Labeled Data

Separate the PCA components of a dataset by class.

We'll cover the following

Chapter Goals:

  • Learn about labeled datasets
  • Separate principle component data by class label

A. Class labels

A big part of data science is classifying observations in a dataset into separate categories, or classes. A popular use case of data classification is in separating a dataset into "good" and "bad" categories. For example, we can classify a dataset of breast tumors as either malignant or benign.

The code below separates a breast cancer dataset into malignant and benign categories. The load_breast_cancer function is part of the scikit-learn library, and its data comes from the Breast Cancer Wisconsin dataset.

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from sklearn.datasets import load_breast_cancer
bc = load_breast_cancer()
print('{}\n'.format(repr(bc.data)))
print('Data shape: {}\n'.format(bc.data.shape))
# Class labels
print('{}\n'.format(repr(bc.target)))
print('Labels shape: {}\n'.format(bc.target.shape))
# Label names
print('{}\n'.format(list(bc.target_names)))
malignant = bc.data[bc.target == 0]
print('Malignant shape: {}\n'.format(malignant.shape))
benign = bc.data[bc.target == 1]
print('Benign shape: {}\n'.format(benign.shape))

In the example above, the bc.data array contains all the dataset values, while the bc.target array contains the class ID labels for each row in bc.data. A class ID of 0 corresponds to a malignant tumor, while a class ID of 1 corresponds to a benign tumor.

Using the bc.target class IDs, we separated the dataset into malignant and benign data arrays. In other words, the malignant array contains the rows of bc.data corresponding to the indexes in bc.target containing 0, while the benign array contains the rows of bc.data corresponding to the indexes in bc.target containing 1. There are 212 malignant data observations and 357 benign observations.

Time to Code!

The coding exercise in this chapter involves completing the separate_components function, which will separate principal component data by class.

To do this, we first need to complete a helper function, get_label_info, which returns the label name and data for an input class label.

The component_data input represents the principal component data.

The labels input is a 1-D array containing the class label IDs corresponding to each row of component_data. We can use it to separate the principle components by class.

The class_label input represents a particular class label ID.

The label_names input represents all the string names for the class labels.

Set label_name equal to the string at index class_label of label_names.

Set label_data equal to the rows of component_data corresponding to the indexes where labels equals class_label. Then return the tuple (label_name, label_data).

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def get_label_info(component_data, labels,
                   class_label, label_names):
  # CODE HERE
  pass

Now, inside the main separate_data function, we'll iterate through each label in the label_names list.

Set separated_data equal to an empty list.

Create a for loop that iterates variable class_label through range(len(label_names)).

Inside the for loop, we can use our helper function to obtain the separated data for each class.

Inside the for loop, call get_label_info with inputs component_data, labels, class_label, and label_names. Append the function’s output to separated_data.

After finalizing the list of principle components separated by class, we return it.

Outside the for loop, return separated_data.

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def separate_data(component_data, labels,
                  label_names):
  # CODE HERE
  pass

The separate_data function is incredibly useful for visualizing the components. We can use matplotlib to create nice plots of the separated data (shown in the code below).

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from sklearn.datasets import load_breast_cancer
from sklearn.decomposition import PCA
bc = load_breast_cancer()
pca_obj = PCA(n_components=2)
component_data = pca_obj.fit_transform(bc.data)
labels = bc.target
label_names = bc.target_names
# Using the completed separate_data function
separated_data = separate_data(component_data,
                               labels, label_names)
# Plotting the data
import matplotlib.pyplot as plt
for label_name, label_data in separated_data:
  col1 = label_data[:, 0]  # 1st column (1st pr. comp.)
  col2 = label_data[:, 1]  # 2nd column (2nd pr. comp.)
  plt.scatter(col1, col2, label=label_name) # scatterplot
plt.legend()  # adds legend to plot
plt.title('Breast Cancer Dataset PCA Plot')
plt.show()

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