Man page - mlpack_lars(1)
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apt-get install mlpack-bin
Manual
mlpack_lars
NAMESYNOPSIS
DESCRIPTION
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION
NAME
mlpack_lars - lars
SYNOPSIS
mlpack_lars [ -i unknown ] [ -m unknown ] [ -l double ] [ -L double ] [ -n bool ] [ -N bool ] [ -r unknown ] [ -t unknown ] [ -c bool ] [ -V bool ] [ -M unknown ] [ -o unknown ] [ -h -v ]
DESCRIPTION
An implementation of LARS: Least Angle Regression (Stagewise/laSso). This is a stage-wise homotopy-based algorithm for L1-regularized linear regression (LASSO) and L1+L2-regularized linear regression (Elastic Net).
This program is able to train a LARS/LASSO/Elastic Net model or load a model from file, output regression predictions for a test set, and save the trained model to a file. The LARS algorithm is described in more detail below:
Let X be a matrix where each row is a point and each column is a dimension, and let y be a vector of targets.
The Elastic Net problem is to solve
min_beta 0.5 ||
X * beta - y ||_2ˆ2 + lambda_1 ||beta||_1 +
0.5 lambda_2 ||beta||_2ˆ2
If lambda1 > 0 and lambda2 = 0, the problem is the LASSO. If lambda1 > 0 and lambda2 > 0, the problem is the Elastic Net. If lambda1 = 0 and lambda2 > 0, the problem is ridge regression. If lambda1 = 0 and lambda2 = 0, the problem is unregularized linear regression.
For efficiency reasons, it is not recommended to use this algorithm with ’ --lambda1 ( -l )’ = 0. In that case, use the ’linear_regression’ program, which implements both unregularized linear regression and ridge regression.
To train a LARS/LASSO/Elastic Net model, the ’ --input_file ( -i )’ and ’ --responses_file ( -r )’ parameters must be given. The ’ --lambda1 ( -l )’, ’ --lambda2 ( -L )’, and ’ --use_cholesky ( -c )’ parameters control the training options. A trained model can be saved with the ’ --output_model_file ( -M )’. If no training is desired at all, a model can be passed via the ’ --input_model_file ( -m )’ parameter.
The program can also provide predictions for test data using either the trained model or the given input model. Test points can be specified with the ’ --test_file ( -t )’ parameter. Predicted responses to the test points can be saved with the ’ --output_predictions_file ( -o )’ output parameter.
For example, the following command trains a model on the data ’data.csv’ and responses ’responses.csv’ with lambda1 set to 0.4 and lambda2 set to 0 (so, LASSO is being solved), and then the model is saved to ’lasso_model.bin’:
$ mlpack_lars --input_file data.csv --responses_file responses.csv --lambda1 0.4 --lambda2 0 --output_model_file lasso_model.bin
The following command uses the ’lasso_model.bin’ to provide predicted responses for the data ’test.csv’ and save those responses to ’test_predictions.csv’:
$ mlpack_lars --input_model_file lasso_model.bin --test_file test.csv --output_predictions_file test_predictions.csv
OPTIONAL INPUT OPTIONS
--help (-h) [ bool ]
Default help info.
--info [string]
Print help on a specific option. Default value ’’.
--input_file (-i) [ unknown ]
Matrix of covariates (X).
--input_model_file (-m) [ unknown ]
Trained LARS model to use.
--lambda1 (-l) [ double ]
Regularization parameter for l1-norm penalty. Default value 0.
--lambda2 (-L) [ double ]
Regularization parameter for l2-norm penalty. Default value 0.
--no_intercept (-n) [ bool ]
Do not fit an intercept in the model.
--no_normalize (-N) [ bool ]
Do not normalize data to unit variance before modeling.
--responses_file (-r) [ unknown ]
Matrix of responses/observations (y).
--test_file (-t) [ unknown ]
Matrix containing points to regress on (test points).
--use_cholesky (-c) [ bool ]
Use Cholesky decomposition during computation rather than explicitly computing the full Gram matrix.
--verbose (-v) [ bool ]
Display informational messages and the full list of parameters and timers at the end of execution.
--version (-V) [ bool ]
Display the version of mlpack.
OPTIONAL OUTPUT OPTIONS
--output_model_file (-M) [ unknown ]
Output LARS model.
--output_predictions_file (-o) [ unknown ]
If --test_file is specified, this file is where the predicted responses will be saved.
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack.