Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
| """Tests for GE LLM dataset builder.""" | |
| import json | |
| from pathlib import Path | |
| import pandas as pd | |
| import pytest | |
| from negbiodb_depmap.llm_dataset import ( | |
| apply_max_per_gene, | |
| assign_splits, | |
| construct_evidence_description, | |
| construct_l3_context, | |
| construct_l4_context, | |
| write_jsonl, | |
| write_dataset_metadata, | |
| ) | |
| def sample_row(): | |
| return pd.Series({ | |
| "gene_symbol": "TP53", | |
| "model_id": "ACH-000001", | |
| "ccle_name": "A549_LUNG", | |
| "lineage": "Lung", | |
| "primary_disease": "Non-Small Cell Lung Cancer", | |
| "gene_effect_score": 0.05, | |
| "dependency_probability": 0.10, | |
| "evidence_type": "crispr_nonessential", | |
| "source_db": "depmap", | |
| "confidence_tier": "gold", | |
| "is_common_essential": 0, | |
| "is_reference_nonessential": 1, | |
| "gene_degree": 1500, | |
| }) | |
| class TestConstructEvidence: | |
| def test_basic_fields(self, sample_row): | |
| text = construct_evidence_description(sample_row) | |
| assert "TP53" in text | |
| assert "A549_LUNG" in text | |
| assert "Lung" in text | |
| assert "CRISPR" in text | |
| def test_gene_effect_included(self, sample_row): | |
| text = construct_evidence_description(sample_row) | |
| assert "0.050" in text | |
| def test_dependency_prob_included(self, sample_row): | |
| text = construct_evidence_description(sample_row) | |
| assert "0.100" in text | |
| def test_rnai_evidence(self, sample_row): | |
| sample_row["evidence_type"] = "rnai_nonessential" | |
| text = construct_evidence_description(sample_row) | |
| assert "RNAi" in text | |
| class TestConstructL3: | |
| def test_includes_gene_function(self, sample_row): | |
| text = construct_l3_context(sample_row, gene_description="Tumor suppressor") | |
| assert "Tumor suppressor" in text | |
| def test_includes_reference_flag(self, sample_row): | |
| text = construct_l3_context(sample_row) | |
| assert "non-essential gene set" in text | |
| def test_includes_degree(self, sample_row): | |
| text = construct_l3_context(sample_row) | |
| assert "1500" in text | |
| class TestConstructL4: | |
| def test_minimal_context(self, sample_row): | |
| text = construct_l4_context(sample_row) | |
| assert "TP53" in text | |
| assert "A549_LUNG" in text | |
| assert "Lung" in text | |
| # Should NOT include scores | |
| assert "0.050" not in text | |
| class TestApplyMaxPerGene: | |
| def test_caps_per_gene(self): | |
| df = pd.DataFrame({ | |
| "gene_id": [1] * 10 + [2] * 5, | |
| "value": range(15), | |
| }) | |
| result = apply_max_per_gene(df, max_per_gene=3) | |
| assert len(result[result["gene_id"] == 1]) == 3 | |
| assert len(result[result["gene_id"] == 2]) == 3 | |
| def test_no_change_below_max(self): | |
| df = pd.DataFrame({ | |
| "gene_id": [1, 2, 3], | |
| "value": [10, 20, 30], | |
| }) | |
| result = apply_max_per_gene(df, max_per_gene=5) | |
| assert len(result) == 3 | |
| class TestAssignSplits: | |
| def test_all_assigned(self): | |
| df = pd.DataFrame({"x": range(100)}) | |
| result = assign_splits(df) | |
| assert "split" in result.columns | |
| assert all(result["split"].isin(["train", "val", "test"])) | |
| def test_approximate_ratios(self): | |
| df = pd.DataFrame({"x": range(1000)}) | |
| result = assign_splits(df) | |
| counts = result["split"].value_counts() | |
| assert 650 < counts["train"] < 750 | |
| assert 50 < counts["val"] < 150 | |
| class TestWriteJsonl: | |
| def test_writes_file(self, tmp_path): | |
| records = [{"id": 1, "text": "hello"}, {"id": 2, "text": "world"}] | |
| path = tmp_path / "test.jsonl" | |
| count = write_jsonl(records, path) | |
| assert count == 2 | |
| assert path.exists() | |
| # Verify content | |
| lines = path.read_text().strip().split("\n") | |
| assert len(lines) == 2 | |
| assert json.loads(lines[0])["text"] == "hello" | |
| class TestWriteMetadata: | |
| def test_writes_metadata(self, tmp_path): | |
| write_dataset_metadata( | |
| tmp_path, task="ge-l1", | |
| stats={"n_records": 100, "split_counts": {"train": 70, "val": 10, "test": 20}}, | |
| ) | |
| meta_path = tmp_path / "ge-l1_metadata.json" | |
| assert meta_path.exists() | |
| meta = json.loads(meta_path.read_text()) | |
| assert meta["task"] == "ge-l1" | |
| assert meta["domain"] == "ge" | |
| assert meta["n_records"] == 100 | |