Merge pull request #32 from javidahmed64592/add-pytest-coverage

NN v1.12.1 Add pytest coverage

Pipfile

@@ -8,12 +8,13 @@ numpy = "*"
 
 [dev-packages]
 pytest = "*"
+pytest-cov = "*"
 ruff = "*"
 ipykernel = "*"
 
 [requires]
 python_version = "3.12"
 
 [scripts]
-test = "python -m pytest -vx"
+test = "python -m pytest -vx --cov=neural_network --cov-report term-missing"
 ruff = "python -m ruff check ."

neural_network/nn/layer.py → neural_network/layer.py

@@ -141,7 +141,7 @@ def __init__(
 
         Parameters:
             size (int): Size of InputLayer
-            activation (ActivationFunction): Layer activation function
+            activation (ActivationFunction): InputLayer activation function
         """
         super().__init__(size, activation, [1, 1], [0, 0])
 
@@ -180,10 +180,10 @@ def __init__(
         Initialise HiddenLayer object with number of nodes, activation function, weights range and bias range.
 
         Parameters:
-            size (int): Size of Layer
-            activation (ActivationFunction): Layer activation function
-            weights_range (tuple[float, float]): Range for Layer weights
-            bias_range (tuple[float, float]): Range for Layer bias
+            size (int): Size of HiddenLayer
+            activation (ActivationFunction): HiddenLayer activation function
+            weights_range (tuple[float, float]): Range for HiddenLayer weights
+            bias_range (tuple[float, float]): Range for HiddenLayer bias
         """
         super().__init__(size, activation, weights_range, bias_range)
 
@@ -205,7 +205,7 @@ def __init__(
 
         Parameters:
             size (int): Size of OutputLayer
-            activation (ActivationFunction): Layer activation function
+            activation (ActivationFunction): OutputLayer activation function
             weights_range (tuple[float, float]): Range for OutputLayer weights
             bias_range (tuple[float, float]): Range for OutputLayer bias
         """

neural_network/math/matrix.py

@@ -20,7 +20,7 @@ def __init__(self, rows: int, cols: int, vals: NDArray | None = None) -> None:
         Parameters:
             rows (int): Number of rows in matrix
             cols (int): Number of columns in matrix
-            vals (Optional[NDArray]): Matrix values if specified
+            vals (NDArray | None): Matrix values if specified
         """
         self._rows = rows
         self._cols = cols

neural_network/neural_network.py

@@ -4,9 +4,9 @@
 
 import numpy as np
 
+from neural_network.layer import HiddenLayer, InputLayer, Layer, OutputLayer
 from neural_network.math.matrix import Matrix
 from neural_network.math.nn_math import calculate_error_from_expected, calculate_next_errors
-from neural_network.nn.layer import HiddenLayer, InputLayer, Layer, OutputLayer
 
 
 class NeuralNetwork:
@@ -98,12 +98,10 @@ def save(self, filepath: str) -> None:
 
     def mutate(self, shift_vals: float) -> None:
         """
-        Mutate NeuralNetwork Layers by adjusting weights and biases, and potentially adding new Nodes.
+        Mutate NeuralNetwork Layers by adjusting weights and biases.
 
         Parameters:
             shift_vals (float): Factor to adjust Layer weights and biases by
-            prob_new_node (float): Probability per Layer for a new Node, range [0, 1]
-            prob_toggle_connection (float): Probability per Layer to toggle a random Node, range[0, 1]
         """
         for layer in self.layers[1:]:
             layer.mutate(shift_vals)

setup.py

@@ -1,6 +1,6 @@
 from setuptools import find_packages, setup
 
-__version__ = "1.12.0"
+__version__ = "1.12.1"
 
 setup(
     name="neural_network",

tests/conftest.py

@@ -1,9 +1,9 @@
 import pytest
 
+from neural_network.layer import HiddenLayer, InputLayer, OutputLayer
 from neural_network.math.activation_functions import LinearActivation
 from neural_network.math.matrix import Matrix
 from neural_network.neural_network import NeuralNetwork
-from neural_network.nn.layer import HiddenLayer, InputLayer, OutputLayer
 
