Package 'aifeducation'

Description

This function is designed for taking the output of summarize_args_for_long_task as input. It adds the missing arguments. In general these are arguments that rely on objects of class R6 which can not be exported to a new R session.

Usage

add_missing_args(args, path_args, meta_args)

Arguments

Value

Returns a named list of all arguments that a method of a specific class requires.

See Also

Other Utils Studio Developers: create_data_embeddings_description(), long_load_target_data(), summarize_args_for_long_task()

Description

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.

Super class

aifeducation::AIFEMaster -> AIFEBaseModel

Methods

Public methods

• AIFEBaseModel$count_parameter()

• AIFEBaseModel$clone()

Method count_parameter()

Method for counting the trainable parameters of a model.

Usage

AIFEBaseModel$count_parameter()

Returns

Returns the number of trainable parameters of the model.

Method clone()

The objects of this class are cloneable with this method.

Usage

AIFEBaseModel$clone(deep = FALSE)

Arguments

See Also

Other R6 Classes for Developers: AIFEMaster, BaseModelCore, ClassifiersBasedOnTextEmbeddings, DataManagerClassifier, LargeDataSetBase, ModelsBasedOnTextEmbeddings, TEClassifiersBasedOnProtoNet, TEClassifiersBasedOnRegular, TokenizerBase

Description

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.

Public fields

Methods

Public methods

• AIFEMaster$get_model_info()

• AIFEMaster$set_publication_info()

• AIFEMaster$get_publication_info()

• AIFEMaster$set_model_license()

• AIFEMaster$get_model_license()

• AIFEMaster$set_documentation_license()

• AIFEMaster$get_documentation_license()

• AIFEMaster$set_model_description()

• AIFEMaster$get_model_description()

• AIFEMaster$get_package_versions()

• AIFEMaster$get_sustainability_data()

• AIFEMaster$get_ml_framework()

• AIFEMaster$is_configured()

• AIFEMaster$is_trained()

• AIFEMaster$get_private()

• AIFEMaster$get_all_fields()

• AIFEMaster$get_model_config()

• AIFEMaster$clone()

Method get_model_info()

Method for requesting the model information.

Usage

AIFEMaster$get_model_info()

Returns

list of all relevant model information.

Method set_publication_info()

Method for setting publication information of the model.

Usage

AIFEMaster$set_publication_info(authors, citation, url = NULL)

Arguments

Returns

Function does not return a value. It is used for setting the private members for publication information.

Method get_publication_info()

Method for requesting the bibliographic information of the model.

Usage

AIFEMaster$get_publication_info()

Returns

list with all saved bibliographic information.

Method set_model_license()

Method for setting the license of the model.

Usage

AIFEMaster$set_model_license(license = "CC BY")

Arguments

Returns

Function does not return a value. It is used for setting the private member for the software license of the model.

Method get_model_license()

Method for getting the license of the model.

Usage

AIFEMaster$get_model_license()

Arguments

Returns

string representing the license for the model.

Method set_documentation_license()

Method for setting the license of the model's documentation.

Usage

AIFEMaster$set_documentation_license(license = "CC BY")

Arguments

Returns

Function does not return a value. It is used for setting the private member for the documentation license of the model.

Method get_documentation_license()

Method for getting the license of the model's documentation.

Usage

AIFEMaster$get_documentation_license()

Arguments

Returns

Returns the license as a string.

Method set_model_description()

Method for setting a description of the model.

Usage

AIFEMaster$set_model_description(
  eng = NULL,
  native = NULL,
  abstract_eng = NULL,
  abstract_native = NULL,
  keywords_eng = NULL,
  keywords_native = NULL
)

Arguments

Returns

Function does not return a value. It is used for setting the private members for the description of the model.

Method get_model_description()

Method for requesting the model description.

Usage

AIFEMaster$get_model_description()

Returns

list with the description of the classifier in English and the native language.

Method get_package_versions()

Method for requesting a summary of the R and python packages' versions used for creating the model.

Usage

AIFEMaster$get_package_versions()

Returns

Returns a list containing the versions of the relevant R and python packages.

Method get_sustainability_data()

Method for requesting a summary of tracked energy consumption during training and an estimate of the resulting CO2 equivalents in kg.

Usage

AIFEMaster$get_sustainability_data(track_mode = "training")

Arguments

Returns

Returns a list containing the tracked energy consumption, CO2 equivalents in kg, information on the tracker used, and technical information on the training infrastructure.

Method get_ml_framework()

Method for requesting the machine learning framework used for the model.

Usage

AIFEMaster$get_ml_framework()

Returns

Returns a string describing the machine learning framework used for the classifier.

