Gerarchie di titoli azionari

Nel capitolo 1 hai usato il clustering k-means per raggruppare le aziende in base agli andamenti dei loro prezzi azionari. Ora eseguirai un clustering gerarchico delle aziende. Ti viene fornito un array NumPy di movimenti di prezzo movements, in cui le righe corrispondono alle aziende, e un elenco dei nomi delle aziende companies. Il clustering gerarchico di SciPy non si integra in una pipeline di sklearn, quindi dovrai usare la funzione normalize() da sklearn.preprocessing invece di Normalizer.

linkage e dendrogram sono già stati importati da scipy.cluster.hierarchy, e PyPlot è stato importato come plt.

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Istruzioni dell'esercizio

Importa normalize da sklearn.preprocessing.
Riscalare gli andamenti dei prezzi per ciascun titolo usando la funzione normalize() su movements.
Applica la funzione linkage() a normalized_movements, usando il collegamento 'complete', per calcolare il clustering gerarchico. Assegna il risultato a mergings.
Traccia un dendrogramma del clustering gerarchico, usando la lista companies dei nomi delle aziende come labels. Inoltre, specifica gli argomenti leaf_rotation=90 e leaf_font_size=6 come hai fatto nell'esercizio precedente.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import normalize
____

# Normalize the movements: normalized_movements
normalized_movements = ____

# Calculate the linkage: mergings
mergings = ____

# Plot the dendrogram
____
plt.show()

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Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

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Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizzare le gerarchie Exercise 2: Quante fusioni?Exercise 3: Clustering gerarchico dei dati sui cereali Exercise 4: Gerarchie di titoli azionari

Esercizio in corso

Exercise 5: Etichette di cluster nel clustering gerarchico Exercise 6: Quali cluster sono più vicini?Exercise 7: Linkage diverso, clustering gerarchico diverso!Exercise 8: Raggruppamenti intermedi Exercise 9: Estrarre le etichette dei cluster Exercise 10: t-SNE per mappe bidimensionali Exercise 11: Visualizzazione t-SNE dell'insieme di dati sui cereali Exercise 12: Una mappa t-SNE del mercato azionario

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Non-negative data Exercise 3: NMF applied to Wikipedia articles Exercise 4: NMF features of the Wikipedia articles Exercise 5: NMF reconstructs samples Exercise 6: NMF learns interpretable parts Exercise 7: NMF learns topics of documents Exercise 8: Explore the LED digits dataset Exercise 9: NMF learns the parts of images Exercise 10: PCA doesn't learn parts Exercise 11: Building recommender systems using NMF Exercise 12: Which articles are similar to 'Cristiano Ronaldo'?Exercise 13: Recommend musical artists part I Exercise 14: Recommend musical artists part II Exercise 15: Final thoughts