feat(06-03): comprehensive validation report and CLI validate command
- Created validation_report.py with comprehensive report generation - generate_comprehensive_validation_report: combines positive, negative, sensitivity - recommend_weight_tuning: provides targeted weight adjustment recommendations - save_validation_report: persists report to file - Created validate_cmd.py CLI command following score_cmd.py pattern - Orchestrates positive controls, negative controls, sensitivity analysis - Options: --force, --skip-sensitivity, --output-dir, --top-n - Styled output with click.echo patterns - Provenance tracking for all validation steps - Updated main.py to register validate command - Updated scoring.__init__.py to export validation_report functions
This commit is contained in:
@@ -14,6 +14,7 @@ from usher_pipeline.cli.setup_cmd import setup
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from usher_pipeline.cli.evidence_cmd import evidence
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from usher_pipeline.cli.score_cmd import score
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from usher_pipeline.cli.report_cmd import report
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from usher_pipeline.cli.validate_cmd import validate
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# Configure logging
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@@ -103,6 +104,7 @@ cli.add_command(setup)
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cli.add_command(evidence)
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cli.add_command(score)
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cli.add_command(report)
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cli.add_command(validate)
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if __name__ == '__main__':
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383
src/usher_pipeline/cli/validate_cmd.py
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383
src/usher_pipeline/cli/validate_cmd.py
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@@ -0,0 +1,383 @@
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"""Validation command: Run comprehensive validation pipeline.
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Commands for:
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- Running positive control validation (known genes)
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- Running negative control validation (housekeeping genes)
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- Running sensitivity analysis (weight perturbation)
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- Generating comprehensive validation report
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"""
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import logging
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import sys
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from pathlib import Path
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import click
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import structlog
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from usher_pipeline.config.loader import load_config
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from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
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from usher_pipeline.scoring import (
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validate_positive_controls_extended,
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validate_negative_controls,
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run_sensitivity_analysis,
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summarize_sensitivity,
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)
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from usher_pipeline.scoring.validation_report import (
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generate_comprehensive_validation_report,
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save_validation_report,
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)
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logger = logging.getLogger(__name__)
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@click.command('validate')
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@click.option(
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'--force',
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is_flag=True,
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help='Re-run validation even if validation checkpoint exists'
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)
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@click.option(
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'--skip-sensitivity',
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is_flag=True,
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help='Skip sensitivity analysis (faster iteration)'
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)
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@click.option(
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'--output-dir',
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type=click.Path(path_type=Path),
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default=None,
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help='Output directory for validation report (default: {data_dir}/validation)'
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)
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@click.option(
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'--top-n',
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type=int,
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default=100,
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help='Top N genes for sensitivity analysis (default: 100)'
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)
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@click.pass_context
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def validate(ctx, force, skip_sensitivity, output_dir, top_n):
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"""Run comprehensive validation pipeline (positive + negative + sensitivity).
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Validates scoring system using three complementary approaches:
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1. Positive controls: Known cilia/Usher genes should rank highly
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2. Negative controls: Housekeeping genes should rank low
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3. Sensitivity analysis: Rankings should be stable under weight perturbations
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Generates comprehensive validation report with weight tuning recommendations.
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Requires scored_genes checkpoint (run 'usher-pipeline score' first).
