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Happy New Year! We’re back! Did you try your hand at any exciting Python projects over the holidays that you’d like to share? If so, reply to this email and let me know. If it’s brilliant, we’ll share what you made in next week's issue.

In today’sExpert Insight we bring you an excerpt from the recently published book, XGBoost for Regression Predictive Modeling and Time Series Analysis, which demonstrates the power of XGBoost's multithreaded capabilities, showcasing how adjusting the nthread parameter can significantly accelerate model training by utilizing multiple CPU cores, as illustrated through a practical example with the California housing dataset.

News Highlights: Python 3.14.0 alpha 4 introduces features like PEP 649 for deferred annotations and improved error messages; Python wins Tiobe's Programming Language of the Year 2024 with a 9.3% popularity surge; and a new PEP proposes SBOMs for better package security and dependency tracking.

My top 5 picks from today’s learning resources:

And, in From the Cutting Edge, we introduce sQUlearn, a Python library for quantum machine learning that integrates seamlessly with classical tools like scikit-learn, offering high-level APIs, low-level customisation, and robust support for NISQ devices.

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In the paper, "sQUlearn – A Python Library for Quantum Machine Learning," Kreplin et al. introduce sQUlearn, a Python library for quantum machine learning (QML), designed to integrate seamlessly with classical machine learning tools like scikit-learn.

Quantum Machine Learning (QML) combines quantum computing and machine learning to harness quantum principles for computational efficiency and enhanced algorithmic capabilities. However, many current QML tools demand in-depth quantum computing expertise. Noisy Intermediate-Scale Quantum (NISQ) devices, while promising, pose significant challenges due to their limitations in handling deep quantum circuits. To bridge these gaps, sQUlearn focuses on NISQ-compatibility, usability, and integration with classical ML tools, particularly scikit-learn.

sQUlearn offers:

sQUlearn simplifies quantum machine learning for both researchers and practitioners. For researchers, it offers a flexible low-level framework for exploring novel QML algorithms and quantum circuit designs. For practitioners it simplifies the deployment of QML solutions with minimal quantum-specific knowledge via high-level interfaces and pre-built models using familiar tools like scikit-learn.

sQUlearn’s dual-layer architecture enables flexibility, with high-level APIs for seamless integration into machine learning workflows and low-level tools for advanced customisation. The Executor module centralises quantum job execution, handling retries, caching results, and transitioning between simulation and real hardware. It supports quantum kernel methods and neural networks while addressing noise challenges on quantum devices through built-in regularisation techniques. This focus on automation and robustness ensures the library is both reliable for practical applications and adaptable for research needs.

You can learn more by reading the entire paper or accessing the library on GitHub.

Here’s an excerpt from “Chapter 13: Deploying Your XGBoost Model” in the book, XGBoost for Regression Predictive Modeling and Time Series Analysis by Partha Pritam Deka and Joyce Weiner.

XGBoost has built-in support for multithreaded computing, which allows you to speed up model training by utilizing multiple CPU cores. You can control this by setting thenthread parameter, which determines the number of threads to use. By default,

XGBoost will automatically use the maximum number of available threads.

It’s important to note that if you’re using Dask, any value you set fornthreadwithin XGBoost will take precedence over Dask’s default configuration. The following example demonstrates how the multithreading parameter works. We’ll revisit theCalifornia housing datasetthat you worked with inChapter 4:

import pandas as pd
import numpy as np
import time
import xgboost as xgb
from sklearn.metrics import r2_score
from sklearn import datasets
from sklearn.model_selection import train_test_split

housingX, housingy = datasets.fetch_california_housing(
return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(
housingX,housingy, test_size=0.2, random_state=17)

dtrain = xgb.DMatrix(X_train, y_train)
dtest = xgb.DMatrix(X_test, y_test)

Now, the data is in a format that XGBoost can manipulate with efficiency. As mentioned inChapter 3, XGBoost does some sorting and performs other operations on the dataset to speedup execution.

train_start = time.time()

param = {"eta": 0.3, "booster": "gbtree",
"nthread": 2}

housevalue_xgb = xgb.train(param, dtrain)
train_end = time.time()

print ("Training time with 2 threads is :{
0:.3f}".format((train_end - train_start) * 10**3),
"ms")

train_start = time.time()
param = {"eta": 0.3, "booster": "gbtree",
"nthread": 8}
housevalue_xgb = xgb.train(param, dtrain)
train_end = time.time()
print ("Training time with 8 threads is :{
0:.3f}".format((train_end - train_start) * 10**3),
"ms")

pred_start = time.time()
ypred = housevalue_xgb.predict(dtest)
pred_end = time.time()
print ("Prediction time is :{0:.3f}".format((
pred_end - pred_start) * 10**3), "ms")
xgb_r2 = r2_score(y_true=y_test, y_pred= ypred)
print ("XGBoost Rsquared is {0:.2f}".format(xgb_r2))

Training time with 2 threads is :237.088 ms
Training time with 8 threads is :130.723 ms
Prediction time is :2.012 ms XGBoost
Rsquared is 0.76

On our computer, going from two to eight threads sped up training by over 44%. This demonstrates the benefit XGBoost provides with multithreading. Recall that by default, it will use the maximum number of threads available. Next, you’ll learn about using XGBoost with distributed compute by using Daskon Linux.

XGBoost for Regression Predictive Modeling and Time Series Analysis was published in December 2024.

And that’s a wrap.

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