491 lines
18 KiB
Python
491 lines
18 KiB
Python
from __future__ import annotations
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import argparse
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import json
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from copy import deepcopy
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from datetime import datetime
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Dict, Iterable, List, Optional, Tuple
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from babelnet_incremental_enricher import (
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DEFAULT_TOPIC,
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merge_babelnet_entries,
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rebuild_enriched,
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)
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from build_babelnet_enrichment import (
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BABELNET_CACHE_PATH,
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BABELNET_ENV_KEY,
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BABELNET_OUTPUT_PATH,
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BabelNetApiCallLimitReached,
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BabelNetKeyUnavailable,
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POS_TO_BABELNET,
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enrich_entry,
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load_babelnet_api_keys,
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load_json,
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write_json,
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)
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from build_enriched_lexicon import ENRICHED_LEXICON_OUTPUT_PATH
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from build_semantic_lexicon import SEMANTIC_LEXICON_OUTPUT_PATH
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LOG_DIR = Path(__file__).with_name("logs")
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DEFAULT_API_CALL_LIMIT = 950
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DEFAULT_PER_KEY_API_CALL_LIMIT = 950
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DEFAULT_WORD_LIMIT = 10_000
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MIN_WORD_LENGTH = 3
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MAX_WORD_LENGTH = 16
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USEFUL_POS_PRIORITY = {
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"NOUN": 6,
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"VERB": 5,
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"ADJ": 4,
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"ADV": 3,
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}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description=(
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"Batch giornaliero per fondere progressivamente ItalWordNet e BabelNet: "
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"arricchisce parole mancanti, aggiorna lexicon_it_babelnet.json e rigenera lexicon_it_enriched.json."
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)
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)
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parser.add_argument(
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"--api-call-limit",
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type=int,
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default=DEFAULT_API_CALL_LIMIT,
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help="Numero massimo complessivo di chiamate API BabelNet reali consentite in questa esecuzione.",
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)
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parser.add_argument(
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"--per-key-api-call-limit",
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type=int,
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default=DEFAULT_PER_KEY_API_CALL_LIMIT,
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help="Numero massimo di chiamate API reali consentite per ciascuna chiave caricata.",
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)
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parser.add_argument(
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"--token-index",
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default=None,
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help="Usa una o piu chiavi locali, contando da 1. Esempi: --token-index 2 oppure --token-index 1,2,3.",
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)
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parser.add_argument(
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"--token-indexes",
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default=None,
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help="Alias esplicito per una lista di chiavi locali. Esempio: --token-indexes 1,2,3.",
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)
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parser.add_argument(
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"--word-limit",
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type=int,
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default=DEFAULT_WORD_LIMIT,
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help="Numero massimo di parole candidate da tentare in questa esecuzione.",
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)
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parser.add_argument(
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"--sleep",
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type=float,
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default=0.2,
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help="Pausa tra richieste API.",
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)
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parser.add_argument(
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"--topic",
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default=None,
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help="Topic opzionale per concentrare il batch su una parte del lessico.",
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)
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parser.add_argument(
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"--include-not-crossword",
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action="store_true",
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help="Include anche voci non marcate allowed_in_crossword.",
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)
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parser.add_argument(
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"--retry-no-match",
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action="store_true",
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help="Riprova anche parole gia marcate come no_match.",
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)
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parser.add_argument(
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"--dry-run",
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action="store_true",
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help="Mostra le prossime parole candidate senza chiamare BabelNet e senza scrivere file.",
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)
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parser.add_argument(
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"--ignore-cache",
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action="store_true",
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help="Ignora la cache in questa esecuzione diagnostica, utile per testare un token specifico.",
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)
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parser.add_argument(
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"--semantic",
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type=Path,
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default=SEMANTIC_LEXICON_OUTPUT_PATH,
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help="Lessico semantico completo di partenza.",
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)
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parser.add_argument(
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"--babelnet",
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type=Path,
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default=BABELNET_OUTPUT_PATH,
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help="Archivio incrementale degli arricchimenti BabelNet.",
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)
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parser.add_argument(
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"--enriched",
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type=Path,
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default=ENRICHED_LEXICON_OUTPUT_PATH,
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help="Lessico fuso da rigenerare dopo il batch.",
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)
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return parser.parse_args()
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def entry_key(entry: Dict[str, object]) -> Tuple[str, str]:
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form = str(entry.get("normalized_form") or entry.get("form") or "").strip().lower()
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pos = str(entry.get("pos") or "").strip().upper()
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return form, pos
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def load_source_payload(enriched_path: Path, semantic_path: Path) -> Dict[str, object]:
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if enriched_path.exists():
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payload = load_json(enriched_path, {})
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if isinstance(payload, dict) and "entries" in payload:
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return payload
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payload = load_json(semantic_path, {})
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if isinstance(payload, dict) and "entries" in payload:
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return payload
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raise ValueError(f"Nessun lessico valido trovato: {enriched_path} / {semantic_path}")
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def babelnet_status(entry: Dict[str, object]) -> str:
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babelnet = entry.get("babelnet", {})
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if isinstance(babelnet, dict):
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return str(babelnet.get("status", "not_requested"))
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return "not_requested"
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def entry_topics(entry: Dict[str, object]) -> set[str]:
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topics = {str(item).lower() for item in entry.get("topics", []) or [] if item}
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semantic = entry.get("semantic", {})
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if isinstance(semantic, dict):
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topics.update(str(item).lower() for item in semantic.get("semantic_topics", []) or [] if item)
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return topics
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def eligible_entry(entry: Dict[str, object], args: argparse.Namespace) -> bool:
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word = str(entry.get("form", "")).strip().lower()
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pos = str(entry.get("pos", "")).strip().upper()
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status = babelnet_status(entry)
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allowed_statuses = {"not_requested", "api_error"}
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if args.retry_no_match:
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allowed_statuses.add("no_match")
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if status not in allowed_statuses:
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return False
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if pos not in POS_TO_BABELNET:
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return False
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if not word.isalpha() or not MIN_WORD_LENGTH <= len(word) <= MAX_WORD_LENGTH:
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return False
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if not args.include_not_crossword and not entry.get("allowed_in_crossword", False):
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return False
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if args.topic and args.topic.strip().lower() not in entry_topics(entry):
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return False
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return True
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def candidate_priority(entry: Dict[str, object]) -> Tuple[int, int, int, int, int, str]:
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word = str(entry.get("form", ""))
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pos = str(entry.get("pos", "")).upper()
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topics = {str(item).lower() for item in entry.get("topics", []) or []}
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semantic = entry.get("semantic", {})
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semantic_topics = set()
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if isinstance(semantic, dict):
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semantic_topics = {str(item).lower() for item in semantic.get("semantic_topics", []) or []}
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useful_topic_bonus = 2 if topics - {DEFAULT_TOPIC, "abstract", "actions"} else 0
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semantic_topic_bonus = 1 if semantic_topics else 0
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length_bonus = 3 if 4 <= len(word) <= 11 else 1
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return (
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useful_topic_bonus,
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semantic_topic_bonus,
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int(entry.get("quality_score", 0)),
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USEFUL_POS_PRIORITY.get(pos, 0),
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length_bonus,
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word,
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)
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def select_candidates(payload: Dict[str, object], args: argparse.Namespace) -> List[Dict[str, object]]:
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candidates = [
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entry
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for entry in payload.get("entries", []) or []
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if isinstance(entry, dict) and eligible_entry(entry, args)
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]
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candidates.sort(key=candidate_priority, reverse=True)
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return candidates[: max(0, args.word_limit)]
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def progress_counts(payload: Dict[str, object]) -> Dict[str, int]:
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counts: Dict[str, int] = {}
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for entry in payload.get("entries", []) or []:
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if not isinstance(entry, dict):
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continue
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status = babelnet_status(entry)
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counts[status] = counts.get(status, 0) + 1
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return counts
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def parse_token_indexes(value: Optional[str], key_count: int, option_name: str) -> Optional[List[int]]:
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if value is None:
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return None
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selected: List[int] = []
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seen = set()
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for raw_part in str(value).replace(";", ",").split(","):
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part = raw_part.strip()
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if not part:
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continue
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try:
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index = int(part)
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except ValueError as exc:
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raise SystemExit(f"{option_name} deve contenere solo numeri separati da virgola.") from exc
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if not 1 <= index <= key_count:
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raise SystemExit(
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f"{option_name} contiene {index}, ma deve essere tra 1 e {key_count}. Chiavi caricate: {key_count}."
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)
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if index in seen:
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continue
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selected.append(index)
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seen.add(index)
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if not selected:
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raise SystemExit(f"{option_name} non contiene nessun indice valido.")
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return selected
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def write_batch_log(payload: Dict[str, object]) -> Path:
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LOG_DIR.mkdir(exist_ok=True)
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timestamp = datetime.now().astimezone().strftime("%Y%m%d_%H%M%S")
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path = LOG_DIR / f"babelnet_batch_{timestamp}.json"
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path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
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return path
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def run_batch(args: argparse.Namespace) -> Dict[str, object]:
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source_payload = load_source_payload(args.enriched, args.semantic)
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candidates = select_candidates(source_payload, args)
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before_counts = progress_counts(source_payload)
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if args.dry_run:
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return {
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"mode": "dry-run",
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"candidate_count": len(candidates),
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"selected_words": [entry.get("form") for entry in candidates[:50]],
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"before_counts": before_counts,
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}
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api_keys = load_babelnet_api_keys()
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if not api_keys:
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raise SystemExit(
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f"Chiave BabelNet mancante. Imposta {BABELNET_ENV_KEY} oppure crea .babelnet_api_key.local."
