Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

Data Labeling in Machine Learning with Python

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Acknowledgments

I extend my heartfelt gratitude to my mother, Rajya Lakshmi Suda, and dedicate this work to the cherished memory of my father, Koteswara Rao Suda. Their sacrifices and unwavering determination have been a profound source of inspiration.

Special thanks to my wife, Radhika, for her enduring support and patience throughout the writing of this book.

To my son, Chandra Suda (Rise Global Winner 2023), and daughter, Akshaya, your talents and creativity have shown me the beautiful evolution of skill.

I am deeply appreciative of my siblings, Rama Devi, Swarna Kumar, and Dr. Sri Kumar, for their continuous support.

A sincere acknowledgment to my mentors and managers, Kevin Fleck and Des Quinta, for their invaluable support and motivation throughout the writing process of this book.

Finally, I want to thank the Packt Publishing team, especially Shreya and Hemangi, for their fantastic support, which made the writing process an absolute pleasure.

Contributors

About the author

Vijaya Kumar Suda is a seasoned data and AI professional, boasting over two decades of expertise collaborating with global clients. Having resided and worked in diverse locations such as Switzerland, Belgium, Mexico, Bahrain, India, Canada, and the USA, Vijaya has successfully assisted customers spanning various industries. Currently serving as a senior data and AI consultant at Microsoft, he is instrumental in guiding industry partners through their digital transformation endeavors using cutting-edge cloud technologies and AI capabilities. His proficiency encompasses architecture, data engineering, machine learning, generative AI, and cloud solutions. Vijaya also shares his insights through engaging videos on the cloud, data, and AI on his YouTube channel, Cloud & Data Science .

About the reviewers

Pritesh Kanani is a full stack developer with experience in data wrangling and supervised machine learning. He helped a major oil and gas company with building a tool to monitor drilling operations and handling thousands of high frequency data streams. He completed a post-graduation course in applied AI and is currently utilizing his full stack data science and cloud computing skills at a leading nuclear and renewable energy organization in Ontario, Canada.

Sourav Roy is a passionate data enthusiast, an experienced machine learning practitioner, and an expert book reviewer with a focus on literature linked to data. He possesses a diverse skill set in data engineering and data analytics, which allows him to combine technical proficiency with a deep passion in his work on data-centric books. Sourav obtained a master’s degree in data science and analytics from Queen’s University. He is presently employed as a data engineer in the banking sector.

Mitesh Mangaonkar is an engineering leader pioneering generative AI to transform data platforms. As a tech lead at Airbnb, he builds cutting-edge data pipelines leveraging big technologies and modern data stacks to power trust and safety products. Previously, at AWS, Mitesh helped Fortune 500 companies migrate their data warehouses to the cloud and engineered highly scalable, resilient systems. An innovator at heart, he combines deep data engineering expertise with a passion for AI to create the next generation of data products. Mitesh is an influential voice shaping the future of data engineering and governance.

Table of Contents

Preface

Part 1: Labeling Tabular Data

1

Exploring Data for Machine Learning

Technical requirements

EDA and data labeling

Understanding the ML project life cycle

Defining the business problem

Data discovery and data collection

Data exploration

Data labeling

Model training

Model evaluation

Model deployment

Introducing Pandas DataFrames

Summary statistics and data aggregates

Summary statistics

Data aggregates of the feature for each target class

Creating visualizations using Seaborn for univariate and bivariate analysis

Univariate analysis

Bivariate analysis

Profiling data using the ydata-profiling library

Variables section

Interactions section

Correlations

Missing values

Sample data

Unlocking insights from data with OpenAI and LangChain

Summary

2

Labeling Data for Classification

Technical requirements

Predicting labels with LLMs for tabular data

Data labeling using Snorkel

What is Snorkel?

Why is Snorkel popular?

Loading unlabeled data

Creating the labeling functions

Labeling rules

Constants

Labeling functions

Creating a label model

Predicting labels

Labeling data using the Compose library

Labeling data using semi-supervised learning

What is semi-supervised learning?

What is pseudo-labeling?

Labeling data using K-means clustering

What is unsupervised learning?

