| Title: | Python-Based Extensions for Data Analytics Workflows |
| Version: | 1.2.727 |
| Description: | Provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. Includes tools for data sampling, transformation, feature selection, balancing strategies (e.g., SMOTE), and model construction. These capabilities leverage Python libraries via the reticulate interface, enabling seamless integration with a broader machine learning ecosystem. Supports instance selection and hybrid workflows that combine R and Python functionalities for flexible and reproducible analytical pipelines. The architecture is inspired by the Experiment Lines approach, which promotes modularity, extensibility, and interoperability across tools. More information on Experiment Lines is available in Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>. |
| License: | MIT + file LICENSE |
| URL: | https://cefet-rj-dal.github.io/daltoolboxdp/, https://github.com/cefet-rj-dal/daltoolboxdp |
| BugReports: | https://github.com/cefet-rj-dal/daltoolboxdp/issues |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 4.1.0) |
| Imports: | tspredit, daltoolbox, leaps, FSelector, doBy, glmnet, smotefamily, reticulate, stats |
| Config/reticulate: | list( packages = list( list(package = "scipy"), list(package = "torch"), list(package = "pandas"), list(package = "numpy"), list(package = "matplotlib"), list(package = "scikit-learn") ) ) |
| NeedsCompilation: | no |
| Packaged: | 2025-06-29 11:44:38 UTC; gpca |
| Author: | Eduardo Ogasawara 
[aut, ths, cre],
Diego Salles [aut],
Janio Lima [aut],
Lucas Tavares [aut],
Eduardo Bezerra [ctb],
CEFET/RJ [cph] |
| Maintainer: | Eduardo Ogasawara <eogasawara@ieee.org> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-29 12:10:09 UTC |
Adversarial Autoencoder - Encode
Description
Creates an deep learning adversarial autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_adv_e(
input_size,
encoding_size,
batch_size = 350,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
a autoenc_adv_e object.
#See an example of using autoenc_adv_e at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_adv_e.md
Adversarial Autoencoder - Encode
Description
Creates an deep learning adversarial autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_adv_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
a autoenc_adv_ed object.
Examples
#See an example of using `autoenc_adv_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_adv_ed.md
Convolutional Autoencoder - Encode
Description
Creates an deep learning convolutional autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_conv_e(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
a autoenc_conv_e object.
Examples
#See an example of using `autoenc_conv_e` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/transf/autoenc_conv_e.md
Convolutional Autoencoder - Encode
Description
Creates an deep learning convolutional autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_conv_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
a autoenc_conv_ed object.
Examples
#See an example of using `autoenc_conv_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/transf/autoenc_conv_ed.md
Denoising Autoencoder - Encode
Description
Creates an deep learning denoising autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_denoise_e(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001,
noise_factor = 0.3
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
noise_factor |
level of noise to be added to the data |
Value
a autoenc_denoise_e_decode object.
Examples
#See an example of using `autoenc_denoise_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_denoise_e.md
Denoising Autoencoder - Encode
Description
Creates an deep learning denoising autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_denoise_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001,
noise_factor = 0.3
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
noise_factor |
level of noise to be added to the data |
Value
a autoenc_denoise_ed object.
Examples
#See an example of using `autoenc_denoise_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_denoise_ed.md
Autoencoder - Encode
Description
Creates an deep learning autoencoder to encode a sequence of observations. It wraps the pytorch and reticulate libraries.
Usage
autoenc_e(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
returns a autoenc_e object.
Examples
#See an example of using `autoenc_e` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_e.md
Autoencoder - Encode-decode
Description
Creates an deep learning autoencoder to encode-decode a sequence of observations. It wraps the pytorch and reticulate libraries.
Usage
autoenc_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
returns a autoenc_ed object.
Examples
#See an example of using `autoenc_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_ed.md
LSTM Autoencoder - Encode
Description
Creates an deep learning LSTM autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_lstm_e(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 50,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
returns a autoenc_lstm_e object.
Examples
#See an example of using `autoenc_lstm_e` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_lstm_e.md
LSTM Autoencoder - Decode
Description
Creates an deep learning LSTM autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_lstm_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 50,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
returns a autoenc_lstm_ed object.
Examples
#See an example of using `autoenc_lstm_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_lstm_ed.md
Stacked Autoencoder - Encode
Description
Creates an deep learning stacked autoencoder to encode a sequence of observations. The autoencoder layers are based on DAL Toolbox Vanilla Autoencoder It wraps the pytorch library.
Usage
autoenc_stacked_e(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001,
k = 3
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
k |
number of AE layers in the stack |
Value
a autoenc_stacked_e_decode object.
#See an example of using autoenc_stacked_e_decode at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_stacked_e.md
Stacked Autoencoder - Encode
Description
Creates an deep learning stacked autoencoder to encode a sequence of observations. The autoencoder layers are based on DAL Toolbox Vanilla Autoencoder It wraps the pytorch library.
Usage
autoenc_stacked_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001,
k = 3
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
k |
number of AE layers in the stack |
Value
a autoenc_stacked_ed object.
Examples
#See an example of using `autoenc_stacked_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_stacked_e.md
Variational Autoencoder - Encode
Description
Creates an deep learning variational autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_variational_e(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
returns a autoenc_variational_e object.
Examples
#See an example of using `autoenc_variational_e` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_variational_e.md
Variational Autoencoder - Encode
Description
Creates an deep learning variational autoencoder to encode a sequence of observations. It wraps the pytorch library.
Usage
autoenc_variational_ed(
input_size,
encoding_size,
batch_size = 32,
num_epochs = 1000,
learning_rate = 0.001
)
Arguments
input_size |
input size |
encoding_size |
encoding size |
batch_size |
size for batch learning |
num_epochs |
number of epochs for training |
learning_rate |
learning rate |
Value
returns a autoenc_variational_ed object.
Examples
#See an example of using `autoenc_variational_ed` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/autoencoder/autoenc_variational_ed.md
Oversampling
Description
Oversampling balances the class distribution of a dataset by increasing the representation of the minority class in the dataset. It wraps the smotefamily library.
Usage
bal_oversampling(attribute)
Arguments
attribute |
The class attribute to target balancing using oversampling. |
Value
A bal_oversampling object.
Examples
data(iris)
mod_iris <- iris[c(1:50,51:71,101:111),]
bal <- bal_oversampling('Species')
bal <- daltoolbox::fit(bal, mod_iris)
adjust_iris <- daltoolbox::transform(bal, mod_iris)
table(adjust_iris$Species)
Subsampling
Description
Subsampling balances the class distribution of a dataset by reducing the representation of the majority class in the dataset.
Usage
bal_subsampling(attribute)
Arguments
attribute |
The class attribute to target balancing using subsampling |
Value
A bal_subsampling object.
Examples
data(iris)
mod_iris <- iris[c(1:50,51:71,101:111),]
bal <- bal_subsampling('Species')
bal <- daltoolbox::fit(bal, mod_iris)
adjust_iris <- daltoolbox::transform(bal, mod_iris)
table(adjust_iris$Species)
Feature Selection
Description
Feature selection is a process of selecting a subset of relevant features from a larger set of features in a dataset for use in model training. The FeatureSelection class in R provides a framework for performing feature selection.
Usage
fs(attribute)
Arguments
attribute |
The target variable. |
Value
An instance of the FeatureSelection class.
Examples
#See ?fs_fss for an example of feature selection
Forward Stepwise Selection
Description
Forward stepwise selection is a technique for feature selection in which attributes are added to a model one at a time based on their ability to improve the model's performance. It stops adding once the candidate addition does not significantly improve model adjustment. It wraps the leaps library.
Usage
fs_fss(attribute)
Arguments
attribute |
The target variable. |
Value
A fs_fss object.
Examples
data(iris)
myfeature <- daltoolbox::fit(fs_fss("Species"), iris)
data <- daltoolbox::transform(myfeature, iris)
head(data)
Information Gain
Description
Information Gain is a feature selection technique based on information theory. It measures the information obtained for the target variable by knowing the presence or absence of a feature. It wraps the FSelector library.
Usage
fs_ig(attribute)
Arguments
attribute |
The target variable. |
Value
A fs_ig object.
Examples
data(iris)
myfeature <- daltoolbox::fit(fs_ig("Species"), iris)
data <- daltoolbox::transform(myfeature, iris)
head(data)
Feature Selection using Lasso
Description
Feature selection using Lasso regression is a technique for selecting a subset of relevant features. It wraps the glmnet library.
Usage
fs_lasso(attribute)
Arguments
attribute |
The target variable. |
Value
A fs_lasso object.
Examples
data(iris)
myfeature <- daltoolbox::fit(fs_lasso("Species"), iris)
data <- daltoolbox::transform(myfeature, iris)
head(data)
Relief
Description
Feature selection using Relief is a technique for selecting a subset of relevant features. It calculates the relevance of a feature by considering the difference in feature values between nearest neighbors of the same and different classes. It wraps the FSelector library.
Usage
fs_relief(attribute)
Arguments
attribute |
The target variable. |
Value
A fs_relief object.
Examples
data(iris)
myfeature <- daltoolbox::fit(fs_relief("Species"), iris)
data <- daltoolbox::transform(myfeature, iris)
head(data)
Gradient Boosting Classifier
Description
Implements a classifier using the Gradient Boosting algorithm. This function wraps the GradientBoostingClassifier from Python's scikit-learn library.
Usage
skcla_gb(
attribute,
slevels,
loss = "log_loss",
learning_rate = 0.1,
n_estimators = 100,
subsample = 1,
criterion = "friedman_mse",
min_samples_split = 2,
min_samples_leaf = 1,
min_weight_fraction_leaf = 0,
max_depth = 3,
min_impurity_decrease = 0,
init = NULL,
random_state = NULL,
max_features = NULL,
verbose = 0,
max_leaf_nodes = NULL,
warm_start = FALSE,
validation_fraction = 0.1,
n_iter_no_change = NULL,
tol = 1e-04,
ccp_alpha = 0
)
Arguments
attribute |
Target attribute name for model building |
slevels |
Possible values for the target classification |
loss |
Loss function to be optimized ('log_loss', 'exponential') |
learning_rate |
Learning rate that shrinks the contribution of each tree |
n_estimators |
Number of boosting stages to perform |
subsample |
Fraction of samples to be used for fitting the individual base learners |
criterion |
Function to measure the quality of a split |
min_samples_split |
Minimum number of samples required to split an internal node |
min_samples_leaf |
Minimum number of samples required to be at a leaf node |
min_weight_fraction_leaf |
Minimum weighted fraction of the sum total of weights |
max_depth |
Maximum depth of the individual regression estimators |
min_impurity_decrease |
Minimum impurity decrease required for split |
init |
Estimator object to initialize the model |
random_state |
Random number generator seed |
max_features |
Number of features to consider for best split |
verbose |
Controls verbosity of the output |
max_leaf_nodes |
Maximum number of leaf nodes |
warm_start |
Whether to reuse solution of previous call |
validation_fraction |
Proportion of training data to set aside for validation |
n_iter_no_change |
Used to decide if early stopping will be used |
tol |
Tolerance for early stopping |
ccp_alpha |
Complexity parameter for cost-complexity pruning |
Details
Tree Boosting
Value
A Gradient Boosting classifier object
skcla_gb object
Examples
#See an example of using `skcla_gb` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_gb.md
K-Nearest Neighbors Classifier
Description
Implements classification using the K-Nearest Neighbors algorithm. This function wraps the KNeighborsClassifier from Python's scikit-learn library.
Usage
skcla_knn(
attribute,
slevels,
n_neighbors = 5,
weights = "uniform",
algorithm = "auto",
leaf_size = 30,
p = 2,
metric = "minkowski",
metric_params = NULL,
n_jobs = NULL
)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
n_neighbors |
Number of neighbors to use for queries |
weights |
Weight function used in prediction ('uniform', 'distance') |
algorithm |
Algorithm used to compute nearest neighbors ('auto', 'ball_tree', 'kd_tree', 'brute') |
leaf_size |
Leaf size passed to BallTree or KDTree |
p |
Power parameter for the Minkowski metric |
metric |
Distance metric for the tree ('euclidean', 'manhattan', 'chebyshev', 'minkowski', etc.) |
metric_params |
Additional parameters for the metric function |
n_jobs |
Number of parallel jobs for neighbor searches |
Details
K-Nearest Neighbors Classifier
Value
A K-Nearest Neighbors classifier object
skcla_knn object
Examples
#See an example of using `skcla_knn` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_knn.md
Multi-layer Perceptron Classifier
Description
Implements classification using Multi-layer Perceptron algorithm. This function wraps the MLPClassifier from Python's scikit-learn library.
Usage
skcla_mlp(
attribute,
slevels,
hidden_layer_sizes = c(100),
activation = "relu",
solver = "adam",
alpha = 1e-04,
batch_size = "auto",
learning_rate = "constant",
learning_rate_init = 0.001,
power_t = 0.5,
max_iter = 200,
shuffle = TRUE,
random_state = NULL,
tol = 1e-04,
verbose = FALSE,
warm_start = FALSE,
momentum = 0.9,
nesterovs_momentum = TRUE,
early_stopping = FALSE,
validation_fraction = 0.1,
beta_1 = 0.9,
beta_2 = 0.999,
epsilon = 1e-08,
n_iter_no_change = 10,
max_fun = 15000
)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
|
Number of neurons in each hidden layer | |
activation |
Activation function for hidden layer ('identity', 'logistic', 'tanh', 'relu') |
solver |
The solver for weight optimization ('lbfgs', 'sgd', 'adam') |
alpha |
L2 penalty (regularization term) parameter |
batch_size |
Size of minibatches for stochastic optimizers |
learning_rate |
Learning rate schedule for weight updates |
learning_rate_init |
Initial learning rate used |
power_t |
Exponent for inverse scaling learning rate |
max_iter |
Maximum number of iterations |
shuffle |
Whether to shuffle samples in each iteration |
random_state |
Seed for random number generation |
tol |
Tolerance for optimization |
verbose |
Whether to print progress messages to stdout |
warm_start |
Whether to reuse previous solution |
momentum |
Momentum for gradient descent update |
nesterovs_momentum |
Whether to use Nesterov's momentum |
early_stopping |
Whether to use early stopping |
validation_fraction |
Proportion of training data for validation |
beta_1 |
Exponential decay rate for estimates of first moment vector |
beta_2 |
Exponential decay rate for estimates of second moment vector |
epsilon |
Value for numerical stability in adam |
n_iter_no_change |
Maximum number of epochs to not meet tol improvement |
max_fun |
Maximum number of loss function calls |
Details
Neural Network Classifier
Value
A Multi-layer Perceptron classifier object
skcla_mlp object
Examples
#See an example of using `skcla_mlp` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_mlp.md
Gaussian Naive Bayes Classifier
Description
Implements classification using the Gaussian Naive Bayes algorithm. This function wraps the GaussianNB from Python's scikit-learn library.
Usage
skcla_nb(attribute, slevels, var_smoothing = 1e-09, priors = NULL)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
var_smoothing |
Portion of the largest variance of all features that is added to variances |
priors |
Prior probabilities of the classes. If specified must be a list of length n_classes |
Details
Naive Bayes Classifier
Value
A Naive Bayes classifier object
skcla_nb object
Examples
#See an example of using `skcla_nb` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_nb.md
Random Forest Classifier
Description
Implements classification using Random Forest algorithm. This function wraps the RandomForestClassifier from Python's scikit-learn library.
Usage
skcla_rf(
attribute,
slevels,
n_estimators = 100,
criterion = "gini",
max_depth = NULL,
min_samples_split = 2,
min_samples_leaf = 1,
min_weight_fraction_leaf = 0,
max_features = "sqrt",
max_leaf_nodes = NULL,
min_impurity_decrease = 0,
bootstrap = TRUE,
oob_score = FALSE,
n_jobs = NULL,
random_state = NULL,
verbose = 0,
warm_start = FALSE,
class_weight = NULL,
ccp_alpha = 0,
max_samples = NULL,
monotonic_cst = NULL
)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
n_estimators |
Number of trees in random forest |
criterion |
Function name for measuring split quality |
max_depth |
Maximum tree depth value |
min_samples_split |
Minimum samples needed for internal node split |
min_samples_leaf |
Minimum samples needed at leaf node |
min_weight_fraction_leaf |
Minimum weighted fraction value |
max_features |
Number of features to consider for best split |
max_leaf_nodes |
Maximum number of leaf nodes |
min_impurity_decrease |
Minimum impurity decrease needed for split |
bootstrap |
Whether to use bootstrap samples |
oob_score |
Whether to use out-of-bag samples |
n_jobs |
Number of parallel jobs |
random_state |
Seed for random number generation |
verbose |
Whether to enable verbose output |
warm_start |
Whether to reuse previous solution |
class_weight |
Weights associated with classes |
ccp_alpha |
Complexity parameter value for pruning |
max_samples |
Number of samples for training estimators |
monotonic_cst |
Monotonicity constraints for features |
Details
Tree Ensemble
Value
A Random Forest classifier object
skcla_rf object
Examples
#See an example of using `skcla_rf` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_rf.md
Support Vector Machine Classification
Description
Implements classification using Support Vector Machine (SVM) algorithm. This function wraps the SVC classifier from Python's scikit-learn library.
Usage
skcla_svc(
attribute,
slevels,
kernel = "rbf",
degree = 3,
gamma = "scale",
coef0 = 0,
tol = 0.001,
C = 1,
shrinking = TRUE,
probability = FALSE,
cache_size = 200,
class_weight = NULL,
verbose = FALSE,
max_iter = -1,
decision_function_shape = "ovr",
break_ties = FALSE,
random_state = NULL
)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
kernel |
Kernel function type ('linear', 'poly', 'rbf', 'sigmoid') |
degree |
Polynomial degree when using 'poly' kernel |
gamma |
Kernel coefficient value |
coef0 |
Independent term value in kernel function |
tol |
Tolerance value for stopping criterion |
C |
Regularization strength parameter |
shrinking |
Whether to use shrinking heuristic |
probability |
Whether to enable probability estimates |
cache_size |
Kernel cache size value in MB |
class_weight |
Weights associated with classes |
verbose |
Whether to enable verbose output |
max_iter |
Maximum number of iterations |
decision_function_shape |
Shape of decision function ('ovo', 'ovr') |
break_ties |
Whether to break tie decisions |
random_state |
Seed for random number generation |
Details
SVM Classifier
Value
An SVM classifier object
skcla_svc object
Examples
#See an example of using `skcla_svc` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/cla_svm.md
Conv1D
Description
Creates a time series prediction object that uses the Conv1D. It wraps the pytorch library.
Usage
ts_conv1d(preprocess = NA, input_size = NA, epochs = 10000L)
Arguments
preprocess |
normalization |
input_size |
input size for machine learning model |
epochs |
maximum number of epochs |
Value
returns a ts_conv1d object.
Examples
#See an example of using `ts_conv1d` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/timeseries/ts_conv1d.md
LSTM
Description
Creates a time series prediction object that uses the LSTM. It wraps the pytorch library.
Usage
ts_lstm(preprocess = NA, input_size = NA, epochs = 10000L)
Arguments
preprocess |
normalization |
input_size |
input size for machine learning model |
epochs |
maximum number of epochs |
Value
returns a ts_lstm object.
Examples
#See an example of using `ts_ts_lstmconv1d` at this
#https://github.com/cefet-rj-dal/daltoolbox/blob/main/timeseries/ts_lstm.md