R/ml_stat.R

Defines functions ml_corr

Documented in ml_corr

#' Compute correlation matrix
#'
#' @param x A \code{tbl_spark}.
#' @param columns The names of the columns to calculate correlations of. If only one
#'   column is specified, it must be a vector column (for example, assembled using
#'   \code{ft_vector_assember()}).
#' @param method The method to use, either \code{"pearson"} or \code{"spearman"}.
#'
#' @return A correlation matrix organized as a data frame.
#'
#' @examples
#' \dontrun{
#' sc <- spark_connect(master = "local")
#' iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
#'
#' features <- c("Petal_Width", "Petal_Length", "Sepal_Length", "Sepal_Width")
#'
#' ml_corr(iris_tbl, columns = features, method = "pearson")
#' }
#' @export
ml_corr <- function(x, columns = NULL, method = c("pearson", "spearman")) {
  spark_require_version(spark_connection(x), "2.2.0")

  method <- match.arg(method)

  columns <- if (rlang::is_null(columns)) {
    colnames(x)
  } else {
    sapply(columns, cast_string)
  }

  col_in_df <- columns %in% colnames(x)
  if (!all(col_in_df)) {
    bad_cols <- paste0(columns[!col_in_df], collapse = ", ")
    stop("All columns specified must be in x. Failed to find ",
      bad_cols, ".",
      call. = FALSE
    )
  }

  if (identical(length(columns), 1L)) {
    # check to see that the column is a VectorUDT

    if (!grepl("VectorUDT", sdf_schema(x)[[columns]][["type"]])) {
      stop("When only one column is specified, it must be a column of Vectors. For example, the output of `ft_vector_assembler()`.",
        call. = FALSE
      )
    }

    num_features <- x %>%
      dplyr::select(!!columns) %>%
      spark_dataframe() %>%
      invoke("first") %>%
      sapply(invoke, "size")
    feature_names <- num_features %>%
      paste0("V", seq_len(.))

    features_col <- columns
    sdf <- spark_dataframe(x)
  } else {
    num_features <- length(columns)
    feature_names <- columns
    features_col <- random_string("features")
    sdf <- x %>%
      ft_vector_assembler(columns, features_col) %>%
      spark_dataframe()
  }

  invoke_static(
    spark_connection(sdf),
    "org.apache.spark.ml.stat.Correlation",
    "corr", sdf, features_col
  ) %>%
    invoke("first") %>%
    sapply(invoke, "toArray") %>%
    matrix(nrow = num_features) %>%
    dplyr::as_tibble(.name_repair = "unique") %>%
    dplyr::rename(!!!rlang::set_names(
      paste0("...", seq_len(num_features)),
      feature_names
    ))
}

#' Chi-square hypothesis testing for categorical data.
#'
#' Conduct Pearson's independence test for every feature against the
#'   label. For each feature, the (feature, label) pairs are converted
#'   into a contingency matrix for which the Chi-squared statistic is
#'   computed. All label and feature values must be categorical.
#'
#' @param x A \code{tbl_spark}.
#' @param features The name(s) of the feature columns. This can also be the name
#'   of a single vector column created using \code{ft_vector_assembler()}.
#' @param label The name of the label column.
#' @return A data frame with one row for each (feature, label) pair with p-values,
#'   degrees of freedom, and test statistics.
#'
#' @examples
#' \dontrun{
#' sc <- spark_connect(master = "local")
#' iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
#'
#' features <- c("Petal_Width", "Petal_Length", "Sepal_Length", "Sepal_Width")
#'
#' ml_chisquare_test(iris_tbl, features = features, label = "Species")
#' }
#' @export
ml_chisquare_test <- function(x, features, label) {
  spark_require_version(spark_connection(x), "2.2.0")

  schema <- sdf_schema(x)

  columns <- c(features, label)
  col_in_df <- columns %in% colnames(x)
  if (!all(col_in_df)) {
    bad_cols <- paste(columns[!col_in_df], collapse = ", ")
    stop("All columns specified must be in x. Failed to find ",
      bad_cols, ".",
      call. = FALSE
    )
  }

  label_is_double <- identical(schema[[label]][["type"]], "DoubleType")
  features_are_assembled <- identical(length(features), 1L) &&
    grepl("VectorUDT", schema[[features]][["type"]])

  if (features_are_assembled) {
    features_col <- features
    feature_names <- x %>%
      dplyr::select(!!features) %>%
      spark_dataframe() %>%
      invoke("first") %>%
      sapply(invoke, "size") %>%
      paste0("feature_", seq_len(.))
    if (label_is_double) {
      label_col <- label
      df <- x
    } else {
      label_col <- random_string("label")
      df <- ft_string_indexer(x, label, label_col)
    }
  } else {
    feature_names <- features
    features_col <- random_string("features")
    if (label_is_double) {
      label_col <- label
      df <- ft_r_formula(x, paste0("~ ", paste0(features, collapse = " + ")),
        features_col = features_col
      )
    } else {
      label_col <- random_string("label")
      df <- ft_r_formula(x, paste0(
        label, " ~ ",
        paste0(features, collapse = " + ")
      ),
      features_col = features_col,
      label_col = label_col, force_index_label = TRUE
      )
    }
  }

  sdf <- spark_dataframe(df)
  sc <- spark_connection(sdf)

  invoke_static(
    sc, "org.apache.spark.ml.stat.ChiSquareTest", "test",
    sdf, features_col, label_col
  ) %>%
    sdf_register() %>%
    dplyr::collect() %>%
    tidyr::unnest() %>%
    dplyr::transmute(
      p_value = !!rlang::sym("pValues"),
      degrees_of_freedom = unlist(!!rlang::sym("degreesOfFreedom")),
      statistic = !!rlang::sym("statistics")
    ) %>%
    dplyr::bind_cols(data.frame(
      feature = feature_names,
      label = label
    ), .)
}

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sparklyr documentation built on Jan. 8, 2022, 5:06 p.m.