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Use stricter matching for bulk matching.
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SergeantPanda
parent
6384f4f56f
commit
c7235f66ba
1 changed files with 37 additions and 14 deletions
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@ -155,6 +155,10 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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"""
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EPG matching logic that finds the best EPG matches for channels using
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multiple matching strategies including fuzzy matching and ML models.
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Automatically uses conservative thresholds for bulk matching (multiple channels)
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to avoid bad matches that create user cleanup work, and aggressive thresholds
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for single channel matching where users specifically requested a match attempt.
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"""
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channels_to_update = []
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matched_channels = []
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@ -169,7 +173,26 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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epg_embeddings = None
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ml_available = use_ml
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# Process each channel
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# Automatically determine matching strategy based on number of channels
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is_bulk_matching = len(channels_data) > 1
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# Adjust matching thresholds based on operation type
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if is_bulk_matching:
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# Conservative thresholds for bulk matching to avoid creating cleanup work
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FUZZY_HIGH_CONFIDENCE = 90 # Only very high fuzzy scores
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FUZZY_MEDIUM_CONFIDENCE = 70 # Higher threshold for ML enhancement
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ML_HIGH_CONFIDENCE = 0.75 # Higher ML confidence required
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ML_LAST_RESORT = 0.65 # More conservative last resort
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FUZZY_LAST_RESORT_MIN = 50 # Higher fuzzy minimum for last resort
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logger.info(f"Using conservative thresholds for bulk matching ({total_channels} channels)")
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else:
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# More aggressive thresholds for single channel matching (user requested specific match)
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FUZZY_HIGH_CONFIDENCE = 85 # Original threshold
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FUZZY_MEDIUM_CONFIDENCE = 40 # Original threshold
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ML_HIGH_CONFIDENCE = 0.65 # Original threshold
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ML_LAST_RESORT = 0.50 # Original desperate threshold
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FUZZY_LAST_RESORT_MIN = 20 # Original minimum
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logger.info("Using aggressive thresholds for single channel matching") # Process each channel
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for index, chan in enumerate(channels_data):
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normalized_tvg_id = chan.get("tvg_id", "")
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fallback_name = chan["tvg_id"].strip() if chan["tvg_id"] else chan["name"]
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@ -254,14 +277,14 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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continue
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# High confidence match - accept immediately
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if best_score >= BEST_FUZZY_THRESHOLD:
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if best_score >= FUZZY_HIGH_CONFIDENCE:
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chan["epg_data_id"] = best_epg["id"]
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channels_to_update.append(chan)
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matched_channels.append((chan['id'], chan['name'], best_epg["tvg_id"]))
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logger.info(f"Channel {chan['id']} '{chan['name']}' => matched tvg_id={best_epg['tvg_id']} (score={best_score})")
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# Medium confidence - use ML if available (lazy load models here)
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elif best_score >= LOWER_FUZZY_THRESHOLD and ml_available:
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elif best_score >= FUZZY_MEDIUM_CONFIDENCE and ml_available:
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# Lazy load ML models only when we actually need them
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if st_model is None:
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st_model, util = get_sentence_transformer()
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@ -288,7 +311,7 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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top_index = int(sim_scores.argmax())
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top_value = float(sim_scores[top_index])
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if top_value >= EMBED_SIM_THRESHOLD:
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if top_value >= ML_HIGH_CONFIDENCE:
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# Find the EPG entry that corresponds to this embedding index
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epg_with_names = [epg for epg in epg_data if epg.get("norm_name")]
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matched_epg = epg_with_names[top_index]
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@ -298,10 +321,10 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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matched_channels.append((chan['id'], chan['name'], matched_epg["tvg_id"]))
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logger.info(f"Channel {chan['id']} '{chan['name']}' => matched EPG tvg_id={matched_epg['tvg_id']} (fuzzy={best_score}, ML-sim={top_value:.2f})")
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else:
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logger.info(f"Channel {chan['id']} '{chan['name']}' => fuzzy={best_score}, ML-sim={top_value:.2f} < {EMBED_SIM_THRESHOLD}, trying last resort...")
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logger.info(f"Channel {chan['id']} '{chan['name']}' => fuzzy={best_score}, ML-sim={top_value:.2f} < {ML_HIGH_CONFIDENCE}, trying last resort...")
