Book Image

Data Processing with Optimus

By : Dr. Argenis Leon, Luis Aguirre
Book Image

Data Processing with Optimus

By: Dr. Argenis Leon, Luis Aguirre

Overview of this book

Optimus is a Python library that works as a unified API for data cleaning, processing, and merging data. It can be used for handling small and big data on your local laptop or on remote clusters using CPUs or GPUs. The book begins by covering the internals of Optimus and how it works in tandem with the existing technologies to serve your data processing needs. You'll then learn how to use Optimus for loading and saving data from text data formats such as CSV and JSON files, exploring binary files such as Excel, and for columnar data processing with Parquet, Avro, and OCR. Next, you'll get to grips with the profiler and its data types - a unique feature of Optimus Dataframe that assists with data quality. You'll see how to use the plots available in Optimus such as histogram, frequency charts, and scatter and box plots, and understand how Optimus lets you connect to libraries such as Plotly and Altair. You'll also delve into advanced applications such as feature engineering, machine learning, cross-validation, and natural language processing functions and explore the advancements in Optimus. Finally, you'll learn how to create data cleaning and transformation functions and add a hypothetical new data processing engine with Optimus. By the end of this book, you'll be able to improve your data science workflow with Optimus easily.
Table of Contents (16 chapters)
1
Section 1: Getting Started with Optimus
4
Section 2: Optimus – Transform and Rollout
10
Section 3: Advanced Features of Optimus

Implementing a train-test split procedure

The main idea of splitting your data into two datasets is that you can train your model in one and then test your model performance over new data. When a dataset is split into a training and testing set, the majority of the data goes to the training set and a small part of it is used for testing.

The subset used to fit a model is known as the training dataset. This contains example inputs and outputs (I/Os) that will train the model fitting the parameters.

On the other hand, when the inputs on the test dataset are provided to the model, the resulting predictions made from those inputs are then compared to the expected values to assess the model's accuracy.

When to use a train-test split procedure

A train-test split evaluation procedure can be used for classification or regression problems.

The dataset to be used should be large enough to represent the problem domain, covering every common case and enough uncommon cases....