Whenever we think of Machine Learning, the first thing that comes to our mind is a dataset. While there are many datasets that you can find on websites such as Kaggle, sometimes it is useful to extract data on your own and generate your own dataset. Generating your own dataset gives you more control over the data and allows you to train your machine-learning model. In this article, we will generate random datasets using sklearn.datasets library in Python.
Generate test datasets for Classification:
Binary Classification
Example 1: The 2d binary classification data generated by make_circles() have a spherical decision boundary.
Python3
# Import necessary librariesfrom sklearn.datasets import make_circlesimport matplotlib.pyplot as plt# Generate 2d classification datasetX, y = make_circles(n_samples=200, shuffle=True, noise=0.1, random_state=42)# Plot the generated datasetsplt.scatter(X[:, 0], X[:, 1], c=y)plt.show() |
Output:
make_circles()
Example 2: Two interlocking half circles represent the 2d binary classification data produced by the make_moons() function.
Python3
#import the necessary librariesfrom sklearn.datasets import make_moonsimport matplotlib.pyplot as plt# generate 2d classification datasetX, y = make_moons(n_samples=500, shuffle=True, noise=0.15, random_state=42)# Plot the generated datasetsplt.scatter(X[:, 0], X[:, 1], c=y)plt.show() |
Output:
make_moons()
Multi-Class Classification
Example 1: Data generated by the function make_blobs() are blobs that can be utilized for clustering.
Python3
#import the necessary librariesfrom sklearn.datasets import make_blobsimport matplotlib.pyplot as plt# Generate 2d classification datasetX, y = make_blobs(n_samples=500, centers=3, n_features=2, random_state=23)# Plot the generated datasetsplt.scatter(X[:, 0], X[:, 1], c=y)plt.show() |
Output:
make_blobs()
Example 2: To generate data by the function make_classification() need to balance between n_informative, n_redundant and n_classes attributes X[:, :n_informative + n_redundant + n_repeated]
Python3
#import the necessary librariesfrom sklearn.datasets import make_classificationimport matplotlib.pyplot as plt# generate 2d classification datasetX, y = make_classification(n_samples = 100, n_features=2, n_redundant=0, n_informative=2, n_repeated=0, n_classes =3, n_clusters_per_class=1)# Plot the generated datasetsplt.scatter(X[:, 0], X[:, 1], c=y)plt.show() |
Output:
make_classification()
Example 3:A random multi-label classification data is created by the function make make_multilabel_classification()
Python3
# Import necessary librariesfrom sklearn.datasets import make_multilabel_classificationimport pandas as pdimport matplotlib.pyplot as plt# Generate 2d classification datasetX, y = make_multilabel_classification(n_samples=500, n_features=2, n_classes=2, n_labels=2, allow_unlabeled=True, random_state=23)# create pandas dataframe from generated datasetdf = pd.concat([pd.DataFrame(X, columns=['X1', 'X2']), pd.DataFrame(y, columns=['Label1', 'Label2'])], axis=1)display(df.head())# Plot the generated datasetsplt.scatter(df['X1'], df['X2'], c=df['Label1'])plt.show() |
Output:
X1 X2 Label1 Label2 0 14.0 34.0 0 1 1 30.0 22.0 1 1 2 29.0 19.0 1 1 3 21.0 19.0 1 1 4 16.0 32.0 0 1
make_multilabel_classification()
Generate test datasets for Regression:
Example 1: Generate a 1-dimensional feature and target for linear regression using make_regression
Python3
# Import necessary librariesfrom sklearn.datasets import make_regressionimport matplotlib.pyplot as plt# Generate 1d Regression datasetX, y = make_regression(n_samples = 50, n_features=1,noise=20, random_state=23)# Plot the generated datasetsplt.scatter(X, y)plt.show() |
Output:
make_regression()
Example 2: Multilabel feature using make_sparse_uncorrelated()
Python3
# Import necessary librariesfrom sklearn.datasets import make_sparse_uncorrelatedimport matplotlib.pyplot as plt# Generate 1d Regression datasetX, y = make_sparse_uncorrelated(n_samples = 100, n_features=4, random_state=23)# Plot the generated datasetsplt.figure(figsize=(12,10))for i in range(4): plt.subplot(2,2, i+1) plt.scatter(X[:,i], y) plt.xlabel('X'+str(i+1)) plt.ylabel('Y')plt.show() |
Output:
make_sparse_uncorrelated()
Example: 3 Multilabel feature using make_friedman2()
Python3
# Import necessary librariesfrom sklearn.datasets import make_friedman2import matplotlib.pyplot as plt# Generate 1d Regression datasetX, y = make_friedman2(n_samples = 100, random_state=23)# Plot the generated datasetsplt.figure(figsize=(12,10))for i in range(4): plt.subplot(2,2, i+1) plt.scatter(X[:,i], y) plt.xlabel('X'+str(i+1)) plt.ylabel('Y')plt.show() |
Output:
make_friedman2()
