Looking for structure in data — Andrews curves plot explained

That may be one of those tools which I will never use in real life, but I thought that it is interesting enough to learn it anyway.

Andrews curves plot is a way of visualizing the structure of independent variables. The possible usage is a lightweight method of checking whether we have enough features to train a classifier or whether we should keep doing feature engineering and cleaning data because there is a mess in data.

We may do it because the independent variables get summarized and the data dimensions get reduced to only two. To be more precise, every observation gets reduced to a curve which can be projected in two-dimensions. When we draw the plot and use colors to distinguish the labels, we see whether all observations are tangled with each other or whether the observations are grouped in separate streams.

In Scikit-learn all we need is one function and preprocessed data (values must be normalized to (0.0, 1.0).

1
2
3
4
5
6
7
8
9
10
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaler.fit(X)

X = pd.DataFrame(scaler.transform(X), columns = ['feature_1', 'feature_2', 'label'])

from pandas.plotting import andrews_curves

andrews_curves(X, 'label', colormap = 'winter')

How to interpret the chart

What do we see here? Both colors are mixed. There is no way to distinguish between them. The same problem exists in the feature dataset. There is no way to predict the target class using those two features.

Did you enjoy reading this article?
Would you like to learn more about software craft in data engineering and MLOps?

Subscribe to the newsletter or add this blog to your RSS reader (does anyone still use them?) to get a notification when I publish a new essay!

Newsletter

Do you enjoy reading my articles?
Subscribe to the newsletter if you don't want to miss the new content, business offers, and free training materials.

Bartosz Mikulski

Bartosz Mikulski

  • Data/MLOps engineer by day
  • DevRel/copywriter by night
  • Python and data engineering trainer
  • Conference speaker
  • Contributed a chapter to the book "97 Things Every Data Engineer Should Know"
  • Twitter: @mikulskibartosz
Newsletter

Do you enjoy reading my articles?
Subscribe to the newsletter if you don't want to miss the new content, business offers, and free training materials.