Feature/p test

.github/workflows/macos.yml

@@ -36,3 +36,10 @@ jobs:
                 pip install --no-cache-dir Cython
                 pip install -r requirements.txt
                 pip install .
+          - name: Install extra dependencies for dataset
+            run: |
+                pip install torch
+                pip install torchvision
+          - name: Test with pytest
+            run: |
+                pytest

.github/workflows/windows.yml

@@ -41,7 +41,13 @@ jobs:
                 pip install --no-cache-dir Cython
                 pip install -r requirements.txt
                 pip install .
-
+          - name: Install extra dependencies for dataset
+            run: |
+                pip install torch
+                pip install torchvision
+          - name: Test with pytest
+            run: |
+                pytest
         # steps:
         #     - uses: actions/checkout@v3
         #     - name: Install miniconda

tests/__init__.py

@@ -1 +0,0 @@
-"""Unit test package for prosemble."""

tests/test_core.py

@@ -0,0 +1 @@
+"""prosemble core test suite"""

tests/test_dataset.py

@@ -0,0 +1,81 @@
+"""prosemble dataset test suite"""
+
+import unittest
+import numpy as np
+from prosemble import datasets
+
+
+class TestWDBC(unittest.TestCase):
+    def setUp(self):
+        self.data,self.labels = datasets.DATA().breast_cancer
+
+    def test_size(self):
+        self.assertEqual(len(self.data), 569)
+        self.assertEqual(len(self.labels), 569)
+
+    def test_dimensions(self):
+        self.assertEqual(self.data.shape[1], 30)
+
+    def test_unique_labels(self):
+        self.assertEqual(len(np.unique(self.labels)), 2)
+
+    def test_dims_selection(self):
+        self.input_features = self.data[:, [0, 1]]
+        self.assertEqual(self.input_features.shape[1], 2)
+
+
+class TestS1(unittest.TestCase):
+    def setUp(self):
+        self.data,self.labels = datasets.DATA().S_1
+
+    def test_size(self):
+        self.assertEqual(len(self.data), 150)
+        self.assertEqual(len(self.labels), 150)
+
+    def test_dimensions(self):
+        self.assertEqual(self.data.shape[1], 2)
+
+    def test_unique_labels(self):
+        self.assertEqual(len(np.unique(self.labels)), 2)
+
+    def test_dims_selection(self):
+        self.input_features = self.data[:, [0, 1]]
+        self.assertEqual(self.data.shape[1], 2)
+
+
+
+
+class TestS2(unittest.TestCase):
+    def setUp(self):
+        self.data,self.labels = datasets.DATA().S_2
+
+    def test_size(self):
+        self.assertEqual(len(self.data), 200)
+        self.assertEqual(len(self.labels), 200)
+
+    def test_dimensions(self):
+        self.assertEqual(self.data.shape[1], 2)
+
+    def test_unique_labels(self):
+        self.assertEqual(len(np.unique(self.labels)), 2)
+
+    def test_dims_selection(self):
+        self.input_features = self.data[:, [0, 1]]
+        self.assertEqual(self.data.shape[1], 2)
+
+
+
+class TestMnits(unittest.TestCase):
+    def setUp(self):
+        self.data,self.labels = datasets.DATA().mnist
+
+    def test_size(self):
+        self.assertEqual(len(self.data), 70000)
+        self.assertEqual(len(self.labels), 70000)
+
+    def test_dimensions(self):
+        self.assertEqual(self.data.shape[1], 28)
+
+    def test_unique_labels(self):
+        self.assertEqual(len(np.unique(self.labels)), 10)
+

