Get the Most out of Scikit-Learn with Object-Oriented Programming

As data scientists, we are all familiar with scikit-learn, one of the most used machine learning and data analysis libraries available in Python. I’ve personally used it, along with pandas, for the majority of my professional projects.

It is only recently, however, that I have gotten the most out of scikit-learn when I needed to build a custom regression estimator. I was pleasantly surprised at how easy it was to create a new and compatible class of estimator, and this is all due to the object-oriented design of the scikit-learn components.

In this article, I will walk you through a common data science workflow and demonstrate a use case for object-oriented programming (OOP). In particular, you will learn how to make a custom transformer object using the concept of inheritance, which allows us to extend the functionality of an existing class. Thanks to inheritance, this transformer will then easily fit into a scikit-learn pipeline to build a simple machine learning model.

A motivating example.

To illustrate things more clearly, let’s work through a practical example inspired by marketing for a retailer. Suppose I have a data set where each record represents a customer and there are measurements (i.e. features) relating to purchase recency, frequency, and monetary value. Using these features, we would like to predict whether or not the customer will join the retailer’s rewards card program. This emulates a real-world scenario where we want to predict which clients are the best targets for marketing. See a sample of the customer record data below:

One noticeable quality of the data is missingness, the presence of missing values in the observations. These values will be rendered as NaN’s once read by pandas and will not work in our machine learning model. To build our model, we will need to impute values (i.e. substitute missing values with inferred quantities) for the missing features. While there are a number of techniques for data imputation, for the sake of simplicity let’s say we want to try either imputing the column mean or median for any missing value.

So, which statistic should we use, the mean or the median? One easy way of finding out is by trying both options and assessing which yields the best performance in a grid search, maybe by using scikit-learn’s GridSearchCV.

One mistake would be to compute the mean and median on the entire training set and then impute with those values. To be more rigorous, we need to compute these statistics on the data excluding the holdout fold. This seems complicated, but thanks to OOP, we can easily implement our own transformer via inheritance which will be compatible with scikit-learn. From there, we can plug the transformer into a scikit-learn Pipeline, an object which sequences together with a list of transformer objects along with an estimator object, to build our model.

#data-science #python

What is GEEK

Buddha Community

Get the Most out of Scikit-Learn with Object-Oriented Programming
Arvel  Parker

Arvel Parker


How to Find Ulimit For user on Linux

How can I find the correct ulimit values for a user account or process on Linux systems?

For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]

MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS

We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.

MEAN Stack tutorial series:

AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II)
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III) 👈 you are here
Before completing the app, let’s cover some background about the this stack. If you rather jump to the hands-on part click here to get started.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]

Creating RESTful APIs with NodeJS and MongoDB Tutorial

Welcome to this tutorial about RESTful API using Node.js (Express.js) and MongoDB (mongoose)! We are going to learn how to install and use each component individually and then proceed to create a RESTful API.

MEAN Stack tutorial series:

AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II) 👈 you are here
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III)

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]

Tyrique  Littel

Tyrique Littel



When I install s3cmd package on my FreeBSD system and try to use the s3cmd command I get the following error:

_ERROR: Test failed: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (ssl.c:1091)

How do I fix this problem on FreeBSD Unix system?

Amazon Simple Storage Service (s3 ) is object storage through a web service interface or API. You can store all sorts of files. FreeBSD is free and open-source operating systems. s3cmd is a command-line utility for the Unix-like system to upload, download files to AWS S3 service from the command line.

ERROR: Test failed: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed error and solution

This error indicates that you don’t have packages correctly installed, especially SSL certificates. Let us see how to fix this problem and install s3cmd correctly on FreeBSD to get rid of the problem.

How to install s3cmd on FreeBSD

Search for s3cmd package:

$ pkg search s3cmd

Execute the following command and make sure you install Python 3.x package as Python 2 will be removed after 2020:

$ sudo pkg install py37-s3cmd-2.1.0

Updating FreeBSD repository catalogue...
FreeBSD repository is up to date.
All repositories are up to date.
Checking integrity... done (0 conflicting)
The following 8 package(s) will be affected (of 0 checked):

New packages to be INSTALLED:
	libffi: 3.2.1_3
	py37-dateutil: 2.8.1
	py37-magic: 5.38
	py37-s3cmd: 2.1.0
	py37-setuptools: 44.0.0
	py37-six: 1.14.0
	python37: 3.7.8
	readline: 8.0.4

Number of packages to be installed: 8

The process will require 118 MiB more space.

Proceed with this action? [y/N]: y
[rsnapshot] [1/8] Installing readline-8.0.4...
[rsnapshot] [1/8] Extracting readline-8.0.4: 100%
[rsnapshot] [2/8] Installing libffi-3.2.1_3...
[rsnapshot] [8/8] Extracting py37-s3cmd-2.1.0: 100%
Message from python37-3.7.8:

Note that some standard Python modules are provided as separate ports
as they require additional dependencies. They are available as:

py37-gdbm       databases/py-gdbm@py37
py37-sqlite3    databases/py-sqlite3@py37
py37-tkinter    x11-toolkits/py-tkinter@py37

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]

CentOS Linux 8.2 Released and Here is How to Upgrade it

CentOS Linux 8.2 (2004) released. It is a Linux distribution derived from RHEL (Red Hat Enterprise Linux) 8.2 source code. CentOS was created when Red Hat stopped providing RHEL free. CentOS 8.2 gives complete control of its open-source software packages and is fully customized for research needs or for running a high-performance website without the need for license fees. Let us see what’s new in CentOS 8.2 (2004) and how to upgrade existing CentOS 8.1.1199 server to 8.2.2004 using the command line.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]