| 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: | 3 |
| NeedsCompilation: | no |
| 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]
Giancarlo Vercellino giancarlo.vercellino@gmail.com
See Also
Useful links: