The text discusses the challenges faced while running Principal Component Analysis (PCA) in PySpark to rank diamonds using machine learning. Despite the excellent documentation, the process of working with machine learning in Spark is not user-friendly. The author outlines the steps of coding, vectorizing the dataset, running PCA, and calculating scores for ranking the diamonds.
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The challenges of running Principal Component Analysis in PySpark
Introduction
Running Principal Component Analysis (PCA) in PySpark can be challenging, especially when dealing with Machine Learning in MLlib, Spark’s Machine Learning Library, designed for Big Data in a parallelized environment.
Coding
To start, we import the necessary modules and load the Diamonds dataset from Databricks sample datasets, removing outliers that can affect PCA.
Dataset
We select the numerical variables carat, table, and depth from the dataset and transform the data to a logarithmic scale for consistency.
Vectorization
We use VectorAssembler to convert the selected numerical values into a single vector for input into the PCA algorithm.
PCA
We run the PCA algorithm using MLlib, fit the data, and analyze the explained variance per Principal Component.
Getting the Transformed Data
We extract the transformed data from the PCA model to understand the direction and value of the components.
Cleaning the Transformed Data
We manipulate the transformed data to prepare it for ranking by removing the square brackets and casting the data to numerical values.
Calculating Scores
We calculate scores by multiplying the transformed data from each Principal Component by the respective explained variance and create a ranking of diamonds based on the combination of carat, table, and depth variables.
Before You Go
PCA in Spark presents challenges, but it can be a valuable tool for dimensionality reduction in Big Data environments, serving as input for further analysis.
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