Package 'pmlbr' reference manual

Title:	Interface to the Penn Machine Learning Benchmarks Data Repository
Description:	Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.
Authors:	Trang Le [aut, cre] (https://trang.page/), makeyourownmaker [aut] (https://github.com/makeyourownmaker), Jason Moore [aut] (http://www.epistasisblog.org/), University of Pennsylvania [cph]
Maintainer:	Trang Le <[email protected]>
License:	GPL-2 \| file LICENSE
Version:	0.2.2
Built:	2024-11-04 14:59:22 UTC
Source:	https://github.com/epistasislab/pmlbr

Names of available classification datasets

Description

A list of the names of available classification datasets

Usage

classification_dataset_names
classification_dataset_names

Format

An object of class character of length 162.

Source

https://github.com/EpistasisLab/pmlb

Computes imbalance value for a given dataset.

Description

Computes imbalance value for a given dataset.

Usage

compute_imbalance(target_col)
compute_imbalance(target_col)

Arguments

target_col

Factor or character vector of target column.

Value

A value of imbalance metric, where zero means that the dataset is perfectly balanced and the higher the value, the more imbalanced the dataset.

Names of all available datasets

Description

A list of the names of available datasets

Usage

dataset_names
dataset_names

Format

An object of class character of length 284.

Source

https://github.com/EpistasisLab/pmlb

fetch_data function

Description

Download a data set from the PMLB repository, (optionally) store it locally, and return the data set. You must be connected to the internet if you are fetching a data set that is not cached locally.

Usage

fetch_data(
  dataset_name,
  return_X_y = FALSE,
  local_cache_dir = NA,
  dropna = TRUE
)
fetch_data(
  dataset_name,
  return_X_y = FALSE,
  local_cache_dir = NA,
  dropna = TRUE
)

Arguments

`dataset_name`	The name of the data set to load from PMLB
`return_X_y`	Boolean. Whether to return the data with the features and labels stored in separate data structures or a single structure (can be TRUE or FALSE, defaults to FALSE)
`local_cache_dir`	The directory on your local machine to store the data files in (defaults to NA, indicating cache will not be used)
`dropna`	Boolean. Whether rows with NAs should be automatically dropped. Default to TRUE.

Examples

# Features and labels in single data frame
penguins <- fetch_data("penguins")
head(penguins)

# Features and labels stored in separate data structures
penguins <- fetch_data("penguins", return_X_y = TRUE)
penguins$x # data frame
penguins$y # vector
# Features and labels in single data frame
penguins <- fetch_data("penguins")
head(penguins)

# Features and labels stored in separate data structures
penguins <- fetch_data("penguins", return_X_y = TRUE)
penguins$x # data frame
penguins$y # vector

Get type/class of given vector.

Description

Get type/class of given vector.

Usage

get_type(x, include_binary = FALSE)
get_type(x, include_binary = FALSE)

Arguments

`x`	Input vector.
`include_binary`	Boolean. Whether binary should be counted separately from categorical.

Value

Type/class of 'x'.

Download a File Gracefully with Retry Mechanism

Description

Attempts to download a file from a specified URL, retrying a set number of times if the download fails. This function meets CRAN's requirement for gracefully handling the use of internet resources by catching errors and returning a warning message if the download ultimately fails.

Usage

graceful_download(url, destfile, retries = 3)
graceful_download(url, destfile, retries = 3)

Arguments

`url`	Character. The URL of the file to download.
`destfile`	Character. The path to the destination file where the downloaded content will be saved.
`retries`	Integer. The maximum number of download attempts (default is 3).

Value

Logical. Returns 'TRUE' if the download succeeds, 'FALSE' otherwise.

Examples

## Not run: 
dataset_url <- "https://example.com/dataset.csv"
tmp <- tempfile(fileext = ".csv")
success <- download_file_gracefully(dataset_url, tmp)
if (!success) {
  message("Continuing gracefully without the dataset.")
}

## End(Not run)
## Not run: 
dataset_url <- "https://example.com/dataset.csv"
tmp <- tempfile(fileext = ".csv")
success <- download_file_gracefully(dataset_url, tmp)
if (!success) {
  message("Continuing gracefully without the dataset.")
}

## End(Not run)

Select nearest datasets given input 'x'.

Description

If 'x' is a data.frame object, computes dataset characteristics. If 'x' is a character object specifying dataset name from PMLB, use the already computed dataset statistics/characteristics in 'summary_stats'.

Usage

nearest_datasets(x, ...)

