376 lines
15 KiB
Python
376 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Universal Clustering Filter
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Filters clustering results for multiple algorithms:
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- DBSCAN: handles noise points, uses density-based selection
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- GMM: uses probability-based selection, no noise points
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- K-Means: standard centroid-based selection
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Uses cosine distance metric for all calculations.
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"""
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import json
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import numpy as np
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from sklearn.preprocessing import normalize
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from sklearn.metrics.pairwise import cosine_distances
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import argparse
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import os
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from pathlib import Path
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class UniversalClusterFilter:
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def __init__(self, embeddings_path, clustering_results_path):
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"""
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Initialize universal cluster filter
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Args:
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embeddings_path: Path to embeddings JSON file
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clustering_results_path: Path to clustering results JSON
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"""
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self.embeddings_path = embeddings_path
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self.clustering_results_path = clustering_results_path
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self.embeddings = None
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self.embeddings_normalized = None
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self.clustering_results = None
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self.filepath_to_embedding = {}
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self.algorithm = None
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def load_data(self):
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"""Load embeddings and clustering results"""
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print("Loading embeddings...")
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with open(self.embeddings_path, 'r') as f:
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embeddings_data = json.load(f)
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# Create mapping from filepath to embedding
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embeddings_list = []
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filepaths = []
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for item in embeddings_data:
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self.filepath_to_embedding[item['filepath']] = item['embedding']
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embeddings_list.append(item['embedding'])
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filepaths.append(item['filepath'])
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self.embeddings = np.array(embeddings_list, dtype=np.float32)
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self.embeddings_normalized = normalize(self.embeddings, norm='l2')
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print(f"Loaded {len(embeddings_list)} embeddings")
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print("Loading clustering results...")
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with open(self.clustering_results_path, 'r') as f:
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self.clustering_results = json.load(f)
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# Detect algorithm type
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self.algorithm = self.clustering_results.get('method', 'UNKNOWN')
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print(f"Detected algorithm: {self.algorithm}")
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print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, "
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f"{self.clustering_results['n_samples']} samples")
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def group_by_clusters(self):
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"""Group data points by cluster labels (algorithm-agnostic)"""
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clusters = {}
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noise_points = []
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for result in self.clustering_results['results']:
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cluster_id = result['cluster']
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filepath = result['filepath']
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# Check for noise points (DBSCAN specific)
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is_noise = result.get('is_noise', False)
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if is_noise or cluster_id == -1:
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noise_points.append({
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'filepath': filepath,
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'embedding': self.filepath_to_embedding[filepath],
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'metadata': result
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})
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else:
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if cluster_id not in clusters:
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clusters[cluster_id] = []
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clusters[cluster_id].append({
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'filepath': filepath,
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'embedding': self.filepath_to_embedding[filepath],
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'metadata': result
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})
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return clusters, noise_points
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def calculate_cluster_centroid(self, cluster_points):
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"""Calculate centroid of a cluster using normalized embeddings"""
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embeddings = np.array([point['embedding'] for point in cluster_points])
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embeddings_normalized = normalize(embeddings, norm='l2')
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# For cosine distance, centroid is the normalized mean
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centroid = np.mean(embeddings_normalized, axis=0)
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centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0]
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return centroid_normalized
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def calculate_cosine_distances_to_centroid(self, cluster_points, centroid):
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"""Calculate cosine distances from each point to cluster centroid"""
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embeddings = np.array([point['embedding'] for point in cluster_points])
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embeddings_normalized = normalize(embeddings, norm='l2')
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# Calculate cosine distances to centroid
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distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten()
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return distances
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def filter_cluster_standard(self, cluster_points, selection_ratio=0.5):
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"""
