Help for package janus

Type:

Package

Title:

Optimized Recommending System Based on 'tensorflow'

Version:

1.0.0

Maintainer:

Giancarlo Vercellino <giancarlo.vercellino@gmail.com>

Description:

Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'.

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.2.1

Imports:

keras (≥ 2.9.0), tensorflow (≥ 2.9.0), dplyr (≥ 1.0.10), purrr (≥ 0.3.4), forcats (≥ 0.5.1), tictoc (≥ 1.0.1), readr (≥ 2.1.2), ggplot2 (≥ 3.3.6), narray (≥ 0.4.1.1), lubridate (≥ 1.7.10), RcppAlgos (≥ 2.6.0), Rmpfr (≥ 0.8-7), Metrics (≥ 0.1.4), StatRank (≥ 0.0.6), hash (≥ 2.2.6.2), reticulate (≥ 1.26)

URL:

https://rpubs.com/giancarlo_vercellino/janus

Suggests:

testthat (≥ 3.0.0)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2022-12-16 05:09:19 UTC; gvercellino

Author:

Giancarlo Vercellino [aut, cre, cph]

Repository:

CRAN

Date/Publication:

2022-12-16 10:00:02 UTC

janus

Description

Coarse-to-fine optimization of a recommending system based on deep neural networks with Tensorflow/Keras back-end

Usage

janus(
  data,
  rating_label,
  rater_label,
  rated_label,
  task,
  skip_shortcut = FALSE,
  rater_embedding_size = c(8, 32),
  rated_embedding_size = c(8, 32),
  layers = c(1, 5),
  activations = c("elu", "selu", "relu", "sigmoid", "softmax", "softplus", "softsign",
    "tanh", "linear", "leaky_relu", "parametric_relu", "thresholded_relu", "swish",
    "gelu", "mish", "bent"),
  nodes = c(8, 512),
  regularization_L1 = c(0, 100),
  regularization_L2 = c(0, 100),
  dropout = c(0, 1),
  batch_size = 64,
  epochs = 10,
  optimizer = c("adam", "sgd", "adamax", "adadelta", "adagrad", "nadam", "rmsprop"),
  opt_metric = "bac",
  folds = 3,
  reps = 1,
  holdout = 0.1,
  n_steps = 3,
  n_samp = 10,
  offset = 0,
  n_top = 3,
  seed = 999,
  verbose = TRUE
)

Arguments

data

A data frame including at least three features: rating actor, rated item and rating value.

rating_label

String. Single label for the feature containing the rating values.

rater_label

String. Single label for the feature containing the rating actors.

rated_label

String. Single label for the feature containing the rated items.

task

String. Available options are: "regr", for regression (when the rating value is numeric); "classif", for classification (when the rating value is a class or a factor).

skip_shortcut

Logical. Option to add a skip shortcut to improve network performance in case of many layers. Default: FALSE.

rater_embedding_size

Integer. Output dimension for embedding the rating actors. Default: coarse-to-fine search (8 to 32).

rated_embedding_size

Integer. Output dimension for embedding the rated items. Default: coarse-to-fine search (8 to 32).

layers

Positive integer. Number of layers for DNN. Default: coarse-to-fine search (1 to 5).

activations

String. String vector with the activation functions for each layer. Default: coarse-to-fine search ("elu", "selu", "relu", "sigmoid", "softmax", "softplus", "softsign", "tanh", "linear", "leaky_relu", "parametric_relu", "thresholded_relu", "swish", "gelu", "mish", "bent").

nodes

Positive integer. Integer vector with nodes for each layer. Default: coarse-to-fine search (8 to 512).

regularization_L1

Positive numeric. Value for L1 regularization of loss function. Default: coarse-to-fine search (0 to 100).

regularization_L2

Positive numeric. Value for L2 regularization of loss function. Default: coarse-to-fine search (0 to 100).

dropout

Positive numeric. Value for dropout parameter at each layer (bounded between 0 and 1). Default: coarse-to-fine search (0 to 1).

batch_size

Positive integer. Maximum batch size for training. Default: 64.

epochs

Positive integer. Maximum number of forward and backward propagation. Default: 10.

optimizer

String. Standard Tensorflow/Keras Optimization methods are available. Default: coarse-to-fine search ("adam", "sgd", "adamax", "adadelta", "adagrad", "nadam", "rmsprop").

opt_metric

String. Error metric to track for the coarse-to-fine optimization. Different options: for regression, "rmse", "mae", "mdae", "mape", "smape", "rae", "rrse"; for classification, "bac", "avs", "avp", "avf", "kend", "ndcg".

folds

Positive integer. Number of folds for repeated cross-validation. Default: 3.

reps

Positive integer. Number of repetitions for repeated cross-validation. Default: 1.

holdout

Positive numeric. Percentage of cases for holdout validation. Default: 0.1.

n_steps

Positive integer. Number of phases for the coarse-to-fine optimization process (minimum 2). Default: 3.

n_samp

Positive integer. Number of sampled models per coarse-to-fine phase. Default: 10.

offset

Positive numeric. Percentage of expansion of numeric boundaries during the coarse-to-fine optimization. Default: 0.

n_top

Positive integer. Number of candidates selected during the coarse-to-fine phase. Default: 3.

seed

Positive integer. Seed value to control random processes. Default: 42.

verbose

Printing specific messages. Default: TRUE.

Value

This function returns a list including:

pipeline:
model:
- configuration: DNN hyper-parameters (layers, activations, regularization_L1, regularization_L2, nodes, dropout)
- model: Keras standard model description
- recommend: function to use to recommend on rating actors
- plot: Keras standard history plot
- training_metrics: tracking of opt_metric across folds and repetitions
- test_frame: testing set with the related predictions, including
- testing_metrics: summary statistics for testing
time_log

Author(s)

Maintainer: Giancarlo Vercellino giancarlo.vercellino@gmail.com [copyright holder]