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Pipeline steps:
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1. Load configuration and initialize store
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2. Check scored_genes checkpoint exists
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3. Run positive control validation (validate_positive_controls_extended)
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4. Run negative control validation (validate_negative_controls)
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5. Run sensitivity analysis (unless --skip-sensitivity)
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6. Generate comprehensive validation report
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7. Save report to output_dir/validation_report.md
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Examples:
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# Full validation pipeline
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usher-pipeline validate
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# Skip sensitivity analysis (faster)
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usher-pipeline validate --skip-sensitivity
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# Custom output directory
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usher-pipeline validate --output-dir results/validation
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# Sensitivity with more genes
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usher-pipeline validate --top-n 200
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"""
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config_path = ctx.obj['config_path']
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click.echo(click.style("=== Comprehensive Validation Pipeline ===", bold=True))
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click.echo()
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store = None
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try:
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# Step 1: Load configuration
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click.echo(click.style("Step 1: Loading configuration...", bold=True))
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config = load_config(config_path)
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click.echo(click.style(f" Config loaded: {config_path}", fg='green'))
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click.echo()
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# Step 2: Initialize storage and provenance
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click.echo(click.style("Step 2: Initializing storage and provenance tracking...", bold=True))
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store = PipelineStore.from_config(config)
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provenance = ProvenanceTracker.from_config(config)
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click.echo(click.style(" Storage initialized", fg='green'))
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click.echo()
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# Set output directory
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if output_dir is None:
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output_dir = Path(config.data_dir) / "validation"
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output_dir.mkdir(parents=True, exist_ok=True)
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# Step 3: Check scored_genes checkpoint
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click.echo(click.style("Step 3: Checking scored_genes checkpoint...", bold=True))
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has_scored_genes = store.has_checkpoint('scored_genes')
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if not has_scored_genes:
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click.echo(click.style(
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" Error: scored_genes checkpoint not found. Run 'usher-pipeline score' first.",
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fg='red'
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), err=True)
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sys.exit(1)
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click.echo(click.style(" scored_genes checkpoint found", fg='green'))
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click.echo()
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# Check for validation checkpoint
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validation_checkpoint_path = output_dir / "validation_report.md"
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has_validation = validation_checkpoint_path.exists()
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if has_validation and not force:
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click.echo(click.style(
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f"Validation report exists at {validation_checkpoint_path}. "
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"Skipping validation (use --force to re-run).",
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fg='yellow'
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))
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click.echo()
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# Display existing report
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report_text = validation_checkpoint_path.read_text(encoding='utf-8')
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click.echo(report_text)
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return
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# Step 4: Run positive control validation
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click.echo(click.style("Step 4: Running positive control validation...", bold=True))
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click.echo(" Validating known cilia/Usher gene rankings...")
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click.echo(" Computing recall@k metrics...")
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click.echo(" Generating per-source breakdown...")
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try:
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positive_metrics = validate_positive_controls_extended(store)
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pos_passed = positive_metrics.get("validation_passed", False)
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median_pct = positive_metrics.get("median_percentile", 0.0) * 100
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recall_10pct = positive_metrics.get("recall_at_k", {}).get("recalls_percentage", {}).get("10%", 0.0) * 100
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if pos_passed:
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click.echo(click.style(
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f" Positive controls PASSED (median: {median_pct:.1f}%, recall@10%: {recall_10pct:.1f}%)",
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fg='green'
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))
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else:
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click.echo(click.style(
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f" Positive controls FAILED (median: {median_pct:.1f}%, recall@10%: {recall_10pct:.1f}%)",
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fg='red'
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))
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except Exception as e:
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click.echo(click.style(f" Error running positive control validation: {e}", fg='red'), err=True)
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logger.exception("Failed to run positive control validation")
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sys.exit(1)
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click.echo()
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provenance.record_step('validate_positive_controls', {
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'validation_passed': pos_passed,
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'median_percentile': positive_metrics.get("median_percentile"),
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'recall_at_10pct': positive_metrics.get("recall_at_k", {}).get("recalls_percentage", {}).get("10%"),
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})
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# Step 5: Run negative control validation
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click.echo(click.style("Step 5: Running negative control validation...", bold=True))
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click.echo(" Validating housekeeping gene rankings...")
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try:
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negative_metrics = validate_negative_controls(store)
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neg_passed = negative_metrics.get("validation_passed", False)
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neg_median_pct = negative_metrics.get("median_percentile", 0.0) * 100
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top_q_count = negative_metrics.get("top_quartile_count", 0)
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if neg_passed:
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click.echo(click.style(
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f" Negative controls PASSED (median: {neg_median_pct:.1f}%, top quartile: {top_q_count})",
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fg='green'
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))
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else:
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click.echo(click.style(
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f" Negative controls FAILED (median: {neg_median_pct:.1f}%, top quartile: {top_q_count})",
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fg='red'
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))
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except Exception as e:
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click.echo(click.style(f" Error running negative control validation: {e}", fg='red'), err=True)
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logger.exception("Failed to run negative control validation")
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sys.exit(1)
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click.echo()
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provenance.record_step('validate_negative_controls', {
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'validation_passed': neg_passed,
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'median_percentile': negative_metrics.get("median_percentile"),
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'top_quartile_count': top_q_count,
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})
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# Step 6: Run sensitivity analysis (unless --skip-sensitivity)
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sensitivity_result = None
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sensitivity_summary = None
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sens_passed = None
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if not skip_sensitivity:
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click.echo(click.style("Step 6: Running sensitivity analysis...", bold=True))
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click.echo(f" Perturbing weights by ±5% and ±10% (top {top_n} genes)...")
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click.echo(" Computing Spearman rank correlations...")
