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docs(03-04): complete subcellular localization evidence layer

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- Created SUMMARY.md with full implementation details
- Updated STATE.md: progress 40%, 8/20 plans complete
- Documented 4 key decisions (evidence terminology, NULL semantics, embedded proteomics, evidence weighting)
- All verification criteria met: 17/17 tests pass, CLI functional, DuckDB integration complete
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gbanyan
2026-02-11 19:08:01 +08:00
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tests/test_literature_integration.py Normal file
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"""Integration tests for literature evidence pipeline."""
import polars as pl
import pytest
from unittest.mock import Mock, patch, MagicMock
from pathlib import Path
import tempfile
from usher_pipeline.evidence.literature import (
process_literature_evidence,
load_to_duckdb,
query_literature_supported,
)
from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
@pytest.fixture
def mock_config():
"""Create a mock PipelineConfig for testing."""
config = Mock()
config.config_hash = Mock(return_value="test_hash_123")
config.versions = Mock()
config.versions.model_dump = Mock(return_value={
"gnomad_version": "v4.1",
"ensembl_version": "111",
})
return config
@pytest.fixture
def mock_entrez_responses():
"""Mock Entrez API responses for testing full pipeline."""
def mock_esearch_side_effect(*args, **kwargs):
"""Return mock counts based on gene and query terms."""
term = kwargs.get('term', '')
# Parse gene symbol from term (format: "({gene}[Gene Name])")
if '(' in term and '[Gene Name]' in term:
gene = term.split('(')[1].split('[')[0].strip()
else:
gene = "UNKNOWN"
# Mock counts for test genes
gene_counts = {
"GENE1": { # Direct experimental evidence
"total": 100,
"cilia": 10,
"sensory": 5,
"cytoskeleton": 8,
"cell_polarity": 3,
"knockout": 3,
"screen": 0,
},
"GENE2": { # Functional mention
"total": 50,
"cilia": 5,
"sensory": 3,
"cytoskeleton": 4,
"cell_polarity": 2,
"knockout": 0,
"screen": 0,
},
"GENE3": { # No evidence
"total": 0,
"cilia": 0,
"sensory": 0,
"cytoskeleton": 0,
"cell_polarity": 0,
"knockout": 0,
"screen": 0,
},
}
counts = gene_counts.get(gene, {"total": 0})
# Determine count based on query terms
if "cilia" in term or "cilium" in term:
count = counts.get("cilia", 0)
elif "retina" in term or "cochlea" in term or "sensory" in term:
count = counts.get("sensory", 0)
elif "cytoskeleton" in term:
count = counts.get("cytoskeleton", 0)
elif "cell polarity" in term:
count = counts.get("cell_polarity", 0)
elif "knockout" in term or "CRISPR" in term:
count = counts.get("knockout", 0)
elif "screen" in term or "proteomics" in term:
count = counts.get("screen", 0)
else:
count = counts.get("total", 0)
# Create mock handle
mock_handle = MagicMock()
mock_handle.__enter__ = Mock(return_value=mock_handle)
mock_handle.__exit__ = Mock(return_value=False)
return mock_handle
def mock_read_side_effect(handle):
"""Return count dict for esearch results."""
# Extract count from the term that was used
# For simplicity, return a range of counts
import random
count = random.randint(0, 100)
return {"Count": str(count)}
return mock_esearch_side_effect, mock_read_side_effect
@pytest.fixture
def temp_duckdb():
"""Create temporary DuckDB for integration testing."""
import tempfile
import os
# Create temp file path but don't create the file yet (DuckDB will create it)
fd, temp_path = tempfile.mkstemp(suffix='.duckdb')
os.close(fd) # Close file descriptor
os.unlink(temp_path) # Delete the empty file - DuckDB will create it properly
db_path = Path(temp_path)
yield db_path
# Cleanup
if db_path.exists():
db_path.unlink()
@pytest.fixture
def gene_test_data():
"""Small gene universe for testing."""
return pl.DataFrame({
"gene_id": [
"ENSG00000001",
"ENSG00000002",
"ENSG00000003",
],
"gene_symbol": [
"GENE1",
"GENE2",
"GENE3",
],
})
def test_full_pipeline_with_mock_pubmed(gene_test_data, mock_entrez_responses, temp_duckdb):
"""Test full literature evidence pipeline with mocked PubMed responses."""
mock_esearch, mock_read = mock_entrez_responses
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
# Configure mocks
mock_entrez.esearch = mock_esearch
mock_entrez.read = mock_read
mock_entrez.email = None
mock_entrez.api_key = None
# Process literature evidence
df = process_literature_evidence(
gene_ids=gene_test_data["gene_id"].to_list(),
gene_symbol_map=gene_test_data,
email="test@example.com",
api_key=None,
batch_size=10,
)
# Verify results
assert len(df) == 3
assert "gene_id" in df.columns
assert "gene_symbol" in df.columns
assert "evidence_tier" in df.columns
assert "literature_score_normalized" in df.columns
# Verify tier classification occurred
tiers = df["evidence_tier"].unique().to_list()
assert len(tiers) > 0
assert all(tier in ["direct_experimental", "functional_mention", "hts_hit", "incidental", "none"] for tier in tiers)
def test_checkpoint_restart(gene_test_data, mock_entrez_responses):
"""Test checkpoint-restart functionality for long-running PubMed queries."""
