preprocess_imputer_main.cpp File Reference

#include <mlpack/prereqs.hpp>
#include <mlpack/core/data/load_impl.hpp>
#include <mlpack/core/util/io.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include <mlpack/core/data/imputer.hpp>
#include <mlpack/core/data/dataset_mapper.hpp>
#include <mlpack/core/data/map_policies/increment_policy.hpp>
#include <mlpack/core/data/map_policies/missing_policy.hpp>
#include <mlpack/core/data/imputation_methods/mean_imputation.hpp>
#include <mlpack/core/data/imputation_methods/median_imputation.hpp>
#include <mlpack/core/data/imputation_methods/custom_imputation.hpp>
#include <mlpack/core/data/imputation_methods/listwise_deletion.hpp>

Include dependency graph for preprocess_imputer_main.cpp:

This graph shows which files directly or indirectly include this file:

Functions
	BINDING_NAME ("Impute Data")
	BINDING_SHORT_DESC ("This utility provides several imputation strategies for missing data. " "Given a dataset with missing values, this can impute according to several " "strategies, including user-defined values.")
	BINDING_LONG_DESC ("This utility takes a dataset and converts a user-defined missing variable " "to another to provide more meaningful analysis." "\" "The program does not modify the original file, but instead makes a " "separate file to save the output data; You can save the output by " "specifying the file name with"+PRINT_PARAM_STRING("output_file")+".")
	BINDING_EXAMPLE ("For example, if we consider 'NULL' in dimension 0 to be a missing " "variable and want to delete whole row containing the NULL in the " "column-wise"+PRINT_DATASET("dataset")+", and save the result to "+PRINT_DATASET("result")+", we could run :" "\"+PRINT_CALL("mlpack_preprocess_imputer", "input_file", "dataset", "output_file", "result", "missing_value", "NULL", "dimension", "0", "strategy", "listwise_deletion"))
	BINDING_SEE_ALSO ("@preprocess_binarize", "#preprocess_binarize")
	BINDING_SEE_ALSO ("@preprocess_describe", "#preprocess_describe")
	BINDING_SEE_ALSO ("@preprocess_split", "#preprocess_split")
	PARAM_STRING_IN_REQ ("input_file", "File containing data.", "i")
	PARAM_STRING_OUT ("output_file", "File to save output into.", "o")
	PARAM_STRING_IN_REQ ("missing_value", "User defined missing value.", "m")
	PARAM_STRING_IN_REQ ("strategy", "imputation strategy to be applied. Strategies " "should be one of 'custom', 'mean', 'median', and 'listwise_deletion'.", "s")
	PARAM_DOUBLE_IN ("custom_value", "User-defined custom imputation value.", "c", 0.0)
	PARAM_INT_IN ("dimension", "The dimension to apply imputation to.", "d", 0)

Detailed Description

Author

Keon Kim

A utility that provides imputation strategies for missing values.

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.