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# Lasso Path Using LARS in Scikit-learn

Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector, and this is displayed as a function of the regularization parameter.

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### Version¶

In [1]:
import sklearn
sklearn.__version__

Out[1]:
'0.18.1'

### Imports¶

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np
from sklearn import linear_model
from sklearn import datasets


### Calculations¶

In [3]:
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target

print("Computing regularization path using the LARS ...")
alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)

xx = np.sum(np.abs(coefs.T), axis=1)
xx /= xx[-1]

Computing regularization path using the LARS ...
.

### Plot Results¶

In [4]:
data = [ ]
for i in range(0, len(coefs)):
trace = go.Scatter(x=xx, y=coefs[i],
mode='lines', showlegend=False)
data.append(trace)

for i in range(0, len(xx)):
trace1 = go.Scatter(x=2* [xx[i]], y=[-800, 800],
mode='lines', showlegend=False,
line=dict(color='black', width=1,
dash='dash')
)
data.append(trace1)

layout = go.Layout(title='LASSO Path',
xaxis=dict(title='|coef| / max|coef|'),
yaxis=dict(title='Coefficients'))
fig = go.Figure(data=data, layout=layout)

In [5]:
py.iplot(fig)

Out[5]:

Author:

     Fabian Pedregosa <fabian.pedregosa@inria.fr>

Alexandre Gramfort <alexandre.gramfort@inria.fr>



     BSD 3 clause