Hyperparameter Tuning with River
37
HPT: River
Hyperparameter Tuning Cookbook
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Preface
Optimization
1
Aircraft Wing Weight Example
2
Introduction to
scipy.optimize
Numerical Methods
3
Simulation and Surrogate Modeling
4
Sampling Plans
5
Constructing a Surrogate
6
Response Surface Methods
7
Polynomial Models
8
Radial Basis Function Models
9
Kriging (Gaussian Process Regression)
10
Matrices
11
Infill Criteria
Sequential Parameter Optimization Toolbox (SPOT)
12
Introduction to Sequential Parameter Optimization
13
Multi-dimensional Functions
14
Isotropic and Anisotropic Kriging
15
Sequential Parameter Optimization: Using
scipy
Optimizers
16
Using
sklearn
Surrogates in
spotpython
17
Sequential Parameter Optimization: Gaussian Process Models
18
Infill Criteria
19
Handling Noise
20
Optimal Computational Budget Allocation in spotpython
21
Kriging with Varying Correlation-p
22
Factorial Variables
23
User-Specified Functions: Extending the Analytical Class
24
Kriging with spotPython based on the Forrester et al. textbook
25
Benchmarking SPOT Kriging with Matern Kernel on 6D Rosenbrock Function and 10D Michalewicz Function
Data-Driven Modeling and Optimization
26
Basic Statistics and Data Analysis
27
Hypothesis Testing
28
Addressing Multicollinearity: Principle Component Analysis (PCA) and Factor Analysis (FA)
29
Regression
30
Classification
31
Clustering
Machine Learning and AI
32
Machine Learning and Artificial Intelligence
Introduction to Hyperparameter Tuning
33
Hyperparameter Tuning
Hyperparameter Tuning with Sklearn
34
HPT: sklearn
35
HPT: sklearn SVC on Moons Data
36
HPT: sklearn SVR on Regression Data
Hyperparameter Tuning with River
37
HPT: River
38
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
39
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
40
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
41
Basic Lightning Module
42
Details of the Lightning Module Integration in spotpython
43
User Specified Basic Lightning Module With spotpython
44
HPT PyTorch Lightning: Data
45
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
46
Early Stopping Explained: HPT with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
47
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
48
Hyperparameter Tuning with PyTorch Lightning and User Models
49
Hyperparameter Tuning with PyTorch Lightning: ResNets
50
Neural ODEs
51
Neural ODE Example
52
Physics Informed Neural Networks
53
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
54
Explainable AI with SpotPython and Pytorch
55
HPT PyTorch Lightning Transformer: Introduction
56
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
57
Saving and Loading
58
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
59
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
60
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning Using a CondNet Model
Multi Objective Optimization
61
Introduction to Desirability Functions
Lernmodule
62
Lernmodul: Aircraft Wing Weight Example (AWWE)
63
Lernmodul: Versuchspläne (Sampling-Pläne) für Computerexperimente
64
Lernmodul: Eine Einführung in Kriging
65
Lernmodul: Die Cholesky-Zerlegung
66
Lernmodul: Erweiterung des Kriging-Modells: Numerische Optimierung der Hyperparameter
67
Lernmodul: Erweiterung des Kriging-Modells zu einer Klasse (Python Code)
68
Lernmodul: Kriging Projekt
69
Lernmodul: Kriging Projekt mit Expected Improvement
Appendices
A
Introduction to Jupyter Notebook
B
Git Introduction
C
Python
D
Gaussian Processes—Some Background Information
E
Datasets
F
Using Slurm
G
Python Package Building
H
Parallelism in Initial Design
I
Solutions to Selected Exercises
References
Table of contents
37.1
Introduction to River
Hyperparameter Tuning with River
37
HPT: River
37
HPT: River
37.1
Introduction to River
36
HPT: sklearn SVR on Regression Data
38
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI