Hyperparameter Tuning with River
34
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
Data-Driven Modeling and Optimization
24
Basic Statistics and Data Analysis
25
Addressing Multicollinearity: Principle Component Analysis (PCA) and Factor Analysis (FA)
26
Regression
27
Classification
28
Clustering
Machine Learning and AI
29
Machine Learning and Artificial Intelligence
Introduction to Hyperparameter Tuning
30
Hyperparameter Tuning
Hyperparameter Tuning with Sklearn
31
HPT: sklearn
32
HPT: sklearn SVC on Moons Data
33
HPT: sklearn SVR on Regression Data
Hyperparameter Tuning with River
34
HPT: River
35
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
36
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
37
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
38
Basic Lightning Module
39
Details of the Lightning Module Integration in spotpython
40
User Specified Basic Lightning Module With spotpython
41
HPT PyTorch Lightning: Data
42
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
43
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
44
Hyperparameter Tuning with PyTorch Lightning and User Models
45
Hyperparameter Tuning with PyTorch Lightning: ResNets
46
Neural ODEs
47
Neural ODE Example
48
Physics Informed Neural Networks
49
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
50
Explainable AI with SpotPython and Pytorch
51
HPT PyTorch Lightning Transformer: Introduction
52
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
53
Saving and Loading
54
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
55
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
56
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning Using a CondNet Model
Multi Objective Optimization
57
Introduction to Desirability Functions
Lernmodule
58
Lernmodul: Aircraft Wing Weight Example (AWWE)
59
Lernmodul: Versuchspläne (Sampling-Pläne) für Computerexperimente
60
Lernmodul: Eine Einführung in Kriging
61
Lernmodul: Die Cholesky-Zerlegung
62
Lernmodul: Erweiterung des Kriging-Modells: Numerische Optimierung der Hyperparameter
63
Lernmodul: Erweiterung des Kriging-Modells zu einer Klasse (Python Code)
64
Lernmodul: Kriging Projekt
65
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
34.1
Introduction to River
Hyperparameter Tuning with River
34
HPT: River
34
HPT: River
34.1
Introduction to River
33
HPT: sklearn SVR on Regression Data
35
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI