From 47a260f8ce50b14dfa127f96dd3bcff82fca2ed3 Mon Sep 17 00:00:00 2001 From: Codex Agent Date: Tue, 29 Sep 2026 17:38:28 +0000 Subject: [PATCH 1/3] Fix duplicate padding in SingleShaMinHash sketches --- datasig/algo.py | 18 ++++++++----- datasig/test/test_single_sha.py | 47 +++++++++++++++++++++++++++++++++ 2 files changed, 58 insertions(+), 7 deletions(-) create mode 100644 datasig/test/test_single_sha.py diff --git a/datasig/algo.py b/datasig/algo.py index 5731a7c..6bdac51 100644 --- a/datasig/algo.py +++ b/datasig/algo.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod import hashlib import struct +from itertools import groupby, islice import numpy as np import datasketch # pyright: ignore[reportMissingTypeStubs] from .fingerprint import ( @@ -147,6 +148,7 @@ class SingleShaMinHash(MinHash): """Generate dataset fingerprint based on a single SHA permutation The figerprint is computed using the MinHash scheme with a single permutation approximated by SHA256. + Each distinct data point hash occupies at most one bottom-k slot. See https://web.eecs.utk.edu/~jplank/plank/classes/cs494/494/notes/Min-Hash/index.html, Min Hash with one hash functions. And https://en.wikipedia.org/wiki/MinHash#Variant_with_a_single_hash_function. @@ -163,15 +165,17 @@ def clone_config(self) -> Self: return self.__class__(nb_signatures=self.nb_signatures) def _get_fingerprint(self) -> DatasetFingerprint: - # Relies on the fact that the data point hashes are sorted - if len(self._hashes) < self.nb_signatures: + # Sampling observations before deduplication lets repeated points + # displace distinct points from the bottom-k set sketch. + # MinHash.digest() has already sorted the hashes. + distinct_hashes = list( + islice((digest for digest, _ in groupby(self._hashes)), self.nb_signatures) + ) + if len(distinct_hashes) < self.nb_signatures: raise ValueError( - f"Not enough data points to compute a fingerprint: we need at least {self.nb_signatures} data points." + f"Not enough distinct data points to compute a fingerprint: we need at least {self.nb_signatures} distinct data points." ) - - res = self._hashes[: self.nb_signatures] - - return BasicDatasetFingerprint(res) # pyright: ignore[reportArgumentType] + return BasicDatasetFingerprint(distinct_hashes) class DatasketchMinHash(MinHash): diff --git a/datasig/test/test_single_sha.py b/datasig/test/test_single_sha.py new file mode 100644 index 0000000..29baabf --- /dev/null +++ b/datasig/test/test_single_sha.py @@ -0,0 +1,47 @@ +"""Regression tests for the bottom-k dataset fingerprint.""" + +from hashlib import sha256 + +import pytest + +from datasig.algo import SingleShaMinHash + + +def slots(signature: bytes) -> list[bytes]: + return [signature[index : index + 32] for index in range(0, len(signature), 32)] + + +@pytest.mark.parametrize("width", [4, 400]) +def test_repeated_low_hash_cannot_hide_an_added_row(width: int) -> None: + candidates = [f"row:{index}".encode() for index in range(width + 1)] + ordered = sorted(candidates, key=lambda row: sha256(row).digest()) + padding, added = ordered[:2] + original = [padding, *ordered[2:], *([padding] * width)] + changed = original + [added] + original_signature = SingleShaMinHash(original, nb_signatures=width).digest().signature() + changed_signature = SingleShaMinHash(changed, nb_signatures=width).digest().signature() + + assert original_signature != changed_signature + assert len(set(slots(original_signature))) == width + assert len(set(slots(changed_signature))) == width + assert sha256(added).digest() in slots(changed_signature) + + +def test_repeated_rows_do_not_satisfy_distinct_requirement_and_updates_work(): + sketch = SingleShaMinHash([b"repeated"] * 4, nb_signatures=2) + with pytest.raises(ValueError, match="Not enough distinct data points"): + sketch.digest() + + sketch.update([b"repeated", b"new"]) + updated = sketch.digest().signature() + assert slots(updated) == sorted({sha256(b"repeated").digest(), sha256(b"new").digest()}) + + +def test_fewer_observations_than_requested_still_raise(): + with pytest.raises(ValueError, match="Not enough distinct data points"): + SingleShaMinHash([b"only one"], nb_signatures=4).digest() + + +def test_empty_dataset_is_rejected(): + with pytest.raises(ValueError, match="Not enough distinct data points"): + SingleShaMinHash().digest() From 1d3647a36a1d3271f195bc16e728a63eaca4a260 Mon Sep 17 00:00:00 2001 From: Lucas Bourtoule Date: Wed, 30 Sep 2026 15:57:58 +0200 Subject: [PATCH 2/3] Require Python 3.9 for torchvision dev deps --- pyproject.toml | 2 +- uv.lock | 2336 +++++------------------------------------------- 2 files changed, 248 insertions(+), 2090 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 72f2687..58bae92 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "datasig" version = "0.0.7" -requires-python = ">= 3.8" +requires-python = ">= 3.9" readme = "README.md" description = "Dataset fingerprinting library" authors = [ diff --git a/uv.lock b/uv.lock index 94ced56..d450c74 100644 --- a/uv.lock +++ b/uv.lock @@ -1,13 +1,12 @@ version = 1 revision = 3 -requires-python = ">=3.8" +requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13'", "python_full_version == '3.12.*'", "python_full_version == '3.11.*'", "python_full_version == '3.10.*'", - 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"https://data.cityofnewyork.us/api/views/c3uy-2p5r/rows.csv", "/tmp/data.csv" -) +def write_csv(path: Path, start: int, end: int) -> Path: + path.write_text("id,sensor,metric,value\n" + "".join(csv_rows(start, end))) + return path -def test_csv_v0(): + +def test_csv_v0(tmp_path: Path): + csv_file = write_csv(tmp_path / "data.csv", 0, 500) dataset = CSVDataset(csv_file, delimiter=",") + fingerprint = KeyedShaMinHash(dataset).digest() - uid = UID(dataset).digest() - # Test UID and fingerprint - pprint(uid, width=70) - pprint(fingerprint.signature(), width=70) + uid = UID(CSVDataset(csv_file, delimiter=",")).digest() + assert ( uid - == ( - b'W\x7f\xb3\xc4\x9d\xfbrr\xf9i\x01,\xc6\x08y9ez\xfb\xd1l\n?\xa4wt(w' - b'526_' - ) + == b"\x86\x02y\xa4(br\x18\x8e1\x0f\xcf\xfc`w1" + b"\xde{\x01$HRK\xb5\xc5\xa10\x8c\n \xc1U" ) assert len(fingerprint) == 400 - assert fingerprint.signature() == ( - b"\x00\x05'_0L\x84\x95\x03\x19\xec\xa2 jT\xdat\xb1 3\xe6\xf9\x80c" - b'?