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R/classifiers.R
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#' @title DecisionTreeClassifier
#'
#' @description Wrapper R6 Class of rpart::rpart function that can be used for LESSRegressor and LESSClassifier
#'
#' @param min_samples_split The minimum number of observations that must exist in a node in order for a split to be attempted (defaults to 2).
#' @param min_samples_leaf The minimum number of observations in any terminal (leaf) node (defaults to 1).
#' @param cp Complexity Parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted.
#' This means that the overall R-squared must increase by cp at each step. The main role of this parameter is
#' to save computing time by pruning off splits that are obviously not worthwhile. (defaults to 0.001)
#' @param xval Number of cross-validations (defaults to 10)
#' @param surrogate_style Controls the selection of a best surrogate. If set to 0 (default) the program uses the total number of correct
#' classification for a potential surrogate variable, if set to 1 it uses the percent correct, calculated over the non-missing values of the surrogate.
#' The first option more severely penalizes covariates with a large number of missing values.
#' @param max_depth The maximum depth of any node of the final tree, with the root node counted as depth 0.
#' Values greater than 30 will give nonsense results on 32-bit machines.
#'
#' @return R6 Class of DecisionTreeClassifier
#' @importFrom rpart rpart
#' @seealso [rpart::rpart()]
#' @export
DecisionTreeClassifier <- R6::R6Class(classname = "DecisionTreeClassifier",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
min_samples_split = NULL,
min_samples_leaf = NULL,
cp = NULL,
xval = NULL,
surrogate_style = NULL,
max_depth = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of DecisionTreeClassifier
#'
#' @examples
#' dt <- DecisionTreeClassifier$new()
#' dt <- DecisionTreeClassifier$new(min_samples_split = 10)
#' dt <- DecisionTreeClassifier$new(min_samples_leaf = 6, cp = 0.01)
initialize = function(min_samples_split = 2, min_samples_leaf = 1, cp = 0.001, xval = 10, surrogate_style = 0, max_depth = 30){
private$min_samples_split = min_samples_split
private$min_samples_leaf = min_samples_leaf
private$cp = cp
private$xval = xval
private$surrogate_style = surrogate_style
private$max_depth = max_depth
},
#' @description Builds a decision tree regressor from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of DecisionTreeClassifier
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' dt <- DecisionTreeClassifier$new()
#' dt$fit(X_train, y_train)
fit = function(X, y) {
df <- prepareDataset(X, y)
private$model <- rpart::rpart(y ~ ., data = df, method = "class", minsplit = private$min_samples_split,
minbucket = private$min_samples_leaf, cp = private$cp, xval = private$xval,
surrogatestyle = private$surrogate_style, maxdepth = private$max_depth)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' dt <- DecisionTreeClassifier$new()
#' dt$fit(X_train, y_train)
#' preds <- dt$predict(X_test)
#'
#' dt <- DecisionTreeClassifier$new()
#' preds <- dt$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- DecisionTreeClassifier$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
predict = function(X0) {
check_is_fitted(self)
data <- prepareXset(X0)
y_pred <- predict(private$model, data, type = "class")
return(y_pred)
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#' @examples
#' dt$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
#' @title Support Vector Classification
#'
#' @description Wrapper R6 Class of e1071::svm function that can be used for LESSRegressor and LESSClassifier
#'
#' @param scale A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed.
#' Per default, data are scaled internally (both x and y variables) to zero mean and unit variance.
