Tutorial 1: Introduction to processing time series
Contents
Tutorial 1: Introduction to processing time series¶
Week 2, Day 5: Time Series And Natural Language Processing
By Neuromatch Academy
Content creators: Lyle Ungar, Kelson Shilling-Scrivo, Alish Dipani
Content reviewers: Kelson Shilling-Scrivo
Content editors: Gagana B, Spiros Chavlis, Kelson Shilling-Scrivo
Production editors: Gagana B, Spiros Chavlis
Based on Content from: Anushree Hede, Pooja Consul, Ann-Katrin Reuel
Tutorial objectives¶
Before we begin exploring how Recurrent Neural Networks (RNNs) excel at modeling sequences, we will explore other ways we can model sequences, encode text, and make meaningful measurements using such encodings and embeddings.
Setup¶
Install dependencies¶
There may be errors and/or warnings reported during the installation. However, they are to be ignored.
# @title Install dependencies
# @markdown There may be *errors* and/or *warnings* reported during the installation. However, they are to be ignored.
!pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 torchtext==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html --quiet
!pip install --upgrade gensim --quiet
!pip install nltk --quiet
!pip install python-Levenshtein --quiet
!pip install git+https://github.com/NeuromatchAcademy/evaltools --quiet
from evaltools.airtable import AirtableForm
atform = AirtableForm('appn7VdPRseSoMXEG', 'W2D5_T1', 'https://portal.neuromatchacademy.org/api/redirect/to/9c55f6cb-cdf9-4429-ac1c-ec44fe64c303')
Install fastText¶
If you want to see the original code, go to repo: https://github.com/facebookresearch/fastText.git
# @title Install fastText
# @markdown If you want to see the original code, go to repo: https://github.com/facebookresearch/fastText.git
# !pip install git+https://github.com/facebookresearch/fastText.git --quiet
import os, zipfile, requests
url = "https://osf.io/vkuz7/download"
fname = "fastText-main.zip"
print('Downloading Started...')
# Downloading the file by sending the request to the URL
r = requests.get(url, stream=True)
# Writing the file to the local file system
with open(fname, 'wb') as f:
f.write(r.content)
print('Downloading Completed.')
# opening the zip file in READ mode
with zipfile.ZipFile(fname, 'r') as zipObj:
# extracting all the files
print('Extracting all the files now...')
zipObj.extractall()
print('Done!')
os.remove(fname)
# Install the package
!pip install fastText-main/ --quiet
Downloading Started...
Downloading Completed.
Extracting all the files now...
Done!
# Imports
import time
import nltk
import fasttext
import numpy as np
import matplotlib.pyplot as plt
from nltk.corpus import brown
from nltk.tokenize import word_tokenize
from gensim.models import Word2Vec
import torch.nn as nn
from torch.nn import functional as F
from torchtext.legacy import data, datasets
from torchtext.vocab import FastText
Figure Settings¶
# @title Figure Settings
import ipywidgets as widgets
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
plt.style.use("https://raw.githubusercontent.com/NeuromatchAcademy/content-creation/main/nma.mplstyle")
Load Dataset from nltk¶
# @title Load Dataset from `nltk`
# No critical warnings, so we suppress it
import warnings
warnings.simplefilter("ignore")
nltk.download('punkt')
nltk.download('brown')
True
Helper functions¶
# @title Helper functions
import requests
def download_file_from_google_drive(id, destination):
URL = "https://docs.google.com/uc?export=download"
session = requests.Session()
response = session.get(URL, params={'id': id}, stream=True)
token = get_confirm_token(response)
if token:
params = {'id': id, 'confirm': token}
response = session.get(URL, params=params, stream=True)
save_response_content(response, destination)
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
def save_response_content(response, destination):
CHUNK_SIZE = 32768
with open(destination, "wb") as f:
for chunk in response.iter_content(CHUNK_SIZE):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
Set random seed¶
Executing set_seed(seed=seed) you are setting the seed
# @title Set random seed
# @markdown Executing `set_seed(seed=seed)` you are setting the seed
# For DL its critical to set the random seed so that students can have a
# baseline to compare their results to expected results.
# Read more here: https://pytorch.org/docs/stable/notes/randomness.html
# Call `set_seed` function in the exercises to ensure reproducibility.
import random
import torch
def set_seed(seed=None, seed_torch=True):
"""
Function that controls randomness.
NumPy and random modules must be imported.
Args:
seed : Integer
A non-negative integer that defines the random state. Default is `None`.
seed_torch : Boolean
If `True` sets the random seed for pytorch tensors, so pytorch module
must be imported. Default is `True`.
Returns:
Nothing.
"""
if seed is None:
seed = np.random.choice(2 ** 32)
random.seed(seed)
np.random.seed(seed)
if seed_torch:
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
print(f'Random seed {seed} has been set.')
# In case that `DataLoader` is used
def seed_worker(worker_id):
"""
DataLoader will reseed workers following randomness in
multi-process data loading algorithm.
Args:
worker_id: integer
ID of subprocess to seed. 0 means that
the data will be loaded in the main process
Refer: https://pytorch.org/docs/stable/data.html#data-loading-randomness for more details
Returns:
Nothing
"""
worker_seed = torch.initial_seed() % 2**32
np.random.seed(worker_seed)
random.seed(worker_seed)
Set device (GPU or CPU). Execute set_device()¶
# @title Set device (GPU or CPU). Execute `set_device()`
# Inform the user if the notebook uses GPU or CPU.
def set_device():
"""
Set the device. CUDA if available, CPU otherwise
Args:
None
Returns:
Nothing
"""
device = "cuda" if torch.cuda.is_available() else "cpu"
if device != "cuda":
print("WARNING: For this notebook to perform best, "
"if possible, in the menu under `Runtime` -> "
"`Change runtime type.` select `GPU` ")
else:
print("GPU is enabled in this notebook.")
return device
DEVICE = set_device()
SEED = 2021
set_seed(seed=SEED)
WARNING: For this notebook to perform best, if possible, in the menu under `Runtime` -> `Change runtime type.` select `GPU`
Random seed 2021 has been set.
Section 1: Intro: What time series are there?¶
Time estimate: 20 mins
Video 1: Time Series and NLP¶
Video 2: What is NLP?¶
Section 2: Embeddings¶
Time estimate: 50 mins
Video 3: Embeddings Rule!¶
Section 2.1: Introduction¶
Word2vec is a group of related models used to produce word embeddings. These models are shallow, two-layer neural networks trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, with each unique word in the corpus being assigned a corresponding vector in the space.
Creating Word Embeddings¶
We will create embeddings for a subset of categories in Brown corpus. To achieve this task we will use gensim library to create word2vec embeddings. Gensim’s word2vec expects a sequence of sentences as its input. Each sentence is a list of words.
Calling Word2Vec(sentences, iter=1) will run two passes over the sentences iterator (generally, iter+1 passes). The first pass collects words and their frequencies to build an internal dictionary tree structure. The second and subsequent passes train the neural model.
Word2vec accepts several parameters that affect both training speed and quality.
One of them is for pruning the internal dictionary. Words that appear only once or twice in a billion-word corpus are probably uninteresting typos and garbage. In addition, there are not enough data to make any meaningful training on those words, so it’s best to ignore them:
model = Word2Vec(sentences, min_count=10) # default value is 5
A reasonable value for min_count is bewteen 0-100, depending on the size of your dataset.
Another parameter is the size of the NN layers, which correspond to the “degrees” of freedom the training algorithm has:
model = Word2Vec(sentences, size=200) # default value is 100
Bigger size values require more training data but can lead to better (more accurate) models. Reasonable values are in the tens to hundreds.