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# Last resort: try ML with very low fuzzy threshold
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if top_value >= 0.45: # Lower ML threshold as last resort
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if top_value >= ML_LAST_RESORT: # Dynamic last resort threshold
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epg_with_names = [epg for epg in epg_data if epg.get("norm_name")]
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matched_epg = epg_with_names[top_index]
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@ -310,7 +333,7 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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matched_channels.append((chan['id'], chan['name'], matched_epg["tvg_id"]))
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logger.info(f"Channel {chan['id']} '{chan['name']}' => LAST RESORT match EPG tvg_id={matched_epg['tvg_id']} (fuzzy={best_score}, ML-sim={top_value:.2f})")
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else:
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logger.info(f"Channel {chan['id']} '{chan['name']}' => even last resort ML-sim {top_value:.2f} < 0.45, skipping")
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logger.info(f"Channel {chan['id']} '{chan['name']}' => even last resort ML-sim {top_value:.2f} < {ML_LAST_RESORT}, skipping")
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except Exception as e:
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logger.warning(f"ML matching failed for channel {chan['id']}: {e}")
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@ -318,7 +341,7 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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logger.info(f"Channel {chan['id']} '{chan['name']}' => fuzzy score {best_score} below threshold, skipping")
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# Last resort: Try ML matching even with very low fuzzy scores
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elif best_score >= 20 and ml_available:
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elif best_score >= FUZZY_LAST_RESORT_MIN and ml_available:
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# Lazy load ML models for last resort attempts
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if st_model is None:
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st_model, util = get_sentence_transformer()
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@ -346,7 +369,7 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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top_index = int(sim_scores.argmax())
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top_value = float(sim_scores[top_index])
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if top_value >= 0.50: # Even lower threshold for last resort
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if top_value >= ML_LAST_RESORT: # Dynamic threshold for desperate attempts
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# Find the EPG entry that corresponds to this embedding index
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epg_with_names = [epg for epg in epg_data if epg.get("norm_name")]
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matched_epg = epg_with_names[top_index]
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@ -356,13 +379,13 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
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matched_channels.append((chan['id'], chan['name'], matched_epg["tvg_id"]))
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logger.info(f"Channel {chan['id']} '{chan['name']}' => DESPERATE LAST RESORT match EPG tvg_id={matched_epg['tvg_id']} (fuzzy={best_score}, ML-sim={top_value:.2f})")
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else:
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logger.info(f"Channel {chan['id']} '{chan['name']}' => desperate last resort ML-sim {top_value:.2f} < 0.50, giving up")
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logger.info(f"Channel {chan['id']} '{chan['name']}' => desperate last resort ML-sim {top_value:.2f} < {ML_LAST_RESORT}, giving up")
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except Exception as e:
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logger.warning(f"Last resort ML matching failed for channel {chan['id']}: {e}")
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logger.info(f"Channel {chan['id']} '{chan['name']}' => best fuzzy score={best_score} < {LOWER_FUZZY_THRESHOLD}, giving up")
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logger.info(f"Channel {chan['id']} '{chan['name']}' => best fuzzy score={best_score} < {FUZZY_MEDIUM_CONFIDENCE}, giving up")
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else:
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# No ML available or very low fuzzy score
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logger.info(f"Channel {chan['id']} '{chan['name']}' => best fuzzy score={best_score} < {LOWER_FUZZY_THRESHOLD}, no ML fallback available")
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logger.info(f"Channel {chan['id']} '{chan['name']}' => best fuzzy score={best_score} < {FUZZY_MEDIUM_CONFIDENCE}, no ML fallback available")
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# Clean up ML models from memory after matching (infrequent operation)
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if _ml_model_cache['sentence_transformer'] is not None:
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@ -430,7 +453,7 @@ def match_epg_channels():
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logger.info(f"Processing {len(channels_data)} channels against {len(epg_data)} EPG entries")
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# Run EPG matching with progress updates
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# Run EPG matching with progress updates - automatically uses conservative thresholds for bulk operations
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result = match_channels_to_epg(channels_data, epg_data, region_code, use_ml=True, send_progress=True)
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channels_to_update_dicts = result["channels_to_update"]
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matched_channels = result["matched_channels"]
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@ -564,7 +587,7 @@ def match_single_channel_epg(channel_id):
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# Send progress for single channel matching
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send_epg_matching_progress(1, 0, current_channel_name=channel.name, stage="matching")
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# Use the EPG matching function (no progress updates for single channel to avoid spam)
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# Use the EPG matching function - automatically uses aggressive thresholds for single channel
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result = match_channels_to_epg([channel_data], epg_data_list, send_progress=False)
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channels_to_update = result.get("channels_to_update", [])
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matched_channels = result.get("matched_channels", [])
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