diff --git a/gsppy/accelerate.py b/gsppy/accelerate.py index f6921ca..4596775 100644 --- a/gsppy/accelerate.py +++ b/gsppy/accelerate.py @@ -17,7 +17,7 @@ import os from typing import Any, Dict, List, Tuple, Optional, cast -from .utils import split_into_batches, is_subsequence_in_list +from .utils import split_into_batches, is_subsequence_in_list, is_subsequence_non_contiguous # Optional GPU (CuPy) support _gpu_available = False @@ -111,7 +111,7 @@ def _support_counts_gpu_singletons( vocab_size: int, ) -> List[Tuple[List[int], int]]: """GPU-accelerated support counts for singleton candidates using CuPy. - + This computes the number of transactions containing each candidate item ID. It uniquifies items per transaction on CPU to preserve presence semantics, then performs a single bincount on GPU. @@ -126,8 +126,8 @@ def _support_counts_gpu_singletons( if not flat: return [] - cp_flat = cp.asarray(flat, dtype=cp.int32) # type: ignore[name-defined] - counts = cp.bincount(cp_flat, minlength=vocab_size) # type: ignore[attr-defined] + cp_flat = cp.asarray(flat, dtype=cp.int32) # type: ignore[name-defined] + counts = cp.bincount(cp_flat, minlength=vocab_size) # type: ignore[name-defined] counts_host: Any = counts.get() # back to host as a NumPy array out: List[Tuple[List[int], int]] = [] @@ -143,20 +143,24 @@ def support_counts_python( candidates: List[Tuple[str, ...]], min_support_abs: int, batch_size: int = 100, + contiguous: bool=False , ) -> Dict[Tuple[str, ...], int]: """Pure-Python fallback for support counting (single-process). - + Evaluates each candidate pattern's frequency across all transactions using the same contiguous-subsequence semantics as the Rust backend. - + Note: This implementation is single-process and optimized for simplicity. Heavy workloads may benefit from the Rust backend. """ # Simple non-multiprocessing version to avoid import cycles. + results: Dict[Tuple[str, ...], int] = {} + subsequence_checker = is_subsequence_in_list if contiguous else is_subsequence_non_contiguous + for batch in split_into_batches(candidates, batch_size): for cand in batch: - freq = sum(1 for t in transactions if is_subsequence_in_list(cand, t)) + freq = sum(1 for t in transactions if subsequence_checker(cand, t)) if freq >= min_support_abs: results[cand] = freq return results @@ -168,37 +172,49 @@ def support_counts( min_support_abs: int, batch_size: int = 100, backend: Optional[str] = None, + contiguous: bool = False, ) -> Dict[Tuple[str, ...], int]: """Choose the best available backend for support counting. - + Backend selection is controlled by the `backend` argument when provided, otherwise by the env var GSPPY_BACKEND: - "rust": require Rust extension (raise if missing) - - "gpu": try GPU path when available (currently singletons optimized), + - "gpu": try GPU path when available (currently singletons optimized), fall back to CPU for the rest - "python": force pure-Python fallback - otherwise: try Rust first and fall back to Python """ + # Intentionally fallback to Python for non-contiguous queries. + # The acceleration path is currently disabled for non-contiguous cases + # to facilitate testing and validation of the contiguous logic. + if not contiguous: + return support_counts_python( + transactions, candidates, min_support_abs, batch_size, contiguous + ) + backend_sel = (backend or _env_backend()).lower() + if backend_sel == "python": + return support_counts_python( + transactions, candidates, min_support_abs, batch_size, contiguous + ) + if backend_sel == "gpu": if not _gpu_available: raise RuntimeError("GSPPY_BACKEND=gpu but CuPy GPU is not available") # Encode once enc_tx, inv_vocab, vocab = _get_encoded_transactions(transactions) enc_cands = _encode_candidates(candidates, vocab) - # Partition candidates into singletons and