From aac96c18f4a231e79c7c557b531275fff6e8ce56 Mon Sep 17 00:00:00 2001 From: Lee Miller <80222060+dleemiller@users.noreply.github.com> Date: Sun, 27 Oct 2024 19:34:07 -0600 Subject: [PATCH] Delete wordllama/algorithms/topic_model.pyx --- wordllama/algorithms/topic_model.pyx | 173 --------------------------- 1 file changed, 173 deletions(-) delete mode 100644 wordllama/algorithms/topic_model.pyx diff --git a/wordllama/algorithms/topic_model.pyx b/wordllama/algorithms/topic_model.pyx deleted file mode 100644 index 7d724cd..0000000 --- a/wordllama/algorithms/topic_model.pyx +++ /dev/null @@ -1,173 +0,0 @@ -# topic_model.pyx - -# Cython directives for optimization -# Disable bounds checking and wraparound for speed -# Ensure that array accesses are safe -# These can also be set in setup.py if preferred -# cython: boundscheck=False -# cython: wraparound=False -# cython: cdivision=True - -import cython -from cython cimport boundscheck, wraparound, cdivision -import numpy as np -cimport numpy as np - -from collections import Counter -from itertools import islice -import tqdm - -# Type definitions for clarity and performance -ctypedef np.float64_t FLOAT -ctypedef np.int64_t INT - -# Function to generate n-grams (remains in Python for flexibility) -def generate_ngrams(token_ids, n=4): - """ - Generate n-grams from the list of token ids. - - Parameters: - - token_ids: List of token IDs. - - n: The number of tokens in each n-gram. - - Returns: - - A generator of n-grams. - """ - return zip(*(islice(token_ids, i, None) for i in range(n))) - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef list combine_overlapping_ngrams_cython(list ngrams_with_scores, int n, int k): - """ - Combine overlapping n-grams within the top-k window. - - Overlapping is defined as sharing (len(ngram) - 1) tokens. - - Parameters: - - ngrams_with_scores: List of tuples ((ngram), score) sorted by score descending. - - n: The length of the n-grams. - - k: The desired number of combined n-grams. - - Returns: - - List of combined n-grams with their aggregated scores. - """ - cdef list combined_ngrams = [] - cdef set used_token_ids = set() - cdef int i, j, overlap, idx - cdef tuple ngram - cdef float score - cdef tuple existing_ngram - cdef float existing_score - cdef tuple merged_ngram - cdef float merged_score - cdef int token_id - cdef bint overlap_flag - - for i in range(len(ngrams_with_scores)): - if len(combined_ngrams) >= k: - break - ngram, score = ngrams_with_scores[i] - - # Check if any token_id is already used - overlap_flag = False - for token_id in ngram: - if token_id in used_token_ids: - overlap_flag = True - break - - if not overlap_flag: - combined_ngrams.append((ngram, score)) - for token_id in ngram: - used_token_ids.add(token_id) - else: - # Attempt to merge with existing n-grams - for idx in range(len(combined_ngrams)): - existing_ngram, existing_score = combined_ngrams[idx] - overlap = 0 - for j in range(1, n): - # Replace negative indices with positive indices - # existing_ngram[-j:] -> existing_ngram[n - j :] - # ngram[:j] remains the same - if existing_ngram[n - j :] == ngram[:j]: - overlap = j - if overlap == n - 1: - # Merge the n-grams - # Replace ngram[-1] with ngram[n - 1] - merged_ngram = existing_ngram + (ngram[n - 1],) - merged_score = existing_score + score # Aggregation method - combined_ngrams[idx] = (merged_ngram, merged_score) - used_token_ids.add(ngram[n - 1]) - break - - return combined_ngrams[:k] - - -def top_k_token_ngrams(texts, wl, int k=10, int n=3): - """ - Extract the top-k non-overlapping n-grams from the texts. - - Parameters: - - texts: List of texts (each text is a string). - - wl: Language model with tokenizer and embeddings. - - k: Number of top n-grams to return. - - n: The number of tokens in each n-gram. - - Returns: - - List of top-k n-grams with their scores. - """ - # Use Python's Counter since it's optimized and efficient - trigram_counter = Counter() - # Ensure wl.embedding is a NumPy array of type float64 - magnitudes_np = np.linalg.norm(wl.embedding, axis=1, keepdims=True) - # Cast to float64 to match FLOAT - magnitudes = magnitudes_np.astype(np.float64) - cdef np.ndarray[FLOAT, ndim=2] magnitudes_c = magnitudes - - # Iterate over each tokenized text (list of token ids) - for batch in tqdm.tqdm(texts, desc="Processing texts"): - tokenized_text = wl.tokenize([batch]) - for x in tokenized_text: - ngrams = generate_ngrams(x.ids, n) - trigram_counter.update(ngrams) - - # Get the top 10 * k most common n-grams - ngrams = trigram_counter.most_common(10 * k) - importances = [] - counts = [] - cdef tuple ngram - cdef int count - cdef float importance - cdef int i - - for ngram, count in ngrams: - importance = 0.0 - for token_id in ngram: - importance += magnitudes_c[token_id, 0] - importances.append(importance) - counts.append(count) - - iar = np.array(importances, dtype=np.float64) - counts_arr = np.array(counts, dtype=np.float64) - - # Compute scores in Python using NumPy's optimized functions - scores = [] - sorted_iar = np.sort(iar) - sorted_counts = np.sort(counts_arr) - for i in range(len(ngrams)): - p0 = np.searchsorted(sorted_iar, iar[i], side='right') / len(iar) - p1 = np.searchsorted(sorted_counts, counts_arr[i], side='right') / len(counts_arr) - score = (p0 + p1) / 2.0 - scores.append(score) - - # Combine ngrams with their scores - ngrams_with_scores = list(zip([ngram for ngram, _ in ngrams], scores)) - - # Sort ngrams by score in descending order - ngrams_with_scores.sort(key=lambda x: x[1], reverse=True) - - # Combine overlapping ngrams using the Cython-optimized function - combined_ngrams = combine_overlapping_ngrams_cython(ngrams_with_scores, n, k) - - return combined_ngrams -