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https://github.com/catlog22/Claude-Code-Workflow.git
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261c98549d
- Add `association_tree` module with components for building and processing call association trees using LSP call hierarchy capabilities. - Introduce `AssociationTreeBuilder` for constructing call trees from seed locations with depth-first expansion. - Create data structures: `TreeNode`, `CallTree`, and `UniqueNode` for representing nodes and relationships in the call tree. - Implement `ResultDeduplicator` to extract unique nodes from call trees and assign relevance scores based on depth, frequency, and kind. - Add unit tests for `AssociationTreeBuilder` and `ResultDeduplicator` to ensure functionality and correctness.
302 lines
9.3 KiB
Python
302 lines
9.3 KiB
Python
"""Result deduplication for association tree nodes.
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Provides functionality to extract unique nodes from a call tree and assign
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relevance scores based on various factors.
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"""
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from __future__ import annotations
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import logging
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from typing import Dict, List, Optional
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from .data_structures import (
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CallTree,
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TreeNode,
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UniqueNode,
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)
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logger = logging.getLogger(__name__)
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# Symbol kind weights for scoring (higher = more relevant)
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KIND_WEIGHTS: Dict[str, float] = {
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# Functions and methods are primary targets
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"function": 1.0,
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"method": 1.0,
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"12": 1.0, # LSP SymbolKind.Function
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"6": 1.0, # LSP SymbolKind.Method
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# Classes are important but secondary
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"class": 0.8,
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"5": 0.8, # LSP SymbolKind.Class
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# Interfaces and types
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"interface": 0.7,
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"11": 0.7, # LSP SymbolKind.Interface
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"type": 0.6,
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# Constructors
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"constructor": 0.9,
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"9": 0.9, # LSP SymbolKind.Constructor
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# Variables and constants
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"variable": 0.4,
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"13": 0.4, # LSP SymbolKind.Variable
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"constant": 0.5,
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"14": 0.5, # LSP SymbolKind.Constant
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# Default for unknown kinds
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"unknown": 0.3,
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}
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class ResultDeduplicator:
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"""Extracts and scores unique nodes from call trees.
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Processes a CallTree to extract unique code locations, merging duplicates
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and assigning relevance scores based on:
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- Depth: Shallower nodes (closer to seeds) score higher
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- Frequency: Nodes appearing multiple times score higher
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- Kind: Function/method > class > variable
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Attributes:
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depth_weight: Weight for depth factor in scoring (default 0.4)
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frequency_weight: Weight for frequency factor (default 0.3)
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kind_weight: Weight for symbol kind factor (default 0.3)
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max_depth_penalty: Maximum depth before full penalty applied
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"""
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def __init__(
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self,
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depth_weight: float = 0.4,
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frequency_weight: float = 0.3,
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kind_weight: float = 0.3,
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max_depth_penalty: int = 10,
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):
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"""Initialize ResultDeduplicator.
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Args:
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depth_weight: Weight for depth factor (0.0-1.0)
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frequency_weight: Weight for frequency factor (0.0-1.0)
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kind_weight: Weight for symbol kind factor (0.0-1.0)
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max_depth_penalty: Depth at which score becomes 0 for depth factor
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"""
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self.depth_weight = depth_weight
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self.frequency_weight = frequency_weight
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self.kind_weight = kind_weight
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self.max_depth_penalty = max_depth_penalty
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def deduplicate(
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self,
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tree: CallTree,
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max_results: Optional[int] = None,
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) -> List[UniqueNode]:
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"""Extract unique nodes from the call tree.
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Traverses the tree, groups nodes by their unique key (file_path,
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start_line, end_line), and merges duplicate occurrences.
