Embedding algorithms, especially word-embedding algorithms, have been one of the recurrent themes of this blog. Word2Vec has been mentioned in a few entries (see this); LDA2Vec has been covered (see this); the mathematical principle of GloVe has been elaborated (see this); I haven’t even covered Facebook’s fasttext; and I have not explained the widely used t-SNE and Kohonen’s map (self-organizing map, SOM).

I have also described the algorithm of Sammon Embedding, (see this) which attempts to capture the likeliness of pairwise Euclidean distances, and I implemented it using Theano. This blog entry is about its implementation in Tensorflow as a demonstration.

Let’s recall the formalism of Sammon Embedding, as outlined in the previous entry:

Assume there are high dimensional data described by $d$-dimensional vectors, $X_i$ where $i=1, 2, \ldots, N$. And they will be mapped into vectors $Y_i$, with dimensions 2 or 3. Denote the distances to be $d_{ij}^{*} = \sqrt{| X_i - X_j|^2}$ and $d_{ij} = \sqrt{| Y_i - Y_j|^2}$. In this problem, $Y_i$ are the variables to be learned. The cost function to minimize is

$E = \frac{1}{c} \sum_{i,

where $c = \sum_{i.

Unlike in previous entry and original paper, I am going to optimize it using first-order gradient optimizer. If you are not familiar with Tensorflow, take a look at some online articles, for example, “Tensorflow demystified.” This demonstration can be found in this Jupyter Notebook in Github.

First of all, import all the libraries required:

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf


Like previously, we want to use the points clustered around at the four nodes of a tetrahedron as an illustration, which is expected to give equidistant clusters. We sample points around them, as shown:

tetrahedron_points = [np.array([0., 0., 0.]), np.array([1., 0., 0.]), np.array([np.cos(np.pi/3), np.sin(np.pi/3), 0.]), np.array([0.5, 0.5/np.sqrt(3), np.sqrt(2./3.)])]

sampled_points = np.concatenate([np.random.multivariate_normal(point, np.eye(3)*0.0001, 10) for point in tetrahedron_points])

init_points = np.concatenate([np.random.multivariate_normal(point[:2], np.eye(2)*0.0001, 10) for point in tetrahedron_points])


Retrieve the number of points, N, and the resulting dimension, d:

N = sampled_points.shape[0]
d = sampled_points.shape[1]


One of the most challenging technical difficulties is to calculate the pairwise distance. Inspired by this StackOverflow thread and Travis Hoppe’s entry on Thomson’s problem, we know it can be computed. Assuming Einstein’s convention of summation over repeated indices, given vectors $a_{ik}$, the distance matrix is:

$D_{ij} = (a_{ik}-a_{jk}) (a_{ik} - a_{jk})^T = a_{ik} a_{ik} + a_{jk} a_{jk} - 2 a_{ik} a_{jk}$,

where the first and last terms are simply the norms of the vectors. After computing the matrix, we will flatten it to vectors, for technical reasons omitted to avoid gradient overflow:

X = tf.placeholder('float')
Xshape = tf.shape(X)

sqX = tf.reduce_sum(X*X, 1)
sqX = tf.reshape(sqX, [-1, 1])
sqDX = sqX - 2*tf.matmul(X, tf.transpose(X)) + tf.transpose(sqX)
sqDXarray = tf.stack([sqDX[i, j] for i in range(N) for j in range(i+1, N)])
DXarray = tf.sqrt(sqDXarray)

Y = tf.Variable(init_points, dtype='float')
sqY = tf.reduce_sum(Y*Y, 1)
sqY = tf.reshape(sqY, [-1, 1])
sqDY = sqY - 2*tf.matmul(Y, tf.transpose(Y)) + tf.transpose(sqY)
sqDYarray = tf.stack([sqDY[i, j] for i in range(N) for j in range(i+1, N)])
DYarray = tf.sqrt(sqDYarray)


And DXarray and DYarray are the vectorized pairwise distances. Then we defined the cost function according to the definition:

Z = tf.reduce_sum(DXarray)*0.5
numerator = tf.reduce_sum(tf.divide(tf.square(DXarray-DYarray), DXarray))*0.5
cost = tf.divide(numerator, Z)


update_rule = tf.assign(Y, Y-0.01*grad_cost/lapl_cost)
init = tf.global_variables_initializer()


The last line initializes all variables in the Tensorflow session when it is run. Then start a Tensorflow session, and initialize all variables globally:

sess = tf.Session()
sess.run(init)


Then run the algorithm:

nbsteps = 1000
c = sess.run(cost, feed_dict={X: sampled_points})
print "epoch: ", -1, " cost = ", c
for i in range(nbsteps):
sess.run(train, feed_dict={X: sampled_points})
c = sess.run(cost, feed_dict={X: sampled_points})
print "epoch: ", i, " cost =


Then extract the points and close the Tensorflow session:

calculated_Y = sess.run(Y, feed_dict={X: sampled_points})
sess.close()


Plot it using matplotlib:

embed1, embed2 = calculated_Y.transpose()
plt.plot(embed1, embed2, 'ro')


This gives, as expected,

This code for Sammon Embedding has been incorporated into the Python package mogu, which is a collection of numerical routines. You can install it, and call:

from mogu.embed import sammon_embedding
calculated_Y = sammon_embedding(sampled_points, init_points)


