Laurae (version 0.0.0.9001)

MGScanning_pred: Multi-Grained Scanning Predictor implementation in R

Description

This function attempts to predict from Multi-Grained Scanning using xgboost.

Usage

MGScanning_pred(model, data, folds = NULL, dimensions = NULL,
  multi_class = NULL, data_start = NULL)

Arguments

model

Type: list. A model trained by MGScanning.

data

Type: data.table. A data to predict on. If passing training data, it will predict as if it was out of fold and you will overfit (so, use the list preds instead please).

folds

Type: list. The folds as list for cross-validation if using the training data. Otherwise, leave NULL. Defaults to NULL.

dimensions

Type: numeric. The dimensions of the data. Only supported is 1 for matrix format, and 2 for list of matrices. Defaults to NULL.

multi_class

Type: numeric. How many classes you got. Set to 2 for binary classification, or regression cases. Set to NULL to let it try guessing by reading the model. Defaults to NULL.

data_start

Type: vector of numeric. The initial prediction labels. Set to NULL if you do not know what you are doing. Defaults to NULL.

Value

A data.table or a list based on data predicted using model.

Details

For implementation details of Cascade Forest / Complete-Random Tree Forest / Multi-Grained Scanning / Deep Forest, check this: https://github.com/Microsoft/LightGBM/issues/331#issuecomment-283942390 by Laurae.

Examples

## Not run: ------------------------------------
# # Load libraries
# library(data.table)
# library(Matrix)
# library(xgboost)
# 
# # Create data
# data(agaricus.train, package = "lightgbm")
# data(agaricus.test, package = "lightgbm")
# agaricus_data_train <- data.table(as.matrix(agaricus.train$data))
# agaricus_data_test <- data.table(as.matrix(agaricus.test$data))
# agaricus_label_train <- agaricus.train$label
# agaricus_label_test <- agaricus.test$label
# folds <- Laurae::kfold(agaricus_label_train, 5)
# 
# # Train a model (binary classification) - FAST VERSION
# model <- MGScanning(data = agaricus_data_train, # Training data
#                     labels = agaricus_label_train, # Training labels
#                     folds = folds, # Folds for cross-validation
#                     dimensions = 1, # Change this for 2 dimensions if needed
#                     depth = 10, # Change this to change the sliding window size
#                     stride = 20, # Change this to change the sliding window speed
#                     nthread = 1, # Change this to use more threads
#                     lr = 1, # Do not touch this unless you are expert
#                     training_start = NULL, # Do not touch this unless you are expert
#                     validation_start = NULL, # Do not touch this unless you are expert
#                     n_forest = 2, # Number of forest models
#                     n_trees = 30, # Number of trees per forest
#                     random_forest = 1, # We want only 2 random forest
#                     seed = 0,
#                     objective = "binary:logistic",
#                     eval_metric = Laurae::df_logloss,
#                     multi_class = 2, # Modify this for multiclass problems)
#                     verbose = TRUE)
# 
# # Train a model (binary classification) - SLOW
# model <- MGScanning(data = agaricus_data_train, # Training data
#                     labels = agaricus_label_train, # Training labels
#                     folds = folds, # Folds for cross-validation
#                     dimensions = 1, # Change this for 2 dimensions if needed
#                     depth = 10, # Change this to change the sliding window size
#                     stride = 1, # Change this to change the sliding window speed
#                     nthread = 1, # Change this to use more threads
#                     lr = 1, # Do not touch this unless you are expert
#                     training_start = NULL, # Do not touch this unless you are expert
#                     validation_start = NULL, # Do not touch this unless you are expert
#                     n_forest = 2, # Number of forest models
#                     n_trees = 30, # Number of trees per forest
#                     random_forest = 1, # We want only 2 random forest
#                     seed = 0,
#                     objective = "binary:logistic",
#                     eval_metric = Laurae::df_logloss,
#                     multi_class = 2, # Modify this for multiclass problems)
#                     verbose = TRUE)
# 
# # Create predictions
# data_predictions <- model$preds
# 
# # Make real predictions
# new_preds <- MGScanning_pred(model, data = agaricus_data_test)
# 
# # We can check whether we have equal predictions, it's all TRUE!
# all.equal(model$preds, MGScanning_pred(model,
#                                        agaricus_data_train,
#                                        folds = folds))
# 
# # Example on fake pictures (matrices) and multiclass problem
# 
# # Generate fake images
# new_data <- list(matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20))
# 
# # Generate fake labels
# new_labels <- c(2, 1, 0, 2, 1, 0, 2, 1, 0, 0)
# 
# # Train a model (multiclass problem)
# model <- MGScanning(data = new_data, # Training data
#                     labels = new_labels, # Training labels
#                     folds = list(1:3, 3:6, 7:10), # Folds for cross-validation
#                     dimensions = 2,
#                     depth = 10,
#                     stride = 1,
#                     nthread = 1, # Change this to use more threads
#                     lr = 1, # Do not touch this unless you are expert
#                     training_start = NULL, # Do not touch this unless you are expert
#                     validation_start = NULL, # Do not touch this unless you are expert
#                     n_forest = 2, # Number of forest models
#                     n_trees = 10, # Number of trees per forest
#                     random_forest = 1, # We want only 2 random forest
#                     seed = 0,
#                     objective = "multi:softprob",
#                     eval_metric = Laurae::df_logloss,
#                     multi_class = 3, # Modify this for multiclass problems)
#                     verbose = TRUE)
# 
# # Matrix output is 10x600
# dim(model$preds)
# 
# # We can check whether we have equal predictions, it's all TRUE!
# all.equal(model$preds, MGScanning_pred(model,
#                                        new_data,
#                                        folds = list(1:3, 3:6, 7:10)))
# 
# # Real predictions on new data
# new_data <- list(matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20),
#                  matrix(rnorm(n = 400), ncol = 20, nrow = 20))
# new_preds <- MGScanning_pred(model, data = new_data)
## ---------------------------------------------