Un array di frequenze delle parole con tf-idf

In questo esercizio creerai un array di frequenze delle parole con tf-idf per una collezione di documenti di esempio. Per farlo, usa TfidfVectorizer da sklearn. Trasforma una lista di documenti in un array di frequenze delle parole, restituendolo come csr_matrix. Ha metodi fit() e transform() come altri oggetti di sklearn.

Ti viene fornita una lista documents di semplici documenti sui pet.

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Istruzioni dell'esercizio

Importa TfidfVectorizer da sklearn.feature_extraction.text.
Crea un'istanza di TfidfVectorizer chiamata tfidf.
Applica il metodo .fit_transform() di tfidf a documents e assegna il risultato a csr_mat. Si tratta di un array di frequenze delle parole in formato csr_matrix.
Ispeziona csr_mat chiamando il suo metodo .toarray() e stampando il risultato. Questo è già stato fatto per te.
Le colonne dell'array corrispondono alle parole. Ottieni l'elenco delle parole chiamando il metodo .get_feature_names_out() di tfidf e assegna il risultato a words.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import TfidfVectorizer
from ____ import ____

# Create a TfidfVectorizer: tfidf
tfidf = ____ 

# Apply fit_transform to document: csr_mat
csr_mat = ____

# Print result of toarray() method
print(csr_mat.toarray())

# Get the words: words
words = ____

# Print words
print(words)

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Questo esercizio fa parte del corso

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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: 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

Esercizio in corso

Exercise 12: Clustering di Wikipedia parte I Exercise 13: Clustering di Wikipedia, parte 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