Embedding has been hot in recent years partly due to the success of Word2Vec, (see demo in my previous entry) although the idea has been around in academia for more than a decade. The idea is to transform a vector of integers into continuous, or embedded, representations. Keras, a Python package that implements neural network models (including the ANN, RNN, CNN etc.) by wrapping Theano or TensorFlow, implemented it, as shown in the example below (which converts a vector of 200 features into a continuous vector of 10):

from keras.layers import Embedding
from keras.models import Sequential

# define and compile the embedding model
model = Sequential()
model.compile('rmsprop', 'mse')  # optimizer: rmsprop; loss function: mean-squared error


We can then convert any features from 0 to 199 into vectors of 20, as shown below:

import numpy as np

model.predict(np.array([10, 90, 151]))


It outputs:

array([[[ 0.02915354,  0.03084954, -0.04160764, -0.01752155, -0.00056815,
-0.02512387, -0.02073313, -0.01154278, -0.00389587, -0.04596512]],

[[ 0.02981793, -0.02618774,  0.04137352, -0.04249889,  0.00456919,
0.04393572,  0.04139435,  0.04415271,  0.02636364, -0.04997493]],

[[ 0.00947296, -0.01643104, -0.03241419, -0.01145032,  0.03437041,
0.00386361, -0.03124221, -0.03837727, -0.04804075, -0.01442516]]])


Of course, one must not omit a similar algorithm called GloVe, developed by the Stanford NLP group. Their codes have been wrapped in both Python (package called glove) and R (library called text2vec).

Besides Word2Vec, there are other word embedding algorithms that try to complement Word2Vec, although many of them are more computationally costly. Previously, I introduced LDA2Vec in my previous entry, an algorithm that combines the locality of words and their global distribution in the corpus. And in fact, word embedding algorithms with a similar ideas are also invented by other scientists, as I have introduced in another entry.

However, there are word embedding algorithms coming out. Since most English words carry more than a single sense, different senses of a word might be best represented by different embedded vectors. Incorporating word sense disambiguation, a method called sense2vec has been introduced by Trask, Michalak, and Liu. (arXiv:1511.06388). Matthew Honnibal wrote a nice blog entry demonstrating its use.

There are also other related work, such as wang2vec that is more sensitive to word orders.

Big Bang Theory (Season 2, Episode 5): Euclid Alternative

DMV staff: Application?
Sheldon: I’m actually more or a theorist.

Note: feature image taken from Big Bang Theory (CBS).

Word2Vec has hit the NLP world for a while, as it is a nice method for word embeddings or word representations. Its use of skip-gram model and deep learning made a big impact too. It has been my favorite toy indeed. However, even though the words do have a correlation across a small segment of text, it is still a local coherence. On the other hand, topic models such as latent Dirichlet allocation (LDA) capture the distribution of words within a topic, and that of topics within a document etc. And it provides a representation of a new document in terms of a topic.

In my previous blog entry, I introduced Chris Moody’s LDA2Vec algorithm (see: his SlideShare). Unfortunately, not many papers or blogs have covered this new algorithm too much despite its potential. The API is not completely well documented yet, although you can see its example from its source code on its Github. In its documentation, it gives an example of deriving topics from an array of random numbers, in its lda2vec/lda2vec.py code:

from lda2vec import LDA2Vec
n_words = 10
n_docs = 15
n_hidden = 8
n_topics = 2
n_obs = 300
words = np.random.randint(n_words, size=(n_obs))
_, counts = np.unique(words, return_counts=True)
model = LDA2Vec(n_words, n_hidden, counts)
model.finalize()
doc_ids = np.arange(n_obs) % n_docs
loss = model.fit_partial(words, 1.0, categorical_features=doc_ids)


A more comprehensive example is in examples/twenty_newsgroup/lda.py .

Besides, LDA2Vec, there are some related research work on topical word embeddings too. A group of Australian and American scientists studied about the topic modeling with pre-trained Word2Vec (or GloVe) before performing LDA. (See: their paper and code) On the other hand, another group with Chinese and Singaporean scientists performs LDA, and then trains a Word2Vec model. (See: their paper and code) And LDA2Vec concatenates the Word2Vec and LDA representation, like an early fusion.

No matter what, representations with LDA models (or related topic modeling such as correlated topic models (CTM)) can be useful even outside NLP. I have found it useful at some intermediate layer of calculation lately.

Both LDA (latent Dirichlet allocation) and Word2Vec are two important algorithms in natural language processing (NLP). LDA is a widely used topic modeling algorithm, which seeks to find the topic distribution in a corpus, and the corresponding word distributions within each topic, with a prior Dirichlet distribution. Word2Vec is a vector-representation model, trained from RNN (recurrent neural network), to seek a continuous representation for words.

They are both very useful, but LDA deals with words and documents globally, and Word2Vec locally (depending on adjacent words in the training data). A LDA vector is so sparse that the users can interpret the topic easily, but it is inflexible. Word2Vec’s representation is not human-interpretable, but it is easy to use. In his slides, Chris Moody recently devises a topic modeling algorithm, called LDA2Vec, which is a hybrid of the two, to get the best out of the two algorithms.

Honestly, I never used this algorithm. I rarely talk about something I didn’t even try, but I want to raise awareness so that more people know about it when I come to use it. To me, it looks like concatenating two vectors with some hyperparameters, but  the source codes rejects this claim. It is a topic model algorithm.

There are not many blogs or papers talking about LDA2Vec yet. I am looking forward to learning more about it when there are more awareness.

Continue reading “LDA2Vec: a hybrid of LDA and Word2Vec”