Create new supervised.py unit tests

.gitignore

@@ -20,4 +20,5 @@ Train_CovMatch.py
 Train_LinearModels.py
 Validate_AE.py
 .ipynb_checkpoints
-/ignore/
+/ignore/
+/parametermatrix_autoencoder/

UnitTests/test_supervised.py

@@ -0,0 +1,122 @@
+import unittest
+import numpy as np
+import pandas as pd
+import mineralML as mm
+
+
+class mineralML_supervised(unittest.TestCase):
+
+    def setUp(self):
+        self.data = {
+            "SampleID": [72065, 72066, 31890, 31891, 59237, 59238, 37643, 37644],
+            "Mineral": [
+                "Amphibole",
+                "Amphibole",
+                "Clinopyroxene",
+                "Clinopyroxene",
+                "Garnet",
+                "Garnet",
+                "Olivine",
+                "Olivine",
+            ],
+            "SiO2": [40, 39.7, 51.49, 51.15, 39.8, 40.2, 40.31, 38.99],
+            "TiO2": [3.1, 3.2, 0.6, 0.53, 0.6, 0.6, 0.01, 0.08],
+            "Al2O3": [16.1, 16, 2.57, 2.57, 22.5, 23.4, 0.01, np.nan],
+            "Cr2O3": [0.08, 0.06, 0.24, 0.19, np.nan, np.nan, 0.06, 0.05],
+            "FeOt": [12, 13, 6.98, 5.55, 16.1, 15.1, 11.88, 19.2],
+            "MnO": [0.16, 0.17, 0.22, 0.16, 0.5, 0.5, 0.18, 0.25],
+            "MgO": [10.2, 9.5, 16.77, 16.37, 9.8, 12.3, 47.02, 40.73],
+            "CaO": [10, 10.7, 19.42, 21.36, 10.7, 7.9, 0.08, 0.26],
+            "Na2O": [3.1, 2.9, 0.25, 0.32, np.nan, np.nan, np.nan, np.nan],
+            "K2O": [1.9, 1.7, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+        }
+
+        self.df = pd.DataFrame(self.data)
+
+
+    def test_load_minclass_nn(self):
+        # Load actual mineral classes
+        min_cat, mapping = mm.load_minclass_nn()
+
+        expected_mapping = {
+            0: 'Amphibole',
+            1: 'Biotite',
+            2: 'Clinopyroxene',
+            3: 'Garnet',
+            4: 'Ilmenite',
+            5: 'KFeldspar',
+            6: 'Magnetite',
+            7: 'Muscovite',
+            8: 'Olivine',
+            9: 'Orthopyroxene',
+            10: 'Plagioclase',
+            11: 'Spinel'
+        }
+
+        expected_min_cat = list(expected_mapping.values())
+
+        # Verify expected output
+        self.assertEqual(min_cat, expected_min_cat)
+        self.assertEqual(mapping, expected_mapping)
+
+
+    def test_prep_df_nn(self):
+        df_cleaned = mm.prep_df_nn(self.df.copy())
+
+        # Verify that NaN values were replaced correctly and DataFrame is cleaned
+        self.assertEqual(
+            df_cleaned.isnull().sum().sum(), 0
+        )  # No NaN values should be present
+        self.assertEqual(df_cleaned.index.name, "SampleID")
+        self.assertEqual(
+            set(df_cleaned.columns),
+            set(
+                [
+                    "SiO2",
+                    "TiO2",
+                    "Al2O3",
+                    "Cr2O3",
+                    "FeOt",
+                    "MnO",
+                    "MgO",
+                    "CaO",
+                    "Na2O",
+                    "K2O",
+                    "Mineral",
+                ]
+            ),
+        )
+
+    def test_norm_data_nn(self):
+
+        df_cleaned = mm.prep_df_nn(self.df.copy())
+        normalized_data = mm.norm_data_nn(df_cleaned)
+
+        # Check the shape of the output
+        self.assertEqual(normalized_data.shape, (8, 10))
+
+        # Expected normalized data
+        expected_normalized_data = np.array([
+            [-0.22643322,  0.08201123,  0.37556234, -0.09862359, -0.32940736,
+             -0.46334456,  0.40043244,  0.33010893, -0.22369372, -0.17032885],
+            [-0.24528221,  0.0942531 ,  0.36763161, -0.04468734, -0.29926058,
+             -0.50116598,  0.48428333,  0.25318957, -0.26727423, -0.17443251],
+            [ 0.49548345, -0.22403538, -0.69746528, -0.36938356, -0.14852665,
+             -0.10836359,  1.52882587, -0.765992  , -0.63770857, -0.13749953],
+            [ 0.47412125, -0.23260468, -0.69746528, -0.4465124 , -0.32940736,
+             -0.12997583,  1.76121262, -0.73907022, -0.63770857, -0.14775869],
+            [-0.23899921, -0.22403538,  0.88312898,  0.12251504,  0.69558336,
+             -0.4849568 ,  0.48428333, -0.8621412 , -0.63770857, -0.18674351],
+            [-0.21386722, -0.22403538,  0.95450554,  0.06857879,  0.69558336,
+             -0.34988033,  0.14887976, -0.8621412 , -0.63770857, -0.18674351],
+            [-0.20695592, -0.29626238, -0.90049194, -0.10509594, -0.26911379,
+              1.52606173, -0.78785449, -0.8621412 , -0.63770857, -0.17443251],
+            [-0.28989151, -0.28769307, -0.90128501,  0.28971741, -0.05808629,
+              1.18620932, -0.76629283, -0.8621412 , -0.63770857, -0.17648434]
+        ])
+
+        np.testing.assert_almost_equal(normalized_data, expected_normalized_data, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()