K-means clustering

Inertia

Dunn's index

Summary

3

Labeling Data for Regression

Technical requirements

Using summary statistics to generate housing price labels

Finding the closest labeled observation to match the label

Using semi-supervised learning to label regression data

Pseudo-labeling

Using data augmentation to label regression data

Using k-means clustering to label regression data

Summary

Part 2: Labeling Image Data

4

Exploring Image Data

Technical requirements

Visualizing image data using Matplotlib in Python

Loading the data

Checking the dimensions

Visualizing the data

Checking for outliers

Performing data preprocessing

Checking for class imbalance

Identifying patterns and relationships

Evaluating the impact of preprocessing

Practice example of visualizing data

Practice example for adding annotations to an image

Practice example of image segmentation

Practice example for feature extraction

Analyzing image size and aspect ratio

Impact of aspect ratios on model performance

Image resizing

Image normalization

Performing transformations on images – image augmentation

Summary

5

Labeling Image Data Using Rules

Technical requirements

Labeling rules based on image visualization

Image labeling using rules with Snorkel

Weak supervision

Rules based on the manual visualization of an image’s object color

Real-world applications

A practical example of plant disease detection

Labeling images using rules based on properties

Bounding boxes

Example 1 – image classification – a bicycle with and without a person

Example 2 – image classification – dog and cat images

Labeling images using transfer learning

Example – digit classification using a pre-trained classifier

Example – person image detection using the YOLO V3 pre-trained classifier

Example – bicycle image detection using the YOLO V3 pre-trained classifier

Labeling images using transformations

Summary

6

Labeling Image Data Using Data Augmentation

Technical requirements

Training support vector machines with augmented image data

Kernel trick

Data augmentation

Image data augmentation

Implementing an SVM with data augmentation in Python

Introducing the CIFAR-10 dataset

Loading the CIFAR-10 dataset in Python

Preprocessing the data for SVM training

Implementing an SVM with the default hyperparameters

Evaluating SVM on the original dataset

Implementing an SVM with an augmented dataset

Training the SVM on augmented data

Evaluating the SVM’s performance on the augmented dataset

Image classification using the SVM with data augmentation on the MNIST dataset

Convolutional neural networks using augmented image data

How CNNs work

Practical example of a CNN using data augmentation

CNN using image data augmentation with the CIFAR-10 dataset

Summary

Part 3: Labeling Text, Audio, and Video Data

7

Labeling Text Data

Technical requirements

Real-world applications of text data labeling

Tools and frameworks for text data labeling

Exploratory data analysis of text

Loading the data

Understanding the data

Cleaning and preprocessing the data

Exploring the text’s content

Analyzing relationships between text and other variables

Visualizing the results

Exploratory data analysis of sample text data set

Exploring Generative AI and OpenAI for labeling text data

GPT models by OpenAI

Zero-shot learning capabilities

Text classification with OpenAI models

Data labeling assistance

OpenAI API overview

Use case 1 – summarizing the text

Use case 2 – topic generation for news articles

Use case 3 – classification of customer queries using the user-defined categories and sub-categories

Use case 4 – information retrieval using entity extraction

Use case 5 – aspect-based sentiment analysis

Hands-on labeling of text data using the Snorkel API

Hands-on text labeling using Logistic Regression

Hands-on label prediction using K-means clustering

Generating labels for customer reviews (sentiment analysis)

Summary

8

Exploring Video Data

Technical requirements

Loading video data using cv2

Extracting frames from video data for analysis

Extracting features from video frames

Color histogram

Optical flow features

Motion vectors

Deep learning features

Appearance and shape descriptors

Visualizing video data using Matplotlib

Frame visualization

Temporal visualization

Motion visualization

Labeling video data using k-means clustering

Overview of data labeling using k-means clustering

Example of video data labeling using k-means clustering with a color histogram

Advanced concepts in video data analysis

Motion analysis in videos

Object tracking in videos

Facial recognition in videos

Video compression techniques

Real-time video processing

Video data formats and quality in machine learning

Common issues in handling video data for ML models

Troubleshooting steps

Summary

9

Labeling Video Data

Technical requirements

Capturing real-time video

Key components and features

A hands-on example to capture real-time video using a webcam

Building a CNN model for labeling video data

Using autoencoders for video data labeling

A hands-on example to label video data using autoencoders

Transfer learning

Using the Watershed algorithm for video data labeling

A hands-on example to label video data segmentation using the Watershed algorithm

