# Implementing PCA and UMAP in Python

You can find the full code for PCA here, and the full code for UMAP here.

Dimensionality reduction is an important part of constructing Machine Learning models. Dimensionality Reduction is basically the process of combining multiple features into a smaller number of features. Features that have a higher contribution to the target value have a greater representation in the final combined feature than features that contribute less. For instance, if you have 8 features, the first 6 of which have a summed contribution of around 95%, and the last 2 of which have a contribution of about only 5%, then those 6 features will have a greater representation in the final combined feature. In terms of advantages, the most significant is less memory storage and hence higher modeling and processing speed. Other advantages include simplicity and easier visualization. For instance, you can easily plot the contribution of two combined features to the target, especially compared to plotting, say, 20 initial features. Another significant aspect is that features will less contribution that would otherwise add useless ‘weight’ to the model are removed early on.

The two methods of dimensionality reduction I will be using are PCA and UMAP. I won’t be going in through how they work as I’ve given a short overview of their purpose above. Instead, I’ll go through the code I implemented for each, and visualize the results. For this exercise, I’m using the WHO Life Expectancy Dataset that can be found on Kaggle, as its very small and easy to work with. My target variable will be life expectancy, and my features will be aspects like adult mortality, schooling, GDP etc. I randomly selected these features from the dataset.

Here is a list of the modules we will be using. train_test_split will help us break out data into a training set and a testing set (about a 7:3 ratio). While this isn’t significant right now, this aids in the detection of under fitting and over fitting. Under-fitting is detected by bad performances on both the training set and the testing set, whereas Over-fitting is detected by really good performance on the training set but bad performance on the testing set. StandardScaler has been used to normalise features. Feature normalisation is a technique that reduces the range of the dataset, or the standard deviation, in layman’s terms. Lastly, we’ve imported both PCA and UMAP, which will be used.

Here we just load our dataset, extract the features that will be used (see column names in the dataframe), and rename them for the sake of simplicity. As you can see, there are some random spaces and not all use underscores as notation, so I decided to have one uniform way of typing out each feature. Now, to extract a feature matrix and a target vector, just drop the life_expectancy column from the dataframe and convert it into a numpy array, and convert the life_expectancy column into a separate numpy array. I won’t show the code for splitting and normalising, because that’s pretty much irrelevant here.

Implementing PCA in itself is very simple, as shown above. You’ll notice that I’ve specified n_components to be equal to 2 above. This is because I just wanted to point out that the number of combined features you want at the end can be set by you. In this case, it doesn’t really matter because PCA will give only two combined features if I do not a specify a pre-set number. After that, I’ve fitted the training_data to PCA.

Here’s a bit of data treatment before I finally plot the results. I’ve basically converted PCA’s output, which was a numpy array, to a pandas dataframe, and then added life_expectancy as a column because that will be used for the color-bar you will see below.

Here is the code for my plot, and here is the plot:

You can see the relative contribution of each componenent to each feature, whose target value, or life expectancy, is represented by the color of the marker. While I don’t see any patterns straightaway (specific colors being clustered somewhere etc.), the primary thing that does stand out is how heavily green dots (~ 70 expectancy) are clustered towards the bottom left. There are other colors as well, but there don’t seem to be many green dots anywhere else.

The code for UMAP is the exact same, except with UMAP as our decomposer instead of PCA. Here’s the plot.

You can straightaway see that the results of UMAP are quite different. Once again, there are no noticeable patterns in terms of specific colors being clustered in specific locations, but the overall structure is quite different from that of PCA. We can see that each color is distributed throughout.

There’s no way to say which method is better without modeling your target variable with respect to both principal components and calculating the accuracy on the testing set. This post just aims to illustrate how both of them work without going into specific details.