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Pliable Lasso

Project description


Author: Stephen Anthony Rose

Original paper: []

pip install plasso


We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of modifying variables. These modifiers might be variables such as gender, age or time. The paradigm is quite general, with each lasso coefficient modified by a sparse linear function of the modifying variables Z. The model is estimated in a hierarchical fashion to control the degrees of freedom and avoid overfitting. The modifying variables may be observed, observed only in the training set, or unobserved overall. There are connections of our proposal to varying coefficient models and high-dimensional interaction models. We present a computationally efficient algorithm for its optimization, with exact screening rules to facilitate application to large numbers of predictors. The method is illustrated on a number of different simulated and real examples.


from plasso import PliableLasso

# Input data looks just like sklearn except an extra matrix Z
y = target_data()
x = data() # Main effects data
z = modifying_data() # Data used to modify the estimate coefficients for X

# Fit model
model = PliableLasso(), z, y)

# Cool things to do afterwards
y_hat = model.predict(x, z)


Check out the `` file to see more ways to use it.

Installation / Usage

To install use pip:

$ pip install plasso

Or clone the repo:

$ git clone
$ python install

Also for large data sets I recommend install `pytorch`


Me (Stephen Anthony Rose)

If you want to add and improve things be my guest.

Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence

Project details

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plasso-1.20190329.1200.tar.gz (12.7 kB view hashes)

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