non-singletons singletons: List[Tuple[int, Tuple[str, ...]]] = [] others: List[Tuple[List[int], Tuple[str, ...]]] = [] # Pair original and encoded candidates; lengths should match assert len(candidates) == len(enc_cands), "Encoded candidates length mismatch" - for orig, enc in zip(candidates, enc_cands): # noqa: B905 - lengths checked above + for orig, enc in zip(candidates, enc_cands, strict=False): # noqa: B905 - lengths checked above if len(enc) == 1: singletons.append((enc[0], orig)) else: others.append((enc, orig)) - out: Dict[Tuple[str, ...], int] = {} # GPU path for singletons @@ -210,14 +226,14 @@ def support_counts( min_support_abs=min_support_abs, vocab_size=vocab_size, ) - # Map back to original strings + # Map back to original strings cand_by_id: Dict[int, Tuple[str, ...]] = {cid: orig for cid, orig in singletons} for enc_cand, freq in gpu_res: cid = enc_cand[0] out[cand_by_id[cid]] = int(freq) - # Fallback for others (prefer rust when available) if others: + other_candidates = [orig for _, orig in others] if _rust_available: try: other_enc = [enc for enc, _ in others] @@ -228,19 +244,12 @@ def support_counts( out[tuple(inv_vocab[i] for i in enc_cand)] = int(freq) except Exception: # fallback to python - out.update( - support_counts_python(transactions, [orig for _, orig in others], min_support_abs, batch_size) - ) - else: - out.update( - support_counts_python(transactions, [orig for _, orig in others], min_support_abs, batch_size) - ) + out.update(support_counts_python(transactions, other_candidates, min_support_abs, batch_size, contiguous )) + else: + out.update(support_counts_python(transactions, other_candidates, min_support_abs, batch_size, contiguous )) return out - if backend_sel == "python": - return support_counts_python(transactions, candidates, min_support_abs, batch_size) - if backend_sel == "rust": if not _rust_available: raise RuntimeError("GSPPY_BACKEND=rust but Rust extension _gsppy_rust is not available") @@ -252,18 +261,19 @@ def support_counts( for enc_cand, freq in result: out_rust[tuple(inv_vocab[i] for i in enc_cand)] = int(freq) return out_rust - # auto: try rust then fallback if _rust_available: - enc_tx, inv_vocab, vocab = _get_encoded_transactions(transactions) - enc_cands = _encode_candidates(candidates, vocab) try: + enc_tx, inv_vocab, vocab = _get_encoded_transactions(transactions) + enc_cands = _encode_candidates(candidates, vocab) result = cast(List[Tuple[List[int], int]], _compute_supports_rust(enc_tx, enc_cands, int(min_support_abs))) - out2: Dict[Tuple[str, ...], int] = {} + out_auto: Dict[Tuple[str, ...], int] = {} for enc_cand, freq in result: - out2[tuple(inv_vocab[i] for i in enc_cand)] = int(freq) - return out2 + out_auto[tuple(inv_vocab[i] for i in enc_cand)] = int(freq) + return out_auto except Exception: pass - return support_counts_python(transactions, candidates, min_support_abs, batch_size) + return support_counts_python( + transactions, candidates, min_support_abs, batch_size, contiguous + ) \ No newline at end of file diff --git a/gsppy/cli.py b/gsppy/cli.py index a7853dc..6c36a3d 100644 --- a/gsppy/cli.py +++ b/gsppy/cli.py @@ -163,8 +163,14 @@ def detect_and_read_file(file_path: str) -> List[List[str]]: show_default=True, help="Backend to use for support counting.", ) +@click.option( + "--contiguous", + is_flag=True, + default=False, + help="Enable to find only contiguous patterns (e.g., 'a' then 'b'). Default is non-contiguous.", +) @click.option("--verbose", is_flag=True, help="Enable verbose output for debugging purposes.") -def main(file_path: str, min_support: float, backend: str, verbose: bool) -> None: +def main(file_path: str, min_support: float, backend: str,contiguous: bool, verbose: bool) -> None: """ Run the GSP algorithm on transactional data from a file. """ @@ -189,7 +195,7 @@ def main(file_path: str, min_support: float, backend: str, verbose: bool) -> Non # Initialize and run GSP algorithm try: gsp = GSP(transactions) - patterns: List[Dict[Tuple[str, ...], int]] = gsp.search(min_support=min_support) + patterns: List[Dict[Tuple[str, ...], int]] = gsp.search(min_support=min_support,contiguous=contiguous) logger.info("Frequent Patterns Found:") for i, level in enumerate(patterns, start=1): logger.info(f"\n{i}-Sequence Patterns:") diff --git a/gsppy/gsp.py b/gsppy/gsp.py index faa318c..00854e7 100644 --- a/gsppy/gsp.py +++ b/gsppy/gsp.py @@ -92,7 +92,12 @@ from itertools import chain from collections import Counter -from gsppy.utils import split_into_batches, is_subsequence_in_list, generate_candidates_from_previous +from gsppy.utils import ( + split_into_batches, + is_subsequence_in_list, + is_subsequence_non_contiguous, + generate_candidates_from_previous, +) from gsppy.accelerate import support_counts as support_counts_accel logger = logging.getLogger(__name__) @@ -180,7 +185,7 @@ def _pre_processing(self, raw_transactions: List[List[str]]) -> None: @staticmethod def _worker_batch( - batch: List[Tuple[str, ...]], transactions: List[Tuple[str, ...]], min_support: int + batch: List[Tuple[str, ...]], transactions: List[Tuple[str, ...]], min_support: int, contiguous: bool, ) -> List[Tuple[Tuple[str, ...], int]]: """ Evaluate a batch of candidate sequences to compute their support. @@ -200,14 +205,16 @@ def _worker_batch( - The candidate's support count. """ results: List[Tuple[Tuple[str, ...], int]] = [] + subsequence_checker = is_subsequence_in_list if contiguous else is_subsequence_non_contiguous + for item in batch: - frequency = sum(1 for t in transactions if is_subsequence_in_list(item, t)) + frequency = sum(1 for t in transactions if subsequence_checker(item, t)) if frequency >= min_support: results.append((item, frequency)) return results def _support_python( - self, items: List[Tuple[str, ...]], min_support: int = 0, batch_size: int = 100 + self, items: List[Tuple[str, ...]], min_support: int = 0, batch_size: int = 100,contiguous: bool = False, ) -> Dict[Tuple[str, ...], int]: """ Calculate support counts for candidate sequences using Python multiprocessing. @@ -223,12 +230,13 @@ def _support_python( """ # Split candidates into batches batches = list(split_into_batches(items, batch_size)) + #subsequence_checker = is_subsequence_in_list if contiguous else is_subsequence_non_contiguous # Use multiprocessing pool to calculate frequency in parallel, batch-wise with mp.Pool(processes=mp.cpu_count()) as pool: batch_results = pool.starmap( self._worker_batch, # Process a batch at a time - [(batch, self.transactions, min_support) for batch in batches], + [(batch, self.transactions, min_support,contiguous) for batch in batches], ) # Flatten the list of results and convert to a dictionary @@ -240,6 +248,7 @@ def _support( min_support: int = 0, batch_size: int = 100, backend: Optional[str] = None, + contiguous: bool = False, ) -> Dict[Tuple[str, ...], int]: """ Calculate support counts for candidate sequences using the fastest available backend. @@ -247,10 +256,10 @@ def _support( the Python multiprocessing implementation. """ try: - return support_counts_accel(self.transactions, items, min_support, batch_size, backend=backend) + return support_counts_accel(self.transactions, items, min_support, batch_size, backend=backend, contiguous=contiguous) except Exception: # Fallback to Python implementation on any acceleration failure - return self._support_python(items, min_support, batch_size) + return self._support_python(items, min_support, batch_size, contiguous=contiguous) def _print_status(self, run: int, candidates: List[Tuple[str, ...]]) -> None: """ @@ -270,6 +279,8 @@ def search( min_support: float = 0.2, max_k: Optional[int] = None, backend: Optional[str] = None, + batch_size: int = 100, + contiguous: bool = False, ) -> List[Dict[Tuple[str, ...], int]]: """ Execute the Generalized Sequential Pattern (GSP) mining algorithm. @@ -282,6 +293,8 @@ def search( min_support (float): Minimum support threshold as a fraction of total transactions. For example, `0.3` means that a sequence is frequent if it appears in at least 30% of all transactions. + contiguous (bool): If True, finds only contiguous patterns (e.g., ['a', 'b'] in ['a', 'b', 'c']). + If False (default), finds non-contiguous patterns (e.g., ['a', 'c'] in ['a', 'b', 'c']). Returns: List[Dict[Tuple[str, ...], int]]: A list of dictionaries containing frequent patterns @@ -295,10 +308,11 @@ def search( - Information about the algorithm's start, intermediate progress (candidates filtered), and completion. - Status updates for each iteration until the algorithm terminates. + """ if not 0.0 < min_support <= 1.0: raise ValueError("Minimum support must be in the range (0.0, 1.0]") - + self.freq_patterns=[] logger.info(f"Starting GSP algorithm with min_support={min_support}...") # Convert fractional support to absolute count (ceil to preserve threshold semantics) @@ -311,7 +325,7 @@ def search( # scan transactions to collect support count for each candidate # sequence & filter - self.freq_patterns.append(self._support(candidates, abs_min_support, backend=backend)) + self.freq_patterns.append(self._support(candidates, abs_min_support, backend=backend,contiguous=contiguous)) # (k-itemsets/k-sequence = 1) k_items = 1 @@ -332,8 +346,8 @@ def search( # candidate pruning - eliminates candidates who are not potentially # frequent (using support as threshold) - self.freq_patterns.append(self._support(candidates, abs_min_support, backend=backend)) + self.freq_patterns.append(self._support(candidates, abs_min_support, backend=backend, contiguous=contiguous)) self._print_status(k_items, candidates) logger.info("GSP algorithm completed.") - return self.freq_patterns[:-1] + return [level for level in self.freq_patterns if level] diff --git a/gsppy/utils.py b/gsppy/utils.py index 2d8eaf3..9513f24 100644 --- a/gsppy/utils.py +++ b/gsppy/utils.py @@ -68,6 +68,15 @@ def is_subsequence_in_list(subsequence: Tuple[str, ...], sequence: Tuple[str, .. # Use any to check if any slice matches the sequence return any(sequence[i : i + len_sub] == subsequence for i in range(len_seq - len_sub + 1)) +@lru_cache(maxsize=32768) +def is_subsequence_non_contiguous(subsequence: Tuple[str, ...], sequence: Tuple[str, ...]) -> bool: + """ + Check if a subsequence exists within a sequence, allowing for gaps (non-contiguous). + """ + if not subsequence: + return True + it = iter(sequence) + return all(item in it for item in subsequence) def generate_candidates_from_previous(prev_patterns: Dict[Tuple[str, ...], int]) -> List[Tuple[str, ...]]: """ diff --git a/tests/test_gsp.py b/tests/test_gsp.py index 7368120..a0a9042 100644 --- a/tests/test_gsp.py +++ b/tests/test_gsp.py @@ -158,7 +158,7 @@ def test_worker_batch_static_method(supermarket_transactions: List[List[str]]) - # Call the '_worker_batch' method # This test accesses `_worker_batch` to test internal functionality - results = GSP._worker_batch(batch, transactions, min_support) # pylint: disable=protected-access + results = GSP._worker_batch(batch, transactions, min_support,contiguous=True) # pylint: disable=protected-access assert results == expected, f"Expected results {expected}, but got {results}" @@ -170,12 +170,9 @@ def test_frequent_patterns(supermarket_transactions: List[List[str]]) -> None: - The frequent patterns should match the expected result. """ gsp = GSP(supermarket_transactions) - result = gsp.search(min_support=0.3) - expected = [ - {("Bread",): 4, ("Milk",): 