An Introduction To Automated Machine Learning With Tpot

Introduction

Automated Machine Learning (AutoML) has become a popular topic in the field of data science. AutoML essentially refers to the automated process of applying machine learning to real-world problems. One of the popular Python libraries that provide AutoML capabilities is TPOT (Tree-Based Pipeline Optimization Tool). In this article, we will delve into a quick overview and demonstration of TPOT.

What is TPOT?

TPOT is an open-source Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. It automates the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

Installation

TPOT is built on top of scikit-learn, so its installation is quite straightforward using pip:

!pip install tpot

Example Usage

Here is a simple example using the inbuilt iris dataset in scikit-learn:

from tpot import TPOTClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load and split data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
                                                    train_size=0.75, test_size=0.25)

# Invoke TPOT estimator
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))

# Export the generated code
tpot.export('output_pipeline.py')

This script will create and train a TPOT autoML estimator on the iris dataset. The 'verbosity' parameter can be set to 2 to display a progress bar. The generations and population_size parameters dictate the number of iterations TPOT goes through to optimize the pipeline (larger values will increase both runtime and potentially better results). After training, TPOT checks the accuracy on the provided testing data.

In the end, the optimized pipeline can be exported as a Python script with the export function.

Wrap Up

The power of TPOT and similar libraries lies in their ability to save the data scientist significant time and effort spent on pre-processing data and tuning hyperparameters. This enables the data scientist to focus on problem-solving aspects of machine learning, providing considerable benefits for both beginners and experts alike.

This was a very basic demonstration of what TPOT is capable of. There are many parameters and methods in TPOT to explore which makes it incredibly flexible and powerful.