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

Training models in Optimus

Now that we know how the test/train, split, and cross-validation processes work, let me tell you something amazing. You don't have to struggle with configuring and writing code to make this process work, as Optimus will do the heavy lifting for you.

Let's see the ML models available in Optimus.

Linear regression

Linear regression is a supervised ML algorithm that is useful for finding out how variables are linked to each other. By assigning a linear equation to the data that we have, we can use fresh data and predict the output, as illustrated in the following diagram:

Figure 8.4 – A line approximated to a cluster of points

In the preceding diagram, we can see a line that approximates a cluster of points. Let's see how to calculate this approximation.

First, let's start by creating a dataset with the following code:

import numpy as np 
size = 10000 
data = {&...