Using decomposition to classify with DictionaryLearning

In this recipe, we'll show how a decomposition method can actually be used for classification. DictionaryLearning attempts to take a dataset and transform it into a sparse representation.

Getting ready

With DictionaryLearning, the idea is that the features are the basis for the resulting datasets. Load the iris dataset:

from sklearn.datasets import load_iris
iris = load_iris()
iris_X = iris.data
y = iris.target

Additionally, create a training set by taking every other element of iris_X and y. Take the remaining elements for testing:

X_train = iris_X[::2]
X_test = iris_X[1::2]
y_train = y[::2]
y_test = y[1::2]

How to do it...

1. Import DictionaryLearning:

from sklearn.decomposition import DictionaryLearning

2. Use three components to represent the three species of iris:

dl = DictionaryLearning(3)

3. Transform every other data point so that we can test the classifier on the resulting data points after the learner is trained:

transformed = dl.fit_transform...