Visualizzare una Random Search

Visualizzare lo spazio di ricerca della random search ti permette di vedere facilmente la copertura di questa tecnica e quindi l’effetto del tuo campionamento sullo spazio di ricerca.

In questo esercizio userai diversi campioni di combinazioni di iperparametri e produrrai visualizzazioni dello spazio di ricerca.

La funzione sample_and_visualize_hyperparameters() accetta un solo argomento (il numero di combinazioni da campionare) e poi campiona casualmente combinazioni di iperparametri, proprio come hai fatto nell’esercizio precedente! La funzione quindi visualizzerà le combinazioni.

Se vuoi vedere la definizione della funzione, puoi usare la comoda libreria inspect di Python, così:

print(inspect.getsource(sample_and_visualize_hyperparameters))

Questo esercizio fa parte del corso

Ottimizzazione degli iperparametri in Python

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Istruzioni dell'esercizio

Verifica quante possibili combinazioni di iperparametri ci sono in combinations_list assegnandole alla variabile number_combs e stampale.
Campiona e visualizza 50, 500 e 1500 combinazioni. Userai un ciclo per essere più sintetico. Cosa noti nella visualizzazione?
Ora campiona e visualizza l’intero set di combinazioni. Hai già creato una variabile per aiutarti in questo. Che aspetto ha?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Confirm how many hyperparameter combinations & print
number_combs = ____(____)
print(____)

# Sample and visualise specified combinations
for x in [____, ____, ____]:
    sample_and_visualize_hyperparameters(x)
    
# Sample all the hyperparameter combinations & visualise
____(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Ottimizzazione degli iperparametri in Python

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In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices.

Exercise 1: Introduction & 'Parameters'Exercise 2: Parameters in Logistic Regression Exercise 3: Extracting a Logistic Regression parameter Exercise 4: Extracting a Random Forest parameter Exercise 5: Introducing Hyperparameters Exercise 6: Hyperparameters in Random Forests Exercise 7: Exploring Random Forest Hyperparameters Exercise 8: Hyperparameters of KNN Exercise 9: Setting & Analyzing Hyperparameter Values Exercise 10: Automating Hyperparameter Choice Exercise 11: Building Learning Curves

This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this.

Exercise 1: Introducing Grid Search Exercise 2: Build Grid Search functions Exercise 3: Iteratively tune multiple hyperparameters Exercise 4: How Many Models?Exercise 5: Grid Search with Scikit Learn Exercise 6: GridSearchCV inputs Exercise 7: GridSearchCV with Scikit Learn Exercise 8: Understanding a grid search output Exercise 9: Using the best outputs Exercise 10: Exploring the grid search results Exercise 11: Analyzing the best results Exercise 12: Using the best results

In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output.

Exercise 1: Introduzione al Random Search Exercise 2: Campiona casualmente gli iperparametri Exercise 3: Ricerca casuale con Random Forest Exercise 4: Visualizzare una Random Search

Esercizio in corso

Exercise 5: Random Search in Scikit Learn Exercise 6: Input di RandomSearchCV Exercise 7: L'oggetto RandomizedSearchCV Exercise 8: RandomizedSearchCV in Scikit Learn Exercise 9: Confrontare Grid Search e Random Search Exercise 10: Confronto tra Random Search e Grid Search Exercise 11: Grid e Random Search a confronto

In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go.

Exercise 1: Informed Search: Coarse to Fine Exercise 2: Visualizing Coarse to Fine Exercise 3: Coarse to Fine Iterations Exercise 4: Informed Search: Bayesian Statistics Exercise 5: Bayes Rule in Python Exercise 6: Bayesian Hyperparameter tuning with Hyperopt Exercise 7: Informed Search: Genetic Algorithms Exercise 8: Genetic Hyperparameter Tuning with TPOT Exercise 9: Analysing TPOT's stability Exercise 10: Congratulations!