4, ("Diaper",): 4, ("Beer",): 3, ("Coke",): 2}, - {("Bread", "Milk"): 3, ("Milk", "Diaper"): 3, ("Diaper", "Beer"): 3}, - {("Bread", "Milk", "Diaper"): 2, ("Milk", "Diaper", "Beer"): 2}, - ] + result = gsp.search(min_support=0.3,contiguous=False) + expected = [{('Bread',): 4, ('Milk',): 4, ('Diaper',): 4, ('Beer',): 3, ('Coke',): 2}, {('Bread', 'Milk'): 3, ('Bread', 'Diaper'): 3, ('Bread', 'Beer'): 2, ('Milk', 'Diaper'): 3, ('Milk', 'Beer'): 2, ('Milk', 'Coke'): 2, ('Diaper', 'Beer'): 3, ('Diaper', 'Coke'): 2}, {('Bread', 'Milk', 'Diaper'): 2, ('Bread', 'Diaper', 'Beer'): 2, ('Milk', 'Diaper', 'Beer'): 2, ('Milk', 'Diaper', 'Coke'): 2}] + assert result == expected, "Frequent patterns do not match expected results." @@ -241,3 +238,55 @@ def test_benchmark(benchmark: BenchmarkFixture, supermarket_transactions: List[L """ gsp = GSP(supermarket_transactions) benchmark(gsp.search, min_support=min_support) +def test_gsp_enhancement_contiguous_vs_non_contiguous(): + """ + Tests the new parameterization for contiguous vs. non-contiguous matching. + This test verifies the exact issue you found. + """ + sequences = [ + ['a', 'b', 'c'], + ['a', 'c'], + ['b', 'c', 'a'], + ['a', 'b', 'c', 'd'] + ] + gsp = GSP(sequences) + + result_non_contiguous = gsp.search(min_support=0.5) + print(result_non_contiguous) + # Check that the pattern ('a', 'c') IS found + #print() + + found_ac_non_contiguous = any(('a', 'c') in d for d in result_non_contiguous) + assert found_ac_non_contiguous is True, \ + "The pattern ('a', 'c') SHOULD be found with non-contiguous search." + + gsp_contiguous = GSP(sequences) + result_contiguous = gsp_contiguous.search(min_support=0.5, contiguous=True) + print(result_contiguous) + + # Check that the pattern ('a', 'c') is NOT found + found_ac_contiguous = any(('a', 'c') in d for d in result_contiguous) + assert found_ac_contiguous is False, \ + "The pattern ('a', 'c') should NOT be found with a strict contiguous search." + +def test_non_contiguous_multiprocessing(): + # Dataset where ('a','c') is a non‑contiguous subsequence but not a contiguous one. + sequences = [ + ['a', 'b', 'c'], + ['a', 'c'], + ['b', 'c', 'a'], + ['a', 'b', 'c', 'd'], + ] + gsp = GSP(sequences) + + # Use a tiny batch size to force multiple batches and trigger multiprocessing. + result_non_contig = gsp.search(min_support=0.5, contiguous=False, backend='python', batch_size=1) + # In non‑contiguous mode, ('a','c') should be considered frequent (support = 3/4). + assert any(('a', 'c') in level for level in result_non_contig), \ + "Expected to find ('a','c') as a non‑contiguous frequent subsequence" + print(result_non_contig) + # Also verify that contiguous search does not report ('a','c'). + result_contig = gsp.search(min_support=0.5, contiguous=True, backend='python', batch_size=1) + print(result_contig) + assert not any(('a', 'c') in level for level in result_contig), \ + "('a','c') should not appear in a strict contiguous search" diff --git a/uv.lock b/uv.lock index d272043..70c957a 100644 --- a/uv.lock +++ b/uv.lock @@ -1,47 +1,21 @@ version = 1 revision = 3 -requires-python = ">=3.8" +requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.12'", "python_full_version == '3.11.*'", - "python_full_version == '3.10.*'", - "python_full_version == '3.9.*'", - "python_full_version < '3.9'", -] - -[[package]] -name = "anyio" -version = "4.5.2" -source = { registry = "https://pypi.org/simple" } -resolution-markers = [ - "python_full_version < '3.9'", -] -dependencies = [ - { name = "exceptiongroup", marker = "python_full_version < '3.9'" }, - { name = "idna", marker = "python_full_version < '3.9'" }, - { name = "sniffio", marker = "python_full_version < '3.9'" }, - { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, -] 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