#' The center and scale values are returned and used for later predictions (default: TRUE)
#' @param kernel The kernel used in training and predicting. Possible values are: "linear", "polynomial", "radial", "sigmoid" (default is "radial")
#' @param degree Parameter needed for kernel of type polynomial (default: 3)
#' @param gamma Parameter needed for all kernels except linear (default: 1/(data dimension))
#' @param coef0 Parameter needed for kernels of type polynomial and sigmoid (default: 0)
#' @param cost Cost of constraints violation (default: 1)—it is the ‘C’-constant of the regularization term in the Lagrange formulation (default: 1)
#' @param cache_size Cache memory in MB (default: 40)
#' @param tolerance Tolerance of termination criterion (default: 0.001)
#' @param epsilon Epsilon in the insensitive-loss function (default: 0.1)
#' @param shrinking Option whether to use the shrinking-heuristics (default: TRUE)
#' @param cross If a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model:
#' the accuracy rate for classification and the Mean Squared Error for regression (default: 0)
#' @param fitted Logical indicating whether the fitted values should be computed and included in the model or not (default: TRUE)
#' @param probability Logical indicating whether the model should allow for probability predictions (default: FALSE)
#'
#' @return R6 Class of SVC
#' @seealso [e1071::svm()]
#' @importFrom e1071 svm
#' @export
SVC <- R6::R6Class(classname = "SVC",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
scale = NULL,
kernel = NULL,
degree = NULL,
gamma = NULL,
coef0 = NULL,
cost = NULL,
cache_size = NULL,
tolerance = NULL,
epsilon = NULL,
shrinking = NULL,
cross = NULL,
probability = NULL,
fitted = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of SVC
#'
#' @examples
#' svc <- SVC$new()
#' svc <- SVC$new(kernel = "polynomial")
initialize = function(scale = TRUE, kernel = "radial", degree = 3, gamma = NULL, coef0 = 0, cost = 1,
cache_size = 40, tolerance = 0.001, epsilon = 0.1, shrinking = TRUE, cross = 0,
probability = FALSE, fitted = TRUE){
private$scale = scale
private$kernel = kernel
private$degree = degree
private$gamma = gamma
private$coef0 = coef0
private$cost = cost
private$cache_size = cache_size
private$tolerance = tolerance
private$epsilon = epsilon
private$shrinking = shrinking
private$cross = cross
private$probability = probability
private$fitted = fitted
},
#' @description Fit the SVM model from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of SVC
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' svc <- SVC$new()
#' svc$fit(X_train, y_train)
fit = function(X, y){
df <- prepareDataset(X, y)
gamma = if (is.vector(X)) 1 else 1 / ncol(X)
df$y <- as.factor(df$y)
private$model <- e1071::svm(y ~ ., data = df, type = 'C-classification', scale = private$scale, kernel = private$kernel,
degree = private$degree, gamma = if (is.null(private$gamma)) gamma else private$gamma, coef0 = private$coef0,
cost = private$cost, cachesize = private$cache_size, tolerance = private$tolerance, epsilon = private$epsilon,
shrinking = private$shrinking, cross = private$cross, probability = private$probability,
fitted = private$fitted)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' svc <- SVC$new()
#' svc$fit(X_train, y_train)
#' preds <- svc$predict(X_test)
#'
#' svc <- SVC$new()
#' preds <- svc$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- SVC$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
predict = function(X0){
check_is_fitted(self)
data <- prepareXset(X0)
y_pred <- predict(private$model, data)
return(y_pred)
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#'
#' @examples
#' svc$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
#' @title RandomForestClassifier
#'
#' @description Wrapper R6 Class of randomForest::randomForest function that can be used for LESSRegressor and LESSClassifier
#'
#' @param n_estimators Number of trees to grow. This should not be set to too small a number,
#' to ensure that every input row gets predicted at least a few times (defaults to 100).
#' @param random_state Seed number to be used for fixing the randomness (default to NULL).
#' @param min_samples_leaf Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown
#' (and thus take less time) (defaults to 1)
#' @param max_leaf_nodes Maximum number of terminal nodes trees in the forest can have.
#' If not given, trees are grown to the maximum possible (subject to limits by nodesize).
#' If set larger than maximum possible, a warning is issued. (defaults to NULL)
#'
#' @return R6 Class of RandomForestClassifier
#' @importFrom randomForest randomForest
#' @seealso [randomForest::randomForest()]
#' @export
RandomForestClassifier <- R6::R6Class(classname = "RandomForestClassifier",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
n_estimators = NULL,
random_state = NULL,
min_samples_leaf = NULL,
max_leaf_nodes = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of RandomForestClassifier
#'
#' @examples
#' rf <- RandomForestClassifier$new()
#' rf <- RandomForestClassifier$new(n_estimators = 500)
#' rf <- RandomForestClassifier$new(n_estimators = 500, random_state = 100)
initialize = function(n_estimators = 100, random_state = NULL, min_samples_leaf = 1,
max_leaf_nodes = NULL){
private$n_estimators = n_estimators
private$random_state = random_state
private$min_samples_leaf = min_samples_leaf
private$max_leaf_nodes = max_leaf_nodes
},
#' @description Builds a random forest regressor from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of RandomForestClassifier
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' rf <- RandomForestClassifier$new()
#' rf$fit(X_train, y_train)
fit = function(X, y){
df <- prepareDataset(X, y)
df$y <- as.factor(df$y)
set.seed(private$random_state)
private$model <- randomForest::randomForest(y ~ ., data = df, ntree = private$n_estimators,
nodesize = private$min_samples_leaf,
maxnodes = private$max_leaf_nodes,
importance = TRUE,
proximity = TRUE)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' rf <- RandomForestClassifier$new()
#' rf$fit(X_train, y_train)
#' preds <- rf$predict(X_test)
#'
#' rf <- RandomForestClassifier$new()
#' preds <- rf$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- RandomForestClassifier$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
predict = function(X0){
check_is_fitted(self)
data <- prepareXset(X0)
predict(private$model, data)
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#'
#' @examples
#' rf$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
#' @title KNeighborsClassifier
#'
#' @description Wrapper R6 Class of caret::knnreg function that can be used for LESSRegressor and LESSClassifier
#'
#' @param k Number of neighbors considered (defaults to 5).
#'
#' @return R6 Class of KNeighborsClassifier
#' @seealso [caret::knn3()]
#' @importFrom caret knn3
#' @export
KNeighborsClassifier <- R6::R6Class(classname = "KNeighborsClassifier",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
k = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of KNeighborsClassifier
#'
#' @examples
#' knc <- KNeighborsClassifier$new()
#' knc <- KNeighborsClassifier$new(k = 5)
initialize = function(k = 5){
private$k = k
},
#' @description Fit the k-nearest neighbors regressor from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of KNeighborsClassifier
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' knc <- KNeighborsClassifier$new()
#' knc$fit(X_train, y_train)
fit = function(X, y){
df <- prepareDataset(X, y)
df$y <- as.factor(df$y)
private$model <- caret::knn3(y ~ ., data = df, k=private$k)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' knc <- KNeighborsClassifier$new()
#' knc$fit(X_train, y_train)
#' preds <- knc$predict(X_test)
#'
#' knc <- KNeighborsClassifier$new()
#' preds <- knc$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- KNeighborsClassifier$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=factor(preds), reference = factor(y_test)))
predict = function(X0){
check_is_fitted(self)
data <- prepareXset(X0)
y_pred <- predict(private$model, data, type = "class")
return(factor(y_pred))
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#'
#' @examples
#' knc$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
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#' @title DecisionTreeClassifier
#'
#' @description Wrapper R6 Class of rpart::rpart function that can be used for LESSRegressor and LESSClassifier
#'
#' @param min_samples_split The minimum number of observations that must exist in a node in order for a split to be attempted (defaults to 2).
#' @param min_samples_leaf The minimum number of observations in any terminal (leaf) node (defaults to 1).
#' @param cp Complexity Parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted.
