ML for Developers

tests/model/test_behavioral.py

@@ -0,0 +1,65 @@
+import pytest
+import utils
+
+
+@pytest.mark.parametrize(
+    "input_a, input_b, label",
+    [
+        (
+            "Transformers applied to NLP have revolutionized machine learning.",
+            "Transformers applied to NLP have disrupted machine learning.",
+            "natural-language-processing",
+        ),
+    ],
+)
+def test_invariance(input_a, input_b, label, predictor):
+    """INVariance via verb injection (changes should not affect outputs)."""
+    label_a = utils.get_label(text=input_a, predictor=predictor)
+    label_b = utils.get_label(text=input_b, predictor=predictor)
+    assert label_a == label_b == label
+
+
+@pytest.mark.parametrize(
+    "input, label",
+    [
+        (
+            "ML applied to text classification.",
+            "natural-language-processing",
+        ),
+        (
+            "ML applied to image classification.",
+            "computer-vision",
+        ),
+        (
+            "CNNs for text classification.",
+            "natural-language-processing",
+        ),
+    ],
+)
+def test_directional(input, label, predictor):
+    """DIRectional expectations (changes with known outputs)."""
+    prediction = utils.get_label(text=input, predictor=predictor)
+    assert label == prediction
+
+
+@pytest.mark.parametrize(
+    "input, label",
+    [
+        (
+            "Natural language processing is the next big wave in machine learning.",
+            "natural-language-processing",
+        ),
+        (
+            "MLOps is the next big wave in machine learning.",
+            "mlops",
+        ),
+        (
+            "This is about graph neural networks.",
+            "other",
+        ),
+    ],
+)
+def test_mft(input, label, predictor):
+    """Minimum Functionality Tests (simple input/output pairs)."""
+    prediction = utils.get_label(text=input, predictor=predictor)
+    assert label == prediction