aifeducation (version 1.0.2)

TEClassifierRegular: Text embedding classifier with a neural net

Description

Abstract class for neural nets with 'keras'/'tensorflow' and ' pytorch'.

Value

Objects of this class are used for assigning texts to classes/categories. For the creation and training of a classifier an object of class EmbeddedText or LargeDataSetForTextEmbeddings on the one hand and a factor on the other hand are necessary.

The object of class EmbeddedText or LargeDataSetForTextEmbeddings contains the numerical text representations (text embeddings) of the raw texts generated by an object of class TextEmbeddingModel. For supporting large data sets it is recommended to use LargeDataSetForTextEmbeddings instead of EmbeddedText.

The factor contains the classes/categories for every text. Missing values (unlabeled cases) are supported and can be used for pseudo labeling.

For predictions an object of class EmbeddedText or LargeDataSetForTextEmbeddings has to be used which was created with the same TextEmbeddingModel as for training.

Super class

aifeducation::AIFEBaseModel -> TEClassifierRegular

Public fields

feature_extractor

('list()')
List for storing information and objects about the feature_extractor.

reliability

('list()')

List for storing central reliability measures of the last training.

• reliability$test_metric: Array containing the reliability measures for the test data for every fold and step (in case of pseudo-labeling).

• reliability$test_metric_mean: Array containing the reliability measures for the test data. The values represent the mean values for every fold.

• reliability$raw_iota_objects: List containing all iota_object generated with the package iotarelr for every fold at the end of the last training for the test data.

• reliability$raw_iota_objects$iota_objects_end: List of objects with class iotarelr_iota2 containing the estimated iota reliability of the second generation for the final model for every fold for the test data.

• reliability$raw_iota_objects$iota_objects_end_free: List of objects with class iotarelr_iota2 containing the estimated iota reliability of the second generation for the final model for every fold for the test data. Please note that the model is estimated without forcing the Assignment Error Matrix to be in line with the assumption of weak superiority.

• reliability$iota_object_end: Object of class iotarelr_iota2 as a mean of the individual objects for every fold for the test data.

• reliability$iota_object_end_free: Object of class iotarelr_iota2 as a mean of the individual objects for every fold. Please note that the model is estimated without forcing the Assignment Error Matrix to be in line with the assumption of weak superiority.

• reliability$standard_measures_end: Object of class list containing the final measures for precision, recall, and f1 for every fold.

• reliability$standard_measures_mean: matrix containing the mean measures for precision, recall, and f1.

Methods

Public methods

• TEClassifierRegular$configure()

• TEClassifierRegular$train()

• TEClassifierRegular$predict()

• TEClassifierRegular$check_embedding_model()

• TEClassifierRegular$check_feature_extractor_object_type()

• TEClassifierRegular$requires_compression()

• TEClassifierRegular$save()

• TEClassifierRegular$load_from_disk()

• TEClassifierRegular$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$get_sustainability_data()
• aifeducation::AIFEBaseModel$get_text_embedding_model()
• aifeducation::AIFEBaseModel$get_text_embedding_model_name()
• aifeducation::AIFEBaseModel$is_configured()
• aifeducation::AIFEBaseModel$load()
• aifeducation::AIFEBaseModel$set_documentation_license()
• aifeducation::AIFEBaseModel$set_model_description()
• aifeducation::AIFEBaseModel$set_model_license()
• aifeducation::AIFEBaseModel$set_publication_info()

---

Method configure()

Creating a new instance of this class.

Usage

TEClassifierRegular$configure(
  ml_framework = "pytorch",
  name = NULL,
  label = NULL,
  text_embeddings = NULL,
  feature_extractor = NULL,
  target_levels = NULL,
  dense_size = 4,
  dense_layers = 0,
  rec_size = 4,
  rec_layers = 2,
  rec_type = "gru",
  rec_bidirectional = FALSE,
  self_attention_heads = 0,
  intermediate_size = NULL,
  attention_type = "fourier",
  add_pos_embedding = TRUE,
  rec_dropout = 0.1,
  repeat_encoder = 1,
  dense_dropout = 0.4,
  recurrent_dropout = 0.4,
  encoder_dropout = 0.1,
  optimizer = "adam"
)

Arguments

ml_framework

string Framework to use for training and inference. ml_framework="tensorflow" for 'tensorflow' and ml_framework="pytorch" for 'pytorch'

name

string Name of the new classifier. Please refer to common name conventions. Free text can be used with parameter label.

label

string Label for the new classifier. Here you can use free text.

text_embeddings

An object of class EmbeddedText or LargeDataSetForTextEmbeddings.

feature_extractor

Object of class TEFeatureExtractor which should be used in order to reduce the number of dimensions of the text embeddings. If no feature extractor should be applied set NULL.

target_levels

vector containing the levels (categories or classes) within the target data. Please not that order matters. For ordinal data please ensure that the levels are sorted correctly with later levels indicating a higher category/class. For nominal data the order does not matter.

dense_size

int Number of neurons for each dense layer.

dense_layers

int Number of dense layers.

rec_size

int Number of neurons for each recurrent layer.

rec_layers

int Number of recurrent layers.

rec_type

string Type of the recurrent layers. rec_type="gru" for Gated Recurrent Unit and rec_type="lstm" for Long Short-Term Memory.

rec_bidirectional

bool If TRUE a bidirectional version of the recurrent layers is used.

