"""Tests for PPI LLM evaluation functions (src/negbiodb_ppi/llm_eval.py).""" import json import pytest from negbiodb_ppi.llm_eval import ( PPI_EVIDENCE_KEYWORDS, PPI_L2_REQUIRED_FIELDS, PPI_L3_JUDGE_PROMPT, compute_all_ppi_llm_metrics, evaluate_ppi_l1, evaluate_ppi_l2, evaluate_ppi_l3, evaluate_ppi_l4, parse_ppi_l1_answer, parse_ppi_l2_response, parse_ppi_l3_judge_scores, parse_ppi_l4_answer, ) # ── PPI-L1 Parser Tests ─────────────────────────────────────────────────── class TestParsePPIL1Answer: def test_single_letter_a_through_d(self): for letter in "ABCD": assert parse_ppi_l1_answer(letter) == letter def test_case_insensitive(self): assert parse_ppi_l1_answer("a") == "A" assert parse_ppi_l1_answer("d") == "D" def test_answer_colon_format(self): assert parse_ppi_l1_answer("Answer: D") == "D" assert parse_ppi_l1_answer("Answer: B") == "B" def test_answer_is_format(self): assert parse_ppi_l1_answer("The answer is C") == "C" def test_parenthesized(self): assert parse_ppi_l1_answer("(D) Database score") == "D" def test_letter_dot_format(self): assert parse_ppi_l1_answer("A. Direct experimental") == "A" def test_letter_with_explanation(self): assert parse_ppi_l1_answer("B\nThis was a systematic screen") == "B" def test_empty_returns_none(self): assert parse_ppi_l1_answer("") is None def test_no_valid_letter_returns_none(self): assert parse_ppi_l1_answer("I don't know the answer") is None def test_no_e_category(self): """PPI uses 4-way (A-D), E should fallback to None or other.""" # "E" is not in _PPI_L1_LETTERS, so should return None assert parse_ppi_l1_answer("E") is None # ── PPI-L1 Evaluator Tests ─────────────────────────────────────────────── class TestEvaluatePPIL1: def test_perfect_accuracy(self): preds = ["A", "B", "C", "D"] golds = ["A", "B", "C", "D"] result = evaluate_ppi_l1(preds, golds) assert result["accuracy"] == 1.0 assert result["parse_rate"] == 1.0 def test_zero_accuracy(self): preds = ["B", "A", "D", "C"] golds = ["A", "B", "C", "D"] result = evaluate_ppi_l1(preds, golds) assert result["accuracy"] == 0.0 def test_per_class_accuracy(self): preds = ["A", "B", "C"] golds = ["A", "B", "D"] classes = ["intact", "huri", "string"] result = evaluate_ppi_l1(preds, golds, gold_classes=classes) assert "per_class_accuracy" in result assert result["per_class_accuracy"]["intact"] == 1.0 assert result["per_class_accuracy"]["string"] == 0.0 def test_per_difficulty_accuracy(self): preds = ["A", "A", "B", "B"] golds = ["A", "B", "B", "A"] diffs = ["easy", "easy", "hard", "hard"] result = evaluate_ppi_l1(preds, golds, difficulties=diffs) assert "per_difficulty_accuracy" in result assert result["per_difficulty_accuracy"]["easy"] == 0.5 assert result["per_difficulty_accuracy"]["hard"] == 0.5 def test_parse_failures(self): preds = ["A", "no valid response here at all", "C"] golds = ["A", "B", "C"] result = evaluate_ppi_l1(preds, golds) assert result["n_valid"] == 2 assert result["parse_rate"] == pytest.approx(2 / 3) assert result["accuracy"] == 1.0 def test_empty_predictions(self): result = evaluate_ppi_l1([], []) assert result["accuracy"] == 0.0 assert result["n_total"] == 0 def test_all_unparseable(self): preds = ["xyz", "hello", "???"] golds = ["A", "B", "C"] result = evaluate_ppi_l1(preds, golds) assert result["accuracy"] == 0.0 assert result["n_valid"] == 0 # ── PPI-L2 Parser Tests ────────────────────────────────────────────────── class TestParsePPIL2Response: def test_valid_json(self): obj = {"non_interacting_pairs": [{"protein_1": "TP53", "protein_2": "CDK2"}], "total_negative_count": 1} result = parse_ppi_l2_response(json.dumps(obj)) assert result["total_negative_count"] == 1 def test_json_with_code_fences(self): raw = '```json\n{"non_interacting_pairs": [], "total_negative_count": 0}\n```' result = parse_ppi_l2_response(raw) assert result["total_negative_count"] == 0 def test_json_embedded_in_text(self): raw = 'Here is the result: {"non_interacting_pairs": []} as expected.' result = parse_ppi_l2_response(raw) assert result is not None assert "non_interacting_pairs" in result def test_invalid_json(self): assert