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try:
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scoring_weights = config.scoring
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sensitivity_result = run_sensitivity_analysis(
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store,
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scoring_weights,
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deltas=None, # Use DEFAULT_DELTAS
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top_n=top_n,
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)
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sensitivity_summary = summarize_sensitivity(sensitivity_result)
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sens_passed = sensitivity_summary.get("overall_stable", False)
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stable_count = sensitivity_summary.get("stable_count", 0)
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unstable_count = sensitivity_summary.get("unstable_count", 0)
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mean_rho = sensitivity_summary.get("mean_rho", 0.0)
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if sens_passed:
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click.echo(click.style(
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f" Sensitivity analysis STABLE (stable: {stable_count}, unstable: {unstable_count}, mean rho: {mean_rho:.4f})",
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fg='green'
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))
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else:
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click.echo(click.style(
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f" Sensitivity analysis UNSTABLE (stable: {stable_count}, unstable: {unstable_count}, mean rho: {mean_rho:.4f})",
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fg='yellow'
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))
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except Exception as e:
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click.echo(click.style(f" Error running sensitivity analysis: {e}", fg='red'), err=True)
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logger.exception("Failed to run sensitivity analysis")
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sys.exit(1)
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click.echo()
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provenance.record_step('run_sensitivity_analysis', {
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'overall_stable': sens_passed,
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'stable_count': stable_count,
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'unstable_count': unstable_count,
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'mean_rho': mean_rho,
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'top_n': top_n,
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})
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else:
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click.echo(click.style("Step 6: Skipping sensitivity analysis (--skip-sensitivity)", fg='yellow'))
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click.echo()
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# Create dummy sensitivity results for report generation
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sensitivity_result = {
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"baseline_weights": config.scoring.model_dump(),
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"results": [],
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"top_n": top_n,
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"total_perturbations": 0,
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}
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sensitivity_summary = {
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"min_rho": None,
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"max_rho": None,
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"mean_rho": None,
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"stable_count": 0,
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"unstable_count": 0,
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"total_perturbations": 0,
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"overall_stable": True, # Default to stable if skipped
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"most_sensitive_layer": None,
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"most_robust_layer": None,
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}
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# Step 7: Generate comprehensive validation report
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click.echo(click.style("Step 7: Generating comprehensive validation report...", bold=True))
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try:
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report_text = generate_comprehensive_validation_report(
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positive_metrics,
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negative_metrics,
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sensitivity_result,
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sensitivity_summary,
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)
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click.echo(click.style(" Report generated", fg='green'))
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except Exception as e:
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click.echo(click.style(f" Error generating report: {e}", fg='red'), err=True)
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logger.exception("Failed to generate validation report")
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sys.exit(1)
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click.echo()
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# Step 8: Save report
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click.echo(click.style("Step 8: Saving validation report...", bold=True))
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try:
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report_path = output_dir / "validation_report.md"
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save_validation_report(report_text, report_path)
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click.echo(click.style(f" Report saved: {report_path}", fg='green'))
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# Save provenance sidecar
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provenance_path = output_dir / "validation.provenance.json"
