Hiërarchieën van aandelen

In hoofdstuk 1 gebruikte je k-means clustering om bedrijven te groeperen op basis van hun aandelenkoersbewegingen. Nu ga je hiërarchische clustering op de bedrijven toepassen. Je krijgt een NumPy-array met koersbewegingen movements, waarbij de rijen overeenkomen met bedrijven, en een lijst met bedrijfsnamen companies. SciPy hiërarchische clustering past niet in een sklearn-pijplijn, dus je moet de functie normalize() uit sklearn.preprocessing gebruiken in plaats van Normalizer.

linkage en dendrogram zijn al geïmporteerd uit scipy.cluster.hierarchy, en PyPlot is geïmporteerd als plt.

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Oefeninstructies

Importeer normalize uit sklearn.preprocessing.
Herschal de koersbewegingen voor elk aandeel met de functie normalize() op movements.
Pas de functie linkage() toe op normalized_movements met 'complete' linkage om de hiërarchische clustering te berekenen. Ken het resultaat toe aan mergings.
Plot een dendrogram van de hiërarchische clustering, met de lijst companies van bedrijfsnamen als labels. Geef daarnaast de keyword-argumenten leaf_rotation=90 en leaf_font_size=6 op, zoals je in de vorige oefening deed.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import normalize
____

# Normalize the movements: normalized_movements
normalized_movements = ____

# Calculate the linkage: mergings
mergings = ____

# Plot the dendrogram
____
plt.show()

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Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Begin de cursus gratis

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: Hiërarchieën visualiseren Exercise 2: Hoeveel samenvoegingen?Exercise 3: Hiërarchische clustering van de graangegevens Exercise 4: Hiërarchieën van aandelen

Huidige oefening

Exercise 5: Clusterlabels bij hiërarchisch clusteren Exercise 6: Welke clusters liggen het dichtst bij elkaar?Exercise 7: Andere linkage, andere hiërarchische clustering!Exercise 8: Tussenliggende clusterings Exercise 9: Clusteringlabels extraheren Exercise 10: t-SNE voor 2D-kaarten Exercise 11: t-SNE-visualisatie van graangegevensset Exercise 12: Een t-SNE-kaart van de aandelenmarkt

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