drop kORE from default ensemble, add --kore flag to opt in
- Remove kORE from top-level imports, _ALGORITHMS, and infer_ensemble body - Add include_kore=False parameter, runs kORE only when opted in - Add --kore CLI flag threaded through all pipeline layers - Remove koreinference from --prefer choices - Keep kore.py module in repo for reference/future use - Update tests: remove test_prefer_koreinference, update algorithm assertions
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tobjend
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e3ad256321
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3 changed files with 65 additions and 57 deletions
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@ -3,7 +3,6 @@
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import re
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from .crx import CRX
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from .idregex import idregex
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from .kore import kOREInference
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from .expr import alphabet
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from .mdl import model_cost, mdl_score
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@ -375,8 +374,21 @@ def _run_idregex(sequences, kmax, N):
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return None, float('inf')
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_ALGO_NAMES = {
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'crx': 'CRX',
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'idregex': 'iDRegEx',
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}
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_ALGORITHMS = {
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'crx': lambda s, k, n: (CRX().infer(s), mdl_score_simple(CRX().infer(s), s)),
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'idregex': _run_idregex,
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}
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def _run_kore(sequences, kmax, N):
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"""Run kOREInference (Algorithm 4 with MDL), return (grammar, score) or (None, inf)."""
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"""Run kOREInference, return (grammar, score) or (None, inf)."""
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from .kore import kOREInference
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kore = kOREInference(k_max=kmax, N=N)
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result = kore.infer(sequences)
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if result:
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@ -385,21 +397,7 @@ def _run_kore(sequences, kmax, N):
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return None, float('inf')
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_ALGO_NAMES = {
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'crx': 'CRX',
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'idregex': 'iDRegEx',
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'koreinference': 'kOREInference',
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}
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_ALGORITHMS = {
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'crx': lambda s, k, n: (CRX().infer(s), mdl_score_simple(CRX().infer(s), s)),
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'idregex': _run_idregex,
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'koreinference': _run_kore,
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}
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def infer_ensemble(sequences, kmax=2, N=3, prefer=None, min_coverage=1.0):
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def infer_ensemble(sequences, kmax=2, N=3, prefer=None, min_coverage=1.0, include_kore=False):
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"""Run all applicable algorithms and return the best by MDL score.
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Args:
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@ -450,10 +448,11 @@ def infer_ensemble(sequences, kmax=2, N=3, prefer=None, min_coverage=1.0):
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if idr_g:
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results.append(('iDRegEx', idr_g, idr_score))
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# 3. kOREInference (Algorithm 4 with MDL scoring)
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kore_g, kore_score = _run_kore(sequences, kmax, N)
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if kore_g:
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results.append(('kOREInference', kore_g, kore_score))
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# 3. kOREInference (opt-in via include_kore=True)
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if include_kore:
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kore_g, kore_score = _run_kore(sequences, kmax, N)
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if kore_g:
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results.append(('kOREInference', kore_g, kore_score))
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results = [r for r in results if r[1] and r[1] != '∅']
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if not results:
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@ -14,6 +14,7 @@ import sys
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import time
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from pathlib import Path
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from collections import Counter
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from concurrent.futures import ProcessPoolExecutor, as_completed
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import pathspec
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@ -203,7 +204,7 @@ def frequency_filter(sequences, min_coverage=0.2):
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return filtered
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def analyze_clusters(file_paths, extension, project_root="", min_coverage=DEFAULT_COVERAGE, prefer=None, kmax=2, N=3):
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def analyze_clusters(file_paths, extension, project_root="", min_coverage=DEFAULT_COVERAGE, prefer=None, kmax=2, N=3, include_kore=False):
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"""Run full pipeline: preprocess → frequency filter → ensemble infer.
