R/sklearn.R

In abresler/sklearn: sklearn port

# https://scikit-learn.org/0.15/index.html

#' Import SK Learn
#'
#' \href{https://scikit-learn.org/stable/modules/classes.html#api-reference}{SK learn API from python}
#'
#' @param assign_to_environment if \code{TRUE} assings to environment
#'
#' @return python object
#' @export
#'
#' @examples
import_sklearn <-
  function(assign_to_environment = T) {
    sklearn <- import("sklearn")
    !'sklearn' %>% exists() & assign_to_environment
    if (assign_to_environment) {
      assign('sklearn', sklearn, envir = .GlobalEnv)
    }
    sklearn
  }

#' SKLearn Clustering
#'
#' Functions for unsupervised clustering algorithms.
#'
#' \itemize{
#' \item \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster}{clustering functions}
#' \item \href{https://scikit-learn.org/stable/modules/clustering.html#clustering}{clustering examples}
#' }
#'
#' @return python object
#' @export
#'
#' @examples
#' clusterer <- sk_cluster()
#'
#' ## DBSCAN
#'
#' dbscan <- clusterer$DBSCAN(algorithm = "auto", leaf_size = 10L)
#' X <- iris %>% select_if(is.numeric) %>% as_tibble()
#' model <- dbscan$fit(X = X)
#' clusters <-
#' model$fit_predict(X = X) + 1
#' df <-
#' iris %>%
#' mutate(cluster = clusters) %>%
#' as_tibble()
#' df
#'
#' ## K-Means
#' X <- iris %>% select_if(is.numeric) %>% as_tibble()
#' iris_clusters <- clusterer$k_means(X = X, n_clusters = 3L,
#' precompute_distances = T, init = "random", max_iter = 100L,
#' return_n_iter = T)
#' iris_clusters
#' kmeans_centroids <-
#' iris_clusters[[1]] %>%
#' t() %>%
#' as_tibble()
#' cluster <- iris_clusters[[2]] + 1
#' intertia <- iris_clusters[[3]]
#' df_iris <-
#' iris %>%
#' as_tibble() %>%
#' mutate(cluster)
#' tibble(intertia, centroids = list(kmeans_centroids),
#' dataClusters = list())


sk_cluster <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$cluster
    obj
  }



#' SKLearn Data
#'
#' Access to the sci-kit learn datasets
#'
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets}{datasets}
#'
#' @return python object
#' @export
#'
#' @examples
sk_datasets <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$datasets
    obj
  }

#' SK Learn Preprocessing
#'
#' Functions to pre-process data
#'
#'\itemize{
#'\item \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing}{preprocessing functions}
#' \item \href{https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing}{preprocessing examples}
#' }
#'
#' @return python object
#' @export
#'
#' @examples
sk_preprocessing <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$preprocessing
    obj
  }


#' SK Learn LM

#' \itemize{
#' \item \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model}{linear model functions}
#' \item \href{https://scikit-learn.org/stable/modules/linear_model.html#linear-model}{linear model examples}
#' }
#'
#' @return python object
#' @export
#'
#' @examples
sk_lm <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$linear_model
    obj
  }

#' Neighbors
#'
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors}{nearest neighbor functions}
#' \href{https://scikit-learn.org/stable/modules/neighbors.html#neighbors}{nearest neighbor examples}
#'
#' @return python object
#' @export
#'
#' @examples
sk_neighbors <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$neighbors
    obj
  }

#' SK Decomposition
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition}{decomposition functions}
#' \href{https://scikit-learn.org/stable/modules/decomposition.html#decompositions}{decomposition example}
#'
#' @return python object
#' @export
#'
#' @examples
sk_decomp <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$decomposition
    obj
  }


