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feat: negative penalization tests, expanded CI, 89 tests, lint clean
- Expand CI to validate evidence certificates, run leakage check, and gate on hidden-active EF >= 1.5 at k=5 (currently achieves 4.0) - Add test_negative_penalization.py: 20 tests verifying that problematic sequences (extreme hydrophobicity, high-cysteine, purely negative charge, long repeat runs) score lower than known AMP-like sequences on activity, safety, and synthesis dimensions - Fix all 10 ruff lint warnings (unused imports) across 7 files - 89 tests passing, ruff clean
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‎.github/workflows/ci.yml‎

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- uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- name: Install
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run: pip install -e .[dev]
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run: pip install -e ".[dev]"
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- name: Lint
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run: ruff check src tests scripts
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- name: Test
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run: pytest -q
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- name: Demo
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run: ruff check src tests
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- name: Unit + integration tests
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run: pytest -q --tb=short
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- name: Demo pipeline
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run: make demo
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- name: Validate evidence certificates
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run: |
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for cert in outputs/evidence/*.json; do
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PYTHONPATH=src python -m openamp_foundry.cli validate \
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--certificate "$cert" \
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--schema schemas/candidate.schema.json
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done
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- name: Leakage check (informational)
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run: make bench-leakage
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- name: Hidden-active benchmark (require EF >= 1.5 at k=5)
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run: |
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PYTHONPATH=src python -c "
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import json, subprocess, sys, os
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result = subprocess.run(
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['python', '-m', 'openamp_foundry.cli', 'bench', 'baseline',
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'--candidates', 'examples/benchmark/mixed_candidates.csv',
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'--positives', 'examples/benchmark/active_labels.csv',
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'--k', '5'],
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capture_output=True, text=True, check=True,
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env={**os.environ, 'PYTHONPATH': 'src'}
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)
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data = json.loads(result.stdout)
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ef = next(r['enrichment_factor'] for r in data['results'] if r['k'] == 5)
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print(f'Enrichment factor at k=5: {ef}')
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if ef < 1.5:
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print(f'FAIL: EF={ef} below minimum 1.5')
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sys.exit(1)
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print('PASS: Pipeline outperforms random ranker on hidden-active benchmark')
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"

‎src/openamp_foundry/pipeline.py‎

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from __future__ import annotations
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3-
import hashlib
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import uuid
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from datetime import datetime, timezone
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from pathlib import Path

‎tests/test_benchmark_evaluate.py‎

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"""Tests for benchmark evaluation: recall@k, enrichment factor, and summary."""
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from __future__ import annotations
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import pytest
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from openamp_foundry.benchmark.evaluate import (
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benchmark_summary,
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enrichment_factor,
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random_recall_at_k,
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recall_at_k,
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top_k_ids,
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)
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from openamp_foundry.features.physchem import compute_features
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from openamp_foundry.types import PeptideCandidate, ScoredCandidate

‎tests/test_cli.py‎

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import json
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import pytest
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from openamp_foundry.cli import main
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"--out", out,
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"--cert-dir", certs,
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])
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import os
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cert_files = list((tmp_path / "certs").glob("*.json"))
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assert cert_files, "No certificates were generated"
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ret = main([

