# [Getting Started Code] Research Paper Classification

In this second challenge of Blitz 9, we are going to use LSTM for multi class text classification

## Starter Code for Research paper Classification

Ok, we learned the fundamentals from Natural Language Processing in our First Challenge and we did Classification of different emotions. Now here the task is still the same - Classification. But the main point of this challange isn't the task itself, but is how we complete the task. There are many cons with the word2vec, which we are trying solve here.

### What we are going to Learn¶

• What is LSTM & why LSTM ?
• Using Tensorflow to create the dataset, converting texts into tokens and encoding them using Vectorization.
• Creating & Training a Tenforflow models with LSTM layers.
• Testing and Submitting the Results to the Challenge.

# Setup AIcrowd Utilities 🛠¶

Here we are installing AIcrowd CLI to download the the challange dataset

In [ ]:
!pip install -q -U aicrowd-cli

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ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.
ERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.


So first, as in the previous challenge, we will first need to download the python library by AIcrowd that will allow us to download the dataset by just inputting the API key.

In [ ]:
API_KEY = '61d7dd898be9a4343531783c2ca4a402' # Please get your your API Key from [https://www.aicrowd.com/participants/me]

API Key valid
Saved API Key successfully!

In [ ]:
# Downloading the Dataset ( removing data and assets folder if existing already and then creating the folder )
!rm -rf data
!mkdir data
!rm -rf assets
!mkdir assets


val.csv:   0% 0.00/883k [00:00<?, ?B/s]
test.csv:   0% 0.00/3.01M [00:00<?, ?B/s]

val.csv: 100% 883k/883k [00:00<00:00, 1.14MB/s]

test.csv: 100% 3.01M/3.01M [00:01<00:00, 2.90MB/s]

train.csv: 100% 8.77M/8.77M [00:01<00:00, 4.71MB/s]


# Define preprocessing code 💻¶

As you probably have guessed, we will be using Tensorflow maily for creating the dataset and training the LSTM model.

In [ ]:
# Importing Libraries
import pandas as pd
import numpy as np
from sklearn.metrics import f1_score, accuracy_score
import os

# Tensorflow
import tensorflow as tf
tf.random.set_seed(42) # Addign seed to reproducability

# To make things more beautiful!
from rich.console import Console
from rich.table import Table
from rich import pretty
pretty.install()

# function to display YouTube videos


Reading the necessary files to train, validation & submit our results!

In [ ]:
train_df = pd.read_csv("data/train.csv")
train_df

Out[ ]:
id text label
0 0 we propose deep network models and learning al... 3
1 1 multi-distance information computed by the MDL... 3
2 2 traditional solutions consider dense pedestria... 2
3 3 in this paper, is used the lagrangian classica... 2
4 4 the aim of this work is to determine how vulne... 3
... ... ... ...
31495 31495 the proposed method is easily programmed by ki... 2
31496 31496 research in unpaired video translation has foc... 3
31497 31497 deep learning models exhibit limited generaliz... 3
31498 31498 in this paper, we aim to incorporate global se... 3
31499 31499 to precisely calculate context-based probabili... 3

31500 rows × 3 columns

In [ ]:
train_df['label'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot object at 0x7efdc64ca610>


Ok, we are seeing that there is quite a big dataset imbalance problem here. But, I will leave this to you to fix it. You know, the starter code will not contain solutions to everything 😉 .

# Creating the Dataset 📁¶

From here, we will be using Tensorflow extensily to create the dataset and in the next section, training and our model and submitting resuts.

One hot encoding is a technique which helps to convert your categorical column ( in this case, the label column ) to a numerial column which we can input into the model. There are many other techniques to do this, one hot encode is very popular and a good technique among them. In simple here's what one hot encoding does --

In [ ]:
train_one_hot_label = pd.get_dummies(train_df['label'])
val_one_hot_label = pd.get_dummies(val_df['label'])

train_one_hot_label[:10]

Out[ ]:
0 1 2 3
0 0 0 0 1
1 0 0 0 1
2 0 0 1 0
3 0 0 1 0
4 0 0 0 1
5 0 0 0 1
6 0 1 0 0
7 0 0 1 0
8 0 0 0 1
9 0 0 0 1
In [ ]:
train_df.head(10)

Out[ ]:
id text label
0 0 we propose deep network models and learning al... 3
1 1 multi-distance information computed by the MDL... 3
2 2 traditional solutions consider dense pedestria... 2
3 3 in this paper, is used the lagrangian classica... 2
4 4 the aim of this work is to determine how vulne... 3
5 5 classification is one of the most well studied... 3
6 6 denoising autoencoders (DAE) are trained to re... 1
7 7 we present a novel haptic teleoperation approa... 2
8 8 deep convolutional neural networks (CNN) have ... 3
9 9 the focus of this work is sign spotting - give... 3

Can you detect the pattern ?

