The Unsupervised Learning Workshop by AARON JONES CHRISTOPHER KRUGER
 BENJAMIN JOHNSTON

Author:AARON JONES, CHRISTOPHER KRUGER
, BENJAMIN JOHNSTON
Language: eng
Format: epub
Publisher: Packt Publishing Pvt. Ltd.
Published: 2020-07-28T00:00:00+00:00

Activity 6.02: t-SNE Wine and Perplexity

In this activity, we will use the Wine dataset to further reinforce the influence of perplexity on the t-SNE visualization process. In this activity, we will try to determine whether we can identify the source of the wine based on its chemical composition. The t-SNE process provides an effective means of representing and possibly identifying the sources.

Note

This dataset is sourced from https://archive.ics.uci.edu/ml/machine-learning-databases/wine/ (UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science). It can be downloaded from https://packt.live/3aPOmRJ.

Import pandas, numpy, and matplotlib, as well as the t-SNE and PCA models from scikit-learn.

Load the Wine dataset and inspect the first five rows.

The first column provides the labels; extract these from the DataFrame and store them in a separate variable. Ensure that the column is removed from the DataFrame.

Execute PCA on the dataset and extract the first six components.

Construct a loop that iterates through the perplexity values (1, 5, 20, 30, 80, 160, 320). For each loop, generate a t-SNE model with the corresponding perplexity and print a scatter plot of the labeled wine classes. Note the effect of different perplexity values.

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