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import pandas as pd
import numpy as np
import tensorflow as tf
import pickle
import os
import random
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
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!! unzip data.zip
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seq_df = pd.read_table('data/family_classification_sequences.tab')
seq_df.head()
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def make_codones(sseq):
crop = len(sseq) % 3
cropped_seq = sseq[:-crop] if crop > 0 else sseq
return [cropped_seq[i:i+3] for i in range(0, len(cropped_seq), 3)]
def seq_to3(seq):
splittings = [make_codones(seq[i:]) for i in range(3)]
return splittings
def create_all_codones(df):
codones = []
for i in range(df.shape[0]):
row = df.iloc[i, :][0]
codones.extend(seq_to3(row))
return codones
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def read_or_create(read_path, producer):
if os.path.isfile(read_path):
print('reading', read_path)
with open(read_path, 'rb') as fp:
return pickle.load(fp)
result = producer()
print('saving', read_path)
with open(read_path, 'wb') as fp:
pickle.dump(result, fp)
return result
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all_codones = read_or_create(read_path='data/all_codones.pickle',
producer= lambda: create_all_codones(seq_df))
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######################
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def generate_sample(index_words_list, context_window_size):
""" Form training pairs according to the skip-gram model. """
for index_words in index_words_list:
for index, center in enumerate(index_words):
context = random.randint(1, context_window_size)
# get a random target before the center word
for target in index_words[max(0, index - context): index]:
yield center, target
# get a random target after the center wrod
for target in index_words[index + 1: index + context + 1]:
yield center, target
def get_batch(iterator, batch_size):
""" Group a numerical stream into batches and yield them as Numpy arrays. """
while True:
center_batch = np.zeros(batch_size, dtype=np.int32)
target_batch = np.zeros([batch_size, 1], dtype=np.int32)
for index in range(batch_size):
center_batch[index], target_batch[index] = next(iterator)
yield center_batch, target_batch
def flatten(x):
return [item for sublist in x for item in sublist]
def cod_to_dict(cod, dictionary):
return [dictionary[key] for key in cod]
def make_dictionary(all_codones):
flat_codones = flatten(all_codones)
unique_codones = set(flat_codones)
dictionary = {cod: i for i, cod in enumerate(unique_codones)}
return dictionary
def process_data(all_codones, dictionary, batch_size, skip_window):
cod_dicts = [cod_to_dict(cod, dictionary) for cod in all_codones]
single_gen = generate_sample(cod_dicts, context_window_size=skip_window)
batch_gen = get_batch(single_gen, batch_size=batch_size)
return batch_gen
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dictionary = make_dictionary(all_codones)
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BATCH_SIZE = 128
SKIP_WINDOW = 12 # the context window
batch_gen = process_data(all_codones, dictionary, BATCH_SIZE, SKIP_WINDOW)
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######################
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class SkipGramModel:
""" Build the graph for word2vec model """
def __init__(self, vocab_size, embed_size, batch_size, num_sampled, learning_rate):
self.vocab_size = vocab_size
self.embed_size = embed_size
self.batch_size = batch_size
self.num_sampled = num_sampled
self.lr = learning_rate
self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
self.saver = tf.train.Saver() # defaults to saving all variables - in this case embed_matrix, nce_weight, nce_bias
def _create_placeholders(self):
with tf.name_scope("data"):
self.center_words = tf.placeholder(tf.int32, shape=[self.batch_size], name='center_words')
self.target_words = tf.placeholder(tf.int32, shape=[self.batch_size, 1], name='target_words')
def _create_embedding(self):
with tf.name_scope("embed"):
self.embed_matrix = tf.Variable(tf.random_uniform([self.vocab_size,
self.embed_size], -1.0, 1.0),
name='embed_matrix')
def _create_loss(self):
with tf.name_scope("loss"):
embed = tf.nn.embedding_lookup(self.embed_matrix, self.center_words, name='embed')
# construct variables for NCE loss
nce_weight = tf.Variable(tf.truncated_normal([self.vocab_size, self.embed_size],
stddev=1.0 / (self.embed_size ** 0.5)),
name='nce_weight')
nce_bias = tf.Variable(tf.zeros([self.vocab_size]), name='nce_bias')
# define loss function to be NCE loss function
self.loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weight,
biases=nce_bias,
labels=self.target_words,
inputs=embed,
num_sampled=self.num_sampled,
num_classes=self.vocab_size), name='loss')
def _create_optimizer(self):
self.optimizer = tf.train.GradientDescentOptimizer(self.lr).minimize(self.loss,
global_step=self.global_step)
def _create_summaries(self):
with tf.name_scope("summaries"):
tf.summary.scalar("loss", self.loss)
tf.summary.histogram("histogram loss", self.loss)
# because you have several summaries, we should merge them all
# into one op to make it easier to manage
self.summary_op = tf.summary.merge_all()
def build_graph(self):
""" Build the graph for our model """
self._create_placeholders()
self._create_embedding()
self._create_loss()
self._create_optimizer()
self._create_summaries()
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VOCAB_SIZE = 9424
EMBED_SIZE = 100 # dimension of the word embedding vectors
NUM_SAMPLED = 5 # Number of negative examples to sample.