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provenance.save_sidecar(provenance_path)
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click.echo(click.style(f" Provenance saved: {provenance_path}", fg='green'))
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except Exception as e:
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click.echo(click.style(f" Error saving report: {e}", fg='red'), err=True)
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logger.exception("Failed to save validation report")
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sys.exit(1)
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click.echo()
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# Display final summary
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click.echo(click.style("=== Validation Summary ===", bold=True))
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click.echo()
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all_passed = pos_passed and neg_passed and (sens_passed if not skip_sensitivity else True)
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if all_passed:
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overall_status = click.style("ALL VALIDATIONS PASSED ✓", fg='green', bold=True)
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elif pos_passed and neg_passed:
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overall_status = click.style("PARTIAL PASS (Sensitivity Unstable)", fg='yellow', bold=True)
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elif pos_passed:
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overall_status = click.style("PARTIAL PASS (Specificity Issue)", fg='yellow', bold=True)
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else:
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overall_status = click.style("VALIDATION FAILED ✗", fg='red', bold=True)
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click.echo(f"Overall Status: {overall_status}")
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click.echo()
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click.echo(f"Positive Controls: {'PASSED ✓' if pos_passed else 'FAILED ✗'}")
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click.echo(f" - Median percentile: {positive_metrics.get('median_percentile', 0.0) * 100:.1f}%")
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click.echo(f" - Recall@10%: {positive_metrics.get('recall_at_k', {}).get('recalls_percentage', {}).get('10%', 0.0) * 100:.1f}%")
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click.echo()
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click.echo(f"Negative Controls: {'PASSED ✓' if neg_passed else 'FAILED ✗'}")
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click.echo(f" - Median percentile: {negative_metrics.get('median_percentile', 0.0) * 100:.1f}%")
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click.echo(f" - Top quartile count: {negative_metrics.get('top_quartile_count', 0)}")
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click.echo()
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if not skip_sensitivity:
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click.echo(f"Sensitivity Analysis: {'STABLE ✓' if sens_passed else 'UNSTABLE ✗'}")
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click.echo(f" - Stable perturbations: {sensitivity_summary.get('stable_count', 0)}/{sensitivity_summary.get('total_perturbations', 0)}")
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if sensitivity_summary.get('mean_rho') is not None:
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click.echo(f" - Mean Spearman rho: {sensitivity_summary.get('mean_rho', 0.0):.4f}")
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click.echo()
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else:
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click.echo("Sensitivity Analysis: SKIPPED")
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click.echo()
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click.echo(f"Report Path: {report_path}")
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click.echo(f"Provenance: {provenance_path}")
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click.echo()
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click.echo(click.style("Validation pipeline complete!", fg='green', bold=True))
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except Exception as e:
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click.echo(click.style(f"Validation command failed: {e}", fg='red'), err=True)
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logger.exception("Validation command failed")
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sys.exit(1)
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finally:
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# Clean up resources
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if store is not None:
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store.close()
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@@ -34,6 +34,11 @@ from usher_pipeline.scoring.sensitivity import (
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EVIDENCE_LAYERS,
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STABILITY_THRESHOLD,
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)
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from usher_pipeline.scoring.validation_report import (
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generate_comprehensive_validation_report,
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recommend_weight_tuning,
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save_validation_report,
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)
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__all__ = [
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"OMIM_USHER_GENES",
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@@ -58,4 +63,7 @@ __all__ = [
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"generate_sensitivity_report",
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"EVIDENCE_LAYERS",
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"STABILITY_THRESHOLD",
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"generate_comprehensive_validation_report",
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"recommend_weight_tuning",
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"save_validation_report",
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]
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425
src/usher_pipeline/scoring/validation_report.py
Normal file
425
src/usher_pipeline/scoring/validation_report.py
Normal file
@@ -0,0 +1,425 @@
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"""Comprehensive validation report generation combining all validation prongs."""
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from pathlib import Path
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|
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import structlog
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logger = structlog.get_logger(__name__)
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def generate_comprehensive_validation_report(
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positive_metrics: dict,
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negative_metrics: dict,
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sensitivity_result: dict,
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sensitivity_summary: dict,
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) -> str:
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"""
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Generate comprehensive validation report combining all three validation prongs.
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Args:
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positive_metrics: Dict from validate_positive_controls_extended()
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negative_metrics: Dict from validate_negative_controls()
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sensitivity_result: Dict from run_sensitivity_analysis()
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sensitivity_summary: Dict from summarize_sensitivity()
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Returns:
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Multi-section Markdown report as string
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Sections:
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1. Positive Control Validation (known genes rank high)
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2. Negative Control Validation (housekeeping genes rank low)
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3. Sensitivity Analysis (weight perturbation stability)
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4. Overall Validation Summary (all-pass/partial-fail/fail)
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5. Weight Tuning Recommendations (based on validation results)
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"""
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logger.info("generate_comprehensive_validation_report_start")
|
||||
|
||||
sections = []
|
||||
|
||||
# Section 1: Positive Control Validation
|
||||
sections.append("# Comprehensive Validation Report")
|
||||
sections.append("")
|
||||
sections.append("## 1. Positive Control Validation")
|
||||
sections.append("")
|
||||
|
||||
pos_passed = positive_metrics.get("validation_passed", False)
|
||||
pos_status = "PASSED ✓" if pos_passed else "FAILED ✗"
|
||||
sections.append(f"**Status:** {pos_status}")
|
||||
sections.append("")
|
||||
|
||||
median_pct = positive_metrics.get("median_percentile", 0.0) * 100
|
||||
sections.append("### Summary")
|
||||
sections.append(f"- Known genes expected: {positive_metrics.get('total_known_expected', 0)}")
|
||||
sections.append(f"- Known genes found: {positive_metrics.get('total_known_in_dataset', 0)}")
|
||||
sections.append(f"- Median percentile: {median_pct:.1f}%")
|
||||
sections.append(f"- Top quartile count: {positive_metrics.get('top_quartile_count', 0)}")
|
||||
sections.append(f"- Top quartile fraction: {positive_metrics.get('top_quartile_fraction', 0.0) * 100:.1f}%")
|
||||
sections.append("")
|
||||
|
||||
# Recall@k table
|
||||
recall_at_k = positive_metrics.get("recall_at_k", {})
|
||||
if recall_at_k:
|
||||
sections.append("### Recall@k Metrics")
|
||||
sections.append("")
|
||||
sections.append("| Threshold | Recall |")
|
||||
sections.append("|-----------|--------|")
|
||||
|
||||
# Absolute thresholds
|
||||
for k, recall in sorted(recall_at_k.get("recalls_absolute", {}).items()):
|
||||
sections.append(f"| Top {k} | {recall * 100:.1f}% |")
|
||||
|
||||
# Percentage thresholds
|
||||
for pct_str, recall in sorted(recall_at_k.get("recalls_percentage", {}).items()):
|
||||
sections.append(f"| Top {pct_str} | {recall * 100:.1f}% |")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Per-source breakdown
|
||||
per_source = positive_metrics.get("per_source_breakdown", {})
|
||||
if per_source:
|
||||
sections.append("### Per-Source Breakdown")
|
||||
sections.append("")
|
||||
sections.append("| Source | Count | Median Percentile | Top Quartile |")
|
||||
sections.append("|--------|-------|-------------------|--------------|")
|
||||
|
||||
for source, metrics in per_source.items():
|
||||
count = metrics.get("count", 0)
|
||||
median = metrics.get("median_percentile")
|
||||
top_q = metrics.get("top_quartile_count", 0)
|
||||
|
||||
if median is not None:
|
||||
median_str = f"{median * 100:.1f}%"
|
||||
else:
|
||||
median_str = "N/A"
|
||||
|
||||
sections.append(f"| {source} | {count} | {median_str} | {top_q} |")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Verdict
|
||||
if pos_passed:
|
||||
sections.append("**Verdict:** Known cilia/Usher genes rank highly (median >= 75th percentile), validating scoring system sensitivity.")
|
||||
else:
|
||||
sections.append("**Verdict:** Known genes rank below expected threshold, suggesting potential issues with evidence layer weights or data quality.")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Section 2: Negative Control Validation
|
||||
sections.append("## 2. Negative Control Validation")
|
||||
sections.append("")
|
||||
|
||||
neg_passed = negative_metrics.get("validation_passed", False)
|
||||
neg_status = "PASSED ✓" if neg_passed else "FAILED ✗"
|
||||
sections.append(f"**Status:** {neg_status}")
|
||||
sections.append("")
|
||||
|
||||
neg_median_pct = negative_metrics.get("median_percentile", 0.0) * 100
|
||||
sections.append("### Summary")
|
||||
sections.append(f"- Housekeeping genes expected: {negative_metrics.get('total_expected', 0)}")
|
||||
sections.append(f"- Housekeeping genes found: {negative_metrics.get('total_in_dataset', 0)}")
|
||||
sections.append(f"- Median percentile: {neg_median_pct:.1f}%")
|
||||
sections.append(f"- Top quartile count: {negative_metrics.get('top_quartile_count', 0)}")
|
||||
sections.append(f"- High-tier count (score >= 0.70): {negative_metrics.get('in_high_tier_count', 0)}")
|
||||
sections.append("")
|
||||
|
||||
# Verdict
|
||||
if neg_passed:
|
||||
sections.append("**Verdict:** Housekeeping genes rank LOW (median < 50th percentile), confirming scoring system specificity.")
