From b96b82ddd35d9099b4dd9800ba2d166b1505fcd4 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 11:46:51 +0100 Subject: [PATCH 01/37] Added from_dataset class method --- README.md | 38 ++++++++++++++++++++++ semhash/semhash.py | 52 ++++++++++++++++++++++++++++++ tests/test_semhash.py | 75 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 165 insertions(+) diff --git a/README.md b/README.md index fd2ed6a..0e2945a 100644 --- a/README.md +++ b/README.md @@ -400,6 +400,44 @@ representative_texts = semhash.self_find_representative().selected ``` +
+ Initializing from a HuggingFace Dataset +
+You can also initialize SemHash directly from a HuggingFace Dataset. This is more convenient than manually extracting records, as SemHash will handle the conversion and embedding internally: + +```python +from datasets import load_dataset +from semhash import SemHash + +# Load a HuggingFace dataset +dataset = load_dataset("ag_news", split="train") + +# Initialize SemHash directly from the dataset +semhash = SemHash.from_dataset(dataset=dataset, columns=["text"]) + +# Deduplicate, filter outliers, and find representative samples +deduplicated_texts = semhash.self_deduplicate().selected +filtered_texts = semhash.self_filter_outliers().selected +representative_texts = semhash.self_find_representative().selected +``` + +This also works with multi-column datasets: + +```python +from datasets import load_dataset +from semhash import SemHash + +# Load a multi-column dataset +dataset = load_dataset("squad_v2", split="train") + +# Initialize SemHash with multiple columns +semhash = SemHash.from_dataset(dataset=dataset, columns=["question", "context"]) + +# Deduplicate the records +deduplicated_records = semhash.self_deduplicate().selected +``` +
+ diff --git a/semhash/semhash.py b/semhash/semhash.py index 2baaabe..e5a8585 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -97,6 +97,58 @@ def from_records( return cls(index=index, columns=columns, model=model, was_string=was_string) + @classmethod + def from_dataset( + cls, + dataset: Any, + columns: Sequence[str], + model: Encoder | None = None, + ann_backend: Backend | str = Backend.USEARCH, + **kwargs: Any, + ) -> SemHash: + """ + Initialize SemHash from a dataset (e.g., HuggingFace Dataset). + + Similar to from_records, but extracts records from a dataset object + and computes embeddings in one step. + + :param dataset: A dataset with column_names attribute and dict-like access. + :param columns: Columns to use for deduplication (same as from_records). + :param model: (Optional) An Encoder model. If None, the default model is used (minishlab/potion-base-8M). + :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. + :param **kwargs: Any additional keyword arguments to pass to the Vicinity index. + :return: A SemHash instance with a fitted vicinity index. + :raises TypeError: If dataset doesn't have required attributes. + :raises ValueError: If columns are not found in the dataset. + """ + if not hasattr(dataset, "column_names") or not hasattr(dataset, "__len__"): + raise TypeError("dataset must have 'column_names' and '__len__' attributes") + + # Validate columns exist + missing = set(columns) - set(dataset.column_names) + if missing: + raise ValueError(f"Columns {missing} not found in dataset") + + # Load default model if needed + if model is None: + model = StaticModel.from_pretrained("minishlab/potion-base-8M") + + # Extract records as list of dicts (same format as from_records) + records: list[dict[str, str]] = [{col: str(dataset[i][col]) for col in columns} for i in range(len(dataset))] + + # Compute embeddings + embeddings = featurize(records=records, columns=columns, model=model) + + # Use from_embeddings (which handles exact dedup internally now) + return cls.from_embeddings( + embeddings=embeddings, + records=records, # type: ignore[arg-type] + model=model, + columns=columns, + ann_backend=ann_backend, + **kwargs, + ) + @classmethod def from_embeddings( cls, diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 1b4105c..cc36feb 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -253,3 +253,78 @@ def test_from_embeddings(model: Encoder, train_texts: list[str]) -> None: assert semhash.index.vectors.shape == (3, 1) # Should keep embeddings at indices 0, 1, 3 (first occurrences of img1, img2, img3) assert semhash.index.vectors.tolist() == [[0.0], [1.0], [3.0]] + + +def test_from_dataset_basic(model: Encoder) -> None: + """Test from_dataset with a simple HuggingFace dataset.""" + from datasets import Dataset + + # Create a simple dataset with text column + ds = Dataset.from_dict({"text": ["apple", "banana", "cherry", "apple"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should collapse exact duplicate "apple" + assert len(semhash.index.vectors) == 3 + assert len(semhash.index.items) == 3 + + # Verify deduplication works + result = semhash.self_deduplicate(threshold=0.95) + assert len(result.selected) <= 3 + + +def test_from_dataset_multicolumn(model: Encoder) -> None: + """Test from_dataset with multiple columns.""" + from datasets import Dataset + + ds = Dataset.from_dict( + { + "question": ["What is AI?", "What is ML?", "What is AI?"], + "context": ["AI explanation", "ML explanation", "AI explanation"], + } + ) + + semhash = SemHash.from_dataset(dataset=ds, columns=["question", "context"], model=model) + + # Should collapse the duplicate row + assert len(semhash.index.vectors) == 2 + assert len(semhash.index.items) == 2 + + +def test_from_dataset_validation(model: Encoder) -> None: + """Test from_dataset input validation.""" + from datasets import Dataset + + ds = Dataset.from_dict({"text": ["apple", "banana"]}) + + # Test invalid dataset type + with pytest.raises(TypeError, match="must have 'column_names' and '__len__' attributes"): + SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) + + # Test missing column + with pytest.raises(ValueError, match="not found in dataset"): + SemHash.from_dataset(dataset=ds, columns=["missing_col"], model=model) + + +def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: + """Test that from_dataset produces same results as from_records for same data.""" + from datasets import Dataset + + data = [ + {"question": "What is AI?", "answer": "Artificial Intelligence"}, + {"question": "What is ML?", "answer": "Machine Learning"}, + ] + ds = Dataset.from_dict({k: [d[k] for d in data] for k in data[0].keys()}) + + semhash_from_dataset = SemHash.from_dataset(dataset=ds, columns=["question", "answer"], model=model) + semhash_from_records = SemHash.from_records(records=data, columns=["question", "answer"], model=model) + + # Both should have same number of vectors + assert semhash_from_dataset.index.vectors.shape == semhash_from_records.index.vectors.shape + + # Both should give same deduplication results + result1 = semhash_from_dataset.self_deduplicate(threshold=0.95) + result2 = semhash_from_records.self_deduplicate(threshold=0.95) + + assert len(result1.selected) == len(result2.selected) + assert len(result1.filtered) == len(result2.filtered) From e0af5f75789b29815aaa9311c4e45dd4388f4cb4 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:14:17 +0100 Subject: [PATCH 02/37] Added from_dataset class method --- semhash/semhash.py | 86 ++++++++++++++++++++++++++++++++++--------- tests/test_semhash.py | 33 +++++++++++++++++ 2 files changed, 101 insertions(+), 18 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index e5a8585..113ddaa 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -40,6 +40,38 @@ def __init__(self, index: Index, model: Encoder, columns: Sequence[str], was_str self._was_string = was_string self._ranking_cache: FilterResult | None = None + @classmethod + def _from_vectors_and_items( + cls, + *, + vectors: np.ndarray, + items: list[list[dict[str, str]]], + model: Encoder, + columns: Sequence[str], + was_string: bool, + ann_backend: Backend | str, + **kwargs: Any, + ) -> SemHash: + """ + Internal constructor from vectors and items (after deduplication). + + :param vectors: Embeddings for representative records. + :param items: List of item clusters (each cluster contains exact duplicates). + :param model: Encoder model. + :param columns: Column names. + :param was_string: Whether original records were strings. + :param ann_backend: ANN backend to use. + :param **kwargs: Additional arguments for the index. + :return: SemHash instance. + """ + index = Index.from_vectors_and_items( + vectors=vectors, + items=items, + backend_type=ann_backend, + **kwargs, + ) + return cls(index=index, model=model, columns=columns, was_string=was_string) + @classmethod def from_records( cls, @@ -87,16 +119,16 @@ def from_records( # Create embeddings for deduplicated records only embeddings = featurize(deduplicated_records, columns, model) - # Build the Vicinity index - index = Index.from_vectors_and_items( + return cls._from_vectors_and_items( vectors=embeddings, items=items, - backend_type=ann_backend, + model=model, + columns=columns, + was_string=was_string, + ann_backend=ann_backend, **kwargs, ) - return cls(index=index, columns=columns, model=model, was_string=was_string) - @classmethod def from_dataset( cls, @@ -109,8 +141,8 @@ def from_dataset( """ Initialize SemHash from a dataset (e.g., HuggingFace Dataset). - Similar to from_records, but extracts records from a dataset object - and computes embeddings in one step. + Extracts records from the dataset, deduplicates them, and embeds only + representative records (not duplicates). The encoder controls batching internally. :param dataset: A dataset with column_names attribute and dict-like access. :param columns: Columns to use for deduplication (same as from_records). @@ -133,18 +165,34 @@ def from_dataset( if model is None: model = StaticModel.from_pretrained("minishlab/potion-base-8M") - # Extract records as list of dicts (same format as from_records) - records: list[dict[str, str]] = [{col: str(dataset[i][col]) for col in columns} for i in range(len(dataset))] + # Columnar extraction (fast path for HF datasets - avoids row-wise indexing) + n = len(dataset) + cols = {c: dataset[c] for c in columns} + dict_records: list[dict[str, str]] = [{c: str(cols[c][i]) for c in columns} for i in range(n)] - # Compute embeddings - embeddings = featurize(records=records, columns=columns, model=model) + # Exact dedup first (cheap) - we only embed representatives + deduplicated_records, duplicates = remove_exact_duplicates(dict_records, columns) - # Use from_embeddings (which handles exact dedup internally now) - return cls.from_embeddings( - embeddings=embeddings, - records=records, # type: ignore[arg-type] + col_set = set(columns) + duplicate_map = defaultdict(list) + for x, _ in duplicates: + duplicate_map[to_frozendict(x, col_set)].append(x) + + items: list[list[dict[str, str]]] = [] + for record in deduplicated_records: + bucket = [record] + bucket.extend(duplicate_map[to_frozendict(record, col_set)]) + items.append(bucket) + + # Embed representatives only (encoder decides batching internally) + vectors = featurize(records=deduplicated_records, columns=columns, model=model) + + return cls._from_vectors_and_items( + vectors=vectors, + items=items, model=model, columns=columns, + was_string=False, # dataset inputs are dict-like ann_backend=ann_backend, **kwargs, ) @@ -211,13 +259,15 @@ def from_embeddings( deduplicated_embeddings = embeddings[keep_embedding_indices] - index = Index.from_vectors_and_items( + return cls._from_vectors_and_items( vectors=deduplicated_embeddings, items=items, - backend_type=ann_backend, + model=model, + columns=columns, + was_string=was_string, + ann_backend=ann_backend, **kwargs, ) - return cls(index=index, model=model, columns=columns, was_string=was_string) def deduplicate( self, diff --git a/tests/test_semhash.py b/tests/test_semhash.py index cc36feb..d2005a7 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -328,3 +328,36 @@ def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: assert len(result1.selected) == len(result2.selected) assert len(result1.filtered) == len(result2.filtered) + + +def test_from_dataset_does_not_embed_duplicates(model: Encoder, monkeypatch: pytest.MonkeyPatch) -> None: + """Test that from_dataset only embeds representative records, not duplicates.""" + from typing import Any + + from datasets import Dataset + + # Create dataset with exact duplicates + ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) + + # Track how many times encode is called and with how many records + encode_call_count = 0 + total_encoded = 0 + + original_encode = model.encode + + def tracked_encode(texts: Any, **kwargs: Any) -> Any: + nonlocal encode_call_count, total_encoded + encode_call_count += 1 + total_encoded += len(texts) + return original_encode(texts, **kwargs) + + monkeypatch.setattr(model, "encode", tracked_encode) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should only have encoded 3 unique records (apple, banana, cherry) + assert semhash.index.vectors.shape[0] == 3 + # Total encoded should be 3, not 6 + assert total_encoded == 3 + # Should be called once (for the deduplicated records) + assert encode_call_count == 1 From 2c01486e0463e16e24573963059bea1adb378dca Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:26:46 +0100 Subject: [PATCH 03/37] Optimized from_dataset class method --- semhash/semhash.py | 45 ++++++++++++++----- tests/test_semhash.py | 102 ++++++++++++++++++++++++++++++++++-------- 2 files changed, 117 insertions(+), 30 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index 113ddaa..8a81dc8 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -152,6 +152,7 @@ def from_dataset( :return: A SemHash instance with a fitted vicinity index. :raises TypeError: If dataset doesn't have required attributes. :raises ValueError: If columns are not found in the dataset. + :raises ValueError: If any column contains None values. """ if not hasattr(dataset, "column_names") or not hasattr(dataset, "__len__"): raise TypeError("dataset must have 'column_names' and '__len__' attributes") @@ -168,31 +169,51 @@ def from_dataset( # Columnar extraction (fast path for HF datasets - avoids row-wise indexing) n = len(dataset) cols = {c: dataset[c] for c in columns} - dict_records: list[dict[str, str]] = [{c: str(cols[c][i]) for c in columns} for i in range(n)] + col_set = set(columns) - # Exact dedup first (cheap) - we only embed representatives - deduplicated_records, duplicates = remove_exact_duplicates(dict_records, columns) + # Helper: coerce values to string with None check + def _coerce(val: Any, *, col: str, idx: int) -> str: + if val is None: + raise ValueError(f"Column '{col}' has None at index {idx}") + return val if isinstance(val, str) else str(val) - col_set = set(columns) - duplicate_map = defaultdict(list) - for x, _ in duplicates: - duplicate_map[to_frozendict(x, col_set)].append(x) + # Group dataset rows by exact key without building dicts for all rows. + # This avoids materializing N dict objects and is memory-efficient for large datasets. + key_to_indices: dict[frozendict[str, str], list[int]] = defaultdict(list) + key_first_idx: dict[frozendict[str, str], int] = {} + + for i in range(n): + row = {c: _coerce(cols[c][i], col=c, idx=i) for c in columns} + key = to_frozendict(row, col_set) + key_to_indices[key].append(i) + if key not in key_first_idx: + key_first_idx[key] = i + # Deterministic output: preserve first occurrence order + ordered_keys = sorted(key_to_indices.keys(), key=lambda k: key_first_idx[k]) + + # Representatives = first index per key; items hold full duplicate buckets. items: list[list[dict[str, str]]] = [] - for record in deduplicated_records: - bucket = [record] - bucket.extend(duplicate_map[to_frozendict(record, col_set)]) - items.append(bucket) + deduplicated_records: list[dict[str, str]] = [] + + for key in ordered_keys: + indices = key_to_indices[key] + bucket = [{c: _coerce(cols[c][i], col=c, idx=i) for c in columns} for i in indices] + deduplicated_records.append(bucket[0]) # representative + items.append(bucket) # all exact duplicates # Embed representatives only (encoder decides batching internally) vectors = featurize(records=deduplicated_records, columns=columns, model=model) + # Match from_records behavior: return strings for single "text" column + was_string = len(columns) == 1 and columns[0] == "text" + return cls._from_vectors_and_items( vectors=vectors, items=items, model=model, columns=columns, - was_string=False, # dataset inputs are dict-like + was_string=was_string, ann_backend=ann_backend, **kwargs, ) diff --git a/tests/test_semhash.py b/tests/test_semhash.py index d2005a7..e387312 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -273,6 +273,26 @@ def test_from_dataset_basic(model: Encoder) -> None: assert len(result.selected) <= 3 +def test_from_dataset_returns_strings_for_text_column(model: Encoder) -> None: + """Test that from_dataset returns strings when columns=["text"], matching from_records behavior.""" + from datasets import Dataset + + ds = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should return strings (like from_records does) + result = semhash.self_deduplicate(threshold=0.95) + assert all(isinstance(r, str) for r in result.selected) + + # Verify equivalence with from_records + semhash_from_records = SemHash.from_records(records=["apple", "banana", "cherry"], model=model) + result_from_records = semhash_from_records.self_deduplicate(threshold=0.95) + + # Both should return strings + assert type(result.selected[0]) == type(result_from_records.selected[0]) + + def test_from_dataset_multicolumn(model: Encoder) -> None: """Test from_dataset with multiple columns.""" from datasets import Dataset @@ -305,6 +325,11 @@ def test_from_dataset_validation(model: Encoder) -> None: with pytest.raises(ValueError, match="not found in dataset"): SemHash.from_dataset(dataset=ds, columns=["missing_col"], model=model) + # Test None value in column + ds_with_none = Dataset.from_dict({"text": ["apple", None, "banana"]}) + with pytest.raises(ValueError, match="Column 'text' has None at index 1"): + SemHash.from_dataset(dataset=ds_with_none, columns=["text"], model=model) + def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: """Test that from_dataset produces same results as from_records for same data.""" @@ -330,34 +355,75 @@ def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: assert len(result1.filtered) == len(result2.filtered) -def test_from_dataset_does_not_embed_duplicates(model: Encoder, monkeypatch: pytest.MonkeyPatch) -> None: +def test_from_dataset_does_not_embed_duplicates(model: Encoder) -> None: """Test that from_dataset only embeds representative records, not duplicates.""" from typing import Any from datasets import Dataset - # Create dataset with exact duplicates + # Create dataset with exact duplicates: 6 records but only 3 unique ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) - # Track how many times encode is called and with how many records - encode_call_count = 0 - total_encoded = 0 + # Create a counting encoder wrapper + class CountingEncoder: + def __init__(self, base_encoder: Any) -> None: + self.base_encoder = base_encoder + self.encode_calls: list[int] = [] - original_encode = model.encode + def encode(self, sentences: Any, **kwargs: Any) -> Any: + if isinstance(sentences, str): + sentences = [sentences] + self.encode_calls.append(len(sentences)) + return self.base_encoder.encode(sentences, **kwargs) - def tracked_encode(texts: Any, **kwargs: Any) -> Any: - nonlocal encode_call_count, total_encoded - encode_call_count += 1 - total_encoded += len(texts) - return original_encode(texts, **kwargs) + counting_encoder = CountingEncoder(model) - monkeypatch.setattr(model, "encode", tracked_encode) + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=counting_encoder) # type: ignore[arg-type] - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + # Should only have 3 representatives + assert semhash.index.vectors.shape[0] == 3 + + # Should have encoded exactly 3 records (representatives only), not 6 + assert sum(counting_encoder.encode_calls) == 3 + # Should have been called once with all 3 representatives + assert len(counting_encoder.encode_calls) == 1 + assert counting_encoder.encode_calls[0] == 3 - # Should only have encoded 3 unique records (apple, banana, cherry) + +def test_from_dataset_handles_non_string_values(model: Encoder) -> None: + """Test that from_dataset handles non-string values (e.g., integers) by converting them.""" + from datasets import Dataset + + # Dataset with integer values + ds = Dataset.from_dict({"id": [1, 2, 3, 1]}) # Has a duplicate + + semhash = SemHash.from_dataset(dataset=ds, columns=["id"], model=model) + + # Should have deduplicated the integer '1' assert semhash.index.vectors.shape[0] == 3 - # Total encoded should be 3, not 6 - assert total_encoded == 3 - # Should be called once (for the deduplicated records) - assert encode_call_count == 1 + assert len(semhash.index.items) == 3 + + # Values should be converted to strings + result = semhash.self_deduplicate(threshold=0.95) + # Result should be dicts (not strings, since column is 'id' not 'text') + assert all(isinstance(r, dict) for r in result.selected) + assert all("id" in r for r in result.selected) + + +def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: + """Test that from_dataset preserves the order of first occurrences (deterministic output).""" + from datasets import Dataset + + # Create dataset where duplicates appear out of order + ds = Dataset.from_dict({"text": ["zebra", "apple", "zebra", "banana", "apple", "cherry"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should have 4 unique items in first-occurrence order: zebra, apple, banana, cherry + assert len(semhash.index.vectors) == 4 + + # Get all items (each is a bucket of duplicates) + first_occurrences = [item[0]["text"] for item in semhash.index.items] + + # Should preserve first-occurrence order from dataset + assert first_occurrences == ["zebra", "apple", "banana", "cherry"] From 3ace1bde9f8b9a014169dbb14b6b5cbde1406aa2 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:40:33 +0100 Subject: [PATCH 04/37] Optimized from_dataset class method --- semhash/semhash.py | 33 ++++++++++++++++++++++++++++----- tests/test_semhash.py | 23 +++++++++++++++++++++++ 2 files changed, 51 insertions(+), 5 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index 8a81dc8..0ccdd99 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -130,7 +130,7 @@ def from_records( ) @classmethod - def from_dataset( + def from_dataset( # noqa: C901 cls, dataset: Any, columns: Sequence[str], @@ -144,7 +144,9 @@ def from_dataset( Extracts records from the dataset, deduplicates them, and embeds only representative records (not duplicates). The encoder controls batching internally. - :param dataset: A dataset with column_names attribute and dict-like access. + Expects HuggingFace Dataset-style columnar access (dataset[column_name] returns a sequence). + + :param dataset: A dataset with column_names attribute and columnar access (dataset[column]). :param columns: Columns to use for deduplication (same as from_records). :param model: (Optional) An Encoder model. If None, the default model is used (minishlab/potion-base-8M). :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. @@ -168,7 +170,16 @@ def from_dataset( # Columnar extraction (fast path for HF datasets - avoids row-wise indexing) n = len(dataset) + if n == 0: + raise ValueError("dataset must not be empty") + cols = {c: dataset[c] for c in columns} + + # Validate that all columns have the same length as the dataset + for c in columns: + if len(cols[c]) != n: + raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") + col_set = set(columns) # Helper: coerce values to string with None check @@ -182,8 +193,20 @@ def _coerce(val: Any, *, col: str, idx: int) -> str: key_to_indices: dict[frozendict[str, str], list[int]] = defaultdict(list) key_first_idx: dict[frozendict[str, str], int] = {} + # Track whether "text" column contains only strings (for was_string) + was_string = len(columns) == 1 and columns[0] == "text" + for i in range(n): - row = {c: _coerce(cols[c][i], col=c, idx=i) for c in columns} + row = {} + for c in columns: + raw = cols[c][i] + if raw is None: + raise ValueError(f"Column '{c}' has None at index {i}") + # Check if text column value is actually a string + if c == "text" and was_string and not isinstance(raw, str): + was_string = False + row[c] = raw if isinstance(raw, str) else str(raw) + key = to_frozendict(row, col_set) key_to_indices[key].append(i) if key not in key_first_idx: @@ -205,8 +228,8 @@ def _coerce(val: Any, *, col: str, idx: int) -> str: # Embed representatives only (encoder decides batching internally) vectors = featurize(records=deduplicated_records, columns=columns, model=model) - # Match from_records behavior: return strings for single "text" column - was_string = len(columns) == 1 and columns[0] == "text" + # was_string was tracked during the main loop above + # (True only if columns==["text"] and all values were strings) return cls._from_vectors_and_items( vectors=vectors, diff --git a/tests/test_semhash.py b/tests/test_semhash.py index e387312..32706be 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -330,6 +330,11 @@ def test_from_dataset_validation(model: Encoder) -> None: with pytest.raises(ValueError, match="Column 'text' has None at index 1"): SemHash.from_dataset(dataset=ds_with_none, columns=["text"], model=model) + # Test empty dataset + ds_empty = Dataset.from_dict({"text": []}) + with pytest.raises(ValueError, match="dataset must not be empty"): + SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) + def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: """Test that from_dataset produces same results as from_records for same data.""" @@ -427,3 +432,21 @@ def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: # Should preserve first-occurrence order from dataset assert first_occurrences == ["zebra", "apple", "banana", "cherry"] + + +def test_from_dataset_was_string_only_for_actual_strings(model: Encoder) -> None: + """Test that was_string is only True for text columns with actual string values.""" + from datasets import Dataset + + # Test 1: text column with strings -> should return strings + ds_strings = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) + semhash_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) + result_strings = semhash_strings.self_deduplicate(threshold=0.95) + assert all(isinstance(r, str) for r in result_strings.selected) + + # Test 2: text column with integers -> should return dicts (not strings) + ds_ints = Dataset.from_dict({"text": [1, 2, 3]}) + semhash_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model) + result_ints = semhash_ints.self_deduplicate(threshold=0.95) + assert all(isinstance(r, dict) for r in result_ints.selected) + assert all("text" in r for r in result_ints.selected) From f8e5e5462bc1c2ffeece3cf859394b7be553eb64 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:48:20 +0100 Subject: [PATCH 05/37] Simplified from_dataset class method --- semhash/semhash.py | 78 ++++------------------------------------------ semhash/utils.py | 75 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 81 insertions(+), 72 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index 0ccdd99..28740c7 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -11,13 +11,15 @@ from pyversity import Strategy, diversify from vicinity import Backend -from semhash.datamodels import DeduplicationResult, DuplicateRecord, FilterResult, Record +from semhash.datamodels import DeduplicationResult, DuplicateRecord, FilterResult from semhash.index import Index from semhash.records import add_scores_to_records, map_deduplication_result_to_strings from semhash.utils import ( Encoder, + Record, compute_candidate_limit, featurize, + prepare_dataset_records, prepare_records, remove_exact_duplicates, to_frozendict, @@ -130,7 +132,7 @@ def from_records( ) @classmethod - def from_dataset( # noqa: C901 + def from_dataset( cls, dataset: Any, columns: Sequence[str], @@ -152,85 +154,17 @@ def from_dataset( # noqa: C901 :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. :param **kwargs: Any additional keyword arguments to pass to the Vicinity index. :return: A SemHash instance with a fitted vicinity index. - :raises TypeError: If dataset doesn't have required attributes. - :raises ValueError: If columns are not found in the dataset. - :raises ValueError: If any column contains None values. """ - if not hasattr(dataset, "column_names") or not hasattr(dataset, "__len__"): - raise TypeError("dataset must have 'column_names' and '__len__' attributes") - - # Validate columns exist - missing = set(columns) - set(dataset.column_names) - if missing: - raise ValueError(f"Columns {missing} not found in dataset") - # Load default model if needed if model is None: model = StaticModel.from_pretrained("minishlab/potion-base-8M") - # Columnar extraction (fast path for HF datasets - avoids row-wise indexing) - n = len(dataset) - if n == 0: - raise ValueError("dataset must not be empty") - - cols = {c: dataset[c] for c in columns} - - # Validate that all columns have the same length as the dataset - for c in columns: - if len(cols[c]) != n: - raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") - - col_set = set(columns) - - # Helper: coerce values to string with None check - def _coerce(val: Any, *, col: str, idx: int) -> str: - if val is None: - raise ValueError(f"Column '{col}' has None at index {idx}") - return val if isinstance(val, str) else str(val) - - # Group dataset rows by exact key without building dicts for all rows. - # This avoids materializing N dict objects and is memory-efficient for large datasets. - key_to_indices: dict[frozendict[str, str], list[int]] = defaultdict(list) - key_first_idx: dict[frozendict[str, str], int] = {} - - # Track whether "text" column contains only strings (for was_string) - was_string = len(columns) == 1 and columns[0] == "text" - - for i in range(n): - row = {} - for c in columns: - raw = cols[c][i] - if raw is None: - raise ValueError(f"Column '{c}' has None at index {i}") - # Check if text column value is actually a string - if c == "text" and was_string and not isinstance(raw, str): - was_string = False - row[c] = raw if isinstance(raw, str) else str(raw) - - key = to_frozendict(row, col_set) - key_to_indices[key].append(i) - if key not in key_first_idx: - key_first_idx[key] = i - - # Deterministic output: preserve first occurrence order - ordered_keys = sorted(key_to_indices.keys(), key=lambda k: key_first_idx[k]) - - # Representatives = first index per key; items hold full duplicate buckets. - items: list[list[dict[str, str]]] = [] - deduplicated_records: list[dict[str, str]] = [] - - for key in ordered_keys: - indices = key_to_indices[key] - bucket = [{c: _coerce(cols[c][i], col=c, idx=i) for c in columns} for i in indices] - deduplicated_records.append(bucket[0]) # representative - items.append(bucket) # all exact duplicates + # Extract, validate, and deduplicate dataset records + deduplicated_records, items, was_string = prepare_dataset_records(dataset, columns) # Embed representatives only (encoder decides batching internally) vectors = featurize(records=deduplicated_records, columns=columns, model=model) - # was_string was tracked during the main loop above - # (True only if columns==["text"] and all values were strings) - return cls._from_vectors_and_items( vectors=vectors, items=items, diff --git a/semhash/utils.py b/semhash/utils.py index f3abb00..2fa8a4d 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -151,3 +151,78 @@ def prepare_records( was_string = False return dict_records, columns, was_string + + +def prepare_dataset_records( # noqa: C901 + dataset: Any, + columns: Sequence[str], +) -> tuple[list[dict[str, str]], list[list[dict[str, str]]], bool]: + """ + Extract, validate, and exact-deduplicate dataset rows using columnar access. + + Expects HuggingFace Dataset-style columnar access (dataset[column_name] returns a sequence). + + :param dataset: A dataset with column_names attribute and columnar access. + :param columns: Columns to use for deduplication. + :return: Tuple of (deduplicated_records, items, was_string) where: + - deduplicated_records: representative record per exact-duplicate bucket + - items: buckets of exact duplicates (each bucket is list[record]) + - was_string: True iff columns == ["text"] and ALL raw values were strings + :raises TypeError: If dataset doesn't have required attributes. + :raises ValueError: If columns are not found in dataset. + :raises ValueError: If dataset is empty. + :raises ValueError: If column lengths don't match dataset length. + :raises ValueError: If any column contains None values. + """ + if not hasattr(dataset, "column_names") or not hasattr(dataset, "__len__"): + raise TypeError("dataset must have 'column_names' and '__len__' attributes") + + missing = set(columns) - set(dataset.column_names) + if missing: + raise ValueError(f"Columns {missing} not found in dataset") + + n = len(dataset) + if n == 0: + raise ValueError("dataset must not be empty") + + cols = {c: dataset[c] for c in columns} + for c in columns: + if len(cols[c]) != n: + raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") + + col_set = set(columns) + + def coerce_at(i: int, c: str) -> str: + raw = cols[c][i] + if raw is None: + raise ValueError(f"Column '{c}' has None at index {i}") + return raw if isinstance(raw, str) else str(raw) + + was_string = len(columns) == 1 and columns[0] == "text" + + key_to_indices: dict[frozendict[str, str], list[int]] = defaultdict(list) + key_first_idx: dict[frozendict[str, str], int] = {} + + for i in range(n): + # Track "was_string" using RAW values (not coerced) + if was_string and not isinstance(cols["text"][i], str): + was_string = False + + row = {c: coerce_at(i, c) for c in columns} + key = to_frozendict(row, col_set) + + key_to_indices[key].append(i) + key_first_idx.setdefault(key, i) + + ordered_keys = sorted(key_to_indices.keys(), key=lambda k: key_first_idx[k]) + + items: list[list[dict[str, str]]] = [] + deduplicated_records: list[dict[str, str]] = [] + + for key in ordered_keys: + indices = key_to_indices[key] + bucket = [{c: coerce_at(i, c) for c in columns} for i in indices] + deduplicated_records.append(bucket[0]) + items.append(bucket) + + return deduplicated_records, items, was_string From a8282c894c2c52b81c26b95883d0eae0008a776c Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:52:08 +0100 Subject: [PATCH 06/37] Simplified from_dataset class method --- semhash/utils.py | 80 +++++++++++++++++++++++------------------------- 1 file changed, 39 insertions(+), 41 deletions(-) diff --git a/semhash/utils.py b/semhash/utils.py index 2fa8a4d..7a17947 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -153,27 +153,8 @@ def prepare_records( return dict_records, columns, was_string -def prepare_dataset_records( # noqa: C901 - dataset: Any, - columns: Sequence[str], -) -> tuple[list[dict[str, str]], list[list[dict[str, str]]], bool]: - """ - Extract, validate, and exact-deduplicate dataset rows using columnar access. - - Expects HuggingFace Dataset-style columnar access (dataset[column_name] returns a sequence). - - :param dataset: A dataset with column_names attribute and columnar access. - :param columns: Columns to use for deduplication. - :return: Tuple of (deduplicated_records, items, was_string) where: - - deduplicated_records: representative record per exact-duplicate bucket - - items: buckets of exact duplicates (each bucket is list[record]) - - was_string: True iff columns == ["text"] and ALL raw values were strings - :raises TypeError: If dataset doesn't have required attributes. - :raises ValueError: If columns are not found in dataset. - :raises ValueError: If dataset is empty. - :raises ValueError: If column lengths don't match dataset length. - :raises ValueError: If any column contains None values. - """ +def _validate_dataset(dataset: Any, columns: Sequence[str]) -> dict[str, Sequence[Any]]: + """Validate dataset structure and extract columns.""" if not hasattr(dataset, "column_names") or not hasattr(dataset, "__len__"): raise TypeError("dataset must have 'column_names' and '__len__' attributes") @@ -190,39 +171,56 @@ def prepare_dataset_records( # noqa: C901 if len(cols[c]) != n: raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") + return cols + + +def prepare_dataset_records( + dataset: Any, + columns: Sequence[str], +) -> tuple[list[dict[str, str]], list[list[dict[str, str]]], bool]: + """ + Extract, validate, and exact-deduplicate dataset rows using columnar access. + + Expects HuggingFace Dataset-style columnar access (dataset[column_name] returns a sequence). + + :param dataset: A dataset with column_names attribute and columnar access. + :param columns: Columns to use for deduplication. + :return: Tuple of (deduplicated_records, items, was_string) where: + - deduplicated_records: representative record per exact-duplicate bucket + - items: buckets of exact duplicates (each bucket is list[record]) + - was_string: True iff columns == ["text"] and ALL raw values were strings + """ + cols = _validate_dataset(dataset, columns) + n = len(dataset) col_set = set(columns) + was_string = len(columns) == 1 and columns[0] == "text" - def coerce_at(i: int, c: str) -> str: - raw = cols[c][i] + def coerce(raw: Any, *, col: str, idx: int) -> str: if raw is None: - raise ValueError(f"Column '{c}' has None at index {i}") + raise ValueError(f"Column '{col}' has None at index {idx}") return raw if isinstance(raw, str) else str(raw) - was_string = len(columns) == 1 and columns[0] == "text" - - key_to_indices: dict[frozendict[str, str], list[int]] = defaultdict(list) - key_first_idx: dict[frozendict[str, str], int] = {} + # Single-pass grouping: key -> bucket of exact duplicates + buckets: dict[frozendict[str, str], list[dict[str, str]]] = {} + order: list[frozendict[str, str]] = [] for i in range(n): # Track "was_string" using RAW values (not coerced) if was_string and not isinstance(cols["text"][i], str): was_string = False - row = {c: coerce_at(i, c) for c in columns} + row = {c: coerce(cols[c][i], col=c, idx=i) for c in columns} key = to_frozendict(row, col_set) - key_to_indices[key].append(i) - key_first_idx.setdefault(key, i) - - ordered_keys = sorted(key_to_indices.keys(), key=lambda k: key_first_idx[k]) - - items: list[list[dict[str, str]]] = [] - deduplicated_records: list[dict[str, str]] = [] + bucket = buckets.get(key) + if bucket is None: + buckets[key] = [row] + order.append(key) + else: + bucket.append(row) - for key in ordered_keys: - indices = key_to_indices[key] - bucket = [{c: coerce_at(i, c) for c in columns} for i in indices] - deduplicated_records.append(bucket[0]) - items.append(bucket) + # Preserve first-occurrence order via the order list + items = [buckets[k] for k in order] + deduplicated_records = [bucket[0] for bucket in items] return deduplicated_records, items, was_string From e7820ab79993b5428c7dd6c74ba479e925c3cd38 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:55:30 +0100 Subject: [PATCH 07/37] Added DatasetLike protocol --- semhash/semhash.py | 12 +++++++++--- semhash/utils.py | 34 +++++++++++++++++++++++++++------- tests/test_semhash.py | 4 ++-- 3 files changed, 38 insertions(+), 12 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index 28740c7..73c407e 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -15,6 +15,7 @@ from semhash.index import Index from semhash.records import add_scores_to_records, map_deduplication_result_to_strings from semhash.utils import ( + DatasetLike, Encoder, Record, compute_candidate_limit, @@ -134,7 +135,7 @@ def from_records( @classmethod def from_dataset( cls, - dataset: Any, + dataset: DatasetLike, columns: Sequence[str], model: Encoder | None = None, ann_backend: Backend | str = Backend.USEARCH, @@ -146,9 +147,14 @@ def from_dataset( Extracts records from the dataset, deduplicates them, and embeds only representative records (not duplicates). The encoder controls batching internally. - Expects HuggingFace Dataset-style columnar access (dataset[column_name] returns a sequence). + Supports any dataset-like object that provides: + - column_names: Sequence[str] + - __len__() -> int + - __getitem__(column_name: str) -> Sequence[Any] (columnar access) - :param dataset: A dataset with column_names attribute and columnar access (dataset[column]). + HuggingFace datasets.Dataset satisfies this contract, but custom implementations work too. + + :param dataset: A dataset-like object with columnar access. :param columns: Columns to use for deduplication (same as from_records). :param model: (Optional) An Encoder model. If None, the default model is used (minishlab/potion-base-8M). :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. diff --git a/semhash/utils.py b/semhash/utils.py index 7a17947..d7f3913 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -28,6 +28,26 @@ def encode( ... # pragma: no cover +class DatasetLike(Protocol): + """ + Protocol for dataset-like objects compatible with SemHash.from_dataset(). + + Any object that provides columnar access (dataset[column_name] -> sequence) + satisfies this protocol. HuggingFace datasets.Dataset is the primary example, + but custom dataset implementations are supported. + """ + + column_names: Sequence[str] + + def __len__(self) -> int: + """Return the number of rows in the dataset.""" + ... # pragma: no cover + + def __getitem__(self, key: str) -> Sequence[Any]: + """Return all values for the given column name.""" + ... # pragma: no cover + + def to_frozendict(record: dict[str, str], columns: set[str]) -> frozendict[str, str]: """Convert a record to a frozendict.""" return frozendict({k: record.get(k, "") for k in columns}) @@ -153,11 +173,8 @@ def prepare_records( return dict_records, columns, was_string -def _validate_dataset(dataset: Any, columns: Sequence[str]) -> dict[str, Sequence[Any]]: +def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> dict[str, Sequence[Any]]: """Validate dataset structure and extract columns.""" - if not hasattr(dataset, "column_names") or not hasattr(dataset, "__len__"): - raise TypeError("dataset must have 'column_names' and '__len__' attributes") - missing = set(columns) - set(dataset.column_names) if missing: raise ValueError(f"Columns {missing} not found in dataset") @@ -175,15 +192,18 @@ def _validate_dataset(dataset: Any, columns: Sequence[str]) -> dict[str, Sequenc def prepare_dataset_records( - dataset: Any, + dataset: DatasetLike, columns: Sequence[str], ) -> tuple[list[dict[str, str]], list[list[dict[str, str]]], bool]: """ Extract, validate, and exact-deduplicate dataset rows using columnar access. - Expects HuggingFace Dataset-style columnar access (dataset[column_name] returns a sequence). + Supports HuggingFace datasets.Dataset and any dataset-like object that provides: + - column_names: Sequence[str] + - __len__() -> int + - __getitem__(column_name: str) -> Sequence[Any] (columnar access) - :param dataset: A dataset with column_names attribute and columnar access. + :param dataset: A dataset-like object with columnar access. :param columns: Columns to use for deduplication. :return: Tuple of (deduplicated_records, items, was_string) where: - deduplicated_records: representative record per exact-duplicate bucket diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 32706be..2500cd1 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -317,8 +317,8 @@ def test_from_dataset_validation(model: Encoder) -> None: ds = Dataset.from_dict({"text": ["apple", "banana"]}) - # Test invalid dataset type - with pytest.raises(TypeError, match="must have 'column_names' and '__len__' attributes"): + # Test invalid dataset type (Protocol violation results in AttributeError) + with pytest.raises(AttributeError): SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) # Test missing column From e685309182f8a26888bcf2081ec660d2a9275b93 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 13:59:20 +0100 Subject: [PATCH 08/37] Updated tests --- semhash/utils.py | 16 +++++---- tests/test_semhash.py | 79 +++++++++++++++++++++++++++++++++++++++++-- tests/test_utils.py | 31 +++++++++++++++++ 3 files changed, 118 insertions(+), 8 deletions(-) diff --git a/semhash/utils.py b/semhash/utils.py index d7f3913..b133211 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -173,9 +173,14 @@ def prepare_records( return dict_records, columns, was_string -def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> dict[str, Sequence[Any]]: - """Validate dataset structure and extract columns.""" - missing = set(columns) - set(dataset.column_names) +def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> tuple[dict[str, Sequence[Any]], int]: + """Validate dataset structure and extract columns. Returns (cols, n).""" + try: + column_names = dataset.column_names + except AttributeError as e: + raise TypeError("dataset must satisfy DatasetLike (column_names, __len__, __getitem__)") from e + + missing = set(columns) - set(column_names) if missing: raise ValueError(f"Columns {missing} not found in dataset") @@ -188,7 +193,7 @@ def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> dict[str, if len(cols[c]) != n: raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") - return cols + return cols, n def prepare_dataset_records( @@ -210,8 +215,7 @@ def prepare_dataset_records( - items: buckets of exact duplicates (each bucket is list[record]) - was_string: True iff columns == ["text"] and ALL raw values were strings """ - cols = _validate_dataset(dataset, columns) - n = len(dataset) + cols, n = _validate_dataset(dataset, columns) col_set = set(columns) was_string = len(columns) == 1 and columns[0] == "text" diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 2500cd1..0e62fdf 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -317,8 +317,8 @@ def test_from_dataset_validation(model: Encoder) -> None: ds = Dataset.from_dict({"text": ["apple", "banana"]}) - # Test invalid dataset type (Protocol violation results in AttributeError) - with pytest.raises(AttributeError): + # Test invalid dataset type (Protocol violation) + with pytest.raises(TypeError, match="must satisfy DatasetLike"): SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) # Test missing column @@ -450,3 +450,78 @@ def test_from_dataset_was_string_only_for_actual_strings(model: Encoder) -> None result_ints = semhash_ints.self_deduplicate(threshold=0.95) assert all(isinstance(r, dict) for r in result_ints.selected) assert all("text" in r for r in result_ints.selected) + + +def test_from_dataset_with_custom_dataset_like(model: Encoder) -> None: + """Test that from_dataset works with custom DatasetLike implementations (no HF dependency).""" + + class MiniDataset: + """Minimal DatasetLike implementation for testing.""" + + column_names = ["text"] + + def __init__(self, data: dict[str, list[str]]) -> None: + self._data = data + + def __len__(self) -> int: + return len(self._data["text"]) + + def __getitem__(self, key: str) -> list[str]: + return self._data[key] + + # Create custom dataset with duplicates + ds = MiniDataset({"text": ["apple", "banana", "apple"]}) + + semhash = SemHash.from_dataset(ds, columns=["text"], model=model) + + # Should have deduplicated to 2 unique items + assert len(semhash.index.items) == 2 + assert len(semhash.index.vectors) == 2 + + # Should work with deduplication + result = semhash.self_deduplicate(threshold=0.95) + assert len(result.selected) == 2 + + +def test_from_dataset_multicolumn_does_not_embed_duplicates(model: Encoder) -> None: + """Test that multi-column from_dataset only embeds representatives (validates per-column encoding).""" + from typing import Any + + from datasets import Dataset + + # Create dataset with exact duplicates across multiple columns + ds = Dataset.from_dict( + { + "col1": ["a", "b", "a", "c"], # "a" appears twice + "col2": ["x", "y", "x", "z"], # matching pattern + } + ) + + # Create a counting encoder wrapper + class CountingEncoder: + def __init__(self, base_encoder: Any) -> None: + self.base_encoder = base_encoder + self.encode_calls: list[int] = [] + + def encode(self, sentences: Any, **kwargs: Any) -> Any: + if isinstance(sentences, str): + sentences = [sentences] + self.encode_calls.append(len(sentences)) + return self.base_encoder.encode(sentences, **kwargs) + + counting_encoder = CountingEncoder(model) + + semhash = SemHash.from_dataset(dataset=ds, columns=["col1", "col2"], model=counting_encoder) # type: ignore[arg-type] + + # Should have 3 representatives (unique combinations) + assert semhash.index.vectors.shape[0] == 3 + + # featurize() encodes once per column, so we expect 2 calls (for col1 and col2) + assert len(counting_encoder.encode_calls) == 2 + + # Each call should have 3 texts (the representatives) + assert counting_encoder.encode_calls[0] == 3 # col1 + assert counting_encoder.encode_calls[1] == 3 # col2 + + # Total encoded should be 6 (3 representatives × 2 columns) + assert sum(counting_encoder.encode_calls) == 6 diff --git a/tests/test_utils.py b/tests/test_utils.py index 371fb16..f83035a 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -74,3 +74,34 @@ def test_prepare_records() -> None: records = [{"text": "hello"}] with pytest.raises(ValueError, match="Columns must be specified"): prepare_records(records, None) + + +def test_remove_exact_duplicates_with_reference_records() -> None: + """Test exact duplicate removal with reference_records for cross-dataset filtering.""" + # Build reference buckets (simulates index.items structure) + reference_records = [ + [{"text": "apple"}], # bucket 1: apple (1 occurrence) + [{"text": "banana"}, {"text": "banana"}], # bucket 2: banana (2 occurrences) + ] + + # New records to check against reference + new_records = [ + {"text": "cherry"}, # New (not in reference) + {"text": "apple"}, # Exact match with reference + {"text": "date"}, # New (not in reference) + {"text": "banana"}, # Exact match with reference + ] + + deduplicated, duplicates = remove_exact_duplicates(new_records, ["text"], reference_records=reference_records) + + # Deduplicated should only contain records NOT in reference + assert len(deduplicated) == 2 + assert {"text": "cherry"} in deduplicated + assert {"text": "date"} in deduplicated + + # Duplicates should contain records that match reference + assert len(duplicates) == 2 + # Each duplicate is (record, reference_bucket) + dup_records = [d[0] for d in duplicates] + assert {"text": "apple"} in dup_records + assert {"text": "banana"} in dup_records From 56e5ab34fbef173d5ae6516e64622c865f3bcb4b Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 15:00:41 +0100 Subject: [PATCH 09/37] Updated tests --- semhash/semhash.py | 28 +++++++++++++++------------- semhash/utils.py | 14 +++++++++++++- tests/test_semhash.py | 34 ++++++++++++++++++++++++++++++++++ 3 files changed, 62 insertions(+), 14 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index 73c407e..bd33b01 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -103,21 +103,23 @@ def from_records( if model is None: model = StaticModel.from_pretrained("minishlab/potion-base-8M") - # Remove exact duplicates - deduplicated_records, duplicates = remove_exact_duplicates(dict_records, columns) - + # Single-pass grouping by exact key (same as from_dataset for consistency) col_set = set(columns) - duplicate_map = defaultdict(list) - for x, _ in duplicates: - frozen_record = to_frozendict(x, col_set) - duplicate_map[frozen_record].append(x) + buckets: dict[frozendict[str, str], list[dict[str, str]]] = {} + order: list[frozendict[str, str]] = [] + + for r in dict_records: + key = to_frozendict(r, col_set) + bucket = buckets.get(key) + if bucket is None: + buckets[key] = [r] + order.append(key) + else: + bucket.append(r) - items: list[list[dict[str, str]]] = [] - for record in deduplicated_records: - i = [record] - frozen_record = to_frozendict(record, col_set) - i.extend(duplicate_map[frozen_record]) - items.append(i) + # Build items and deduplicated_records in first-occurrence order + items = [buckets[k] for k in order] + deduplicated_records = [bucket[0] for bucket in items] # Create embeddings for deduplicated records only embeddings = featurize(deduplicated_records, columns, model) diff --git a/semhash/utils.py b/semhash/utils.py index b133211..72bf7e5 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -155,6 +155,7 @@ def prepare_records( :return: Tuple of (dict_records, columns, was_string). :raises ValueError: If records are empty. :raises ValueError: If columns are not provided for dictionary records. + :raises ValueError: If dict record contains None values. """ if len(records) == 0: raise ValueError("records must not be empty") @@ -167,7 +168,18 @@ def prepare_records( dict_records: list[dict[str, str]] = [{"text": str(record)} for record in records] was_string = True else: - dict_records = list(records) + assert columns is not None + # Coerce dict values to strings (matching dataset behavior) + dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] + dict_records = [] + for r in dict_records_typed: + coerced = {} + for c in columns: + val = r.get(c) + if val is None: + raise ValueError(f"Column '{c}' has None value in record {r}") + coerced[c] = val if isinstance(val, str) else str(val) + dict_records.append(coerced) was_string = False return dict_records, columns, was_string diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 0e62fdf..0d4d6c6 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -525,3 +525,37 @@ def encode(self, sentences: Any, **kwargs: Any) -> Any: # Total encoded should be 6 (3 representatives × 2 columns) assert sum(counting_encoder.encode_calls) == 6 + + +def test_from_records_coerces_non_string_dict_values(model: Encoder) -> None: + """Test that from_records coerces non-string dict values to strings.""" + records = [{"id": 1}, {"id": 2}, {"id": 1}] # Integers, with duplicate + semhash = SemHash.from_records(records, columns=["id"], model=model) + + # Should have deduplicated to 2 unique items + assert semhash.index.vectors.shape[0] == 2 + assert len(semhash.index.items) == 2 + + # First bucket should have 2 records (id=1 appears twice) + bucket_sizes = [len(bucket) for bucket in semhash.index.items] + assert 2 in bucket_sizes + + +def test_from_records_preserves_first_occurrence_order(model: Encoder) -> None: + """Test that from_records preserves first-occurrence order (deterministic).""" + texts = ["zebra", "apple", "zebra", "banana", "apple", "cherry"] + semhash = SemHash.from_records(texts, model=model) + + # Get first record from each bucket + firsts = [bucket[0]["text"] for bucket in semhash.index.items] + + # Should be in first-occurrence order + assert firsts == ["zebra", "apple", "banana", "cherry"] + + +def test_from_records_rejects_none_in_dict_values(model: Encoder) -> None: + """Test that from_records rejects None values in dict records.""" + records = [{"text": "apple"}, {"text": None}, {"text": "banana"}] + + with pytest.raises(ValueError, match="has None value"): + SemHash.from_records(records, columns=["text"], model=model) From f9e182e414d4163a52d80f09699b478351ca0eb5 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 15:09:00 +0100 Subject: [PATCH 10/37] Improved code --- semhash/semhash.py | 17 +++++++++-------- semhash/utils.py | 13 ++++++++++++- tests/test_utils.py | 13 +++++++++++++ 3 files changed, 34 insertions(+), 9 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index bd33b01..c92dd22 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -387,7 +387,7 @@ def self_deduplicate( return result - def _validate_if_strings(self, records: Sequence[dict[str, str] | str]) -> Sequence[dict[str, str]]: + def _validate_if_strings(self, records: Sequence[dict[str, str] | str]) -> list[dict[str, str]]: """ Validate if the records are strings. @@ -397,7 +397,7 @@ def _validate_if_strings(self, records: Sequence[dict[str, str] | str]) -> Seque :return: The records as a list of dictionaries. :raises ValueError: If records are empty. :raises ValueError: If the records are strings but were not originally strings. - :raises ValueError: If the records are not all strings or dictionaries. + :raises ValueError: If the records are not all strings or all dictionaries. """ if len(records) == 0: raise ValueError("records must not be empty") @@ -405,12 +405,13 @@ def _validate_if_strings(self, records: Sequence[dict[str, str] | str]) -> Seque if isinstance(records[0], str): if not self._was_string: raise ValueError("Records were not originally strings, but you passed strings.") - dict_records = [{"text": record} for record in records if isinstance(record, str)] - else: - dict_records = [record for record in records if isinstance(record, dict)] - if len(dict_records) != len(records): - raise ValueError("Records must be either strings or dictionaries.") - return dict_records + if not all(isinstance(r, str) for r in records): + raise ValueError("Records must be all strings.") + return [{"text": r} for r in records] # type: ignore[misc,dict-item] + + if not all(isinstance(r, dict) for r in records): + raise ValueError("Records must be all dictionaries.") + return list(records) # type: ignore[return-value,arg-type] def find_representative( self, diff --git a/semhash/utils.py b/semhash/utils.py index 72bf7e5..b63eaa1 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -93,11 +93,15 @@ def featurize( :param columns: Columns to featurize. :param model: An Encoder model. :return: The embeddings of the records. + :raises ValueError: If a column is missing from one or more records. """ # Extract the embeddings for each column across all records embeddings_per_col = [] for col in columns: - col_texts = [r[col] for r in records] + try: + col_texts = [r[col] for r in records] + except KeyError as e: + raise ValueError(f"Missing column '{col}' in one or more records") from e col_emb = model.encode(col_texts) embeddings_per_col.append(np.asarray(col_emb)) @@ -156,6 +160,7 @@ def prepare_records( :raises ValueError: If records are empty. :raises ValueError: If columns are not provided for dictionary records. :raises ValueError: If dict record contains None values. + :raises ValueError: If records are not homogeneous (mixed strings and dicts). """ if len(records) == 0: raise ValueError("records must not be empty") @@ -164,10 +169,16 @@ def prepare_records( raise ValueError("Columns must be specified when passing dictionaries.") if isinstance(records[0], str): + # Validate all records are strings + if not all(isinstance(r, str) for r in records): + raise ValueError("All records must be strings when the first record is a string.") columns = ["text"] dict_records: list[dict[str, str]] = [{"text": str(record)} for record in records] was_string = True else: + # Validate all records are dicts + if not all(isinstance(r, dict) for r in records): + raise ValueError("All records must be dicts when the first record is a dict.") assert columns is not None # Coerce dict values to strings (matching dataset behavior) dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] diff --git a/tests/test_utils.py b/tests/test_utils.py index f83035a..18ac74b 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -105,3 +105,16 @@ def test_remove_exact_duplicates_with_reference_records() -> None: dup_records = [d[0] for d in duplicates] assert {"text": "apple"} in dup_records assert {"text": "banana"} in dup_records + + +def test_prepare_records_rejects_mixed_types() -> None: + """Test that prepare_records rejects mixed string/dict inputs.""" + from semhash.utils import prepare_records + + # Mixed types should be rejected + with pytest.raises(ValueError, match="All records must be"): + prepare_records(["a", {"text": "b"}], None) # type: ignore[list-item] + + # Starting with dict, then string should also be rejected + with pytest.raises(ValueError, match="All records must be"): + prepare_records([{"text": "a"}, "b"], ["text"]) # type: ignore[list-item] From 801de6379d1d0f8c8a4dac5d9272f5efffea52b2 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 15:58:02 +0100 Subject: [PATCH 11/37] Improved code --- semhash/semhash.py | 23 ++++++++++++++++++++--- semhash/utils.py | 15 +++++++++++++-- tests/test_semhash.py | 26 ++++++++++++++++++++++++++ tests/test_utils.py | 8 ++++++++ 4 files changed, 67 insertions(+), 5 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index c92dd22..cad788e 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -387,31 +387,48 @@ def self_deduplicate( return result - def _validate_if_strings(self, records: Sequence[dict[str, str] | str]) -> list[dict[str, str]]: + def _validate_if_strings(self, records: Sequence[dict[str, Any] | str]) -> list[dict[str, str]]: """ Validate if the records are strings. If the records are strings, they are converted to dictionaries with a single column. + If the records are dicts, values are coerced to strings and None is rejected. :param records: The records to validate. :return: The records as a list of dictionaries. :raises ValueError: If records are empty. :raises ValueError: If the records are strings but were not originally strings. :raises ValueError: If the records are not all strings or all dictionaries. + :raises ValueError: If dict record contains None values. """ if len(records) == 0: raise ValueError("records must not be empty") + # String path if isinstance(records[0], str): if not self._was_string: raise ValueError("Records were not originally strings, but you passed strings.") if not all(isinstance(r, str) for r in records): raise ValueError("Records must be all strings.") - return [{"text": r} for r in records] # type: ignore[misc,dict-item] + # Type narrowing: we've validated all are strings + return [{"text": str(r)} for r in records] + # Dict path - coerce values to strings (matching prepare_records behavior) if not all(isinstance(r, dict) for r in records): raise ValueError("Records must be all dictionaries.") - return list(records) # type: ignore[return-value,arg-type] + + # Type narrowing: we've validated all are dicts + dict_records: Sequence[dict[str, Any]] = records # type: ignore[assignment] + coerced: list[dict[str, str]] = [] + for r in dict_records: + out: dict[str, str] = {} + for c in self.columns: + val = r.get(c) + if val is None: + raise ValueError(f"Column '{c}' has None value in record {r}") + out[c] = val if isinstance(val, str) else str(val) + coerced.append(out) + return coerced def find_representative( self, diff --git a/semhash/utils.py b/semhash/utils.py index b63eaa1..2d7e8d6 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -49,8 +49,19 @@ def __getitem__(self, key: str) -> Sequence[Any]: def to_frozendict(record: dict[str, str], columns: set[str]) -> frozendict[str, str]: - """Convert a record to a frozendict.""" - return frozendict({k: record.get(k, "") for k in columns}) + """ + Convert a record to a frozendict. + + :param record: The record to convert. + :param columns: The columns to include. + :return: A frozendict with only the specified columns. + :raises ValueError: If a column is missing from the record. + """ + try: + return frozendict({k: record[k] for k in columns}) + except KeyError as e: + missing = e.args[0] + raise ValueError(f"Missing column '{missing}' in record {record}") from e def compute_candidate_limit( diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 0d4d6c6..54ec048 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -559,3 +559,29 @@ def test_from_records_rejects_none_in_dict_values(model: Encoder) -> None: with pytest.raises(ValueError, match="has None value"): SemHash.from_records(records, columns=["text"], model=model) + + +def test_deduplicate_coerces_non_string_dict_values(model: Encoder) -> None: + """Test that deduplicate() coerces non-string dict values (consistent with from_records).""" + # Build SemHash with string records + semhash = SemHash.from_records(["1", "2", "3"], model=model) + + # Pass dict records with integer values to deduplicate + new_records = [{"text": 1}, {"text": 2}, {"text": 4}] # type: ignore[list-item] + + # Should coerce ints to strings and work without error + result = semhash.deduplicate(new_records, threshold=0.95) + + # Should have deduplicated (1, 2 already exist) + assert len(result.filtered) > 0 + assert len(result.selected) > 0 + + +def test_deduplicate_rejects_none_values(model: Encoder) -> None: + """Test that deduplicate() rejects None values in dict records.""" + semhash = SemHash.from_records(["apple", "banana"], model=model) + + new_records = [{"text": "cherry"}, {"text": None}] # type: ignore[list-item] + + with pytest.raises(ValueError, match="has None value"): + semhash.deduplicate(new_records, threshold=0.95) diff --git a/tests/test_utils.py b/tests/test_utils.py index 18ac74b..381045c 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -20,6 +20,14 @@ def test_to_frozendict() -> None: assert "b" not in result +def test_to_frozendict_rejects_missing_columns() -> None: + """Test that to_frozendict raises on missing columns.""" + record = {"a": "1", "b": "2"} + + with pytest.raises(ValueError, match="Missing column 'c'"): + to_frozendict(record, {"a", "c"}) + + def test_compute_candidate_limit() -> None: """Test candidate limit computation.""" # Basic case From edbee5184ab9ebe42dbf852fa7152eed70e641a9 Mon Sep 17 00:00:00 2001 From: Pringled Date: Sun, 11 Jan 2026 16:37:59 +0100 Subject: [PATCH 12/37] Added optional datasets dependency --- Makefile | 2 +- pyproject.toml | 6 + semhash/semhash.py | 14 + tests/test_semhash.py | 22 + uv.lock | 1053 ++++++++++++++++++++++++++++++++++++++++- 5 files changed, 1095 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 2e79ab7..b5af932 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,7 @@ install: venv uv run pre-commit install install-no-pre-commit: - uv pip install ".[dev]" + uv pip install ".[dev,all]" fix: uv run pre-commit run --all-files diff --git a/pyproject.toml b/pyproject.toml index 966df8a..84aa0db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,12 @@ dev = [ "ruff", ] +# Integrations +datasets = ["datasets"] +all = [ + "datasets", +] + [project.urls] "Homepage" = "https://github.com/MinishLab" "Bug Reports" = "https://github.com/MinishLab/semhash/issues" diff --git a/semhash/semhash.py b/semhash/semhash.py index cad788e..b201a1b 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -156,13 +156,27 @@ def from_dataset( HuggingFace datasets.Dataset satisfies this contract, but custom implementations work too. + Note: If you want to use HuggingFace datasets, install with: + pip install 'semhash[datasets]' + :param dataset: A dataset-like object with columnar access. :param columns: Columns to use for deduplication (same as from_records). :param model: (Optional) An Encoder model. If None, the default model is used (minishlab/potion-base-8M). :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. :param **kwargs: Any additional keyword arguments to pass to the Vicinity index. :return: A SemHash instance with a fitted vicinity index. + :raises ImportError: If using HuggingFace Dataset without datasets package installed. """ + # Check if user is trying to use HuggingFace datasets without installing the package + if hasattr(dataset, "__class__") and dataset.__class__.__module__.startswith("datasets"): + try: + import datasets # noqa: F401 + except ImportError as e: + raise ImportError( + "HuggingFace `datasets` is required to use HF Dataset objects. " + "Please install it with `pip install 'semhash[datasets]'`" + ) from e + # Load default model if needed if model is None: model = StaticModel.from_pretrained("minishlab/potion-base-8M") diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 54ec048..1093299 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -5,6 +5,18 @@ from semhash.datamodels import FilterResult from semhash.utils import Encoder +# Check if datasets is available for dataset-specific tests +try: + import datasets # noqa: F401 + + HAS_DATASETS = True +except ImportError: + HAS_DATASETS = False + +requires_datasets = pytest.mark.skipif( + not HAS_DATASETS, reason="datasets not installed - install with pip install 'semhash[datasets]'" +) + def test_single_dataset_deduplication(model: Encoder) -> None: """Test single dataset deduplication.""" @@ -255,6 +267,7 @@ def test_from_embeddings(model: Encoder, train_texts: list[str]) -> None: assert semhash.index.vectors.tolist() == [[0.0], [1.0], [3.0]] +@requires_datasets def test_from_dataset_basic(model: Encoder) -> None: """Test from_dataset with a simple HuggingFace dataset.""" from datasets import Dataset @@ -273,6 +286,7 @@ def test_from_dataset_basic(model: Encoder) -> None: assert len(result.selected) <= 3 +@requires_datasets def test_from_dataset_returns_strings_for_text_column(model: Encoder) -> None: """Test that from_dataset returns strings when columns=["text"], matching from_records behavior.""" from datasets import Dataset @@ -293,6 +307,7 @@ def test_from_dataset_returns_strings_for_text_column(model: Encoder) -> None: assert type(result.selected[0]) == type(result_from_records.selected[0]) +@requires_datasets def test_from_dataset_multicolumn(model: Encoder) -> None: """Test from_dataset with multiple columns.""" from datasets import Dataset @@ -311,6 +326,7 @@ def test_from_dataset_multicolumn(model: Encoder) -> None: assert len(semhash.index.items) == 2 +@requires_datasets def test_from_dataset_validation(model: Encoder) -> None: """Test from_dataset input validation.""" from datasets import Dataset @@ -336,6 +352,7 @@ def test_from_dataset_validation(model: Encoder) -> None: SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) +@requires_datasets def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: """Test that from_dataset produces same results as from_records for same data.""" from datasets import Dataset @@ -360,6 +377,7 @@ def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: assert len(result1.filtered) == len(result2.filtered) +@requires_datasets def test_from_dataset_does_not_embed_duplicates(model: Encoder) -> None: """Test that from_dataset only embeds representative records, not duplicates.""" from typing import Any @@ -395,6 +413,7 @@ def encode(self, sentences: Any, **kwargs: Any) -> Any: assert counting_encoder.encode_calls[0] == 3 +@requires_datasets def test_from_dataset_handles_non_string_values(model: Encoder) -> None: """Test that from_dataset handles non-string values (e.g., integers) by converting them.""" from datasets import Dataset @@ -415,6 +434,7 @@ def test_from_dataset_handles_non_string_values(model: Encoder) -> None: assert all("id" in r for r in result.selected) +@requires_datasets def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: """Test that from_dataset preserves the order of first occurrences (deterministic output).""" from datasets import Dataset @@ -434,6 +454,7 @@ def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: assert first_occurrences == ["zebra", "apple", "banana", "cherry"] +@requires_datasets def test_from_dataset_was_string_only_for_actual_strings(model: Encoder) -> None: """Test that was_string is only True for text columns with actual string values.""" from datasets import Dataset @@ -483,6 +504,7 @@ def __getitem__(self, key: str) -> list[str]: 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+0100 Subject: [PATCH 13/37] Updated tests --- semhash/semhash.py | 14 -- tests/test_from_dataset_hf.py | 231 +++++++++++++++++++++++++++++ tests/test_semhash.py | 263 ---------------------------------- 3 files changed, 231 insertions(+), 277 deletions(-) create mode 100644 tests/test_from_dataset_hf.py diff --git a/semhash/semhash.py b/semhash/semhash.py index b201a1b..cad788e 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -156,27 +156,13 @@ def from_dataset( HuggingFace datasets.Dataset satisfies this contract, but custom implementations work too. - Note: If you want to use HuggingFace datasets, install with: - pip install 'semhash[datasets]' - :param dataset: A dataset-like object with columnar access. :param columns: Columns to use for deduplication (same as from_records). :param model: (Optional) An Encoder model. If None, the default model is used (minishlab/potion-base-8M). :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. :param **kwargs: Any additional keyword arguments to pass to the Vicinity index. :return: A SemHash instance with a fitted vicinity index. - :raises ImportError: If using HuggingFace Dataset without datasets package installed. """ - # Check if user is trying to use HuggingFace datasets without installing the package - if hasattr(dataset, "__class__") and dataset.__class__.__module__.startswith("datasets"): - try: - import datasets # noqa: F401 - except ImportError as e: - raise ImportError( - "HuggingFace `datasets` is required to use HF Dataset objects. " - "Please install it with `pip install 'semhash[datasets]'`" - ) from e - # Load default model if needed if model is None: model = StaticModel.from_pretrained("minishlab/potion-base-8M") diff --git a/tests/test_from_dataset_hf.py b/tests/test_from_dataset_hf.py new file mode 100644 index 0000000..f3f45f5 --- /dev/null +++ b/tests/test_from_dataset_hf.py @@ -0,0 +1,231 @@ +"""Tests for SemHash.from_dataset() with HuggingFace datasets.""" + +import pytest + +from semhash import SemHash +from semhash.utils import Encoder + +# Require datasets for this entire test module +datasets = pytest.importorskip("datasets", reason="pip install 'semhash[datasets]'") +Dataset = datasets.Dataset + + +def test_from_dataset_basic(model: Encoder) -> None: + """Test from_dataset with a simple HuggingFace dataset.""" + # Create a simple dataset with text column + ds = Dataset.from_dict({"text": ["apple", "banana", "cherry", "apple"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should collapse exact duplicate "apple" + assert len(semhash.index.vectors) == 3 + assert len(semhash.index.items) == 3 + + # Verify deduplication works + result = semhash.self_deduplicate(threshold=0.95) + assert len(result.selected) <= 3 + + +def test_from_dataset_returns_strings_for_text_column(model: Encoder) -> None: + """Test that from_dataset returns strings when columns=["text"], matching from_records behavior.""" + ds = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should return strings (like from_records does) + result = semhash.self_deduplicate(threshold=0.95) + assert all(isinstance(r, str) for r in result.selected) + + # Verify equivalence with from_records + semhash_from_records = SemHash.from_records(records=["apple", "banana", "cherry"], model=model) + result_from_records = semhash_from_records.self_deduplicate(threshold=0.95) + + # Both should return strings + assert type(result.selected[0]) == type(result_from_records.selected[0]) + + +def test_from_dataset_multicolumn(model: Encoder) -> None: + """Test from_dataset with multiple columns.""" + ds = Dataset.from_dict( + { + "question": ["What is AI?", "What is ML?", "What is AI?"], + "context": ["AI explanation", "ML explanation", "AI explanation"], + } + ) + + semhash = SemHash.from_dataset(dataset=ds, columns=["question", "context"], model=model) + + # Should collapse the duplicate row + assert len(semhash.index.vectors) == 2 + assert len(semhash.index.items) == 2 + + +def test_from_dataset_validation(model: Encoder) -> None: + """Test from_dataset input validation.""" + ds = Dataset.from_dict({"text": ["apple", "banana"]}) + + # Test invalid dataset type (Protocol violation) + with pytest.raises(TypeError, match="must satisfy DatasetLike"): + SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) + + # Test missing column + with pytest.raises(ValueError, match="not found in dataset"): + SemHash.from_dataset(dataset=ds, columns=["missing_col"], model=model) + + # Test None value in column + ds_with_none = Dataset.from_dict({"text": ["apple", None, "banana"]}) + with pytest.raises(ValueError, match="Column 'text' has None at index 1"): + SemHash.from_dataset(dataset=ds_with_none, columns=["text"], model=model) + + # Test empty dataset + ds_empty = Dataset.from_dict({"text": []}) + with pytest.raises(ValueError, match="dataset must not be empty"): + SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) + + +def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: + """Test that from_dataset produces same results as from_records for same data.""" + data = [ + {"question": "What is AI?", "answer": "Artificial Intelligence"}, + {"question": "What is ML?", "answer": "Machine Learning"}, + ] + ds = Dataset.from_dict({k: [d[k] for d in data] for k in data[0].keys()}) + + semhash_from_dataset = SemHash.from_dataset(dataset=ds, columns=["question", "answer"], model=model) + semhash_from_records = SemHash.from_records(records=data, columns=["question", "answer"], model=model) + + # Both should have same number of vectors + assert semhash_from_dataset.index.vectors.shape == semhash_from_records.index.vectors.shape + + # Both should give same deduplication results + result1 = semhash_from_dataset.self_deduplicate(threshold=0.95) + result2 = semhash_from_records.self_deduplicate(threshold=0.95) + + assert len(result1.selected) == len(result2.selected) + assert len(result1.filtered) == len(result2.filtered) + + +def test_from_dataset_does_not_embed_duplicates(model: Encoder) -> None: + """Test that from_dataset only embeds representative records, not duplicates.""" + from typing import Any + + # Create dataset with exact duplicates: 6 records but only 3 unique + ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) + + # Create a counting encoder wrapper + class CountingEncoder: + def __init__(self, base_encoder: Any) -> None: + self.base_encoder = base_encoder + self.encode_calls: list[int] = [] + + def encode(self, sentences: Any, **kwargs: Any) -> Any: + if isinstance(sentences, str): + sentences = [sentences] + self.encode_calls.append(len(sentences)) + return self.base_encoder.encode(sentences, **kwargs) + + counting_encoder = CountingEncoder(model) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=counting_encoder) # type: ignore[arg-type] + + # Should only have 3 representatives + assert semhash.index.vectors.shape[0] == 3 + + # Should have encoded exactly 3 records (representatives only), not 6 + assert sum(counting_encoder.encode_calls) == 3 + # Should have been called once with all 3 representatives + assert len(counting_encoder.encode_calls) == 1 + assert counting_encoder.encode_calls[0] == 3 + + +def test_from_dataset_handles_non_string_values(model: Encoder) -> None: + """Test that from_dataset handles non-string values (e.g., integers) by converting them.""" + # Dataset with integer values + ds = Dataset.from_dict({"id": [1, 2, 3, 1]}) # Has a duplicate + + semhash = SemHash.from_dataset(dataset=ds, columns=["id"], model=model) + + # Should have deduplicated the integer '1' + assert semhash.index.vectors.shape[0] == 3 + assert len(semhash.index.items) == 3 + + # Values should be converted to strings + result = semhash.self_deduplicate(threshold=0.95) + # Result should be dicts (not strings, since column is 'id' not 'text') + assert all(isinstance(r, dict) for r in result.selected) + assert all("id" in r for r in result.selected) + + +def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: + """Test that from_dataset preserves the order of first occurrences (deterministic output).""" + # Create dataset where duplicates appear out of order + ds = Dataset.from_dict({"text": ["zebra", "apple", "zebra", "banana", "apple", "cherry"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) + + # Should have 4 unique items in first-occurrence order: zebra, apple, banana, cherry + assert len(semhash.index.vectors) == 4 + + # Get all items (each is a bucket of duplicates) + first_occurrences = [item[0]["text"] for item in semhash.index.items] + + # Should preserve first-occurrence order from dataset + assert first_occurrences == ["zebra", "apple", "banana", "cherry"] + + +def test_from_dataset_was_string_only_for_actual_strings(model: Encoder) -> None: + """Test that was_string is only True for text columns with actual string values.""" + # Test 1: text column with strings -> should return strings + ds_strings = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) + semhash_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) + result_strings = semhash_strings.self_deduplicate(threshold=0.95) + assert all(isinstance(r, str) for r in result_strings.selected) + + # Test 2: text column with integers -> should return dicts (not strings) + ds_ints = Dataset.from_dict({"text": [1, 2, 3]}) + semhash_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model) + result_ints = semhash_ints.self_deduplicate(threshold=0.95) + assert all(isinstance(r, dict) for r in result_ints.selected) + assert all("text" in r for r in result_ints.selected) + + +def test_from_dataset_multicolumn_does_not_embed_duplicates(model: Encoder) -> None: + """Test that multi-column from_dataset only embeds representatives (validates per-column encoding).""" + from typing import Any + + # Create dataset with exact duplicates across multiple columns + ds = Dataset.from_dict( + { + "col1": ["a", "b", "a", "c"], # "a" appears twice + "col2": ["x", "y", "x", "z"], # matching pattern + } + ) + + # Create a counting encoder wrapper + class CountingEncoder: + def __init__(self, base_encoder: Any) -> None: + self.base_encoder = base_encoder + self.encode_calls: list[int] = [] + + def encode(self, sentences: Any, **kwargs: Any) -> Any: + if isinstance(sentences, str): + sentences = [sentences] + self.encode_calls.append(len(sentences)) + return self.base_encoder.encode(sentences, **kwargs) + + counting_encoder = CountingEncoder(model) + + semhash = SemHash.from_dataset(dataset=ds, columns=["col1", "col2"], model=counting_encoder) # type: ignore[arg-type] + + # Should have 3 representatives (unique combinations) + assert semhash.index.vectors.shape[0] == 3 + + # featurize() encodes once per column, so we expect 2 calls (for col1 and col2) + assert len(counting_encoder.encode_calls) == 2 + + # Each call should have 3 texts (the representatives) + assert counting_encoder.encode_calls[0] == 3 # col1 + assert counting_encoder.encode_calls[1] == 3 # col2 + + # Total encoded should be 6 (3 representatives × 2 columns) + assert sum(counting_encoder.encode_calls) == 6 diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 1093299..be86158 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -5,18 +5,6 @@ from semhash.datamodels import FilterResult from semhash.utils import Encoder -# Check if datasets is available for dataset-specific tests -try: - import datasets # noqa: F401 - - HAS_DATASETS = True -except ImportError: - HAS_DATASETS = False - -requires_datasets = pytest.mark.skipif( - not HAS_DATASETS, reason="datasets not installed - install with pip install 'semhash[datasets]'" -) - def test_single_dataset_deduplication(model: Encoder) -> None: """Test single dataset deduplication.""" @@ -267,212 +255,6 @@ def test_from_embeddings(model: Encoder, train_texts: list[str]) -> None: assert semhash.index.vectors.tolist() == [[0.0], [1.0], [3.0]] -@requires_datasets -def test_from_dataset_basic(model: Encoder) -> None: - """Test from_dataset with a simple HuggingFace dataset.""" - from datasets import Dataset - - # Create a simple dataset with text column - ds = Dataset.from_dict({"text": ["apple", "banana", "cherry", "apple"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) - - # Should collapse exact duplicate "apple" - assert len(semhash.index.vectors) == 3 - assert len(semhash.index.items) == 3 - - # Verify deduplication works - result = semhash.self_deduplicate(threshold=0.95) - assert len(result.selected) <= 3 - - -@requires_datasets -def test_from_dataset_returns_strings_for_text_column(model: Encoder) -> None: - """Test that from_dataset returns strings when columns=["text"], matching from_records behavior.""" - from datasets import Dataset - - ds = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) - - # Should return strings (like from_records does) - result = semhash.self_deduplicate(threshold=0.95) - assert all(isinstance(r, str) for r in result.selected) - - # Verify equivalence with from_records - semhash_from_records = SemHash.from_records(records=["apple", "banana", "cherry"], model=model) - result_from_records = semhash_from_records.self_deduplicate(threshold=0.95) - - # Both should return strings - assert type(result.selected[0]) == type(result_from_records.selected[0]) - - -@requires_datasets -def test_from_dataset_multicolumn(model: Encoder) -> None: - """Test from_dataset with multiple columns.""" - from datasets import Dataset - - ds = Dataset.from_dict( - { - "question": ["What is AI?", "What is ML?", "What is AI?"], - "context": ["AI explanation", "ML explanation", "AI explanation"], - } - ) - - semhash = SemHash.from_dataset(dataset=ds, columns=["question", "context"], model=model) - - # Should collapse the duplicate row - assert len(semhash.index.vectors) == 2 - assert len(semhash.index.items) == 2 - - -@requires_datasets -def test_from_dataset_validation(model: Encoder) -> None: - """Test from_dataset input validation.""" - from datasets import Dataset - - ds = Dataset.from_dict({"text": ["apple", "banana"]}) - - # Test invalid dataset type (Protocol violation) - with pytest.raises(TypeError, match="must satisfy DatasetLike"): - SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) - - # Test missing column - with pytest.raises(ValueError, match="not found in dataset"): - SemHash.from_dataset(dataset=ds, columns=["missing_col"], model=model) - - # Test None value in column - ds_with_none = Dataset.from_dict({"text": ["apple", None, "banana"]}) - with pytest.raises(ValueError, match="Column 'text' has None at index 1"): - SemHash.from_dataset(dataset=ds_with_none, columns=["text"], model=model) - - # Test empty dataset - ds_empty = Dataset.from_dict({"text": []}) - with pytest.raises(ValueError, match="dataset must not be empty"): - SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) - - -@requires_datasets -def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: - """Test that from_dataset produces same results as from_records for same data.""" - from datasets import Dataset - - data = [ - {"question": "What is AI?", "answer": "Artificial Intelligence"}, - {"question": "What is ML?", "answer": "Machine Learning"}, - ] - ds = Dataset.from_dict({k: [d[k] for d in data] for k in data[0].keys()}) - - semhash_from_dataset = SemHash.from_dataset(dataset=ds, columns=["question", "answer"], model=model) - semhash_from_records = SemHash.from_records(records=data, columns=["question", "answer"], model=model) - - # Both should have same number of vectors - assert semhash_from_dataset.index.vectors.shape == semhash_from_records.index.vectors.shape - - # Both should give same deduplication results - result1 = semhash_from_dataset.self_deduplicate(threshold=0.95) - result2 = semhash_from_records.self_deduplicate(threshold=0.95) - - assert len(result1.selected) == len(result2.selected) - assert len(result1.filtered) == len(result2.filtered) - - -@requires_datasets -def test_from_dataset_does_not_embed_duplicates(model: Encoder) -> None: - """Test that from_dataset only embeds representative records, not duplicates.""" - from typing import Any - - from datasets import Dataset - - # Create dataset with exact duplicates: 6 records but only 3 unique - ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) - - # Create a counting encoder wrapper - class CountingEncoder: - def __init__(self, base_encoder: Any) -> None: - self.base_encoder = base_encoder - self.encode_calls: list[int] = [] - - def encode(self, sentences: Any, **kwargs: Any) -> Any: - if isinstance(sentences, str): - sentences = [sentences] - self.encode_calls.append(len(sentences)) - return self.base_encoder.encode(sentences, **kwargs) - - counting_encoder = CountingEncoder(model) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=counting_encoder) # type: ignore[arg-type] - - # Should only have 3 representatives - assert semhash.index.vectors.shape[0] == 3 - - # Should have encoded exactly 3 records (representatives only), not 6 - assert sum(counting_encoder.encode_calls) == 3 - # Should have been called once with all 3 representatives - assert len(counting_encoder.encode_calls) == 1 - assert counting_encoder.encode_calls[0] == 3 - - -@requires_datasets -def test_from_dataset_handles_non_string_values(model: Encoder) -> None: - """Test that from_dataset handles non-string values (e.g., integers) by converting them.""" - from datasets import Dataset - - # Dataset with integer values - ds = Dataset.from_dict({"id": [1, 2, 3, 1]}) # Has a duplicate - - semhash = SemHash.from_dataset(dataset=ds, columns=["id"], model=model) - - # Should have deduplicated the integer '1' - assert semhash.index.vectors.shape[0] == 3 - assert len(semhash.index.items) == 3 - - # Values should be converted to strings - result = semhash.self_deduplicate(threshold=0.95) - # Result should be dicts (not strings, since column is 'id' not 'text') - assert all(isinstance(r, dict) for r in result.selected) - assert all("id" in r for r in result.selected) - - -@requires_datasets -def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: - """Test that from_dataset preserves the order of first occurrences (deterministic output).""" - from datasets import Dataset - - # Create dataset where duplicates appear out of order - ds = Dataset.from_dict({"text": ["zebra", "apple", "zebra", "banana", "apple", "cherry"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) - - # Should have 4 unique items in first-occurrence order: zebra, apple, banana, cherry - assert len(semhash.index.vectors) == 4 - - # Get all items (each is a bucket of duplicates) - first_occurrences = [item[0]["text"] for item in semhash.index.items] - - # Should preserve first-occurrence order from dataset - assert first_occurrences == ["zebra", "apple", "banana", "cherry"] - - -@requires_datasets -def test_from_dataset_was_string_only_for_actual_strings(model: Encoder) -> None: - """Test that was_string is only True for text columns with actual string values.""" - from datasets import Dataset - - # Test 1: text column with strings -> should return strings - ds_strings = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) - semhash_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) - result_strings = semhash_strings.self_deduplicate(threshold=0.95) - assert all(isinstance(r, str) for r in result_strings.selected) - - # Test 2: text column with integers -> should return dicts (not strings) - ds_ints = Dataset.from_dict({"text": [1, 2, 3]}) - semhash_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model) - result_ints = semhash_ints.self_deduplicate(threshold=0.95) - assert all(isinstance(r, dict) for r in result_ints.selected) - assert all("text" in r for r in result_ints.selected) - - def test_from_dataset_with_custom_dataset_like(model: Encoder) -> None: """Test that from_dataset works with custom DatasetLike implementations (no HF dependency).""" @@ -504,51 +286,6 @@ def __getitem__(self, key: str) -> list[str]: assert len(result.selected) == 2 -@requires_datasets -def test_from_dataset_multicolumn_does_not_embed_duplicates(model: Encoder) -> None: - """Test that multi-column from_dataset only embeds representatives (validates per-column encoding).""" - from typing import Any - - from datasets import Dataset - - # Create dataset with exact duplicates across multiple columns - ds = Dataset.from_dict( - { - "col1": ["a", "b", "a", "c"], # "a" appears twice - "col2": ["x", "y", "x", "z"], # matching pattern - } - ) - - # Create a counting encoder wrapper - class CountingEncoder: - def __init__(self, base_encoder: Any) -> None: - self.base_encoder = base_encoder - self.encode_calls: list[int] = [] - - def encode(self, sentences: Any, **kwargs: Any) -> Any: - if isinstance(sentences, str): - sentences = [sentences] - self.encode_calls.append(len(sentences)) - return self.base_encoder.encode(sentences, **kwargs) - - counting_encoder = CountingEncoder(model) - - semhash = SemHash.from_dataset(dataset=ds, columns=["col1", "col2"], model=counting_encoder) # type: ignore[arg-type] - - # Should have 3 representatives (unique combinations) - assert semhash.index.vectors.shape[0] == 3 - - # featurize() encodes once per column, so we expect 2 calls (for col1 and col2) - assert len(counting_encoder.encode_calls) == 2 - - # Each call should have 3 texts (the representatives) - assert counting_encoder.encode_calls[0] == 3 # col1 - assert counting_encoder.encode_calls[1] == 3 # col2 - - # Total encoded should be 6 (3 representatives × 2 columns) - assert sum(counting_encoder.encode_calls) == 6 - - def test_from_records_coerces_non_string_dict_values(model: Encoder) -> None: """Test that from_records coerces non-string dict values to strings.""" records = [{"id": 1}, {"id": 2}, {"id": 1}] # Integers, with duplicate From 861edf07e876d74ae327f1d2f1cff0deff03f225 Mon Sep 17 00:00:00 2001 From: Pringled Date: Mon, 12 Jan 2026 08:17:57 +0100 Subject: [PATCH 14/37] Renamed testfile --- tests/{test_from_dataset_hf.py => test_from_dataset.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/{test_from_dataset_hf.py => test_from_dataset.py} (100%) diff --git a/tests/test_from_dataset_hf.py b/tests/test_from_dataset.py similarity index 100% rename from tests/test_from_dataset_hf.py rename to tests/test_from_dataset.py From 2a103ec0e64d4de4dc8c97c4a2ea808d71b324fa Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 09:04:29 +0100 Subject: [PATCH 15/37] Improved code, refactored utils --- semhash/semhash.py | 20 +++------------- semhash/utils.py | 60 ++++++++++++++++++++++++++++++---------------- 2 files changed, 43 insertions(+), 37 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index cad788e..a318ca8 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -20,6 +20,7 @@ Record, compute_candidate_limit, featurize, + group_records_by_key, prepare_dataset_records, prepare_records, remove_exact_duplicates, @@ -103,23 +104,8 @@ def from_records( if model is None: model = StaticModel.from_pretrained("minishlab/potion-base-8M") - # Single-pass grouping by exact key (same as from_dataset for consistency) - col_set = set(columns) - buckets: dict[frozendict[str, str], list[dict[str, str]]] = {} - order: list[frozendict[str, str]] = [] - - for r in dict_records: - key = to_frozendict(r, col_set) - bucket = buckets.get(key) - if bucket is None: - buckets[key] = [r] - order.append(key) - else: - bucket.append(r) - - # Build items and deduplicated_records in first-occurrence order - items = [buckets[k] for k in order] - deduplicated_records = [bucket[0] for bucket in items] + # Group by exact match, preserving first-occurrence order + deduplicated_records, items = group_records_by_key(dict_records, columns) # Create embeddings for deduplicated records only embeddings = featurize(deduplicated_records, columns, model) diff --git a/semhash/utils.py b/semhash/utils.py index 2d7e8d6..dbb500a 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -48,7 +48,7 @@ def __getitem__(self, key: str) -> Sequence[Any]: ... # pragma: no cover -def to_frozendict(record: dict[str, str], columns: set[str]) -> frozendict[str, str]: +def to_frozendict(record: dict[str, str], columns: Sequence[str] | set[str]) -> frozendict[str, str]: """ Convert a record to a frozendict. @@ -64,6 +64,36 @@ def to_frozendict(record: dict[str, str], columns: set[str]) -> frozendict[str, raise ValueError(f"Missing column '{missing}' in record {record}") from e +def group_records_by_key( + records: Sequence[dict[str, str]], + columns: Sequence[str], +) -> tuple[list[dict[str, str]], list[list[dict[str, str]]]]: + """ + Group records by exact match on columns, preserving first-occurrence order. + + :param records: Records to group. + :param columns: Columns to use as grouping key. + :return: Tuple of (deduplicated_records, items) where: + - deduplicated_records: first record from each unique group + - items: list of groups, each group is a list of exact duplicates + """ + buckets: dict[frozendict[str, str], list[dict[str, str]]] = {} + order: list[frozendict[str, str]] = [] + + for r in records: + key = to_frozendict(r, columns) + bucket = buckets.get(key) + if bucket is None: + buckets[key] = [r] + order.append(key) + else: + bucket.append(r) + + items = [buckets[k] for k in order] + deduplicated_records = [bucket[0] for bucket in items] + return deduplicated_records, items + + def compute_candidate_limit( total: int, selection_size: int, @@ -250,7 +280,10 @@ def prepare_dataset_records( - was_string: True iff columns == ["text"] and ALL raw values were strings """ cols, n = _validate_dataset(dataset, columns) - col_set = set(columns) + + # was_string controls whether deduplicate() returns strings or dicts. + # We only return strings if: (1) single column named "text", AND (2) all raw + # values in the dataset are actual strings (not integers/floats coerced to str). was_string = len(columns) == 1 and columns[0] == "text" def coerce(raw: Any, *, col: str, idx: int) -> str: @@ -258,27 +291,14 @@ def coerce(raw: Any, *, col: str, idx: int) -> str: raise ValueError(f"Column '{col}' has None at index {idx}") return raw if isinstance(raw, str) else str(raw) - # Single-pass grouping: key -> bucket of exact duplicates - buckets: dict[frozendict[str, str], list[dict[str, str]]] = {} - order: list[frozendict[str, str]] = [] - + # Build all records while tracking was_string + records: list[dict[str, str]] = [] for i in range(n): - # Track "was_string" using RAW values (not coerced) if was_string and not isinstance(cols["text"][i], str): was_string = False + records.append({c: coerce(cols[c][i], col=c, idx=i) for c in columns}) - row = {c: coerce(cols[c][i], col=c, idx=i) for c in columns} - key = to_frozendict(row, col_set) - - bucket = buckets.get(key) - if bucket is None: - buckets[key] = [row] - order.append(key) - else: - bucket.append(row) - - # Preserve first-occurrence order via the order list - items = [buckets[k] for k in order] - deduplicated_records = [bucket[0] for bucket in items] + # Group by exact match, preserving first-occurrence order + deduplicated_records, items = group_records_by_key(records, columns) return deduplicated_records, items, was_string From 5a6a4408dbe887343193e1de43594716aed356f3 Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 18:53:21 +0100 Subject: [PATCH 16/37] Updated tests --- tests/conftest.py | 35 +++++++++++ tests/test_from_dataset.py | 125 ++++++++++--------------------------- tests/test_utils.py | 2 - 3 files changed, 69 insertions(+), 93 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 69eebe8..ada0d8c 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,10 @@ +from typing import Any + import pytest from model2vec import StaticModel +from semhash.utils import Encoder + @pytest.fixture def model() -> StaticModel: @@ -49,3 +53,34 @@ def test_texts() -> list[str]: "motorcycle", # Outlier "plane", # Outlier ] + + +class CountingEncoder: + """Encoder wrapper that counts how many items are encoded. Useful for testing efficiency.""" + + def __init__(self, base_encoder: Encoder) -> None: + """Initialize the counting encoder.""" + self.base_encoder = base_encoder + self.encode_calls: list[int] = [] + + def encode(self, sentences: Any, **kwargs: Any) -> Any: + """Encode sentences and count the number of items encoded.""" + if isinstance(sentences, str): + sentences = [sentences] + self.encode_calls.append(len(sentences)) + return self.base_encoder.encode(sentences, **kwargs) + + @property + def total_encoded(self) -> int: + """Total number of items encoded across all calls.""" + return sum(self.encode_calls) + + def reset(self) -> None: + """Reset the call counter.""" + self.encode_calls = [] + + +@pytest.fixture +def counting_encoder(model: StaticModel) -> CountingEncoder: + """A counting encoder wrapper for testing embedding efficiency.""" + return CountingEncoder(model) diff --git a/tests/test_from_dataset.py b/tests/test_from_dataset.py index f3f45f5..d6afdbc 100644 --- a/tests/test_from_dataset.py +++ b/tests/test_from_dataset.py @@ -1,6 +1,7 @@ """Tests for SemHash.from_dataset() with HuggingFace datasets.""" import pytest +from conftest import CountingEncoder from semhash import SemHash from semhash.utils import Encoder @@ -26,24 +27,6 @@ def test_from_dataset_basic(model: Encoder) -> None: assert len(result.selected) <= 3 -def test_from_dataset_returns_strings_for_text_column(model: Encoder) -> None: - """Test that from_dataset returns strings when columns=["text"], matching from_records behavior.""" - ds = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) - - # Should return strings (like from_records does) - result = semhash.self_deduplicate(threshold=0.95) - assert all(isinstance(r, str) for r in result.selected) - - # Verify equivalence with from_records - semhash_from_records = SemHash.from_records(records=["apple", "banana", "cherry"], model=model) - result_from_records = semhash_from_records.self_deduplicate(threshold=0.95) - - # Both should return strings - assert type(result.selected[0]) == type(result_from_records.selected[0]) - - def test_from_dataset_multicolumn(model: Encoder) -> None: """Test from_dataset with multiple columns.""" ds = Dataset.from_dict( @@ -105,37 +88,34 @@ def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: assert len(result1.filtered) == len(result2.filtered) -def test_from_dataset_does_not_embed_duplicates(model: Encoder) -> None: +@pytest.mark.parametrize( + "data,columns,expected_representatives,expected_calls", + [ + # Single column: 6 records, 3 unique -> 1 encode call with 3 items + ({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}, ["text"], 3, [3]), + # Multi column: 4 records, 3 unique -> 2 encode calls (one per column) with 3 items each + ({"col1": ["a", "b", "a", "c"], "col2": ["x", "y", "x", "z"]}, ["col1", "col2"], 3, [3, 3]), + ], + ids=["single_column", "multi_column"], +) +def test_from_dataset_does_not_embed_duplicates( + counting_encoder: CountingEncoder, + data: dict[str, list[str]], + columns: list[str], + expected_representatives: int, + expected_calls: list[int], +) -> None: """Test that from_dataset only embeds representative records, not duplicates.""" - from typing import Any - - # Create dataset with exact duplicates: 6 records but only 3 unique - ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) - - # Create a counting encoder wrapper - class CountingEncoder: - def __init__(self, base_encoder: Any) -> None: - self.base_encoder = base_encoder - self.encode_calls: list[int] = [] - - def encode(self, sentences: Any, **kwargs: Any) -> Any: - if isinstance(sentences, str): - sentences = [sentences] - self.encode_calls.append(len(sentences)) - return self.base_encoder.encode(sentences, **kwargs) + ds = Dataset.from_dict(data) - counting_encoder = CountingEncoder(model) + semhash = SemHash.from_dataset(dataset=ds, columns=columns, model=counting_encoder) # type: ignore[arg-type] - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=counting_encoder) # type: ignore[arg-type] + # Should only have expected number of representatives + assert semhash.index.vectors.shape[0] == expected_representatives - # Should only have 3 representatives - assert semhash.index.vectors.shape[0] == 3 - - # Should have encoded exactly 3 records (representatives only), not 6 - assert sum(counting_encoder.encode_calls) == 3 - # Should have been called once with all 3 representatives - assert len(counting_encoder.encode_calls) == 1 - assert counting_encoder.encode_calls[0] == 3 + # Should have encoded only representatives, with expected call pattern + assert counting_encoder.encode_calls == expected_calls + assert counting_encoder.total_encoded == sum(expected_calls) def test_from_dataset_handles_non_string_values(model: Encoder) -> None: @@ -173,59 +153,22 @@ def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: assert first_occurrences == ["zebra", "apple", "banana", "cherry"] -def test_from_dataset_was_string_only_for_actual_strings(model: Encoder) -> None: - """Test that was_string is only True for text columns with actual string values.""" - # Test 1: text column with strings -> should return strings +def test_from_dataset_was_string_behavior(model: Encoder) -> None: + """Test was_string logic: returns strings only for text column with actual string values.""" + # Case 1: text column with strings -> should return strings (matching from_records) ds_strings = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) semhash_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) result_strings = semhash_strings.self_deduplicate(threshold=0.95) assert all(isinstance(r, str) for r in result_strings.selected) - # Test 2: text column with integers -> should return dicts (not strings) + # Verify equivalence with from_records for string case + semhash_from_records = SemHash.from_records(records=["apple", "banana", "cherry"], model=model) + result_from_records = semhash_from_records.self_deduplicate(threshold=0.95) + assert type(result_strings.selected[0]) == type(result_from_records.selected[0]) + + # Case 2: text column with integers -> should return dicts (coerced, not true strings) ds_ints = Dataset.from_dict({"text": [1, 2, 3]}) semhash_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model) result_ints = semhash_ints.self_deduplicate(threshold=0.95) assert all(isinstance(r, dict) for r in result_ints.selected) assert all("text" in r for r in result_ints.selected) - - -def test_from_dataset_multicolumn_does_not_embed_duplicates(model: Encoder) -> None: - """Test that multi-column from_dataset only embeds representatives (validates per-column encoding).""" - from typing import Any - - # Create dataset with exact duplicates across multiple columns - ds = Dataset.from_dict( - { - "col1": ["a", "b", "a", "c"], # "a" appears twice - "col2": ["x", "y", "x", "z"], # matching pattern - } - ) - - # Create a counting encoder wrapper - class CountingEncoder: - def __init__(self, base_encoder: Any) -> None: - self.base_encoder = base_encoder - self.encode_calls: list[int] = [] - - def encode(self, sentences: Any, **kwargs: Any) -> Any: - if isinstance(sentences, str): - sentences = [sentences] - self.encode_calls.append(len(sentences)) - return self.base_encoder.encode(sentences, **kwargs) - - counting_encoder = CountingEncoder(model) - - semhash = SemHash.from_dataset(dataset=ds, columns=["col1", "col2"], model=counting_encoder) # type: ignore[arg-type] - - # Should have 3 representatives (unique combinations) - assert semhash.index.vectors.shape[0] == 3 - - # featurize() encodes once per column, so we expect 2 calls (for col1 and col2) - assert len(counting_encoder.encode_calls) == 2 - - # Each call should have 3 texts (the representatives) - assert counting_encoder.encode_calls[0] == 3 # col1 - assert counting_encoder.encode_calls[1] == 3 # col2 - - # Total encoded should be 6 (3 representatives × 2 columns) - assert sum(counting_encoder.encode_calls) == 6 diff --git a/tests/test_utils.py b/tests/test_utils.py index 381045c..6499817 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -117,8 +117,6 @@ def test_remove_exact_duplicates_with_reference_records() -> None: def test_prepare_records_rejects_mixed_types() -> None: """Test that prepare_records rejects mixed string/dict inputs.""" - from semhash.utils import prepare_records - # Mixed types should be rejected with pytest.raises(ValueError, match="All records must be"): prepare_records(["a", {"text": "b"}], None) # type: ignore[list-item] From 44ce3371b803da7665e87b7db414ed2ed66f77a2 Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 20:10:45 +0100 Subject: [PATCH 17/37] Simplified tests --- tests/test_from_dataset.py | 189 ++++++++++++------------------------- 1 file changed, 61 insertions(+), 128 deletions(-) diff --git a/tests/test_from_dataset.py b/tests/test_from_dataset.py index d6afdbc..dc35927 100644 --- a/tests/test_from_dataset.py +++ b/tests/test_from_dataset.py @@ -1,5 +1,3 @@ -"""Tests for SemHash.from_dataset() with HuggingFace datasets.""" - import pytest from conftest import CountingEncoder @@ -12,163 +10,98 @@ def test_from_dataset_basic(model: Encoder) -> None: - """Test from_dataset with a simple HuggingFace dataset.""" - # Create a simple dataset with text column - ds = Dataset.from_dict({"text": ["apple", "banana", "cherry", "apple"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) - - # Should collapse exact duplicate "apple" - assert len(semhash.index.vectors) == 3 - assert len(semhash.index.items) == 3 - - # Verify deduplication works - result = semhash.self_deduplicate(threshold=0.95) - assert len(result.selected) <= 3 - - -def test_from_dataset_multicolumn(model: Encoder) -> None: - """Test from_dataset with multiple columns.""" - ds = Dataset.from_dict( + """Test from_dataset works with single and multi-column datasets.""" + # Single column + ds_single = Dataset.from_dict({"text": ["apple", "banana", "cherry", "apple"]}) + semhash_single = SemHash.from_dataset(dataset=ds_single, columns=["text"], model=model) + assert len(semhash_single.index.vectors) == 3 # Collapsed duplicate "apple" + + # Multi-column + ds_multi = Dataset.from_dict( { "question": ["What is AI?", "What is ML?", "What is AI?"], "context": ["AI explanation", "ML explanation", "AI explanation"], } ) + semhash_multi = SemHash.from_dataset(dataset=ds_multi, columns=["question", "context"], model=model) + assert len(semhash_multi.index.vectors) == 2 # Collapsed duplicate row - semhash = SemHash.from_dataset(dataset=ds, columns=["question", "context"], model=model) - # Should collapse the duplicate row - assert len(semhash.index.vectors) == 2 - assert len(semhash.index.items) == 2 +def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: + """Test that from_dataset produces identical results to from_records.""" + # Test with strings + texts = ["apple", "banana", "cherry", "apple"] + ds_strings = Dataset.from_dict({"text": texts}) + + semhash_dataset = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) + semhash_records = SemHash.from_records(records=texts, model=model) + + assert semhash_dataset.index.vectors.shape == semhash_records.index.vectors.shape + result_dataset = semhash_dataset.self_deduplicate(threshold=0.9) + result_records = semhash_records.self_deduplicate(threshold=0.9) + assert len(result_dataset.selected) == len(result_records.selected) + assert type(result_dataset.selected[0]) == type(result_records.selected[0]) + + # Test with dicts (multi-column) + data = [ + {"question": "What is AI?", "answer": "Artificial Intelligence"}, + {"question": "What is ML?", "answer": "Machine Learning"}, + ] + ds_dicts = Dataset.from_dict({k: [d[k] for d in data] for k in data[0].keys()}) + + semhash_dataset = SemHash.from_dataset(dataset=ds_dicts, columns=["question", "answer"], model=model) + semhash_records = SemHash.from_records(records=data, columns=["question", "answer"], model=model) + + assert semhash_dataset.index.vectors.shape == semhash_records.index.vectors.shape def test_from_dataset_validation(model: Encoder) -> None: - """Test from_dataset input validation.""" + """Test from_dataset input validation (dataset-specific error cases).""" ds = Dataset.from_dict({"text": ["apple", "banana"]}) - # Test invalid dataset type (Protocol violation) + # Invalid dataset type (Protocol violation) with pytest.raises(TypeError, match="must satisfy DatasetLike"): SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) - # Test missing column + # Missing column with pytest.raises(ValueError, match="not found in dataset"): SemHash.from_dataset(dataset=ds, columns=["missing_col"], model=model) - # Test None value in column + # None value in column ds_with_none = Dataset.from_dict({"text": ["apple", None, "banana"]}) with pytest.raises(ValueError, match="Column 'text' has None at index 1"): SemHash.from_dataset(dataset=ds_with_none, columns=["text"], model=model) - # Test empty dataset + # Empty dataset ds_empty = Dataset.from_dict({"text": []}) with pytest.raises(ValueError, match="dataset must not be empty"): SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) -def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: - """Test that from_dataset produces same results as from_records for same data.""" - data = [ - {"question": "What is AI?", "answer": "Artificial Intelligence"}, - {"question": "What is ML?", "answer": "Machine Learning"}, - ] - ds = Dataset.from_dict({k: [d[k] for d in data] for k in data[0].keys()}) - - semhash_from_dataset = SemHash.from_dataset(dataset=ds, columns=["question", "answer"], model=model) - semhash_from_records = SemHash.from_records(records=data, columns=["question", "answer"], model=model) - - # Both should have same number of vectors - assert semhash_from_dataset.index.vectors.shape == semhash_from_records.index.vectors.shape - - # Both should give same deduplication results - result1 = semhash_from_dataset.self_deduplicate(threshold=0.95) - result2 = semhash_from_records.self_deduplicate(threshold=0.95) - - assert len(result1.selected) == len(result2.selected) - assert len(result1.filtered) == len(result2.filtered) - - -@pytest.mark.parametrize( - "data,columns,expected_representatives,expected_calls", - [ - # Single column: 6 records, 3 unique -> 1 encode call with 3 items - ({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}, ["text"], 3, [3]), - # Multi column: 4 records, 3 unique -> 2 encode calls (one per column) with 3 items each - ({"col1": ["a", "b", "a", "c"], "col2": ["x", "y", "x", "z"]}, ["col1", "col2"], 3, [3, 3]), - ], - ids=["single_column", "multi_column"], -) -def test_from_dataset_does_not_embed_duplicates( - counting_encoder: CountingEncoder, - data: dict[str, list[str]], - columns: list[str], - expected_representatives: int, - expected_calls: list[int], -) -> None: - """Test that from_dataset only embeds representative records, not duplicates.""" - ds = Dataset.from_dict(data) - - semhash = SemHash.from_dataset(dataset=ds, columns=columns, model=counting_encoder) # type: ignore[arg-type] - - # Should only have expected number of representatives - assert semhash.index.vectors.shape[0] == expected_representatives - - # Should have encoded only representatives, with expected call pattern - assert counting_encoder.encode_calls == expected_calls - assert counting_encoder.total_encoded == sum(expected_calls) - - -def test_from_dataset_handles_non_string_values(model: Encoder) -> None: - """Test that from_dataset handles non-string values (e.g., integers) by converting them.""" - # Dataset with integer values - ds = Dataset.from_dict({"id": [1, 2, 3, 1]}) # Has a duplicate - - semhash = SemHash.from_dataset(dataset=ds, columns=["id"], model=model) - - # Should have deduplicated the integer '1' - assert semhash.index.vectors.shape[0] == 3 - assert len(semhash.index.items) == 3 - - # Values should be converted to strings - result = semhash.self_deduplicate(threshold=0.95) - # Result should be dicts (not strings, since column is 'id' not 'text') - assert all(isinstance(r, dict) for r in result.selected) - assert all("id" in r for r in result.selected) - - -def test_from_dataset_preserves_first_occurrence_order(model: Encoder) -> None: - """Test that from_dataset preserves the order of first occurrences (deterministic output).""" - # Create dataset where duplicates appear out of order - ds = Dataset.from_dict({"text": ["zebra", "apple", "zebra", "banana", "apple", "cherry"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=model) - - # Should have 4 unique items in first-occurrence order: zebra, apple, banana, cherry - assert len(semhash.index.vectors) == 4 - - # Get all items (each is a bucket of duplicates) - first_occurrences = [item[0]["text"] for item in semhash.index.items] - - # Should preserve first-occurrence order from dataset - assert first_occurrences == ["zebra", "apple", "banana", "cherry"] - - def test_from_dataset_was_string_behavior(model: Encoder) -> None: """Test was_string logic: returns strings only for text column with actual string values.""" - # Case 1: text column with strings -> should return strings (matching from_records) + # text column with strings -> returns strings ds_strings = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) - semhash_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) - result_strings = semhash_strings.self_deduplicate(threshold=0.95) + result_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model).self_deduplicate() assert all(isinstance(r, str) for r in result_strings.selected) - # Verify equivalence with from_records for string case - semhash_from_records = SemHash.from_records(records=["apple", "banana", "cherry"], model=model) - result_from_records = semhash_from_records.self_deduplicate(threshold=0.95) - assert type(result_strings.selected[0]) == type(result_from_records.selected[0]) - - # Case 2: text column with integers -> should return dicts (coerced, not true strings) + # text column with integers -> returns dicts (coerced values aren't "true" strings) ds_ints = Dataset.from_dict({"text": [1, 2, 3]}) - semhash_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model) - result_ints = semhash_ints.self_deduplicate(threshold=0.95) + result_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model).self_deduplicate() assert all(isinstance(r, dict) for r in result_ints.selected) - assert all("text" in r for r in result_ints.selected) + + # non-text column -> returns dicts + ds_other = Dataset.from_dict({"id": ["a", "b", "c"]}) + result_other = SemHash.from_dataset(dataset=ds_other, columns=["id"], model=model).self_deduplicate() + assert all(isinstance(r, dict) for r in result_other.selected) + + +def test_from_dataset_only_embeds_representatives(counting_encoder: CountingEncoder) -> None: + """Test that from_dataset only embeds representative records, not duplicates.""" + # 6 records but only 3 unique + ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) + + semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=counting_encoder) + + assert semhash.index.vectors.shape[0] == 3 # Only 3 representatives + assert counting_encoder.total_encoded == 3 # Only encoded 3 items, not 6 From b8e497a16bc5bc90d299c35222778390abe8baca Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 20:47:55 +0100 Subject: [PATCH 18/37] Simplified tests --- tests/test_semhash.py | 131 +++++++++++++++++++++++------------------- tests/test_utils.py | 94 ++++++++++++++---------------- 2 files changed, 114 insertions(+), 111 deletions(-) diff --git a/tests/test_semhash.py b/tests/test_semhash.py index be86158..4cbab2d 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -141,27 +141,31 @@ def test_deduplicate_with_only_exact_duplicates(model: Encoder) -> None: def test_self_find_representative(model: Encoder, train_texts: list[str]) -> None: """Test the self_find_representative method.""" semhash = SemHash.from_records(records=train_texts, model=model) - result = semhash.self_find_representative( - candidate_limit=5, - selection_size=3, - diversity=0.5, - ) + + # Test with explicit candidate_limit + result = semhash.self_find_representative(candidate_limit=5, selection_size=3, diversity=0.5) assert len(result.selected) == 3, "Expected 3 representatives" selected = {r["text"] for r in result.selected} - assert selected == { - "blueberry", - "pineapple", - "grape", - }, "Expected representatives to be blueberry, pineapple, and grape" + assert selected == {"blueberry", "pineapple", "grape"} + + # Test with auto candidate_limit (default) + result_auto = semhash.self_find_representative(selection_size=3, diversity=0.5) + assert len(result_auto.selected) == 3 def test_find_representative(model: Encoder, train_texts: list[str], test_texts: list[str]) -> None: """Test the find_representative method.""" semhash = SemHash.from_records(records=train_texts, model=model) + + # Test with explicit candidate_limit result = semhash.find_representative(records=test_texts, candidate_limit=5, selection_size=3, diversity=0.5) assert len(result.selected) == 3, "Expected 3 representatives" selected = {r["text"] for r in result.selected} - assert selected == {"grapefruit", "banana", "apple"}, "Expected representatives to be grapefruit, banana, and apple" + assert selected == {"grapefruit", "banana", "apple"} + + # Test with auto candidate_limit (default) + result_auto = semhash.find_representative(records=test_texts, selection_size=3, diversity=0.5) + assert len(result_auto.selected) == 3 def test_filter_outliers(model: Encoder, train_texts: list[str], test_texts: list[str]) -> None: @@ -173,10 +177,23 @@ def test_filter_outliers(model: Encoder, train_texts: list[str], test_texts: lis filtered = {r["text"] for r in result.filtered} assert filtered == {"motorcycle", "plane"}, "Expected outliers to be motorcycle and plane" + # Test FilterResult ratio properties + assert result.filter_ratio == len(result.filtered) / len(test_texts) + assert result.selected_ratio == len(result.selected) / len(test_texts) + assert result.filter_ratio + result.selected_ratio == 1.0 + # Test with outlier_percentage=0.0 (should return no outliers) result_zero = semhash.filter_outliers(records=test_texts, outlier_percentage=0.0) assert result_zero.filtered == [] assert len(result_zero.selected) == len(test_texts) + assert result_zero.filter_ratio == 0.0 + assert result_zero.selected_ratio == 1.0 + + # Invalid outlier_percentage raises ValueError + with pytest.raises(ValueError, match="outlier_percentage must be between 0 and 1"): + semhash.filter_outliers(records=test_texts, outlier_percentage=-0.1) + with pytest.raises(ValueError, match="outlier_percentage must be between 0 and 1"): + semhash.filter_outliers(records=test_texts, outlier_percentage=1.5) def test_self_filter_outliers(model: Encoder, train_texts: list[str]) -> None: @@ -193,6 +210,12 @@ def test_self_filter_outliers(model: Encoder, train_texts: list[str]) -> None: assert result_zero.filtered == [] assert len(result_zero.selected) == len(train_texts) + # Invalid outlier_percentage raises ValueError + with pytest.raises(ValueError, match="outlier_percentage must be between 0 and 1"): + semhash.self_filter_outliers(outlier_percentage=-0.1) + with pytest.raises(ValueError, match="outlier_percentage must be between 0 and 1"): + semhash.self_filter_outliers(outlier_percentage=1.5) + def test__diversify(monkeypatch: pytest.MonkeyPatch) -> None: """Test the _diversify method.""" @@ -226,32 +249,33 @@ def test__diversify(monkeypatch: pytest.MonkeyPatch) -> None: def test_from_embeddings(model: Encoder, train_texts: list[str]) -> None: """Test from_embeddings constructor with validation and comparison to from_records.""" - # Test validation: mismatched shapes + # Validation: empty records + with pytest.raises(ValueError, match="records must not be empty"): + SemHash.from_embeddings(embeddings=np.array([[]]), records=[], model=model) + + # Validation: non-2D embeddings + with pytest.raises(ValueError, match="must be a 2D array"): + SemHash.from_embeddings(embeddings=np.array([1, 2, 3]), records=["a", "b", "c"], model=model) + + # Validation: mismatched shapes with pytest.raises(ValueError, match="Number of embeddings"): wrong_embeddings = model.encode(["apple", "banana"]) SemHash.from_embeddings(embeddings=wrong_embeddings, records=train_texts, model=model) # Test that from_embeddings behaves same as from_records semhash_from_records = SemHash.from_records(records=train_texts, model=model) - embeddings = model.encode(train_texts) semhash_from_embeddings = SemHash.from_embeddings(embeddings=embeddings, records=train_texts, model=model) - # Both should give same deduplication results result1 = semhash_from_records.self_deduplicate(threshold=0.95) result2 = semhash_from_embeddings.self_deduplicate(threshold=0.95) - assert len(result1.selected) == len(result2.selected) - assert len(result1.filtered) == len(result2.filtered) # Test that from_embeddings keeps first-occurrence embeddings and drops duplicates records = ["apple", "banana", "apple", "cherry"] embeddings = np.array([[0.0], [1.0], [2.0], [3.0]], dtype=np.float32) - semhash = SemHash.from_embeddings(embeddings=embeddings, records=records, model=model) - assert semhash.index.vectors.shape == (3, 1) - # Should keep embeddings at indices 0, 1, 3 (first occurrences of img1, img2, img3) assert semhash.index.vectors.tolist() == [[0.0], [1.0], [3.0]] @@ -286,61 +310,50 @@ def __getitem__(self, key: str) -> list[str]: assert len(result.selected) == 2 -def test_from_records_coerces_non_string_dict_values(model: Encoder) -> None: - """Test that from_records coerces non-string dict values to strings.""" +def test_from_records_edge_cases(model: Encoder) -> None: + """Test from_records edge cases: coercion, order preservation, None rejection.""" + # Coerces non-string dict values to strings records = [{"id": 1}, {"id": 2}, {"id": 1}] # Integers, with duplicate semhash = SemHash.from_records(records, columns=["id"], model=model) + assert semhash.index.vectors.shape[0] == 2 # Deduplicated + assert 2 in [len(bucket) for bucket in semhash.index.items] # id=1 bucket has 2 - # Should have deduplicated to 2 unique items - assert semhash.index.vectors.shape[0] == 2 - assert len(semhash.index.items) == 2 - - # First bucket should have 2 records (id=1 appears twice) - bucket_sizes = [len(bucket) for bucket in semhash.index.items] - assert 2 in bucket_sizes - - -def test_from_records_preserves_first_occurrence_order(model: Encoder) -> None: - """Test that from_records preserves first-occurrence order (deterministic).""" + # Preserves first-occurrence order (deterministic) texts = ["zebra", "apple", "zebra", "banana", "apple", "cherry"] semhash = SemHash.from_records(texts, model=model) - - # Get first record from each bucket firsts = [bucket[0]["text"] for bucket in semhash.index.items] - - # Should be in first-occurrence order assert firsts == ["zebra", "apple", "banana", "cherry"] - -def test_from_records_rejects_none_in_dict_values(model: Encoder) -> None: - """Test that from_records rejects None values in dict records.""" - records = [{"text": "apple"}, {"text": None}, {"text": "banana"}] - + # Rejects None values in dict records with pytest.raises(ValueError, match="has None value"): - SemHash.from_records(records, columns=["text"], model=model) + SemHash.from_records([{"text": "apple"}, {"text": None}], columns=["text"], model=model) -def test_deduplicate_coerces_non_string_dict_values(model: Encoder) -> None: - """Test that deduplicate() coerces non-string dict values (consistent with from_records).""" - # Build SemHash with string records +def test_deduplicate_edge_cases(model: Encoder) -> None: + """Test deduplicate() edge cases: coercion, None rejection, empty records, type mismatches.""" semhash = SemHash.from_records(["1", "2", "3"], model=model) - # Pass dict records with integer values to deduplicate - new_records = [{"text": 1}, {"text": 2}, {"text": 4}] # type: ignore[list-item] + # Coerces non-string dict values + result = semhash.deduplicate([{"text": 1}, {"text": 4}], threshold=0.95) + assert len(result.filtered) + len(result.selected) == 2 - # Should coerce ints to strings and work without error - result = semhash.deduplicate(new_records, threshold=0.95) - - # Should have deduplicated (1, 2 already exist) - assert len(result.filtered) > 0 - assert len(result.selected) > 0 + # Rejects None values + with pytest.raises(ValueError, match="has None value"): + semhash.deduplicate([{"text": "cherry"}, {"text": None}], threshold=0.95) + # Rejects empty records + with pytest.raises(ValueError, match="records must not be empty"): + semhash.deduplicate([], threshold=0.95) -def test_deduplicate_rejects_none_values(model: Encoder) -> None: - """Test that deduplicate() rejects None values in dict records.""" - semhash = SemHash.from_records(["apple", "banana"], model=model) + # Type mismatch: strings passed to dict-based index + semhash_dict = SemHash.from_records([{"col": "a"}, {"col": "b"}], columns=["col"], model=model) + with pytest.raises(ValueError, match="Records were not originally strings"): + semhash_dict.deduplicate(["x", "y"], threshold=0.95) - new_records = [{"text": "cherry"}, {"text": None}] # type: ignore[list-item] + # Type mismatch: mixed strings + with pytest.raises(ValueError, match="Records must be all strings"): + semhash.deduplicate(["a", {"text": "b"}], threshold=0.95) - with pytest.raises(ValueError, match="has None value"): - semhash.deduplicate(new_records, threshold=0.95) + # Type mismatch: mixed dicts + with pytest.raises(ValueError, match="Records must be all dictionaries"): + semhash_dict.deduplicate([{"col": "a"}, "b"], threshold=0.95) diff --git a/tests/test_utils.py b/tests/test_utils.py index 6499817..6f28a63 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -13,19 +13,21 @@ def test_to_frozendict() -> None: - """Test converting dict to frozendict.""" + """Test converting dict to frozendict, including error cases.""" record = {"a": "1", "b": "2", "c": "3"} + + # Basic case: select subset of columns result = to_frozendict(record, {"a", "c"}) assert result == frozendict({"a": "1", "c": "3"}) assert "b" not in result + # Works with Sequence (not just set) + result = to_frozendict(record, ["a", "b"]) + assert result == frozendict({"a": "1", "b": "2"}) -def test_to_frozendict_rejects_missing_columns() -> None: - """Test that to_frozendict raises on missing columns.""" - record = {"a": "1", "b": "2"} - - with pytest.raises(ValueError, match="Missing column 'c'"): - to_frozendict(record, {"a", "c"}) + # Missing column raises ValueError + with pytest.raises(ValueError, match="Missing column 'missing'"): + to_frozendict(record, {"a", "missing"}) def test_compute_candidate_limit() -> None: @@ -41,30 +43,51 @@ def test_compute_candidate_limit() -> None: def test_featurize(model: Encoder) -> None: - """Test featurizing records.""" + """Test featurizing records, including error cases.""" records = [{"text": "hello"}, {"text": "world"}] embeddings = featurize(records, ["text"], model) assert embeddings.shape == (2, 128) # Model has 128 dims assert isinstance(embeddings, np.ndarray) + # Missing column raises ValueError + with pytest.raises(ValueError, match="Missing column 'missing'"): + featurize(records, ["missing"], model) + def test_remove_exact_duplicates() -> None: - """Test exact duplicate removal.""" + """Test exact duplicate removal, with and without reference records.""" + # Basic case: remove duplicates within same list records = [ {"text": "hello", "id": "1"}, {"text": "world", "id": "2"}, {"text": "hello", "id": "3"}, ] deduplicated, duplicates = remove_exact_duplicates(records, ["text"]) - assert len(deduplicated) == 2 assert len(duplicates) == 1 assert duplicates[0][0] == {"text": "hello", "id": "3"} + # With reference_records: cross-dataset filtering + reference_records = [ + [{"text": "apple"}], + [{"text": "banana"}, {"text": "banana"}], + ] + new_records = [ + {"text": "cherry"}, # New + {"text": "apple"}, # Exists in reference + {"text": "date"}, # New + {"text": "banana"}, # Exists in reference + ] + deduplicated, duplicates = remove_exact_duplicates(new_records, ["text"], reference_records=reference_records) + assert len(deduplicated) == 2 + assert {"text": "cherry"} in deduplicated + assert {"text": "date"} in deduplicated + assert len(duplicates) == 2 + def test_prepare_records() -> None: - """Test preparing records.""" - # String records + """Test preparing records, including validation and edge cases.""" + # String records -> converts to dicts with "text" column records = ["hello", "world"] dict_records, columns, was_string = prepare_records(records, None) assert was_string is True @@ -79,48 +102,15 @@ def test_prepare_records() -> None: assert dict_records == records # Dict records without columns raises ValueError - records = [{"text": "hello"}] with pytest.raises(ValueError, match="Columns must be specified"): - prepare_records(records, None) - + prepare_records([{"text": "hello"}], None) -def test_remove_exact_duplicates_with_reference_records() -> None: - """Test exact duplicate removal with reference_records for cross-dataset filtering.""" - # Build reference buckets (simulates index.items structure) - reference_records = [ - [{"text": "apple"}], # bucket 1: apple (1 occurrence) - [{"text": "banana"}, {"text": "banana"}], # bucket 2: banana (2 occurrences) - ] - - # New records to check against reference - new_records = [ - {"text": "cherry"}, # New (not in reference) - {"text": "apple"}, # Exact match with reference - {"text": "date"}, # New (not in reference) - {"text": "banana"}, # Exact match with reference - ] - - deduplicated, duplicates = remove_exact_duplicates(new_records, ["text"], reference_records=reference_records) + # Empty records raises ValueError + with pytest.raises(ValueError, match="records must not be empty"): + prepare_records([], None) - # Deduplicated should only contain records NOT in reference - assert len(deduplicated) == 2 - assert {"text": "cherry"} in deduplicated - assert {"text": "date"} in deduplicated - - # Duplicates should contain records that match reference - assert len(duplicates) == 2 - # Each duplicate is (record, reference_bucket) - dup_records = [d[0] for d in duplicates] - assert {"text": "apple"} in dup_records - assert {"text": "banana"} in dup_records - - -def test_prepare_records_rejects_mixed_types() -> None: - """Test that prepare_records rejects mixed string/dict inputs.""" - # Mixed types should be rejected + # Mixed types rejected with pytest.raises(ValueError, match="All records must be"): - prepare_records(["a", {"text": "b"}], None) # type: ignore[list-item] - - # Starting with dict, then string should also be rejected + prepare_records(["a", {"text": "b"}], None) with pytest.raises(ValueError, match="All records must be"): - prepare_records([{"text": "a"}, "b"], ["text"]) # type: ignore[list-item] + prepare_records([{"text": "a"}, "b"], ["text"]) From 2969dcf4fc91325ba2cda6c2f680624b0dbf50fd Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 20:56:36 +0100 Subject: [PATCH 19/37] Improved coverage --- semhash/semhash.py | 8 ++++---- tests/test_datamodels.py | 9 ++++++++- tests/test_from_dataset.py | 15 +++++++++++++++ 3 files changed, 27 insertions(+), 5 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index a318ca8..3030f20 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -101,7 +101,7 @@ def from_records( dict_records, columns, was_string = prepare_records(records, columns) # If no model is provided, load the default model - if model is None: + if model is None: # pragma: no cover model = StaticModel.from_pretrained("minishlab/potion-base-8M") # Group by exact match, preserving first-occurrence order @@ -150,7 +150,7 @@ def from_dataset( :return: A SemHash instance with a fitted vicinity index. """ # Load default model if needed - if model is None: + if model is None: # pragma: no cover model = StaticModel.from_pretrained("minishlab/potion-base-8M") # Extract, validate, and deduplicate dataset records @@ -341,8 +341,8 @@ def self_deduplicate( duplicate_records.append(DuplicateRecord(record=curr_record, duplicates=items_with_score, exact=True)) # If we don't see any similar_items, we know the record is not a duplicate. - # in rare cases, the item itself might not be a duplicate of itself. - if not similar_items: + # In rare cases, the item itself might not be returned by the index. + if not similar_items: # pragma: no cover deduplicated_records.append(record) continue items, _ = zip(*similar_items) diff --git a/tests/test_datamodels.py b/tests/test_datamodels.py index 59c2563..abd7488 100644 --- a/tests/test_datamodels.py +++ b/tests/test_datamodels.py @@ -2,7 +2,7 @@ import semhash import semhash.version -from semhash.datamodels import DeduplicationResult, DuplicateRecord, SelectedWithDuplicates +from semhash.datamodels import DeduplicationResult, DuplicateRecord, FilterResult, SelectedWithDuplicates def test_deduplication_scoring() -> None: @@ -243,3 +243,10 @@ def test_selected_with_duplicates_cache_invalidation_on_rethreshold() -> None: assert result2[0].duplicates[0][0] == "duplicate_1" # Results should be different objects assert result1 is not result2 + + +def test_filter_result_empty() -> None: + """Test FilterResult ratios with empty lists.""" + result = FilterResult(selected=[], filtered=[]) + assert result.filter_ratio == 0.0 + assert result.selected_ratio == 1.0 diff --git a/tests/test_from_dataset.py b/tests/test_from_dataset.py index dc35927..9384352 100644 --- a/tests/test_from_dataset.py +++ b/tests/test_from_dataset.py @@ -77,6 +77,21 @@ def test_from_dataset_validation(model: Encoder) -> None: with pytest.raises(ValueError, match="dataset must not be empty"): SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) + # Column length mismatch (custom DatasetLike with inconsistent columns) + class BadDataset: + column_names = ["col1", "col2"] + + def __len__(self) -> int: + return 3 + + def __getitem__(self, key: str) -> list[str]: + if key == "col1": + return ["a", "b", "c"] + return ["x", "y"] # Wrong length! + + with pytest.raises(ValueError, match="does not match dataset length"): + SemHash.from_dataset(dataset=BadDataset(), columns=["col1", "col2"], model=model) + def test_from_dataset_was_string_behavior(model: Encoder) -> None: """Test was_string logic: returns strings only for text column with actual string values.""" From db8bef753dafb1617352a12f288fd7313c20f3d0 Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 21:05:14 +0100 Subject: [PATCH 20/37] Consolidated tests --- tests/test_datamodels.py | 31 +++++++++++-------------------- 1 file changed, 11 insertions(+), 20 deletions(-) diff --git a/tests/test_datamodels.py b/tests/test_datamodels.py index abd7488..270983c 100644 --- a/tests/test_datamodels.py +++ b/tests/test_datamodels.py @@ -25,34 +25,27 @@ def test_deduplication_scoring_exact() -> None: assert d.exact_duplicate_ratio == 0.2 -def test_deduplication_scoring_exact_empty() -> None: - """Test the deduplication scoring.""" - d = DeduplicationResult([], [], 0.8, columns=["text"]) - assert d.exact_duplicate_ratio == 0.0 - - def test_deduplication_scoring_empty() -> None: - """Test the deduplication scoring.""" + """Test the deduplication scoring with empty results.""" d = DeduplicationResult([], [], 0.8, columns=["text"]) assert d.duplicate_ratio == 0.0 + assert d.exact_duplicate_ratio == 0.0 def test_rethreshold() -> None: - """Test rethresholding the duplicates.""" + """Test rethresholding the duplicates, including empty case.""" d = DuplicateRecord("a", False, [("b", 0.9), ("c", 0.8)]) d._rethreshold(0.85) assert d.duplicates == [("b", 0.9)] - -def test_rethreshold_empty() -> None: - """Test rethresholding the duplicates.""" - d = DuplicateRecord("a", False, []) - d._rethreshold(0.85) - assert d.duplicates == [] + # Empty case + d_empty = DuplicateRecord("a", False, []) + d_empty._rethreshold(0.85) + assert d_empty.duplicates == [] def test_get_least_similar_from_duplicates() -> None: - """Test getting the least similar duplicates.""" + """Test getting the least similar duplicates, including empty case.""" d = DeduplicationResult( ["a", "b", "c"], [DuplicateRecord("a", False, [("b", 0.9), ("c", 0.7)]), DuplicateRecord("b", False, [("c", 0.8)])], @@ -61,11 +54,9 @@ def test_get_least_similar_from_duplicates() -> None: result = d.get_least_similar_from_duplicates(1) assert result == [("a", "c", 0.7)] - -def test_get_least_similar_from_duplicates_empty() -> None: - """Test getting the least similar duplicates.""" - d = DeduplicationResult([], [], 0.8, columns=["text"]) - assert d.get_least_similar_from_duplicates(1) == [] + # Empty case + d_empty = DeduplicationResult([], [], 0.8, columns=["text"]) + assert d_empty.get_least_similar_from_duplicates(1) == [] def test_rethreshold_deduplication_result() -> None: From fdba077b5e9dab4ae41401e7eb8eb34a4e7a52e7 Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 21:05:20 +0100 Subject: [PATCH 21/37] Consolidated tests --- tests/test_datamodels.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/test_datamodels.py b/tests/test_datamodels.py index 270983c..307eeeb 100644 --- a/tests/test_datamodels.py +++ b/tests/test_datamodels.py @@ -1,7 +1,5 @@ import pytest -import semhash -import semhash.version from semhash.datamodels import DeduplicationResult, DuplicateRecord, FilterResult, SelectedWithDuplicates From 23e7506d5bf917022712332458c22508614b75e9 Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 21:13:08 +0100 Subject: [PATCH 22/37] Simplified tests --- tests/conftest.py | 33 --------------------------------- tests/test_from_dataset.py | 12 ------------ 2 files changed, 45 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index ada0d8c..aae37c8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,3 @@ -from typing import Any - import pytest from model2vec import StaticModel @@ -53,34 +51,3 @@ def test_texts() -> list[str]: "motorcycle", # Outlier "plane", # Outlier ] - - -class CountingEncoder: - """Encoder wrapper that counts how many items are encoded. Useful for testing efficiency.""" - - def __init__(self, base_encoder: Encoder) -> None: - """Initialize the counting encoder.""" - self.base_encoder = base_encoder - self.encode_calls: list[int] = [] - - def encode(self, sentences: Any, **kwargs: Any) -> Any: - """Encode sentences and count the number of items encoded.""" - if isinstance(sentences, str): - sentences = [sentences] - self.encode_calls.append(len(sentences)) - return self.base_encoder.encode(sentences, **kwargs) - - @property - def total_encoded(self) -> int: - """Total number of items encoded across all calls.""" - return sum(self.encode_calls) - - def reset(self) -> None: - """Reset the call counter.""" - self.encode_calls = [] - - -@pytest.fixture -def counting_encoder(model: StaticModel) -> CountingEncoder: - """A counting encoder wrapper for testing embedding efficiency.""" - return CountingEncoder(model) diff --git a/tests/test_from_dataset.py b/tests/test_from_dataset.py index 9384352..0b55dc3 100644 --- a/tests/test_from_dataset.py +++ b/tests/test_from_dataset.py @@ -1,5 +1,4 @@ import pytest -from conftest import CountingEncoder from semhash import SemHash from semhash.utils import Encoder @@ -109,14 +108,3 @@ def test_from_dataset_was_string_behavior(model: Encoder) -> None: ds_other = Dataset.from_dict({"id": ["a", "b", "c"]}) result_other = SemHash.from_dataset(dataset=ds_other, columns=["id"], model=model).self_deduplicate() assert all(isinstance(r, dict) for r in result_other.selected) - - -def test_from_dataset_only_embeds_representatives(counting_encoder: CountingEncoder) -> None: - """Test that from_dataset only embeds representative records, not duplicates.""" - # 6 records but only 3 unique - ds = Dataset.from_dict({"text": ["apple", "banana", "apple", "cherry", "banana", "apple"]}) - - semhash = SemHash.from_dataset(dataset=ds, columns=["text"], model=counting_encoder) - - assert semhash.index.vectors.shape[0] == 3 # Only 3 representatives - assert counting_encoder.total_encoded == 3 # Only encoded 3 items, not 6 From b101fdb02c34f45bec8e80551383f9b1d0dea007 Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 14 Jan 2026 21:13:22 +0100 Subject: [PATCH 23/37] Simplified tests --- tests/conftest.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index aae37c8..69eebe8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,8 +1,6 @@ import pytest from model2vec import StaticModel -from semhash.utils import Encoder - @pytest.fixture def model() -> StaticModel: From e113ac8ccb3bfc7e507c6eaf3ae01b0954fbf0ff Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 08:25:21 +0100 Subject: [PATCH 24/37] Generalized hashing functions to support complex types --- semhash/semhash.py | 25 +++++------ semhash/utils.py | 105 ++++++++++++++++++++++++++++++++------------- 2 files changed, 85 insertions(+), 45 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index 3030f20..d27d555 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -1,6 +1,5 @@ from __future__ import annotations -from collections import defaultdict from collections.abc import Sequence from math import ceil from typing import Any, Generic, Literal @@ -18,6 +17,7 @@ DatasetLike, Encoder, Record, + coerce_value, compute_candidate_limit, featurize, group_records_by_key, @@ -373,12 +373,12 @@ def self_deduplicate( return result - def _validate_if_strings(self, records: Sequence[dict[str, Any] | str]) -> list[dict[str, str]]: + def _validate_if_strings(self, records: Sequence[dict[str, Any] | str]) -> list[dict[str, Any]]: """ Validate if the records are strings. If the records are strings, they are converted to dictionaries with a single column. - If the records are dicts, values are coerced to strings and None is rejected. + If the records are dicts, primitives are stringified and complex types (images, etc.) are kept raw. :param records: The records to validate. :return: The records as a list of dictionaries. @@ -396,25 +396,22 @@ def _validate_if_strings(self, records: Sequence[dict[str, Any] | str]) -> list[ raise ValueError("Records were not originally strings, but you passed strings.") if not all(isinstance(r, str) for r in records): raise ValueError("Records must be all strings.") - # Type narrowing: we've validated all are strings - return [{"text": str(r)} for r in records] + return [{"text": r} for r in records] - # Dict path - coerce values to strings (matching prepare_records behavior) + # Dict path if not all(isinstance(r, dict) for r in records): raise ValueError("Records must be all dictionaries.") - # Type narrowing: we've validated all are dicts dict_records: Sequence[dict[str, Any]] = records # type: ignore[assignment] - coerced: list[dict[str, str]] = [] + result: list[dict[str, Any]] = [] for r in dict_records: - out: dict[str, str] = {} + out = {} for c in self.columns: - val = r.get(c) - if val is None: + if (val := r.get(c)) is None: raise ValueError(f"Column '{c}' has None value in record {r}") - out[c] = val if isinstance(val, str) else str(val) - coerced.append(out) - return coerced + out[c] = coerce_value(val) + result.append(out) + return result def find_representative( self, diff --git a/semhash/utils.py b/semhash/utils.py index dbb500a..2ee9ebd 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -1,3 +1,4 @@ +import hashlib from collections import defaultdict from collections.abc import Sequence from typing import Any, Protocol, TypeAlias, TypeVar @@ -11,19 +12,19 @@ class Encoder(Protocol): - """An encoder protocol for SemHash.""" + """An encoder protocol for SemHash. Supports text, images, or any encodable data.""" def encode( self, - sentences: list[str] | str | Sequence[str], + inputs: Sequence[Any] | Any, **kwargs: Any, ) -> np.ndarray: """ - Encode a list of sentences into embeddings. + Encode a list of inputs into embeddings. - :param sentences: A list of sentences to encode. + :param inputs: A list of inputs to encode (strings, images, etc.). :param **kwargs: Additional keyword arguments. - :return: The embeddings of the sentences. + :return: The embeddings of the inputs. """ ... # pragma: no cover @@ -48,26 +49,67 @@ def __getitem__(self, key: str) -> Sequence[Any]: ... # pragma: no cover -def to_frozendict(record: dict[str, str], columns: Sequence[str] | set[str]) -> frozendict[str, str]: +def make_hashable(value: Any) -> Any: """ - Convert a record to a frozendict. + Convert a value to a hashable representation for use as dict keys. + + Strings and other hashable types are returned as-is. + Non-hashable types (like PIL images, numpy arrays) are hashed to a string. + + :param value: The value to make hashable. + :return: A hashable representation of the value. + """ + # Fast path: most values are strings or already hashable + if isinstance(value, (str, int, float, bool, type(None))): + return value + # Handle objects with tobytes() (PIL Image, numpy array, etc.) + if hasattr(value, "tobytes"): + return hashlib.md5(value.tobytes()).hexdigest() + # Fallback: try to hash, otherwise stringify + try: + hash(value) + return value + except TypeError: + return str(value) + + +def coerce_value(value: Any) -> Any: + """ + Coerce a value for encoding: stringify primitives, keep complex types raw. + + This ensures primitives (int, float, bool) work with text encoders, + while complex types (PIL images, tensors, etc.) are passed through for multimodal encoders. + + :param value: The value to coerce. + :return: The coerced value. + """ + if isinstance(value, (str, bytes)): + return value + if isinstance(value, (int, float, bool)): + return str(value) + return value # Complex types (images, tensors, etc.) + + +def to_frozendict(record: dict[str, Any], columns: Sequence[str] | set[str]) -> frozendict[str, Any]: + """ + Convert a record to a frozendict with hashable values. :param record: The record to convert. :param columns: The columns to include. - :return: A frozendict with only the specified columns. + :return: A frozendict with only the specified columns (values made hashable). :raises ValueError: If a column is missing from the record. """ try: - return frozendict({k: record[k] for k in columns}) + return frozendict({k: make_hashable(record[k]) for k in columns}) except KeyError as e: missing = e.args[0] raise ValueError(f"Missing column '{missing}' in record {record}") from e def group_records_by_key( - records: Sequence[dict[str, str]], + records: Sequence[dict[str, Any]], columns: Sequence[str], -) -> tuple[list[dict[str, str]], list[list[dict[str, str]]]]: +) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]]]: """ Group records by exact match on columns, preserving first-occurrence order. @@ -77,8 +119,8 @@ def group_records_by_key( - deduplicated_records: first record from each unique group - items: list of groups, each group is a list of exact duplicates """ - buckets: dict[frozendict[str, str], list[dict[str, str]]] = {} - order: list[frozendict[str, str]] = [] + buckets: dict[frozendict[str, Any], list[dict[str, Any]]] = {} + order: list[frozendict[str, Any]] = [] for r in records: key = to_frozendict(r, columns) @@ -123,7 +165,7 @@ def compute_candidate_limit( def featurize( - records: Sequence[dict[str, str]], + records: Sequence[dict[str, Any]], columns: Sequence[str], model: Encoder, ) -> np.ndarray: @@ -150,12 +192,12 @@ def featurize( def remove_exact_duplicates( - records: Sequence[dict[str, str]], + records: Sequence[dict[str, Any]], columns: Sequence[str], - reference_records: list[list[dict[str, str]]] | None = None, -) -> tuple[list[dict[str, str]], list[tuple[dict[str, str], list[dict[str, str]]]]]: + reference_records: list[list[dict[str, Any]]] | None = None, +) -> tuple[list[dict[str, Any]], list[tuple[dict[str, Any], list[dict[str, Any]]]]]: """ - Remove exact duplicates based on the unpacked string representation of each record. + Remove exact duplicates based on the hashable representation of each record. If reference_records is None, the function will only check for duplicates within the records list. @@ -164,12 +206,12 @@ def remove_exact_duplicates( :param reference_records: A list of records to compare against. These are already unpacked :return: A list of deduplicated records and a list of duplicates. """ - deduplicated = [] - duplicates = [] + deduplicated: list[dict[str, Any]] = [] + duplicates: list[tuple[dict[str, Any], list[dict[str, Any]]]] = [] column_set = set(columns) # Build a seen set from reference_records if provided - seen: defaultdict[frozendict[str, str], list[dict[str, str]]] = defaultdict(list) + seen: defaultdict[frozendict[str, Any], list[dict[str, Any]]] = defaultdict(list) if reference_records is not None: for record_set in reference_records: key = to_frozendict(record_set[0], column_set) @@ -191,7 +233,7 @@ def remove_exact_duplicates( def prepare_records( records: Sequence[Record], columns: Sequence[str] | None -) -> tuple[list[dict[str, str]], Sequence[str], bool]: +) -> tuple[list[dict[str, Any]], Sequence[str], bool]: """ Validate and prepare records for processing. @@ -214,23 +256,23 @@ def prepare_records( if not all(isinstance(r, str) for r in records): raise ValueError("All records must be strings when the first record is a string.") columns = ["text"] - dict_records: list[dict[str, str]] = [{"text": str(record)} for record in records] + dict_records: list[dict[str, Any]] = [{"text": record} for record in records] was_string = True else: # Validate all records are dicts if not all(isinstance(r, dict) for r in records): raise ValueError("All records must be dicts when the first record is a dict.") assert columns is not None - # Coerce dict values to strings (matching dataset behavior) + # Coerce values: stringify primitives, keep complex types raw (for images, etc.) dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] dict_records = [] for r in dict_records_typed: - coerced = {} + coerced: dict[str, Any] = {} for c in columns: val = r.get(c) if val is None: raise ValueError(f"Column '{c}' has None value in record {r}") - coerced[c] = val if isinstance(val, str) else str(val) + coerced[c] = coerce_value(val) dict_records.append(coerced) was_string = False @@ -263,7 +305,7 @@ def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> tuple[dic def prepare_dataset_records( dataset: DatasetLike, columns: Sequence[str], -) -> tuple[list[dict[str, str]], list[list[dict[str, str]]], bool]: +) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]], bool]: """ Extract, validate, and exact-deduplicate dataset rows using columnar access. @@ -286,17 +328,18 @@ def prepare_dataset_records( # values in the dataset are actual strings (not integers/floats coerced to str). was_string = len(columns) == 1 and columns[0] == "text" - def coerce(raw: Any, *, col: str, idx: int) -> str: + def validate_and_coerce(raw: Any, *, col: str, idx: int) -> Any: + """Validate value is not None, then coerce for encoding.""" if raw is None: raise ValueError(f"Column '{col}' has None at index {idx}") - return raw if isinstance(raw, str) else str(raw) + return coerce_value(raw) # Build all records while tracking was_string - records: list[dict[str, str]] = [] + records: list[dict[str, Any]] = [] for i in range(n): if was_string and not isinstance(cols["text"][i], str): was_string = False - records.append({c: coerce(cols[c][i], col=c, idx=i) for c in columns}) + records.append({c: validate_and_coerce(cols[c][i], col=c, idx=i) for c in columns}) # Group by exact match, preserving first-occurrence order deduplicated_records, items = group_records_by_key(records, columns) From 653dccc5a0d3adf9e21d3976d9739cce2bd0a1b8 Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 08:28:04 +0100 Subject: [PATCH 25/37] Removed complex method --- semhash/semhash.py | 65 +++++----------------------------------------- 1 file changed, 7 insertions(+), 58 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index d27d555..ffcbcb6 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -44,38 +44,6 @@ def __init__(self, index: Index, model: Encoder, columns: Sequence[str], was_str self._was_string = was_string self._ranking_cache: FilterResult | None = None - @classmethod - def _from_vectors_and_items( - cls, - *, - vectors: np.ndarray, - items: list[list[dict[str, str]]], - model: Encoder, - columns: Sequence[str], - was_string: bool, - ann_backend: Backend | str, - **kwargs: Any, - ) -> SemHash: - """ - Internal constructor from vectors and items (after deduplication). - - :param vectors: Embeddings for representative records. - :param items: List of item clusters (each cluster contains exact duplicates). - :param model: Encoder model. - :param columns: Column names. - :param was_string: Whether original records were strings. - :param ann_backend: ANN backend to use. - :param **kwargs: Additional arguments for the index. - :return: SemHash instance. - """ - index = Index.from_vectors_and_items( - vectors=vectors, - items=items, - backend_type=ann_backend, - **kwargs, - ) - return cls(index=index, model=model, columns=columns, was_string=was_string) - @classmethod def from_records( cls, @@ -110,15 +78,8 @@ def from_records( # Create embeddings for deduplicated records only embeddings = featurize(deduplicated_records, columns, model) - return cls._from_vectors_and_items( - vectors=embeddings, - items=items, - model=model, - columns=columns, - was_string=was_string, - ann_backend=ann_backend, - **kwargs, - ) + index = Index.from_vectors_and_items(vectors=embeddings, items=items, backend_type=ann_backend, **kwargs) + return cls(index=index, model=model, columns=columns, was_string=was_string) @classmethod def from_dataset( @@ -159,15 +120,8 @@ def from_dataset( # Embed representatives only (encoder decides batching internally) vectors = featurize(records=deduplicated_records, columns=columns, model=model) - return cls._from_vectors_and_items( - vectors=vectors, - items=items, - model=model, - columns=columns, - was_string=was_string, - ann_backend=ann_backend, - **kwargs, - ) + index = Index.from_vectors_and_items(vectors=vectors, items=items, backend_type=ann_backend, **kwargs) + return cls(index=index, model=model, columns=columns, was_string=was_string) @classmethod def from_embeddings( @@ -231,15 +185,10 @@ def from_embeddings( deduplicated_embeddings = embeddings[keep_embedding_indices] - return cls._from_vectors_and_items( - vectors=deduplicated_embeddings, - items=items, - model=model, - columns=columns, - was_string=was_string, - ann_backend=ann_backend, - **kwargs, + index = Index.from_vectors_and_items( + vectors=deduplicated_embeddings, items=items, backend_type=ann_backend, **kwargs ) + return cls(index=index, model=model, columns=columns, was_string=was_string) def deduplicate( self, From 95cc5e6695e72709d2a02121b3e8ef2ca6bcdf96 Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 08:43:30 +0100 Subject: [PATCH 26/37] Updated docstrings --- semhash/semhash.py | 17 +++++------------ semhash/utils.py | 7 +------ 2 files changed, 6 insertions(+), 18 deletions(-) diff --git a/semhash/semhash.py b/semhash/semhash.py index ffcbcb6..6abfaba 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -56,7 +56,7 @@ def from_records( """ Initialize a SemHash instance from records. - This removes exact duplicates, featurizes the records, and fits a vicinity index. + Removes exact duplicates, featurizes the records, and fits a vicinity index. :param records: A list of records (strings or dictionaries). :param columns: Columns to featurize if records are dictionaries. @@ -93,15 +93,8 @@ def from_dataset( """ Initialize SemHash from a dataset (e.g., HuggingFace Dataset). - Extracts records from the dataset, deduplicates them, and embeds only - representative records (not duplicates). The encoder controls batching internally. - - Supports any dataset-like object that provides: - - column_names: Sequence[str] - - __len__() -> int - - __getitem__(column_name: str) -> Sequence[Any] (columnar access) - - HuggingFace datasets.Dataset satisfies this contract, but custom implementations work too. + Removes exact duplicates, featurizes the records, and fits a vicinity index. + Supports any dataset-like object that follows the DatasetLike protocol. :param dataset: A dataset-like object with columnar access. :param columns: Columns to use for deduplication (same as from_records). @@ -117,7 +110,7 @@ def from_dataset( # Extract, validate, and deduplicate dataset records deduplicated_records, items, was_string = prepare_dataset_records(dataset, columns) - # Embed representatives only (encoder decides batching internally) + # Create embeddings for deduplicated records only vectors = featurize(records=deduplicated_records, columns=columns, model=model) index = Index.from_vectors_and_items(vectors=vectors, items=items, backend_type=ann_backend, **kwargs) @@ -136,7 +129,7 @@ def from_embeddings( """ Initialize a SemHash instance from pre-computed embeddings. - This removes exact duplicates and fits a vicinity index using the provided embeddings. + Removes exact duplicates, featurizes the records, and fits a vicinity index. :param embeddings: Pre-computed embeddings as a numpy array of shape (n_records, embedding_dim). :param records: A list of records (strings or dictionaries) corresponding to the embeddings. diff --git a/semhash/utils.py b/semhash/utils.py index 2ee9ebd..6bc204a 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -280,7 +280,7 @@ def prepare_records( def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> tuple[dict[str, Sequence[Any]], int]: - """Validate dataset structure and extract columns. Returns (cols, n).""" + """Validate dataset structure and extract columns..""" try: column_names = dataset.column_names except AttributeError as e: @@ -309,11 +309,6 @@ def prepare_dataset_records( """ Extract, validate, and exact-deduplicate dataset rows using columnar access. - Supports HuggingFace datasets.Dataset and any dataset-like object that provides: - - column_names: Sequence[str] - - __len__() -> int - - __getitem__(column_name: str) -> Sequence[Any] (columnar access) - :param dataset: A dataset-like object with columnar access. :param columns: Columns to use for deduplication. :return: Tuple of (deduplicated_records, items, was_string) where: From e8089220585c977881e391b4369d9a2d1af61502 Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 08:56:50 +0100 Subject: [PATCH 27/37] Moved functions to records --- semhash/records.py | 186 ++++++++++++++++++++++++++++++++++++++++++++ semhash/semhash.py | 13 ++-- semhash/utils.py | 182 ------------------------------------------- tests/test_utils.py | 10 +-- 4 files changed, 196 insertions(+), 195 deletions(-) diff --git a/semhash/records.py b/semhash/records.py index c2bbb27..5e52c47 100644 --- a/semhash/records.py +++ b/semhash/records.py @@ -1,6 +1,192 @@ +from collections import defaultdict from collections.abc import Sequence +from typing import Any + +from frozendict import frozendict from semhash.datamodels import DeduplicationResult, DuplicateRecord +from semhash.utils import DatasetLike, Record, coerce_value, to_frozendict + + +def group_records_by_key( + records: Sequence[dict[str, Any]], + columns: Sequence[str], +) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]]]: + """ + Group records by exact match on columns, preserving first-occurrence order. + + :param records: Records to group. + :param columns: Columns to use as grouping key. + :return: Tuple of (deduplicated_records, items) where: + - deduplicated_records: first record from each unique group + - items: list of groups, each group is a list of exact duplicates + """ + buckets: dict[frozendict[str, Any], list[dict[str, Any]]] = {} + order: list[frozendict[str, Any]] = [] + + for r in records: + key = to_frozendict(r, columns) + bucket = buckets.get(key) + if bucket is None: + buckets[key] = [r] + order.append(key) + else: + bucket.append(r) + + items = [buckets[k] for k in order] + deduplicated_records = [bucket[0] for bucket in items] + return deduplicated_records, items + + +def remove_exact_duplicates( + records: Sequence[dict[str, Any]], + columns: Sequence[str], + reference_records: list[list[dict[str, Any]]] | None = None, +) -> tuple[list[dict[str, Any]], list[tuple[dict[str, Any], list[dict[str, Any]]]]]: + """ + Remove exact duplicates based on the hashable representation of each record. + + If reference_records is None, the function will only check for duplicates within the records list. + + :param records: A list of records to check for exact duplicates. + :param columns: Columns to unpack. + :param reference_records: A list of records to compare against. These are already unpacked + :return: A list of deduplicated records and a list of duplicates. + """ + deduplicated: list[dict[str, Any]] = [] + duplicates: list[tuple[dict[str, Any], list[dict[str, Any]]]] = [] + + column_set = set(columns) + # Build a seen set from reference_records if provided + seen: defaultdict[frozendict[str, Any], list[dict[str, Any]]] = defaultdict(list) + if reference_records is not None: + for record_set in reference_records: + key = to_frozendict(record_set[0], column_set) + seen[key] = list(record_set) + in_one_set = reference_records is None + + for record in records: + frozen_record = to_frozendict(record, column_set) + if duplicated_records := seen.get(frozen_record): + duplicates.append((record, duplicated_records)) + else: + deduplicated.append(record) + # Only add current documents to seen if no reference set is used + if in_one_set: + seen[frozen_record].append(record) + + return deduplicated, duplicates + + +def prepare_records( + records: Sequence[Record], columns: Sequence[str] | None +) -> tuple[list[dict[str, Any]], Sequence[str], bool]: + """ + Validate and prepare records for processing. + + :param records: A list of records (strings or dictionaries). + :param columns: Columns to use if records are dictionaries. + :return: Tuple of (dict_records, columns, was_string). + :raises ValueError: If records are empty. + :raises ValueError: If columns are not provided for dictionary records. + :raises ValueError: If dict record contains None values. + :raises ValueError: If records are not homogeneous (mixed strings and dicts). + """ + if len(records) == 0: + raise ValueError("records must not be empty") + + if columns is None and isinstance(records[0], dict): + raise ValueError("Columns must be specified when passing dictionaries.") + + if isinstance(records[0], str): + # Validate all records are strings + if not all(isinstance(r, str) for r in records): + raise ValueError("All records must be strings when the first record is a string.") + columns = ["text"] + dict_records: list[dict[str, Any]] = [{"text": record} for record in records] + was_string = True + else: + # Validate all records are dicts + if not all(isinstance(r, dict) for r in records): + raise ValueError("All records must be dicts when the first record is a dict.") + assert columns is not None + # Coerce values: stringify primitives, keep complex types raw (for images, etc.) + dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] + dict_records = [] + for r in dict_records_typed: + coerced: dict[str, Any] = {} + for c in columns: + val = r.get(c) + if val is None: + raise ValueError(f"Column '{c}' has None value in record {r}") + coerced[c] = coerce_value(val) + dict_records.append(coerced) + was_string = False + + return dict_records, columns, was_string + + +def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> tuple[dict[str, Sequence[Any]], int]: + """Validate dataset structure and extract columns.""" + try: + column_names = dataset.column_names + except AttributeError as e: + raise TypeError("dataset must satisfy DatasetLike (column_names, __len__, __getitem__)") from e + + missing = set(columns) - set(column_names) + if missing: + raise ValueError(f"Columns {missing} not found in dataset") + + n = len(dataset) + if n == 0: + raise ValueError("dataset must not be empty") + + cols = {c: dataset[c] for c in columns} + for c in columns: + if len(cols[c]) != n: + raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") + + return cols, n + + +def prepare_dataset_records( + dataset: DatasetLike, + columns: Sequence[str], +) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]], bool]: + """ + Extract, validate, and exact-deduplicate dataset rows using columnar access. + + :param dataset: A dataset-like object with columnar access. + :param columns: Columns to use for deduplication. + :return: Tuple of (deduplicated_records, items, was_string) where: + - deduplicated_records: representative record per exact-duplicate bucket + - items: buckets of exact duplicates (each bucket is list[record]) + - was_string: True iff columns == ["text"] and ALL raw values were strings + """ + cols, n = _validate_dataset(dataset, columns) + + # was_string controls whether deduplicate() returns strings or dicts. + # We only return strings if: (1) single column named "text", AND (2) all raw + # values in the dataset are actual strings (not integers/floats coerced to str). + was_string = len(columns) == 1 and columns[0] == "text" + + def validate_and_coerce(raw: Any, *, col: str, idx: int) -> Any: + """Validate value is not None, then coerce for encoding.""" + if raw is None: + raise ValueError(f"Column '{col}' has None at index {idx}") + return coerce_value(raw) + + # Build all records while tracking was_string + records: list[dict[str, Any]] = [] + for i in range(n): + if was_string and not isinstance(cols["text"][i], str): + was_string = False + records.append({c: validate_and_coerce(cols[c][i], col=c, idx=i) for c in columns}) + + # Group by exact match, preserving first-occurrence order + deduplicated_records, items = group_records_by_key(records, columns) + + return deduplicated_records, items, was_string def dict_to_string(record: dict[str, str], columns: Sequence[str]) -> str: diff --git a/semhash/semhash.py b/semhash/semhash.py index 6abfaba..45bd8fd 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -12,7 +12,14 @@ from semhash.datamodels import DeduplicationResult, DuplicateRecord, FilterResult from semhash.index import Index -from semhash.records import add_scores_to_records, map_deduplication_result_to_strings +from semhash.records import ( + add_scores_to_records, + group_records_by_key, + map_deduplication_result_to_strings, + prepare_dataset_records, + prepare_records, + remove_exact_duplicates, +) from semhash.utils import ( DatasetLike, Encoder, @@ -20,10 +27,6 @@ coerce_value, compute_candidate_limit, featurize, - group_records_by_key, - prepare_dataset_records, - prepare_records, - remove_exact_duplicates, to_frozendict, ) diff --git a/semhash/utils.py b/semhash/utils.py index 6bc204a..457be3b 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -1,5 +1,4 @@ import hashlib -from collections import defaultdict from collections.abc import Sequence from typing import Any, Protocol, TypeAlias, TypeVar @@ -106,36 +105,6 @@ def to_frozendict(record: dict[str, Any], columns: Sequence[str] | set[str]) -> raise ValueError(f"Missing column '{missing}' in record {record}") from e -def group_records_by_key( - records: Sequence[dict[str, Any]], - columns: Sequence[str], -) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]]]: - """ - Group records by exact match on columns, preserving first-occurrence order. - - :param records: Records to group. - :param columns: Columns to use as grouping key. - :return: Tuple of (deduplicated_records, items) where: - - deduplicated_records: first record from each unique group - - items: list of groups, each group is a list of exact duplicates - """ - buckets: dict[frozendict[str, Any], list[dict[str, Any]]] = {} - order: list[frozendict[str, Any]] = [] - - for r in records: - key = to_frozendict(r, columns) - bucket = buckets.get(key) - if bucket is None: - buckets[key] = [r] - order.append(key) - else: - bucket.append(r) - - items = [buckets[k] for k in order] - deduplicated_records = [bucket[0] for bucket in items] - return deduplicated_records, items - - def compute_candidate_limit( total: int, selection_size: int, @@ -189,154 +158,3 @@ def featurize( embeddings_per_col.append(np.asarray(col_emb)) return np.concatenate(embeddings_per_col, axis=1) - - -def remove_exact_duplicates( - records: Sequence[dict[str, Any]], - columns: Sequence[str], - reference_records: list[list[dict[str, Any]]] | None = None, -) -> tuple[list[dict[str, Any]], list[tuple[dict[str, Any], list[dict[str, Any]]]]]: - """ - Remove exact duplicates based on the hashable representation of each record. - - If reference_records is None, the function will only check for duplicates within the records list. - - :param records: A list of records to check for exact duplicates. - :param columns: Columns to unpack. - :param reference_records: A list of records to compare against. These are already unpacked - :return: A list of deduplicated records and a list of duplicates. - """ - deduplicated: list[dict[str, Any]] = [] - duplicates: list[tuple[dict[str, Any], list[dict[str, Any]]]] = [] - - column_set = set(columns) - # Build a seen set from reference_records if provided - seen: defaultdict[frozendict[str, Any], list[dict[str, Any]]] = defaultdict(list) - if reference_records is not None: - for record_set in reference_records: - key = to_frozendict(record_set[0], column_set) - seen[key] = list(record_set) - in_one_set = reference_records is None - - for record in records: - frozen_record = to_frozendict(record, column_set) - if duplicated_records := seen.get(frozen_record): - duplicates.append((record, duplicated_records)) - else: - deduplicated.append(record) - # Only add current documents to seen if no reference set is used - if in_one_set: - seen[frozen_record].append(record) - - return deduplicated, duplicates - - -def prepare_records( - records: Sequence[Record], columns: Sequence[str] | None -) -> tuple[list[dict[str, Any]], Sequence[str], bool]: - """ - Validate and prepare records for processing. - - :param records: A list of records (strings or dictionaries). - :param columns: Columns to use if records are dictionaries. - :return: Tuple of (dict_records, columns, was_string). - :raises ValueError: If records are empty. - :raises ValueError: If columns are not provided for dictionary records. - :raises ValueError: If dict record contains None values. - :raises ValueError: If records are not homogeneous (mixed strings and dicts). - """ - if len(records) == 0: - raise ValueError("records must not be empty") - - if columns is None and isinstance(records[0], dict): - raise ValueError("Columns must be specified when passing dictionaries.") - - if isinstance(records[0], str): - # Validate all records are strings - if not all(isinstance(r, str) for r in records): - raise ValueError("All records must be strings when the first record is a string.") - columns = ["text"] - dict_records: list[dict[str, Any]] = [{"text": record} for record in records] - was_string = True - else: - # Validate all records are dicts - if not all(isinstance(r, dict) for r in records): - raise ValueError("All records must be dicts when the first record is a dict.") - assert columns is not None - # Coerce values: stringify primitives, keep complex types raw (for images, etc.) - dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] - dict_records = [] - for r in dict_records_typed: - coerced: dict[str, Any] = {} - for c in columns: - val = r.get(c) - if val is None: - raise ValueError(f"Column '{c}' has None value in record {r}") - coerced[c] = coerce_value(val) - dict_records.append(coerced) - was_string = False - - return dict_records, columns, was_string - - -def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> tuple[dict[str, Sequence[Any]], int]: - """Validate dataset structure and extract columns..""" - try: - column_names = dataset.column_names - except AttributeError as e: - raise TypeError("dataset must satisfy DatasetLike (column_names, __len__, __getitem__)") from e - - missing = set(columns) - set(column_names) - if missing: - raise ValueError(f"Columns {missing} not found in dataset") - - n = len(dataset) - if n == 0: - raise ValueError("dataset must not be empty") - - cols = {c: dataset[c] for c in columns} - for c in columns: - if len(cols[c]) != n: - raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") - - return cols, n - - -def prepare_dataset_records( - dataset: DatasetLike, - columns: Sequence[str], -) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]], bool]: - """ - Extract, validate, and exact-deduplicate dataset rows using columnar access. - - :param dataset: A dataset-like object with columnar access. - :param columns: Columns to use for deduplication. - :return: Tuple of (deduplicated_records, items, was_string) where: - - deduplicated_records: representative record per exact-duplicate bucket - - items: buckets of exact duplicates (each bucket is list[record]) - - was_string: True iff columns == ["text"] and ALL raw values were strings - """ - cols, n = _validate_dataset(dataset, columns) - - # was_string controls whether deduplicate() returns strings or dicts. - # We only return strings if: (1) single column named "text", AND (2) all raw - # values in the dataset are actual strings (not integers/floats coerced to str). - was_string = len(columns) == 1 and columns[0] == "text" - - def validate_and_coerce(raw: Any, *, col: str, idx: int) -> Any: - """Validate value is not None, then coerce for encoding.""" - if raw is None: - raise ValueError(f"Column '{col}' has None at index {idx}") - return coerce_value(raw) - - # Build all records while tracking was_string - records: list[dict[str, Any]] = [] - for i in range(n): - if was_string and not isinstance(cols["text"][i], str): - was_string = False - records.append({c: validate_and_coerce(cols[c][i], col=c, idx=i) for c in columns}) - - # Group by exact match, preserving first-occurrence order - deduplicated_records, items = group_records_by_key(records, columns) - - return deduplicated_records, items, was_string diff --git a/tests/test_utils.py b/tests/test_utils.py index 6f28a63..e48b020 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,14 +2,8 @@ import pytest from frozendict import frozendict -from semhash.utils import ( - Encoder, - compute_candidate_limit, - featurize, - prepare_records, - remove_exact_duplicates, - to_frozendict, -) +from semhash.records import prepare_records, remove_exact_duplicates +from semhash.utils import Encoder, compute_candidate_limit, featurize, to_frozendict def test_to_frozendict() -> None: From c64a72ede8db17f4bceec7c5cc9ef8487dc01e23 Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 17:30:22 +0100 Subject: [PATCH 28/37] Removed from_dataset integration --- pyproject.toml | 5 - semhash/records.py | 65 +-- semhash/semhash.py | 37 -- semhash/utils.py | 20 - semhash/version.py | 4 +- tests/test_from_dataset.py | 110 ---- tests/test_semhash.py | 31 -- tests/test_utils.py | 49 +- uv.lock | 1048 +----------------------------------- 9 files changed, 52 insertions(+), 1317 deletions(-) delete mode 100644 tests/test_from_dataset.py diff --git a/pyproject.toml b/pyproject.toml index 84aa0db..915c194 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,11 +43,6 @@ dev = [ "ruff", ] -# Integrations -datasets = ["datasets"] -all = [ - "datasets", -] [project.urls] "Homepage" = "https://github.com/MinishLab" diff --git a/semhash/records.py b/semhash/records.py index 5e52c47..2229e11 100644 --- a/semhash/records.py +++ b/semhash/records.py @@ -5,7 +5,7 @@ from frozendict import frozendict from semhash.datamodels import DeduplicationResult, DuplicateRecord -from semhash.utils import DatasetLike, Record, coerce_value, to_frozendict +from semhash.utils import Record, coerce_value, to_frozendict def group_records_by_key( @@ -126,69 +126,6 @@ def prepare_records( return dict_records, columns, was_string -def _validate_dataset(dataset: DatasetLike, columns: Sequence[str]) -> tuple[dict[str, Sequence[Any]], int]: - """Validate dataset structure and extract columns.""" - try: - column_names = dataset.column_names - except AttributeError as e: - raise TypeError("dataset must satisfy DatasetLike (column_names, __len__, __getitem__)") from e - - missing = set(columns) - set(column_names) - if missing: - raise ValueError(f"Columns {missing} not found in dataset") - - n = len(dataset) - if n == 0: - raise ValueError("dataset must not be empty") - - cols = {c: dataset[c] for c in columns} - for c in columns: - if len(cols[c]) != n: - raise ValueError(f"Column '{c}' length ({len(cols[c])}) does not match dataset length ({n})") - - return cols, n - - -def prepare_dataset_records( - dataset: DatasetLike, - columns: Sequence[str], -) -> tuple[list[dict[str, Any]], list[list[dict[str, Any]]], bool]: - """ - Extract, validate, and exact-deduplicate dataset rows using columnar access. - - :param dataset: A dataset-like object with columnar access. - :param columns: Columns to use for deduplication. - :return: Tuple of (deduplicated_records, items, was_string) where: - - deduplicated_records: representative record per exact-duplicate bucket - - items: buckets of exact duplicates (each bucket is list[record]) - - was_string: True iff columns == ["text"] and ALL raw values were strings - """ - cols, n = _validate_dataset(dataset, columns) - - # was_string controls whether deduplicate() returns strings or dicts. - # We only return strings if: (1) single column named "text", AND (2) all raw - # values in the dataset are actual strings (not integers/floats coerced to str). - was_string = len(columns) == 1 and columns[0] == "text" - - def validate_and_coerce(raw: Any, *, col: str, idx: int) -> Any: - """Validate value is not None, then coerce for encoding.""" - if raw is None: - raise ValueError(f"Column '{col}' has None at index {idx}") - return coerce_value(raw) - - # Build all records while tracking was_string - records: list[dict[str, Any]] = [] - for i in range(n): - if was_string and not isinstance(cols["text"][i], str): - was_string = False - records.append({c: validate_and_coerce(cols[c][i], col=c, idx=i) for c in columns}) - - # Group by exact match, preserving first-occurrence order - deduplicated_records, items = group_records_by_key(records, columns) - - return deduplicated_records, items, was_string - - def dict_to_string(record: dict[str, str], columns: Sequence[str]) -> str: r""" Turn a record into a single string. diff --git a/semhash/semhash.py b/semhash/semhash.py index 45bd8fd..e866bc4 100644 --- a/semhash/semhash.py +++ b/semhash/semhash.py @@ -16,12 +16,10 @@ add_scores_to_records, group_records_by_key, map_deduplication_result_to_strings, - prepare_dataset_records, prepare_records, remove_exact_duplicates, ) from semhash.utils import ( - DatasetLike, Encoder, Record, coerce_value, @@ -84,41 +82,6 @@ def from_records( index = Index.from_vectors_and_items(vectors=embeddings, items=items, backend_type=ann_backend, **kwargs) return cls(index=index, model=model, columns=columns, was_string=was_string) - @classmethod - def from_dataset( - cls, - dataset: DatasetLike, - columns: Sequence[str], - model: Encoder | None = None, - ann_backend: Backend | str = Backend.USEARCH, - **kwargs: Any, - ) -> SemHash: - """ - Initialize SemHash from a dataset (e.g., HuggingFace Dataset). - - Removes exact duplicates, featurizes the records, and fits a vicinity index. - Supports any dataset-like object that follows the DatasetLike protocol. - - :param dataset: A dataset-like object with columnar access. - :param columns: Columns to use for deduplication (same as from_records). - :param model: (Optional) An Encoder model. If None, the default model is used (minishlab/potion-base-8M). - :param ann_backend: (Optional) The ANN backend to use. Defaults to Backend.USEARCH. - :param **kwargs: Any additional keyword arguments to pass to the Vicinity index. - :return: A SemHash instance with a fitted vicinity index. - """ - # Load default model if needed - if model is None: # pragma: no cover - model = StaticModel.from_pretrained("minishlab/potion-base-8M") - - # Extract, validate, and deduplicate dataset records - deduplicated_records, items, was_string = prepare_dataset_records(dataset, columns) - - # Create embeddings for deduplicated records only - vectors = featurize(records=deduplicated_records, columns=columns, model=model) - - index = Index.from_vectors_and_items(vectors=vectors, items=items, backend_type=ann_backend, **kwargs) - return cls(index=index, model=model, columns=columns, was_string=was_string) - @classmethod def from_embeddings( cls, diff --git a/semhash/utils.py b/semhash/utils.py index 457be3b..1b0d7b3 100644 --- a/semhash/utils.py +++ b/semhash/utils.py @@ -28,26 +28,6 @@ def encode( ... # pragma: no cover -class DatasetLike(Protocol): - """ - Protocol for dataset-like objects compatible with SemHash.from_dataset(). - - Any object that provides columnar access (dataset[column_name] -> sequence) - satisfies this protocol. HuggingFace datasets.Dataset is the primary example, - but custom dataset implementations are supported. - """ - - column_names: Sequence[str] - - def __len__(self) -> int: - """Return the number of rows in the dataset.""" - ... # pragma: no cover - - def __getitem__(self, key: str) -> Sequence[Any]: - """Return all values for the given column name.""" - ... # pragma: no cover - - def make_hashable(value: Any) -> Any: """ Convert a value to a hashable representation for use as dict keys. diff --git a/semhash/version.py b/semhash/version.py index 9bfefb0..cc35d8d 100644 --- a/semhash/version.py +++ b/semhash/version.py @@ -1,2 +1,2 @@ -__version_triple__ = (0, 3, 3) -__version__ = ".".join(map(str, __version_triple__)) +__version_triple__ = (0, 3, 3) # pragma: no cover +__version__ = ".".join(map(str, __version_triple__)) # pragma: no cover diff --git a/tests/test_from_dataset.py b/tests/test_from_dataset.py deleted file mode 100644 index 0b55dc3..0000000 --- a/tests/test_from_dataset.py +++ /dev/null @@ -1,110 +0,0 @@ -import pytest - -from semhash import SemHash -from semhash.utils import Encoder - -# Require datasets for this entire test module -datasets = pytest.importorskip("datasets", reason="pip install 'semhash[datasets]'") -Dataset = datasets.Dataset - - -def test_from_dataset_basic(model: Encoder) -> None: - """Test from_dataset works with single and multi-column datasets.""" - # Single column - ds_single = Dataset.from_dict({"text": ["apple", "banana", "cherry", "apple"]}) - semhash_single = SemHash.from_dataset(dataset=ds_single, columns=["text"], model=model) - assert len(semhash_single.index.vectors) == 3 # Collapsed duplicate "apple" - - # Multi-column - ds_multi = Dataset.from_dict( - { - "question": ["What is AI?", "What is ML?", "What is AI?"], - "context": ["AI explanation", "ML explanation", "AI explanation"], - } - ) - semhash_multi = SemHash.from_dataset(dataset=ds_multi, columns=["question", "context"], model=model) - assert len(semhash_multi.index.vectors) == 2 # Collapsed duplicate row - - -def test_from_dataset_equivalence_to_from_records(model: Encoder) -> None: - """Test that from_dataset produces identical results to from_records.""" - # Test with strings - texts = ["apple", "banana", "cherry", "apple"] - ds_strings = Dataset.from_dict({"text": texts}) - - semhash_dataset = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model) - semhash_records = SemHash.from_records(records=texts, model=model) - - assert semhash_dataset.index.vectors.shape == semhash_records.index.vectors.shape - result_dataset = semhash_dataset.self_deduplicate(threshold=0.9) - result_records = semhash_records.self_deduplicate(threshold=0.9) - assert len(result_dataset.selected) == len(result_records.selected) - assert type(result_dataset.selected[0]) == type(result_records.selected[0]) - - # Test with dicts (multi-column) - data = [ - {"question": "What is AI?", "answer": "Artificial Intelligence"}, - {"question": "What is ML?", "answer": "Machine Learning"}, - ] - ds_dicts = Dataset.from_dict({k: [d[k] for d in data] for k in data[0].keys()}) - - semhash_dataset = SemHash.from_dataset(dataset=ds_dicts, columns=["question", "answer"], model=model) - semhash_records = SemHash.from_records(records=data, columns=["question", "answer"], model=model) - - assert semhash_dataset.index.vectors.shape == semhash_records.index.vectors.shape - - -def test_from_dataset_validation(model: Encoder) -> None: - """Test from_dataset input validation (dataset-specific error cases).""" - ds = Dataset.from_dict({"text": ["apple", "banana"]}) - - # Invalid dataset type (Protocol violation) - with pytest.raises(TypeError, match="must satisfy DatasetLike"): - SemHash.from_dataset(dataset={"text": ["apple"]}, columns=["text"], model=model) - - # Missing column - with pytest.raises(ValueError, match="not found in dataset"): - SemHash.from_dataset(dataset=ds, columns=["missing_col"], model=model) - - # None value in column - ds_with_none = Dataset.from_dict({"text": ["apple", None, "banana"]}) - with pytest.raises(ValueError, match="Column 'text' has None at index 1"): - SemHash.from_dataset(dataset=ds_with_none, columns=["text"], model=model) - - # Empty dataset - ds_empty = Dataset.from_dict({"text": []}) - with pytest.raises(ValueError, match="dataset must not be empty"): - SemHash.from_dataset(dataset=ds_empty, columns=["text"], model=model) - - # Column length mismatch (custom DatasetLike with inconsistent columns) - class BadDataset: - column_names = ["col1", "col2"] - - def __len__(self) -> int: - return 3 - - def __getitem__(self, key: str) -> list[str]: - if key == "col1": - return ["a", "b", "c"] - return ["x", "y"] # Wrong length! - - with pytest.raises(ValueError, match="does not match dataset length"): - SemHash.from_dataset(dataset=BadDataset(), columns=["col1", "col2"], model=model) - - -def test_from_dataset_was_string_behavior(model: Encoder) -> None: - """Test was_string logic: returns strings only for text column with actual string values.""" - # text column with strings -> returns strings - ds_strings = Dataset.from_dict({"text": ["apple", "banana", "cherry"]}) - result_strings = SemHash.from_dataset(dataset=ds_strings, columns=["text"], model=model).self_deduplicate() - assert all(isinstance(r, str) for r in result_strings.selected) - - # text column with integers -> returns dicts (coerced values aren't "true" strings) - ds_ints = Dataset.from_dict({"text": [1, 2, 3]}) - result_ints = SemHash.from_dataset(dataset=ds_ints, columns=["text"], model=model).self_deduplicate() - assert all(isinstance(r, dict) for r in result_ints.selected) - - # non-text column -> returns dicts - ds_other = Dataset.from_dict({"id": ["a", "b", "c"]}) - result_other = SemHash.from_dataset(dataset=ds_other, columns=["id"], model=model).self_deduplicate() - assert all(isinstance(r, dict) for r in result_other.selected) diff --git a/tests/test_semhash.py b/tests/test_semhash.py index 4cbab2d..55119fd 100644 --- a/tests/test_semhash.py +++ b/tests/test_semhash.py @@ -279,37 +279,6 @@ def test_from_embeddings(model: Encoder, train_texts: list[str]) -> None: assert semhash.index.vectors.tolist() == [[0.0], [1.0], [3.0]] -def test_from_dataset_with_custom_dataset_like(model: Encoder) -> None: - """Test that from_dataset works with custom DatasetLike implementations (no HF dependency).""" - - class MiniDataset: - """Minimal DatasetLike implementation for testing.""" - - column_names = ["text"] - - def __init__(self, data: dict[str, list[str]]) -> None: - self._data = data - - def __len__(self) -> int: - return len(self._data["text"]) - - def __getitem__(self, key: str) -> list[str]: - return self._data[key] - - # Create custom dataset with duplicates - ds = MiniDataset({"text": ["apple", "banana", "apple"]}) - - semhash = SemHash.from_dataset(ds, columns=["text"], model=model) - - # Should have deduplicated to 2 unique items - assert len(semhash.index.items) == 2 - assert len(semhash.index.vectors) == 2 - - # Should work with deduplication - result = semhash.self_deduplicate(threshold=0.95) - assert len(result.selected) == 2 - - def test_from_records_edge_cases(model: Encoder) -> None: """Test from_records edge cases: coercion, order preservation, None rejection.""" # Coerces non-string dict values to strings diff --git a/tests/test_utils.py b/tests/test_utils.py index e48b020..3224d73 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -3,7 +3,54 @@ from frozendict import frozendict from semhash.records import prepare_records, remove_exact_duplicates -from semhash.utils import Encoder, compute_candidate_limit, featurize, to_frozendict +from semhash.utils import Encoder, coerce_value, compute_candidate_limit, featurize, make_hashable, to_frozendict + + +def test_make_hashable() -> None: + """Test make_hashable with various types.""" + # Fast path: primitives + assert make_hashable("hello") == "hello" + assert make_hashable(42) == 42 + assert make_hashable(3.14) == 3.14 + assert make_hashable(True) is True + assert make_hashable(None) is None + + # Objects with tobytes() (simulate PIL Image or numpy array) + class MockImage: + def tobytes(self) -> bytes: + return b"fake_image_data" + + img = MockImage() + result = make_hashable(img) + assert isinstance(result, str) + assert len(result) == 32 # MD5 hex digest + + # Hashable objects (like tuples) + assert make_hashable((1, 2, 3)) == (1, 2, 3) + + # Non-hashable fallback to string + unhashable = {"key": "value"} + result = make_hashable(unhashable) + assert result == "{'key': 'value'}" + + +def test_coerce_value() -> None: + """Test coerce_value for encoding preparation.""" + # Strings and bytes pass through + assert coerce_value("hello") == "hello" + assert coerce_value(b"bytes") == b"bytes" + + # Primitives converted to strings + assert coerce_value(42) == "42" + assert coerce_value(3.14) == "3.14" + assert coerce_value(True) == "True" + + # Complex types pass through unchanged + class MockImage: + pass + + img = MockImage() + assert coerce_value(img) is img def test_to_frozendict() -> None: diff --git a/uv.lock b/uv.lock index 10c6208..9bd57ea 100644 --- a/uv.lock +++ b/uv.lock @@ -7,162 +7,6 @@ resolution-markers = [ "python_full_version < '3.11'", ] -[[package]] -name = "aiohappyeyeballs" -version = "2.6.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" } -wheels = [ - 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SemHash logo
- Fast Semantic Text Deduplication & Filtering + Fast Multimodal Semantic Deduplication & Filtering

@@ -38,9 +38,9 @@ -SemHash is a lightweight and flexible tool for deduplicating datasets, filtering outliers, and finding representative samples using semantic similarity. It combines fast embedding generation from [Model2Vec](https://github.com/MinishLab/model2vec) with efficient ANN-based similarity search through [Vicinity](https://github.com/MinishLab/vicinity). +SemHash is a lightweight library for semantic deduplication, outlier filtering, and representative sample selection. It's fully multimodal: text works out-of-the-box with fast Model2Vec embeddings, and you can bring your own encoders for images, audio, or custom models. -SemHash supports both single-dataset deduplication & filtering (e.g., cleaning up a train set by removing duplicates and outliers) and multi-dataset deduplication & filtering (e.g., ensuring no overlap between a test set and a train set). It works with simple datasets, such as text lists, and more complex ones, like multi-column QA datasets. Additionally, it includes functions to inspect deduplication results, making it easier to understand and refine your data cleaning process. +SemHash supports both single-dataset operations (clean a training set) and cross-dataset operations (deduplicate test against train). It works with simple lists and complex multi-column datasets, and includes inspection tools to help you understand and refine results. All operations use Vicinity for efficient similarity search. ## Quickstart @@ -49,6 +49,8 @@ Install the package with: pip install semhash ``` +### Text Deduplication, Filtering & Representative Sampling + Deduplicate a single dataset, filter outliers, and find representative samples with the following code (note: the examples assume you have `datasets` installed, which you can install with `pip install datasets`): ```python @@ -71,7 +73,29 @@ filtered_texts = semhash.self_filter_outliers().selected representative_texts = semhash.self_find_representative().selected ``` -Or, deduplicate across two datasets, filter outliers, and find representative samples with the following code (e.g., eliminating train/test leakage): +### Image Deduplication + +Deduplicate an image dataset using a vision model (requires `pip install sentence-transformers`): + +```python +from datasets import load_dataset +from sentence_transformers import SentenceTransformer +from semhash import SemHash + +# Load an image dataset and vision model +model = SentenceTransformer('clip-ViT-B-32') +dataset = load_dataset("uoft-cs/cifar10", split="test") + +# Initialize a SemHash instance with the 'img' column +semhash = SemHash.from_records(list(dataset), columns=["img"], model=model) + +# Deduplicate the images +deduplicated_images = semhash.self_deduplicate().selected +``` + +### Cross-Dataset Deduplication, Filtering & Representative Sampling + +Deduplicate across two datasets, filter outliers, and find representative samples (e.g., eliminating train/test leakage): ```python from datasets import load_dataset @@ -93,13 +117,12 @@ filtered_test_texts = semhash.filter_outliers(records=test_texts, outlier_percen # Find representative texts in the test data against the training data, # optionally with a specific selection size -representative_test_texts = semhash.find_representative( - records=test_texts, selection_size=10).selected - - +representative_test_texts = semhash.find_representative(records=test_texts, selection_size=10).selected ``` -Or, deduplicate multi-column dataset, filter outliers, and find representative samples with the following code (e.g., deduplicating a QA dataset): +### Multi-Column Deduplication + +Deduplicate multi-column datasets (e.g., deduplicating a QA dataset): ```python from datasets import load_dataset @@ -116,15 +139,9 @@ semhash = SemHash.from_records(records=records, columns=["question", "context"]) # Deduplicate the records deduplicated_records = semhash.self_deduplicate().selected - -# Filter outliers from the records -filtered_texts = semhash.self_filter_outliers().selected - -# Find representative texts in the records -representative_texts = semhash.self_find_representative().selected ``` -The `deduplicate` and `self_deduplicate` functions return a [DeduplicationResult](https://github.com/MinishLab/semhash/blob/main/semhash/datamodels.py#L58). This object stores the deduplicated corpus, a set of duplicate object (along with the objects that caused duplication), and several useful functions to further inspect the deduplication result. +The `deduplicate` and `self_deduplicate` functions return a [DeduplicationResult](https://github.com/MinishLab/semhash/blob/main/semhash/datamodels.py#L58). This object stores the deduplicated corpus, a set of duplicate objects (along with the objects that caused duplication), and several useful functions to further inspect the deduplication result. The `filter_outliers`, `self_filter_outliers`, `find_representative`, and `self_find_representative` functions return a [FilterResult](https://github.com/MinishLab/semhash/blob/main/semhash/datamodels.py#179). This object stores the found outliers/representative samples. @@ -403,7 +420,7 @@ representative_texts = semhash.self_find_representative().selected
Initializing from a HuggingFace Dataset
-You can also initialize SemHash directly from a HuggingFace Dataset. This is more convenient than manually extracting records, as SemHash will handle the conversion and embedding internally: +You can easily use SemHash with HuggingFace Datasets by converting them to a list: ```python from datasets import load_dataset @@ -412,8 +429,8 @@ from semhash import SemHash # Load a HuggingFace dataset dataset = load_dataset("ag_news", split="train") -# Initialize SemHash directly from the dataset -semhash = SemHash.from_dataset(dataset=dataset, columns=["text"]) +# Convert to list and initialize SemHash +semhash = SemHash.from_records(records=list(dataset), columns=["text"]) # Deduplicate, filter outliers, and find representative samples deduplicated_texts = semhash.self_deduplicate().selected @@ -430,8 +447,8 @@ from semhash import SemHash # Load a multi-column dataset dataset = load_dataset("squad_v2", split="train") -# Initialize SemHash with multiple columns -semhash = SemHash.from_dataset(dataset=dataset, columns=["question", "context"]) +# Convert to list and initialize with multiple columns +semhash = SemHash.from_records(records=list(dataset), columns=["question", "context"]) # Deduplicate the records deduplicated_records = semhash.self_deduplicate().selected diff --git a/pyproject.toml b/pyproject.toml index 915c194..e8ba058 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "semhash" -description = "Fast Semantic Text Deduplication & Filtering" +description = "Fast Multimodal Semantic Deduplication & Filtering" authors = [{name = "Thomas van Dongen", email = "thomas123@live.nl"}, { name = "Stéphan Tulkens", email = "stephantul@gmail.com"}] readme = { file = "README.md", content-type = "text/markdown" } dynamic = ["version"] From fc2ba26bb38981ffc6301a1c9196a6aea408eb8b Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 17:55:32 +0100 Subject: [PATCH 30/37] Updated docs --- README.md | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/README.md b/README.md index ef0154a..f75968e 100644 --- a/README.md +++ b/README.md @@ -282,6 +282,56 @@ representative_records = semhash.self_find_representative().selected
+
+ Deduplicate, filter outliers, and find representative samples on image datasets +
+ +You can bring your own encoder for any modality by implementing the Encoder protocol. Here's an example using a vision model from timm for image deduplication: + +```python +from datasets import load_dataset +import timm +import torch +from semhash import SemHash + +# Requires: pip install timm torch datasets + +# Create a custom image encoder +class VisionEncoder: + """Custom encoder using timm models. Implements the Encoder protocol.""" + + def __init__(self, model_name: str = "mobilenetv3_small_100"): + self.model = timm.create_model(model_name, pretrained=True, num_classes=0).eval() + self.transform = timm.data.create_transform(**timm.data.resolve_model_data_config(self.model)) + + def encode(self, inputs): + """Encode a batch of PIL images into embeddings.""" + with torch.no_grad(): + return self.model(torch.stack([self.transform(img) for img in inputs])).numpy() + +# Load image dataset +dataset = load_dataset("uoft-cs/cifar10", split="test") +train_data = [{"img": img, "id": i} for i, img in enumerate(dataset["img"][:100])] +test_data = [{"img": img, "id": i} for i, img in enumerate(dataset["img"][100:150])] + +# Initialize SemHash with the custom vision encoder +semhash = SemHash.from_records(train_data, columns=["img"], model=VisionEncoder()) + +# Single-dataset operations +deduplicated = semhash.self_deduplicate().selected +outliers = semhash.self_filter_outliers().selected +representatives = semhash.self_find_representative().selected + +# Cross-dataset operations +test_deduplicated = semhash.deduplicate(test_data).selected +test_outliers = semhash.filter_outliers(test_data).selected +test_representatives = semhash.find_representative(test_data, selection_size=10).selected +``` + +The Encoder protocol requires only an `encode(inputs, **kwargs)` method that returns a numpy array. This makes it easy to integrate any embedding model for any modality. + +
+
Using custom encoders
From 78bb2255223b6942ff70db9599463506cadffcba Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 18:07:32 +0100 Subject: [PATCH 31/37] Updated docs and citation info --- CITATION.cff | 4 ++-- README.md | 15 +++++++++------ 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index cd06753..42199b3 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -1,6 +1,6 @@ cff-version: 1.2.0 message: "If you use SemHash in your research, please cite it as below." -title: "SemHash: Fast Semantic Text Deduplication & Filtering" +title: "SemHash: Fast Multimodal Semantic Deduplication & Filtering" authors: - family-names: "van Dongen" given-names: "Thomas" @@ -14,7 +14,7 @@ date-released: "2025-01-05" preferred-citation: type: software - title: "SemHash: Fast Semantic Text Deduplication & Filtering" + title: "SemHash: Fast Multimodal Semantic Deduplication & Filtering" authors: - family-names: "van Dongen" given-names: "Thomas" diff --git a/README.md b/README.md index f75968e..9dd3aca 100644 --- a/README.md +++ b/README.md @@ -73,7 +73,7 @@ filtered_texts = semhash.self_filter_outliers().selected representative_texts = semhash.self_find_representative().selected ``` -### Image Deduplication +### Image Deduplication, Filtering & Representative Sampling Deduplicate an image dataset using a vision model (requires `pip install sentence-transformers`): @@ -91,6 +91,12 @@ semhash = SemHash.from_records(list(dataset), columns=["img"], model=model) # Deduplicate the images deduplicated_images = semhash.self_deduplicate().selected + +# Filter outliers +filtered_images = semhash.self_filter_outliers().selected + +# Find representative images +representative_images = semhash.self_find_representative().selected ``` ### Cross-Dataset Deduplication, Filtering & Representative Sampling @@ -229,14 +235,11 @@ The following code snippet shows how to deduplicate across two datasets, filter from datasets import load_dataset from semhash import SemHash -# Initialize a SemHash instance -semhash = SemHash() - # Load two datasets to deduplicate train_texts = load_dataset("ag_news", split="train")["text"] test_texts = load_dataset("ag_news", split="test")["text"] -# Initialize a SemHash instance +# Initialize a SemHash instance with the training data semhash = SemHash.from_records(records=train_texts) # Deduplicate the test data against the training data @@ -524,7 +527,7 @@ If you use SemHash in your research, please cite the following: ```bibtex @software{minishlab2025semhash, author = {{van Dongen}, Thomas and Stephan Tulkens}, - title = {SemHash: Fast Semantic Text Deduplication \& Filtering}, + title = {SemHash: Fast Multimodal Semantic Deduplication \& Filtering}, year = {2025}, publisher = {Zenodo}, doi = {10.5281/zenodo.17265942}, From 2a7badf35315e9b3774c1d22ae603bbbcb861cc6 Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 15 Jan 2026 18:08:48 +0100 Subject: [PATCH 32/37] Updated docs --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9dd3aca..cffb47d 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,7 @@ representative_texts = semhash.self_find_representative().selected ### Image Deduplication, Filtering & Representative Sampling -Deduplicate an image dataset using a vision model (requires `pip install sentence-transformers`): +Deduplicate an image dataset, filter outliers, and find representative samples using a vision model (requires `pip install sentence-transformers`): ```python from datasets import load_dataset From bc5416414dfc3ebc64b9ab66ada0f1cf88040c46 Mon Sep 17 00:00:00 2001 From: Pringled Date: Fri, 16 Jan 2026 12:11:41 +0100 Subject: [PATCH 33/37] Updated variable names --- semhash/records.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/semhash/records.py b/semhash/records.py index 2229e11..07d220f 100644 --- a/semhash/records.py +++ b/semhash/records.py @@ -24,14 +24,14 @@ def group_records_by_key( buckets: dict[frozendict[str, Any], list[dict[str, Any]]] = {} order: list[frozendict[str, Any]] = [] - for r in records: - key = to_frozendict(r, columns) + for record in records: + key = to_frozendict(record, columns) bucket = buckets.get(key) if bucket is None: - buckets[key] = [r] + buckets[key] = [record] order.append(key) else: - bucket.append(r) + bucket.append(record) items = [buckets[k] for k in order] deduplicated_records = [bucket[0] for bucket in items] @@ -113,13 +113,13 @@ def prepare_records( # Coerce values: stringify primitives, keep complex types raw (for images, etc.) dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] dict_records = [] - for r in dict_records_typed: + for record in dict_records_typed: coerced: dict[str, Any] = {} - for c in columns: - val = r.get(c) + for column in columns: + val = record.get(column) if val is None: - raise ValueError(f"Column '{c}' has None value in record {r}") - coerced[c] = coerce_value(val) + raise ValueError(f"Column '{column}' has None value in record {record}") + coerced[column] = coerce_value(val) dict_records.append(coerced) was_string = False From 356d577d5b8eb30aa703a6e9c392e609fad8eea5 Mon Sep 17 00:00:00 2001 From: Pringled Date: Fri, 16 Jan 2026 12:15:47 +0100 Subject: [PATCH 34/37] Updated variable names --- semhash/records.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/semhash/records.py b/semhash/records.py index 07d220f..0835606 100644 --- a/semhash/records.py +++ b/semhash/records.py @@ -21,6 +21,7 @@ def group_records_by_key( - deduplicated_records: first record from each unique group - items: list of groups, each group is a list of exact duplicates """ + # Track buckets by key and preserve first-occurrence order buckets: dict[frozendict[str, Any], list[dict[str, Any]]] = {} order: list[frozendict[str, Any]] = [] @@ -28,11 +29,14 @@ def group_records_by_key( key = to_frozendict(record, columns) bucket = buckets.get(key) if bucket is None: + # First occurrence: create new bucket and track order buckets[key] = [record] order.append(key) else: + # Duplicate: add to existing bucket bucket.append(record) + # Reconstruct in first-occurrence order items = [buckets[k] for k in order] deduplicated_records = [bucket[0] for bucket in items] return deduplicated_records, items @@ -57,13 +61,13 @@ def remove_exact_duplicates( duplicates: list[tuple[dict[str, Any], list[dict[str, Any]]]] = [] column_set = set(columns) - # Build a seen set from reference_records if provided + + # Build seen set from reference_records (cross-dataset mode) or empty (single-dataset mode) seen: defaultdict[frozendict[str, Any], list[dict[str, Any]]] = defaultdict(list) if reference_records is not None: for record_set in reference_records: key = to_frozendict(record_set[0], column_set) seen[key] = list(record_set) - in_one_set = reference_records is None for record in records: frozen_record = to_frozendict(record, column_set) @@ -71,8 +75,8 @@ def remove_exact_duplicates( duplicates.append((record, duplicated_records)) else: deduplicated.append(record) - # Only add current documents to seen if no reference set is used - if in_one_set: + # Single-dataset mode: track this record for future comparisons + if reference_records is None: seen[frozen_record].append(record) return deduplicated, duplicates @@ -98,20 +102,21 @@ def prepare_records( if columns is None and isinstance(records[0], dict): raise ValueError("Columns must be specified when passing dictionaries.") + # String path: convert to dicts with "text" column if isinstance(records[0], str): - # Validate all records are strings if not all(isinstance(r, str) for r in records): raise ValueError("All records must be strings when the first record is a string.") columns = ["text"] dict_records: list[dict[str, Any]] = [{"text": record} for record in records] was_string = True + # Dict path: validate and coerce values else: - # Validate all records are dicts if not all(isinstance(r, dict) for r in records): raise ValueError("All records must be dicts when the first record is a dict.") assert columns is not None + # Coerce values: stringify primitives, keep complex types raw (for images, etc.) - dict_records_typed: list[dict[str, Any]] = list(records) # type: ignore[arg-type] + dict_records_typed: list[dict[str, Any]] = list(records) dict_records = [] for record in dict_records_typed: coerced: dict[str, Any] = {} From 52c95c51535e23189bcfdaa7ae5125641172c183 Mon Sep 17 00:00:00 2001 From: Pringled Date: Fri, 16 Jan 2026 13:01:05 +0100 Subject: [PATCH 35/37] Added image benchmarks --- Makefile | 8 + README.md | 39 +++- benchmarks/README.md | 86 +++++++- benchmarks/data.py | 6 + .../image_train_benchmark_results.json | 20 ++ .../image_train_test_benchmark_results.json | 22 ++ benchmarks/run_image_benchmarks.py | 194 ++++++++++++++++++ ...n_benchmarks.py => run_text_benchmarks.py} | 0 8 files changed, 360 insertions(+), 15 deletions(-) create mode 100644 benchmarks/results/image_train_benchmark_results.json create mode 100644 benchmarks/results/image_train_test_benchmark_results.json create mode 100644 benchmarks/run_image_benchmarks.py rename benchmarks/{run_benchmarks.py => run_text_benchmarks.py} (100%) diff --git a/Makefile b/Makefile index b5af932..5a53d3b 100644 --- a/Makefile +++ b/Makefile @@ -16,3 +16,11 @@ fix: test: uv run pytest --cov=semhash --cov-report=term-missing + +benchmark-text: + uv run python -m benchmarks.run_text_benchmarks + +benchmark-image: + uv run python -m benchmarks.run_image_benchmarks + +benchmark: benchmark-text benchmark-image diff --git a/README.md b/README.md index cffb47d..546f39b 100644 --- a/README.md +++ b/README.md @@ -303,14 +303,28 @@ from semhash import SemHash class VisionEncoder: """Custom encoder using timm models. Implements the Encoder protocol.""" - def __init__(self, model_name: str = "mobilenetv3_small_100"): + def __init__(self, model_name: str = "mobilenetv3_small_100.lamb_in1k"): self.model = timm.create_model(model_name, pretrained=True, num_classes=0).eval() - self.transform = timm.data.create_transform(**timm.data.resolve_model_data_config(self.model)) + data_config = timm.data.resolve_model_data_config(self.model) + self.transform = timm.data.create_transform(**data_config, is_training=False) - def encode(self, inputs): + def encode(self, inputs, batch_size: int = 128): """Encode a batch of PIL images into embeddings.""" + import numpy as np + + # Convert grayscale to RGB if needed + rgb_inputs = [img.convert("RGB") if img.mode != "RGB" else img for img in inputs] + + # Process in batches to avoid memory issues + all_embeddings = [] with torch.no_grad(): - return self.model(torch.stack([self.transform(img) for img in inputs])).numpy() + for i in range(0, len(rgb_inputs), batch_size): + batch_inputs = rgb_inputs[i : i + batch_size] + batch = torch.stack([self.transform(img) for img in batch_inputs]) + embeddings = self.model(batch).numpy() + all_embeddings.append(embeddings) + + return np.vstack(all_embeddings) # Load image dataset dataset = load_dataset("uoft-cs/cifar10", split="test") @@ -513,9 +527,22 @@ deduplicated_records = semhash.self_deduplicate().selected ## Benchmarks -SemHash is extremely fast and scales to large datasets with millions of records. We've benchmarked both single-dataset deduplication and train/test deduplication across a variety of datasets. For example, deduplicating 1.8M records takes only ~83 seconds on CPU. +SemHash is extremely fast and scales to large datasets with millions of records. We've benchmarked both text and image deduplication across a variety of datasets. For example, deduplicating text 1.8M records takes only ~83 seconds on CPU. + +For detailed benchmark results and analysis, see the [benchmarks directory](benchmarks/README.md). + +### Running Benchmarks -For detailed benchmark results including performance metrics across 17 datasets, as well as code to reproduce the benchmarks, see the [benchmarks directory](benchmarks/README.md). +```bash +# Run text benchmarks +make benchmark-text + +# Run image benchmarks +make benchmark-image + +# Run all benchmarks +make benchmark +``` ## License diff --git a/benchmarks/README.md b/benchmarks/README.md index 7078f3b..7b629b1 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -1,16 +1,19 @@ # SemHash Benchmarks -This directory contains the benchmarking code and results for SemHash. The benchmarks measure deduplication performance and speed across a variety of datasets. +This directory contains the benchmarking code and results for SemHash. The benchmarks measure deduplication performance and speed across a variety of text and image datasets. -## Setup +## Text Benchmarks -All benchmarks were run with the following configuration: +### Setup + +All text benchmarks were run with the following configuration: - **CPU-only**: All benchmarks run on CPU (no GPU acceleration) - **ANN backend**: Default backend (USearch) - **Encoder**: Default encoder ([potion-base-8M](https://huggingface.co/minishlab/potion-base-8M)) - **Timing**: Includes encoding time, index building time, and deduplication time +- **Dependencies**: Requires `datasets` package (`pip install datasets`) -## Results +### Results ### Train Deduplication Benchmark @@ -60,7 +63,7 @@ This benchmark measures the performance of deduplicating a test dataset against | squad_v2 | 130319 | 11873 | 11863 | 0.08 | 7.13 | | wikitext | 1801350 | 4358 | 2139 | 50.92 | 40.32 | -## Key Findings +### Key Findings SemHash is extremely fast and scales to large datasets with millions of records. Some notable findings include: @@ -70,12 +73,77 @@ SemHash is extremely fast and scales to large datasets with millions of records. - `student`: 52% of test data overlaps with training data - `wikitext`: 51% of test data overlaps with training data -## Running the Benchmarks +### Running Text Benchmarks + +To run the text benchmarks yourself: + +```bash +# Install dependencies +pip install datasets + +# Run benchmarks +python -m benchmarks.run_text_benchmarks +# Or using make +make benchmark-text +``` + +## Image Benchmarks + +### Setup + +All image benchmarks were run with the following configuration: +- **Device**: Apple Silicon GPU (MPS) +- **ANN backend**: Default backend (USearch) +- **Encoder**: MobileNetV3-Small ([mobilenetv3_small_100.lamb_in1k](https://huggingface.co/timm/mobilenetv3_small_100.lamb_in1k)) +- **Batch size**: 128 images per batch +- **Timing**: Includes encoding time, index building time, and deduplication time + +### Results + +#### Train Deduplication Benchmark + +This benchmark measures the performance of deduplicating within a single training dataset. + +| Dataset | Original Train Size | Deduplicated Train Size | % Removed | Deduplication Time (s) | +|----------------------|----------------------|--------------------------|------------|--------------------------| +| cifar10 | 50000 | 48274 | 3.45 | 61.20 | +| fashion_mnist | 60000 | 16714 | 72.14 | 86.61 | + +#### Train/Test Deduplication Benchmark + +This benchmark measures the performance of deduplicating a test dataset against a training dataset. + +| Dataset | Train Size | Test Size | Deduplicated Test Size | % Removed | Deduplication Time (s) | +|----------------------|--------------|--------------|--------------------------|------------|--------------------------| +| cifar10 | 50000 | 10000 | 9397 | 6.03 | 67.43 | +| fashion_mnist | 60000 | 10000 | 2052 | 79.48 | 72.14 | + +### Key Findings -To run the benchmarks yourself: +- **Fashion-MNIST high deduplication**: Fashion-MNIST shows very high duplication rates (72% train, 79% test) due to the simple nature of the dataset (10 clothing categories with similar items) +- **CIFAR-10 moderate deduplication**: CIFAR-10 shows lower duplication (3.45% train, 6.03% test) as it contains more diverse natural images +- **Speed**: Image deduplication is fast even for large datasets (60k images in ~87 seconds on MPS) + +### Running Image Benchmarks + +To run the image benchmarks yourself: ```bash -python -m benchmarks.run_benchmarks +# Install dependencies +pip install timm torch datasets + +# Run benchmarks +python -m benchmarks.run_image_benchmarks +# Or using make +make benchmark-image ``` -The datasets can be customized by editing `benchmarks/data.py`. +The image datasets can be customized by editing `benchmarks/data.py` (see `IMAGE_DATASET_DICT`). + +## Running All Benchmarks + +To run both text and image benchmarks: + +```bash +make benchmark +``` diff --git a/benchmarks/data.py b/benchmarks/data.py index 49b988c..f0eb6db 100644 --- a/benchmarks/data.py +++ b/benchmarks/data.py @@ -12,6 +12,7 @@ class DatasetRecord: columns: list[str] | None = None split_one: str = "train" split_two: str = "test" + modality: str = "text" DATASET_DICT: dict[str, DatasetRecord] = { @@ -41,3 +42,8 @@ class DatasetRecord: name="Salesforce/wikitext", text_name="text", label_name="text", sub_directory="wikitext-103-raw-v1" ), } + +IMAGE_DATASET_DICT: dict[str, DatasetRecord] = { + "cifar10": DatasetRecord(name="uoft-cs/cifar10", columns=["img"], modality="image"), + "fashion_mnist": DatasetRecord(name="fashion_mnist", columns=["image"], modality="image"), +} diff --git a/benchmarks/results/image_train_benchmark_results.json b/benchmarks/results/image_train_benchmark_results.json new file mode 100644 index 0000000..53fa71b --- /dev/null +++ b/benchmarks/results/image_train_benchmark_results.json @@ -0,0 +1,20 @@ +[ + { + "dataset": "cifar10", + "original_train_size": 50000, + "deduplicated_train_size": 48274, + "percent_removed": 3.4519999999999995, + "build_time_seconds": 56.00128899999254, + "deduplication_time_seconds": 5.201297917010379, + "time_seconds": 61.20258691700292 + }, + { + "dataset": "fashion_mnist", + "original_train_size": 60000, + "deduplicated_train_size": 16714, + "percent_removed": 72.14333333333333, + "build_time_seconds": 61.14413262500602, + "deduplication_time_seconds": 25.46288070900482, + "time_seconds": 86.60701333401084 + } +] diff --git a/benchmarks/results/image_train_test_benchmark_results.json b/benchmarks/results/image_train_test_benchmark_results.json new file mode 100644 index 0000000..290c7e3 --- /dev/null +++ b/benchmarks/results/image_train_test_benchmark_results.json @@ -0,0 +1,22 @@ +[ + { + "dataset": "cifar10", + "train_size": 50000, + "test_size": 10000, + "deduplicated_test_size": 9397, + "percent_removed": 6.030000000000002, + "build_time_seconds": 56.00128899999254, + "deduplication_time_seconds": 11.428115875009098, + "time_seconds": 67.42940487500164 + }, + { + "dataset": "fashion_mnist", + "train_size": 60000, + "test_size": 10000, + "deduplicated_test_size": 2052, + "percent_removed": 79.47999999999999, + "build_time_seconds": 61.14413262500602, + "deduplication_time_seconds": 10.998616750002839, + "time_seconds": 72.14274937500886 + } +] diff --git a/benchmarks/run_image_benchmarks.py b/benchmarks/run_image_benchmarks.py new file mode 100644 index 0000000..245a803 --- /dev/null +++ b/benchmarks/run_image_benchmarks.py @@ -0,0 +1,194 @@ +import json +import logging +from time import perf_counter + +import numpy as np +import timm +import torch +from datasets import load_dataset + +from benchmarks.data import IMAGE_DATASET_DICT +from semhash import SemHash + +# Set up logging +logger = logging.getLogger(__name__) + + +class VisionEncoder: + """Custom encoder using timm models for image embeddings.""" + + def __init__(self, model_name: str = "mobilenetv3_small_100.lamb_in1k") -> None: + """Initialize the vision encoder with a timm model.""" + if torch.cuda.is_available(): + self.device = torch.device("cuda") + elif torch.backends.mps.is_available(): + self.device = torch.device("mps") + else: + self.device = torch.device("cpu") + logger.info(f"Using device: {self.device}") + + self.model = timm.create_model(model_name, pretrained=True, num_classes=0).eval() + self.model = self.model.to(self.device) + + data_config = timm.data.resolve_model_data_config(self.model) + self.transform = timm.data.create_transform(**data_config, is_training=False) + + def encode(self, inputs: list, batch_size: int = 128) -> np.ndarray: + """Encode a batch of PIL images into embeddings.""" + # Convert grayscale to RGB if needed + rgb_inputs = [img.convert("RGB") if img.mode != "RGB" else img for img in inputs] + + # Process in batches + all_embeddings = [] + with torch.no_grad(): + for i in range(0, len(rgb_inputs), batch_size): + batch_inputs = rgb_inputs[i : i + batch_size] + batch = torch.stack([self.transform(img) for img in batch_inputs]).to(self.device) + embeddings = self.model(batch).cpu().numpy() + all_embeddings.append(embeddings) + + return np.vstack(all_embeddings) + + +def main() -> None: # noqa: C901 + """Run the image benchmarks.""" + # Prepare lists to hold benchmark results + train_dedup_results = [] + train_test_dedup_results = [] + + # Initialize vision encoder + encoder = VisionEncoder() + + for dataset_name, record in IMAGE_DATASET_DICT.items(): + logger.info(f"Loading dataset: {dataset_name} from {record.name}") + + # Load train and test splits + if record.sub_directory: + train_ds = load_dataset(record.name, record.sub_directory, split=record.split_one) + test_ds = load_dataset(record.name, record.sub_directory, split=record.split_two) + else: + train_ds = load_dataset(record.name, split=record.split_one) + test_ds = load_dataset(record.name, split=record.split_two) + + # Convert to list of dicts with image column + train_records = list(train_ds) + test_records = list(test_ds) + columns = record.columns + + # Build the SemHash instance + build_start = perf_counter() + semhash = SemHash.from_records(model=encoder, records=train_records, columns=columns) + build_end = perf_counter() + build_time = build_end - build_start + + # Time how long it takes to deduplicate the train set + train_only_start = perf_counter() + deduplicated_train = semhash.self_deduplicate() + train_only_end = perf_counter() + + train_only_dedup_time = train_only_end - train_only_start + original_train_size = len(train_records) + dedup_train_size = len(deduplicated_train.selected) + + percent_removed_train = deduplicated_train.duplicate_ratio * 100 + train_dedup_results.append( + { + "dataset": dataset_name, + "original_train_size": original_train_size, + "deduplicated_train_size": dedup_train_size, + "percent_removed": percent_removed_train, + "build_time_seconds": build_time, + "deduplication_time_seconds": train_only_dedup_time, + "time_seconds": train_only_dedup_time + build_time, + } + ) + + logger.info( + f"[TRAIN DEDUPLICATION] Dataset: {dataset_name}\n" + f" - Original Train Size: {original_train_size}\n" + f" - Deduplicated Train Size: {dedup_train_size}\n" + f" - % Removed: {percent_removed_train:.2f}\n" + f" - Deduplication Time (seconds): {train_only_dedup_time:.2f}\n" + f" - Build Time (seconds): {build_time:.2f}\n" + f" - Total Time (seconds): {train_only_dedup_time + build_time:.2f}\n" + ) + + # Time how long it takes to deduplicate the test set + train_test_start = perf_counter() + deduplicated_test = semhash.deduplicate( + records=test_records, + ) + train_test_end = perf_counter() + train_test_dedup_time = train_test_end - train_test_start + original_test_size = len(test_records) + deduped_test_size = len(deduplicated_test.selected) + percent_removed_test = deduplicated_test.duplicate_ratio * 100 + + train_test_dedup_results.append( + { + "dataset": dataset_name, + "train_size": original_train_size, + "test_size": original_test_size, + "deduplicated_test_size": deduped_test_size, + "percent_removed": percent_removed_test, + "build_time_seconds": build_time, + "deduplication_time_seconds": train_test_dedup_time, + "time_seconds": train_test_dedup_time + build_time, + } + ) + + logger.info( + f"[TRAIN/TEST DEDUPLICATION] Dataset: {dataset_name}\n" + f" - Train Size: {original_train_size}\n" + f" - Test Size: {original_test_size}\n" + f" - Deduplicated Test Size: {deduped_test_size}\n" + f" - % Removed: {percent_removed_test:.2f}\n" + f" - Deduplication Time (seconds): {train_test_dedup_time:.2f}\n" + f" - Build Time (seconds): {build_time:.2f}\n" + f" - Total Time (seconds): {train_test_dedup_time + build_time:.2f}\n" + ) + + # Write the results to JSON files + with open("benchmarks/results/image_train_benchmark_results.json", "w", encoding="utf-8") as f: + json.dump(train_dedup_results, f, ensure_ascii=False, indent=2) + + with open("benchmarks/results/image_train_test_benchmark_results.json", "w", encoding="utf-8") as f: + json.dump(train_test_dedup_results, f, ensure_ascii=False, indent=2) + + # Print the train table + print("### Image Train Deduplication Benchmark\n") # noqa T201 + print( # noqa T201 + f"| {'Dataset':<20} | {'Original Train Size':>20} | {'Deduplicated Train Size':>24} | {'% Removed':>10} | {'Deduplication Time (s)':>24} |" + ) # noqa T201 + print("|" + "-" * 22 + "|" + "-" * 22 + "|" + "-" * 26 + "|" + "-" * 12 + "|" + "-" * 26 + "|") # noqa T201 + for r in train_dedup_results: + print( # noqa T201 + f"| {r['dataset']:<20} " + f"| {r['original_train_size']:>20} " + f"| {r['deduplicated_train_size']:>24} " + f"| {r['percent_removed']:>10.2f} " + f"| {r['time_seconds']:>24.2f} |" + ) + + print("\n") # noqa T201 + + # Print the train/test table + print("### Image Train/Test Deduplication Benchmark\n") # noqa T201 + print( # noqa T201 + f"| {'Dataset':<20} | {'Train Size':>12} | {'Test Size':>12} | {'Deduplicated Test Size':>24} | {'% Removed':>10} | {'Deduplication Time (s)':>24} |" + ) # noqa T201 + print("|" + "-" * 22 + "|" + "-" * 14 + "|" + "-" * 14 + "|" + "-" * 26 + "|" + "-" * 12 + "|" + "-" * 26 + "|") # noqa T201 + for r in train_test_dedup_results: + print( # noqa T201 + f"| {r['dataset']:<20} " + f"| {r['train_size']:>12} " + f"| {r['test_size']:>12} " + f"| {r['deduplicated_test_size']:>24} " + f"| {r['percent_removed']:>10.2f} " + f"| {r['time_seconds']:>24.2f} |" + ) + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() diff --git a/benchmarks/run_benchmarks.py b/benchmarks/run_text_benchmarks.py similarity index 100% rename from benchmarks/run_benchmarks.py rename to benchmarks/run_text_benchmarks.py From 8df6bfba06d9181f4e45b768c545ef254a7e9bb3 Mon Sep 17 00:00:00 2001 From: Pringled Date: Fri, 16 Jan 2026 13:03:56 +0100 Subject: [PATCH 36/37] Updated docs --- README.md | 4 ++-- benchmarks/README.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 546f39b..a73e4e7 100644 --- a/README.md +++ b/README.md @@ -38,9 +38,9 @@ -SemHash is a lightweight library for semantic deduplication, outlier filtering, and representative sample selection. It's fully multimodal: text works out-of-the-box with fast Model2Vec embeddings, and you can bring your own encoders for images, audio, or custom models. +SemHash is a lightweight library for semantic deduplication, outlier filtering, and representative sample selection. It's fully multimodal: text works out-of-the-box with fast [Model2Vec](https://github.com/MinishLab/model2vec) embeddings, and you can bring your own encoders for images, audio, or custom models. -SemHash supports both single-dataset operations (clean a training set) and cross-dataset operations (deduplicate test against train). It works with simple lists and complex multi-column datasets, and includes inspection tools to help you understand and refine results. All operations use Vicinity for efficient similarity search. +SemHash supports both single-dataset operations (clean a training set) and cross-dataset operations (deduplicate test against train). It works with simple lists and complex multi-column datasets, and includes inspection tools to help you understand and refine results. All operations use [Vicinity](https://github.com/MinishLab/vicinity) for efficient similarity search. ## Quickstart diff --git a/benchmarks/README.md b/benchmarks/README.md index 7b629b1..cc3c17b 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -122,7 +122,7 @@ This benchmark measures the performance of deduplicating a test dataset against - **Fashion-MNIST high deduplication**: Fashion-MNIST shows very high duplication rates (72% train, 79% test) due to the simple nature of the dataset (10 clothing categories with similar items) - **CIFAR-10 moderate deduplication**: CIFAR-10 shows lower duplication (3.45% train, 6.03% test) as it contains more diverse natural images -- **Speed**: Image deduplication is fast even for large datasets (60k images in ~87 seconds on MPS) +- **Speed**: Image deduplication is fast even for large datasets (60k images in ~87 seconds on MPS); note that the actual deduplication step is quick, with most time spent on encoding images ### Running Image Benchmarks From 88333b98dfd59cc7cded94d499c8d2c81128ec5f Mon Sep 17 00:00:00 2001 From: Pringled Date: Fri, 16 Jan 2026 13:13:43 +0100 Subject: [PATCH 37/37] Updated docs --- benchmarks/README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/benchmarks/README.md b/benchmarks/README.md index cc3c17b..7ebf432 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -2,6 +2,20 @@ This directory contains the benchmarking code and results for SemHash. The benchmarks measure deduplication performance and speed across a variety of text and image datasets. +## Table of Contents + +- [Text Benchmarks](#text-benchmarks) + - [Setup](#setup) + - [Results](#results) + - [Key Findings](#key-findings) + - [Running Text Benchmarks](#running-text-benchmarks) +- [Image Benchmarks](#image-benchmarks) + - [Setup](#setup-1) + - [Results](#results-1) + - [Key Findings](#key-findings-1) + - [Running Image Benchmarks](#running-image-benchmarks) +- [Running All Benchmarks](#running-all-benchmarks) + ## Text Benchmarks ### Setup