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Fits model to data

Description

Generic function to fit models (inherited frommlapiEstimation)

Usage

fit(x, model, y = NULL, ...)## S3 method for class 'Matrix'fit(x, model, y = NULL, ...)## S3 method for class 'matrix'fit(x, model, y = NULL, ...)

Arguments

Value

invisible(object$self())

Fit model to the data, then transforms data

Description

Generic function to fit transformers (inherits frommlapiTransformation)

Usage

fit_transform(x, model, y = NULL, ...)## S3 method for class 'Matrix'fit_transform(x, model, y = NULL, ...)## S3 method for class 'matrix'fit_transform(x, model, y = NULL, ...)

Arguments

Value

Transformed version of thex

Base abstract class for all decompositions

Description

Base class for alldecompositions which are methods which can decompose matrix into2 low-dimensional matricesx = f(A, B).(Think of this Latent Dirichlet Allocation, Non-negative Matrix Factorization, etc).It iherits frommlapiTransformation and additionally requires to implementcomponents member.

Base class for alldecompositions which are methods which can decompose matrix into2 low-dimensional matricesx = f(A, B)incrementally.It iherits frommlapiDecomposition and additionally requiresto implementpartial_fit method which can learncomponents incrementally.

Usage

mlapiDecompositionmlapiDecompositionOnline

Format

R6Class object.

Fields

components

features embeddings. So if matrix is decomposed in a formx = f(A, B) whereX = n\*m, A = n\*k, B = k\*m themB = components

components

features embeddings. So if matrix is decomposed in a formx = f(A, B) whereX = n\*m, A = n\*k, B = k\*m themB = components

Methods

$fit_transform(x, y = NULL, ...)

$transform(x, ...)

Performs transformation of the new data (after model was trained)

$fit_transform(x, y = NULL, ...)

$partial_fit(x, y = NULL, ...)

$transform(x, ...)

Performs transformation of the new data (after model was trained)

Arguments

x

A matrix like object, shouldinherit fromMatrix ormatrix.Allowed classes should be defined in child classes.

y

NULL. Optional taget variable. Usually this should beNULL.There few cases when it could be used.

...

additional parameterswith default values

x

A matrix like object, shouldinherit fromMatrix ormatrix.Allowed classes should be defined in child classes.

y

NULL. Optional taget variable. Usually this should beNULL.There few cases when it could be used.

...

additional parameterswith default values

Examples

TruncatedSVD = R6::R6Class(  classname = "TruncatedSVD",  inherit = mlapi::mlapiDecomposition,  public = list(    initialize = function(rank = 10) {      private$rank = rank      super$set_internal_matrix_formats(dense = "matrix", sparse = NULL)    },    fit_transform = function(x, ...) {      x = super$check_convert_input(x)      private$n_features = ncol(x)      svd_fit = svd(x, nu = private$rank, nv = private$rank, ...)      sing_values = svd_fit$d[seq_len(private$rank)]      result = svd_fit$u %*% diag(x = sqrt(sing_values))      private$components_ = t(svd_fit$v %*% diag(x = sqrt(sing_values)))      rm(svd_fit)      rownames(result) = rownames(x)      colnames(private$components_) = colnames(x)      private$fitted = TRUE      invisible(result)    },    transform = function(x, ...) {      if (private$fitted) {        stopifnot(ncol(x) == ncol(private$components_))        lhs = tcrossprod(private$components_)        rhs = as.matrix(tcrossprod(private$components_, x))        t(solve(lhs, rhs))      }      else        stop("Fit the model first woth model$fit_transform()!")    }  ),  private = list(    rank = NULL,    n_features = NULL,    fitted = NULL  ))set.seed(1)model = TruncatedSVD$new(2)x = matrix(sample(100 * 10, replace = TRUE), ncol = 10)x_trunc = model$fit_transform(x)dim(x_trunc)x_trunc_2 = model$transform(x)sum(x_trunc_2 - x_trunc)#' check pipe-compatible S3 interfacex_trunc_2_s3 = transform(x, model)identical(x_trunc_2, x_trunc_2_s3)

Base abstract class for all classification/regression models

Description

Base class for all estimators.Defines minimal set of members and methods(with signatires) which have to be implemented in child classes.

