Base class for regular classifiers relying on EmbeddedText or LargeDataSetForTextEmbeddings as input

Abstract class for all regular classifiers that use numerical representations of texts instead of words.

Objects of this class containing fields and methods used in several other classes in 'AI for Education'.

This class is not designed for a direct application and should only be used by developers.

Value

A new object of this class.

See also

Other R6 Classes for Developers: AIFEBaseModel, ClassifiersBasedOnTextEmbeddings, LargeDataSetBase, ModelsBasedOnTextEmbeddings, TEClassifiersBasedOnProtoNet

Super classes

aifeducation::AIFEBaseModel -> aifeducation::ModelsBasedOnTextEmbeddings -> aifeducation::ClassifiersBasedOnTextEmbeddings -> TEClassifiersBasedOnRegular

Methods

Public methods

• TEClassifiersBasedOnRegular$train()

• TEClassifiersBasedOnRegular$clone()

Inherited methods

• aifeducation::AIFEBaseModel$count_parameter()
• aifeducation::AIFEBaseModel$get_all_fields()
• aifeducation::AIFEBaseModel$get_documentation_license()
• aifeducation::AIFEBaseModel$get_ml_framework()
• aifeducation::AIFEBaseModel$get_model_description()
• aifeducation::AIFEBaseModel$get_model_info()
• aifeducation::AIFEBaseModel$get_model_license()
• aifeducation::AIFEBaseModel$get_package_versions()
• aifeducation::AIFEBaseModel$get_private()
• aifeducation::AIFEBaseModel$get_publication_info()
• aifeducation::AIFEBaseModel$get_sustainability_data()
• aifeducation::AIFEBaseModel$is_configured()
• aifeducation::AIFEBaseModel$is_trained()
• aifeducation::AIFEBaseModel$load()
• aifeducation::AIFEBaseModel$set_documentation_license()
• aifeducation::AIFEBaseModel$set_model_description()
• aifeducation::AIFEBaseModel$set_model_license()
• aifeducation::AIFEBaseModel$set_publication_info()
• aifeducation::ModelsBasedOnTextEmbeddings$get_text_embedding_model()
• aifeducation::ModelsBasedOnTextEmbeddings$get_text_embedding_model_name()
• aifeducation::ClassifiersBasedOnTextEmbeddings$adjust_target_levels()
• aifeducation::ClassifiersBasedOnTextEmbeddings$check_embedding_model()
• aifeducation::ClassifiersBasedOnTextEmbeddings$check_feature_extractor_object_type()
• aifeducation::ClassifiersBasedOnTextEmbeddings$load_from_disk()
• aifeducation::ClassifiersBasedOnTextEmbeddings$plot_coding_stream()
• aifeducation::ClassifiersBasedOnTextEmbeddings$plot_training_history()
• aifeducation::ClassifiersBasedOnTextEmbeddings$predict()
• aifeducation::ClassifiersBasedOnTextEmbeddings$requires_compression()
• aifeducation::ClassifiersBasedOnTextEmbeddings$save()

---

Method train()

Method for training a neural net.

Training includes a routine for early stopping. In the case that loss<0.0001 and Accuracy=1.00 and Average Iota=1.00 training stops. The history uses the values of the last trained epoch for the remaining epochs.

After training the model with the best values for Average Iota, Accuracy, and Loss on the validation data set is used as the final model.

Usage

TEClassifiersBasedOnRegular$train(
  data_embeddings = NULL,
  data_targets = NULL,
  data_folds = 5,
  data_val_size = 0.25,
  loss_balance_class_weights = TRUE,
  loss_balance_sequence_length = TRUE,
  loss_cls_fct_name = "FocalLoss",
  use_sc = FALSE,
  sc_method = "knnor",
  sc_min_k = 1,
  sc_max_k = 10,
  use_pl = FALSE,
  pl_max_steps = 3,
  pl_max = 1,
  pl_anchor = 1,
  pl_min = 0,
  sustain_track = TRUE,
  sustain_iso_code = NULL,
  sustain_region = NULL,
  sustain_interval = 15,
  epochs = 40,
  batch_size = 32,
  trace = TRUE,
  ml_trace = 1,
  log_dir = NULL,
  log_write_interval = 10,
  n_cores = auto_n_cores(),
  lr_rate = 0.001,
  lr_warm_up_ratio = 0.02,
  optimizer = "AdamW"
)

