1mlpack_lars(1)                   User Commands                  mlpack_lars(1)
2
3
4

NAME

6       mlpack_lars - lars
7

SYNOPSIS

9        mlpack_lars [-i string] [-m unknown] [-l double] [-L double] [-r string] [-t string] [-c bool] [-V bool] [-M unknown] [-o string] [-h -v]
10

DESCRIPTION

12       An  implementation  of  LARS: Least Angle Regression (Stagewise/laSso).
13       This is a stage-wise homotopy-based algorithm for L1-regularized linear
14       regression  (LASSO)  and  L1+L2-regularized  linear regression (Elastic
15       Net).
16
17       This program is able to train a LARS/LASSO/Elastic Net model or load  a
18       model from file, output regression predictions for a test set, and save
19       the trained model to a file. The LARS algorithm is  described  in  more
20       detail below:
21
22       Let X be a matrix where each row is a point and each column is a dimen‐
23       sion, and let y be a vector of targets.
24
25       The Elastic Net problem is to solve
26
27         min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 +
28           0.5 lambda_2 ||beta||_2^2
29
30       If lambda1 > 0 and lambda2 = 0, the problem is the LASSO.  If lambda1 >
31       0  and lambda2 > 0, the problem is the Elastic Net.  If lambda1 = 0 and
32       lambda2 > 0, the problem is ridge  regression.   If  lambda1  =  0  and
33       lambda2 = 0, the problem is unregularized linear regression.
34
35       For  efficiency  reasons,  it  is not recommended to use this algorithm
36       with ’--lambda1 (-l)' = 0. In that case,  use  the  'linear_regression'
37       program,  which  implements  both  unregularized  linear regression and
38       ridge regression.
39
40       To train a LARS/LASSO/Elastic Net model, the  '--input_file  (-i)'  and
41       ’--responses_file (-r)' parameters must be given. The '--lambda1 (-l)',
42       ’--lambda2 (-L)', and  '--use_cholesky  (-c)'  parameters  control  the
43       training  options.  A  trained  model  can  be  saved  with the '--out‐
44       put_model_file (-M)'.  If no training is desired at all, a model can be
45       passed via the ’--input_model_file (-m)' parameter.
46
47       The program can also provide predictions for test data using either the
48       trained model or the given input model. Test points  can  be  specified
49       with  the ’--test_file (-t)' parameter. Predicted responses to the test
50       points can be saved with the  '--output_predictions_file  (-o)'  output
51       parameter.
52
53       For  example,  the  following  command  trains  a  model  on  the  data
54       'data.csv' and responses 'responses.csv' with lambda1 set  to  0.4  and
55       lambda2  set  to  0  (so, LASSO is being solved), and then the model is
56       saved to 'lasso_model.bin':
57
58       $ lars --input_file data.csv --responses_file  responses.csv  --lambda1
59       0.4 --lambda2 0 --output_model_file lasso_model.bin
60
61       The  following  command uses the 'lasso_model.bin' to provide predicted
62       responses  for  the  data  'test.csv'  and  save  those  responses   to
63       ’test_predictions.csv':
64
65       $  lars  --input_model_file lasso_model.bin --test_file test.csv --out‐
66       put_predictions_file test_predictions.csv
67

OPTIONAL INPUT OPTIONS

69       --help (-h) [bool]
70              Default help info.
71
72       --info [string]
73              Get help on a specific module or option.  Default value ''.
74
75       --input_file (-i) [string]
76              Matrix of covariates (X). Default value ''.
77
78       --input_model_file (-m) [unknown]
79              Trained LARS model to use. Default value ''.
80
81       --lambda1 (-l) [double]
82              Regularization parameter for l1-norm penalty.  Default value 0.
83
84       --lambda2 (-L) [double]
85              Regularization parameter for l2-norm penalty.  Default value 0.
86
87       --responses_file (-r) [string]
88              Matrix of responses/observations (y). Default value ''.
89
90       --test_file (-t) [string]
91              Matrix containing points to regress on  (test  points).  Default
92              value ''.
93
94       --use_cholesky (-c) [bool]
95              Use   Cholesky  decomposition  during  computation  rather  than
96              explicitly computing the full Gram matrix.
97
98       --verbose (-v) [bool]
99              Display informational messages and the full list  of  parameters
100              and timers at the end of execution.
101
102       --version (-V) [bool]
103              Display the version of mlpack.
104

OPTIONAL OUTPUT OPTIONS

106       --output_model_file (-M) [unknown]
107              Output LARS model. Default value ''.
108
109       --output_predictions_file (-o) [string]
110              If  --test_file  is  specified, this file is where the predicted
111              responses will be saved. Default value ''.
112

ADDITIONAL INFORMATION

114       For further information, including relevant papers, citations, and the‐
115       ory,  consult  the  documentation  found  at  http://www.mlpack.org  or
116       included with your distribution of mlpack.
117
118
119
120mlpack-3.0.4                   21 February 2019                 mlpack_lars(1)