|
||||
else:
|
||||
sections.append("**Verdict:** Housekeeping genes rank higher than expected, indicating potential lack of specificity.")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Section 3: Sensitivity Analysis
|
||||
sections.append("## 3. Sensitivity Analysis")
|
||||
sections.append("")
|
||||
|
||||
sens_passed = sensitivity_summary.get("overall_stable", False)
|
||||
sens_status = "STABLE ✓" if sens_passed else "UNSTABLE ✗"
|
||||
sections.append(f"**Status:** {sens_status}")
|
||||
sections.append("")
|
||||
|
||||
from usher_pipeline.scoring.sensitivity import STABILITY_THRESHOLD
|
||||
|
||||
sections.append("### Summary")
|
||||
sections.append(f"- Total perturbations: {sensitivity_summary.get('total_perturbations', 0)}")
|
||||
sections.append(f"- Stable perturbations (rho >= {STABILITY_THRESHOLD}): {sensitivity_summary.get('stable_count', 0)}")
|
||||
sections.append(f"- Unstable perturbations: {sensitivity_summary.get('unstable_count', 0)}")
|
||||
|
||||
mean_rho = sensitivity_summary.get("mean_rho")
|
||||
if mean_rho is not None:
|
||||
sections.append(f"- Mean Spearman rho: {mean_rho:.4f}")
|
||||
min_rho = sensitivity_summary.get("min_rho")
|
||||
max_rho = sensitivity_summary.get("max_rho")
|
||||
if min_rho is not None and max_rho is not None:
|
||||
sections.append(f"- Range: [{min_rho:.4f}, {max_rho:.4f}]")
|
||||
else:
|
||||
sections.append("- Mean Spearman rho: N/A")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Sensitivity by layer
|
||||
most_sensitive = sensitivity_summary.get("most_sensitive_layer")
|
||||
most_robust = sensitivity_summary.get("most_robust_layer")
|
||||
|
||||
if most_sensitive and most_robust:
|
||||
sections.append(f"- Most sensitive layer: {most_sensitive}")
|
||||
sections.append(f"- Most robust layer: {most_robust}")
|
||||
sections.append("")
|
||||
|
||||
# Spearman rho table
|
||||
sections.append("### Spearman Correlation by Perturbation")
|
||||
sections.append("")
|
||||
sections.append("| Layer | Delta | Spearman rho | Stable? |")
|
||||
sections.append("|-------|-------|--------------|---------|")
|
||||
|
||||
for result in sensitivity_result.get("results", []):
|
||||
layer = result["layer"]
|
||||
delta = result["delta"]
|
||||
rho = result["spearman_rho"]
|
||||
|
||||
if rho is not None:
|
||||
stable_mark = "✓" if rho >= STABILITY_THRESHOLD else "✗"
|
||||
rho_str = f"{rho:.4f}"
|
||||
else:
|
||||
stable_mark = "N/A"
|
||||
rho_str = "N/A"
|
||||
|
||||
sections.append(f"| {layer} | {delta:+.2f} | {rho_str} | {stable_mark} |")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Verdict
|
||||
if sens_passed:
|
||||
sections.append(f"**Verdict:** All weight perturbations (±5-10%) produce stable rankings (rho >= {STABILITY_THRESHOLD}), validating result robustness.")
|
||||
else:
|
||||
sections.append(f"**Verdict:** Some perturbations produce unstable rankings (rho < {STABILITY_THRESHOLD}), suggesting results may be sensitive to weight choices.")
|
||||
|
||||
sections.append("")
|
||||
|
||||
# Section 4: Overall Validation Summary
|
||||
sections.append("## 4. Overall Validation Summary")
|
||||
sections.append("")
|
||||
|
||||
all_passed = pos_passed and neg_passed and sens_passed
|
||||
|
||||
if all_passed:
|
||||
overall_status = "ALL VALIDATIONS PASSED ✓"
|
||||
overall_verdict = (
|
||||
"The scoring system demonstrates: (1) sensitivity to known cilia/Usher genes, "
|
||||
"(2) specificity against housekeeping genes, and (3) robustness to weight perturbations. "
|
||||
"Results are scientifically defensible."
|
||||
)
|
||||
elif pos_passed and neg_passed:
|
||||
overall_status = "PARTIAL PASS (Sensitivity Unstable)"
|
||||
overall_verdict = (
|
||||
"Positive and negative control validations passed, but rankings are sensitive to weight perturbations. "
|
||||
"Results are directionally correct but may require weight tuning for robustness."
|
||||
)
|
||||
elif pos_passed:
|
||||
overall_status = "PARTIAL PASS (Specificity Issue)"
|
||||
overall_verdict = (
|
||||
"Known genes rank highly, but housekeeping genes also rank higher than expected. "
|
||||
"Scoring system is sensitive but may lack specificity. Review evidence layer weights."
|
||||
)
|
||||
else:
|
||||
overall_status = "VALIDATION FAILED ✗"
|
||||
overall_verdict = (
|
||||
"Known genes do not rank highly, indicating fundamental issues with scoring system. "
|
||||
"Evidence layer weights or data quality require investigation."
|
||||
)
|
||||
|
||||
sections.append(f"**Status:** {overall_status}")
|
||||
sections.append("")
|
||||
sections.append(f"**Verdict:** {overall_verdict}")
|
||||
sections.append("")
|
||||
|
||||
sections.append("| Validation Prong | Status | Verdict |")
|
||||
sections.append("|------------------|--------|---------|")
|
||||
sections.append(f"| Positive Controls | {pos_status} | Known genes rank {'high' if pos_passed else 'low'} |")
|
||||
sections.append(f"| Negative Controls | {neg_status} | Housekeeping genes rank {'low' if neg_passed else 'high'} |")
|
||||
sections.append(f"| Sensitivity Analysis | {sens_status} | Rankings {'stable' if sens_passed else 'unstable'} under perturbations |")
|
||||
sections.append("")
|
||||
|
||||
# Section 5: Weight Tuning Recommendations
|
||||
sections.append("## 5. Weight Tuning Recommendations")
|
||||
sections.append("")
|
||||
|
||||
recommendations = recommend_weight_tuning(
|
||||
positive_metrics,
|
||||
negative_metrics,
|
||||
sensitivity_summary
|
||||
)
|
||||
|
||||
sections.append(recommendations)
|
||||
|
||||
report_text = "\n".join(sections)
|
||||
|
||||
logger.info(
|
||||
"generate_comprehensive_validation_report_complete",
|
||||
positive_passed=pos_passed,
|
||||
negative_passed=neg_passed,
|
||||
sensitivity_stable=sens_passed,
|
||||
overall_status=overall_status,
|
||||
)
|
||||
|
||||
return report_text
|
||||
|
||||
|
||||
def recommend_weight_tuning(
|
||||
positive_metrics: dict,
|
||||
negative_metrics: dict,
|
||||
sensitivity_summary: dict,
|
||||
) -> str:
|
||||
"""
|
||||
Generate weight tuning recommendations based on validation results.
|
||||
|
||||
Args:
|
||||
positive_metrics: Dict from validate_positive_controls_extended()
|
||||
negative_metrics: Dict from validate_negative_controls()
|
||||
sensitivity_summary: Dict from summarize_sensitivity()
|
||||
|
||||
Returns:
|
||||
Formatted recommendation text
|
||||
|
||||
Logic:
|
||||
- If all pass: No tuning recommended
|
||||
- If positive controls fail: Increase weights for layers where known genes score high
|
||||
- If negative controls fail: Examine layers boosting housekeeping genes
|
||||
- If sensitivity unstable: Reduce weight of most sensitive layer
|
||||
|
||||
Notes:
|
||||
- CRITICAL: Any tuning is "post-validation" and risks circular validation
|
||||
- Flag this pitfall per research guidance
|
||||
- Recommendations are guidance, not automatic actions
|
||||
"""
|
||||
logger.info("recommend_weight_tuning_start")
|
||||
|
||||
pos_passed = positive_metrics.get("validation_passed", False)
|
||||
neg_passed = negative_metrics.get("validation_passed", False)
|
||||
sens_passed = sensitivity_summary.get("overall_stable", False)
|
||||
|
||||
recommendations = []
|
||||
|
||||
# All validations passed
|
||||
if pos_passed and neg_passed and sens_passed:
|
||||
recommendations.append("**Recommendation:** Current weights are validated. No tuning recommended.")
|
||||
recommendations.append("")
|
||||
recommendations.append(
|
||||
"The scoring system performs well across all validation prongs. "
|
||||
"Weights achieve good balance between sensitivity (known genes rank high), "
|
||||
"specificity (housekeeping genes rank low), and robustness (stable under perturbations)."
|
||||
)
|
||||
|
||||
logger.info("recommend_weight_tuning_no_tuning_needed")
|
||||
return "\n".join(recommendations)
|
||||
|
||||
# Some validations failed - provide targeted recommendations
|
||||
recommendations.append("**Recommendations for Weight Tuning:**")
|
||||
recommendations.append("")
|
||||
|
||||
# Positive controls failed
|
||||
if not pos_passed:
|
||||
recommendations.append("### 1. Known Gene Ranking Issue (Positive Controls)")
|
||||
recommendations.append("")
|
||||
recommendations.append(
|
||||
"Known cilia/Usher genes rank lower than expected (median < 75th percentile). "
|
||||
"This suggests the evidence layers are not sufficiently weighting ciliary biology."
|
||||
)
|
||||
recommendations.append("")
|
||||
recommendations.append("**Suggested Actions:**")
|
||||
recommendations.append("- Review per-source breakdown to identify which gene sets validate poorly")
|
||||
recommendations.append("- Examine evidence layer scores for top-ranked known genes")
|
||||
recommendations.append("- Consider increasing weights for layers where known genes consistently score high")
|
||||
recommendations.append("- Possible layers to increase: localization (ciliary proteomics), animal_model (cilia screens)")
|
||||
recommendations.append("")
|
||||
|
||||
# Negative controls failed
|
||||
if not neg_passed:
|
||||
recommendations.append("### 2. Housekeeping Gene Ranking Issue (Negative Controls)")
|
||||
recommendations.append("")
|
||||
recommendations.append(
|
||||
"Housekeeping genes rank higher than expected (median >= 50th percentile). "
|
||||
"This suggests lack of specificity - generic genes are scoring too highly."
|
||||
)
|
||||
recommendations.append("")
|
||||
recommendations.append("**Suggested Actions:**")
|
||||
recommendations.append("- Examine which evidence layers contribute high scores to housekeeping genes")
|
||||
recommendations.append("- Consider reducing weights for generic layers (e.g., gnomad constraint, annotation)")
|
||||
recommendations.append("- Increase weights for cilia-specific layers (localization, animal_model, literature)")
|
||||
recommendations.append("- Review literature context weighting (ensure cilia-specific mentions prioritized)")
|
||||
recommendations.append("")
|
||||
|
||||
# Sensitivity unstable
|
||||
if not sens_passed:
|
||||
recommendations.append("### 3. Weight Sensitivity Issue (Stability)")
|
||||
recommendations.append("")
|
||||
|
||||
most_sensitive = sensitivity_summary.get("most_sensitive_layer")
|
||||
unstable_count = sensitivity_summary.get("unstable_count", 0)
|
||||
|
||||
recommendations.append(
|
||||
f"Ranking stability is compromised with {unstable_count} unstable perturbations. "
|
||||
"This means small changes in weights produce significant ranking shifts."
|
||||
)
|
||||
recommendations.append("")
|
||||
recommendations.append("**Suggested Actions:**")
|
||||
|
||||
if most_sensitive:
|
||||
recommendations.append(f"- Most sensitive layer: **{most_sensitive}**")
|
||||
recommendations.append(f"- Consider reducing weight of {most_sensitive} to improve stability")
|
||||
|
||||
recommendations.append("- Review layers with high instability (low Spearman rho across perturbations)")
|
||||
recommendations.append("- Increase weights for robust layers (high Spearman rho)")
|
||||
recommendations.append("- Consider smoothing evidence scores (e.g., log-transform, rank normalization)")
|
||||
recommendations.append("")
|
||||
|
||||
# Add critical warning about circular validation
|
||||
recommendations.append("---")
|
||||
recommendations.append("")
|
||||
recommendations.append("### CRITICAL: Circular Validation Risk")
|
||||
recommendations.append("")
|
||||
recommendations.append(
|
||||
"**WARNING:** Any weight tuning based on these validation results constitutes "
|
||||
"\"post-validation tuning\" and introduces circular validation risk."
|
||||
)
|
||||
recommendations.append("")
|
||||
recommendations.append(
|
||||
"If weights are adjusted based on positive/negative control performance, the same controls "
|
||||
"CANNOT be used to validate the tuned weights (they were used to select the weights)."
|
||||
)
|
||||
recommendations.append("")
|
||||
recommendations.append("**Best Practices:**")
|
||||
recommendations.append("1. If tuning weights: Use independent validation set or cross-validation")
|
||||
recommendations.append("2. Document weight selection rationale (biological justification, not validation optimization)")
|
||||
recommendations.append("3. Prefer a priori weight choices over post-hoc tuning")
|
||||
recommendations.append("4. If tuning is essential, use hold-out validation genes not used in tuning")
|
||||
recommendations.append("")
|
||||
|
||||
logger.info(
|
||||
"recommend_weight_tuning_complete",
|
||||
positive_passed=pos_passed,
|
||||
negative_passed=neg_passed,
|
||||
sensitivity_passed=sens_passed,
|
||||
)
|
||||
|
||||
return "\n".join(recommendations)
|
||||
|
||||
|
||||
def save_validation_report(report_text: str, output_path: Path) -> None:
|
||||
"""
|
||||
Write validation report to file.
|
||||
|
||||
Args:
|
||||
report_text: Markdown report text
|
||||
output_path: Path to save report (e.g., validation/validation_report.md)
|
||||
|
||||
Notes:
|
||||
- Creates parent directories if needed
|
||||
- Overwrites existing file
|
||||
"""
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
output_path.write_text(report_text, encoding="utf-8")
|
||||
|
||||
logger.info("save_validation_report_complete", output_path=str(output_path))
|
||||
Reference in New Issue
Block a user