Computational complexity

Performance metrics

Real-world examples for video data labeling

Advances in video data labeling and classification

Summary

10

Exploring Audio Data

Technical requirements

Real-life applications for labeling audio data

Audio data fundamentals

Hands-on with analyzing audio data

Example code for loading and analyzing sample audio file

Best practices for audio format conversion

Example code for audio data cleaning

Extracting properties from audio data

Tempo

Chroma features

Mel-frequency cepstral coefficients (MFCCs)

Zero-crossing rate

Spectral contrast

Considerations for extracting properties

Visualizing audio data with matplotlib and Librosa

Waveform visualization

Loudness visualization

Spectrogram visualization

Mel spectrogram visualization

Considerations for visualizations

Ethical implications of audio data

Recent advances in audio data analysis

Troubleshooting common issues during data analysis

Troubleshooting common installation issues for audio libraries

Summary

11

Labeling Audio Data

Technical requirements

Downloading FFmpeg

Azure Machine Learning

Real-time voice classification with Random Forest

Transcribing audio using the OpenAI Whisper model

Step 1 – importing the Whisper model

Step 2 – loading the base Whisper model

Step 3 – setting up FFmpeg

Step 4 – transcribing the YouTube audio using the Whisper model

Classifying a transcription using Hugging Face transformers

Hands-on – labeling audio data using a CNN

Exploring audio data augmentation

Introducing Azure Cognitive Services – the speech service

Creating an Azure Speech service

Speech to text

Speech translation

Summary

12

Hands-On Exploring Data Labeling Tools

Technical requirements

Azure Machine Learning data labeling

Label Studio

pyOpenAnnotate

Data labeling using Azure Machine Learning

Benefits of data labeling with Azure Machine Learning

Data labeling steps using Azure Machine Learning

Image data labeling with Azure Machine Learning

Text data labeling with Azure Machine Learning

Audio data labeling using Azure Machine Learning

Integration of the Azure Machine Learning pipeline with the labeled dataset

Exploring Label Studio

Labeling the image data

Labeling the text data

Labeling the video data

pyOpenAnnotate

Computer Vision Annotation Tool

Comparison of data labeling tools

Advanced methods in data labeling

Active learning

Semi-automated labeling

Summary

Index

Other Books You May Enjoy

Preface

In today’s data-driven era, where more than 2.5 quintillion bytes of data are produced daily in various forms such as text, image, audio, and video, data stands as the cornerstone of the AI revolution. However, the majority of real-world data available for training supervised machine learning models lacks labels, or we encounter limited labeled data. This presents a significant challenge, as labeled data is essential for training any supervised machine learning model and fine-tuning large language models in the age of generative AI.

To address the scarcity of labeled data and facilitate the preparation of labeled data for training supervised machine learning models and fine-tuning large language models, this book introduces various methods for programmatic data labeling using Python libraries and methods, including semi-supervised and unsupervised learning.

This book guides you through the process of loading and analyzing tabular data, images, videos, audio, and text using various Python libraries, the OpenAI API, LangChain, and Azure Machine Learning. It explores techniques such as weak supervision, pseudo-labeling, and K-means clustering for classification and labeling, while also providing data augmentation methods to enhance accuracy. Utilizing the Azure OpenAI API and LangChain, the book demonstrates the automation of data analysis using natural language without the need to acquire any programming skills. It also encompasses the classification and data labeling of text data using OpenAI and large language models (LLMs). This book covers a wide variety of open source data annotation tools, along with Azure Machine Learning, and compares the pros and cons of these tools.

Real-world examples from various industries are incorporated to illustrate the application of these methods to tabular, text, image, video, and audio data.

By the conclusion of this book, you will have acquired the skills to explore different types of data using Python and OpenAI LLMs. You will have learned how to prepare data with labels, whether for training machine learning models or unlocking insights about the data to leverage for business use cases across industries.

Who this book is for

This book is for aspiring AI engineers, machine learning engineers, data scientists, and data engineers who want to learn about data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn about data exploration and annotation using Python libraries.

What this book covers

Chapter 1, Exploring Data for Machine Learning, provides an overview of data analysis and visualization methods using various Python libraries. Additionally, it deep dives into unlocking data insights with natural language using OpenAI LLMs.

Chapter 2, Labeling Data for Classification, covers the process of labeling tabular data for training classification models. Various methods, such as Snorkel Python functions, semi-supervised learning, and clustering data using K-means, are explored.