#' This means that the overall R-squared must increase by cp at each step. The main role of this parameter is
#' to save computing time by pruning off splits that are obviously not worthwhile. (defaults to 0.001)
#' @param xval Number of cross-validations (defaults to 10)
#' @param surrogate_style Controls the selection of a best surrogate. If set to 0 (default) the program uses the total number of correct
#' classification for a potential surrogate variable, if set to 1 it uses the percent correct, calculated over the non-missing values of the surrogate.
#' The first option more severely penalizes covariates with a large number of missing values.
#' @param max_depth The maximum depth of any node of the final tree, with the root node counted as depth 0.
#' Values greater than 30 will give nonsense results on 32-bit machines.
#'
#' @return R6 Class of DecisionTreeClassifier
#' @importFrom rpart rpart
#' @seealso [rpart::rpart()]
#' @export
DecisionTreeClassifier <- R6::R6Class(classname = "DecisionTreeClassifier",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
min_samples_split = NULL,
min_samples_leaf = NULL,
cp = NULL,
xval = NULL,
surrogate_style = NULL,
max_depth = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of DecisionTreeClassifier
#'
#' @examples
#' dt <- DecisionTreeClassifier$new()
#' dt <- DecisionTreeClassifier$new(min_samples_split = 10)
#' dt <- DecisionTreeClassifier$new(min_samples_leaf = 6, cp = 0.01)
initialize = function(min_samples_split = 2, min_samples_leaf = 1, cp = 0.001, xval = 10, surrogate_style = 0, max_depth = 30){
private$min_samples_split = min_samples_split
private$min_samples_leaf = min_samples_leaf
private$cp = cp
private$xval = xval
private$surrogate_style = surrogate_style
private$max_depth = max_depth
},
#' @description Builds a decision tree regressor from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of DecisionTreeClassifier
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' dt <- DecisionTreeClassifier$new()
#' dt$fit(X_train, y_train)
fit = function(X, y) {
df <- prepareDataset(X, y)
private$model <- rpart::rpart(y ~ ., data = df, method = "class", minsplit = private$min_samples_split,
minbucket = private$min_samples_leaf, cp = private$cp, xval = private$xval,
surrogatestyle = private$surrogate_style, maxdepth = private$max_depth)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' dt <- DecisionTreeClassifier$new()
#' dt$fit(X_train, y_train)
#' preds <- dt$predict(X_test)
#'
#' dt <- DecisionTreeClassifier$new()
#' preds <- dt$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- DecisionTreeClassifier$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
predict = function(X0) {
check_is_fitted(self)
data <- prepareXset(X0)
y_pred <- predict(private$model, data, type = "class")
return(y_pred)
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#' @examples
#' dt$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
#' @title Support Vector Classification
#'
#' @description Wrapper R6 Class of e1071::svm function that can be used for LESSRegressor and LESSClassifier
#'
#' @param scale A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed.
#' Per default, data are scaled internally (both x and y variables) to zero mean and unit variance.
