| Title: | Artificial Neural Networks for Anomaly Detection |
|---|---|
| Description: | Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning. |
| Authors: | Bart Lammers |
| Maintainer: | Bart Lammers <[email protected]> |
| License: | GPL (>= 3) | file LICENSE |
| Version: | 2.3.4 |
| Built: | 2024-11-26 04:37:37 UTC |
| Source: | https://github.com/bflammers/ann2 |
Construct and train an Autoencoder by setting the target variables equal to the input variables. The number of nodes in the middle layer should be smaller than the number of input variables in X in order to create a bottleneck layer.
autoencoder( X, hidden.layers, standardize = TRUE, loss.type = "squared", huber.delta = 1, activ.functions = "tanh", step.H = 5, step.k = 100, optim.type = "sgd", learn.rates = 1e-04, L1 = 0, L2 = 0, sgd.momentum = 0.9, rmsprop.decay = 0.9, adam.beta1 = 0.9, adam.beta2 = 0.999, n.epochs = 100, batch.size = 32, drop.last = TRUE, val.prop = 0.1, verbose = TRUE, random.seed = NULL )autoencoder( X, hidden.layers, standardize = TRUE, loss.type = "squared", huber.delta = 1, activ.functions = "tanh", step.H = 5, step.k = 100, optim.type = "sgd", learn.rates = 1e-04, L1 = 0, L2 = 0, sgd.momentum = 0.9, rmsprop.decay = 0.9, adam.beta1 = 0.9, adam.beta2 = 0.999, n.epochs = 100, batch.size = 32, drop.last = TRUE, val.prop = 0.1, verbose = TRUE, random.seed = NULL )
X |
matrix with explanatory variables |
|
vector specifying the number of nodes in each layer. The
number of hidden layers in the network is implicitly defined by the length of
this vector. Set |
|
standardize |
logical indicating if X and Y should be standardized before
training the network. Recommended to leave at |
loss.type |
which loss function should be used. Options are "squared", "absolute", "huber" and "pseudo-huber" |
huber.delta |
used only in case of loss functions "huber" and "pseudo-huber". This parameter controls the cut-off point between quadratic and absolute loss. |
activ.functions |
character vector of activation functions to be used in each hidden layer. Possible options are 'tanh', 'sigmoid', 'relu', 'linear', 'ramp' and 'step'. Should be either the size of the number of hidden layers or equal to one. If a single activation type is specified, this type will be broadcasted across the hidden layers. |
step.H |
number of steps of the step activation function. Only applicable if activ.functions includes 'step' |
step.k |
parameter controlling the smoothness of the step activation function. Larger values lead to a less smooth step function. Only applicable if activ.functions includes 'step'. |
optim.type |
type of optimizer to use for updating the parameters. Options are 'sgd', 'rmsprop' and 'adam'. SGD is implemented with momentum. |
learn.rates |
the size of the steps to make in gradient descent. If set too large, the optimization might not converge to optimal values. If set too small, convergence will be slow. Should be either the size of the number of hidden layers plus one or equal to one. If a single learn rate is specified, this learn rate will be broadcasted across the layers. |
L1 |
L1 regularization. Non-negative number. Set to zero for no regularization. |
L2 |
L2 regularization. Non-negative number. Set to zero for no regularization. |
sgd.momentum |
numeric value specifying how much momentum should be used. Set to zero for no momentum, otherwise a value between zero and one. |
rmsprop.decay |
level of decay in the rms term. Controls the strength of the exponential decay of the squared gradients in the term that scales the gradient before the parameter update. Common values are 0.9, 0.99 and 0.999 |
adam.beta1 |
level of decay in the first moment estimate (the mean). The recommended value is 0.9 |
adam.beta2 |
level of decay in the second moment estimate (the uncentered variance). The recommended value is 0.999 |
n.epochs |
the number of epochs to train. One epoch is a single iteration through the training data. |
batch.size |
the number of observations to use in each batch. Batch learning is computationally faster than stochastic gradient descent. However, large batches might not result in optimal learning, see Efficient Backprop by LeCun for details. |
drop.last |
logical. Only applicable if the size of the training set is not perfectly devisible by the batch size. Determines if the last chosen observations should be discarded (in the current epoch) or should constitute a smaller batch. Note that a smaller batch leads to a noisier approximation of the gradient. |
val.prop |
proportion of training data to use for tracking the loss on a validation set during training. Useful for assessing the training process and identifying possible overfitting. Set to zero for only tracking the loss on the training data. |
verbose |
logical indicating if additional information should be printed |
random.seed |
optional seed for the random number generator |
A function for training Autoencoders. During training, the network will learn a generalised representation of the data (generalised since the middle layer acts as a bottleneck, resulting in reproduction of only the most important features of the data). As such, the network models the normal state of the data and therefore has a denoising property. This property can be exploited to detect anomalies by comparing input to reconstruction. If the difference (the reconstruction error) is large, the observation is a possible anomaly.
An ANN object. Use function plot(<object>) to assess
loss on training and optionally validation data during training process. Use
function predict(<object>, <newdata>) for prediction.
# Autoencoder example X <- USArrests AE <- autoencoder(X, c(10,2,10), loss.type = 'pseudo-huber', activ.functions = c('tanh','linear','tanh'), batch.size = 8, optim.type = 'adam', n.epochs = 1000, val.prop = 0) # Plot loss during training plot(AE) # Make reconstruction and compression plots reconstruction_plot(AE, X) compression_plot(AE, X) # Reconstruct data and show states with highest anomaly scores recX <- reconstruct(AE, X) sort(recX$anomaly_scores, decreasing = TRUE)[1:5]# Autoencoder example X <- USArrests AE <- autoencoder(X, c(10,2,10), loss.type = 'pseudo-huber', activ.functions = c('tanh','linear','tanh'), batch.size = 8, optim.type = 'adam', n.epochs = 1000, val.prop = 0) # Plot loss during training plot(AE) # Make reconstruction and compression plots reconstruction_plot(AE, X) compression_plot(AE, X) # Reconstruct data and show states with highest anomaly scores recX <- reconstruct(AE, X) sort(recX$anomaly_scores, decreasing = TRUE)[1:5]
plot compressed observation in pairwise dimensions
compression_plot(object, ...) ## S3 method for class 'ANN' compression_plot(object, X, colors = NULL, jitter = FALSE, ...)compression_plot(object, ...) ## S3 method for class 'ANN' compression_plot(object, X, colors = NULL, jitter = FALSE, ...)
object |
autoencoder object of class |
... |
arguments to be passed to |
X |
data matrix with original values to be compressed and plotted |
colors |
optional vector of discrete colors |
jitter |
logical specifying whether to apply jitter to the compressed values. Especially useful whith step activation function that clusters the compressions and reconstructions. |
Matrix plot of pairwise dimensions
Plots
Decompress low-dimensional representation resulting from the nodes
of the middle layer. Output are the reconstructed inputs to function encode()
decode(object, ...) ## S3 method for class 'ANN' decode(object, compressed, compression.layer = NULL, ...)decode(object, ...) ## S3 method for class 'ANN' decode(object, compressed, compression.layer = NULL, ...)
object |
Object of class |
... |
arguments to be passed down |
compressed |
Compressed data |
compression.layer |
Integer specifying which hidden layer is the compression layer. If NULL this parameter is inferred from the structure of the network (hidden layer with smallest number of nodes) |
Compress data according to trained replicator or autoencoder.
Outputs are the activations of the nodes in the middle layer for each
observation in newdata
encode(object, ...) ## S3 method for class 'ANN' encode(object, newdata, compression.layer = NULL, ...)encode(object, ...) ## S3 method for class 'ANN' encode(object, newdata, compression.layer = NULL, ...)
object |
Object of class |
... |
arguments to be passed down |
newdata |
Data to compress |
compression.layer |
Integer specifying which hidden layer is the compression layer. If NULL this parameter is inferred from the structure of the network (hidden layer with smallest number of nodes) |
Construct and train a Multilayer Neural Network for regression or classification
neuralnetwork( X, y, hidden.layers, regression = FALSE, standardize = TRUE, loss.type = "log", huber.delta = 1, activ.functions = "tanh", step.H = 5, step.k = 100, optim.type = "sgd", learn.rates = 1e-04, L1 = 0, L2 = 0, sgd.momentum = 0.9, rmsprop.decay = 0.9, adam.beta1 = 0.9, adam.beta2 = 0.999, n.epochs = 100, batch.size = 32, drop.last = TRUE, val.prop = 0.1, verbose = TRUE, random.seed = NULL )neuralnetwork( X, y, hidden.layers, regression = FALSE, standardize = TRUE, loss.type = "log", huber.delta = 1, activ.functions = "tanh", step.H = 5, step.k = 100, optim.type = "sgd", learn.rates = 1e-04, L1 = 0, L2 = 0, sgd.momentum = 0.9, rmsprop.decay = 0.9, adam.beta1 = 0.9, adam.beta2 = 0.999, n.epochs = 100, batch.size = 32, drop.last = TRUE, val.prop = 0.1, verbose = TRUE, random.seed = NULL )
X |
matrix with explanatory variables |
y |
matrix with dependent variables. For classification this should be a one-columns matrix containing the classes - classes will be one-hot encoded. |
|
vector specifying the number of nodes in each layer. The
number of hidden layers in the network is implicitly defined by the length of
this vector. Set |
|
regression |
logical indicating regression or classification. In case of TRUE (regression), the activation function in the last hidden layer will be the linear activation function (identity function). In case of FALSE (classification), the activation function in the last hidden layer will be the softmax, and the log loss function should be used. |
standardize |
logical indicating if X and Y should be standardized before
training the network. Recommended to leave at |
loss.type |
which loss function should be used. Options are "log", "squared", "absolute", "huber" and "pseudo-huber". The log loss function should be used for classification (regression = FALSE), and ONLY for classification. |
huber.delta |
used only in case of loss functions "huber" and "pseudo-huber". This parameter controls the cut-off point between quadratic and absolute loss. |
activ.functions |
character vector of activation functions to be used in each hidden layer. Possible options are 'tanh', 'sigmoid', 'relu', 'linear', 'ramp' and 'step'. Should be either the size of the number of hidden layers or equal to one. If a single activation type is specified, this type will be broadcasted across the hidden layers. |
step.H |
number of steps of the step activation function. Only applicable if activ.functions includes 'step'. |
step.k |
parameter controlling the smoothness of the step activation function. Larger values lead to a less smooth step function. Only applicable if activ.functions includes 'step'. |
optim.type |
type of optimizer to use for updating the parameters. Options are 'sgd', 'rmsprop' and 'adam'. SGD is implemented with momentum. |
learn.rates |
the size of the steps to make in gradient descent. If set too large, the optimization might not converge to optimal values. If set too small, convergence will be slow. Should be either the size of the number of hidden layers plus one or equal to one. If a single learn rate is specified, this learn rate will be broadcasted across the layers. |
L1 |
L1 regularization. Non-negative number. Set to zero for no regularization. |
L2 |
L2 regularization. Non-negative number. Set to zero for no regularization. |
sgd.momentum |
numeric value specifying how much momentum should be used. Set to zero for no momentum, otherwise a value between zero and one. |
rmsprop.decay |
level of decay in the rms term. Controls the strength of the exponential decay of the squared gradients in the term that scales the gradient before the parameter update. Common values are 0.9, 0.99 and 0.999. |
adam.beta1 |
level of decay in the first moment estimate (the mean). The recommended value is 0.9. |
adam.beta2 |
level of decay in the second moment estimate (the uncentered variance). The recommended value is 0.999. |
n.epochs |
the number of epochs to train. One epoch is a single iteration through the training data. |
batch.size |
the number of observations to use in each batch. Batch learning is computationally faster than stochastic gradient descent. However, large batches might not result in optimal learning, see Efficient Backprop by LeCun for details. |
drop.last |
logical. Only applicable if the size of the training set is not perfectly devisible by the batch size. Determines if the last chosen observations should be discarded (in the current epoch) or should constitute a smaller batch. Note that a smaller batch leads to a noisier approximation of the gradient. |
val.prop |
proportion of training data to use for tracking the loss on a validation set during training. Useful for assessing the training process and identifying possible overfitting. Set to zero for only tracking the loss on the training data. |
verbose |
logical indicating if additional information should be printed |
random.seed |
optional seed for the random number generator |
A genereric function for training Neural Networks for classification and regression problems. Various types of activation and loss functions are supported, as well as L1 and L2 regularization. Possible optimizer include SGD (with or without momentum), RMSprop and Adam.
An ANN object. Use function plot(<object>) to assess
loss on training and optionally validation data during training process. Use
function predict(<object>, <newdata>) for prediction.
LeCun, Yann A., et al. "Efficient backprop." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 9-48.
# Example on iris dataset # Prepare test and train sets random_draw <- sample(1:nrow(iris), size = 100) X_train <- iris[random_draw, 1:4] y_train <- iris[random_draw, 5] X_test <- iris[setdiff(1:nrow(iris), random_draw), 1:4] y_test <- iris[setdiff(1:nrow(iris), random_draw), 5] # Train neural network on classification task NN <- neuralnetwork(X = X_train, y = y_train, hidden.layers = c(5, 5), optim.type = 'adam', learn.rates = 0.01, val.prop = 0) # Plot the loss during training plot(NN) # Make predictions y_pred <- predict(NN, newdata = X_test) # Plot predictions correct <- (y_test == y_pred$predictions) plot(X_test, pch = as.numeric(y_test), col = correct + 2)# Example on iris dataset # Prepare test and train sets random_draw <- sample(1:nrow(iris), size = 100) X_train <- iris[random_draw, 1:4] y_train <- iris[random_draw, 5] X_test <- iris[setdiff(1:nrow(iris), random_draw), 1:4] y_test <- iris[setdiff(1:nrow(iris), random_draw), 5] # Train neural network on classification task NN <- neuralnetwork(X = X_train, y = y_train, hidden.layers = c(5, 5), optim.type = 'adam', learn.rates = 0.01, val.prop = 0) # Plot the loss during training plot(NN) # Make predictions y_pred <- predict(NN, newdata = X_test) # Plot predictions correct <- (y_test == y_pred$predictions) plot(X_test, pch = as.numeric(y_test), col = correct + 2)
plot Generate plots of the loss against epochs
## S3 method for class 'ANN' plot(x, max.points = 1000, ...)## S3 method for class 'ANN' plot(x, max.points = 1000, ...)
x |
Object of class |
max.points |
Maximum number of points to plot, set to NA, NULL or Inf to include all points in the plot |
... |
further arguments to be passed to plot |
A generic function for plot loss of neural net
Plots
predict Predict class or value for new data
## S3 method for class 'ANN' predict(object, newdata, ...)## S3 method for class 'ANN' predict(object, newdata, ...)
object |
Object of class |
newdata |
Data to make predictions on |
... |
further arguments (not in use) |
A genereric function for training neural nets
A list with predicted classes for classification and fitted probabilities
Print info on trained Neural Network
## S3 method for class 'ANN' print(x, ...)## S3 method for class 'ANN' print(x, ...)
x |
Object of class |
... |
Further arguments |
Deserialize ANN object from binary file
read_ANN(file)read_ANN(file)
file |
character specifying file path |
Object of class ANN
reconstruct takes new data as input and reconstructs the observations using
a trained replicator or autoencoder object.
reconstruct(object, X)reconstruct(object, X)
object |
Object of class |
X |
data matrix to reconstruct |
A genereric function for training neural nets
Reconstructed observations and anomaly scores (reconstruction errors)
plots original and reconstructed data points in a single plot with connecting lines between original value and corresponding reconstruction
reconstruction_plot(object, ...) ## S3 method for class 'ANN' reconstruction_plot(object, X, colors = NULL, ...)reconstruction_plot(object, ...) ## S3 method for class 'ANN' reconstruction_plot(object, X, colors = NULL, ...)
object |
autoencoder object of class |
... |
arguments to be passed down |
X |
data matrix with original values to be reconstructed and plotted |
colors |
optional vector of discrete colors. The reconstruction errors are are used as color if this argument is not specified |
Matrix plot of pairwise dimensions
Plots
Continue training of a neural network object returned by neuralnetwork()
or autoencoder()
train( object, X, y = NULL, n.epochs = 100, batch.size = 32, drop.last = TRUE, val.prop = 0.1, random.seed = NULL )train( object, X, y = NULL, n.epochs = 100, batch.size = 32, drop.last = TRUE, val.prop = 0.1, random.seed = NULL )
object |
object of class |
X |
matrix with explanatory variables |
y |
matrix with dependent variables. Not required if object is an autoencoder |
n.epochs |
the number of epochs to train. This parameter largely determines the training time (one epoch is a single iteration through the training data). |
batch.size |
the number of observations to use in each batch. Batch learning is computationally faster than stochastic gradient descent. However, large batches might not result in optimal learning, see Efficient Backprop by Le Cun for details. |
drop.last |
logical. Only applicable if the size of the training set is not perfectly devisible by the batch size. Determines if the last chosen observations should be discarded (in the current epoch) or should constitute a smaller batch. Note that a smaller batch leads to a noisier approximation of the gradient. |
val.prop |
proportion of training data to use for tracking the loss on a validation set during training. Useful for assessing the training process and identifying possible overfitting. Set to zero for only tracking the loss on the training data. |
random.seed |
optional seed for the random number generator |
A new validation set is randomly chosen. This can result in irregular jumps
in the plot given by plot.ANN().
An ANN object. Use function plot(<object>) to assess
loss on training and optionally validation data during training process. Use
function predict(<object>, <newdata>) for prediction.
LeCun, Yann A., et al. "Efficient backprop." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 9-48.
# Train a neural network on the iris dataset X <- iris[,1:4] y <- iris$Species NN <- neuralnetwork(X, y, hidden.layers = 10, sgd.momentum = 0.9, learn.rates = 0.01, val.prop = 0.3, n.epochs = 100) # Plot training and validation loss during training plot(NN) # Continue training for 1000 epochs train(NN, X, y, n.epochs = 200, val.prop = 0.3) # Again plot the loss - note the jump in the validation loss at the 100th epoch # This is due to the random selection of a new validation set plot(NN)# Train a neural network on the iris dataset X <- iris[,1:4] y <- iris$Species NN <- neuralnetwork(X, y, hidden.layers = 10, sgd.momentum = 0.9, learn.rates = 0.01, val.prop = 0.3, n.epochs = 100) # Plot training and validation loss during training plot(NN) # Continue training for 1000 epochs train(NN, X, y, n.epochs = 200, val.prop = 0.3) # Again plot the loss - note the jump in the validation loss at the 100th epoch # This is due to the random selection of a new validation set plot(NN)
Serialize ANN object to binary file
write_ANN(object, file)write_ANN(object, file)
object |
Object of class |
file |
character specifying file path |