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Returns:
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@ -231,13 +232,25 @@ def analyze_clusters(file_paths, extension, project_root="", min_coverage=DEFAUL
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packages = _top_packages(cluster_fps, project_root)
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symbol_seqs = [[text for _, text, _ in seq] for seq in sequences]
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result = infer_ensemble(symbol_seqs, kmax=kmax, N=N, prefer=prefer, min_coverage=min_coverage)
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result = infer_ensemble(symbol_seqs, kmax=kmax, N=N, prefer=prefer, min_coverage=min_coverage, include_kore=include_kore)
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meta = {"files": cluster_fps, "imports": imports, "arg_patterns": arg_patterns, "packages": packages}
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return [("(all methods)", result, len(sequences), meta)]
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def analyze_by_package(file_paths, extension, project_root="", min_coverage=DEFAULT_COVERAGE, prefer=None, kmax=2, N=3, min_pkg_size=3):
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def _infer_group(label, group_seqs, group_files, project_root, min_coverage, prefer, kmax, N, include_kore=False):
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"""Infer grammar for one package group. Module-level for ProcessPoolExecutor."""
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filtered = frequency_filter(group_seqs, min_coverage=0.2)
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imports = _extract_imports(group_files)
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arg_patterns = _build_arg_patterns(group_files)
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packages = _top_packages(group_files, project_root)
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symbol_seqs = [[text for _, text, _ in seq] for seq in filtered]
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result = infer_ensemble(symbol_seqs, kmax=kmax, N=N, prefer=prefer, min_coverage=min_coverage, include_kore=include_kore)
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meta = {"files": group_files, "imports": imports, "arg_patterns": arg_patterns, "packages": packages}
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return (label, result, len(filtered), meta)
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def analyze_by_package(file_paths, extension, project_root="", min_coverage=DEFAULT_COVERAGE, prefer=None, kmax=2, N=3, min_pkg_size=3, include_kore=False):
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"""Preprocess and group by package directory, infer per group.
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Groups methods by their file's relative directory path, merging
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@ -273,25 +286,23 @@ def analyze_by_package(file_paths, extension, project_root="", min_coverage=DEFA
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_vprint(f" └ (other) ({len(ungrouped)} methods)")
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results = []
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for label, indices in groups:
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t1 = time.time()
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group_seqs = [sequences[i] for i in indices]
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group_files = set(seq_files[i] for i in indices)
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n_workers = os.cpu_count()
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_vprint(f"Inferring {len(groups)} groups across {n_workers} workers ...")
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with ProcessPoolExecutor(max_workers=n_workers) as ex:
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futures = {}
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for label, indices in groups:
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gs = [sequences[i] for i in indices]
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gf = set(seq_files[i] for i in indices)
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f = ex.submit(_infer_group, label, gs, gf, project_root,
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min_coverage, prefer, kmax, N, include_kore)
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futures[f] = label
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group_seqs = frequency_filter(group_seqs, min_coverage=0.2)
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for f in as_completed(futures):
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label, result, count, meta = f.result()
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results.append((label, result, count, meta))
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_vprint(f"Infer {label} ({count} methods) done")
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imports = _extract_imports(group_files)
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arg_patterns = _build_arg_patterns(group_files)
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packages = _top_packages(group_files, project_root)
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group_prefer = prefer
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_vprint(f"Infer {label} ({len(group_seqs)} methods, full ensemble) ... ", end="")
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symbol_seqs = [[text for _, text, _ in seq] for seq in group_seqs]
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result = infer_ensemble(symbol_seqs, kmax=kmax, N=N, prefer=group_prefer, min_coverage=min_coverage)
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_vprint(f"done ({time.time()-t1:.1f}s)")
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meta = {"files": group_files, "imports": imports, "arg_patterns": arg_patterns, "packages": packages}
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results.append((label, result, len(group_seqs), meta))
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results.sort(key=lambda x: x[0])
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if ungrouped:
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ungrouped_files = set(seq_files[i] for i in ungrouped)
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@ -301,7 +312,7 @@ def analyze_by_package(file_paths, extension, project_root="", min_coverage=DEFA
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return results
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def infer(file_paths, extension, min_coverage=DEFAULT_COVERAGE, prefer=None, kmax=2, N=3):
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def infer(file_paths, extension, min_coverage=DEFAULT_COVERAGE, prefer=None, kmax=2, N=3, include_kore=False):
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"""Run full pipeline: preprocess → frequency filter → ensemble infer.
