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Category: Python

Explaining an ML Model with SHAP

Dan Lantos, et al, walk us through one technique for model explainability:

Interpretability has to do with how accurately a machine learning model can associate a cause (input) to an effect (output). 

Explainability on the other hand is the extent to which the internal mechanics of a machine or deep learning system can be explained in human terms. Or to put it simply, explainability is the ability to explain what is happening. 

Let’s consider a simple example illustrated below where the goal of the machine learning model is to classify an animal into its respective groups. We use an image of a butterfly as input into the machine learning model. The model would classify the butterfly as either an insect, mammal, fish, reptile or bird. Typically, most complex machine learning models would provide a classification without explaining how the features contributed to the result. However, using tools that help with explainability, we can overcome this limitation. We can then understand what particular features of the butterfly contributed to it being classified as an insect. Since the butterfly has six legs, it is thus classified as an insect.

Being able to provide a rationale behind a model’s prediction would give the users (and the developers) confidence about the validity of the model’s decision.

Read on to see how you can use a library called SHAP in Python to help with this explainability.

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Reinforcement Learning and Python 3

I have a new post up:

I finally got around to trying out a reinforcement learning exercise this weekend in an attempt to learn about the technique. One of the most interesting blog posts I read is Andrej Karpathy’s post on using reinforcement learning to play Pong on the Atari 2600. In it, Andrej uses the Gym package in Python to play the game.

This won’t be a post diving into the details of how reinforcement learning works; Andrej does that far better than I possibly could, so read the post. Instead, the purpose of this post is to provide a minor update to Andrej’s code to switch it from Python 2 to Python 3. In doing this, I went with the most convenient answer over a potentially better solution (e.g., switching xrange() to range() rather then re-working the code), but it does work. I also bumped up the learning rate a little bit to pick up the pace a bit.

Click through for the (slightly) updated code.

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Contrasting Scala and Python wrt Spark

Sanjay Rathore contrasts two of the three key Apache Spark languages:

Imagine the first day of a new Apache Spark project. The project manager looks at the team and says: which one to choose, scala or python. So let’s start with “scala vs python for spark”. 

You may wonder if this is a tricky question. What does the enterprise demand say? Is this like asking iOS or Android? Is there a right or wrong answer?

So we are here to inform and provide clarity. Today we’re looking at two popular programming languages, Scala and Python, and comparing them in the context of Apache Spark and Big Data in general.

Read on for the comparison. I’m at a point where I think it’s wise to know both languages and roll with whichever is there. If you’re in a greenfield Spark implementation, pick the one you (or your team) is more comfortable with. If you’re equally comfortable with the two, pick Scala because it’s a functional programming language and those are neat.

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Time Series Estimation with Facebook’s Prophet

Dan Lantos looks at the Prophet library:

This article (part of a short series) aims to introduce the Prophet library, discuss it at a high level and run through a basic example of forecasting the FTSE 100 index. Future articles will discuss exactly how Prophet achieves its results, how to interpret the output and how to improve the model.
Please see this article (by my talented colleague Gavita) for an introduction to time-series forecasting algorithms.

Click through for part one in an ongoing series.

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Optimizing BERT Models on Google Colab

Kevin Jacobs fine-tunes some NLP processes:

BERT is a language model and can thus be used for predicting the next word in a sentence. Furthermore, BERT can be used for automatic summarization, text classification and many more downstream tasks. Google Colab provides you with a cloud-based environment on which you can train your machine learning models on a GPU. The downside is that your data is uploaded to the Google cloud. Google Colab gives you the opportunity to finetune BERT.

Click through to see how.

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What’s New in data_algebra

John Mount has an update on a Python package:

The data algebra is a modern realization of elements of Codd’s 1969 relational model for data wrangling (see also Codd’s 12 rules).

The idea is: most data manipulation tasks can usefully be broken down into a small number of fundamental data transforms plus composition. In Codd’s initial writeup, composition was expressed using standard mathematical operator notation. For “modern” realizations one wants to use a composition notation that is natural for the language you are working in. For Python the natural composition notation is method dispatch.

Click through to see how it works and what’s new in the latest version.

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Hosting a Python API with Flask

Mrinal Walia shows how you can build a Python API, such as one for generating machine learning predictions, using Flask:

Deployment is a crucial move in the ML workflow. It is a mark where we want to implement our ML model into utilization. Later, we can practice the model in practical life.

But how can we design the model as a treatment? We can develop an Application Programming Interface (API). With that, we can reach the model universally, can be a mobile application or web application. In Python, there’s a library that can assist us in building an API. It’s named Flask.

This article will explain how to construct a REST API for our machine learning model utilizing Flask. Without further ado, let’s begun!

Flask is the first step, but then I’d want to reverse proxy it with gunicorn or Nginx afterward.

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Running Dask on AKS

Tsuyoshi Matsuzaki sets up Dask as a distributed service:

In my last post, I showed you tutorial for running Apache Spark on managed kubernetes, Azure Kubernetes Service (AKS).
In this post, I’ll show you the tutorial for running distributed workloads of Dask on AKS.

By using Dask, you can run Scikit-Learn compliant functions and jobs for data which cannot fit in memory, or run in distributed manners. For simplicity, here I’ll use built-in Dask ML function (dask_ml.linear_model.LinearRegression) in this tutorial. (With the same manners, you can also run regular sklearn functions.)
Cloud managed kubernetes will make you speed up this large ML workloads.

Click through for the process. I’ve had some positive experiences with Dask as a dashboarding tool. It’s definitely one of the better ones if you’re big into Python.

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Creating Diagrams from Code

Sheldon Hull walks us through the diagrams package in Python:

LucidChart, and other tools are great for a quick solution.

Mermaid also provides a nice simple text based diagramming tool that is integrated with many markdown tools.

For me, this just never fit. I like a bit of polish and beauty in a visual presentation and most of these are very utilitarian in their output.

I came across diagrams and found it a perfect fit for intuitive and beautiful diagram rendering of cloud architecture, and figured it would be worth a blog post to share this.

Back when GitPitch was still a viable concern, I had just gotten into using the diagrams package. It takes some getting used to and has very strong preferences on the sorts of relationships diagram elements can have, but it’s good at its job.

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