Add Python tests to module

Makefile

@@ -10,6 +10,7 @@ clean: ## Clean up
 	if [ -d fimdlp/testcpp/lcoverage ]; then rm -fr fimdlp/testcpp/lcoverage/*  ; fi;
 
 test:
+	python -m unittest -v fimdlp.tests
 	cd fimdlp/testcpp && ./test
 
 coverage:

fimdlp/mdlp.py

@@ -90,10 +90,6 @@ class FImdlp(TransformerMixin, BaseEstimator):
 
         # Input validation
         X = check_array(X)
-        if (X != self.X_).any():
-            raise ValueError(
-                "X values are not the same as the ones used to fit the model."
-            )
 
         # Check that the input is of the same shape as the one passed
         # during fit.
@@ -109,6 +105,12 @@ class FImdlp(TransformerMixin, BaseEstimator):
             )
         return result
 
+    def get_cut_points(self):
+        result = []
+        for feature in range(self.n_features_):
+            result.append(self.cut_points_[feature][:-1])
+        return result
+
     def test(self):
         print("Calculating cut points in python for first feature")
         yz = self.y_.copy()

fimdlp/tests/FImdlp_test.py

@@ -0,0 +1,65 @@
+import unittest
+import sklearn
+from sklearn.datasets import load_iris
+import numpy as np
+from ..mdlp import FImdlp
+
+
+class FImdlpTest(unittest.TestCase):
+    def test_init(self):
+        clf = FImdlp()
+        self.assertTrue(clf.proposal)
+        clf = FImdlp(proposal=False)
+        self.assertFalse(clf.proposal)
+
+    def test_fit(self):
+        clf = FImdlp()
+        clf.fit([[1, 2], [3, 4]], [1, 2])
+        self.assertEqual(clf.n_features_, 2)
+        self.assertListEqual(clf.X_.tolist(), [[1, 2], [3, 4]])
+        self.assertListEqual(clf.y_.tolist(), [1, 2])
+        self.assertListEqual([[], []], clf.get_cut_points())
+        X, y = load_iris(return_X_y=True)
+        clf.fit(X, y)
+        self.assertEqual(clf.n_features_, 4)
+        self.assertTrue(np.array_equal(X, clf.X_))
+        self.assertTrue(np.array_equal(y, clf.y_))
+        expected = [
+            [4.900000095367432, 5.0, 5.099999904632568, 5.400000095367432],
+            [2.6999998092651367, 2.9000000953674316],
+            [2.3499999046325684, 4.5],
+            [0.75, 1.399999976158142, 1.5],
+        ]
+        self.assertListEqual(expected, clf.get_cut_points())
+        with self.assertRaises(ValueError):
+            clf.fit([[1, 2], [3, 4]], [1, 2, 3])
+
+    def test_transform(self):
+        clf = FImdlp()
+        clf.fit([[1, 2], [3, 4]], [1, 2])
+        self.assertEqual(
+            clf.transform([[1, 2], [3, 4]]).tolist(), [[0, 0], [0, 0]]
+        )
+        X, y = load_iris(return_X_y=True)
+        clf.fit(X, y)
+        self.assertEqual(clf.n_features_, 4)
+        self.assertTrue(np.array_equal(X, clf.X_))
+        self.assertTrue(np.array_equal(y, clf.y_))
+        self.assertListEqual(
+            clf.transform(X).tolist(), clf.fit(X, y).transform(X).tolist()
+        )
+        expected = [
+            [4, 0, 1, 1],
+            [4, 2, 2, 2],
+            [4, 0, 1, 1],
+            [1, 0, 1, 1],
+            [4, 1, 1, 1],
+            [4, 2, 1, 1],
+            [4, 1, 1, 1],
+        ]
+        self.assertTrue(np.array_equal(clf.transform(X[90:97]), expected))
+        with self.assertRaises(ValueError):
+            clf.transform([[1, 2, 3], [4, 5, 6]])
+        with self.assertRaises(sklearn.exceptions.NotFittedError):
+            clf = FImdlp()
+            clf.transform([[1, 2], [3, 4]])

fimdlp/tests/__init__.py

@@ -0,0 +1,3 @@
+from .FImdlp_test import FImdlpTest
+
+all = ["FImdlpTest"]