The last of the major parameters (full list here) is for training parallelization, to speed up training:
model = Word2Vec(sentences, workers=4) # default = 1 worker = no parallelization
# Categories used for the Brown corpus
category = ['editorial', 'fiction', 'government', 'mystery', 'news', 'religion',
'reviews', 'romance', 'science_fiction']
Word2Vec model
# @markdown Word2Vec model
def create_word2vec_model(category='news', size=50, sg=1, min_count=5):
sentences = brown.sents(categories=category)
model = Word2Vec(sentences, vector_size=size,
sg=sg, min_count=min_count)
return model
def model_dictionary(model):
print(w2vmodel.wv)
words = list(w2vmodel.wv)
return words
def get_embedding(word, model):
if word in w2vmodel.wv:
return model.wv[word]
else:
return None
The cell will take 30-45 seconds to run.
# Create a word2vec model based on categories from Brown corpus
w2vmodel = create_word2vec_model(category)
You can get the embedding vector for a word in the dictionary.
# get word list from Brown corpus
brown_wordlist = list(brown.words(categories=category))
# generate a random word
random_word = random.sample(brown_wordlist, 1)[0]
# get embedding of the random word
random_word_embedding = get_embedding(random_word, w2vmodel)
print(f'Embedding of "{random_word}" is {random_word_embedding}')
Embedding of "company" is [ 0.23516876 -0.15470655 -0.01185721 0.12713455 -0.26822397 0.17749003
0.32861358 0.05686535 0.08575693 -0.04536838 0.15720847 0.13255735
0.1601964 0.01249241 -0.33314878 0.08529221 0.31413293 0.02816943
0.01610847 -0.16778266 0.24895108 -0.12095504 0.1894225 -0.14280955
-0.03438921 0.28925756 -0.3131631 0.05379526 -0.28573197 0.00451465
0.1953645 -0.07176255 0.5144542 0.02566252 0.01421514 -0.04448826
0.37668723 0.26506782 0.33221996 -0.37479606 0.18085435 0.09222476
-0.4489369 -0.06901337 0.39898348 0.02793003 0.1467646 -0.15631539
0.21513721 0.3699941 ]
Visualizing Word Embeddings¶
We can now obtain the word embeddings for any word in the dictionary using word2vec. Let’s visualize these embeddings to get an intuition of what these embeddings mean. The word embeddings obtained from the word2vec model are in high dimensional space, and we will use tSNE to pick the two features that capture the most variance in the embeddings to represent them in a 2D space.
For each word in keys, we pick the top 10 similar words (using cosine similarity) and plot them.
Before you run the code, spend some time to think:
What should be the arrangement of similar words?
What should be the arrangement of the critical clusters with respect to each other?
keys = ['voters', 'magic', 'love', 'God', 'evidence', 'administration', 'governments']
# @markdown ### Cluster embeddings related functions
# @markdown **Note:** We import [sklearn.manifold.TSNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)
from sklearn.manifold import TSNE
import matplotlib.cm as cm
def get_cluster_embeddings(keys):
embedding_clusters = []
word_clusters = []
# find closest words and add them to cluster
for word in keys:
embeddings = []
words = []
if not word in w2vmodel.wv:
print(f'The word {word} is not in the dictionary')
continue
for similar_word, _ in w2vmodel.wv.most_similar(word, topn=10):
words.append(similar_word)
embeddings.append(w2vmodel.wv[similar_word])
embeddings.append(get_embedding(word, w2vmodel))
words.append(word)
embedding_clusters.append(embeddings)
word_clusters.append(words)
# get embeddings for the words in clusers
embedding_clusters = np.array(embedding_clusters)
n, m, k = embedding_clusters.shape
tsne_model_en_2d = TSNE(perplexity=10, n_components=2, init='pca', n_iter=3500, random_state=32)
embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)
return embeddings_en_2d, word_clusters
def tsne_plot_similar_words(title, labels, embedding_clusters,
word_clusters, opacity, filename=None):
plt.figure(figsize=(16, 9))
colors = cm.rainbow(np.linspace(0, 1, len(labels)))
for label, embeddings, words, color in zip(labels, embedding_clusters, word_clusters, colors):
x = embeddings[:, 0]
y = embeddings[:, 1]
plt.scatter(x, y, color=color, alpha=opacity, label=label)
# Plot the cluster centroids
plt.plot(np.mean(x), np.mean(y), 'x', color=color, markersize=16)
for i, word in enumerate(words):
size = 10 if i < 10 else 14
plt.annotate(word, alpha=0.5, xy=(x[i], y[i]), xytext=(5, 2),
textcoords='offset points',
ha='right', va='bottom', size=size)
plt.legend()
plt.title(title)
plt.grid(True)
if filename:
plt.savefig(filename, format='png', dpi=150, bbox_inches='tight')
plt.show()
# Get closest words to the keys and get clusters of these words
embeddings_en_2d, word_clusters = get_cluster_embeddings(keys)
# tSNE plot of similar words to keys
tsne_plot_similar_words(title='Similar words from Brown Corpus',
labels=keys,
embedding_clusters=embeddings_en_2d,
word_clusters=word_clusters,
opacity=0.7,
filename='similar_words.png')
Think! 2.1¶
What does having higher similarity between two word embeddings mean?