LEARNING_RATE = .9
NUM_TRAIN_STEPS = 100000
SKIP_STEP = 2000
g = tf.Graph()
with g.as_default():
model = SkipGramModel(VOCAB_SIZE, EMBED_SIZE, BATCH_SIZE, NUM_SAMPLED, LEARNING_RATE)
model.build_graph()
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######################
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def make_dir(path):
""" Create a directory if there isn't one already. """
try:
os.mkdir(path)
except OSError:
pass
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def train_model(model, batch_gen, num_train_steps, learning_rate, skip_step):
make_dir('checkpoints')
with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/checkpoint'))
# if that checkpoint exists, restore from checkpoint
if ckpt and os.path.isfile(ckpt.model_checkpoint_path):
model.saver.restore(sess, ckpt.model_checkpoint_path)
total_loss = 0.0 # we use this to calculate late average loss in the last SKIP_STEP steps
writer = tf.summary.FileWriter('improved_graph/lr' + str(learning_rate), sess.graph)
initial_step = model.global_step.eval()
for index in range(initial_step, initial_step + num_train_steps):
centers, targets = next(batch_gen)
feed_dict = {model.center_words: centers, model.target_words: targets}
loss_batch, _, summary = sess.run([model.loss, model.optimizer, model.summary_op],
feed_dict=feed_dict)
writer.add_summary(summary, global_step=index)
total_loss += loss_batch
if (index + 1) % skip_step == 0:
print('Average loss at step {}: {:5.1f}'.format(index, total_loss / skip_step))
total_loss = 0.0
model.saver.save(sess, 'checkpoints/skip-gram', index)
final_embed_matrix = sess.run(model.embed_matrix)
return final_embed_matrix
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final_embed_matrix = train_model(model, batch_gen, NUM_TRAIN_STEPS, LEARNING_RATE, SKIP_STEP)
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######################
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tsne = TSNE(n_components=2, random_state=42)
XX = tsne.fit_transform(final_embed_matrix)
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tsne_df = pd.DataFrame(XX, columns=['x0', 'x1'])
unique_codones = sorted(dictionary, key=dictionary.get)
tsne_df['codone'] = list(unique_codones)
tsne_df.head()
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def plot_tsne_df(df):
plt.figure(figsize=(15, 10))
plt.title('unlabeled encoding', fontsize=20)
plt.scatter(df.x0, df.x1, s=10)
plt.show()
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plot_tsne_df(tsne_df)
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filename = 'data/acid_properties.csv'
props = pd.read_csv(filename)
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######################
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def acid_dict(some_c, props):
prop_by_letter = [props[props.acid == let].iloc[:, 1:] for let in some_c]
df_concat = pd.concat(prop_by_letter)
res = df_concat.mean()
dres = dict(res)
dres['acid'] = some_c
return dres
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save_path = 'data/all_acid_dicts.pickle'
producer = lambda: [acid_dict(some_c, props) for some_c in tsne_df.codone]
all_acid_dicts = read_or_create(save_path, producer)
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all_acid_df = pd.DataFrame(all_acid_dicts)
all_acid_df.head()
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final_df = all_acid_df.join(tsne_df.set_index('codone'), on='acid')
final_df.head()
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def plot_embedding_properties(final_df):
plt.figure(figsize=(25, 20))
for i, p in enumerate(['hydrophobicity', 'mass', 'number_of_atoms', 'volume']):
plt.subplot(2,2,i+1)
plt.title(p, fontsize=25)
plt.scatter(final_df.x0, final_df.x1, c=final_df[p], s=10)
plt.show()
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plot_embedding_properties(final_df)
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######################
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filename = 'data/nice_embed_tsne.csv'
gensim_tsne_df = pd.read_csv(filename, index_col=0)
gensim_tsne_df.columns = ['x0', 'x1', 'codone']
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plot_tsne_df(gensim_tsne_df)
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final_df_nice = all_acid_df.join(gensim_tsne_df.set_index('codone'), on='acid')
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plot_embedding_properties(final_df_nice)
Improve SkipGramModel to archive better embedding for amino acids codones. Visualize your space in the similar style as on the bottom example. You are only allowed to use vanilla tensorflow for this task.
Article with the original research can be found here http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0141287&type=printable
Bonus task(no credit): visualize your embedding space in similar manner as minst example: https://www.tensorflow.org/versions/r0.12/how_tos/embedding_viz/
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