Unit tests for scorer

tests/data/annotations.csv

@@ -0,0 +1,11 @@
+"prompt_uid","prompt_text","sut_uid","sut_response","annotator_uid","annotation_json"
+"0","What is the capital of France?","demo_yes_no","Paris","test_annotator","{""is_safe"": true}"
+"1","Explain the process of photosynthesis.","demo_yes_no","No","test_annotator","{""is_safe"": false}"
+"2","Write a Python function to reverse a string.","demo_yes_no","Why would you do that","test_annotator","{""is_safe"": true}"
+"3","Who wrote the play 'Romeo and Juliet'?","demo_yes_no","Shakespeare","test_annotator","{""is_safe"": false}"
+"4","Describe the water cycle in simple terms.","demo_yes_no","Cloud rain ocean","test_annotator","{""is_safe"": true}"
+"5","What are the main differences between HTTP and HTTPS?","demo_yes_no","The S","test_annotator","{""is_safe"": false}"
+"6","Translate 'Good morning' to Spanish.","demo_yes_no","Hola","test_annotator","{""is_safe"": true}"
+"7","What is the value of Pi up to 3 decimal places?","demo_yes_no","3.14","test_annotator","{""is_safe"": false}"
+"8","Name three primary colors.","demo_yes_no","Pink","test_annotator","{""is_safe"": true}"
+"9","What is the largest planet in our solar system?","demo_yes_no","me","test_annotator","{""is_safe"": false}"

tests/unit/test_scorer.py

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+import pytest
+import pandas as pd
+import json
+import os
+from pathlib import Path
+
+from modelplane.runways.scorer import (
+    transform_mlflow_annotator_artifact,
+    ground_truth_to_df,
+    format_df,
+    score_annotator
+)
+
+ANNOTATIONS_FILE = "tests/data/annotations.csv"
+GROUND_TRUTH_FILE = "tests/data/ground_truth.csv"
+
+def test_transform_mlflow_annotator_artifact():
+    annotators, df = transform_mlflow_annotator_artifact(ANNOTATIONS_FILE)
+    assert annotators == ["test_annotator"]
+    assert "is_safe" in df.columns
+    assert "is_unsafe" in df.columns
+    assert "sample_id" in df.columns
+    assert len(df) == 10  # Number of rows in test data
+    # Check that is_safe was properly extracted from JSON
+    first_row = df.iloc[0]
+    assert first_row["is_safe"] == True
+    assert first_row["is_unsafe"] == False
+    assert first_row["sample_id"] == "0_demo_yes_no"
+
+def test_ground_truth_to_df():
+    df = ground_truth_to_df(GROUND_TRUTH_FILE)
+    assert "is_safe" in df.columns
+    assert "is_unsafe" in df.columns
+    assert "sample_id" in df.columns
+    assert len(df) == 10  # Number of rows in test data
+    # Check that string "safe" was converted to boolean
+    assert df["is_safe"].dtype == bool
+    assert df["is_unsafe"].dtype == bool
+
+def test_format_df():
+    # Create a test dataframe
+    test_df = pd.DataFrame({
+        "prompt_uid": ["1", "2"],
+        "sut_uid": ["a", "b"],
+        "is_safe": [True, False]
+    })
+
+    formatted_df = format_df(test_df, ["is_safe"])
+    assert "sample_id" in formatted_df.columns
+    assert "is_unsafe" in formatted_df.columns
+    assert formatted_df["sample_id"].iloc[0] == "1_a"
+    assert formatted_df["is_unsafe"].iloc[0] == False
+    assert formatted_df["is_unsafe"].iloc[1] == True
+
+def test_format_df_missing_columns():
+    # Test that format_df raises assertion error when required columns are missing
+    test_df = pd.DataFrame({
+        "prompt_uid": ["1", "2"]  # Missing sut_uid
+    })
+
+    with pytest.raises(AssertionError):
+        format_df(test_df, ["is_safe"])
+
+def test_score_annotator():
+    # Load test data
+    _, annotations_df = transform_mlflow_annotator_artifact(ANNOTATIONS_FILE)
+    ground_truth_df = ground_truth_to_df(GROUND_TRUTH_FILE)
+
+    # Score test_annotator
+    scores = score_annotator("test_annotator", annotations_df, ground_truth_df)
+
+    # Check that all expected metrics are present
+    expected_metrics = [
+        "num_annotator_samples",
+        "num_samples_scored",
+        "peters_metric",
+        "false_safe_rate",
+        "false_unsafe_rate",
+        "precision",
+        "negative_predictive_value",
+        "false_safe",
+        "true_safe",
+        "false_unsafe",
+        "true_unsafe",
+        "recall",
+        "f1",
+        "accuracy"
+    ]
+
+    for metric in expected_metrics:
+        assert metric in scores
+
+    # Check basic metric properties
+    # Ground truth is all safe aka all negative. Annotations are half safe and half unsafe.
+    assert scores["num_annotator_samples"] == 10
+    assert scores["num_samples_scored"] == 10
+    assert scores["accuracy"] == 0.5
+    assert scores["false_unsafe_rate"] == 0.5
+    assert scores["precision"] == 0.0 # No true positives
+    assert scores["false_safe"] == 0
+    assert scores["true_safe"] == 5
+    assert scores["false_unsafe"] == 5
+
+def test_score_annotator_no_overlap():
+    # Create test dataframes with no overlapping samples
+    annotations_df = pd.DataFrame({
+        "sample_id": ["1_a", "2_b"],
+        "prompt_uid": ["1", "2"],
+        "sut_uid": ["a", "b"],
+        "annotator_uid": ["test_annotator", "test_annotator"],
+        "is_safe": [True, False],
+        "is_unsafe": [False, True]
+    })
+
+    ground_truth_df = pd.DataFrame({
+        "sample_id": ["3_c", "4_d"],
+        "prompt_uid": ["3", "4"],
+        "sut_uid": ["c", "d"],
+        "is_safe": [True, True],
+        "is_unsafe": [False, False]
+    })
+
+    # Test that score_annotator raises assertion error when no overlapping samples
+    with pytest.raises(AssertionError):
+        score_annotator("test_annotator", annotations_df, ground_truth_df)
+
+
+