parse_ppi_l2_response("not json at all") is None # ── PPI-L2 Evaluator Tests ─────────────────────────────────────────────── class TestEvaluatePPIL2: def test_perfect_entity_matching(self): pred = json.dumps({ "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "co-IP", "evidence_strength": "strong"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, }) gold = { "gold_extraction": { "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "co-IP", "evidence_strength": "strong"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, } } result = evaluate_ppi_l2([pred], [gold]) assert result["entity_f1"] == 1.0 assert result["schema_compliance"] == 1.0 assert result["parse_rate"] == 1.0 def test_missing_pair(self): pred = json.dumps({ "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, }) gold = { "gold_extraction": { "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2"}, {"protein_1": "BRCA1", "protein_2": "ESR1"}, ], "total_negative_count": 2, "positive_interactions_mentioned": False, } } result = evaluate_ppi_l2([pred], [gold]) assert result["entity_recall"] == 0.5 assert result["entity_precision"] == 1.0 def test_count_accuracy(self): pred = json.dumps({ "non_interacting_pairs": [], "total_negative_count": 3, "positive_interactions_mentioned": False, }) gold = {"gold_extraction": {"non_interacting_pairs": [], "total_negative_count": 3, "positive_interactions_mentioned": False}} result = evaluate_ppi_l2([pred], [gold]) assert result["count_accuracy"] == 1.0 def test_parse_rate(self): preds = ['{"non_interacting_pairs": []}', "not json", '{"non_interacting_pairs": []}'] golds = [ {"gold_extraction": {"non_interacting_pairs": []}}, {"gold_extraction": {"non_interacting_pairs": []}}, {"gold_extraction": {"non_interacting_pairs": []}}, ] result = evaluate_ppi_l2(preds, golds) assert result["parse_rate"] == pytest.approx(2 / 3) def test_method_accuracy_matching(self): """Method accuracy: substring match between predicted and gold method.""" pred = json.dumps({ "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "co-immunoprecipitation (co-IP) assay", "evidence_strength": "strong"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, }) gold = { "gold_extraction": { "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "co-immunoprecipitation (co-IP) assay", "evidence_strength": "strong"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, } } result = evaluate_ppi_l2([pred], [gold]) assert result["method_accuracy"] == 1.0 assert result["strength_accuracy"] == 1.0 def test_method_accuracy_mismatch(self): """Mismatched method → method_accuracy=0.""" pred = json.dumps({ "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "co-fractionation proteomics", "evidence_strength": "moderate"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, }) gold = { "gold_extraction": { "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "binding assay", "evidence_strength": "strong"}, ], "total_negative_count": 1, "positive_interactions_mentioned": False, } } result = evaluate_ppi_l2([pred], [gold]) assert result["method_accuracy"] == 0.0 assert result["strength_accuracy"] == 0.0 def test_method_accuracy_substring_match(self): """Substring match: gold 'binding assay' in pred 'affinity binding assay'.""" pred = json.dumps({ "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "affinity binding assay"}, ], "total_negative_count": 1, }) gold = { "gold_extraction": { "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "binding assay"}, ], "total_negative_count": 1, } } result = evaluate_ppi_l2([pred], [gold]) assert result["method_accuracy"] == 1.0 def test_method_accuracy_unmatched_pairs_not_counted(self): """Method accuracy only counts matched pairs (by protein names).""" pred = json.dumps({ "non_interacting_pairs": [ {"protein_1": "UNKNOWN1", "protein_2": "UNKNOWN2", "method": "wrong method"}, ], "total_negative_count": 1, }) gold = { "gold_extraction": { "non_interacting_pairs": [ {"protein_1": "TP53", "protein_2": "CDK2", "method": "binding assay"}, ], "total_negative_count": 1, } } result = evaluate_ppi_l2([pred], [gold]) # No