Let's get right into this post! Before you read this post, be sure to check out my original post on word embeddings. In this post, we will be using data from this blog to create and visualize a word embedding.

To recap, let's first examine a small example using the Gensim Word2Vec model:

```
from gensim.models import Word2Vec
# defines some dummy data
sentences = [["cat", "say", "meow"], ["dog", "say", "woof"], ["man", "say", "dam"]]
# creates and trains model
model = Word2Vec(min_count=1, size=10)
model.build_vocab(sentences)
model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs)
# saves the model so that you can load it later with model.load
model.save("basic-word2vec.model")
```

With this word2vec model, we can do things like get the most similar words and get the word vectorized.

```
model.wv.most_similar("dog")
[('woof', 0.3232297897338867),
('dam', 0.14384251832962036),
('man', 0.11316978931427002),
('cat', -0.06251632422208786),
('say', -0.1781214326620102),
('meow', -0.21009384095668793)]
```

```
print(model.wv.get_vector("dog"))
[ 0.04777663 0.01543251 -0.04632503 0.03601828 -0.00572644 0.00553683
-0.04476452 -0.0274465 0.0047655 0.00508591]
```

Using the TSNE method, we can reduce the dimensionality so that we can visualize it.

```
from sklearn.decomposition import IncrementalPCA # inital reduction
from sklearn.manifold import TSNE # final reduction
import numpy as np
def reduce_dimensions(model):
num_dimensions = 2 # final num dimensions (2D, 3D, etc)
vectors = [] # positions in vector space
labels = [] # keep track of words to label our data again later
for word in model.wv.vocab:
vectors.append(model.wv[word])
labels.append(word)
# convert both lists into numpy vectors for reduction
vectors = np.asarray(vectors)
labels = np.asarray(labels)
# reduce using t-SNE
vectors = np.asarray(vectors)
tsne = TSNE(n_components=num_dimensions, random_state=0)
vectors = tsne.fit_transform(vectors)
x_vals = [v[0] for v in vectors]
y_vals = [v[1] for v in vectors]
return x_vals, y_vals, labels
x_vals, y_vals, labels = reduce_dimensions(model)
```

```
print(labels)
print(x_vals)
print(y_vals)
```

```
['cat' 'say' 'meow' 'dog' 'woof' 'man' 'dam']
[-29.594002, -45.996586, 20.368856, 53.92877, -12.437127, 3.9659712, 37.524284]
[60.112713, 11.891685, 70.019325, 31.70431, -26.423267, 21.79772, -16.517805]
```

Due to simplicity, we are using a basic matplotlib scatter plot to visualize it. Since it is messy to visualize every label on larger models, we are reducing the number of labels we are picking to display on the graph. However, in this first example, we can visualize all the labels.

```
import matplotlib.pyplot as plt
import random
def plot_with_matplotlib(x_vals, y_vals, labels, num_to_label):
plt.figure(figsize=(5, 5))
plt.scatter(x_vals, y_vals)
plt.title("Embedding Space")
indices = list(range(len(labels)))
selected_indices = random.sample(indices, num_to_label)
for i in selected_indices:
plt.annotate(labels[i], (x_vals[i], y_vals[i]))
plt.savefig('ex.png')
plot_with_matplotlib(x_vals, y_vals, labels, 7)
```

Since my blog is written in markdown files, we need to extract all the code blocks out of it so that our model does not get fooled by things that are not "English."

```
def process_file(fileName):
result = ""
tempResult = ""
inCodeBlock = False
with open(fileName) as file:
for line in file:
if line.startswith("```"):
inCodeBlock = not inCodeBlock
elif inCodeBlock:
pass
else:
for word in line.split():
if "http" not in word and "media/"not in word:
result = result + " " + word
return result
print(process_file("data/ackermann-function-written-in-java.md"))
"The Ackermann function is a classic example of a function that is not primitive recursive – you cannot solve it using loops like Fibonacci. In other words, you have to use recursion to solve for values of the Ackermann function. For more information on the Ackermann function [click"
```

Although this script takes us most of the way there with removing the code block, we still want to remove stopper words and punctuations. The Gensim library has a function that already does that for us.

```
from gensim import utils
print(utils.simple_preprocess(process_file("data/ackermann-function-written-in-java.md")))
['the', 'ackermann', 'function', 'is', 'classic', 'example', 'of', 'function', 'that', 'is', 'not', 'primitive', 'recursive', 'you', 'cannot', 'solve', 'it', 'using', 'loops', 'like', 'fibonacci', 'in', 'other', 'words', 'you', 'have', 'to', 'use', 'recursion', 'to', 'solve', 'for', 'values', 'of', 'the', 'ackermann', 'function', 'for', 'more', 'information', 'on', 'the', 'ackermann', 'function', 'click']
```

Loading everything in as memory and feeding it into an extensive list like the dummy example, although possible, is not feasible or efficient for large data sets. In Gensim, it is typical to create a Corpus: a corpus is a collection of documents used for training. To generate the corpus based on my prior blog posts, I need to process each blog post and store the cleaned version in a file.

```
import os
file = open("jrtechs.cor", "w+")
for file_name in os.listdir("data"):
file.write(process_file("data/" + file_name) + "\n")
file.close()
```

To read our text version corpus, we create an iterable source that reads out each line from our text file. In our case, each line is an entire blog post.

```
from gensim.test.utils import datapath
class MyCorpus(object):
"""An interator that yields sentences (lists of str)."""
def __iter__(self):
corpus_path = "jrtechs.cor"
for line in open(corpus_path):
# assume there's one document per line, tokens separated by whitespace
yield utils.simple_preprocess(line)
```

Now we can create a new model using our custom corpus. We will want to change the embedding size and the number of epochs we train our model.

```
sentences = MyCorpus()
model = Word2Vec(min_count=1, size=20, sentences=sentences)
model.train(sentences, total_examples=model.corpus_count, epochs=500)
model.save("jrtechs-word2vec-500.model")
```

With our model trained, we can run word similarity algorithms.

```
model.wv.most_similar("however")
[('that', 0.7954616546630859),
('also', 0.7654829025268555),
('but', 0.7392309904098511),
('since', 0.7380496859550476),
('although', 0.7112778425216675),
('why', 0.7025969624519348),
('encouragement', 0.6704230308532715),
('because', 0.6692429184913635),
('faster', 0.6633850932121277),
('so', 0.6504907011985779)]
```

```
model.wv.most_similar("method")
[('function', 0.8110014200210571),
('grouping', 0.7134513854980469),
('import', 0.7052735090255737),
('select', 0.6987707614898682),
('max', 0.6856316328048706),
('authentication', 0.6762576103210449),
('pseudo', 0.663935124874115),
('namespace', 0.6549093723297119),
('matrix', 0.6515613198280334),
('zero', 0.6360148191452026)]
```

`x_vals, y_vals, labels = reduce_dimensions(model)`

If you run the TSNE algorithm with different training parameters, you will get various shapes in your output. I noticed that for my data, if I ran it with five epochs, I got a curvy distribution where if I trained the model with over 100 epochs, I just got a single cluster shape.

`plot_with_matplotlib(x_vals, y_vals, labels, 10)`

If we zoom in on the embedding space, we can get a better idea of what clusters close to each other. This visualization is not perfect because we are trying to visualize 15 dimensions down into a two-dimensional graph.

```
import matplotlib.pyplot as plt
import random
def plot_with_matplotlib(x_vals, y_vals, labels, xmin=3, xmax=7, ymin=3, ymax=7):
plt.figure(figsize=(5, 5))
plt.scatter(x_vals, y_vals)
plt.title("Embedding Space")
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
for x, y, l in zip(x_vals, y_vals, labels):
plt.annotate(l, (x, y))
plt.savefig('smallex.png')
plot_with_matplotlib(x_vals, y_vals, labels)
```

It was impressive the results I got on my limited data scope. The Google news dataset explored last time was far more accurate; however, the model was quite large, and I was unable to run the TSNE algorithm on it without crashing my computer. Training your own word embedding model is useful because words can mean different things in different applications and contexts. Right now, there is a significant push at seeing how we can leverage pre-trained models. Pre-trained models enable you to take google's word2vec model trained on 100 billion words and tweak it using your data to fit your application better. Maybe I'll make that part three of this blog post series on embeddings.