Multi-Label Text Classification on Stack Overflow Tag Prediction

Published by Revathi on

Multi-Label Text Classification

In this notebook, we will use the dataset “StackSample:10% of Stack Overflow Q&A” and we use the questions and the tags data. We will be developing a text classification model that analyzes a textual comment and predicts multiple labels associated with the questions. We will implement a tag suggestion system using Multi-Label Text Classification which is a subset of multiple output model.

  • Text preprocessing is performed on the text data and the cleaned data is loaded for text classification.
  • We will be implementing text Vectorization on text data, encode the tag labels using MultilabelBinarizer and model Classical classifiers(SGC classifier, MultiNomial Naive Bayes Classifier, Random Forest Classfier,…) for modelling and compare the results.

In machine learning,Classification is a type of supervised learning. classification refers to a predictive modeling problem where a class label is predicted for a given input samples. It specifies the class to which data point belong to and is best used when the output has finite and discrete values.

There are 4 types of classification tasks that you encounter, they are

  • Binary Classification 
  • Multiclass Classification 
  • MultiLabel Classification
  • Imbalanced classification

Binary classification refers to predicting one of two classes.

  • Email spam detection (spam or not)
  • Churn prediction (churn or not).
  • Conversion prediction (buy or not).

Multi-class classification task with more than two classes in which each sample is assigned to one and only one class.

  • a fruit can be either an apple or a pear but not both at the same time. 

Multi-label classification refers to those classification tasks that have two or more class labels, where one or more class labels may be predicted for each sample.

Consider the example of photo classification, where a given photo may have multiple objects in the scene and a model may predict the presence of multiple known objects in the photo, such as “bicycle,” “apple,” “person,” etc.

Imbalanced classification refers to classification tasks where the number of examples in each class is unequally distributed.

  • Fraud detection.
  • Outlier detection.
image.png

Source:cse-iitk

Notebook Setup

Import libraries

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split

from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC

from sklearn.multiclass import OneVsRestClassifier

Use the Pandas method read_csv() to load the data. load data and store in dataframe df

df = pd.read_csv('https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/master/stackoverflow.csv', index_col=0)

Use the method head() to display the first five rows of the dataframe

df.head()
TextTags
2aspnet site maps has anyone got experience cre…[‘sql’, ‘asp.net’]
4adding scripting functionality to net applicat…[‘c#’, ‘.net’]
5should i use nested classes in this case i am …[‘c++’]
6homegrown consumption of web services i have b…[‘.net’]
8automatically update version number i would li…[‘c#’]
type(df['Tags'].iloc[0])
str
df['Tags'].iloc[0]
"['sql', 'asp.net']"

The ast module helps Python applications to process trees of the Python abstract syntax grammar. This module helps to find out programmatically what the current grammar looks like.

ast.literal_eval Safely evaluate an expression node or a string containing a Python literal. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, None, bytes and sets.

Here we need only string literals in Tags which acts as labels for the Target variable.

import ast
ast.literal_eval(df['Tags'].iloc[0])
['sql', 'asp.net']
df['Tags'] = df['Tags'].apply(lambda x: ast.literal_eval(x))
df.head()
TextTags
2aspnet site maps has anyone got experience cre…[sql, asp.net]
4adding scripting functionality to net applicat…[c#, .net]
5should i use nested classes in this case i am …[c++]
6homegrown consumption of web services i have b…[.net]
8automatically update version number i would li…[c#]

Encoding Categorical features

A categorical variable is a variable whose values take on the value of labels. Almost all ML algorithms require data in numerical form. The encoding methods are necessary for the machine to interpret the Qualitative data(categorical data or labelled data) into numerical representation and learn the model from it.

Below are the most commonly used categorical variables/features using Scikit learn and pandas libraries.

1. LabelEncoder:

LabelEncoderis used to transform non-numerical labels to numerical labels. It encodes labels with a value between 0 and n_classes-1, where n is no. of distinct labels. Mostly Binary Columns are encoded using Label Encoder. It may be a perfect encoding method for categorical data as it has an order for example YES/NO,Male/Female etc. LabelEncoder can also be used to normalize labels.