mock_esearch, mock_read = mock_entrez_responses
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
mock_entrez.esearch = mock_esearch
mock_entrez.read = mock_read
# First batch: process 2 genes
first_batch = gene_test_data.head(2)
df1 = process_literature_evidence(
gene_ids=first_batch["gene_id"].to_list(),
gene_symbol_map=first_batch,
email="test@example.com",
api_key=None,
)
assert len(df1) == 2
# Second batch: resume from checkpoint with full dataset
# The fetch function should skip already-processed genes
# Note: This requires checkpoint_df parameter support in fetch_literature_evidence
# For now, just verify we can process the full dataset
df2 = process_literature_evidence(
gene_ids=gene_test_data["gene_id"].to_list(),
gene_symbol_map=gene_test_data,
email="test@example.com",
api_key=None,
)
assert len(df2) == 3
def test_duckdb_persistence(gene_test_data, mock_entrez_responses, temp_duckdb, mock_config):
"""Test saving and loading literature evidence to/from DuckDB."""
mock_esearch, mock_read = mock_entrez_responses
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
mock_entrez.esearch = mock_esearch
mock_entrez.read = mock_read
# Process literature evidence
df = process_literature_evidence(
gene_ids=gene_test_data["gene_id"].to_list(),
gene_symbol_map=gene_test_data,
email="test@example.com",
api_key=None,
)
# Save to DuckDB
store = PipelineStore(temp_duckdb)
provenance = ProvenanceTracker(
pipeline_version="1.0.0",
config=mock_config,
)
load_to_duckdb(
df=df,
store=store,
provenance=provenance,
description="Test literature evidence"
)
# Verify checkpoint exists
assert store.has_checkpoint('literature_evidence')
# Load back from DuckDB
loaded_df = store.load_dataframe('literature_evidence')
assert loaded_df is not None
assert len(loaded_df) == len(df)
# Verify columns preserved
assert "gene_id" in loaded_df.columns
assert "evidence_tier" in loaded_df.columns
assert "literature_score_normalized" in loaded_df.columns
store.close()
def test_provenance_recording(gene_test_data, mock_entrez_responses, temp_duckdb, mock_config):
"""Test that provenance metadata is correctly recorded."""
mock_esearch, mock_read = mock_entrez_responses
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
mock_entrez.esearch = mock_esearch
mock_entrez.read = mock_read
# Process literature evidence
df = process_literature_evidence(
gene_ids=gene_test_data["gene_id"].to_list(),
gene_symbol_map=gene_test_data,
email="test@example.com",
api_key="test_key",
)
# Save to DuckDB with provenance
store = PipelineStore(temp_duckdb)
provenance = ProvenanceTracker(
pipeline_version="1.0.0",
config=mock_config,
)
load_to_duckdb(
df=df,
store=store,
provenance=provenance,
description="Test literature evidence"
)
# Verify provenance step was recorded
steps = provenance.get_steps()
assert len(steps) > 0
assert any(step["step_name"] == "load_literature_evidence" for step in steps)
# Verify provenance contains expected fields
load_step = next(step for step in steps if step["step_name"] == "load_literature_evidence")
assert "row_count" in load_step["details"]
assert "tier_distribution" in load_step["details"]
assert "estimated_pubmed_queries" in load_step["details"]
store.close()
def test_query_literature_supported(gene_test_data, mock_entrez_responses, temp_duckdb, mock_config):
"""Test querying genes with literature support by tier."""
mock_esearch, mock_read = mock_entrez_responses
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
mock_entrez.esearch = mock_esearch
mock_entrez.read = mock_read
# Process and save literature evidence
df = process_literature_evidence(
gene_ids=gene_test_data["gene_id"].to_list(),
gene_symbol_map=gene_test_data,
email="test@example.com",
api_key=None,
)
store = PipelineStore(temp_duckdb)
provenance = ProvenanceTracker(
pipeline_version="1.0.0",
config=mock_config,
)
load_to_duckdb(df=df, store=store, provenance=provenance)
# Query for direct experimental evidence
direct_genes = query_literature_supported(
store=store,
min_tier="direct_experimental"
)
# Should only return genes with direct_experimental tier
assert all(tier == "direct_experimental" for tier in direct_genes["evidence_tier"].to_list())
# Query for functional mention or better
functional_genes = query_literature_supported(
store=store,
min_tier="functional_mention"
)
# Should return direct_experimental OR functional_mention
assert all(
tier in ["direct_experimental", "functional_mention"]
for tier in functional_genes["evidence_tier"].to_list()
)
store.close()
def test_null_handling_in_pipeline(temp_duckdb, mock_config):
"""Test that NULL values from failed queries are preserved through pipeline."""
# Create test data with NULL counts (simulating failed PubMed queries)
df_with_nulls = pl.DataFrame({
"gene_id": ["ENSG00000001", "ENSG00000002"],
"gene_symbol": ["GENE1", "GENE2"],
"total_pubmed_count": [100, None], # GENE2 failed query
"cilia_context_count": [10, None],
"sensory_context_count": [5, None],
"cytoskeleton_context_count": [8, None],
"cell_polarity_context_count": [3, None],
"direct_experimental_count": [3, None],
"hts_screen_count": [0, None],
})
# Process through classification and scoring
from usher_pipeline.evidence.literature import classify_evidence_tier, compute_literature_score
df = classify_evidence_tier(df_with_nulls)
df = compute_literature_score(df)
# Save to DuckDB
store = PipelineStore(temp_duckdb)
provenance = ProvenanceTracker(
pipeline_version="1.0.0",
config=mock_config,
)
load_to_duckdb(df=df, store=store, provenance=provenance)
# Load back
loaded_df = store.load_dataframe('literature_evidence')
# Verify NULL preservation
gene2 = loaded_df.filter(pl.col("gene_symbol") == "GENE2")
assert gene2["total_pubmed_count"][0] is None
assert gene2["literature_score_normalized"][0] is None
assert gene2["evidence_tier"][0] == "none" # NULL counts -> "none" tier
store.close()