NMF mempelajari topik dokumen

Dalam video, Anda mempelajari bahwa ketika NMF diterapkan pada dokumen, komponen-komponennya berkorespondensi dengan topik dokumen, dan fitur NMF merekonstruksi dokumen dari topik-topik tersebut. Verifikasi sendiri untuk model NMF yang Anda bangun sebelumnya menggunakan artikel Wikipedia. Sebelumnya, Anda melihat bahwa nilai fitur NMF ke-3 tinggi untuk artikel tentang aktor Anne Hathaway dan Denzel Washington. Pada latihan ini, identifikasi topik dari komponen NMF yang bersesuaian.

Model NMF yang Anda bangun sebelumnya tersedia sebagai model, sementara words adalah daftar kata yang menjadi label kolom pada array frekuensi kata.

Setelah selesai, luangkan waktu sejenak untuk mengenali topik yang sama-sama dimiliki oleh artikel tentang Anne Hathaway dan Denzel Washington!

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

Petunjuk latihan

Impor pandas sebagai pd.
Buat DataFrame components_df dari model.components_, atur columns=words sehingga kolom-kolomnya dilabeli oleh kata-kata.
Cetak components_df.shape untuk memeriksa dimensi DataFrame.
Gunakan pengakses .iloc[] pada DataFrame components_df untuk memilih baris 3. Tetapkan hasilnya ke component.
Panggil metode .nlargest() pada component, dan cetak hasilnya. Ini akan memberikan lima kata dengan nilai tertinggi untuk komponen tersebut.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import pandas
import pandas as pd

# Create a DataFrame: components_df
components_df = ____

# Print the shape of the DataFrame
print(components_df.shape)

# Select row 3: component
component = ____

# Print result of nlargest
print(component.nlargest())

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: 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: Data non-negatif Exercise 3: Penerapan NMF pada artikel Wikipedia Exercise 4: Fitur NMF dari artikel Wikipedia Exercise 5: NMF merekonstruksi sampel Exercise 6: NMF mempelajari komponen yang dapat ditafsirkan Exercise 7: NMF mempelajari topik dokumen

Latihan Saat Ini

Exercise 8: Jelajahi himpunan data digit LED Exercise 9: NMF mempelajari bagian-bagian dari citra Exercise 10: PCA tidak mempelajari bagian-bagian Exercise 11: Membangun sistem rekomendasi menggunakan NMF Exercise 12: Artikel mana yang mirip dengan 'Cristiano Ronaldo'?Exercise 13: Rekomendasikan artis musik bagian I Exercise 14: Rekomendasikan artis musik bagian II Exercise 15: Penutup