added bert_topic train.py and infer.py

pipelines/bert_topic/train.py

@@ -0,0 +1,119 @@
+"""Nornsight — BERTopic POC Training Script.
+
+Pulls a small stratified sample (~11.5k posts) from main.posts,
+trains BERTopic with MiniBatchKMeans on Jeeves, and saves the model locally.
+
+POC sample rate: random() < 0.00005 (~0.005% of 230M = ~11.5k posts)
+Full training rate will be: random() < 0.005 (~1.08M posts)
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from pathlib import Path
+
+from bertopic import BERTopic
+from sklearn.cluster import MiniBatchKMeans
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
+
+from pipelines.config import BertTopicTrainConfig, get_bertopic_train_config
+from pipelines.orm.common import get_postgres_engine
+from pipelines.orm.data_science_dev.posts import Posts
+from pipelines.orm.data_science_dev.posts.lang_filters import ENGLISH_LANGS
+from pipelines.pipelines.common import configure_logger
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    """Train and persist the BERTopic model."""
+    configure_logger()
+
+    config = get_bertopic_train_config()
+    docs = load_sample(config)
+    if not docs:
+        logger.warning("No training documents were selected")
+        return
+
+    train(docs, config)
+    logger.info(f"Done. Model saved as version {config.model_version}")
+    logger.info("Next: run infer.py to label a sample of posts in the database")
+
+
+def load_sample(config: BertTopicTrainConfig) -> list[str]:
+    logger.info("Connecting to PostgreSQL via SQLAlchemy")
+    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
+
+    logger.info(f"Pulling sample from main.posts (sample_rate={config.sample_rate})")
+    start = time.perf_counter()
+
+    with Session(engine) as session:
+        texts = session.scalars(
+            select(Posts.text).where(
+                Posts.text.is_not(None),
+                Posts.langs.in_(ENGLISH_LANGS),
+                func.length(Posts.text) > config.min_text_length,
+                func.random() < config.sample_rate,
+            )
+        ).all()
+
+    elapsed = time.perf_counter() - start
+    logger.info(f"Fetched {len(texts)} rows in {elapsed:.1f}s")
+
+    # Basic cleaning — strip whitespace and deduplicate
+    docs = list({text.strip() for text in texts})
+    logger.info(f"After cleaning and dedup: {len(docs)} posts")
+
+    return docs
+
+
+def train(docs: list[str], config: BertTopicTrainConfig) -> None:
+    logger.info(
+        f"Initialising BERTopic with MiniBatchKMeans (n_topics={config.n_topics})"
+    )
+
+    cluster_model = MiniBatchKMeans(
+        n_clusters=config.n_topics,
+        random_state=42,
+        batch_size=1024,
+        n_init=3,
+        verbose=1,
+    )
+
+    topic_model = BERTopic(
+        hdbscan_model=cluster_model,
+        language="english",
+        calculate_probabilities=False,  # saves memory
+        verbose=True,
+    )
+
+    logger.info(f"Starting fit_transform on {len(docs)} posts (CPU)")
+    start = time.perf_counter()
+
+    topic_model.fit_transform(docs)
+
+    elapsed = time.perf_counter() - start
+    logger.info(f"Training complete in {elapsed:.1f}s ({elapsed / 60:.1f} min)")
+
+    # Log topic summary for quick inspection
+    topic_info = topic_model.get_topic_info()
+    logger.info(f"Topics found: {len(topic_info)}")
+    logger.info(f"\n{topic_info.to_string()}")
+
+    model_save_path = Path(config.model_save_path)
+    model_save_path.mkdir(parents=True, exist_ok=True)
+    logger.info(f"Saving model to {model_save_path}")
+
+    topic_model.save(
+        str(model_save_path),
+        serialization="safetensors",
+        save_ctfidf=True,
+        save_embedding_model=True,
+    )
+    logger.info("Model saved")
+
+
+if __name__ == "__main__":
+    main()