\x8e\x82\xce\xa5\xc6\xe6U\x00\x03\\]t\x05\x95\xdf\x92\xc7\x8f(' - 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b'\xeb>s\xdc\x00\x0em\xc3l\xe6\x1d\xdc/\x1b|1\xf6\xcbc\xab@\x986\xc1' - b'\xbc7s\xda\xd9\x91\xafwcN\x9c\x96\x00\x021\x15\x8d^\x98\xff' - b'U\x83\xbd\x8b\xa9\xaf\x16\xd2hn\x924\xb9j\xda@1b~\x1e' - b'\x85\n\xcf\xbb\x00\x06\xd5lm\xd1y\xa0\xb2\x07\x0e\xe1\xf8\x86\xafi' - b'\xdc\x03T\xea\x18\x9drr\xf4\xc5\x1d9r\xae\xc5!\x00\x04\x10\xf4' - b'\xb9G\xf2\xb8p&\x04\xab\xee\xf6)\xed\x02\xd2b\\%\xf8\xe6P' - b'O\x0b\xe2\x80q\xac\xe76\x00\x07*F\x9bp\xdb]p\xfaR.\xe51V4' - b'\xf7P\n\xe5\x84\xe12\xb1;\x93\x00\xaa{M\xd7\xb9\x00\x00\xed\x1c' - b'\x817\x03\xb5\t"M\x80\xc4\x94\xd2\x92\xf3\x00p\xf8q\x8aY\x02' - b'6Q\x90\xe4\x90B\x18\x0c\x00\x07\x07\x00\xfc\xae\xd8\xa3\xd2G{\x7f' - b'\x93\xa8\x98\xa7\x99\x0f8[K\xdb\x00\xb6\xa7i\x80\x08vi\xeb\xfb' - b'\x00\x01\x12\xfb\xbd\xf2\xe1\xd3\x0b\xf8\x0c\xa9j@E\xcem\xab\xeef' - b'N*\xadfF\xf5\x95\xfa\xfe\xee\xe14\x00\x02\xb9|\x06c\xf4\xe2' - b'\x7f`\xf9\xe0\xb4\xdc\x0e\xef7\x1a\x0c\xec\xf1\xe3\x9c+R.A%' - b'\xd0\xbc\xd1\xf5\x00\x01\xc73\xfe\xff\xeb\x00\x06\xceb\x91' - b'\xcf\x03y\xeb\xea\xb8E\xad\xe029\xf6\x97\xa5\xac\xa6' - b'\x94\xe2\xe0\xd4\x00\x03\xb8b\xd5\x1f\x1f\xf1H\x19A\xfaNT\x85\xf6' - b'\nU\x15sQpX\xbaH\x89)\xb5q\xa3Z\xbe\x00\x02\xd9\xf5' - b'\x0e\x0c\x04\xbb\xd6;\x06w\x85_\x13g\x06,\xbd\xfb]:\x86\r' - b'\xed\xe7F\xd0\xfa\xc4\x93\x11\x00\x04\xad\x97\xcb\x97#\xdaF(?\xd5' - b'\x85\xb9\x93\xf9\x93\xcb\xb4\x7fh\t\n\x00\x10\xe9&@y\xc3Aq' - b'\x00\x02w\x18N\xa6w\x87#\xc4(\x81l\xd7C(t\xe5?r\x12\xc8V\xd8' - b'*\xb8\xc7\x1d\x12V\x9d\x99' + assert ( + fingerprint.signature() + == KeyedShaMinHash(CSVDataset(csv_file, delimiter=",")).digest().signature() ) -def test_similarity(): - data1 = extract_csv_range(csv_file, 0, 5000, "/tmp/data1.csv") - data2 = extract_csv_range(csv_file, 5000, 10000, "/tmp/data2.csv") - data3 = extract_csv_range(csv_file, 0, 10000, "/tmp/data3.csv") - data4 = extract_csv_range(csv_file, 5000, 15000, "/tmp/data4.csv") - d1 = CSVDataset(data1) - d2 = CSVDataset(data2) - d3 = CSVDataset(data3) - d4 = CSVDataset(data4) +def test_similarity(): + d1 = CSVDataset(csv_rows(0, 5000)) + d2 = CSVDataset(csv_rows(5000, 10000)) + d3 = CSVDataset(csv_rows(0, 10000)) + d4 = CSVDataset(csv_rows(5000, 15000)) # Identical datasets assert_fingerprint_similarity(d1, d1, 1.0) @@ -662,12 +49,24 @@ def test_similarity(): assert_fingerprint_similarity(d1, d3, 0.5) # Completely different datasets assert_fingerprint_similarity(d1, d2, 0.0) - # 1/3th in common + # 1/3rd in common assert_fingerprint_similarity(d3, d4, 0.33) def test_serialization(): - data_point = ["166679", "375", "Nitrogen dioxide (NO2)", "Mean", "ppb", "CD", "414", "Rockaway and Broad Channel (CD14)", "Summer 2009", "06/01/2009", "8.44"] + data_point = [ + "166679", + "375", + "Nitrogen dioxide (NO2)", + "Mean", + "ppb", + "CD", + "414", + "Rockaway and Broad Channel (CD14)", + "Summer 2009", + "06/01/2009", + "8.44", + ] serialized = CSVDataset().serialize_data_point(data_point) deserialized = CSVDataset().deserialize_data_point(serialized) assert data_point == deserialized