Method is_configured()

Method for checking if the model was successfully configured. An object can only be used if this value is TRUE.

Usage

AIFEMaster$is_configured()

Returns

bool TRUE if the model is fully configured. FALSE if not.

Method is_trained()

Check if the TEFeatureExtractor is trained.

Usage

AIFEMaster$is_trained()

Returns

Returns TRUE if the object is trained and FALSE if not.

Method get_private()

Method for requesting all private fields and methods. Used for loading and updating an object.

Usage

AIFEMaster$get_private()

Returns

Returns a list with all private fields and methods.

Method get_all_fields()

Return all fields.

Usage

AIFEMaster$get_all_fields()

Returns

Method returns a list containing all public and private fields of the object.

Method get_model_config()

Method for requesting the model configuration.

Usage

AIFEMaster$get_model_config()

Returns

Returns a list with all configuration parameters used during configuration.

Method clone()

The objects of this class are cloneable with this method.

Usage

AIFEMaster$clone(deep = FALSE)

Arguments

See Also

Other R6 Classes for Developers: AIFEBaseModel, BaseModelCore, ClassifiersBasedOnTextEmbeddings, DataManagerClassifier, LargeDataSetBase, ModelsBasedOnTextEmbeddings, TEClassifiersBasedOnProtoNet, TEClassifiersBasedOnRegular, TokenizerBase

Description

Function for getting the number of cores that should be used for parallel processing of tasks. The number of cores is set to 75 % of the available cores. If the environment variable CI is set to "true" or if the process is running on cran 2 is returned.

Usage

auto_n_cores()

Value

Returns int as the number of cores.

See Also

Other Utils Developers: create_object(), create_synthetic_units_from_matrix(), generate_id(), get_n_chunks(), get_synthetic_cases_from_matrix(), get_time_stamp(), matrix_to_array_c(), tensor_to_matrix_c(), to_categorical_c()

Description

Represents models based on BERT.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::BaseModelCore -> BaseModelBert

Methods

Public methods

• BaseModelBert$configure()

• BaseModelBert$clone()

Method configure()

Configures a new object of this class. Please ensure that your chosen configuration comply with the following guidelines:

• hidden_size is a multiple of num_attention_heads.

Usage

BaseModelBert$configure(
  tokenizer,
  max_position_embeddings = 512L,
  hidden_size = 768L,
  num_hidden_layers = 12L,
  num_attention_heads = 12L,
  intermediate_size = 3072L,
  hidden_act = "GELU",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1
)

Arguments

Returns

Does nothing return.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelBert$clone(deep = FALSE)

Arguments

References

Devlin, J., Chang, M.‑W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North (pp. 4171–4186). Association for Computational Linguistics. doi:10.18653/v1/N19-1423

See Also

Other Base Model: BaseModelDebertaV2, BaseModelFunnel, BaseModelMPNet, BaseModelModernBert, BaseModelRoberta

Description

This class contains all methods shared by all BaseModels.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> BaseModelCore

Public fields

Methods

Public methods

• BaseModelCore$create_from_hf()

• BaseModelCore$train()

• BaseModelCore$count_parameter()

• BaseModelCore$plot_training_history()

• BaseModelCore$get_special_tokens()

• BaseModelCore$get_tokenizer_statistics()

• BaseModelCore$fill_mask()

• BaseModelCore$save()

• BaseModelCore$load_from_disk()

• BaseModelCore$get_model()

• BaseModelCore$get_model_type()

• BaseModelCore$get_final_size()

• BaseModelCore$get_n_layers()

• BaseModelCore$get_flops_estimates()

• BaseModelCore$set_publication_info()

• BaseModelCore$estimate_sustainability_inference_fill_mask()

• BaseModelCore$calc_flops_architecture_based()

• BaseModelCore$clone()

Method create_from_hf()

Creates BaseModel from a pretrained model

Usage

BaseModelCore$create_from_hf(model_dir = NULL, tokenizer_dir = NULL)

Arguments

Returns

Does return a new object of this class.

Method train()

Traines a BaseModel

Usage

BaseModelCore$train(
  text_dataset,
  p_mask = 0.15,
  whole_word = TRUE,
  val_size = 0.1,
  n_epoch = 1L,
  batch_size = 12L,
  max_sequence_length = 250L,
  full_sequences_only = FALSE,
  min_seq_len = 50L,
  learning_rate = 0.003,
  sustain_track = FALSE,
  sustain_iso_code = NULL,
  sustain_region = NULL,
  sustain_interval = 15L,
  sustain_log_level = "warning",
  trace = TRUE,
  pytorch_trace = 1L,
  log_dir = NULL,
  log_write_interval = 2L
)

Arguments

Returns

Does nothing return.

Method count_parameter()

Method for counting the trainable parameters of a model.

Usage

BaseModelCore$count_parameter()

Returns

Returns the number of trainable parameters of the model.

Method plot_training_history()

Method for requesting a plot of the training history. This method requires the R package 'ggplot2' to work.

Usage

BaseModelCore$plot_training_history(
  x_min = NULL,
  x_max = NULL,
  y_min = NULL,
  y_max = NULL,
  ind_best_model = TRUE,
  text_size = 10L
)

Arguments

Returns

Returns a plot of class ggplot visualizing the training process.

Method get_special_tokens()

Method for receiving the special tokens of the model

Usage

BaseModelCore$get_special_tokens()

Returns

Returns a matrix containing the special tokens in the rows and their type, token, and id in the columns.

Method get_tokenizer_statistics()

Tokenizer statistics

Usage

BaseModelCore$get_tokenizer_statistics()

Returns

Returns a data.frame containing the tokenizer's statistics.

Method fill_mask()

Method for calculating tokens behind mask tokens.

Usage

BaseModelCore$fill_mask(masked_text, n_solutions = 5L)

Arguments

Returns

Returns a list containing a data.frame for every mask. The data.frame contains the solutions in the rows and reports the score, token id, and token string in the columns.

Method save()

Method for saving a model on disk.

Usage

BaseModelCore$save(dir_path, folder_name)

Arguments

Returns

Function does nothing return. It is used to save an object on disk.

Method load_from_disk()

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

Usage

BaseModelCore$load_from_disk(dir_path)

Arguments

Returns

Function does nothin return. It loads an object from disk.

Method get_model()

Get 'PyTorch' model

Usage

BaseModelCore$get_model()

Returns

Returns the underlying 'PyTorch' model.

Method get_model_type()

Type of the underlying model.

Usage

BaseModelCore$get_model_type()

Returns

Returns a string describing the model's architecture.

Method get_final_size()

Size of the final layer.

Usage

BaseModelCore$get_final_size()

Returns

Returns an int describing the number of dimensions of the last hidden layer.

Method get_n_layers()

Number of layers.

Usage

BaseModelCore$get_n_layers()

Returns

Returns an int describing the number of layers available for embedding.

Method get_flops_estimates()

Flop estimates

Usage

BaseModelCore$get_flops_estimates()

Returns

Returns a data.frame containing statistics about the flops.

Method set_publication_info()

Method for setting the bibliographic information of the model.

Usage

BaseModelCore$set_publication_info(type, authors, citation, url = NULL)

Arguments

Returns

Function does not return a value. It is used to set the private members for publication information of the model.

Method estimate_sustainability_inference_fill_mask()

Calculates the energy consumption for inference of the given task.

Usage

BaseModelCore$estimate_sustainability_inference_fill_mask(
  text_dataset = NULL,
  n_samples = NULL,
  sustain_iso_code = NULL,
  sustain_region = NULL,
  sustain_interval = 15L,
  sustain_log_level = "warning",
  trace = TRUE
)

Arguments

Returns

Returns nothing. Method saves the statistics internally. The statistics can be accessed with the method get_sustainability_data("inference")

Method calc_flops_architecture_based()

Calculates FLOPS based on model's architecture.

Usage

BaseModelCore$calc_flops_architecture_based(batch_size, n_batches, n_epoch)

Arguments

Returns

Returns a data.frame storing the estimates.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelCore$clone(deep = FALSE)

Arguments

See Also

Other R6 Classes for Developers: AIFEBaseModel, AIFEMaster, ClassifiersBasedOnTextEmbeddings, DataManagerClassifier, LargeDataSetBase, ModelsBasedOnTextEmbeddings, TEClassifiersBasedOnProtoNet, TEClassifiersBasedOnRegular, TokenizerBase

Description

Represents models based on DeBERTa version 2.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::BaseModelCore -> BaseModelDebertaV2

Methods

Public methods

• BaseModelDebertaV2$configure()

• BaseModelDebertaV2$clone()

Method configure()

Configures a new object of this class. Please ensure that your chosen configuration comply with the following guidelines:

• hidden_size is a multiple of num_attention_heads.

Usage

BaseModelDebertaV2$configure(
  tokenizer,
  max_position_embeddings = 512L,
  hidden_size = 768L,
  num_hidden_layers = 12L,
  num_attention_heads = 12L,
  intermediate_size = 3072L,
  hidden_act = "GELU",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1
)

Arguments

Returns

Does nothing return.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelDebertaV2$clone(deep = FALSE)

Arguments

References

He, P., Liu, X., Gao, J. & Chen, W. (2020). DeBERTa: Decoding-enhanced BERT with Disentangled Attention. doi:10.48550/arXiv.2006.03654

See Also

Other Base Model: BaseModelBert, BaseModelFunnel, BaseModelMPNet, BaseModelModernBert, BaseModelRoberta

Description

Represents models based on the Funnel-Transformer.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::BaseModelCore -> BaseModelFunnel

Methods

Public methods

• BaseModelFunnel$configure()

• BaseModelFunnel$get_n_layers()

• BaseModelFunnel$clone()

Method configure()

Configures a new object of this class. Please ensure that your chosen configuration comply with the following guidelines:

• hidden_size is a multiple of num_attention_heads.

Usage

BaseModelFunnel$configure(
  tokenizer,
  max_position_embeddings = 512L,
  hidden_size = 768L,
  block_sizes = c(4L, 4L, 4L),
  num_attention_heads = 12L,
  intermediate_size = 3072L,
  num_decoder_layers = 2L,
  d_head = 64L,
  funnel_pooling_type = "Mean",
  hidden_act = "GELU",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1,
  activation_dropout = 0
)

Arguments

Returns

Does nothing return.

Method get_n_layers()

Number of layers.

Usage

BaseModelFunnel$get_n_layers()

Returns

Returns an int describing the number of layers available for embedding.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelFunnel$clone(deep = FALSE)

Arguments

References

Dai, Z., Lai, G., Yang, Y. & Le, Q. V. (2020). Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. doi:10.48550/arXiv.2006.03236

See Also

Other Base Model: BaseModelBert, BaseModelDebertaV2, BaseModelMPNet, BaseModelModernBert, BaseModelRoberta

Description

Represents models based on Modern Bert.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::BaseModelCore -> BaseModelModernBert

Methods

Public methods

• BaseModelModernBert$configure()

• BaseModelModernBert$clone()

Method configure()

Configures a new object of this class. Please ensure that your chosen configuration comply with the following guidelines:

• hidden_size is a multiple of num_attention_heads.

• hidden_size/num_attention_heads must be a multiple of 2.

• global_attn_every_n_layers is equal or smaller as num_hidden_layers.

Usage

BaseModelModernBert$configure(
  tokenizer,
  max_position_embeddings = 512L,
  hidden_size = 768L,
  num_hidden_layers = 12L,
  num_attention_heads = 12L,
  global_attn_every_n_layers = 3L,
  intermediate_size = 3072L,
  hidden_activation = "GELU",
  embedding_dropout = 0.1,
  mlp_dropout = 0.1,
  attention_dropout = 0.1
)

Arguments

Returns

Does nothing return.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelModernBert$clone(deep = FALSE)

Arguments

References

Devlin, J., Chang, M.‑W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North (pp. 4171–4186). Association for Computational Linguistics. doi:10.18653/v1/N19-1423

See Also

Other Base Model: BaseModelBert, BaseModelDebertaV2, BaseModelFunnel, BaseModelMPNet, BaseModelRoberta

Description

Represents models based on MPNet.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::BaseModelCore -> BaseModelMPNet

Methods

Public methods

• BaseModelMPNet$configure()

• BaseModelMPNet$train()

• BaseModelMPNet$clone()

Method configure()

Configures a new object of this class. Please ensure that your chosen configuration comply with the following guidelines:

• hidden_size is a multiple of num_attention_heads.

Usage

BaseModelMPNet$configure(
  tokenizer,
  max_position_embeddings = 512L,
  hidden_size = 768L,
  num_hidden_layers = 12L,
  num_attention_heads = 12L,
  intermediate_size = 3072L,
  hidden_act = "GELU",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1
)

Arguments

Returns

Does nothing return.

Method train()

Traines a BaseModel

Usage

BaseModelMPNet$train(
  text_dataset,
  p_mask = 0.15,
  p_perm = 0.15,
  whole_word = TRUE,
  val_size = 0.1,
  n_epoch = 1L,
  batch_size = 12L,
  max_sequence_length = 250L,
  full_sequences_only = FALSE,
  min_seq_len = 50L,
  learning_rate = 0.003,
  sustain_track = FALSE,
  sustain_iso_code = NULL,
  sustain_region = NULL,
  sustain_interval = 15L,
  sustain_log_level = "warning",
  trace = TRUE,
  pytorch_trace = 1L,
  log_dir = NULL,
  log_write_interval = 2L
)

Arguments

Returns

Does nothing return.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelMPNet$clone(deep = FALSE)

Arguments

References

Song,K., Tan, X., Qin, T., Lu, J. & Liu, T.-Y. (2020). MPNet: Masked and Permuted Pre-training for Language Understanding. doi:10.48550/arXiv.2004.09297

See Also

Other Base Model: BaseModelBert, BaseModelDebertaV2, BaseModelFunnel, BaseModelModernBert, BaseModelRoberta

Description

Represents models based on RoBERTa.