 
 @pytest.fixture

tests/math/test_activation_functions.py

@@ -0,0 +1,55 @@
+import numpy as np
+
+from neural_network.math.activation_functions import LinearActivation, ReluActivation, SigmoidActivation
+
+
+class TestLinearActivation:
+    def test_given_x_when_calculating_y_then_check_calculated_correctly(self) -> None:
+        x = 5
+        expected_y = x
+        actual_y = LinearActivation.func(x)
+        assert actual_y == expected_y
+
+    def test_given_x_when_calculating_derivative_then_check_calculated_correctly(self) -> None:
+        x = 5
+        expected_y = 1
+        actual_y = LinearActivation.derivative(x)
+        assert actual_y == expected_y
+
+
+class TestReluActivation:
+    def test_given_x_when_calculating_y_then_check_calculated_correctly(self) -> None:
+        x = -5
+        expected_y = 0
+        actual_y = ReluActivation.func(x)
+        assert actual_y == expected_y
+
+        x = 5
+        expected_y = x
+        actual_y = ReluActivation.func(x)
+        assert actual_y == expected_y
+
+    def test_given_x_when_calculating_derivative_then_check_calculated_correctly(self) -> None:
+        x = -5
+        expected_y = 0
+        actual_y = ReluActivation.derivative(x)
+        assert actual_y == expected_y
+
+        x = 5
+        expected_y = 1
+        actual_y = ReluActivation.derivative(x)
+        assert actual_y == expected_y
+
+
+class TestSigmoidActivation:
+    def test_given_x_when_calculating_y_then_check_calculated_correctly(self) -> None:
+        x = 5
+        expected_y = 1 / (1 + np.exp(-x))
+        actual_y = SigmoidActivation.func(x)
+        assert actual_y == expected_y
+
+    def test_given_x_when_calculating_derivative_then_check_calculated_correctly(self) -> None:
+        x = 5
+        expected_y = x * (1 - x)
+        actual_y = SigmoidActivation.derivative(x)
+        assert actual_y == expected_y

tests/math/test_matrix.py

@@ -5,6 +5,12 @@
 
 
 class TestMatrix:
+    def test_given_no_vals_when_creating_matrix_then_check_matrix_has_zero_vals(
+        self, mock_len_inputs: int, mock_len_outputs: int
+    ) -> None:
+        test_matrix = Matrix(rows=mock_len_inputs, cols=mock_len_outputs)
+        assert not np.any(test_matrix.vals)
+
     def test_given_shape_when_creating_random_matrix_then_check_matrix_has_correct_shape(
         self, mock_weights_range: list[float], mock_len_inputs: int, mock_len_outputs: int
     ) -> None:
@@ -161,3 +167,14 @@ def test_given_matrices_when_mixing_then_check_output_has_correct_shape(self) ->
         new_matrix = Matrix.mix_matrices(matrix_in_1, matrix_in_2, matrix_out)
 
         assert np.all(new_matrix.shape == matrix_out.shape)
+
+    def test_given_matrix_when_shifting_vals_then_check_vals_are_different(self) -> None:
+        array = np.array([[1, 2], [4, 3], [2, 4]])
+
+        matrix_1 = Matrix.from_array(array)
+        matrix_2 = Matrix.from_array(array)
+
+        assert np.all(matrix_1.vals == matrix_2.vals)
+
+        matrix_1.shift_vals(0.5)
+        assert not np.all(matrix_1.vals == matrix_2.vals)

tests/nn/test_layer.py → tests/test_layer.py

@@ -1,5 +1,5 @@
+from neural_network.layer import HiddenLayer, InputLayer
 from neural_network.math.matrix import Matrix
-from neural_network.nn.layer import HiddenLayer, InputLayer
 
 
 class TestLayer:

tests/test_neural_network.py

@@ -1,6 +1,6 @@
+from neural_network.layer import HiddenLayer, InputLayer, OutputLayer
 from neural_network.math.activation_functions import ActivationFunction
 from neural_network.neural_network import NeuralNetwork
-from neural_network.nn.layer import HiddenLayer, InputLayer, OutputLayer
 
 
 def make_hidden_layer(