#' SK Manifold
#'
#' Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high.
#'
#'
#' \itemize{
#' \item \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold}{manifold functions}
#' \item \href{https://scikit-learn.org/stable/modules/manifold.html#manifold}{manifold examples}
#' }
#'
#' @return python object
#' @export
#'
#' @examples
#'
#' library(sklearn)
#' library(tidyverse)
#' manifold <- sk_manifold()
#' data_sets <- sk_datasets()
#'
#' ## isomap
#' digits <- data_sets$load_digits(n_class = 6)
#' X <- digits$data %>% as.matrix()
#' y <- digits$target
#' n_neighbors <- 30L
#' n_features <- ncol(X) %>% as.integer()
#' n_samples <- nrow(X) %>% as.integer()
#' isomap <- manifold$Isomap(n_neighbors = n_neighbors, n_components = 2L)
#' model <- isomap$fit(X = X, y = y)
#'
#' df <-
#' model$fit_transform(X = X) %>%
#' sk_tibble(c("iso1", "iso2")) %>%
#' mutate(digit = glue::glue("# {y}") %>% as.character())
#'
#' df %>%
#' ggplot(aes(x = iso1, y= iso2, color = digit)) +
#' geom_jitter(size = .25) +
#' theme_minimal() +
#' ggtitle("MNIST Digit Isomap Embeddings")
#'
#'
#' ### Linear Embedding
#' linear_embedding <- manifold$LocallyLinearEmbedding(n_neighbors = n_neighbors, n_components = 2L)
#'
#' model <- linear_embedding$fit(X = X, y = y)
#' df_linear_embed <-
#' model$fit_transform(X = X) %>%
#' sk_tibble(c("linear1", "linear2")) %>%
#' mutate(digit = glue::glue("# {y}") %>% as.character())
#'
#' df_linear_embed %>%
#' ggplot(aes(x = linear1, y= linear2, color = digit)) +
#' geom_jitter(size = .25) +
#' theme_minimal() +
#' ggtitle("MNIST Digit Linear Embeddings")
#'
#'
#' ### MDS
#' clf <-  manifold$MDS(n_components = 2L,
#' n_init = 1L,
#' max_iter = 100L)
#'
#' model <- clf$fit(X = X)
#'
#' df_clf <-
#' model$fit_transform(X = X) %>%
#' sk_tibble(c("mds1", "mds2")) %>%
#' mutate(digit = glue::glue("# {y}") %>% as.character())
#'
#' df_clf %>%
#' ggplot(aes(x = mds1, y= mds2, color = digit)) +
#' geom_jitter(size = .05) +
#' theme_minimal() +
#' ggtitle("MNIST Digit MDS Embeddings")
#'
#' ### Spectral Clustering
#'
#' embedder <-
#' manifold$SpectralEmbedding(
#' n_components = 2L,
#' random_state = 0L,
#' eigen_solver = "arpack"
#' )
#'
#' df_spectral <-
#' embedder$fit_transform(X = X) %>%
#' sk_tibble(c("spectral1", "spectral2")) %>%
#' mutate(digit = glue::glue("# {y}") %>% as.character())
#'
#' df_spectral %>%
#' ggplot(aes(x = spectral1, y= spectral2, color = digit)) +
#' geom_jitter(size = .05) +
#' theme_minimal() +
#' ggtitle("MNIST Digit Spectral Clustering Embeddings")
#'
#' ## TSNE
#'
#'
#' tsne <- manifold$TSNE(n_components = 2L,
#' init = 'pca',
#' random_state = 0L)
#'
#' df_tsne <-
#' tsne$fit_transform(X = X) %>%#'
#' sk_tibble(c("tsne1", "tsne2")) %>%
#' mutate(digit = glue::glue("# {y}") %>% as.character())
#'
#' df_tsne %>%
#' ggplot(aes(x = tsne1, y= tsne2, color = digit)) +
#' geom_jitter(size = .05) +
#' theme_minimal() +
#' ggtitle("MNIST Digit T-SNE Embeddings")


sk_manifold <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$manifold
    obj
  }


#' SK Ensemble
#'
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble}{ensemble functions}
#' \href{https://scikit-learn.org/stable/modules/ensemble.html#ensemble}{ensemble examples}
#'
#' @return python object
#' @export
#'
#' @examples
sk_ensemble <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$ensemble
    obj
  }

#' SK Covariance
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.covariance}{co-variance functions}
#' \href{https://scikit-learn.org/stable/modules/covariance.html#covariance}{co-variance examples}
#' @return python object
#' @export
#'
#' @examples
sk_covariance <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$covariance
    obj
  }

#' SK Model Selection
#'
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.covariance}{model selection functions}
#' \href{https://scikit-learn.org/stable/modules/covariance.html#covariance}{model selectionexamples}
#'
#' @return python object
#' @export
#'
#' @examples
sk_model_selection <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$model_selection
    obj
  }

#' SK Tree Models
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree}{tree functions}
#' \href{https://scikit-learn.org/stable/modules/tree.html#tree}{tree examples}
#'
#' @return python object
#' @export
#'
#' @examples
#' library(tidyverse)
#' X <- iris %>% select_if(is.numeric) %>% as_tibble()
#' target <- iris %>% pull(Species)
#' tree <- sk_tree()
#' tree_classifier <- tree$DecisionTreeClassifier()
#' model <- tree_classifier$fit(X = X, y = target)
#' model$predict(X = X)
sk_tree <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$tree
    obj
  }

#' SK Utilities
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.utils}{utility functions}
#'
#' @return python object
#' @export
#'
#' @examples
sk_utils <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$utils
    obj
  }

#' SK SVM
#'
#' @return
#' @export
#'
#' @examples
sk_svm <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$svm
    obj
  }


#' SK Bayes
#' These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions.
#'
#' \itemize{
#' \item \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes}{Bayes functions}
#' \item \href{https://scikit-learn.org/stable/modules/naive_bayes.html#naive-bayes}{Bayes examples}
#' }
#'
#' @return python object
#' @export
#'
#' @examples
#' library(tidyverse)
#' library(sklearn)
#' library(caret)
#' bayes <- sk_bayes()
#' X <- iris %>% as_tibble %>% select_if(is.numeric)
#' target <- iris %>% pull(Species)
#' gnb <- bayes$GaussianNB()
#' model <- gnb$fit(X = as.matrix(X), y = target)
#' predictions <- model$predict(X = X)
#' result_table <- table(target, predictions)
#' confusionMatrix(result_table)
#' ### Class probability
#' options(digits = 3, scipen = 9999)
#' df_prob <-
#' gnb$predict_proba(X = X) %>%
#' as_tibble() %>%
#' set_names(target %>% unique())
#'
#' df_prob

sk_bayes <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <- sklearn$naive_bayes
    obj
  }