tests/test_models.py

@@ -0,0 +1,247 @@
+"""prosemble.models test suite. """
+
+import numpy as np
+import prosemble.models as ps
+
+
+input_data = np.array(
+    [
+        [1, 2, 3, 4, 1, 3],
+        [1, 0, 3, 4, 1, 3],
+        [1, 2, 3, 9, 1, 3],
+        [6, 2, 5, 4, 1, 3],
+        [1, 0, 3, 2, 1, 7],
+        [9, 2, 6, 4, 8, 3],
+        [2, 2, 1, 4, 7, 3],
+        [2, 2, 0, 4, 7, 3],
+        [0, 2, 1, 4, 7, 3],
+        [2, 2, 3, 4, 7, 3],
+        [1, 2, 1, 4, 7, 3],
+        [1, 5, 1, 4, 1, 3],
+        [3, 1, 1, 2, 6, 1],
+        [9, 1, 3, 1, 6, 4],
+    ]
+)
+
+
+
+
+def test_hcm_model_build():
+    model = ps.Kmeans(
+    data=input_data,
+    c=3,
+    num_inter=100,
+    epsilon=0.00001,
+    ord='fro',
+    plot_steps=True
+)
+
+def test_afcm_model_build():
+    model = ps.AFCM(
+    data=input_data,
+    c=3,
+    num_iter=1000,
+    epsilon=0.00001,
+    ord='fro',
+    m=2,
+    a=2,
+    b=2,
+    k=1,
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+def test_bgpc_model_build():
+    model = ps.BGPC(
+    data=input_data,
+    c=3,
+    a_f=2,
+    b_f=0.5,
+    num_iter=100,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+def test_fcm_model_build():
+    model = ps.FCM(
+    data=input_data,
+    c=3,
+    m=2,
+    num_iter=100,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix=None,
+    plot_steps=True
+)
+
+
+def test_fpcm_model_build():
+    model = ps.FPCM(
+    data=input_data,
+    c=3,
+    m=2,
+    eta=2,
+    num_iter=1000,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+def test_ipcm_model_build():
+    model= ps.IPCM1(
+    data=input_data,
+    c=3,
+    m_f=2,
+    m_p=2,
+    k=1,
+    num_iter=None,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+def test_ipcm_2_model_build():
+    model = ps.IPCM2(
+    data=input_data,
+    c=3,
+    m_f=2,
+    m_p=2,
+    num_iter=None,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+
+def test_kafcm_model_build():
+    model = ps.KAFCM(
+    data=input_data,
+    c=3,
+    num_iter=1000,
+    epsilon=0.00001,
+    ord='fro',
+    m=2,
+    a=3,
+    b=3,
+    k=1,
+    sigma=1,
+    set_centroids='kfcm',
+    set_U_matrix='kfcm',
+    plot_steps=True
+)
+
+
+def test_kfcm_model_build():
+    model = ps.KFCM(
+    data=input_data,
+    c=3, 
+    num_iter=1000,
+    epsilon=0.001,
+    ord='fro',
+    set_prototypes=None,
+    m=2,
+    sigma=1,
+    set_U_matrix=None,
+    plot_steps=True
+)
+
+
+def test_kpfcm_model_build():
+    model = ps.KFPCM(
+    data=input_data,
+    c=3,
+    num_iter=100,
+    epsilon=0.0001,
+    ord='fro',
+    m=2,
+    sigma=1,
+    eta=2,
+    set_centroids=None,
+    set_U_matrix='kfcm',
+    plot_steps=True
+)
+
+
+def test_kipcm_model_build():
+    model = ps.KIPCM(
+    data=input_data,
+    c=3,
+    num_iter=1000,
+    epsilon=0.001,
+    ord='fro',
+    m_f=2,
+    m_p=2,
+    k=1,
+    sigma=10,
+    set_centroids='fcm',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+
+
+def test_kipcm2_model_build():
+    model = ps.KIPCM2(
+    data=input_data,
+    c=3,
+    num_iter=100,
+    epsilon=0.0001,
+    ord='fro',
+    m=2,
+    k=2,
+    sigma=10,
+    set_centroids='fcm',
+    set_U_matrix='fcm',
+    plot_steps=True
+)   
+
+def test_kpcm_model_build():
+    model = ps.KPCM(
+    data=input_data,
+    c=3,
+    num_iter=100,
+    epsilon=0.001,
+    ord='fro',
+    m=2,
+    k=0.06,
+    sigma=1,
+    set_centroids=None,
+    set_U_matrix='kfcm',
+    plot_steps=True
+)
+
+
+def test_pcm_model_build():
+    model= ps.PCM(
+    data=input_data,
+    c=3,
+    m=2,
+    k=0.001,
+    num_iter=1000,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+
+def test_pfcm_model_buidl():
+    model= ps.PFCM(
+    data=input_data,
+    c=3,
+    m=2,
+    eta=2,
+    k=1,
+    a=2,
+    b=2,
+    num_iter=1000,
+    epsilon=0.00001,
+    ord='fro',
+    set_U_matrix='fcm',
+    plot_steps=True
+)
+