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GloVe embedding for fMRI language encoding.
Uses the pre-trained GloVe 840B 300-d vectors.
Download from:
https://nlp.stanford.edu/data/glove.840B.300d.zip (~2.0 GB compressed)
Place the decompressed text file at:
lab3/data/raw/glove.840B.300d.txt
Alternatively the smaller 6B / 100-d set works too:
https://nlp.stanford.edu/data/glove.6B.zip → glove.6B.100d.txt (100-d)
The pipeline mirrors bow.py and word2vec.py.
"""
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
DEFAULT_GLOVE_PATH = os.path.join(
os.path.dirname(__file__), "../../data/raw/glove.840B.300d.txt"
)
GLOVE_DIM = 300 # change to 100 if using the 6B/100d file
def load_glove(glove_path: str = DEFAULT_GLOVE_PATH) -> dict:
"""Load GloVe vectors from a plain-text file into a dict {word: np.ndarray}."""
print(f"Loading GloVe from {glove_path} …")
embeddings = {}
with open(glove_path, "r", encoding="utf-8") as f:
for line in f:
parts = line.rstrip().split(" ")
word = parts[0]
vec = np.array(parts[1:], dtype=np.float32)
embeddings[word] = vec
print(f"Loaded {len(embeddings):,} GloVe vectors (dim={next(iter(embeddings.values())).shape[0]})")
return embeddings
def get_glove_vectors(wordseqs: dict, glove: dict, dim: int = GLOVE_DIM) -> dict:
"""Look up each word token in the GloVe dictionary.
OOV words receive a zero vector.
Returns:
{story: np.ndarray of shape (num_words, dim)}
"""
word_vectors = {}
oov_total = 0
for story, ds in wordseqs.items():
vecs = []
for word in ds.data:
w = word.lower()
if w in glove:
vecs.append(glove[w])
else:
vecs.append(np.zeros(dim, dtype=np.float32))
oov_total += 1
word_vectors[story] = np.array(vecs, dtype=np.float32)
if oov_total:
print(f"GloVe OOV tokens: {oov_total}")
return word_vectors
def process_glove(stories_train, stories_test, wordseqs,
glove_path=DEFAULT_GLOVE_PATH, dim=GLOVE_DIM,
trim_start=5, trim_end=10, delays=range(1, 5)):
"""Full GloVe pipeline: embed → downsample → trim → lag."""
glove = load_glove(glove_path)
all_stories = list(set(stories_train) | set(stories_test))
word_vectors = get_glove_vectors(
{s: wordseqs[s] for s in all_stories}, glove, dim
)
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]
glove_path = sys.argv[5] if len(sys.argv) > 5 else DEFAULT_GLOVE_PATH
X_train, X_test = process_glove(train_list, test_list, wordseqs, glove_path)
np.save(f"{out_prefix}_train_glove_embeddings.npy", X_train)
np.save(f"{out_prefix}_test_glove_embeddings.npy", X_test)
print(f"Saved GloVe embeddings: train {X_train.shape}, test {X_test.shape}")
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