fix(safaa): warn when threshold argument cannot be honored

Safaa/src/safaa/Safaa.py

@@ -9,6 +9,7 @@
 import os
 import re
 import spacy
+import warnings
 from joblib import load, dump
 from importlib.resources import files, as_file
 from importlib.metadata import version
@@ -235,7 +236,14 @@ def predict(self, data, threshold=0.5):
         Parameters:
         data (iterable): The data to predict.
         threshold (float): The probability threshold for classification.
-                           Defaults to 0.5.
+                           Defaults to 0.5. Only used when the loaded
+                           classifier supports predict_proba (e.g., SGD
+                           with loss='log_loss' or 'modified_huber').
+                           The default shipped model uses loss='hinge'
+                           and does not produce probabilities, so this
+                           argument is ignored for that model. A
+                           UserWarning is emitted when a non-default
+                           threshold is passed but the model cannot use it.
 
         Returns:
         list: The predictions.
@@ -258,6 +266,17 @@ def predict(self, data, threshold=0.5):
                 for prediction in predictions
             ]
 
+        if threshold != 0.5:
+            warnings.warn(
+                "The loaded false positive detector does not support "
+                "probability estimates (predict_proba); the 'threshold' "
+                "argument is being ignored. Train a classifier with "
+                "loss='log_loss' or 'modified_huber' if threshold control "
+                "is required.",
+                UserWarning,
+                stacklevel=2,
+            )
+
         # Get binary predictions from the model if probability prediction is not
         # supported
         return [

pyproject.toml

@@ -41,6 +41,7 @@ psycopg2-binary = '>=2.9'
 requests = '>=2.28'
 flake8 = '*'
 build = '*'
+pytest = '*'
 
 [project]
 name = 'safaa'

tests/__init__.py

@@ -0,0 +1,3 @@
+# SPDX-FileCopyrightText: © 2026 RAJVEER42 <irajveer.bishnoi2310@gmail.com>
+#
+# SPDX-License-Identifier: LGPL-2.1-only

tests/test_safaa.py

@@ -0,0 +1,102 @@
+# SPDX-FileCopyrightText: © 2026 RAJVEER42 <irajveer.bishnoi2310@gmail.com>
+#
+# SPDX-License-Identifier: LGPL-2.1-only
+
+import warnings
+
+import pytest
+
+from safaa.Safaa import SafaaAgent
+
+
+@pytest.fixture(scope="module")
+def agent():
+    return SafaaAgent()
+
+
+# ---------------------------------------------------------------------------
+# predict — threshold argument behavior
+# ---------------------------------------------------------------------------
+
+class _FakeProbaClassifier:
+    """Test double: predict_proba returns a fixed probability for class 1."""
+
+    def __init__(self, prob_class_one):
+        self.prob_class_one = prob_class_one
+
+    def predict_proba(self, X):
+        n = X.shape[0]
+        p1 = self.prob_class_one
+        return [[1.0 - p1, p1] for _ in range(n)]
+
+    def predict(self, X):
+        # Not used when predict_proba exists; included so introspection
+        # sees a normal estimator interface.
+        return [1 if self.prob_class_one >= 0.5 else 0 for _ in range(X.shape[0])]
+
+
+class TestPredictThreshold:
+
+    SAMPLE = "Copyright 2024 Siemens AG"
+
+    def test_no_warning_with_default_threshold_on_hinge_model(self, agent):
+        # Shipped model is SGD(loss='hinge') which has no predict_proba.
+        # Calling predict() with the default threshold must NOT warn.
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")  # promote any warning to an error
+            agent.predict([self.SAMPLE])
+
+    def test_warning_fires_on_non_default_threshold_without_predict_proba(self, agent):
+        with pytest.warns(UserWarning, match="threshold"):
+            agent.predict([self.SAMPLE], threshold=0.3)
+
+    def test_warning_message_mentions_predict_proba(self, agent):
+        with pytest.warns(UserWarning) as record:
+            agent.predict([self.SAMPLE], threshold=0.7)
+        assert any("predict_proba" in str(w.message) for w in record)
+
+    def test_warning_fires_even_at_extreme_thresholds(self, agent):
+        for t in (0.0, 1.0, 0.99, 0.01):
+            with pytest.warns(UserWarning):
+                agent.predict([self.SAMPLE], threshold=t)
+
+    def test_no_warning_when_threshold_explicitly_passed_as_default(self, agent):
+        # User explicitly passes 0.5 — equivalent to default, must not warn
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            agent.predict([self.SAMPLE], threshold=0.5)
+
+    def test_prediction_still_returns_valid_output_when_warning_fires(self, agent):
+        with pytest.warns(UserWarning):
+            result = agent.predict([self.SAMPLE], threshold=0.1)
+        assert result in (["t"], ["f"])
+
+    def test_threshold_actually_controls_proba_classifier(self, agent, monkeypatch):
+        # Swap in a fake classifier whose probability for class 1 is 0.6.
+        # threshold=0.5 → 0.6 >= 0.5 → 'f'
+        # threshold=0.7 → 0.6 <  0.7 → 't'
+        monkeypatch.setattr(
+            agent, "false_positive_detector", _FakeProbaClassifier(prob_class_one=0.6)
+        )
+        assert agent.predict([self.SAMPLE], threshold=0.5) == ["f"]
+        assert agent.predict([self.SAMPLE], threshold=0.7) == ["t"]
+        assert agent.predict([self.SAMPLE], threshold=0.3) == ["f"]
+
+    def test_no_warning_when_proba_classifier_used_with_custom_threshold(
+        self, agent, monkeypatch
+    ):
+        # When the loaded model DOES support predict_proba, a non-default
+        # threshold is honored and no warning should fire.
+        monkeypatch.setattr(
+            agent, "false_positive_detector", _FakeProbaClassifier(prob_class_one=0.6)
+        )
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            agent.predict([self.SAMPLE], threshold=0.8)
+
+    def test_threshold_boundary_inclusive(self, agent, monkeypatch):
+        # Threshold check uses >=, so prediction at the exact boundary should be 'f'
+        monkeypatch.setattr(
+            agent, "false_positive_detector", _FakeProbaClassifier(prob_class_one=0.5)
+        )
+        assert agent.predict([self.SAMPLE], threshold=0.5) == ["f"]