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Args:
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tree: CallTree to process
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max_results: Maximum number of results to return (None = all)
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Returns:
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List of UniqueNode objects, sorted by score descending
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"""
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if not tree.node_list:
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return []
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# Group nodes by unique key
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unique_map: Dict[tuple, UniqueNode] = {}
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for node in tree.node_list:
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if node.is_cycle:
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# Skip cycle markers - they point to already-counted nodes
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continue
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key = self._get_node_key(node)
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if key in unique_map:
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# Update existing unique node
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unique_node = unique_map[key]
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unique_node.occurrences += 1
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unique_node.min_depth = min(unique_node.min_depth, node.depth)
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unique_node.add_path(node.path_from_root)
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# Collect context from relationships
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for parent in node.parents:
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if not parent.is_cycle:
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unique_node.context_nodes.append(parent.node_id)
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for child in node.children:
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if not child.is_cycle:
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unique_node.context_nodes.append(child.node_id)
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else:
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# Create new unique node
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unique_node = UniqueNode(
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file_path=node.item.file_path,
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name=node.item.name,
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kind=node.item.kind,
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range=node.item.range,
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min_depth=node.depth,
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occurrences=1,
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paths=[node.path_from_root.copy()],
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context_nodes=[],
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score=0.0,
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)
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# Collect initial context
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for parent in node.parents:
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if not parent.is_cycle:
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unique_node.context_nodes.append(parent.node_id)
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for child in node.children:
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if not child.is_cycle:
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unique_node.context_nodes.append(child.node_id)
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unique_map[key] = unique_node
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# Calculate scores for all unique nodes
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unique_nodes = list(unique_map.values())
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# Find max frequency for normalization
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max_frequency = max((n.occurrences for n in unique_nodes), default=1)
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for node in unique_nodes:
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node.score = self._score_node(node, max_frequency)
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# Sort by score descending
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unique_nodes.sort(key=lambda n: n.score, reverse=True)
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# Apply max_results limit
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if max_results is not None and max_results > 0:
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unique_nodes = unique_nodes[:max_results]
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logger.debug(
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"Deduplicated %d tree nodes to %d unique nodes",
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len(tree.node_list),
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len(unique_nodes),
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)
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return unique_nodes
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def _score_node(
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self,
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node: UniqueNode,
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max_frequency: int,
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) -> float:
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"""Calculate composite score for a unique node.
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Score = depth_weight * depth_score +
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frequency_weight * frequency_score +
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kind_weight * kind_score
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Args:
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node: UniqueNode to score
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max_frequency: Maximum occurrence count for normalization
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Returns:
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Composite score between 0.0 and 1.0
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"""
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# Depth score: closer to root = higher score
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# Score of 1.0 at depth 0, decreasing to 0.0 at max_depth_penalty
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depth_score = max(
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0.0,
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1.0 - (node.min_depth / self.max_depth_penalty),
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)
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# Frequency score: more occurrences = higher score
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frequency_score = node.occurrences / max_frequency if max_frequency > 0 else 0.0
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# Kind score: function/method > class > variable
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kind_str = str(node.kind).lower()
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kind_score = KIND_WEIGHTS.get(kind_str, KIND_WEIGHTS["unknown"])
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# Composite score
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score = (
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self.depth_weight * depth_score
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+ self.frequency_weight * frequency_score
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+ self.kind_weight * kind_score
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)
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return score
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def _get_node_key(self, node: TreeNode) -> tuple:
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"""Get unique key for a tree node.
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Uses (file_path, start_line, end_line) as the unique identifier.
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Args:
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node: TreeNode
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Returns:
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Tuple key for deduplication
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"""
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return (
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node.item.file_path,
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node.item.range.start_line,
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node.item.range.end_line,
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)
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def filter_by_kind(
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self,
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nodes: List[UniqueNode],
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kinds: List[str],
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) -> List[UniqueNode]:
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"""Filter unique nodes by symbol kind.
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Args:
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nodes: List of UniqueNode to filter
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kinds: List of allowed kinds (e.g., ["function", "method"])
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Returns:
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Filtered list of UniqueNode
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"""
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kinds_lower = [k.lower() for k in kinds]
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return [
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node
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for node in nodes
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if str(node.kind).lower() in kinds_lower
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]
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def filter_by_file(
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self,
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nodes: List[UniqueNode],
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file_patterns: List[str],
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) -> List[UniqueNode]:
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"""Filter unique nodes by file path patterns.
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Args:
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nodes: List of UniqueNode to filter
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file_patterns: List of path substrings to match
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Returns:
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Filtered list of UniqueNode
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"""
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return [
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node
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for node in nodes
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if any(pattern in node.file_path for pattern in file_patterns)
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]
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def to_dict_list(self, nodes: List[UniqueNode]) -> List[Dict]:
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"""Convert list of UniqueNode to JSON-serializable dicts.
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Args:
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nodes: List of UniqueNode
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Returns:
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List of dictionaries
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"""
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return [
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{
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"file_path": node.file_path,
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"name": node.name,
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"kind": node.kind,
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"range": {
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"start_line": node.range.start_line,
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"start_character": node.range.start_character,
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"end_line": node.range.end_line,
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"end_character": node.range.end_character,
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},
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"min_depth": node.min_depth,
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"occurrences": node.occurrences,
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"path_count": len(node.paths),
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"score": round(node.score, 4),
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}
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for node in nodes
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]
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