| Type: | Package |
| Title: | Simple Neural Application |
| Version: | 1.1.0 |
| Author: | Giancarlo Vercellino |
| Maintainer: | Giancarlo Vercellino <giancarlo.vercellino@gmail.com> |
| Description: | A simple wrapper to easily design vanilla deep neural networks using 'Tensorflow'/'Keras' backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection). |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 3.6) |
| Imports: | keras (≥ 2.3.0.0), tensorflow (≥ 2.2.0), dplyr (≥ 1.0.2), purrr (≥ 0.3.4), forcats (≥ 0.5.0), tictoc (≥ 1.0.1), readr (≥ 1.4.0), ggplot2 (≥ 3.3.3), CORElearn (≥ 1.54.2), dbscan (≥ 1.1-5), stringr (≥ 1.4.0), reticulate (≥ 1.18) |
| URL: | https://rpubs.com/giancarlo_vercellino/snap |
| NeedsCompilation: | no |
| Packaged: | 2021-06-30 07:41:14 UTC; gvercellino |
| Repository: | CRAN |
| Date/Publication: | 2021-06-30 08:30:02 UTC |
snap
Description
A simple wrapper to easily design vanilla deep neural networks using 'Tensorflow'/'Keras' backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).
Usage
snap(
data,
target,
task = NULL,
positive = NULL,
skip_shortcut = FALSE,
embedding = "none",
embedding_size = 10,
folds = 3,
reps = 1,
holdout = 0.3,
layers = 1,
activations = "relu",
regularization_L1 = 0,
regularization_L2 = 0,
nodes = 32,
dropout = 0,
span = 0.2,
min_delta = 0,
batch_size = 32,
epochs = 50,
imp_thresh = 0,
anom_thresh = 1,
output_activation = NULL,
optimizer = "Adam",
loss = NULL,
metrics = NULL,
winsor = FALSE,
q_min = 0.01,
q_max = 0.99,
normalization = TRUE,
seed = 42,
verbose = 0
)
Arguments
data |
A data frame including all the features and targets. |
target |
String. Single label for target feature when task is "regr" or "classif". String vector with multiple labels for target features when task is "multilabel". |
task |
String. Inferred by data type of target feature(s). Available options are: "regr", "classif", "multilabel". Default: NULL. |
positive |
String. Positive class label (only for classification task). Default: NULL. |
skip_shortcut |
Logical. Option to add a skip shortcut to improve network performance in case of many layers. Default: FALSE. |
embedding |
String. Available options are: "none", "global" (when identical values for different features hold different meanings), "sequence" (when identical values for different features hold the same meaning). Default: NULL. |
embedding_size |
Integer. Output dimension for the embedding layer. Default: 10. |
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: 0.3. |
layers |
Positive integer. Number of layers for the neural net. Default: 1. |
activations |
String. String vector with the activation functions for each layer (for example, a neural net with 3 layers may have activations = c("relu", "gelu", "tanh")). Besides standard Tensorflow/Keras activations, you can also choose: "swish", "mish", "gelu", "bent". Default: "relu". |
regularization_L1 |
Positive numeric. Value for L1 regularization of the loss function. Default: 0. |
regularization_L2 |
Positive numeric. Value for L2 regularization of the loss function. Default: 0. |
nodes |
Positive integer. Integer vector with the nodes for each layer (for example, a neural net with 3 layers may have nodes = c(32, 64, 16)). Default: 32. |
dropout |
Positive numeric. Value for the dropout parameter for each layer (for example, a neural net with 3 layers may have dropout = c(0, 0.5, 0.3)). Default: 0. |
span |
Positive numeric. Percentage of epoch for the patience parameter. Default: 0.2. |
min_delta |
Positive numeric. Minimum improvement on metric to trigger the early stop. Default: 0. |
batch_size |
Positive integer. Maximum batch size for training. Default: 32. |
epochs |
Positive integer. Maximum number of forward and backward propagations. Default: 50. |
imp_thresh |
Positive numeric. Importance threshold (in percentiles) above which the features are included in the model (using ReliefFbestK metric by CORElearn). Default: 0 (all features included). |
anom_thresh |
Positive numeric. Anomaly threshold (in percentiles) above which the instances are excluded by the model (using lof by dbscan). Default: 1 (all instances included). |
output_activation |
String. Default: NULL. If not specified otherwise, it will be "Linear" for regression task, "Softmax" for classification task, "Sigmoid" for multilabel task. |
optimizer |
String. Standard Tensorflow/Keras Optimization methods are available. Default: "Adam". |
loss |
Default: NULL. If not specified otherwise, it will be "mean_absolute_error" for regression task, "categorical_crossentropy" for classification task, "binary_crossentropy" for multilabel task. |
metrics |
Default: NULL. If not specified otherwise, it will be "mean_absolute_error" for regression task, "categorical_crossentropy" for classification task, "binary_crossentropy" for multilabel task. |
winsor |
Logical. Set to TRUE in case you want to perform Winsorization on regression tasks. Default: FALSE. |
q_min |
Positive numeric. Minimum quantile threshold for Winsorization. Default: 0.01. |
q_max |
Positive numeric. Maximum quantile threshold for Winsorization. Default: 0.99. |
normalization |
Logical. After each layer it performs a batch normalization. Default: TRUE. |
seed |
Positive integer. Seed value to control random processes. Default: 42. |
verbose |
Positive integer. Set the level of information from Keras. Default: 0. |
Value
This function returns a list including:
task: kind of task solved
configuration: main hyper-parameters describing the neural net (layers, activations, regularization_L1, regularization_L2, nodes, dropout)
model: Keras standard model description
pred_fun: function to use on the same data scheme to predict new values
plot: Keras standard history plot
testing_frame: testing set with the related predictions, including
trials: statistics for each trial during the repeated cross-validation (train set and validation set):
task "classif": balanced accuracy (bac), precision (prc), sensitivity (sen), critical success index (csi), FALSE-score (fsc), Kappa (kpp), Kendall (kdl)
task "regr": root mean square error(rmse), mean absolute error (mae), median absolute error (mdae), relative root square error (rrse), relative absolute error (rae), Pearson (prsn)
task "multilabel": macro bac, macro prc, macro sensitivity, macro sen, macro csi, macro fsc, micro kpp, micro kdl
metrics: summary statistics as above for training, validation (both averaged over trials) and testing
selected_feat: labels of features included within the model
selected_inst: index of instances included within the model
time_log
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
See Also
Useful links:
Examples
## Not run:
snap(friedman3, target="y")
snap(threenorm, target="classes", imp_thresh = 0.3, anom_thresh = 0.95)
snap(threenorm, "classes", layers = 2, activations = c("gelu", "swish"), nodes = c(32, 64))
## End(Not run)
friedman3 data set
Description
Data set to demonstrate regression task.
Usage
friedman3
Format
A dummy data frame with 5 columns and 150 rows created using Benchmark Problem Friedman 3 by mlbench. The target feature is "y".
Source
mlbench.friedman3(n = 150, sd = 3)
threenorm data set
Description
Data set to demonstrate classification task.
Usage
threenorm
Format
A dummy data frame with 5 columns and 150 rows created using Threenorm Benchmark Problem by mlbench. The target feature is "classes".
Source
mlbench.threenorm(150, d = 20)