For Multiclass it will give different (0 to n_classes-1) values for different classes eg. 0,1,2,3,….,n-1; which are actually categorical in nature and don’t have any intrinsic order. If LabelEncoder is used for multiclass columns the model will misinterpret the encoded data to be in some kind of order, (0<1<2).

2. OneHotEncoder:

One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction. One Hot Encoder can handle input string categorical and numeric data as well. It takes a column which has categorical data,splits the column into multiple columns based on labels or classes. The labels are replaced by 1s and 0s, depending on which column has what value. Consider a example that feature having 3 class labels: There are three columns, but logically thinking if you know one if one class is present (1) then automatically the other 2 classes are absent (0). Hence the two columns will be highly correlated which is not desired. This is called the variable-trap. In order to solve this, you can use the drop parameter in the OneHotEncoder. This way variable trap is eliminated.

image.png

3. get_dummies:

Pandas get_dummies method takes the categorical feature as an argument. Then it creates a Dummy Variable for every label in the feature, such that each dummy variable holds data as 1 or 01 indicates the presence of a particular label and 0 indicates the absence of a particular label. Pandas get_dummies method is a very straight forward one-step procedure to get the dummy variables for categorical features.

The prefixsep(“”) is used to write column name [ “ColumnName”+ “” + “Class_Name” ] and drop_first = True is used to avoid variable trap, which is also called multicollinearity.

4. MultiLabel Binarize:

Multilabelbinarizer allows you to encode multiple labels per instance. It is used when any column has multiple labels. The input to this transformer should be an array-like of integers or strings,denoting the values taken on by categorical (discrete) features.

y = df['Tags']
y
2          [sql, asp.net]
4              [c#, .net]
5                   [c++]
6                  [.net]
8                    [c#]
                ...      
1262668             [c++]
1262834             [c++]
1262915          [python]
1263065          [python]
1263454             [c++]
Name: Tags, Length: 48976, dtype: object
multilabel = MultiLabelBinarizer()
y = multilabel.fit_transform(df['Tags'])
y
array([[0, 0, 1, ..., 0, 0, 1],
       [1, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]])
multilabel.classes_
array(['.net', 'android', 'asp.net', 'c', 'c#', 'c++', 'css', 'html',
       'ios', 'iphone', 'java', 'javascript', 'jquery', 'mysql',
       'objective-c', 'php', 'python', 'ruby', 'ruby-on-rails', 'sql'],
      dtype=object)
pd.DataFrame(y, columns=multilabel.classes_)
.netandroidasp.netcc#c++csshtmliosiphonejavajavascriptjquerymysqlobjective-cphppythonrubyruby-on-railssql
000100000000000000001
110001000000000000000
200000100000000000000
310000000000000000000
400001000000000000000
4897100000100000000000000
4897200000100000000000000
4897300000000000000001000
4897400000000000000001000
4897500000100000000000000

48976 rows × 20 columns

Text Vectorization

Word Embeddings or Word vectorization is a methodology in NLP to map words or phrases from vocabulary to a corresponding vector of real numbers which are used to find word predictions, word similarities/semantics. The process of converting words into numbers are called Vectorization.

Machine learning algorithms cannot work with raw text directly.We need to transform that text into numbers. This process is called Text Vectorization.

Text Vectorization uses bag-of-words model to represents text data into vectors,when modeling text with machine learning algorithms.

we can do it in 3 ways using Scikit Learn library.

  • Word Counts with CountVectorizer Convert a collection of text documents to a matrix of token counts.
  • Word Frequencies with TfidfVectorizer Convert a collection of raw documents to a matrix of TF-IDF features.
  • Hashing with HashingVectorizer The main difference is that HashingVectorizer applies a hashing functionto term frequency counts in each document, where TfidfVectorizer scales those term frequency counts in each document by penalising terms that appear more widely across the corpus.

TF-IDF is a statistical measure that evaluates how relevant a word is to a document in a collection of documents.

