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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.

The ordinary linear regressor is sensitive to outliers, and the fitted line can easily be skewed away from the true underlying relationship of data.

The RANSAC regressor automatically splits the data into inliers and outliers, and the fitted line is determined only by the identified inliers.

plot ransac

Estimated coefficients (true, linear regression, RANSAC):
82.1903908407869 [54.17236387] [82.08533159]

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importnumpyasnp
frommatplotlibimport pyplot as plt
fromsklearnimport datasets, linear_model
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 ()