Why are cluster centroids (represented with X in the plot) close to some keys (represented with larger fonts) but farther from others?
Student Response¶
# @title Student Response
from ipywidgets import widgets
text=widgets.Textarea(
value='Type your answer here and click on `Submit!`',
placeholder='Type something',
description='',
disabled=False
)
button = widgets.Button(description="Submit!")
display(text,button)
def on_button_clicked(b):
atform.add_answer('q1' , text.value)
print("Submission successful!")
button.on_click(on_button_clicked)
Section 2.2: Embedding exploration¶
Video 4: NLP tokenization¶
Video 5: Distributional Similarity¶
Words or subword units such as morphemes are the basic units we use to express meaning in language. The technique of mapping words to vectors of real numbers is known as word embedding.
In this section, we will use pretrained fastText embeddings, a context-oblivious embedding similar to word2vec.
Embedding Manipulation¶
Let’s use the FastText library to manipulate the embeddings. First, find the embedding for the word “King”
# @markdown ### Download FastText English Embeddings of dimension 100
# @markdown This will take 1-2 minutes to run
import os, zipfile, requests
url = "https://osf.io/2frqg/download"
fname = "cc.en.100.bin.gz"
print('Downloading Started...')
# Downloading the file by sending the request to the URL
r = requests.get(url, stream=True)
# Writing the file to the local file system
with open(fname, 'wb') as f:
f.write(r.content)
print('Downloading Completed.')
# opening the zip file in READ mode
with zipfile.ZipFile(fname, 'r') as zipObj:
# extracting all the files
print('Extracting all the files now...')
zipObj.extractall()
print('Done!')
os.remove(fname)
Downloading Started...
Downloading Completed.
Extracting all the files now...
Done!
# Load 100 dimension FastText Vectors using FastText library
ft_en_vectors = fasttext.load_model('cc.en.100.bin')
print(f"Length of the embedding is: {len(ft_en_vectors.get_word_vector('king'))}")
print(f"\nEmbedding for the word King is:\n {ft_en_vectors.get_word_vector('king')}")
Length of the embedding is: 100
Embedding for the word King is:
[-0.04045481 -0.10617249 -0.27222311 0.06879666 0.16408321 0.00276707
0.27080125 -0.05805573 -0.31865698 0.03748008 -0.00254088 0.13805169
-0.00182498 -0.08973497 0.00319015 -0.19619396 -0.09858181 -0.10103802
-0.08279888 0.0082208 0.13119364 -0.15956607 0.17203182 0.0315701
-0.25064597 0.06182072 0.03929246 0.05157393 0.03543638 0.13660161
0.05473648 0.06072914 -0.04709269 0.17394426 -0.02101276 -0.11402624
-0.24489872 -0.08576579 -0.00322696 -0.04509873 -0.00614253 -0.05772085
-0.073414 -0.06718913 -0.06057961 0.10963406 0.1245006 -0.04819863
0.11408057 0.11081408 0.06752145 -0.01689911 -0.01186301 -0.11716368
-0.01287614 0.10639337 -0.04243141 0.01057278 -0.0230855 -0.04930984
0.04717607 0.03696446 0.0015999 -0.02193867 -0.01331578 0.11102925
0.1686794 0.05814958 -0.00296521 -0.04252011 -0.00352389 0.06267346
-0.07747819 -0.08959802 -0.02445797 -0.08913022 0.13422231 0.1258949
-0.01296814 0.0531218 -0.00541025 -0.16908626 0.06323182 -0.11510128
-0.08352032 -0.07224389 0.01023453 0.08263734 -0.03859017 -0.00798539
-0.01498295 0.05448429 0.02708506 0.00549948 0.14634523 -0.12550676
0.04641578 -0.10164826 0.05370862 0.01217492]
Cosine similarity is used for similarities between words. Similarity is a scalar between 0 and 1. Higher scalar value corresponds to higher similarity.