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)
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token_indexes = parse_token_indexes(args.token_index, len(api_keys), "--token-index")
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token_indexes_alias = parse_token_indexes(args.token_indexes, len(api_keys), "--token-indexes")
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if token_indexes and token_indexes_alias:
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raise SystemExit("Usa solo uno tra --token-index e --token-indexes.")
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selected_token_indexes = token_indexes or token_indexes_alias
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if selected_token_indexes:
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api_keys = [api_keys[index - 1] for index in selected_token_indexes]
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cache = {} if args.ignore_cache else load_json(BABELNET_CACHE_PATH, {})
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if not isinstance(cache, dict):
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cache = {}
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babelnet_payload = load_json(args.babelnet, {"entries": []})
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if not isinstance(babelnet_payload, dict):
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babelnet_payload = {"entries": []}
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global_stats = {
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"api_calls": 0,
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"cache_hits": 0,
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"responses": 0,
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"api_call_limit": max(0, args.api_call_limit),
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}
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per_key_limit = max(0, args.per_key_api_call_limit)
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key_stats = [
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{
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"key_index": selected_token_indexes[index] if selected_token_indexes else index + 1,
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"local_key_index": index + 1,
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"api_calls": 0,
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"cache_hits": 0,
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"responses": 0,
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"api_call_limit": per_key_limit,
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}
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for index, _ in enumerate(api_keys)
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]
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enriched_entries: List[Dict[str, object]] = []
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word_logs = []
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stopped_reason = "completed"
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def select_key_index() -> Optional[int]:
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available = [
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(stats["api_calls"], index)
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for index, stats in enumerate(key_stats)
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if stats["api_calls"] < stats["api_call_limit"]
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]
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if not available:
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return None
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available.sort()
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return available[0][1]
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for index, entry in enumerate(candidates, start=1):
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if global_stats["api_calls"] >= global_stats["api_call_limit"]:
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stopped_reason = "api_call_limit"
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break
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key_index = select_key_index()
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if key_index is None:
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stopped_reason = "per_key_api_call_limit"
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break
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before_api_calls = global_stats["api_calls"]
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before_cache_hits = global_stats["cache_hits"]
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before_responses = global_stats["responses"]
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before_key_api_calls = key_stats[key_index]["api_calls"]
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before_key_cache_hits = key_stats[key_index]["cache_hits"]
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before_key_responses = key_stats[key_index]["responses"]
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updated = deepcopy(entry)
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updated.pop("babelnet", None)
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try:
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updated["babelnet"] = enrich_entry(updated, api_keys[key_index], cache, args.sleep, key_stats[key_index])
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except BabelNetApiCallLimitReached:
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global_stats["api_calls"] += key_stats[key_index]["api_calls"] - before_key_api_calls
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global_stats["cache_hits"] += key_stats[key_index]["cache_hits"] - before_key_cache_hits
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global_stats["responses"] += key_stats[key_index]["responses"] - before_key_responses
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stopped_reason = "per_key_api_call_limit"
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break
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except BabelNetKeyUnavailable as exc:
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global_stats["api_calls"] += key_stats[key_index]["api_calls"] - before_key_api_calls
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global_stats["cache_hits"] += key_stats[key_index]["cache_hits"] - before_key_cache_hits
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global_stats["responses"] += key_stats[key_index]["responses"] - before_key_responses
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key_stats[key_index]["api_calls"] = key_stats[key_index]["api_call_limit"]
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word_logs.append(
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{
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"index": index,
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"word": updated.get("form"),
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"pos": updated.get("pos"),
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"key_index": key_stats[key_index]["key_index"],
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"api_calls": global_stats["api_calls"] - before_api_calls,
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"cache_hits": global_stats["cache_hits"] - before_cache_hits,
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"responses": global_stats["responses"] - before_responses,
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"matched": False,
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"synsets": 0,
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"reason": "key_unavailable_or_daily_limit",
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"error": str(exc),
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}
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)
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print(
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f"[{index}/{len(candidates)}] {updated.get('form')}: "
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f"token={key_stats[key_index]['key_index']} non disponibile o limite giornaliero raggiunto"
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)
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if select_key_index() is None:
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stopped_reason = "all_keys_unavailable_or_daily_limit"
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break
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continue
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global_stats["api_calls"] += key_stats[key_index]["api_calls"] - before_key_api_calls
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global_stats["cache_hits"] += key_stats[key_index]["cache_hits"] - before_key_cache_hits
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global_stats["responses"] += key_stats[key_index]["responses"] - before_key_responses
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enriched_entries.append(updated)
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write_json(BABELNET_CACHE_PATH, cache)
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word_log = {
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"index": index,
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"word": updated.get("form"),
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"pos": updated.get("pos"),
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"key_index": key_stats[key_index]["key_index"],
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"api_calls": global_stats["api_calls"] - before_api_calls,
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"cache_hits": global_stats["cache_hits"] - before_cache_hits,
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"responses": global_stats["responses"] - before_responses,
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"matched": bool(updated.get("babelnet", {}).get("matched")),
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"synsets": len(updated.get("babelnet", {}).get("synsets", []) or []),
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"reason": updated.get("babelnet", {}).get("reason"),
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}
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word_logs.append(word_log)
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print(
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f"[{index}/{len(candidates)}] {word_log['word']}: "
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f"token={word_log['key_index']} api_calls={word_log['api_calls']} cache_hits={word_log['cache_hits']} "
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f"match={word_log['matched']} tot_api={global_stats['api_calls']}/{global_stats['api_call_limit']}"
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)
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merged_babelnet = merge_babelnet_entries(
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babelnet_payload,
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enriched_entries,
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args.topic or "all",
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"all",
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)
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write_json(args.babelnet, merged_babelnet)
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enriched_payload = rebuild_enriched(
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args.semantic,
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args.babelnet,
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args.enriched,
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args.topic or DEFAULT_TOPIC,
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)
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after_counts = progress_counts(enriched_payload)
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total_entries = int(enriched_payload.get("meta", {}).get("entry_count", 0))
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covered = total_entries - after_counts.get("not_requested", 0)
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coverage = covered / total_entries if total_entries else 0.0
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result = {
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"mode": "batch",
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"started_topic": args.topic,
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"stopped_reason": stopped_reason,
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"candidate_count": len(candidates),
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"attempted_words": len(enriched_entries),
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"matched_words": sum(1 for entry in enriched_entries if entry.get("babelnet", {}).get("matched")),
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"api_calls": global_stats["api_calls"],
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"cache_hits": global_stats["cache_hits"],
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"responses": global_stats["responses"],
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"api_call_limit": global_stats["api_call_limit"],
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"api_key_count": len(api_keys),
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"forced_token_indexes": selected_token_indexes,
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"per_key_api_call_limit": per_key_limit,
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"per_key_stats": key_stats,
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"before_counts": before_counts,
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"after_counts": after_counts,
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"total_entries": total_entries,
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"covered_entries": covered,
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"coverage_ratio": coverage,
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"word_logs": word_logs,
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}
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log_path = write_batch_log(result)
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result["log_path"] = str(log_path)
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return result
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def print_result(result: Dict[str, object]) -> None:
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if result["mode"] == "dry-run":
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print("Dry-run batch BabelNet")
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print(f"Candidate selezionate: {result['candidate_count']}")
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print(f"Stati iniziali: {result['before_counts']}")
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print("Prime parole:")
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for index, word in enumerate(result["selected_words"], start=1):
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print(f"{index:>2}. {word}")
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|
return
|
|
|
|
print("Batch BabelNet completato")
|
|
print(f"- motivo stop: {result['stopped_reason']}")
|
|
print(f"- parole tentate: {result['attempted_words']}/{result['candidate_count']}")
|
|
print(f"- parole con match: {result['matched_words']}")
|
|
print(f"- chiamate API reali: {result['api_calls']}/{result['api_call_limit']}")
|
|
print(f"- chiavi caricate: {result['api_key_count']} (limite per chiave: {result['per_key_api_call_limit']})")
|
|
if result.get("forced_token_indexes"):
|
|
print(f"- token forzati: {', '.join('#' + str(index) for index in result['forced_token_indexes'])}")
|
|
for item in result["per_key_stats"]:
|
|
print(f" chiave #{item['key_index']}: {item['api_calls']}/{item['api_call_limit']} chiamate API")
|
|
print(f"- cache hit: {result['cache_hits']}")
|
|
print(f"- copertura lessico: {result['covered_entries']}/{result['total_entries']} ({result['coverage_ratio'] * 100:.1f}%)")
|
|
print(f"- stati dopo: {result['after_counts']}")
|
|
print(f"- log: {result['log_path']}")
|
|
|
|
|
|
def main() -> None:
|
|
args = parse_args()
|
|
result = run_batch(args)
|
|
print_result(result)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|