## Default S3 method:
nearest_datasets(x, ...)

## S3 method for class 'character'
nearest_datasets(
  x,
  n_neighbors = 5,
  dimensions = c("n_instances", "n_features"),
  target_name = "target",
  ...
)

## S3 method for class 'data.frame'
nearest_datasets(
  x,
  y = NULL,
  n_neighbors = 5,
  dimensions = c("n_instances", "n_features"),
  task = c("classification", "regression"),
  target_name = "target",
  ...
)
nearest_datasets(x, ...)

## Default S3 method:
nearest_datasets(x, ...)

## S3 method for class 'character'
nearest_datasets(
  x,
  n_neighbors = 5,
  dimensions = c("n_instances", "n_features"),
  target_name = "target",
  ...
)

## S3 method for class 'data.frame'
nearest_datasets(
  x,
  y = NULL,
  n_neighbors = 5,
  dimensions = c("n_instances", "n_features"),
  task = c("classification", "regression"),
  target_name = "target",
  ...
)

Arguments

`x`	Character string of dataset name from PMLB, or data.frame of n_samples x n_features(or n_features+1 with a target column)
`...`	Further arguments passed to each method.
`n_neighbors`	Integer. The number of dataset names to return as neighbors.
`dimensions`	Character vector specifying dataset characteristics to include in similarity calculation. Dimensions must correspond to numeric columns of [all_summary_stats.tsv](https://github.com/EpistasisLab/pmlb/blob/master/pmlb/all_summary_stats.tsv). If 'all' (default), uses all numeric columns.
`target_name`	Character string specifying column of target/dependent variable.
`y`	Vector of target column. Required when 'x“ does not contain the target column.
`task`	Character string specifying classification or regression for summary stat generation.

Value

Character string of names of most similar datasets to df, most similar dataset first.

Examples

nearest_datasets('penguins')
nearest_datasets(fetch_data('penguins'))

nearest_datasets('penguins')
nearest_datasets(fetch_data('penguins'))

pmlb: R interface to the Penn Machine Learning Benchmarks data repository

Description

The PMLB repository contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are approximately 290 data sets included in the PMLB repository and there are no missing values in these data sets.

Details

This R library includes summaries of the classification and regression data sets but does NOT include any of the PMLB data sets. The data sets can be downloaded using the fetch_data function which is similar to the corresponding PMLB python function.

See fetch_data, summary_stats for usage examples and further information.

If you use PMLB in a scientific publication, please consider citing the following paper:

Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore (2017).

PMLB: a large benchmark suite for machine learning evaluation and comparison

https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0154-4

BioData Mining 10, page 36.

Author(s)

Maintainer: Trang Le [email protected] (https://trang.page/)

Authors:

makeyourownmaker [email protected] (https://github.com/makeyourownmaker)
Jason Moore [email protected] (http://www.epistasisblog.org/)

Other contributors:

University of Pennsylvania [copyright holder]

Names of available regression datasets

Description

A list of the names of available regression datasets

Usage

regression_dataset_names
regression_dataset_names

Format

An object of class character of length 122.

Source

https://github.com/EpistasisLab/pmlb

Summary statistics for the all datasets

Description

Summary statistics for the all datasets

Usage

summary_stats
summary_stats

Format

A data frame with 10 variables:

dataset:: Dataset name
n_instances:: Number of data observations (equal to number of rows)
n_features:: Total number of features (number of columns - 1)
n_binary_features:: Number of binary features
n_categorical_features:: Number of categorical features
n_continuous_features:: Number of continuous features
n_classes:: Number of classes in target variable
endpoint_type:: Value type of endpoint/target (can be binary, categorical or continuous)
imbalance:: Imbalance metric, where zero means that the dataset is perfectly balanced and the higher the value, the more imbalanced the dataset
task:: Type of problem/task. Can be classification or regression.

Source

https://github.com/EpistasisLab/pmlb

Package 'pmlbr'

Help Index

Names of available classification datasets

Description

Usage

Format

Source

Computes imbalance value for a given dataset.

Description

Usage

Arguments

Value

Names of all available datasets

Description

Usage

Format

Source

fetch_data function

Description

Usage

Arguments

See Also

Examples

Get type/class of given vector.

Description

Usage

Arguments

Value

Download a File Gracefully with Retry Mechanism

Description

Usage

Arguments

Value

Examples

Select nearest datasets given input 'x'.

Description

Usage

Arguments

Value

Examples

pmlb: R interface to the Penn Machine Learning Benchmarks data repository

Description

Details

Author(s)

See Also

Names of available regression datasets

Description

Usage

Format

Source

Summary statistics for the all datasets

Description

Usage

Format

Source