Now find the 10 most similar words to “king”.
ft_en_vectors.get_nearest_neighbors("king", 10) # Most similar by key
[(0.8168574571609497, 'prince'),
(0.796097457408905, 'emperor'),
(0.7907207608222961, 'kings'),
(0.7655220627784729, 'lord'),
(0.7435404062271118, 'king-'),
(0.7394551634788513, 'chieftain'),
(0.7307553291320801, 'tyrant'),
(0.7226710319519043, 'conqueror'),
(0.719561755657196, 'kingly'),
(0.718187689781189, 'queen')]
Word Similarity¶
More on similarity between words. Let’s check how similar different pairs of word are.
def cosine_similarity(vec_a, vec_b):
"""Compute cosine similarity between vec_a and vec_b"""
return np.dot(vec_a, vec_b) / (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))
def getSimilarity(word1, word2):
v1 = ft_en_vectors.get_word_vector(word1)
v2 = ft_en_vectors.get_word_vector(word2)
return cosine_similarity(v1, v2)
print(f"Similarity between the words King and Queen: {getSimilarity('king', 'queen')}")
print(f"Similarity between the words King and Knight: {getSimilarity('king', 'knight')}")
print(f"Similarity between the words King and Rock: {getSimilarity('king', 'rock')}")
print(f"Similarity between the words King and Twenty: {getSimilarity('king', 'twenty')}")
print(f"\nSimilarity between the words Dog and Cat: {getSimilarity('dog', 'cat')}")
print(f"Similarity between the words Ascending and Descending: {getSimilarity('ascending', 'descending')}")
print(f"Similarity between the words Victory and Defeat: {getSimilarity('victory', 'defeat')}")
print(f"Similarity between the words Less and More: {getSimilarity('less', 'more')}")
print(f"Similarity between the words True and False: {getSimilarity('true', 'false')}")
Similarity between the words King and Queen: 0.7181877493858337
Similarity between the words King and Knight: 0.6881008744239807
Similarity between the words King and Rock: 0.2892838716506958
Similarity between the words King and Twenty: 0.19655467569828033
Similarity between the words Dog and Cat: 0.833964467048645
Similarity between the words Ascending and Descending: 0.8707448840141296
Similarity between the words Victory and Defeat: 0.7478055953979492
Similarity between the words Less and More: 0.8461978435516357
Similarity between the words True and False: 0.595384955406189
Interactive Demo 2.2.1¶
Check similarity between words
# @title Interactive Demo 2.2.1
# @markdown Check similarity between words
word1 = 'King' # @param \ {type:"string"}
word2 = 'Frog' # @param \ {type:"string"}
word_similarity = getSimilarity(word1, word2)
print(f'Similarity between {word1} and {word2}: {word_similarity}')
Similarity between King and Frog: 0.5649225115776062
Using embeddings, we can find the words that appear in similar contexts. But, what happens if the word has several different meanings?
