 5], nn.Tanh(), 1e-8)
         ann.fit(x, y)
         test_y = ann.predict(x)
         np.testing.assert_array_almost_equal(y, test_y, decimal=3)
+
+    def test_convert_numpy_to_torch(self):
+        arr = [1.0, 2.0, 3.0, 4.0, 5.0]
+
+        ann = ANN([10, 5], nn.Tanh(), 20000)
+
+        value = ann._convert_numpy_to_torch(np.array(arr))
+        assert isinstance(value, Tensor)
+        for i in range(len(arr)):
+            assert value[i] == arr[i]
+
+    def test_convert_torch_to_numpy(self):
+        arr = [1.0, 2.0, 3.0, 4.0, 5.0]
+        tensor = from_numpy(np.array(arr)).float()
+
+        ann = ANN([10, 5], nn.Tanh(), 20000)
+
+        value = ann._convert_torch_to_numpy(tensor)
+        assert isinstance(value, np.ndarray)
+        for i in range(len(arr)):
+            assert value[i] == arr[i]
+
+    def test_build_model(self):
+        passed_func = nn.Tanh()
+        ann = ANN([10, 5, 2], passed_func, 20000)
+
+        ann._build_model(np.array([[1,2],[3,4]]), np.array([[5,6],[7,8]]))
+
+        assert len(ann.model) == 6 + 1
+        for i in range(7):
+            layer = ann.model[i]
+            # the last layer, I keep the separated for clarity
+            if i == 6:
+                assert isinstance(layer, nn.Linear)
+            elif i % 2 == 0:
+                assert isinstance(layer, nn.Linear)
+            else:
+                assert layer == passed_func
+
+        # check input and output
+        assert ann.model[6].out_features == np.array([[5,6],[7,8]]).shape[1]
+        assert ann.model[0].in_features == np.array([[1,2],[3,4]]).shape[1]
+
+        for i in range(0, 5, 2):
+            assert ann.model[i].out_features == ann.model[i+2].in_features