Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate ...for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Cassiterite is a weathering-resistant mineral, which can incorporate a variety of trace elements. Trace elements in cassiterite samples collected from twelve deposits in the Herberton Mineral Field, ...Australia, were measured with the use of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The results were combined with published data from other tin fields, including the Andean Sn belt in South America; the Karagwe Ankole belt in Rwanda; and, from China, the Kangxiwa-Dahongliutan pegmatite field, the Youjiang basin, the Nanling belt and the Da Hinggan Range belt. Tin deposits in the dataset can be subdivided into four deposit types: 1) greisen and veins; 2) skarns; 3) Li-Cs-Ta pegmatites; and 4) polymetallic veins. The cassiterite dataset was analyzed using basic descriptive statistics, principal component analysis (PCA), and cluster analysis. Cassiterite grains from greisen and vein deposits are characterized by high concentrations of Ti (avg. 1751 ppm) and moderate concentrations of Al (avg. 97 ppm), whereas cassiterite grains from skarn deposits generally contain lower concentrations of Ti and Al. Chemical compositional boundaries in cassiterite from different deposits were recognized with cluster analysis. The relative enrichment of Al and Ti in cassiterite grains from greisen and vein deposits is likely due to greisenization reactions. The Ti vs. Al diagram can be used to differentiate between cassiterite grains derived from greisen and vein deposits, as compared to cassiterite grains derived from skarn deposits, whereas Sb vs. V diagram can be used to differentiate between cassiterite grains from polymetallic vein deposits. Zirconium and Nb concentrations are useful in identifying cassiterite grains sourced from LCT pegmatite deposits. The discrimination diagrams developed in this study through cluster analysis indicate that cassiterite grains sourced from different deposit types can be differentiated based on their trace element geochemistry and this can be a useful tool in critical mineral exploration. Therefore, these diagrams can be used effectively to understand metal association and deposit types in a region with detrital cassiterite from stream sediments, till and heavy mineral placer deposits.
•Compilation of cassiterite trace element data from seven major tin belts.•Discrimination plots for identifying cassiterite source.•Application of cassiterite discrimination diagrams in critical metal exploration.
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of ...activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past ...20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.
GraKeL: A Graph Kernel Library in Python Siglidis, Giannis; Nikolentzos, Giannis; Limnios, Stratis ...
Journal of machine learning research,
01/2020
Journal Article
Peer reviewed
Open access
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this ...problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/ GraKeL.
Summary
With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high‐performance batteries like lithium‐ion (Li‐ion) batteries have soared. Li‐ion battery systems ...require the use of a battery management system (BMS) to perform safely and efficiently. Accurate and reliable battery modeling is important for the BMS to function properly. Currently, many BMS applications use the equivalent circuit model due to its simplicity. However, with the development of a cloud BMS, machine learning battery models can be utilized, which can potentially improve the accuracy and reliability of the BMS. This work investigates the performance of four different machine learning models used to predict the thermal (temperature) and electrical (voltage) behaviors of Li‐ion battery cells. A prismatic Li‐ion battery cell with a capacity of 25 Ah was cycled under a constant current profile at three different ambient temperatures, and the surface temperature and voltage of the battery were measured. The four machine learning regression models—linear regression, k‐nearest neighbors, random forest, and decision tree—were developed using the scikit‐learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R2 metric. The decision tree‐based model, with an R2 score of 0.99, was determined to be the best model in this case study.
metric-learn is an open source Python package implementing supervised and weaklysupervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unified interface ...compatible with scikit-learn which allows to easily perform cross-validation, model selection, and pipelining with other machine learning estimators. metric-learn is thoroughly tested and available on PyPi under the MIT license.
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