Homonym Similarity¶
Homonyms are words that have the same spelling or pronunciation but different meanings depending on the context. Let’s explore how these words are embedded and their similarity in different contexts.
####################### Words with multiple meanings ##########################
print(f"Similarity between the words Cricket and Insect: {getSimilarity('cricket', 'insect')}")
print(f"Similarity between the words Cricket and Sport: {getSimilarity('cricket', 'sport')}")
Similarity between the words Cricket and Insect: 0.4072215259075165
Similarity between the words Cricket and Sport: 0.5812374353408813
Interactive Demo 2.2.2¶
# @title Interactive Demo 2.2.2
# @markdown Explore homonyms \\
# @markdown examples - minute (time/small), pie (graph/food)
word = 'minute' # @param \ {type:"string"}
context_word_1 = 'time' # @param \ {type:"string"}
context_word_2 = 'small' # @param \ {type:"string"}
word_similarity_1 = getSimilarity(word, context_word_1)
word_similarity_2 = getSimilarity(word, context_word_2)
print(f'Similarity between {word} and {context_word_1}: {word_similarity_1}')
print(f'Similarity between {word} and {context_word_2}: {word_similarity_2}')
Similarity between minute and time: 0.7297980785369873
Similarity between minute and small: 0.340322345495224
Word Analogies¶
Embeddings can be used to find word analogies. Let’s try it:
Man : Woman :: King : _____
Germany: Berlin :: France : _____
Leaf : Tree :: Petal : _____
## Use get_analogies() funnction.
# The words have to be in the order Positive, negative, Positve
# Man : Woman :: King : _____
# Positive=(woman, king), Negative=(man)
print(ft_en_vectors.get_analogies("woman", "man", "king", 1))
# Germany: Berlin :: France : ______
# Positive=(berlin, frannce), Negative=(germany)
print(ft_en_vectors.get_analogies("berlin", "germany", "france", 1))
# Leaf : Tree :: Petal : _____
# Positive=(tree, petal), Negative=(leaf)
print(ft_en_vectors.get_analogies("tree", "leaf", "petal", 1))
[(0.8162637948989868, 'queen')]
[(0.8568049669265747, 'paris')]
[(0.7037209272384644, 'flower')]
But, does it always work?
Poverty : Wealth :: Sickness : _____
train : board :: horse : _____
# Poverty : Wealth :: Sickness : _____
print(ft_en_vectors.get_analogies("wealth", "poverty", "sickness", 1))
# train : board :: horse : _____
print(ft_en_vectors.get_analogies("board", "train", "horse", 1))
[(0.615874171257019, 'affliction')]
[(0.5437814593315125, 'bull')]
Section 2.3: Neural Net with word embeddings¶
Video 6: Using Embeddings¶
Training context-oblivious word embeddings is relatively cheap, but most people still use pre-trained word embeddings. After we cover context-sensitive word embeddings, we’ll see how to “fine tune” embeddings (adjust them to the task at hand).
Let’s use the pretrained FastText embeddings to train a neural network on the IMDB dataset.
The data consists of reviews and sentiments attached to it, and it is a binary classification task.
Coding Exercise 1: Simple feed forward net¶
Define a vanilla neural network with linear layers. Then average the word embeddings to get an embedding for the entire review. The neural net will have one hidden layer of size 128.
class NeuralNet(nn.Module):
""" A vanilla neural network. """
def __init__(self, batch_size, output_size, hidden_size, vocab_size,
embedding_length, word_embeddings):
"""
Constructs a vanilla Neural Network Instance.
Args:
batch_size: Integer
Specifies probability of dropout hyperparameter
output_size: Integer
Specifies the size of output vector
hidden_size: Integer
Specifies the size of hidden layer
vocab_size: Integer
Specifies the size of the vocabulary
i.e. the number of tokens in the vocabulary
embedding_length: Integer
Specifies the size of the embedding vector
word_embeddings
Specifies the weights to create embeddings from
voabulary.
Returns:
Nothing
"""
super(NeuralNet, self).__init__()
self.batch_size = batch_size
self.output_size = output_size
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.embedding_length = embedding_length
self.word_embeddings = nn.Embedding(vocab_size, embedding_length)
self.word_embeddings.weight = nn.Parameter(word_embeddings, requires_grad=False)
self.fc1 = nn.Linear(embedding_length, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, inputs):
"""
Compute the final labels by taking tokens as input.
Args:
inputs: Tensor
Tensor of tokens in the text
Returns:
out: Tensor
Final prediction Tensor
"""
input = self.word_embeddings(inputs) # convert text to embeddings
#################################################
# Implement a vanilla neural network
raise NotImplementedError("Neural Net `forward`")
#################################################
# Average the word embedddings in a sentence
# Use torch.nn.functional.avg_pool2d to compute the averages
pooled = F.avg_pool2d(..., (input.shape[1], 1)).squeeze(1)
# Pass the embeddings through the neural net
# Use ReLU as the non-linearity
x = ...
x = ...
x = ...
output = F.log_softmax(x, dim=1)
return output
# add event to airtable
atform.add_event('Coding Exercise 1: Neural Net for text classification')
# @markdown ### Helper functions
# @markdown - `train(model, device, train_iter, valid_iter, epochs, learning_rate)`
# @markdown - `test(model, device, test_iter)`
# @markdown - `load_dataset(emb_vectors, seed, sentence_length=50, batch_size=32)`
# @markdown - `plot_train_val(x, train, val, train_label, val_label, title)`
# Training
def train(model, device, train_iter, valid_iter, epochs, learning_rate):
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
train_loss, validation_loss = [], []
train_acc, validation_acc = [], []
for epoch in range(epochs):
# train
model.train()
running_loss = 0.
correct, total = 0, 0
steps = 0
for idx, batch in enumerate(train_iter):
text = batch.text[0]
target = batch.label
target = torch.autograd.Variable(target).long()
text, target = text.to(device), target.to(device)
# add micro for coding training loop
optimizer.zero_grad()
output = model(text)
loss = criterion(output, target)
loss.backward()
optimizer.step()
steps += 1
running_loss += loss.item()
# get accuracy
_, predicted = torch.max(output, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
train_loss.append(running_loss/len(train_iter))
train_acc.append(correct/total)
print(f'Epoch: {epoch + 1}, Training Loss: {running_loss/len(train_iter):.4f}, Training Accuracy: {100*correct/total: .2f}%')
# evaluate on validation data
model.eval()
running_loss = 0.
correct, total = 0, 0
with torch.no_grad():
for idx, batch in enumerate(valid_iter):
text = batch.text[0]
target = batch.label
target = torch.autograd.Variable(target).long()
text, target = text.to(device), target.to(device)
optimizer.zero_grad()
output = model(text)
loss = criterion(output, target)
running_loss += loss.item()
# get accuracy
_, predicted = torch.max(output, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
validation_loss.append(running_loss/len(valid_iter))
validation_acc.append(correct/total)
print (f'Validation Loss: {running_loss/len(valid_iter):.4f}, Validation Accuracy: {100*correct/total: .2f}% \n')
return train_loss, train_acc, validation_loss, validation_acc
# Testing
def test(model, device, test_iter):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for idx, batch in enumerate(test_iter):
text = batch.text[0]
target = batch.label
target = torch.autograd.Variable(target).long()
text, target = text.to(device), target.to(device)
outputs = model(text)
_, predicted = torch.max(outputs, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
acc = 100 * correct / total
return acc
def download_osf():
# Download IMDB dataset from OSF
import tarfile, requests, os
url = "https://osf.io/dvse9/download"
fname = "aclImdb_v1.tar.gz"
print('Downloading Started...')
# Downloading the file by sending the request to the URL
r = requests.get(url, stream=True)
# Writing the file to the local file system
with open(fname, 'wb') as f:
f.write(r.content)
print('Downloading Completed.')
with tarfile.open(fname) as f:
# extracting all the files
print('Extracting all the files now...')
f.extractall('.data/imdb') # specify which folder to extract to
print('Done!')
os.remove(fname)
def load_dataset(emb_vectors, seed, sentence_length=50, batch_size=32):
download_osf()
print("Dataset loading...")
TEXT = data.Field(sequential=True, tokenize=tokenize, lower=True,
include_lengths=True, batch_first=True,
fix_length=sentence_length)
LABEL = data.LabelField(dtype=torch.float)
train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train_data, vectors=emb_vectors)
LABEL.build_vocab(train_data)
train_data, valid_data = train_data.split(split_ratio=0.7,
random_state=random.seed(seed))
datasets_ = (train_data, valid_data, test_data)
train_iter, valid_iter, test_iter = data.BucketIterator.splits(datasets_,
batch_size=batch_size,
sort_key=lambda x: len(x.text),
repeat=False,
shuffle=True)
vocab_size = len(TEXT.vocab)
print("Done!")
return TEXT, vocab_size, train_iter, valid_iter, test_iter
# Plotting
def plot_train_val(x, train, val, train_label, val_label, title, ylabel):
plt.plot(x, train, label=train_label)
plt.plot(x, val, label=val_label)
plt.legend()
plt.xlabel('epoch')
plt.ylabel(ylabel)
plt.title(title)
plt.show()
# Dataset
def tokenize(sentences):
# Tokenize the sentence
# from nltk.tokenize library use word_tokenize
token = word_tokenize(sentences)
return token
# @markdown ### Download embeddings and load the dataset
# @markdown This will load 300 dim FastText embeddings.
# @markdown It will take around 3-4 minutes.
embedding_fasttext = FastText('simple')
TEXT, vocab_size, train_iter, valid_iter, test_iter = load_dataset(embedding_fasttext, seed=SEED)
Downloading Started...
Downloading Completed.
Extracting all the files now...
Done!
Dataset loading...
Done!
learning_rate = 0.0003
batch_size = 32
output_size = 2
hidden_size = 128
embedding_length = 300
epochs = 15
word_embeddings = TEXT.vocab.vectors
vocab_size = len(TEXT.vocab)
nn_model = NeuralNet(batch_size, output_size, hidden_size, vocab_size, embedding_length, word_embeddings)
nn_model.to(DEVICE)
nn_start_time = time.time()
nn_train_loss, nn_train_acc, nn_validation_loss, nn_validation_acc = train(nn_model, DEVICE, train_iter, valid_iter, epochs, learning_rate)
print()
print(f"--- Time taken to train = {time.time() - nn_start_time} seconds ---")
test_accuracy = test(nn_model, DEVICE, test_iter)
print()
print(f'Test Accuracy: {test_accuracy}%')
plot_train_val(np.arange(0, epochs), nn_train_acc, nn_validation_acc,
'training_accuracy', 'validation_accuracy',
'Neural Net on IMDB text classification', 'accuracy')
plot_train_val(np.arange(0, epochs), nn_train_loss, nn_validation_loss,
'training_loss', 'validation_loss',
'Neural Net on IMDB text classification', 'loss')
Summary¶
In this tutorial, we introduced how to process time series by taking language as an example. To process time series, we should convert them into embeddings.
We can first tokenize the words for text and then create either context-oblivious or context-dependent embeddings.
Finally, we saw how these word embeddings could be processed for applications such as text classification.
Airtable Submission Link¶
# @title Airtable Submission Link
from IPython import display as IPydisplay
IPydisplay.HTML(
f"""
<div>
<a href= "{atform.url()}" target="_blank">
<img src="https://github.com/NeuromatchAcademy/course-content-dl/blob/main/tutorials/static/AirtableSubmissionButton.png?raw=1"
alt="button link to Airtable" style="width:410px"></a>
</div>""" )
If you want to learn about Multilingual Embeddings see the Bonus tutorial on colab or kaggle. But first, we suggest completing the tutorial 2!