How to run tsne on word2vec created from gensim?

Question

I want to visualize a word2vec created from gensim library. I tried sklearn but it seems I need to install a developer version to get it. I tried installing the developer ve

Answer 1

Use the code below, instead of X concat all your word embeddings vertically using numpy.vstack into a matrix X and then fit_transform it.

import numpy as np
from sklearn.manifold import TSNE
X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
model = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
model.fit_transform(X) 

the output of fit_transform has shape vocab_size x 2 so you can visualise it.

vocab = sorted(word2vec_model.get_vocab()) #not sure the exact api
emb_tuple = tuple([word2vec_model[v] for v in vocab])
X = numpy.vstack(emb_tuple)

Answer 2

You don't need a developer version of scikit-learn - just install scikit-learn the usual way via pip or conda.

To access the word vectors created by word2vec simply use the word dictionary as index into the model:

X = model[model.wv.vocab]

Following is a simple but complete code example which loads some newsgroup data, applies very basic data preparation (cleaning and breaking up sentences), trains a word2vec model, reduces the dimensions with t-SNE, and visualizes the output.

from gensim.models.word2vec import Word2Vec
from sklearn.manifold import TSNE
from sklearn.datasets import fetch_20newsgroups
import re
import matplotlib.pyplot as plt

# download example data ( may take a while)
train = fetch_20newsgroups()

def clean(text):
    """Remove posting header, split by sentences and words, keep only letters"""
    lines = re.split('[?!.:]\s', re.sub('^.*Lines: \d+', '', re.sub('\n', ' ', text)))
    return [re.sub('[^a-zA-Z]', ' ', line).lower().split() for line in lines]

sentences = [line for text in train.data for line in clean(text)]

model = Word2Vec(sentences, workers=4, size=100, min_count=50, window=10, sample=1e-3)

print (model.wv.most_similar('memory'))

X = model.wv[model.wv.vocab]

tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(X)

plt.scatter(X_tsne[:, 0], X_tsne[:, 1])
plt.show()