lars_main.cpp File Reference

#include <mlpack/prereqs.hpp>
#include <mlpack/core/util/io.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include "lars.hpp"

Include dependency graph for lars_main.cpp:

Functions
	BINDING_NAME ("LARS")
	BINDING_SHORT_DESC ("An implementation of Least Angle Regression (Stagewise/laSso), also known" " as LARS. This can train a LARS/LASSO/Elastic Net model and use that " "model or a pre-trained model to output regression predictions for a test " "set.")
	BINDING_LONG_DESC ("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 ||_2^2 + lambda_1 ||beta||_1 +\ " 0.5 lambda_2||beta||_2^2" "\\" "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" " "+PRINT_PARAM_STRING("lambda1")+"=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 "+PRINT_PARAM_STRING("input")+" and "+PRINT_PARAM_STRING("responses")+" parameters must be given. The "+PRINT_PARAM_STRING("lambda1")+", "+PRINT_PARAM_STRING("lambda2")+", and "+PRINT_PARAM_STRING("use_cholesky")+" parameters control the training " "options. A trained model can be saved with the "+PRINT_PARAM_STRING("output_model")+". If no training is desired at all," " a model can be passed via the "+PRINT_PARAM_STRING("input_model")+" 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 "+PRINT_PARAM_STRING("test")+" parameter. Predicted responses " "to the test points can be saved with the "+PRINT_PARAM_STRING("output_predictions")+" output parameter.")
	BINDING_EXAMPLE ("For example, the following command trains a model on the data "+PRINT_DATASET("data")+" and responses "+PRINT_DATASET("responses")+" with lambda1 set to 0.4 and lambda2 set to 0 (so, LASSO is being " "solved), and then the model is saved to "+PRINT_MODEL("lasso_model")+":" "\"+PRINT_CALL("lars", "input", "data", "responses", "responses", "lambda1", 0.4, "lambda2", 0.0, "output_model", "lasso_model")+"\" "The following command uses the "+PRINT_MODEL("lasso_model")+" to " "provide predicted responses for the data "+PRINT_DATASET("test")+" " "and save those responses to "+PRINT_DATASET("test_predictions")+": " "\"+PRINT_CALL("lars", "input_model", "lasso_model", "test", "test", "output_predictions", "test_predictions"))
	BINDING_SEE_ALSO ("@linear_regression", "#linear_regression")
	BINDING_SEE_ALSO ("Least angle regression (pdf)", "http://mlpack.org/papers/lars.pdf")
	BINDING_SEE_ALSO ("mlpack::regression::LARS C++ class documentation", "@doxygen/classmlpack_1_1regression_1_1LARS.html")
	PARAM_TMATRIX_IN ("input", "Matrix of covariates (X).", "i")
	PARAM_MATRIX_IN ("responses", "Matrix of responses/observations (y).", "r")
	PARAM_MODEL_IN (LARS, "input_model", "Trained LARS model to use.", "m")
	PARAM_MODEL_OUT (LARS, "output_model", "Output LARS model.", "M")
	PARAM_TMATRIX_IN ("test", "Matrix containing points to regress on (test " "points).", "t")
	PARAM_TMATRIX_OUT ("output_predictions", "If --test_file is specified, this " "file is where the predicted responses will be saved.", "o")
	PARAM_DOUBLE_IN ("lambda1", "Regularization parameter for l1-norm penalty.", "l", 0)
	PARAM_DOUBLE_IN ("lambda2", "Regularization parameter for l2-norm penalty.", "L", 0)
	PARAM_FLAG ("use_cholesky", "Use Cholesky decomposition during computation " "rather than explicitly computing the full Gram matrix.", "c")

Detailed Description

Author

Nishant Mehta

Executable for LARS.

mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.