#7: Cria testes de unidades básicos

requirements.txt

@@ -1,26 +1,33 @@
 asttokens==2.0.5
 backcall==0.2.0
+black==22.3.0
+click==8.1.3
 decorator==5.1.1
 executing==0.8.3
 ipdb==0.13.9
-ipython==8.2.0
+ipython==8.3.0
 jedi==0.18.1
 joblib==1.1.0
 matplotlib-inline==0.1.3
+mock==4.0.3
+mypy-extensions==0.4.3
 numpy==1.22.3
 parso==0.8.3
+pathspec==0.9.0
 pexpect==4.8.0
 pickleshare==0.7.5
+platformdirs==2.5.2
 prompt-toolkit==3.0.29
 ptyprocess==0.7.0
 pure-eval==0.2.2
-Pygments==2.11.2
+Pygments==2.12.0
 scikit-learn==1.0.2
 scipy==1.8.0
 six==1.16.0
 sklearn==0.0
 stack-data==0.2.0
 threadpoolctl==3.1.0
 toml==0.10.2
+tomli==2.0.1
 traitlets==5.1.1
 wcwidth==0.2.5

test/test_flexcon.py

@@ -0,0 +1,124 @@
+from unittest import TestCase
+
+from mock import Mock, patch
+
+from src.flexcon import FlexConC
+
+
+class SelfTrainingClassifierMock(Mock):
+    ...
+
+
+class GenerateMemory():
+    def pred_1_it(self):
+        return {
+            1: {
+                'confidence': 0.3,
+                'classes': 0
+            },
+            2: {
+                'confidence': 0.2,
+                'classes': 0
+            },
+            3: {
+                'confidence': 0.97,
+                'classes': 0
+            },
+            4: {
+                'confidence': 0.97,
+                'classes': 1
+            },
+            5: {
+                'confidence': 0.98,
+                'classes': 1
+            }
+        }
+
+    def pred_x_it(self):
+        return {
+            1: {
+                'confidence': 0.3,
+                'classes': 0
+            },
+            2: {
+                'confidence': 0.92,
+                'classes': 1
+            },
+            3: {
+                'confidence': 0.96,
+                'classes':1
+            },
+            4: {
+                'confidence': 0.7,
+                'classes': 1
+            },
+            5: {
+                'confidence': 0.99,
+                'classes': 1
+            }
+        }
+
+class TestFlexCon(TestCase):
+    @patch("src.flexcon.clone")
+    @patch("src.flexcon.SelfTrainingClassifier")
+    def setUp(self, super_class, model_clone):
+        super_class.return_value = SelfTrainingClassifierMock()
+        model_clone.return_value = ""
+
+        self.flexcon = FlexConC("")
+
+    def test_update_model_memory(self):
+        instances = [i for i in range(10)]
+        labels = [0, 0, 0, 1, 1, 1, 1, 1, 0, 1]
+        weights = [0.3, 0.2, 0.5, 0.6, 0.7, 0.1, 0.8, 0.4, 0.9, 0.57]
+        output_without_weights = [
+            [0.3, 0],
+            [0.2, 0],
+            [0.5, 0],
+            [0, 0.6],
+            [0, 0.7],
+            [0, 0.1],
+            [0, 0.8],
+            [0, 0.4],
+            [0.9, 0],
+            [0, 0.57],
+        ]
+
+        self.flexcon.cl_memory = [[0] * 2 for _ in range(len(instances))]
+        self.flexcon.update_memory(instances, labels, weights)
+        self.assertListEqual(self.flexcon.cl_memory, output_without_weights)
+
+        expected_output_weights = [
+            [1, 0],
+            [1, 0],
+            [1, 0],
+            [0, 1],
+            [0, 1],
+            [0, 1],
+            [0, 1],
+            [0, 1],
+            [1, 0],
+            [0, 1],
+        ]
+        self.flexcon.cl_memory = [[0] * 2 for _ in range(len(instances))]
+        self.flexcon.update_memory(instances, labels)
+        self.assertListEqual(self.flexcon.cl_memory, expected_output_weights)
+
+    @patch("src.flexcon.FlexConC.remember")
+    def test_rules(self, remaind):
+        remaind.return_value = [0]
+        self.flexcon.threshold = 0.9
+        preds = GenerateMemory()
+        self.flexcon.pred_x_it = preds.pred_x_it()
+        self.flexcon.dict_first = preds.pred_1_it()
+        # labels from dict (class1 == class2)
+        expected_rule1 = ([5], [1])
+        expected_rule2 = ([4, 5], [1, 1])
+        # labels from mock (class1 != class2)
+        expected_rule3 = ([3], [0])
+        expected_rule4 = ([2, 3], [0])
+
+        self.assertTupleEqual(self.flexcon.rule_1(), expected_rule1)
+        self.assertTupleEqual(self.flexcon.rule_2(), expected_rule2)
+        self.assertTupleEqual(self.flexcon.rule_3(), expected_rule3)
+        self.assertTupleEqual(self.flexcon.rule_4(), expected_rule4)