| """ |
| Word2Vec embedding for fMRI language encoding. |
| |
| Uses the pre-trained Google News Word2Vec model (300-d). |
| Download from: |
| https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing |
| (GoogleNews-vectors-negative300.bin.gz, ~1.5 GB) |
| |
| Place the decompressed .bin file at: |
| lab3/data/raw/GoogleNews-vectors-negative300.bin |
| |
| The pipeline mirrors bow.py: embed each word token → downsample to TR-rate |
| via Lanczos interpolation → trim edges → add temporal lags. |
| """ |
|
|
| import sys |
| import os |
| import numpy as np |
|
|
| sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) |
| from preprocessing import downsample_word_vectors, make_delayed |
|
|
| W2V_DIM = 300 |
| DEFAULT_W2V_PATH = os.path.join( |
| os.path.dirname(__file__), "../../data/raw/GoogleNews-vectors-negative300.bin" |
| ) |
|
|
|
|
| def load_word2vec(model_path: str = DEFAULT_W2V_PATH): |
| """Load the binary Word2Vec model via gensim.""" |
| try: |
| from gensim.models import KeyedVectors |
| except ImportError: |
| raise ImportError("Install gensim: pip install gensim") |
| print(f"Loading Word2Vec from {model_path} …") |
| model = KeyedVectors.load_word2vec_format(model_path, binary=True) |
| return model |
|
|
|
|
| def get_word2vec_vectors(wordseqs: dict, model) -> dict: |
| """Look up each word token in the Word2Vec model. |
| |
| OOV words receive a zero vector. |
| |
| Returns: |
| {story: np.ndarray of shape (num_words, 300)} |
| """ |
| word_vectors = {} |
| for story, ds in wordseqs.items(): |
| vecs = [] |
| for word in ds.data: |
| w = word.lower() |
| if w in model: |
| vecs.append(model[w]) |
| else: |
| vecs.append(np.zeros(W2V_DIM, dtype=np.float32)) |
| word_vectors[story] = np.array(vecs, dtype=np.float32) |
| return word_vectors |
|
|
|
|
| def process_word2vec(stories_train, stories_test, wordseqs, |
| model_path=DEFAULT_W2V_PATH, |
| trim_start=5, trim_end=10, delays=range(1, 5)): |
| """Full Word2Vec pipeline: embed → downsample → trim → lag.""" |
| model = load_word2vec(model_path) |
|
|
| all_stories = list(set(stories_train) | set(stories_test)) |
| word_vectors = get_word2vec_vectors( |
| {s: wordseqs[s] for s in all_stories}, model |
| ) |
|
|
| downsampled = downsample_word_vectors(all_stories, word_vectors, wordseqs) |
|
|
| def _trim_and_lag(stories): |
| mats = [] |
| for story in stories: |
| ds = downsampled[story] |
| trimmed = ds[trim_start: len(ds) - trim_end] |
| lagged = make_delayed(trimmed, list(delays)) |
| mats.append(lagged) |
| return np.vstack(mats) |
|
|
| X_train = _trim_and_lag(stories_train) |
| X_test = _trim_and_lag(stories_test) |
| return X_train, X_test |
|
|
|
|
| if __name__ == "__main__": |
| import pickle |
| wordseqs = pickle.load(open(sys.argv[1], "rb")) |
| train_list = sys.argv[2].split(",") |
| test_list = sys.argv[3].split(",") |
| out_prefix = sys.argv[4] |
| model_path = sys.argv[5] if len(sys.argv) > 5 else DEFAULT_W2V_PATH |
|
|
| X_train, X_test = process_word2vec(train_list, test_list, wordseqs, model_path) |
| np.save(f"{out_prefix}_train_word2vec_embeddings.npy", X_train) |
| np.save(f"{out_prefix}_test_word2vec_embeddings.npy", X_test) |
| print(f"Saved Word2Vec embeddings: train {X_train.shape}, test {X_test.shape}") |
|
|