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Standard filtering: 25% center + 75% border of selected points
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"""
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if len(cluster_points) == 0:
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return []
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# Calculate how many points to select
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total_points = len(cluster_points)
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num_to_select = max(1, int(total_points * selection_ratio))
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# If we need to select all or almost all points, just return all
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if num_to_select >= total_points:
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return cluster_points
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# Calculate centroid
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centroid = self.calculate_cluster_centroid(cluster_points)
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# Calculate distances to centroid
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distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
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# Create list of (point, distance) pairs
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point_distance_pairs = list(zip(cluster_points, distances))
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# Sort by distance (closest to furthest from centroid)
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point_distance_pairs.sort(key=lambda x: x[1])
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# Calculate how many points to select from center and border
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center_count = max(1, int(num_to_select * 0.25)) # 25% from center
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border_count = num_to_select - center_count # 75% from border
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selected_points = []
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# Select center points (closest to centroid)
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center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
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for point in center_points:
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point['selection_type'] = 'center'
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selected_points.extend(center_points)
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# Select border points (furthest from centroid)
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if border_count > 0:
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border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
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for point in border_points:
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point['selection_type'] = 'border'
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selected_points.extend(border_points)
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print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
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f"({center_count} center + {border_count} border)")
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return selected_points
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def filter_cluster_gmm(self, cluster_points, selection_ratio=0.5):
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"""
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GMM-specific filtering: consider probability scores if available
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"""
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if len(cluster_points) == 0:
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return []
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# Check if we have probability scores
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has_probabilities = any('probability' in point['metadata'] for point in cluster_points)
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if has_probabilities:
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# Use probability-based selection
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total_points = len(cluster_points)
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num_to_select = max(1, int(total_points * selection_ratio))
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if num_to_select >= total_points:
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return cluster_points
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# Sort by probability (highest confidence first)
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sorted_points = sorted(cluster_points,
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key=lambda x: x['metadata'].get('probability', 0),
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reverse=True)
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# Take top probability points
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selected_points = sorted_points[:num_to_select]
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for point in selected_points:
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point['selection_type'] = 'high_probability'
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print(f"GMM Cluster with {total_points} points -> selected {len(selected_points)} points "
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f"(top probability)")
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return selected_points
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else:
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# Fall back to standard filtering
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return self.filter_cluster_standard(cluster_points, selection_ratio)
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def filter_all_clusters(self, selection_ratio=0.5):
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"""Filter all clusters according to algorithm-specific criteria"""
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print("\n" + "="*60)
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print(f"FILTERING {self.algorithm} CLUSTERING RESULTS")
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print("="*60)
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clusters, noise_points = self.group_by_clusters()
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print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points")
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filtered_results = []
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# Process each cluster
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for cluster_id, cluster_points in clusters.items():
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print(f"\nProcessing Cluster {cluster_id}:")
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# Choose filtering method based on algorithm
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if self.algorithm.upper() == 'GMM' or 'GAUSSIAN' in self.algorithm.upper():
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filtered_points = self.filter_cluster_gmm(cluster_points, selection_ratio)
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else:
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filtered_points = self.filter_cluster_standard(cluster_points, selection_ratio)
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# Add cluster information
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for point in filtered_points:
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filtered_results.append({
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'filepath': point['filepath'],
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'cluster': cluster_id,
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'is_noise': False,
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'selection_type': point.get('selection_type', 'cluster_filtered'),
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'original_metadata': point['metadata']
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})
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# Add all noise points (DBSCAN only)
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if noise_points:
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print(f"\nAdding all {len(noise_points)} noise points...")