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Args:
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@ -397,6 +408,7 @@ def analyze_directory(
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slice="flat",
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include=None,
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exclude=None,
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include_kore=False,
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):
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"""Scan a directory and run analysis for each language found.
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@ -427,6 +439,7 @@ def analyze_directory(
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min_coverage=min_coverage,
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prefer=prefer,
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kmax=kmax,
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include_kore=include_kore,
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)
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else:
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results[ext] = analyze_clusters(
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@ -435,6 +448,7 @@ def analyze_directory(
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min_coverage=min_coverage,
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prefer=prefer,
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kmax=kmax,
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include_kore=include_kore,
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)
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return results
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@ -470,9 +484,13 @@ def _parse_args(argv=None):
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parser.add_argument("directory", help="Directory to scan")
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parser.add_argument(
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"--prefer",
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choices=["crx", "idregex", "koreinference"],
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choices=["crx", "idregex"],
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help="Skip ensemble, use only this algorithm",
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)
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parser.add_argument(
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"--kore", action="store_true",
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help="Include kORE in ensemble (off by default for speed)",
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)
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parser.add_argument(
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"--kmax", type=int, default=2,
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help="Maximum k for k-ORE algorithms (default: 2)",
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@ -522,6 +540,7 @@ def main():
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slice=args.slice,
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include=args.include,
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exclude=args.exclude,
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include_kore=args.kore,
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)
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if args.json_flag or args.format == "json":
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@ -1,8 +1,7 @@
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"""Tests for infer_ensemble — runs CRX, iDRegEx, and kOREInference, picks best by MDL."""
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"""Tests for infer_ensemble — runs CRX and iDRegEx, picks best by MDL."""
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from bex.ensemble import infer_ensemble
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from bex.idregex import is_deterministic
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from bex.kore import kOREInference
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# ── Basic ensemble runs ──
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@ -21,16 +20,15 @@ def test_ensemble_best_not_none():
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result = infer_ensemble(seqs, kmax=2, N=3)
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assert result['best'] is not None
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assert result['best']['grammar'] is not None
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assert result['best']['algorithm'] in ('CRX', 'iDRegEx', 'kOREInference')
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assert result['best']['algorithm'] in ('CRX', 'iDRegEx')
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assert result['best']['mdl_score'] is not None
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def test_ensemble_runs_all_three():
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def test_ensemble_runs_both():
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seqs = [['a', 'b', 'c'], ['a', 'b', 'c', 'd']]
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result = infer_ensemble(seqs, kmax=2, N=3)
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algos = {a['algorithm'] for a in result['all']}
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assert 'CRX' in algos
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# iDRegEx and kOREInference may fail stochastically, so at least CRX
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assert len(result['all']) >= 1
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@ -67,13 +65,6 @@ def test_prefer_idregex():
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assert len(result['all']) == 1
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def test_prefer_koreinference():
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seqs = [['a', 'b'], ['a', 'b', 'c']]
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result = infer_ensemble(seqs, prefer='koreinference', kmax=2, N=5)
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assert result['best']['algorithm'] == 'kOREInference'
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assert len(result['all']) == 1
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def test_prefer_case_insensitive():
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seqs = [['a', 'b']]
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r1 = infer_ensemble(seqs, prefer='CRX')
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@ -229,12 +220,11 @@ def run_all():
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tests = [
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test_ensemble_returns_dict,
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test_ensemble_best_not_none,
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test_ensemble_runs_all_three,
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test_ensemble_runs_both,
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test_ensemble_all_results_have_scores,
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test_ensemble_deterministic_results,
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test_prefer_crx,
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test_prefer_idregex,
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test_prefer_koreinference,
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test_prefer_case_insensitive,
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test_prefer_unknown_falls_back,
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test_ensemble_empty_input,
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