#' The center and scale values are returned and used for later predictions (default: TRUE)
#' @param kernel The kernel used in training and predicting. Possible values are: "linear", "polynomial", "radial", "sigmoid" (default is "radial")
#' @param degree Parameter needed for kernel of type polynomial (default: 3)
#' @param gamma Parameter needed for all kernels except linear (default: 1/(data dimension))
#' @param coef0 Parameter needed for kernels of type polynomial and sigmoid (default: 0)
#' @param cost Cost of constraints violation (default: 1)—it is the ‘C’-constant of the regularization term in the Lagrange formulation (default: 1)
#' @param cache_size Cache memory in MB (default: 40)
#' @param tolerance Tolerance of termination criterion (default: 0.001)
#' @param epsilon Epsilon in the insensitive-loss function (default: 0.1)
#' @param shrinking Option whether to use the shrinking-heuristics (default: TRUE)
#' @param cross If a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model:
#' the accuracy rate for classification and the Mean Squared Error for regression (default: 0)
#' @param fitted Logical indicating whether the fitted values should be computed and included in the model or not (default: TRUE)
#' @param probability Logical indicating whether the model should allow for probability predictions (default: FALSE)
#'
#' @return R6 Class of SVC
#' @seealso [e1071::svm()]
#' @importFrom e1071 svm
#' @export
SVC <- R6::R6Class(classname = "SVC",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
scale = NULL,
kernel = NULL,
degree = NULL,
gamma = NULL,
coef0 = NULL,
cost = NULL,
cache_size = NULL,
tolerance = NULL,
epsilon = NULL,
shrinking = NULL,
cross = NULL,
probability = NULL,
fitted = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of SVC
#'
#' @examples
#' svc <- SVC$new()
#' svc <- SVC$new(kernel = "polynomial")
initialize = function(scale = TRUE, kernel = "radial", degree = 3, gamma = NULL, coef0 = 0, cost = 1,
cache_size = 40, tolerance = 0.001, epsilon = 0.1, shrinking = TRUE, cross = 0,
probability = FALSE, fitted = TRUE){
private$scale = scale
private$kernel = kernel
private$degree = degree
private$gamma = gamma
private$coef0 = coef0
private$cost = cost
private$cache_size = cache_size
private$tolerance = tolerance
private$epsilon = epsilon
private$shrinking = shrinking
private$cross = cross
private$probability = probability
private$fitted = fitted
},
#' @description Fit the SVM model from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of SVC
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' svc <- SVC$new()
#' svc$fit(X_train, y_train)
fit = function(X, y){
df <- prepareDataset(X, y)
gamma = if (is.vector(X)) 1 else 1 / ncol(X)
df$y <- as.factor(df$y)
private$model <- e1071::svm(y ~ ., data = df, type = 'C-classification', scale = private$scale, kernel = private$kernel,
degree = private$degree, gamma = if (is.null(private$gamma)) gamma else private$gamma, coef0 = private$coef0,
cost = private$cost, cachesize = private$cache_size, tolerance = private$tolerance, epsilon = private$epsilon,
shrinking = private$shrinking, cross = private$cross, probability = private$probability,
fitted = private$fitted)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' svc <- SVC$new()
#' svc$fit(X_train, y_train)
#' preds <- svc$predict(X_test)
#'
#' svc <- SVC$new()
#' preds <- svc$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- SVC$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
predict = function(X0){
check_is_fitted(self)
data <- prepareXset(X0)
y_pred <- predict(private$model, data)
return(y_pred)
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#'
#' @examples
#' svc$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
#' @title RandomForestClassifier
#'
#' @description Wrapper R6 Class of randomForest::randomForest function that can be used for LESSRegressor and LESSClassifier
#'
#' @param n_estimators Number of trees to grow. This should not be set to too small a number,
#' to ensure that every input row gets predicted at least a few times (defaults to 100).
#' @param random_state Seed number to be used for fixing the randomness (default to NULL).
#' @param min_samples_leaf Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown
#' (and thus take less time) (defaults to 1)
#' @param max_leaf_nodes Maximum number of terminal nodes trees in the forest can have.
#' If not given, trees are grown to the maximum possible (subject to limits by nodesize).
