CBDA.training {CBDA} | R Documentation |

This CBDA function comprises all the input specifications to run a set M of subsamples from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized. This version 1.0.0 is fully tested ONLY on continuous features Xtemp and binary outcome Ytemp. It only performs the Training/Learning step of the CBDA protocol.

CBDA.training(Ytemp, Xtemp, label = "CBDA_package_test", alpha = 0.2, Kcol_min = 5, Kcol_max = 15, Nrow_min = 30, Nrow_max = 50, misValperc = 0, M = 3000, N_cores = 1, top = 1000, workspace_directory = tempdir(), max_covs = 100, min_covs = 5, algorithm_list = c("SL.glm", "SL.xgboost", "SL.glmnet", "SL.svm", "SL.randomForest", "SL.bartMachine"))

`Ytemp` |
This is the output variable (vector) in the original Big Data |

`Xtemp` |
This is the input variable (matrix) in the original Big Data |

`label` |
This is the label appended to RData workspaces generated within the CBDA calls |

`alpha` |
Percentage of the Big Data to hold off for Validation |

`Kcol_min` |
Lower bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR) |

`Kcol_max` |
Upper bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR) |

`Nrow_min` |
Lower bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR) |

`Nrow_max` |
Upper bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR) |

`misValperc` |
Percentage of missing values to introduce in BigData (used just for testing, to mimic real cases). |

`M` |
Number of the BigData subsets on which perform Knockoff Filtering and SuperLearner feature mining |

`N_cores` |
Number of Cores to use in the parallel implementation (default is set to 1 core) |

`top` |
Top predictions to select out of the M (must be < M, optimal ~0.1*M) |

`workspace_directory` |
Directory where the results and workspaces are saved (set by default to tempdir()) |

`max_covs` |
Top features to display and include in the Validation Step where nested models are tested |

`min_covs` |
Minimum number of top features to include in the initial model for the Validation Step (it must be greater than 2) |

`algorithm_list` |
List of algorithms/wrappers used by the SuperLearner. By default is set to the following list algorithm_list <- c("SL.glm","SL.xgboost", "SL.glmnet","SL.svm","SL.randomForest","SL.bartMachine") |

This function comprises all the input specifications to run a set M of subsamples from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized. After the necessary data wrangling (i.e., imputation, normalization and rebalancing), an ensemble predictor (i.e., SuperLearner) is applied to each subsample for training/learning. The list of algorithms used by the SuperLearner is supplied by an external file to be placed in the working directory (e.g.: CBDA_SL_library.m in our release). The file can contain any SuperLearner wrapper and any wrappers properly defined by the user. The ensemble predictive model is then validated on a fraction alpha of the Big Data. Each subsample generates a predictive model that is ranked based on performance metrics (e.g., Mean Square Error-MSE and Accuracy) during the first validation step. IMPORTANT - Memory limits to run CBDA: see https://stat.ethz.ch/R-manual/R-devel/library/base/html/Memory-limits.html for various limitations on memory needs while running R under different OS. As far as CBDA is concerned, a CBDA object can be up to 200-300 Mb. The space needed to save all the workspaces however may need to be as large as 1-5 Gb, depending on the number of subsamples. We are working on an new CBDA implementation that reduces the storage constraints.

CBDA object with validation results and 3 RData workspaces

See https://github.com/SOCR/CBDA/releases for details on the CBDA protocol and the manuscript "Controlled Feature Selection and Compressive Big Data Analytics: Applications to Big Biomedical and Health Studiesâ€ť [under review] authored by Simeone Marino, Jiachen Xu, Yi Zhao, Nina Zhou, Yiwang Zhou, Ivo D. Dinov from the University of Michigan

# Installation # Please upload the Windows binary and/or source CBDA_1.0.0 files from # the CBDA Github repository https://github.com/SOCR/CBDA/releases ## Not run: # Installation from the Windows binary (recommended for Windows systems) install.packages("/filepath/CBDA_1.0.0_binary_Windows.zip", repos = NULL, type = "win.binary") # Installation from the source (recommended for Macs and Linux systems) install.packages("/filepath/CBDA_1.0.0_source_.tar.gz", repos = NULL, type = "source") # Initialization # This function call installs (if needed) and attaches all the necessary packages to run # the CBDA package v1.0.0. It should be run before any production run or test. # The output shows a table where for each package a TRUE or FALSE is displayed. # Thus the necessary steps can be pursued in case some package has a FALSE. CBDA_initialization() # Set the specs for the synthetic dataset to be tested n = 300 # number of observations p = 100 # number of variables # Generate a nxp matrix of IID variables (e.g., ~N(0,1)) X1 = matrix(rnorm(n*p), nrow=n, ncol=p) # Setting the nonzero variables - signal variables nonzero=c(1,100,200,300,400,500,600,700,800,900) # Set the signal amplitude (for noise level = 1) amplitude = 10 # Allocate the nonzero coefficients in the correct places beta = amplitude * (1:p %in% nonzero) # Generate a linear model with a bias (e.g., white noise ~N(0,1)) ztemp <- function() X1 %*% beta + rnorm(n) z = ztemp() # Pass it through an inv-logit function to # generate the Bernoulli response variable Ytemp pr = 1/(1+exp(-z)) Ytemp = rbinom(n,1,pr) X2 <- cbind(Ytemp,X1) dataset_file ="Binomial_dataset_3.txt" # Save the synthetic dataset a <- tempdir() write.table(X2, file = paste0(file.path(a),'/',dataset_file), sep=",") # The file is now stored in the directory a a list.files(a) # Load the Synthetic dataset Data = read.csv(paste0(file.path(a),'/',dataset_file),header = TRUE) Ytemp <- Data[,1] # set the outcome original_names_Data <- names(Data) cols_to_eliminate=1 Xtemp <- Data[-cols_to_eliminate] # set the matrix X of features/covariates original_names_Xtemp <- names(Xtemp) # Add more wrappers/algorithms to the SuperLearner ensemble predictor # It can be commented out if only the default set of algorithms are used, # e.g., algorithm_list = c("SL.glm","SL.xgboost","SL.glmnet","SL.svm", # "SL.randomForest","SL.bartMachine") # This defines a "new" wrapper, based on the default SL.glmnet SL.glmnet.0.75 <- function(..., alpha = 0.75,family="binomial"){ SL.glmnet(..., alpha = alpha, family = family)} test_example <- c("SL.glmnet","SL.glmnet.0.75") # Call the CBDA function # Multicore functionality NOT enabled CBDA_object <- CBDA.training(Ytemp , Xtemp , M = 12 , Nrow_min = 50, Nrow_max = 70, top = 10, max_covs = 8 , min_covs = 3,algorithm_list = test_example , workspace_directory = a) # Multicore functionality enabled test_example <- c("SL.xgboost","SL.svm") CBDA_test <- CBDA.training(Ytemp , Xtemp , M = 40 , Nrow_min = 50, Nrow_max = 70, N_cores = 2 , top = 30, max_covs = 20 , min_covs = 5 , algorithm_list = test_example , workspace_directory = a) ## End(Not run)

[Package *CBDA* version 1.0.0 Index]