Value

Does return a new object of this class.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::BaseModelCore -> BaseModelRoberta

Methods

Public methods

• BaseModelRoberta$configure()

• BaseModelRoberta$clone()

Method configure()

Configures a new object of this class. Please ensure that your chosen configuration comply with the following guidelines:

• hidden_size is a multiple of num_attention_heads.

Usage

BaseModelRoberta$configure(
  tokenizer,
  max_position_embeddings = 512L,
  hidden_size = 768L,
  num_hidden_layers = 12L,
  num_attention_heads = 12L,
  intermediate_size = 3072L,
  hidden_act = "GELU",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1
)

Arguments

Returns

Does nothing return.

Method clone()

The objects of this class are cloneable with this method.

Usage

BaseModelRoberta$clone(deep = FALSE)

Arguments

References

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. doi:10.48550/arXiv.1907.11692

See Also

Other Base Model: BaseModelBert, BaseModelDebertaV2, BaseModelFunnel, BaseModelMPNet, BaseModelModernBert

Description

Named list containing all BaseModels as a string.

Usage

BaseModelsIndex

Format

An object of class list of length 6.

See Also

Other Parameter Dictionary: DataSetsIndex, TokenizerIndex, doc_formula(), get_TEClassifiers_class_names(), get_called_args(), get_depr_obj_names(), get_magnitude_values(), get_param_def(), get_param_dict(), get_param_doc_desc()

Description

Function for generating the documentation of a model.

Usage

build_documentation_for_model(
  model_name,
  cls_type = NULL,
  core_type = NULL,
  input_type = "text_embeddings"
)

Arguments

Value

Returns a string containing the description written in rmarkdown.

Note

Function is designed to be used with roxygen2 in the regular documentation.

See Also

Other Utils Documentation: build_layer_stack_documentation_for_vignette(), get_desc_for_core_model_architecture(), get_dict_cls_type(), get_dict_core_models(), get_dict_input_types(), get_layer_dict(), get_layer_documentation(), get_parameter_documentation()

Description

Function for generating the whole documentation for an article used on the package's home page.

Usage

build_layer_stack_documentation_for_vignette()

Value

Returns a string containing the description written in rmarkdown.

Note

Function is designed to be used with inline r code in rmarkdown vignettes/articles.

See Also

Other Utils Documentation: build_documentation_for_model(), get_desc_for_core_model_architecture(), get_dict_cls_type(), get_dict_core_models(), get_dict_input_types(), get_layer_dict(), get_layer_documentation(), get_parameter_documentation()

Description

Function for calculating recall, precision, and f1-scores.

Usage

calc_standard_classification_measures(true_values, predicted_values)

Arguments

Value

Returns a matrix which contains the cases categories in the rows and the measures (precision, recall, f1) in the columns.

See Also

Other performance measures: cohens_kappa(), fleiss_kappa(), get_coder_metrics(), gwet_ac(), kendalls_w(), kripp_alpha()

Description

Function for estimating the tokenizer statistics described by Kaya & Tantuğ (2024).

Usage

calc_tokenizer_statistics(
  dataset,
  step = "creation",
  statistics_max_tokens_length = 512L
)

Arguments

Value

Returns a list with the following entries:

• n_sequences: Number of sequences

• n_words: Number for words in whole corpus

• n_tokens: Number of tokens in the whole corpus

• mu_t: eqn(n_tokens/n_sequences)

• mu_w: eqn(n_words/n_sequences)

• mu_g: eqn(n_tokens/n_words)

References

Kaya, Y. B., & Tantuğ, A. C. (2024). Effect of tokenization granularity for Turkish large language models. Intelligent Systems with Applications, 21, 200335. https://doi.org/10.1016/j.iswa.2024.200335

Description

Prints a message msg if trace parameter is TRUE with current date with cat() function.

Usage

cat_message(msg, trace)

Arguments

Value

This function returns nothing.

See Also

Other Utils Log Developers: clean_pytorch_log_transformers(), output_message(), print_message(), read_log(), read_loss_log(), reset_log(), reset_loss_log(), write_log()

Description

Depending on the test environment, the function adjusts the number of samples. For continuous integration, it is limited to a random sample of combinations. The same applies if CUDA is unavailable.

Usage

check_adjust_n_samples_on_CI(n_samples_requested, n_CI = 50L)

Arguments

Value

Returns an int depending on the test environment.

See Also

Other Utils TestThat Developers: generate_args_for_tests(), generate_embeddings(), generate_tensors(), get_current_args_for_print(), get_fixed_test_tensor(), get_test_data_for_classifiers(), monitor_test_time_on_CI(), random_bool_on_CI()

Description

This function checks if all python modules necessary for the package 'aifeducation' to work are available.

Usage

check_aif_py_modules(trace = TRUE)

Arguments

Value

The function prints a table with all relevant packages and shows which modules are available or unavailable.

If all relevant modules are available, the functions returns TRUE. In all other cases it returns FALSE

See Also

Other Installation and Configuration: get_recommended_py_versions(), install_aifeducation(), install_aifeducation_studio(), install_py_modules(), prepare_session(), set_transformers_logger(), update_aifeducation()

Description

This function performs checks for every provided argument. It can only check arguments that are defined in the central parameter dictionary. See get_param_dict for more details.

Usage

check_all_args(args)

Arguments

Value

Function does nothing return. It raises an error the arguments are not valid.

See Also

Other Utils Checks Developers: check_class_and_type()

Description

Function for checking if an object is of a specific type or class.

Usage

check_class_and_type(
  object,
  object_name = NULL,
  type_classes = "bool",
  allow_NULL = FALSE,
  min = NULL,
  max = NULL,
  allowed_values = NULL
)

Arguments

Value

Function does nothing return. It raises an error if the object is not of the specified type.

Note

parameter min, max, and allowed_values do not apply if type_classes is a class.

allowed_values does only apply if type_classes is string.

See Also

Other Utils Checks Developers: check_all_args()

Description

Function converts a vector of class indices into an arrow data set.

Usage

class_vector_to_py_dataset(vector)

Arguments

Value

Returns a data set of class datasets.arrow_dataset.Dataset containing the class indices.

See Also

Other Utils Python Data Management Developers: create_py_dataset_cache_file_path(), data.frame_to_py_dataset(), extract_column_from_py_dataset(), get_batches_index(), prepare_r_array_for_dataset(), py_dataset_to_embeddings(), reduce_to_unique(), tensor_list_to_numpy(), tensor_to_numpy()

Description

Base class for classifiers relying on EmbeddedText or LargeDataSetForTextEmbeddings generated with a TextEmbeddingModel.

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.

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::ModelsBasedOnTextEmbeddings -> ClassifiersBasedOnTextEmbeddings

Public fields

Methods

Public methods

• ClassifiersBasedOnTextEmbeddings$predict()

• ClassifiersBasedOnTextEmbeddings$check_embedding_model()

• ClassifiersBasedOnTextEmbeddings$check_feature_extractor_object_type()

• ClassifiersBasedOnTextEmbeddings$requires_compression()

• ClassifiersBasedOnTextEmbeddings$save()

• ClassifiersBasedOnTextEmbeddings$load_from_disk()

• ClassifiersBasedOnTextEmbeddings$adjust_target_levels()

• ClassifiersBasedOnTextEmbeddings$plot_training_history()

• ClassifiersBasedOnTextEmbeddings$plot_coding_stream()

• ClassifiersBasedOnTextEmbeddings$clone()

Method predict()

Method for predicting new data with a trained neural net.

Usage

ClassifiersBasedOnTextEmbeddings$predict(
  newdata,
  batch_size = 32L,
  ml_trace = 1L
)

Arguments

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

ClassifiersBasedOnTextEmbeddings$check_embedding_model(
  text_embeddings,
  require_compressed = FALSE
)

Arguments

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

ClassifiersBasedOnTextEmbeddings$check_feature_extractor_object_type(
  feature_extractor
)

Arguments

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

ClassifiersBasedOnTextEmbeddings$requires_compression(text_embeddings)

Arguments

Returns

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

Method save()

Method for saving a model.

Usage

ClassifiersBasedOnTextEmbeddings$save(dir_path, folder_name)

Arguments

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

ClassifiersBasedOnTextEmbeddings$load_from_disk(dir_path)

Arguments

Returns

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

Method adjust_target_levels()

Method transforms the levels of a factor into numbers corresponding to the models definition.

Usage

ClassifiersBasedOnTextEmbeddings$adjust_target_levels(data_targets)

Arguments

Returns

Method returns a factor containing the numerical representation of categories/classes.

Method plot_training_history()

Method for requesting a plot of the training history. This method requires the R package 'ggplot2' to work.