#' If set larger than maximum possible, a warning is issued. (defaults to NULL)
#'
#' @return R6 Class of RandomForestClassifier
#' @importFrom randomForest randomForest
#' @seealso [randomForest::randomForest()]
#' @export
RandomForestClassifier <- R6::R6Class(classname = "RandomForestClassifier",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
n_estimators = NULL,
random_state = NULL,
min_samples_leaf = NULL,
max_leaf_nodes = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of RandomForestClassifier
#'
#' @examples
#' rf <- RandomForestClassifier$new()
#' rf <- RandomForestClassifier$new(n_estimators = 500)
#' rf <- RandomForestClassifier$new(n_estimators = 500, random_state = 100)
initialize = function(n_estimators = 100, random_state = NULL, min_samples_leaf = 1,
max_leaf_nodes = NULL){
private$n_estimators = n_estimators
private$random_state = random_state
private$min_samples_leaf = min_samples_leaf
private$max_leaf_nodes = max_leaf_nodes
},
#' @description Builds a random forest regressor from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of RandomForestClassifier
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' rf <- RandomForestClassifier$new()
#' rf$fit(X_train, y_train)
fit = function(X, y){
df <- prepareDataset(X, y)
df$y <- as.factor(df$y)
set.seed(private$random_state)
private$model <- randomForest::randomForest(y ~ ., data = df, ntree = private$n_estimators,
nodesize = private$min_samples_leaf,
maxnodes = private$max_leaf_nodes,
importance = TRUE,
proximity = TRUE)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' rf <- RandomForestClassifier$new()
#' rf$fit(X_train, y_train)
#' preds <- rf$predict(X_test)
#'
#' rf <- RandomForestClassifier$new()
#' preds <- rf$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- RandomForestClassifier$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
predict = function(X0){
check_is_fitted(self)
data <- prepareXset(X0)
predict(private$model, data)
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#'
#' @examples
#' rf$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
#' @title KNeighborsClassifier
#'
#' @description Wrapper R6 Class of caret::knnreg function that can be used for LESSRegressor and LESSClassifier
#'
#' @param k Number of neighbors considered (defaults to 5).
#'
#' @return R6 Class of KNeighborsClassifier
#' @seealso [caret::knn3()]
#' @importFrom caret knn3
#' @export
KNeighborsClassifier <- R6::R6Class(classname = "KNeighborsClassifier",
inherit = SklearnEstimator,
private = list(
estimator_type = "classifier",
model = NULL,
k = NULL
),
public = list(
#' @description Creates a new instance of R6 Class of KNeighborsClassifier
#'
#' @examples
#' knc <- KNeighborsClassifier$new()
#' knc <- KNeighborsClassifier$new(k = 5)
initialize = function(k = 5){
private$k = k
},
#' @description Fit the k-nearest neighbors regressor from the training set (X, y).
#'
#' @param X 2D matrix or dataframe that includes predictors
#' @param y 1D vector or (n,1) dimensional matrix/dataframe that includes labels
#'
#' @return Fitted R6 Class of KNeighborsClassifier
#'
#' @examples
#' data(iris)
#' split_list <- train_test_split(iris, test_size = 0.3)
#' X_train <- split_list[[1]]
#' X_test <- split_list[[2]]
#' y_train <- split_list[[3]]
#' y_test <- split_list[[4]]
#'
#' knc <- KNeighborsClassifier$new()
#' knc$fit(X_train, y_train)
fit = function(X, y){
df <- prepareDataset(X, y)
df$y <- as.factor(df$y)
private$model <- caret::knn3(y ~ ., data = df, k=private$k)
private$isFitted <- TRUE
invisible(self)
},
#' @description Predict regression value for X0.
#'
#' @param X0 2D matrix or dataframe that includes predictors
#'
#' @return Factor of the predict classes.
#'
#' @examples
#' knc <- KNeighborsClassifier$new()
#' knc$fit(X_train, y_train)
#' preds <- knc$predict(X_test)
#'
#' knc <- KNeighborsClassifier$new()
#' preds <- knc$fit(X_train, y_train)$predict(X_test)
#'
#' preds <- KNeighborsClassifier$new()$fit(X_train, y_train)$predict(X_test)
#' print(caret::confusionMatrix(data=factor(preds), reference = factor(y_test)))
predict = function(X0){
check_is_fitted(self)
data <- prepareXset(X0)
y_pred <- predict(private$model, data, type = "class")
return(factor(y_pred))
},
#' @description Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
#'
#' @examples
#' knc$get_estimator_type()
get_estimator_type = function() {
return(private$estimator_type)
}
))
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