Usage

mlapiEstimation

Format

R6Class object.

Methods

$fit(x, y, ...)

$predict(x, ...)

Makes predictions on new data (after model was trained)

Arguments

x

A matrix like object, shouldinherit fromMatrix ormatrix.Allowed classes should be defined in child classes.

y

target - usuallyvector, but also can be a matrix like object.Allowed classes should be defined in child classes.

...

additional parameterswith default values

Examples

SimpleLinearModel = R6::R6Class(classname = "mlapiSimpleLinearModel",inherit = mlapi::mlapiEstimation,public = list(  initialize = function(tol = 1e-7) {    private$tol = tol    super$set_internal_matrix_formats(dense = "matrix", sparse = NULL)  },  fit = function(x, y, ...) {    x = super$check_convert_input(x)   stopifnot(is.vector(y))   stopifnot(is.numeric(y))   stopifnot(nrow(x) == length(y))   private$n_features = ncol(x)   private$coefficients = .lm.fit(x, y, tol = private$tol)[["coefficients"]] }, predict = function(x) {   stopifnot(ncol(x) == private$n_features)   x %*% matrix(private$coefficients, ncol = 1) }),private = list(  tol = NULL,  coefficients = NULL,  n_features = NULL))set.seed(1)model = SimpleLinearModel$new()x = matrix(sample(100 * 10, replace = TRUE), ncol = 10)y = sample(c(0, 1), 100, replace = TRUE)model$fit(as.data.frame(x), y)res1 = model$predict(x)# check pipe-compatible S3 interfaceres2 = predict(x, model)identical(res1, res2)

Base abstract class for all classification/regression modelswhich can betrained incremendally (online)

Description

Base class for all online estimators. This class inherits frommlapiEstimation andadditionally requires to implement$partial_fit(x, y, ...) method. Idea is that user can passx, y in chunks and model will be updated/refined incrementally.

Usage

mlapiEstimationOnline

Format

R6Class object.

Methods

$fit(x, y, ...)

$partial_fit(x, y, ...)

$predict(x, ...)

Makes predictions on new data (after model was trained)

Arguments

x

A matrix like object, shouldinherit fromMatrix ormatrix.Allowed classes should be defined in child classes.

y

target - usuallyvector, but also can be a matrix like object.Allowed classes should be defined in child classes.

...

additional parameterswith default values

Base abstract class for all transformations

Description

Base class for all online transformations.

Usage

mlapiTransformation

Format

R6Class object.

Methods

$fit_transform(x, y = NULL, ...)

$transform(x, ...)

Performs transformation of the new data (after model was trained)

Arguments

x

A matrix like object, shouldinherit fromMatrix ormatrix.Allowed classes should be defined in child classes.

y

NULL. Optional taget variable. Usually this should beNULL.There few cases when it could be used.

...

additional parameterswith default values

Base abstract class for all transformationswhich can betrained incremendally (online)

Description

Base class for all online transformations. This class inherits frommlapiTransformation andadditionally requires to implement$partial_fit(x, y, ...) method. Idea is that user can passx, y in chunks and model will be updated/refined incrementally.

Usage

mlapiTransformationOnline

Format

R6Class object.

Methods

$fit_transform(x, y = NULL, ...)

$transform(x, ...)

Performs transformation of the new data (after model was trained)

Arguments

x

A matrix like object, shouldinherit fromMatrix ormatrix.Allowed classes should be defined in child classes.

y

NULL. Optional taget variable. Usually this should beNULL.There few cases when it could be used.

...

additional parameterswith default values

Makes predictions on new data using pre-trained model

Description

Makes predictions on new data using pre-trainedmodel (inherits frommlapiEstimation)

Usage

## S3 method for class 'matrix'predict(object, model, ...)## S3 method for class 'Matrix'predict(object, model, ...)

Arguments

Transforms new data using pre-trained model

Description

Generic function to transform data with pre-trainedmodel (inherits frommlapiTransformation)

Usage

## S3 method for class 'Matrix'transform(`_data`, model, ...)## S3 method for class 'matrix'transform(`_data`, model, ...)

Arguments