R: Confusion matrix

confmatrix_treedamage {rTLsDeep}

R Documentation

Confusion matrix

Description

This function calculates a cross-tabulation of reference and predicted classes with associated statistics based on the deep learning models.

Usage

confmatrix_treedamage(predict_class, test_classes, class_list)

Arguments

`predict_class`	A vector with the predicted classes. This is the output from the predict_treedamage function.
`test_classes`	A vector with the predicted classes. This is the output from the get_validation_classes function.
`class_list`	A character string or numeric value describing the post-hurricane individual tree level damage classes, e.g.: c("1","2","3","4","5","6").

Value

Returns the confusion matrix comparing predictions with the reference from validation dataset.

Examples


# Set directory to tensorflow (python environment)
# This is required if running deep learning local computer with GPU
# Guide to install here: https://doi.org/10.5281/zenodo.3929709
tensorflow_dir = NA

# define model type
model_type = "simple"
#model_type = "vgg"
#model_type = "inception"
#model_type = "resnet"
#model_type = "densenet"
#model_type = "efficientnet"

# Image and model properties
# path to image folders - black
train_image_files_path <- system.file('extdata', 'train', package='rTLsDeep')
test_image_files_path <- system.file('extdata', 'validation', package='rTLsDeep')
img_width <- 256
img_height <- 256
class_list_train = unique(list.files(train_image_files_path))
class_list_test = unique(list.files(test_image_files_path))
lr_rate = 0.00003
target_size = c(img_width, img_height)
channels = 4
batch_size = 8L
epochs = 4L

# get model
model = get_dl_model(model_type=model_type,
                    img_width=img_width,
                    img_height=img_height,
                    channels=channels,
                    lr_rate = lr_rate,
                    tensorflow_dir = tensorflow_dir,
                    class_list = class_list_train)


# train model and return best weights
weights = fit_dl_model(model = model,
                                train_input_path = train_image_files_path,
                                test_input_path = test_image_files_path,
                                target_size = target_size,
                                batch_size = batch_size,
                                class_list = class_list_train,
                                epochs = epochs,
                                lr_rate = lr_rate)


# Predicting post-hurricane damage at the tree-level
tree_damage<-predict_treedamage(model=model,
                           input_file_path=test_image_files_path,
                           weights=weights,
                           target_size = c(256,256),
                           class_list=class_list_test,
                           batch_size = batch_size)

# Get damage classes for test datasets
classes<-get_validation_classes(file_path=test_image_files_path)

# Calculate, print and return confusion matrix
cm = confmatrix_treedamage(predict_class = tree_damage,
                          test_classes=classes,
                          class_list = class_list_test)