self_attention_heads

int determining the number of attention heads for a self-attention layer. Only relevant if attention_type="multihead"

intermediate_size

int determining the size of the projection layer within a each transformer encoder.

attention_type

string Choose the relevant attention type. Possible values are fourier and multihead. Please note that you may see different values for a case for different input orders if you choose fourier on linux.

add_pos_embedding

bool TRUE if positional embedding should be used.

rec_dropout

int ranging between 0 and lower 1, determining the dropout between bidirectional recurrent layers.

repeat_encoder

int determining how many times the encoder should be added to the network.

dense_dropout

int ranging between 0 and lower 1, determining the dropout between dense layers.

recurrent_dropout

int ranging between 0 and lower 1, determining the recurrent dropout for each recurrent layer. Only relevant for keras models.

encoder_dropout

int ranging between 0 and lower 1, determining the dropout for the dense projection within the encoder layers.

optimizer

string "adam" or "rmsprop" .

Returns

Returns an object of class TEClassifierRegular which is ready for training.

---

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

TEClassifierRegular$train(
  data_embeddings,
  data_targets,
  data_folds = 5,
  data_val_size = 0.25,
  balance_class_weights = TRUE,
  balance_sequence_length = TRUE,
  use_sc = TRUE,
  sc_method = "dbsmote",
  sc_min_k = 1,
  sc_max_k = 10,
  use_pl = TRUE,
  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,
  dir_checkpoint,
  trace = TRUE,
  ml_trace = 1,
  log_dir = NULL,
  log_write_interval = 10,
  n_cores = auto_n_cores()
)

Arguments

data_embeddings

Object of class EmbeddedText or LargeDataSetForTextEmbeddings.

data_targets

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

data_folds

int determining the number of cross-fold samples.

data_val_size

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

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.

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.

use_sc

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

sc_method

vector containing the method for generating synthetic cases. Possible are sc_method="adas", sc_method="smote", and sc_method="dbsmote".

sc_min_k

int determining the minimal number of k which is used for creating synthetic units.

sc_max_k

int determining the maximal number of k which is used for creating synthetic units.

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.

pl_max

double between 0 and 1, setting the maximal level of confidence for considering a case for pseudo-labeling.

pl_anchor

double between 0 and 1 indicating the reference point for sorting the new cases of every label. See notes for more details.

pl_min

double between 0 and 1, setting the minimal level of confidence for considering a case for pseudo-labeling.

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.

sustain_region

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

sustain_interval

int Interval in seconds for measuring power usage.

epochs

int Number of training epochs.

batch_size

int Size of the batches for training.

dir_checkpoint

string Path to the directory where the checkpoint during training should be saved. If the directory does not exist, it is created.

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.

log_dir

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

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.

n_cores

int Number of cores which should be used during the calculation of synthetic cases. Only relevant if use_sc=TRUE.

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.

---

Method predict()

Method for predicting new data with a trained neural net.

Usage

TEClassifierRegular$predict(newdata, batch_size = 32, ml_trace = 1)

Arguments

newdata

Object of class TextEmbeddingModel or LargeDataSetForTextEmbeddings for which predictions should be made. In addition, this method allows to use objects of class array and datasets.arrow_dataset.Dataset. However, these should be used only by developers.

batch_size

int Size of batches.

ml_trace

int ml_trace=0 does not print any information on the process from the machine learning framework.

Returns

Returns a data.frame containing the predictions and the probabilities of the different labels for each case.

---

Method check_embedding_model()

Method for checking if the provided text embeddings are created with the same TextEmbeddingModel as the classifier.

Usage

TEClassifierRegular$check_embedding_model(
  text_embeddings,
  require_compressed = FALSE
)

Arguments

text_embeddings

Object of class EmbeddedText or LargeDataSetForTextEmbeddings.

require_compressed

TRUE if a compressed version of the embeddings are necessary. Compressed embeddings are created by an object of class TEFeatureExtractor.

Returns

TRUE if the underlying TextEmbeddingModel is the same. FALSE if the models differ.

---

Method check_feature_extractor_object_type()

Method for checking an object of class TEFeatureExtractor.

Usage

TEClassifierRegular$check_feature_extractor_object_type(feature_extractor)

Arguments

feature_extractor

Object of class TEFeatureExtractor

Returns

This method does nothing returns. It raises an error if

• the object is NULL

• the object does not rely on the same machine learning framework as the classifier

• the object is not trained.

---

Method requires_compression()

Method for checking if provided text embeddings must be compressed via a TEFeatureExtractor before processing.

Usage

TEClassifierRegular$requires_compression(text_embeddings)

Arguments

text_embeddings

Object of class EmbeddedText, LargeDataSetForTextEmbeddings, array or datasets.arrow_dataset.Dataset.

Returns

Return TRUE if a compression is necessary and FALSE if not.

---

Method save()

Method for saving a model.

Usage

TEClassifierRegular$save(dir_path, folder_name)

Arguments

dir_path

string Path of the directory where the model should be saved.

folder_name

string Name of the folder that should be created within the directory.

Returns

Function does not return a value. It saves the model to disk.

---

Method load_from_disk()

loads an object from disk and updates the object to the current version of the package.

Usage

TEClassifierRegular$load_from_disk(dir_path)

Arguments

dir_path

Path where the object set is stored.

Returns

Method does not return anything. It loads an object from disk.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TEClassifierRegular$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other Classification: TEClassifierProtoNet