The from_tensor_slices helps to convert the dataset from numpy array to a Tensorflow Dataset which we can them use a tons of other functions to create batches and inputting our dataset into the model

In [ ]:
X_train, y_train = train_df['text'].values.astype(str), np.asarray(train_one_hot_label.values).astype(np.float32)

X_val, y_val = val_df['text'].values.astype(str), np.asarray(val_one_hot_label.values).astype(np.float32)

In [ ]:
# Inputting the X ( features ) and y ( labels )
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
validation_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))

train_dataset

<TensorSliceDataset shapes: ((), (4,)), types: (tf.string, tf.float32)>

In [ ]:
# Setting up batch size
BATCH_SIZE = 64

train_dataset = train_dataset.batch(BATCH_SIZE)
validation_dataset = validation_dataset.batch(BATCH_SIZE)

train_dataset

<BatchDataset shapes: ((None,), (None, 4)), types: (tf.string, tf.float32)>

In [ ]:
# Reading sample text and labels from the dataset

for example, label in train_dataset.take(1):
print('Text : ', example.numpy()[0])
print('Label : ', label.numpy()[0])

Text :  b'we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners . the novelty of our network design is that we constrain one hidden layer to directly output the binary codes . resulting optimizations involving these binary, independence, and balance constraints are difficult to solve .'
Label :  [0. 0. 0. 1.]


There will be a lot going in the upcoming cells let's debrief here -

1. The TextVectorization basically helps us to convert your texts into vectors ( as you can probably guessed by the function name )

There are several steps inside the TextVectorization function -

1. Doing little bit of preprocessing/clearning the text.
2. Converting all of the sentences into words ( tokens )
3. Assigning a unique numerical ID to each token and output the vector.
In [ ]:
VOCAB_SIZE = 10000

encoder = tf.keras.layers.experimental.preprocessing.TextVectorization(
max_tokens=VOCAB_SIZE, )


In [ ]:
# Printing the individual tokens in the vocabulary ( first 10 )

vocab = np.array(encoder.get_vocabulary())
print("Tokens : ", vocab[:10])
print("Number of tokens : ", len(vocab))

Tokens :  ['' '[UNK]' 'the' 'of' 'and' 'to' 'in' 'a' 'we' 'is']
Number of tokens :  10000


The [UNK] is an unknown word, If there we any word found in text which was not in vocabulary ( for example - in testing dataset ), the UNK token will be applied

In [ ]:
# Vectorization

text = example[0].numpy()
encoded_text = encoder(example)[0].numpy()
print("Text : ", text, "\n", "Encoded Text : ", encoded_text)

Text :  b'we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners . the novelty of our network design is that we constrain one hidden layer to directly output the binary codes . resulting optimizations involving these binary, independence, and balance constraints are difficult to solve .'
Encoded Text :  [   8   22   43   36   49    4   29   90   10   29  567 4901 2392  261
24  232  165   63  320    4  421    1    2 2580    3   17   36  155
9   13    8    1   80 1067  316    5  416  340    2  567 2392  270
7895 1463   57  567 1590    4 1306  293   16  611    5  264    0    0
0    0    0    0    0    0    0]


# Creating the Model¶

We are getting close, here we are creating our models and layers like Embedding, LSTM and simple layers like Dense and Dopout, let's dig in and learn model about LSTM.

Now, you might be asking - Let's just do the same as first challenge, why going so advance.

LSTM Neural Network are trying to sove a problem that we never discused about in the previous challange. Texts are Sequences, means that. If we want to predict a next word in a text, we need to know about the previous text(s), that's exactly that LSTM do, they take each word one by one and proprocess them and then they gives the output. While the sklearn mode didn't had that capability to do so.

But, how does LSTM work ? Good question, here a really good video around how LSTM works.

In [ ]:
YouTubeVideo('QciIcRxJvsM')

Out[ ]:

If you want to go a bit more advance about LSTM, Understanding LSTM Networks is a really good blog by colah

In [ ]:
# Creating a Sequential Model
model = tf.keras.Sequential([
encoder,

# Word embedding are very similar to word2vec that we used in the previous challanges, but in this, this will train as the model trains

# Creating the LSTM layers, the return_sequences is set to True when there is also LSTM layer after it.
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,  return_sequences=True)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, )),

tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.2),

# Output layer with 4 neurons. ( 4 classes )
tf.keras.layers.Dense(4)
])

In [ ]:
# Configuring the models and settup up parameters, including optimizer, loss and metrics.
model.compile(loss=tf.keras.losses.binary_crossentropy,
metrics=['accuracy'])

In [ ]:
# predict on a sample text
sample_text = train_df.text[0]
predictions = model.predict(np.array([sample_text]))
print(predictions[0])

[-0.00113596 -0.00072253  0.00577792  0.00659498]


There are the probabilies for each class

### Training the Model 🚆¶

In [ ]:
# Let's goo!
history = model.fit(train_dataset, epochs=10)

Epoch 1/10
493/493 [==============================] - 27s 31ms/step - loss: 0.3927 - accuracy: 0.6994
Epoch 2/10
493/493 [==============================] - 16s 32ms/step - loss: 0.3089 - accuracy: 0.7814
Epoch 3/10
493/493 [==============================] - 16s 32ms/step - loss: 0.3501 - accuracy: 0.7545
Epoch 4/10
493/493 [==============================] - 16s 32ms/step - loss: 0.3033 - accuracy: 0.8117
Epoch 5/10
493/493 [==============================] - 16s 32ms/step - loss: 0.2207 - accuracy: 0.8658
Epoch 6/10
493/493 [==============================] - 15s 31ms/step - loss: 0.2464 - accuracy: 0.8154
Epoch 7/10
493/493 [==============================] - 15s 31ms/step - loss: 0.2649 - accuracy: 0.8294
Epoch 8/10
493/493 [==============================] - 15s 31ms/step - loss: 0.2802 - accuracy: 0.8463
Epoch 9/10
493/493 [==============================] - 15s 31ms/step - loss: 0.2299 - accuracy: 0.8530
Epoch 10/10
493/493 [==============================] - 15s 31ms/step - loss: 0.2084 - accuracy: 0.8863


## Validation¶

Now, we have done the training, let's test our model on unseen ( validation dataset ) to see how well our model performs!

In [ ]:
validation_predictions = model.predict(validation_dataset, verbose=1)
validation_predictions[0]

43/43 [==============================] - 4s 10ms/step

array([0.13306706, 0.35485056, 0.136038  , 0.4757739 ], dtype=float32)

In [ ]:
# Converting the predictions from probabilites into binary
y_pred_encoded = np.argmax(validation_predictions, axis=1)

# Getting the labels from binary using  train_one_hot_label
y_pred = [train_one_hot_label.columns[i]  for i in y_pred_encoded]

In [ ]:
print("F1 Score : ", f1_score(val_df['label'], y_pred, average="weighted"))

F1 Score :  0.8246149113453235


# Prediction phase 🔎¶

Again! Let's make our predictions just like in previous challange!

In [ ]:
# Loading the test dataset, model & train_one_hot_label


In [ ]:
# Making the predictions and convert them into actual labels
X_test = test_df['text'].values.astype(str)

model_results = model.predict(X_test)

encoded_results = np.argmax(model_results, axis=1)
results = [train_one_hot_label.columns[i]  for i in encoded_results]

In [ ]:
# Putting the results into the column of test dataset
test_df['label'] = results
test_df

Out[ ]:
id text label
0 0 we propose a lightweight framework to detect i... 3
1 1 the proposed method presents an alternate solu... 2
2 2 proposed ear identification method fusing SIFT... 3
3 3 a method to reconstruct the three-dimensional ... 3
4 4 strong local consistencies can improve their p... 0
... ... ... ...
10795 10795 whole-body gradient echo scans of 240 subjects... 3
10796 10796 we present a tracker that accomplishes trackin... 3
10797 10797 the most popular FL algorithm is Federated Ave... 1
10798 10798 in the field of Autonomous Driving, the system... 2
10799 10799 our method takes as an input a foreground imag... 3

10800 rows × 3 columns

Note : Please make sure that there should be filename submission.csv in assets folder before submitting it

In [ ]:
# Saving out results in submission.csv
test_df.to_csv(os.path.join("assets", 'submission.csv'), index=False)


# Submit to AIcrowd 🚀¶

Note : Please save the notebook before submitting it (Ctrl + S)

In [ ]:
!aicrowd notebook submit -c research-paper-classification -a assets --no-verify

Mounting Google Drive 💾

4/1AY0e-g4g70Q147Eq7NhbJrvtK2OIUq166FvWgTI_F5Qn3eMHwxSoZ5XyHG0
Mounted at /content/drive
Using notebook: /content/drive/MyDrive/Colab Notebooks/Copy of Research Paper Classification for submission...
Scrubbing API keys from the notebook...
Collecting notebook...
submission.zip ━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 2.1/2.1 MB • 3.4 MB/s • 0:00:00
╭─────────────────────────╮
│ Successfully submitted! │
╰─────────────────────────╯
┌──────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/research-paper-classification/submissions/144549              │
│                  │                                                                                                                      │
│  All submissions │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/research-paper-classification/submissions?my_submissions=true │
│                  │                                                                                                                      │
│                  │                                                                                                                      │
│ Discussion forum │ https://discourse.aicrowd.com/c/ai-blitz-9                                                                           │
│                  │                                                                                                                      │
│   Challenge page │ https://www.aicrowd.com/challenges/ai-blitz-9/problems/research-paper-classification                                 │
└──────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘


Congratulations 🎉 you did it, but there still a lot of improvement that can be made, here are some suggestions -

1. Try out to solve the dataset imbalance issue
2. Try changing parameters, or adding more LSTM layers in the tensorflow model.

And btw -

Don't be shy to ask question related to any errors you are getting or doubts in any part of this notebook in discussion forum or in AIcrowd Discord sever, AIcrew will be happy to help you :)

Also, wanna give us your valuable feedback for next blitz or wanna work with us creating blitz challanges ? Let us know!

In [ ]:


1285