This is done by multiplying two metrics:Term frequency gives how many times a word appears in a document and the inverse document frequency of the word across a set of documents.Multiplying these two metrics results in the TF-IDF score of a word in a document.

The higher the score, the more relevant that word is in that particular document.

    \begin{align*} tfidf(t,d,D) = tf(t,d) \times idf(t,D) \end{align*}

Where t denotes the terms; d denotes each document; D denotes the collection of documents.

Once you’ve transformed words into vectors or numbers using TF-IDF Vectorizer,TF-IDF score can be fed to Machine learning algorithms for the classification.

tfidf = TfidfVectorizer(analyzer='word', max_features=10000, ngram_range=(1,3), stop_words='english')
X = tfidf.fit_transform(df['Text'])
X.shape, y.shape
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

Build Model

sgd = SGDClassifier()
lr = LogisticRegression(solver='lbfgs')
svc = LinearSVC()

Metrics for Multi-label classification

Multi-label classification problems must be assessed using different performance measures than single-label classification problems. Two of the most common performance metrics are hamming loss and Jaccard similarity.

Hamming loss is the average fraction of incorrect labels. Note that hamming loss is a loss function and that the perfect score is 0.

    \begin{align*} Hamming loss = \frac{TP + TN}{T} \end{align*}

where TP + TN + FP + FN =L(the total number of labels).

Jaccard similarity or the Jaccard index, is the size of the intersection of the predicted labels and the true labels divided by the size of the union of the predicted and true labels. It ranges from 0 to 1, and 1 is the perfect score.

    \begin{align*} Jaccard Score  = \frac{TP }{TP+FP+FN}\, if \,TN \neq 1 \end{align*}

Hamming and Jaccard similarity can be represented in terms of true/false positive/negative counts.

def j_score(y_true, y_pred):
  jaccard = np.minimum(y_true, y_pred).sum(axis = 1)/np.maximum(y_true, y_pred).sum(axis = 1)
  return jaccard.mean()*100


def print_score(y_pred, clf):
  print("Clf: ", clf.__class__.__name__)
  print('Jacard score: {}'.format(j_score(y_test, y_pred)))
  print('----')

OneVsRest Classifier

when we want to do multiclass or multilabel classification and it’s strategy consists of fitting one classifier per class. For each classifier, the class is fitted against all the other classes.It means that problem of multiclass/multilabel classification is broken down to multiple binary classification problems.

It involves splitting the multi-class dataset into multiple binary classification problems. A binary classifier is then trained on each binary classification problem and predictions are made using the model that is the most confident.

The scikit-learn library also provides a separate OneVsRestClassifier class that allows the one-vs-rest strategy to be used with any classifier.This class can be used to use a binary classifier like LinearSVC , SGDClassifier or Logistic Regression for multi-Label classification or even other classifiers that natively support multi-label classification.

for classifier in [LinearSVC(C=1.5, penalty = 'l1', dual=False)]:
  clf = OneVsRestClassifier(classifier)
  clf.fit(X_train, y_train)
  y_pred = clf.predict(X_test)
  print_score(y_pred, classifier)
for classifier in [sgd, lr, svc]:
  clf = OneVsRestClassifier(classifier)
  clf.fit(X_train, y_train)
  y_pred = clf.predict(X_test)
  print_score(y_pred, classifier)

Model Test with Real Data

x = [ 'how to write ml code in python and java i have data but do not know how to do it']
xt = tfidf.transform(x)
clf.predict(xt)
multilabel.inverse_transform(clf.predict(xt))

Conclusion:

  • First, We have loaded the text pre-processed dataset using pandas dataframe and also evaluated the string tags using AST module and encoded the tags using Multilabelbinarizer.
  • Thereafter, we have performed the Text Vectorization on Question type using TfidfVectorizer. Finally, we have fit the model on various classifers like LinearSVC,SGDClassifier or Logistic Regression for multi- Label classification and predicted the output on real data.
  • For better accuracy and prediction we may also model Multilabel text clssification using RNN, LSTM’s, bi- directional LSTM’s etc..

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