Multilabel Dataset Generation with Scikit-Learn

Introduction

In this lab, we will learn how to generate a multilabel dataset using the make_multilabel_classification function of Scikit-Learn library. The function generates random samples of multilabel data, where each sample has counts of two features, which are differently distributed in each of two classes.

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Skills Graph

Import Required Libraries and Define Constants

First, we need to import the required libraries and define the colors and random seed constant for generating the multilabel dataset.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_multilabel_classification as make_ml_clf

COLORS = np.array(
    [
        "!",
        "#FF3333",  ## red
        "#0198E1",  ## blue
        "#BF5FFF",  ## purple
        "#FCD116",  ## yellow
        "#FF7216",  ## orange
        "#4DBD33",  ## green
        "#87421F",  ## brown
    ]
)

## Use same random seed for multiple calls to make_multilabel_classification to
## ensure same distributions
RANDOM_SEED = np.random.randint(2**10)

Define the Plot Function

Next, we define a function plot_2d that plots the randomly generated multilabel dataset. It takes three arguments n_labels, n_classes, and length.

def plot_2d(ax, n_labels=1, n_classes=3, length=50):
    X, Y, p_c, p_w_c = make_ml_clf(
        n_samples=150,
        n_features=2,
        n_classes=n_classes,
        n_labels=n_labels,
        length=length,
        allow_unlabeled=False,
        return_distributions=True,
        random_state=RANDOM_SEED,
    )

    ax.scatter(
        X[:, 0], X[:, 1], color=COLORS.take((Y * [1, 2, 4]).sum(axis=1)), marker="."
    )
    ax.scatter(
        p_w_c[0] * length,
        p_w_c[1] * length,
        marker="*",
        linewidth=0.5,
        edgecolor="black",
        s=20 + 1500 * p_c**2,
        color=COLORS.take([1, 2, 4]),
    )
    ax.set_xlabel("Feature 0 count")
    return p_c, p_w_c

This function generates the dataset using the make_multilabel_classification function with the specified parameters. Then, it plots the dataset using the scatter function of Matplotlib library. The function returns the class probabilities and feature probabilities.

Plot the Dataset

Now, we plot the randomly generated multilabel dataset using the plot_2d function. We create a figure with two subplots and call the plot_2d function for each subplot with different parameter values.

_, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
plt.subplots_adjust(bottom=0.15)

p_c, p_w_c = plot_2d(ax1, n_labels=1)
ax1.set_title("n_labels=1, length=50")
ax1.set_ylabel("Feature 1 count")

plot_2d(ax2, n_labels=3)
ax2.set_title("n_labels=3, length=50")
ax2.set_xlim(left=0, auto=True)
ax2.set_ylim(bottom=0, auto=True)

plt.show()

Print the Class and Feature Probabilities

Lastly, we print the class and feature probabilities for each class using the class probabilities and feature probabilities returned by the plot_2d function.

print("The data was generated from (random_state=%d):" % RANDOM_SEED)
print("Class", "P(C)", "P(w0|C)", "P(w1|C)", sep="\t")
for k, p, p_w in zip(["red", "blue", "yellow"], p_c, p_w_c.T):
    print("%s\t%0.2f\t%0.2f\t%0.2f" % (k, p, p_w[0], p_w[1]))

Summary

In this lab, we learned how to generate a multilabel dataset using the make_multilabel_classification function of Scikit-Learn library. We also learned how to plot the dataset and print the class and feature probabilities.