Chapter 3, Labeling Data for Regression, addresses the labeling of tabular data for training regression models. Techniques include leveraging summary statistics, creating pseudo labels, employing data augmentation methods, and utilizing K-means clustering.

Chapter 4, Exploring Image Data, covers the analysis and visualization of image data and feature extraction from images using various Python libraries.

Chapter 5, Labeling Image Data Using Rules, discusses labeling images based on heuristics and image properties such as aspect ratio, and also covers image classification using pre-trained classifiers such as YOLO.

Chapter 6, Labeling Image Data Using Data Augmentation, explores methods of image data augmentation for training support vector machines and Convolutional Neural Networks (CNNs), as well as addressing image data labeling.

Chapter 7, Labeling Text Data, covers generative AI and various methods for labeling text data. This includes Azure OpenAI with real-world use cases, text classification, and sentiment analysis using Snorkel and K-means clustering.

Chapter 8, Exploring Video Data, focuses on loading video data, extracting features, visualizing video data, and clustering video data using K-means clustering.

Chapter 9, Labeling Video Data, delves into labeling video data using CNNs, segmenting video data with the watershed algorithm, and capturing important features using autoencoders, accompanied by real-world examples.

Chapter 10, Exploring Audio Data, provides the fundamentals of audio data, loading and visualizing audio data, extracting features, and real-life applications.

Chapter 11, Labeling Audio Data, covers transcribing audio data using OpenAI’s Whisper model, labeling the transcription, creating spectrograms for audio data classification, augmenting audio data, and using Azure Cognitive Services for speech.

Chapter 12, Hands-On Exploring Data Labeling Tools, covers various data labeling tools, including open source tools such as Label Studio, CVAT, pyOpenAnnotate, and Azure Machine Learning. It also includes a comparison of various data labeling tools for image, text, audio, and video data.

To get the most out of this book

Basic Python knowledge is beneficial but not necessary to get the most out of this book.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: Now let us generate the augmented data by calling the noise, scale, and rotation augmentation functions, as follows.

A block of code is set as follows:

# Train a linear regression model on the labeled data

regressor = LinearRegression()

regressor.fit(train_data, train_labels)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

news_headline="

Label the following news headline into 1 of the following categories: Business, Tech, Politics, Sport, Entertainment\n\n Headline 1: Trump is ready to contest in nov 2024 elections\nCategory:

",

response = openai.Completion.create(

engine=model_deployment_name,

prompt= news_headline,

temperature=0,

Any command-line input or output is written as follows:

pip install keras

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: Change System preferences | Security and privacy | General, and then select Open anyway.

Tips or important notes

Appear like this.

Get in touch

Feedback from our readers is always welcome.

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Part 1: Labeling Tabular Data

This part of the book will guide you in exploring tabular data and programmatically labeling the data using Python libraries, such as Snorkel labeling functions. You will be able to achieve this without requiring any prior data science knowledge. Additionally, it covers data labeling using K-means clustering.

This part comprises the following chapters:

Chapter 1, Exploring Data for Machine Learning

Chapter 2, Labeling Data for Classification

Chapter 3, Labeling Data for Regression

1

Exploring Data for Machine Learning

Imagine embarking on a journey through an expansive ocean of data, where within this vastness are untold stories, patterns, and insights waiting to be discovered. Welcome to the world of data exploration in machine learning (ML). In this chapter, I encourage you to put on your analytical lenses as we embark on a thrilling quest. Here, we will delve deep into the heart of your data, armed with powerful techniques and heuristics, to uncover its secrets. As you embark on this adventure, you will discover that beneath the surface of raw numbers and statistics, there exists a treasure trove of patterns that, once revealed, can transform your data into a valuable asset. The journey begins with exploratory data analysis (EDA), a crucial phase where we unravel the mysteries of data, laying the foundation for automated labeling and, ultimately, building smarter and more accurate ML models. In this age of generative AI, the preparation of quality training data is essential to the fine-tuning of domain-specific large language models (LLMs). Fine-tuning involves the curation of additional domain-specific labeled data for training publicly available LLMs. So, fasten your seatbelts for a captivating voyage into the art and science of data exploration for data labeling.

First, let’s start with the question: What is data exploration? It is the initial phase of data analysis, where raw data is examined, visualized, and summarized to uncover patterns, trends, and insights. It serves as a crucial step in understanding the nature of the data before applying advanced analytics or ML techniques.

In this chapter, we will explore tabular data using various libraries and packages in Python, including Pandas, NumPy, and Seaborn. We will also plot different bar charts and histograms to visualize data to find the relationships between various features, which is useful for labeling data. We will be exploring the Income dataset located in this book’s GitHub repository (a link for which is located in the Technical requirements section). A good understanding of the data is necessary in order to define business rules, identify matching patterns, and, subsequently, label the data using Python labeling functions.

By the end of this chapter, we will be able to generate summary statistics for the given dataset. We will derive aggregates of the features for each target group. We will also learn how to perform univariate and bivariate analyses of the features in the given dataset. We will create a report using the ydata-profiling library.

We’re going to cover the following main topics:

EDA and data labeling

Summary statistics and data aggregates with Pandas

Data visualization with Seaborn for univariate and bivariate analysis

Profiling data using the ydata-profiling library

Unlocking insights from data with OpenAI and LangChain

Technical requirements

One of the following Python IDE and software tools needs to be installed before running the notebook in this chapter:

Anaconda Navigator: Download and install the open source Anaconda Navigator from the following URL:

https://docs.anaconda.com/navigator/install/#system-requirements

Jupyter Notebook: Download and install Jupyter Notebook:

https://jupyter.org/install

We can also use open source, online Python editors such as Google Colab (https://colab.research.google.com/) or Replit (https://replit.com/)

The Python source code and the entire notebook created in this chapter are available in this book’s GitHub repository:

https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python

You also need to create an Azure account and add an OpenAI resource for working with generative AI. To sign up for a free Azure subscription, visit https://azure.microsoft.com/free. To request access to the Azure OpenAI service, visit https://aka.ms/oaiapply.

Once you have provisioned the Azure OpenAI service, deploy the LLM model – either GPT-3.5-Turbo or GPT 4.0 – from Azure OpenAI Studio. Then copy the keys for OpenAI from OpenAI Studio and set up the following environment variables:

os.environ['AZURE_OPENAI_KEY'] = 'your_api_key'

os.environ['AZURE_OPENAI_ENDPOINT") ='your_azure_openai_endpoint'

Your endpoint should look like this: https://YOUR_RESOURCE_NAME.openai.azure.com/.

EDA and data labeling

In this section, we will gain an understanding of what EDA is. We will see why we need to perform it and discuss its advantages. We will also look at the life cycle of an ML project and learn about the role of data labeling in this cycle.

EDA comprises data discovery, data collection, data cleaning, and data exploration. These steps are part of any machine learning project. The data exploration step includes tasks such as data visualization, summary statistics, correlation analysis, and data distribution analysis. We will dive deep into these steps in the upcoming sections.

Here are some real-world examples of EDA:

Customer churn analysis: Suppose you work for a telecommunications company and you want to understand why customers are churning (canceling their subscriptions); in this case, conducting EDA on customer churn data can provide valuable insights.

Income data analysis: EDA on the Income dataset with predictive features such as education, employment status, and marital status helps to predict whether the salary of a person is greater than $50K.

EDA is a critical process for any ML or data science project, and it allows us to understand the data and gain some valuable insights into the data domain and business.

In this chapter, we will use various Python libraries, such as Pandas, and call the describe and info functions on Pandas to generate data summaries. We will discover anomalies in the data and any outliers in the given dataset. We will also figure out various data types and any missing values in the data. We will understand whether any data type conversions are required, such as converting string to float, for performing further analysis. We will also analyze the data formats and see whether any transformations are required to standardize them, such as the date format. We will analyze the counts of different labels and understand whether the dataset is balanced or imbalanced. We will understand the relationships between various features in the data and calculate the correlations between features.

To summarize, we will understand the patterns in the given dataset and also identify the relationships between various features in the data samples. Finally, we will come up with a strategy and domain rules for data cleaning and transformation. This helps us to predict labels for unlabeled data.

We will plot various data visualizations using Python libraries such as seaborn and matplotlib. We will create bar charts, histograms, heatmaps, and various charts to visualize the importance of features in the dataset and how they depend on each other.

Understanding the ML project life cycle

The following are the major steps in an ML project: