Clustering di Wikipedia, parte II

È il momento di mettere al lavoro la pipeline dell'esercizio precedente! Ti vengono forniti un array articles con le frequenze tf-idf delle parole di alcuni popolari articoli di Wikipedia e una lista titles con i loro titoli. Usa la tua pipeline per raggruppare (clusterizzare) gli articoli di Wikipedia.

Una soluzione all'esercizio precedente è già stata caricata per te, quindi hai a disposizione una Pipeline pipeline che collega TruncatedSVD con KMeans.

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

Visualizza il corso

Istruzioni dell'esercizio

Importa pandas come pd.
Adatta la pipeline alla matrice di frequenze delle parole articles.
Predici le etichette dei cluster.
Allinea le etichette dei cluster con la lista titles dei titoli degli articoli creando un DataFrame df con labels e titles come colonne. Questo è già stato fatto per te.
Usa il metodo .sort_values() di df per ordinare il DataFrame in base alla colonna 'label' e stampa il risultato.
Premi Invia risposta e prenditi un momento per esplorare il tuo fantastico clustering delle pagine di Wikipedia!

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import pandas
____

# Fit the pipeline to articles
____

# Calculate the cluster labels: labels
labels = ____

# Create a DataFrame aligning labels and titles: df
df = pd.DataFrame({'label': labels, 'article': titles})

# Display df sorted by cluster label
print(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

IntermediárioNível de habilidade

4.8+

Inizia il corso 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: Visualizing hierarchies Exercise 2: How many merges?Exercise 3: Hierarchical clustering of the grain data Exercise 4: Hierarchies of stocks Exercise 5: Cluster labels in hierarchical clustering Exercise 6: Which clusters are closest?Exercise 7: Different linkage, different hierarchical clustering!Exercise 8: Intermediate clusterings Exercise 9: Extracting the cluster labels Exercise 10: t-SNE for 2-dimensional maps Exercise 11: t-SNE visualization of grain dataset Exercise 12: A t-SNE map of the stock market

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: Visualizzare la trasformazione PCA Exercise 2: Dati correlati in natura Exercise 3: Decorrelare le misurazioni dei cereali con la PCA Exercise 4: Componenti principali Exercise 5: Dimensione intrinseca Exercise 6: La prima componente principale Exercise 7: Varianza delle feature della PCA Exercise 8: Dimensione intrinseca dei dati sui pesci Exercise 9: Riduzione della dimensione con PCA Exercise 10: Riduzione della dimensionalità delle misurazioni dei pesci Exercise 11: Un array di frequenze delle parole con tf-idf Exercise 12: Clustering di Wikipedia parte I Exercise 13: Clustering di Wikipedia, parte II

Esercizio in corso

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