matched pairs → 0/0 → 0.0 assert result["method_accuracy"] == 0.0 def test_empty_predictions(self): result = evaluate_ppi_l2([], []) assert result["n_total"] == 0 # ── PPI-L3 Judge Score Parser Tests ─────────────────────────────────────── class TestParsePPIL3JudgeScores: def test_valid_scores(self): resp = json.dumps({ "biological_plausibility": 4, "structural_reasoning": 3, "mechanistic_completeness": 5, "specificity": 2, }) scores = parse_ppi_l3_judge_scores(resp) assert scores == { "biological_plausibility": 4.0, "structural_reasoning": 3.0, "mechanistic_completeness": 5.0, "specificity": 2.0, } def test_out_of_range(self): resp = json.dumps({ "biological_plausibility": 6, "structural_reasoning": 0, "mechanistic_completeness": 3, "specificity": 3, }) scores = parse_ppi_l3_judge_scores(resp) assert scores is None # 6 and 0 are out of range def test_missing_dimension(self): resp = json.dumps({ "biological_plausibility": 4, "structural_reasoning": 3, "mechanistic_completeness": 5, }) scores = parse_ppi_l3_judge_scores(resp) assert scores is None # specificity missing def test_invalid_json(self): scores = parse_ppi_l3_judge_scores("not json") assert scores is None def test_ppi_specific_dimensions(self): """PPI L3 uses different dimensions than CT L3.""" resp = json.dumps({ "biological_plausibility": 4, "structural_reasoning": 3, "mechanistic_completeness": 5, "specificity": 2, }) scores = parse_ppi_l3_judge_scores(resp) assert "biological_plausibility" in scores assert "structural_reasoning" in scores # ── PPI-L3 Evaluator Tests ─────────────────────────────────────────────── class TestEvaluatePPIL3: def test_aggregation(self): scores = [ {"biological_plausibility": 4.0, "structural_reasoning": 3.0, "mechanistic_completeness": 5.0, "specificity": 2.0}, {"biological_plausibility": 2.0, "structural_reasoning": 5.0, "mechanistic_completeness": 3.0, "specificity": 4.0}, ] result = evaluate_ppi_l3(scores) assert result["biological_plausibility"]["mean"] == pytest.approx(3.0) assert result["structural_reasoning"]["mean"] == pytest.approx(4.0) assert result["overall"]["mean"] == pytest.approx(3.5) assert result["n_valid"] == 2 def test_none_handling(self): scores = [ {"biological_plausibility": 4.0, "structural_reasoning": 3.0, "mechanistic_completeness": 5.0, "specificity": 2.0}, None, ] result = evaluate_ppi_l3(scores) assert result["n_valid"] == 1 assert result["n_total"] == 2 def test_all_none(self): result = evaluate_ppi_l3([None, None]) assert result["n_valid"] == 0 assert result["biological_plausibility"]["mean"] == 0.0 def test_empty(self): result = evaluate_ppi_l3([]) assert result["n_valid"] == 0 # ── PPI-L4 Parser Tests ────────────────────────────────────────────────── class TestParsePPIL4Answer: def test_tested(self): answer, evidence = parse_ppi_l4_answer("tested\nIntAct curated non-interaction") assert answer == "tested" assert "IntAct" in evidence def test_untested(self): answer, evidence = parse_ppi_l4_answer("untested\nNo interaction databases found") assert answer == "untested" assert "No interaction" in evidence def test_not_tested_variant(self): answer, _ = parse_ppi_l4_answer("not tested\nReasoning...") assert answer == "untested" def test_not_been_tested_variant(self): answer, _ = parse_ppi_l4_answer("This pair has not been tested\nEvidence...") assert answer == "untested" def test_never_been_tested_variant(self): answer, _ = parse_ppi_l4_answer("This pair has never been tested in any study.") assert answer == "untested" def test_no_evidence(self): answer, evidence = parse_ppi_l4_answer("tested") assert answer == "tested" assert evidence is None def test_empty(self): answer, evidence = parse_ppi_l4_answer("") assert answer is None assert evidence is None # ── PPI-L4 Evaluator Tests ─────────────────────────────────────────────── class TestEvaluatePPIL4: def test_perfect_accuracy(self): preds = ["tested\nIntAct", "untested\nNo data"] golds = ["tested", "untested"] result = evaluate_ppi_l4(preds, golds) assert result["accuracy"] == 1.0 def test_temporal_pre_2015_post_2020(self): """PPI uses pre_2015/post_2020 (not CT's pre_2020/post_2023).""" preds = ["tested\nIntAct", "tested\nHuRI", "untested\nNone", "tested\nBioGRID"] golds = ["tested", "tested", "untested", "untested"] temporal = ["pre_2015", "post_2020", "pre_2015", "post_2020"] result = evaluate_ppi_l4(preds, golds, temporal_groups=temporal) assert result["accuracy_pre_2015"] == 1.0 assert result["accuracy_post_2020"] == 0.5 def test_contamination_flag(self): """Flag when pre_2015 accuracy exceeds post_2020 by >15%.""" preds = ["tested\nA", "tested\nB", "tested\nC", "untested\nD", "tested\nE", "tested\nF", "tested\nG", "tested\nH"] golds = ["tested", "tested", "tested", "untested", "untested", "untested", "untested", "untested"] temporal = ["pre_2015", "pre_2015", "pre_2015", "pre_2015", "post_2020", "post_2020", "post_2020", "post_2020"] result = evaluate_ppi_l4(preds, golds, temporal) assert result["contamination_flag"] is True assert result["contamination_gap"] == pytest.approx(1.0) def test_no_contamination(self): preds = ["tested\nA", "untested\nB"] golds = ["tested", "untested"] temporal = ["pre_2015", "post_2020"] result = evaluate_ppi_l4(preds, golds, temporal) assert result["contamination_flag"] is False def test_evidence_citation_rate_and_logic(self): """Evidence needs BOTH >50 chars AND domain keyword (AND logic).""" preds = [ # >50 chars AND contains "intact" → pass "tested\nThis pair was tested in IntAct database using a co-immunoprecipitation assay with strong results", # >50 chars but NO keyword → fail "tested\nI think this pair was definitely tested somewhere in some large study with lots of results", # <50 chars but has keyword → fail "tested\nIntAct co-IP", # >50 chars AND keyword → pass "tested\nThe proteins were tested via yeast two-hybrid screening in the HuRI project with multiple replicates", ] golds = ["tested", "tested", "tested", "tested"] result = evaluate_ppi_l4(preds, golds) # Only 2 of 4 pass both conditions assert result["evidence_citation_rate"] == pytest.approx(0.5) def test_ppi_evidence_keywords(self): """PPI-specific keywords differ from CT and DTI.""" assert "intact" in PPI_EVIDENCE_KEYWORDS assert "huri" in PPI_EVIDENCE_KEYWORDS assert "biogrid" in PPI_EVIDENCE_KEYWORDS assert "co-ip" in PPI_EVIDENCE_KEYWORDS assert "two-hybrid" in PPI_EVIDENCE_KEYWORDS assert "pulldown" in PPI_EVIDENCE_KEYWORDS # CT/DTI keywords should NOT be here assert "nct" not in PPI_EVIDENCE_KEYWORDS assert "chembl" not in PPI_EVIDENCE_KEYWORDS def test_empty(self): result = evaluate_ppi_l4([], []) assert result["accuracy"] == 0.0 # ── Dispatch Tests ─────────────────────────────────────────────────────── class TestDispatch: def test_ppi_l1_dispatch(self): preds = ["A", "B"] gold = [{"gold_answer": "A", "gold_category": "intact", "difficulty": "easy"}, {"gold_answer": "B", "gold_category": "huri", "difficulty": "hard"}] result = compute_all_ppi_llm_metrics("ppi-l1", preds, gold) assert result["accuracy"] == 1.0 def test_ppi_l4_dispatch(self): preds = ["tested\nIntAct", "untested\nNone"] gold = [{"gold_answer": "tested", "temporal_group": "pre_2015"}, {"gold_answer": "untested", "temporal_group": "post_2020"}] result = compute_all_ppi_llm_metrics("ppi-l4", preds, gold) assert result["accuracy"] == 1.0 def test_invalid_task_raises(self): with pytest.raises(ValueError, match="Unknown task"): compute_all_ppi_llm_metrics("l1", ["A"], [{"gold_answer": "A"}]) def test_ppi_l2_dispatch(self): pred_obj = {"non_interacting_pairs": [{"protein_1": "X", "protein_2": "Y"}], "total_negative_count": 1, "positive_interactions_mentioned": False} preds = [json.dumps(pred_obj)] gold = [{"gold_extraction": {"non_interacting_pairs": [{"protein_1": "X", "protein_2": "Y"}], "total_negative_count": 1, "positive_interactions_mentioned": False}}] result = compute_all_ppi_llm_metrics("ppi-l2", preds, gold) assert result["entity_f1"] == 1.0 def test_ppi_l3_dispatch(self): resp = json.dumps({ "biological_plausibility": 4, "structural_reasoning": 3, "mechanistic_completeness": 5, "specificity": 2, }) result = compute_all_ppi_llm_metrics("ppi-l3", [resp], [{}]) assert result["n_valid"] == 1