#' SK Calibration
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.calibration}{Calibration functions}
#' \href{https://scikit-learn.org/stable/modules/calibration.html#calibration}{Calibration examples}
#'
#'
#' @return python object
#' @export
#'
#' @examples
sk_calibration <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$calibration
    obj
  }

#' SK Cross Decomposition
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cross_decomposition}{Cross-decomposition functions}
#' \href{https://scikit-learn.org/stable/modules/cross_decomposition.html#cross-decomposition}{Cross decomposition examples}
#' @return python object
#' @export
#'
#' @examples
sk_cross_decomposition <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$cross_decomposition
    obj
  }

#' SK Dummy Variables
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.dummy}{Dummy variable functions}
#' @return python object
#' @export
#'
#' @examples
sk_dummy <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$dummy
    obj
  }

#' SK Discriminant Models
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.discriminant_analysis}{Discriminant functions}
#' \href{https://scikit-learn.org/stable/modules/lda_qda.html#lda-qda}{Discriminant examples}
#'
#' @return python object
#' @export
#'
#' @examples
sk_discriminant_analysis <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$discriminant_analysis
    obj
  }


#' SK Feature Selection
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection}{Feature selection functions}
#' \href{https://scikit-learn.org/stable/modules/feature_selection.html#feature-selection}{feature selection examples}
#' @return python object
#' @export
#'
#' @examples
sk_feature_selection <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$feature_selection
    obj
  }

#' SK Feature extraction
#'
#' @return python object
#' @export
#'
#' @examples
sk_feature_extraction <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$feature_extraction
    obj
  }

#' SK Neural Net
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network}{Neural Network functions}
#' \href{https://scikit-learn.org/stable/modules/neural_networks_supervised.html#neural-networks-supervised}{Neural Network examples}
#' @return python object
#' @export
#'
#' @examples
sk_nnet <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$neural_network
    obj
  }

#' SK Gaussian Process
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.gaussian_process}{Gaussian process functions}
#' \href{https://scikit-learn.org/stable/modules/gaussian_process.html#gaussian-process}{Gaussian process examples}
#'
#' @return python jobject
#' @export
#'
#' @examples
sk_gaussian_process <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$gaussian_process
    obj
  }

#' SK Mixture
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture}{Mixture functions}
#' @return python object
#' @export
#'
#' @examples
sk_mixture <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$mixture
    obj
  }


#' SK Isotonic
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.isotonic}{Isotonic functions}
#' \href{https://scikit-learn.org/stable/modules/isotonic.html#isotonic}{Isotonic examples}
#' @return python object
#' @export
#'
#' @examples
sk_isotonic <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$isotonic
    obj
  }
#' SK Impute
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute}{impute functions}
#' @return python object
#' @export
#'
#' @examples
sk_impute <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$impute
    obj
  }
#' SK Kernel Approxomation
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation}{Kernel Approxomation functions}
#' \href{https://scikit-learn.org/stable/modules/kernel_approximation.html#kernel-approximation}{Kernel Approxomation examples}
#' @return python object
#' @export
#'
#' @examples
sk_kernel_approximation  <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$kernel_approximation
    obj
  }
#' SK Kernel Ridge
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_ridge}{Kernel Ridge functions}
#' \href{https://scikit-learn.org/stable/modules/kernel_ridge.html#kernel-ridge}{Kernel Ridge examples}
#' @return python object
#' @export
#'
#' @examples
sk_kernel_ridge <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$kernel_ridge
    obj
  }
#' SK Metrics
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics}{metrics functions}
#' @return python object
#' @export
#'
#' @examples
sk_metrics <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$metrics
    obj
  }
#' SK Multi Class
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multiclass}{multi-class functions}
#' \href{https://scikit-learn.org/stable/modules/multiclass.html#multiclass}{multiclass examples}
#' @return
#' @export
#'
#' @examples
sk_multi_class <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$multi_class
    obj
  }
#' SK Multioutput
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multioutput}{Multioutput functions}
#' \href{https://scikit-learn.org/stable/modules/multiclass.html#multiclass}{multioutput examples}
#' @return python object
#' @export
#'
#' @examples
sk_multioutput <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$multioutput
    obj
  }

#' SK Random Projection
#' \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.random_projection}{Random projection functions}
#' \href{https://scikit-learn.org/stable/modules/random_projection.html#random-projection}{Random Projection examples}
#' @return python object
#' @export
#'
#' @examples
sk_random_projection <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$random_projection
    obj
  }

#' SK Semi Supervised
#'
#' @return
#' @export
#'
#' @examples
sk_semi_supervised <-
  function() {
    sklearn <- import_sklearn(assign_to_environment = F)
    obj <-
      sklearn$semi_supervised
    obj
  }