Usage

ClassifiersBasedOnTextEmbeddings$plot_training_history(
  final_training = FALSE,
  pl_step = NULL,
  measure = "loss",
  ind_best_model = TRUE,
  ind_selected_model = TRUE,
  x_min = NULL,
  x_max = NULL,
  y_min = NULL,
  y_max = NULL,
  add_min_max = TRUE,
  text_size = 10L
)

Arguments

Returns

Returns a plot of class ggplot visualizing the training process.

Method plot_coding_stream()

Method for requesting a plot the coding stream. The plot shows how the cases of different categories/classes are assigned to a the available classes/categories. The visualization is helpful for analyzing the consequences of coding errors.

Usage

ClassifiersBasedOnTextEmbeddings$plot_coding_stream(
  label_categories_size = 3L,
  key_size = 0.5,
  text_size = 10L
)

Arguments

Returns

Returns a plot of class ggplot visualizing the training process.

Method clone()

The objects of this class are cloneable with this method.

Usage

ClassifiersBasedOnTextEmbeddings$clone(deep = FALSE)

Arguments

See Also

Other R6 Classes for Developers: AIFEBaseModel, AIFEMaster, BaseModelCore, DataManagerClassifier, LargeDataSetBase, ModelsBasedOnTextEmbeddings, TEClassifiersBasedOnProtoNet, TEClassifiersBasedOnRegular, TokenizerBase

Description

Function for preparing and cleaning the log created by an object of class Trainer from the python library 'transformer's.

Usage

clean_pytorch_log_transformers(log)

Arguments

Value

Returns a data.frame containing epochs, loss, and val_loss.

See Also

Other Utils Log Developers: cat_message(), output_message(), print_message(), read_log(), read_loss_log(), reset_log(), reset_loss_log(), write_log()

Description

This function calculates different version of Cohen's Kappa.

Usage

cohens_kappa(rater_one, rater_two)

Arguments

Value

Returns a list containing the results for Cohen' Kappa if no weights are applied (kappa_unweighted), if weights are applied and the weights increase linear (kappa_linear), and if weights are applied and the weights increase quadratic (kappa_squared).

References

Cohen, J (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220. https://doi.org/10.1037/h0026256

Cohen, J (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104

See Also

Other performance measures: calc_standard_classification_measures(), fleiss_kappa(), get_coder_metrics(), gwet_ac(), kendalls_w(), kripp_alpha()

Description

Check whether the passed dir_path directory exists. If not, creates a new directory and prints a msg message if trace is TRUE.

Usage

create_dir(dir_path, trace, msg = "Creating Directory", msg_fun = TRUE)

Arguments

Value

TRUE or FALSE depending on whether the shiny app is active.

See Also

Other Utils File Management Developers: get_file_extension()

Description

Support function for creating objects.

Usage

create_object(class)

Arguments

Value

Returns an object of the requested class.

See Also

Other Utils Developers: auto_n_cores(), create_synthetic_units_from_matrix(), generate_id(), get_n_chunks(), get_synthetic_cases_from_matrix(), get_time_stamp(), matrix_to_array_c(), tensor_to_matrix_c(), to_categorical_c()

Description

Function creates a valid file path for the argument cache_file_name of classes "datasets.arrow_dataset.Dataset" from the python library 'datasets'. The aim of the function is to ensure compatibility between different versions of 'datasets'.

Usage

create_py_dataset_cache_file_path(file_path)

Arguments

Value

Returns a file path as string.

See Also

Other Utils Python Data Management Developers: class_vector_to_py_dataset(), data.frame_to_py_dataset(), extract_column_from_py_dataset(), get_batches_index(), prepare_r_array_for_dataset(), py_dataset_to_embeddings(), reduce_to_unique(), tensor_list_to_numpy(), tensor_to_numpy()

Description

Function for creating synthetic cases in order to balance the data for training with TEClassifierRegular or TEClassifierProtoNet]. This is an auxiliary function for use with get_synthetic_cases_from_matrix to allow parallel computations.

Usage

create_synthetic_units_from_matrix(
  matrix_form,
  target,
  required_cases,
  k,
  method,
  cat
)

Arguments

Value

Returns a list which contains the text embeddings of the new synthetic cases as a named data.frame and their labels as a named factor.

See Also

Other Utils Developers: auto_n_cores(), create_object(), generate_id(), get_n_chunks(), get_synthetic_cases_from_matrix(), get_time_stamp(), matrix_to_array_c(), tensor_to_matrix_c(), to_categorical_c()

Description

Function for converting a data.frame into a pyarrow data set.

Usage

data.frame_to_py_dataset(data_frame)

Arguments

Value

Returns the data.frame as a pyarrow data set of class datasets.arrow_dataset.Dataset.

