Agrupación Wikipedia parte I

Como has visto en el vídeo,TruncatedSVDes capaz de aplicar el PCA a arreglos dispersos en formato csr_matrix, como los arreglos de frecuencia de palabras. Combina tus conocimientos sobre TruncatedSVD y k-means para agrupar en clústeres algunas páginas populares de Wikipedia. En este ejercicio, construye la canalización. En el siguiente ejercicio, lo aplicarás a la matriz de frecuencia de palabras de algunos artículos de Wikipedia.

Crea un objeto Pipeline que consista en un TruncatedSVD seguido de KMeans. (Esta vez, hemos calculado previamente la matriz de frecuencia de palabras por ti, por lo que no es necesario un TfidfVectorizer).

El conjunto de datos de Wikipedia con el que vas a trabajar se obtuvo de aquí.

Este ejercicio forma parte del curso

Aprendizaje no supervisado en Python

Instrucciones del ejercicio

Importa:
- TruncatedSVD de sklearn.decomposition.
- KMeans de sklearn.cluster.
- make_pipeline de sklearn.pipeline.
Crea una instancia de TruncatedSVD llamada svd con n_components=50.
Crea una instancia de KMeans llamada kmeans con n_clusters=6.
Crea una canalización llamada pipeline formada por svd y kmeans.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Perform the necessary imports
from ____ import ____
from ____ import ____
from ____ import ____

# Create a TruncatedSVD instance: svd
svd = ____

# Create a KMeans instance: kmeans
kmeans = ____

# Create a pipeline: pipeline
pipeline = ____

Editar y ejecutar código

Este ejercicio forma parte del curso

Aprendizaje no supervisado en Python

IntermedioNivel de habilidad

4.8+

Comienza el curso 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: Visualización de la transformación PCA Exercise 2: Datos correlacionados en la naturaleza Exercise 3: Descorrelación de las mediciones de grano mediante el análisis de componentes principales (PCA)Exercise 4: Componentes principales Exercise 5: Dimensión intrínseca Exercise 6: El primer componente principal Exercise 7: Varianza de las características del PCA Exercise 8: Dimensión intrínseca de los datos de los peces Exercise 9: Reducción de dimensiones con el PCA Exercise 10: Reducción dimensional de las medidas de los peces Exercise 11: Una matriz tf-idf de frecuencia de palabras Exercise 12: Agrupación Wikipedia parte I

Ejercicio actual

Exercise 13: Agrupación de 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