Arguments

data_embeddings

EmbeddedText, LargeDataSetForTextEmbeddings Object of class EmbeddedText or LargeDataSetForTextEmbeddings.

data_targets

factor containing the labels for cases stored in embeddings. Factor must be named and has to use the same names as used in in the embeddings. .

data_folds

int determining the number of cross-fold samples. Allowed values: 1 <= x

data_val_size

double between 0 and 1, indicating the proportion of cases which should be used for the validation sample during the estimation of the model. The remaining cases are part of the training data. Allowed values: 0 < x < 1

loss_balance_class_weights

bool If TRUE class weights are generated based on the frequencies of the training data with the method Inverse Class Frequency. If FALSE each class has the weight 1.

loss_balance_sequence_length

bool If TRUE sample weights are generated for the length of sequences based on the frequencies of the training data with the method Inverse Class Frequency. If FALSE each sequences length has the weight 1.

loss_cls_fct_name

string Name of the loss function to use during training. Allowed values: 'FocalLoss', 'CrossEntropyLoss'

use_sc

bool TRUE if the estimation should integrate synthetic cases. FALSE if not.

sc_method

string containing the method for generating synthetic cases. Allowed values: 'knnor'

sc_min_k

int determining the minimal number of k which is used for creating synthetic units. Allowed values: 1 <= x

sc_max_k

int determining the maximal number of k which is used for creating synthetic units. Allowed values: 1 <= x

use_pl

bool TRUE if the estimation should integrate pseudo-labeling. FALSE if not.

pl_max_steps

int determining the maximum number of steps during pseudo-labeling. Allowed values: 1 <= x

pl_max

double setting the maximal level of confidence for considering a case for pseudo-labeling. Allowed values: 0 < x <= 1

pl_anchor

double indicating the reference point for sorting the new cases of every label. Allowed values: 0 <= x <= 1

pl_min

double setting the mnimal level of confidence for considering a case for pseudo-labeling. Allowed values: 0 <= x < 1

sustain_track

bool If TRUE energy consumption is tracked during training via the python library 'codecarbon'.

sustain_iso_code

string ISO code (Alpha-3-Code) for the country. This variable must be set if sustainability should be tracked. A list can be found on Wikipedia: https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes. Allowed values: any

sustain_region

string Region within a country. Only available for USA and Canada See the documentation of codecarbon for more information. https://mlco2.github.io/codecarbon/parameters.html Allowed values: any

sustain_interval

int Interval in seconds for measuring power usage. Allowed values: 1 <= x

epochs

int Number of training epochs. Allowed values: 1 <= x

batch_size

int Size of the batches for training. Allowed values: 1 <= x

trace

bool TRUE if information about the estimation phase should be printed to the console.

ml_trace

int ml_trace=0 does not print any information about the training process from pytorch on the console. Allowed values: 0 <= x <= 1

log_dir

string Path to the directory where the log files should be saved. If no logging is desired set this argument to NULL. Allowed values: any

log_write_interval

int Time in seconds determining the interval in which the logger should try to update the log files. Only relevant if log_dir is not NULL. Allowed values: 1 <= x

n_cores

int Number of cores which should be used during the calculation of synthetic cases. Only relevant if use_sc=TRUE. Allowed values: 1 <= x

lr_rate

double Initial learning rate for the training. Allowed values: 0 < x <= 1

lr_warm_up_ratio

double Number of epochs used for warm up. Allowed values: 0 < x < 0.5

optimizer

string determining the optimizer used for training. Allowed values: 'Adam', 'RMSprop', 'AdamW', 'SGD'

Details

• sc_max_k: All values from sc_min_k up to sc_max_k are successively used. If the number of sc_max_k is too high, the value is reduced to a number that allows the calculating of synthetic units.

• pl_anchor: With the help of this value, the new cases are sorted. For this aim, the distance from the anchor is calculated and all cases are arranged into an ascending order.

Returns

Function does not return a value. It changes the object into a trained classifier.

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Method clone()

The objects of this class are cloneable with this method.

Usage

TEClassifiersBasedOnRegular$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.