Robust linear model estimation using RANSAC

 Code 1

(.env) [boris@fedora34server NUMPY]$ cat plot_ransac.py

"""

===========================================

Robust linear model estimation using RANSAC

===========================================

In this example we see how to robustly fit a linear model to faulty data using

the RANSAC algorithm.

"""

import numpy as np

from matplotlib import pyplot as plt

from sklearn import linear_model, datasets

n_samples = 1000

n_outliers = 50

X, y, coef = datasets.make_regression(

    n_samples=n_samples,

    n_features=1,

    n_informative=1,

    noise=10,

    coef=True,

    random_state=0,

)

# Add outlier data

np.random.seed(0)

X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))

y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)

# Fit line using all data

lr = linear_model.LinearRegression()

lr.fit(X, y)

# Robustly fit linear model with RANSAC algorithm

ransac = linear_model.RANSACRegressor()

ransac.fit(X, y)

inlier_mask = ransac.inlier_mask_

outlier_mask = np.logical_not(inlier_mask)

# Predict data of estimated models

line_X = np.arange(X.min(), X.max())[:, np.newaxis]

line_y = lr.predict(line_X)

line_y_ransac = ransac.predict(line_X)

# Compare estimated coefficients

print("Estimated coefficients (true, linear regression, RANSAC):")

print(coef, lr.coef_, ransac.estimator_.coef_)

lw = 2

plt.scatter(

    X[inlier_mask], y[inlier_mask], color="yellowgreen", marker=".", label="Inliers"

)

plt.scatter(

    X[outlier_mask], y[outlier_mask], color="gold", marker=".", label="Outliers"

)

plt.plot(line_X, line_y, color="navy", linewidth=lw, label="Linear regressor")

plt.plot(

    line_X,

    line_y_ransac,

    color="cornflowerblue",

    linewidth=lw,

    label="RANSAC regressor",

)

plt.legend(loc="lower right")

plt.xlabel("Input")

plt.ylabel("Response")

plt.show()

References

https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html