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Bayesian Ridge Regression in Scikit-learn

Computes a Bayesian Ridge Regression on a synthetic dataset.

See Bayesian Ridge Regression for more information on the regressor.

Compared to the OLS (ordinary least squares) estimator, the coefficient weights are slightly shifted toward zeros, which stabilises them.

As the prior on the weights is a Gaussian prior, the histogram of the estimated weights is Gaussian. The estimation of the model is done by iteratively maximizing the marginal log-likelihood of the observations.

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18.1'

Imports

This tutorial imports BayesianRidge and LinearRegression.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np
from scipy import stats

from sklearn.linear_model import BayesianRidge, LinearRegression
Automatically created module for IPython interactive environment

Calculations

Generating simulated data with Gaussian weights

In [3]:
np.random.seed(0)
n_samples, n_features = 100, 100
X = np.random.randn(n_samples, n_features)  # Create Gaussian data
# Create weights with a precision lambda_ of 4.
lambda_ = 4.
w = np.zeros(n_features)
# Only keep 10 weights of interest
relevant_features = np.random.randint(0, n_features, 10)
for i in relevant_features:
    w[i] = stats.norm.rvs(loc=0, scale=1. / np.sqrt(lambda_))
# Create noise with a precision alpha of 50.
alpha_ = 50.
noise = stats.norm.rvs(loc=0, scale=1. / np.sqrt(alpha_), size=n_samples)
# Create the target
y = np.dot(X, w) + noise

Fit the Bayesian Ridge Regression and an OLS for comparison

In [4]:
clf = BayesianRidge(compute_score=True)
clf.fit(X, y)

ols = LinearRegression()
ols.fit(X, y)
Out[4]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Plot Results

Weights of the model

In [5]:
lw = 2

p1 = go.Scatter(y=clf.coef_,
                mode='lines', 
                line=dict(color='lightgreen', width=lw),
                name="Bayesian Ridge estimate")

p2 = go.Scatter(y=w, 
                mode='lines', 
                line=dict(color='gold', width=lw), 
                name="Ground truth")

p3 = go.Scatter(y=ols.coef_, 
                mode='lines', 
                line=dict(color='navy', dash='dash'),
                name="OLS estimate")

layout = go.Layout(title="Weights of the model",
                   xaxis=dict(title="Features"),
                   yaxis=dict(title="Values of the weights")
                  )
fig = go.Figure(data=[p1, p2, p3], layout=layout)
py.iplot(fig)
Out[5]:

Histogram of the weights

In [9]:
p1 = go.Histogram(x=clf.coef_, nbinsx=n_features, 
                  marker=dict(color='gold', 
                              line=dict(color='black', width=1)),
                  showlegend=False)

p2 = go.Scatter(x=clf.coef_[relevant_features], y=5 * np.ones(len(relevant_features)),
                mode='markers',
                marker=dict(color='navy'),
                name="Relevant features")

layout = go.Layout(title="Histogram of the weights",
                   xaxis=dict(title="Features"),
                   yaxis=dict(title="Values of the weights", type='log')
                  )
fig = go.Figure(data=[p1, p2], layout=layout)
py.iplot(fig)
Out[9]:

Marginal log-likelihood

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