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for point in noise_points:
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filtered_results.append({
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'filepath': point['filepath'],
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'cluster': -1,
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'is_noise': True,
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'selection_type': 'noise',
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'original_metadata': point['metadata']
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})
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return filtered_results
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def save_filtered_results(self, filtered_results, output_path=None):
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"""Save filtered results to JSON file"""
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if output_path is None:
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base_name = Path(self.clustering_results_path).stem
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output_path = f"{base_name}_filtered.json"
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# Create summary statistics
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cluster_stats = {}
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noise_count = 0
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selection_type_stats = {}
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for result in filtered_results:
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# Cluster stats
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if result['is_noise']:
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noise_count += 1
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else:
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cluster_id = result['cluster']
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if cluster_id not in cluster_stats:
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cluster_stats[cluster_id] = 0
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cluster_stats[cluster_id] += 1
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# Selection type stats
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sel_type = result['selection_type']
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selection_type_stats[sel_type] = selection_type_stats.get(sel_type, 0) + 1
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# Prepare output data
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output_data = {
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"method": f"{self.algorithm}_FILTERED",
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"original_algorithm": self.algorithm,
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"original_n_clusters": self.clustering_results['n_clusters'],
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"original_n_samples": self.clustering_results['n_samples'],
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"filtered_n_samples": len(filtered_results),
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"filtering_criteria": {
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"cluster_selection_ratio": 0.5,
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"center_points_ratio": 0.25,
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"border_points_ratio": 0.75,
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"noise_points": "all_selected" if noise_count > 0 else "none_present"
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},
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"cluster_statistics": cluster_stats,
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"selection_type_statistics": selection_type_stats,
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"noise_points": noise_count,
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"results": filtered_results
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}
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(output_data, f, indent=4, ensure_ascii=False)
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print("\n" + "="*60)
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print("FILTERING SUMMARY")
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print("="*60)
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print(f"Algorithm: {self.algorithm}")
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print(f"Original samples: {self.clustering_results['n_samples']}")
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print(f"Filtered samples: {len(filtered_results)}")
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print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}")
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print("\nCluster breakdown:")
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for cluster_id, count in sorted(cluster_stats.items()):
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print(f" Cluster {cluster_id}: {count} points")
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if noise_count > 0:
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print(f" Noise points: {noise_count} points")
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print("\nSelection type breakdown:")
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for sel_type, count in selection_type_stats.items():
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print(f" {sel_type}: {count} points")
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print(f"\nFiltered results saved to: {output_path}")
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return output_path
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def create_filepath_list(self, filtered_results, output_txt_path=None):
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"""Create a simple text file with filtered filepaths"""
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if output_txt_path is None:
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base_name = Path(self.clustering_results_path).stem
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output_txt_path = f"{base_name}_filtered_filepaths.txt"
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filepaths = [result['filepath'] for result in filtered_results]
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with open(output_txt_path, 'w', encoding='utf-8') as f:
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for filepath in filepaths:
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f.write(f"{filepath}\n")
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print(f"Filepath list saved to: {output_txt_path}")
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return output_txt_path
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def main():
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parser = argparse.ArgumentParser(description="Universal filter for clustering results")
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parser.add_argument("--embeddings_path", required=True,
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help="Path to embeddings JSON file")
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parser.add_argument("--clustering_results_path", required=True,
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help="Path to clustering results JSON file")
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parser.add_argument("--output_path",
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help="Output path for filtered results (optional)")
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parser.add_argument("--selection_ratio", type=float, default=0.5,
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help="Ratio of points to select from each cluster (default: 0.5)")
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parser.add_argument("--create_filepath_list", action="store_true",
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help="Also create a simple text file with filtered filepaths")
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args = parser.parse_args()
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# Validate input files exist
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if not os.path.exists(args.embeddings_path):
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print(f"Error: Embeddings file not found: {args.embeddings_path}")
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return
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if not os.path.exists(args.clustering_results_path):
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print(f"Error: Clustering results file not found: {args.clustering_results_path}")
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return
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# Initialize filter
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filter_obj = UniversalClusterFilter(args.embeddings_path, args.clustering_results_path)
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# Load data
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filter_obj.load_data()
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# Filter clusters
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filtered_results = filter_obj.filter_all_clusters(args.selection_ratio)
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# Save results
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filter_obj.save_filtered_results(filtered_results, args.output_path)
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# Create filepath list if requested
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if args.create_filepath_list:
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filter_obj.create_filepath_list(filtered_results)
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print("\nFiltering completed successfully!")
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if __name__ == "__main__":
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main() |