TF-IDF delle trame dei film

Usiamo le trame di film selezionati casualmente per effettuare il clustering dei documenti. Prima di eseguire il clustering sui documenti, occorre ripulirli da rumore indesiderato (come caratteri speciali e stop word) e convertirli in una matrice sparsa tramite il TF-IDF dei documenti.

Usa la classe TfidfVectorizer per calcolare il TF-IDF delle trame dei film salvate nella lista plots. La funzione remove_noise() è disponibile da usare come tokenizer nella classe TfidfVectorizer. Il metodo .fit_transform() adatta i dati agli oggetti TfidfVectorizer e poi genera la matrice sparsa TF-IDF.

Nota: l'esecuzione del metodo .fit_transform() richiede alcuni secondi.

Questo esercizio fa parte del corso

Analisi di cluster in Python

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Istruzioni dell'esercizio

Importa la classe TfidfVectorizer da sklearn.
Inizializza la classe TfidfVectorizer con frequenze minima e massima pari a 0.1 e 0.75, e 50 feature massime.
Usa il metodo fit_transform() sull'istanza di TfidfVectorizer inizializzata con la lista plots.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import TfidfVectorizer class from sklearn
from sklearn.feature_extraction.text import ____

# Initialize TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(____)

# Use the .fit_transform() method on the list plots
tfidf_matrix = ____

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi di cluster in Python

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Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.

Exercise 1: Unsupervised learning: basics Exercise 2: Unsupervised learning in real world Exercise 3: Pokémon sightings Exercise 4: Basics of cluster analysis Exercise 5: Pokémon sightings: hierarchical clustering Exercise 6: Pokémon sightings: k-means clustering Exercise 7: Data preparation for cluster analysis Exercise 8: Normalize basic list data Exercise 9: Visualize normalized data Exercise 10: Normalization of small numbers Exercise 11: FIFA 18: Normalize data

This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.

Exercise 1: Basics of hierarchical clustering Exercise 2: Hierarchical clustering: ward method Exercise 3: Hierarchical clustering: single method Exercise 4: Hierarchical clustering: complete method Exercise 5: Visualize clusters Exercise 6: Visualize clusters with matplotlib Exercise 7: Visualize clusters with seaborn Exercise 8: How many clusters?Exercise 9: Create a dendrogram Exercise 10: How many clusters in comic con data?Exercise 11: Limitations of hierarchical clustering Exercise 12: Timing run of hierarchical clustering Exercise 13: FIFA 18: exploring defenders

This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.

Exercise 1: Basics of k-means clustering Exercise 2: K-means clustering: first exercise Exercise 3: Runtime of k-means clustering Exercise 4: How many clusters?Exercise 5: Elbow method on distinct clusters Exercise 6: Elbow method on uniform data Exercise 7: Limitations of k-means clustering Exercise 8: Impact of seeds on distinct clusters Exercise 9: Uniform clustering patterns Exercise 10: FIFA 18: defenders revisited

Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.

Exercise 1: Colori dominanti nelle immagini Exercise 2: Estrai i valori RGB dall'immagine Exercise 3: Quanti colori dominanti?Exercise 4: Visualizza i colori dominanti Exercise 5: Clustering di documenti Exercise 6: TF-IDF delle trame dei film

Esercizio in corso

Exercise 7: Termini principali nei cluster di film Exercise 8: Clustering con più caratteristiche Exercise 9: Clustering con molte feature Exercise 10: Verifiche di base sui cluster Exercise 11: FIFA 18: cosa rende un giocatore completo?Exercise 12: Arrivederci!