Initial commit: TheAuditor v1.0.1 - AI-centric SAST and Code Intelligence Platform

theauditor/commands/ml.py

@@ -0,0 +1,165 @@
+"""Machine learning commands for TheAuditor."""
+
+import click
+from pathlib import Path
+
+
+@click.command(name="learn")
+@click.option("--db-path", default="./.pf/repo_index.db", help="Database path")
+@click.option("--manifest", default="./.pf/manifest.json", help="Manifest file path")
+@click.option("--journal", default="./.pf/journal.ndjson", help="Journal file path")
+@click.option("--fce", default="./.pf/fce.json", help="FCE file path")
+@click.option("--ast", default="./.pf/ast_proofs.json", help="AST proofs file path")
+@click.option("--enable-git", is_flag=True, help="Enable git churn features")
+@click.option("--model-dir", default="./.pf/ml", help="Model output directory")
+@click.option("--window", default=50, type=int, help="Journal window size")
+@click.option("--seed", default=13, type=int, help="Random seed")
+@click.option("--feedback", help="Path to human feedback JSON file")
+@click.option("--train-on", type=click.Choice(["full", "diff", "all"]), default="full", help="Type of historical runs to train on")
+@click.option("--print-stats", is_flag=True, help="Print training statistics")
+def learn(db_path, manifest, journal, fce, ast, enable_git, model_dir, window, seed, feedback, train_on, print_stats):
+    """Train ML models from audit artifacts to predict risk and root causes."""
+    from theauditor.ml import learn as ml_learn
+    
+    click.echo(f"[ML] Training models from audit artifacts (using {train_on} runs)...")
+    
+    result = ml_learn(
+        db_path=db_path,
+        manifest_path=manifest,
+        journal_path=journal,
+        fce_path=fce,
+        ast_path=ast,
+        enable_git=enable_git,
+        model_dir=model_dir,
+        window=window,
+        seed=seed,
+        print_stats=print_stats,
+        feedback_path=feedback,
+        train_on=train_on,
+    )
+    
+    if result.get("success"):
+        stats = result.get("stats", {})
+        click.echo(f"[OK] Models trained successfully")
+        click.echo(f"  * Training data: {train_on} runs from history")
+        click.echo(f"  * Files analyzed: {result.get('source_files', 0)}")
+        click.echo(f"  * Features: {stats.get('n_features', 0)} dimensions")
+        click.echo(f"  * Root cause ratio: {stats.get('root_cause_positive_ratio', 0):.2%}")
+        click.echo(f"  * Risk mean: {stats.get('mean_risk', 0):.3f}")
+        if stats.get('cold_start'):
+            click.echo(f"  [WARN] Cold-start mode (<500 samples)")
+        click.echo(f"  * Models saved to: {result.get('model_dir')}")
+    else:
+        click.echo(f"[FAIL] Training failed: {result.get('error')}", err=True)
+        raise click.ClickException(result.get("error"))
+
+
+@click.command(name="suggest")
+@click.option("--db-path", default="./.pf/repo_index.db", help="Database path")
+@click.option("--manifest", default="./.pf/manifest.json", help="Manifest file path")
+@click.option("--workset", default="./.pf/workset.json", help="Workset file path")
+@click.option("--fce", default="./.pf/fce.json", help="FCE file path")
+@click.option("--ast", default="./.pf/ast_proofs.json", help="AST proofs file path")
+@click.option("--model-dir", default="./.pf/ml", help="Model directory")
+@click.option("--topk", default=10, type=int, help="Top K files to suggest")
+@click.option("--out", default="./.pf/insights/ml_suggestions.json", help="Output file path")
+@click.option("--print-plan", is_flag=True, help="Print suggestions to console")
+def suggest(db_path, manifest, workset, fce, ast, model_dir, topk, out, print_plan):
+    """Generate ML-based suggestions for risky files and likely root causes."""
+    from theauditor.ml import suggest as ml_suggest
+    
+    click.echo("[ML] Generating suggestions from trained models...")
+    
+    result = ml_suggest(
+        db_path=db_path,
+        manifest_path=manifest,
+        workset_path=workset,
+        fce_path=fce,
+        ast_path=ast,
+        model_dir=model_dir,
+        topk=topk,
+        out_path=out,
+        print_plan=print_plan,
+    )
+    
+    if result.get("success"):
+        click.echo(f"[OK] Suggestions generated")
+        click.echo(f"  * Workset size: {result.get('workset_size', 0)} files")
+        click.echo(f"  * Source files analyzed: {result.get('workset_size', 0)}")
+        click.echo(f"  * Non-source excluded: {result.get('excluded_count', 0)}")
+        click.echo(f"  * Top {result.get('topk', 10)} suggestions saved to: {result.get('out_path')}")
+    else:
+        click.echo(f"[FAIL] Suggestion generation failed: {result.get('error')}", err=True)
+        raise click.ClickException(result.get("error"))
+
+
+@click.command(name="learn-feedback")
+@click.option("--feedback-file", required=True, help="Path to feedback JSON file")
+@click.option("--db-path", default="./.pf/repo_index.db", help="Database path")
+@click.option("--manifest", default="./.pf/manifest.json", help="Manifest file path")
+@click.option("--model-dir", default="./.pf/ml", help="Model output directory")
+@click.option("--train-on", type=click.Choice(["full", "diff", "all"]), default="full", help="Type of historical runs to train on")
+@click.option("--print-stats", is_flag=True, help="Print training statistics")
+def learn_feedback(feedback_file, db_path, manifest, model_dir, train_on, print_stats):
+    """
+    Re-train models with human feedback for improved accuracy.
+    
+    The feedback file should be a JSON file with the format:
+    {
+        "path/to/file.py": {
+            "is_risky": true,
+            "is_root_cause": false,
+            "will_need_edit": true
+        },
+        ...
+    }
+    """
+    from theauditor.ml import learn as ml_learn
+    
+    # Validate feedback file exists
+    if not Path(feedback_file).exists():
+        click.echo(f"[FAIL] Feedback file not found: {feedback_file}", err=True)
+        raise click.ClickException(f"Feedback file not found: {feedback_file}")
+    
+    # Validate feedback file format
+    try:
+        import json
+        with open(feedback_file) as f:
+            feedback_data = json.load(f)
+        
+        if not isinstance(feedback_data, dict):
+            raise ValueError("Feedback file must contain a JSON object")
+        
+        # Count feedback entries
+        feedback_count = len(feedback_data)
+        click.echo(f"[ML] Loading human feedback for {feedback_count} files...")
+        
+    except Exception as e:
+        click.echo(f"[FAIL] Invalid feedback file format: {e}", err=True)
+        raise click.ClickException(f"Invalid feedback file: {e}")
+    
+    click.echo(f"[ML] Re-training models with human feedback (using {train_on} runs)...")
+    
+    result = ml_learn(
+        db_path=db_path,
+        manifest_path=manifest,
+        model_dir=model_dir,
+        print_stats=print_stats,
+        feedback_path=feedback_file,
+        train_on=train_on,
+        # Use default paths for historical data from .pf/history
+        enable_git=False,  # Disable git for speed in feedback mode
+    )
+    
+    if result.get("success"):
+        stats = result.get("stats", {})
+        click.echo(f"[OK] Models re-trained with human feedback")
+        click.echo(f"  * Training data: {train_on} runs from history")
+        click.echo(f"  * Files analyzed: {result.get('source_files', 0)}")
+        click.echo(f"  * Human feedback incorporated: {feedback_count} files")
+        click.echo(f"  * Features: {stats.get('n_features', 0)} dimensions")
+        click.echo(f"  * Models saved to: {result.get('model_dir')}")
+        click.echo(f"\n[TIP] The models have learned from your feedback and will provide more accurate predictions.")
+    else:
+        click.echo(f"[FAIL] Re-training failed: {result.get('error')}", err=True)
+        raise click.ClickException(result.get("error"))