Hyperparameter Tuning with River
33
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
Sequential Parameter Optimization Toolbox (SPOT)
11
Introduction to Sequential Parameter Optimization
12
Multi-dimensional Functions
13
Isotropic and Anisotropic Kriging
14
Sequential Parameter Optimization: Using
scipy
Optimizers
15
Using
sklearn
Surrogates in
spotpython
16
Sequential Parameter Optimization: Gaussian Process Models
17
Expected Improvement
18
Handling Noise
19
Optimal Computational Budget Allocation in spotpython
20
Kriging with Varying Correlation-p
21
Factorial Variables
22
User-Specified Functions: Extending the Analytical Class
Data-Driven Modeling and Optimization
23
Basic Statistics and Data Analysis
24
Addressing Multicollinearity: Principle Component Analysis (PCA) and Factor Analysis (FA)
25
Regression
26
Classification
27
Clustering
Machine Learning and AI
28
Machine Learning and Artificial Intelligence
Introduction to Hyperparameter Tuning
29
Hyperparameter Tuning
Hyperparameter Tuning with Sklearn
30
HPT: sklearn
31
HPT: sklearn SVC on Moons Data
32
HPT: sklearn SVR on Regression Data
Hyperparameter Tuning with River
33
HPT: River
34
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
35
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
36
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
37
Basic Lightning Module
38
Details of the Lightning Module Integration in spotpython
39
User Specified Basic Lightning Module With spotpython
40
HPT PyTorch Lightning: Data
41
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
42
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
43
Hyperparameter Tuning with PyTorch Lightning and User Models
44
Hyperparameter Tuning with PyTorch Lightning: ResNets
45
Neural ODEs
46
Neural ODE Example
47
Physics Informed Neural Networks
48
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
49
Explainable AI with SpotPython and Pytorch
50
HPT PyTorch Lightning Transformer: Introduction
51
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
52
Saving and Loading
53
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
54
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
55
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning Using a CondNet Model
Multi Objective Optimization
56
Introduction to Desirability Functions
Lernmodule
57
Lernmodul: Aircraft Wing Weight Example (AWWE)
58
Lernmodul: Versuchspläne (Sampling-Pläne) für Computerexperimente
59
Lernmodul: Eine Einführung in Kriging
60
Lernmodul: Die Cholesky-Zerlegung
61
Lernmodul: Erweiterung des Kriging-Modells: Numerische Optimierung der Hyperparameter
62
Lernmodul: Erweiterung des Kriging-Modells zu einer Klasse (Python Code)
63
Lernmodul: Kriging Projekt
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
33.1
Introduction to River
Hyperparameter Tuning with River
33
HPT: River
33
HPT: River
33.1
Introduction to River
32
HPT: sklearn SVR on Regression Data
34
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI