# Robust linear estimator fitting¶

Here a sine function is fit with a polynomial of order 3, for values close to zero.

Robust fitting is demoed in different situations:

• No measurement errors, only modelling errors (fitting a sine with a polynomial)

• Measurement errors in X

• Measurement errors in y

The median absolute deviation to non corrupt new data is used to judge the quality of the prediction.

What we can see that:

• RANSAC is good for strong outliers in the y direction

• TheilSen is good for small outliers, both in direction X and y, but has a break point above which it performs worse than OLS.

• The scores of HuberRegressor may not be compared directly to both TheilSen and RANSAC because it does not attempt to completely filter the outliers but lessen their effect.

• • • • • from matplotlib import pyplot as plt
import numpy as np

from sklearn.linear_model import (
LinearRegression,
TheilSenRegressor,
RANSACRegressor,
HuberRegressor,
)
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

np.random.seed(42)

X = np.random.normal(size=400)
y = np.sin(X)
# Make sure that it X is 2D
X = X[:, np.newaxis]

X_test = np.random.normal(size=200)
y_test = np.sin(X_test)
X_test = X_test[:, np.newaxis]

y_errors = y.copy()
y_errors[::3] = 3

X_errors = X.copy()
X_errors[::3] = 3

y_errors_large = y.copy()
y_errors_large[::3] = 10

X_errors_large = X.copy()
X_errors_large[::3] = 10

estimators = [
("OLS", LinearRegression()),
("Theil-Sen", TheilSenRegressor(random_state=42)),
("RANSAC", RANSACRegressor(random_state=42)),
("HuberRegressor", HuberRegressor()),
]
colors = {
"OLS": "turquoise",
"Theil-Sen": "gold",
"RANSAC": "lightgreen",
"HuberRegressor": "black",
}
linestyle = {"OLS": "-", "Theil-Sen": "-.", "RANSAC": "--", "HuberRegressor": "--"}
lw = 3

x_plot = np.linspace(X.min(), X.max())
for title, this_X, this_y in [
("Modeling Errors Only", X, y),
("Corrupt X, Small Deviants", X_errors, y),
("Corrupt y, Small Deviants", X, y_errors),
("Corrupt X, Large Deviants", X_errors_large, y),
("Corrupt y, Large Deviants", X, y_errors_large),
]:
plt.figure(figsize=(5, 4))
plt.plot(this_X[:, 0], this_y, "b+")

for name, estimator in estimators:
model = make_pipeline(PolynomialFeatures(3), estimator)
model.fit(this_X, this_y)
mse = mean_squared_error(model.predict(X_test), y_test)
y_plot = model.predict(x_plot[:, np.newaxis])
plt.plot(
x_plot,
y_plot,
color=colors[name],
linestyle=linestyle[name],
linewidth=lw,
label="%s: error = %.3f" % (name, mse),
)

legend_title = "Error of Mean\nAbsolute Deviation\nto Non-corrupt Data"
legend = plt.legend(
loc="upper right", frameon=False, title=legend_title, prop=dict(size="x-small")
)
plt.xlim(-4, 10.2)
plt.ylim(-2, 10.2)
plt.title(title)
plt.show()


Total running time of the script: ( 0 minutes 2.060 seconds)

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