‎tests/test_hidden_active_recovery.py‎

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import json
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import pytest
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from openamp_foundry.benchmark.evaluate import (
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benchmark_summary,
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"""Tests verifying that known problematic sequences are down-ranked.
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Per AGENTS.md Phase 2: 'Toxicity penalty — Predicted hemolytic/toxic candidates
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are down-ranked.' These tests check that the safety and activity scores penalise
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sequences with properties associated with mammalian toxicity risk:
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- Extreme hydrophobicity (hemolysis risk)
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- All-cysteine (aggregation, disulfide chaos)
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- Purely negative charge (anti-AMP, repelled by bacterial membranes)
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- Very long repeat runs (low complexity, synthesis issues)
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No claims of actual biological toxicity are made. These are heuristic proxy checks.
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"""
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from __future__ import annotations
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from openamp_foundry.features.physchem import compute_features
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from openamp_foundry.scoring.activity import activity_likeness_score
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from openamp_foundry.scoring.safety import safety_score
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from openamp_foundry.scoring.synthesis import synthesis_feasibility_score
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def score_seq(seq: str) -> dict:
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f = compute_features(seq)
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return {
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"features": f,
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"activity": activity_likeness_score(f),
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"safety": safety_score(f),
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"synthesis": synthesis_feasibility_score(f, valid_sequence=True),
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}
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REFERENCE_AMP = "KWKLFKKIGAVLKVL" # Magainin analogue, classic AMP
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class TestActivityPenalization:
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def test_negative_charge_sequence_has_low_activity(self):
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# Purely negative charge — repelled by bacterial membranes
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result = score_seq("DEDEDEDEDEDE")
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assert result["activity"] < score_seq(REFERENCE_AMP)["activity"]
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def test_low_charge_sequence_has_low_activity(self):
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# No charge → low charge_density → low charge_score
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result = score_seq("AAAAAAAAGGGGGGGG")
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ref = score_seq(REFERENCE_AMP)
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assert result["activity"] < ref["activity"]
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def test_reference_amp_scores_above_low_charge(self):
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ref = score_seq(REFERENCE_AMP)
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bad = score_seq("GGGGGGGGGGGG")
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assert ref["activity"] > bad["activity"]
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class TestSafetyPenalization:
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def test_extreme_hydrophobicity_penalizes_safety(self):
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# LLLLLLLLLLLL — very hydrophobic → hemolysis risk proxy
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result = score_seq("LLLLLLLLLLLL")
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assert result["safety"] < 1.0
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def test_high_cysteine_penalizes_safety(self):
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# All cys → risk of aggregation/disulfide chaos
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result = score_seq("CCCCCCCCCCCC")
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assert result["safety"] < 0.9
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def test_very_long_repeat_run_penalizes_safety(self):
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# 8+ same residue in a row triggers safety penalty
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result = score_seq("KKKKKKKKLLLL")
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assert result["safety"] < 1.0
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def test_reference_amp_has_high_safety(self):
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# Known AMP with moderate hydrophobicity should score well
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result = score_seq(REFERENCE_AMP)
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assert result["safety"] >= 0.8
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def test_pure_negative_charge_has_lower_safety_than_amp(self):
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neg = score_seq("DEDEDEDEDEDE")
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ref = score_seq(REFERENCE_AMP)
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# Safety differs because charge_density penalizes extremes
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assert ref["safety"] >= neg["safety"]
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class TestSynthesisPenalization:
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def test_very_long_sequence_penalizes_synthesis(self):
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long_seq = "KWKLFKKIGAVLKVL" * 3 # 45 residues
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result = score_seq(long_seq)
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assert result["synthesis"] < 1.0
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def test_high_cysteine_penalizes_synthesis(self):
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result = score_seq("CCCCCCCCCCCC")
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assert result["synthesis"] < 0.9
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def test_short_repeat_penalizes_synthesis(self):
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result = score_seq("KKKKKKKKK") # long repeat run
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assert result["synthesis"] < 1.0
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def test_reference_amp_fully_feasible(self):
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# 15-residue AMP with no special challenges should be fully feasible
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result = score_seq(REFERENCE_AMP)
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assert result["synthesis"] == 1.0
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class TestNegativesVsPositivesSummary:
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"""Aggregate check: negatives from demo should average below known AMPs."""
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KNOWN_AMPS = [
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"KWKLFKKIGAVLKVL",
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"GIGKFLHSAKKFGKAFVGEIMNS",
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"GLFDIVKKVVGALGSL",
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"RRWQWRMKKLG",
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]
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KNOWN_NEGATIVES = [
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"AAAAAAAAAAAA",
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"DEDEDEDEDEDE",
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"GGGGGGGGGGGG",
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]
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def _avg_activity(self, seqs: list[str]) -> float:
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scores = [score_seq(s)["activity"] for s in seqs]
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return sum(scores) / len(scores)
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def test_amp_average_activity_exceeds_negative_average(self):
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amp_avg = self._avg_activity(self.KNOWN_AMPS)
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neg_avg = self._avg_activity(self.KNOWN_NEGATIVES)
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assert amp_avg > neg_avg, (
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f"AMP avg activity {amp_avg:.4f} should exceed negative avg {neg_avg:.4f}"
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)
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def test_amp_activity_margin_over_negatives(self):
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amp_avg = self._avg_activity(self.KNOWN_AMPS)
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neg_avg = self._avg_activity(self.KNOWN_NEGATIVES)
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margin = amp_avg - neg_avg
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assert margin >= 0.15, f"Activity margin {margin:.4f} below minimum 0.15"

‎tests/test_physchem_amphipathicity.py‎

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"""Tests for hydrophobic moment (amphipathicity) feature."""
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from __future__ import annotations
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import math
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from openamp_foundry.features.physchem import compute_features, hydrophobic_moment
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‎tests/test_pipeline_filters.py‎

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from __future__ import annotations
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import json
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from pathlib import Path
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import pytest
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from openamp_foundry.pipeline import _passes_length_filter, run_ranking_pipeline, score_candidates
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