add select_representive_sample_uids.py (#662)

Author: lixinqi

sqlite/util/filter_valid_groups.py …h_net/sqlite_util/filter_valid_groups.pysqlite/util/filter_valid_groups.py renamed to graph_net/sqlite_util/filter_valid_groups.py sqlite/util/filter_valid_groups.py renamed to graph_net/sqlite_util/filter_valid_groups.py

@@ -4,6 +4,7 @@
 
 import math
 from collections import Counter
+from typing import Callable
 
 import numpy as np
 from scipy import sparse
@@ -88,12 +89,17 @@ def _compute_metrics(
     return result
 
 
-def _greedy_select(metrics: list[dict], k: int, rarity_weight: float) -> list[int]:
+def _greedy_select(
+    metrics: list[dict], k: int, rarity_weight: float, selected: list[int] = None
+) -> list[int]:
     """Greedily maximise edge coverage, weighted by rarity score."""
     target = min(k, len(metrics))
     rarity_norm = _min_max_normalize([m["rarity_score"] for m in metrics])
 
-    selected: list[int] = []
+    if selected is None:
+        selected: list[int] = []
+    else:
+        assert isinstance(selected, list)
     selected_set: set[int] = set()
     covered_edges: set[tuple[str, str]] = set()
 
@@ -123,6 +129,7 @@ def select_evaluation_subset(
     *,
     smoothing_alpha: float = 1e-3,
     rarity_weight: float = 1,
+    is_selected: Callable[tuple[str, ...], bool] = lambda x: False,
 ) -> list[tuple[str, ...]]:
     """Select k sequences from op_seqs using Markov-based greedy coverage.
 
@@ -140,4 +147,8 @@ def select_evaluation_subset(
 
     op_to_id, count_matrix, row_sums = _build_markov_model(seqs)
     metrics = _compute_metrics(seqs, op_to_id, count_matrix, row_sums, smoothing_alpha)
-    return [seqs[i] for i in _greedy_select(metrics, k, rarity_weight)]
+    selected_indexes = [i for i, seq in enumerate(seqs) if is_selected(seq)]
+    return [
+        seqs[i]
+        for i in _greedy_select(metrics, k, rarity_weight, selected=selected_indexes)
+    ]

…ite/util/graph_net_sample_groups_util.py …ite_util/graph_net_sample_groups_util.pysqlite/util/graph_net_sample_groups_util.py renamed to graph_net/sqlite_util/graph_net_sample_groups_util.py sqlite/util/graph_net_sample_groups_util.py renamed to graph_net/sqlite_util/graph_net_sample_groups_util.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+"""
+select_representative_sample_uids.py
+
+Reads a TSV file of (sample_uid, op_seq) pairs and a file listing selected op_seqs.
+For each selected op_seq, picks the sample_uid with the maximum string length from its group,
+and outputs them in the same order as the selected op_seqs.
+"""
+
+import argparse
+import sys
+from collections import defaultdict
+
+
+def read_tsv_pairs(file_path):
+    """
+    Read a tab-separated file where each line contains sample_uid and op_seq.
+    Returns a list of (sample_uid, op_seq) tuples.
+    """
+    pairs = []
+    with open(file_path, "r", encoding="utf-8") as f:
+        for line_num, line in enumerate(f, 1):
+            line = line.strip()
+            if not line:
+                continue
+            parts = line.split("\t")
+            if len(parts) != 2:
+                print(
+                    f"Warning: {file_path}:{line_num} invalid format, skipped: {line}",
+                    file=sys.stderr,
+                )
+                continue
+            sample_uid, op_seq = parts
+            pairs.append((sample_uid, op_seq))
+    return pairs
+
+
+def read_op_seq_list(file_path):
+    """
+    Read a file with one op_seq per line. Returns a list preserving order.
+    """
+    op_seqs = []
+    with open(file_path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                op_seqs.append(line)
+    return op_seqs
+
+
+def get_max_len_string(strings):
+    """
+    Return the string with the maximum length from a list.
+    If multiple strings have the same max length, return the first encountered.
+    """
+    if not strings:
+        return None
+    # max with key returns the first element in case of ties (Python's max is stable)
+    return max(strings, key=lambda s: len(s))
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Select representative sample UIDs by max string length per op_seq group."
+    )
+    parser.add_argument(
+        "pairs_file", help="TSV file with two columns: sample_uid and op_seq"
+    )
+    parser.add_argument(
+        "opseq_file", help="File with one op_seq per line (order matters)"
+    )
+    parser.add_argument("-o", "--output", help="Output file path (default: stdout)")
+    args = parser.parse_args()
+
+    # 1. Load input data
+    pairs = read_tsv_pairs(args.pairs_file)
+    selected_op_seqs = read_op_seq_list(args.opseq_file)
+
+    # 2. Group sample_uids by op_seq
+    groups = defaultdict(list)
+    for sample_uid, op_seq in pairs:
+        groups[op_seq].append(sample_uid)
+
+    # 3. For each op_seq, find the longest sample_uid
+    op_seq_to_max_uid = {}
+    for op_seq, uid_list in groups.items():
+        max_uid = get_max_len_string(uid_list)
+        if max_uid is not None:
+            op_seq_to_max_uid[op_seq] = max_uid
+
+    # 4. Collect results in the order of selected_op_seqs
+    result_uids = []
+    for op_seq in selected_op_seqs:
+        if op_seq in op_seq_to_max_uid:
+            result_uids.append(op_seq_to_max_uid[op_seq])
+
+    # 5. Write output
+    out_fh = open(args.output, "w", encoding="utf-8") if args.output else sys.stdout
+    try:
+        for uid in result_uids:
+            print(uid, file=out_fh)
+    finally:
+        if args.output:
+            out_fh.close()
+
+
+if __name__ == "__main__":
+    main()