See Also

Other Utils Python Data Management Developers: class_vector_to_py_dataset(), create_py_dataset_cache_file_path(), extract_column_from_py_dataset(), get_batches_index(), prepare_r_array_for_dataset(), py_dataset_to_embeddings(), reduce_to_unique(), tensor_list_to_numpy(), tensor_to_numpy()

Description

Abstract class for managing the data and samples during training a classifier. DataManagerClassifier is used with all classifiers based on text embeddings.

Value

Objects of this class are used for ensuring the correct data management for training different types of classifiers. They are also used for data augmentation by creating synthetic cases with different techniques.

Public fields

Methods

Public methods

• DataManagerClassifier$new()

• DataManagerClassifier$get_config()

• DataManagerClassifier$get_labeled_data()

• DataManagerClassifier$get_unlabeled_data()

• DataManagerClassifier$get_samples()

• DataManagerClassifier$set_state()

• DataManagerClassifier$get_n_folds()

• DataManagerClassifier$get_n_classes()

• DataManagerClassifier$get_statistics()

• DataManagerClassifier$contains_unlabeled_data()

• DataManagerClassifier$get_dataset()

• DataManagerClassifier$get_val_dataset()

• DataManagerClassifier$get_test_dataset()

• DataManagerClassifier$create_synthetic()

• DataManagerClassifier$add_replace_pseudo_data()

• DataManagerClassifier$clone()

Method new()

Creating a new instance of this class.

Usage

DataManagerClassifier$new(
  data_embeddings,
  data_targets,
  class_levels,
  folds = 5L,
  val_size = 0.25,
  pad_value = -100L,
  one_hot_encoding = TRUE,
  add_matrix_map = TRUE,
  sc_methods = "knnor",
  sc_min_k = 1L,
  sc_max_k = 10L,
  trace = TRUE,
  n_cores = auto_n_cores()
)

Arguments

Returns

Method returns an initialized object of class DataManagerClassifier.

Method get_config()

Method for requesting the configuration of the DataManagerClassifier.

Usage

DataManagerClassifier$get_config()

Returns

Returns a list storing the configuration of the DataManagerClassifier.

Method get_labeled_data()

Method for requesting the complete labeled data set.

Usage

DataManagerClassifier$get_labeled_data()

Returns

Returns an object of class datasets.arrow_dataset.Dataset containing all cases with labels.

Method get_unlabeled_data()

Method for requesting the complete unlabeled data set.

Usage

DataManagerClassifier$get_unlabeled_data()

Returns

Returns an object of class datasets.arrow_dataset.Dataset containing all cases without labels.

Method get_samples()

Method for requesting the assignments to train, validation, and test data sets for every fold and the final training.

Usage

DataManagerClassifier$get_samples()

Returns

Returns a list storing the assignments to a train, validation, and test data set for every fold. In the case of the sample for the final training the test data set is always empty (NULL).

Method set_state()

Method for setting the current state of the DataManagerClassifier.

Usage

DataManagerClassifier$set_state(iteration, step = NULL)

Arguments

Returns

Method does not return anything. It is used for setting the internal state of the DataManager.

Method get_n_folds()

Method for requesting the number of folds the DataManagerClassifier can use with the current data.

Usage

DataManagerClassifier$get_n_folds()

Returns

Returns the number of folds the DataManagerClassifier uses.

Method get_n_classes()

Method for requesting the number of classes.

Usage

DataManagerClassifier$get_n_classes()

Returns

Returns the number classes.

Method get_statistics()

Method for requesting descriptive sample statistics.

Usage

DataManagerClassifier$get_statistics()

Returns

Returns a table describing the absolute frequencies of the labeled and unlabeled data. The rows contain the length of the sequences while the columns contain the labels.

Method contains_unlabeled_data()

Method for checking if the dataset contains cases without labels.

Usage

DataManagerClassifier$contains_unlabeled_data()

Returns

Returns TRUE if the dataset contains cases without labels. Returns FALSE if all cases have labels.

Method get_dataset()

Method for requesting a data set for training depending in the current state of the DataManagerClassifier.

Usage

DataManagerClassifier$get_dataset(
  inc_labeled = TRUE,
  inc_unlabeled = FALSE,
  inc_synthetic = FALSE,
  inc_pseudo_data = FALSE
)

Arguments

Returns

Returns an object of class datasets.arrow_dataset.Dataset containing the requested kind of data along with all requested transformations for training. Please note that this method returns a data sets that is designed for training only. The corresponding validation data set is requested with get_val_dataset and the corresponding test data set with get_test_dataset.

Method get_val_dataset()

Method for requesting a data set for validation depending in the current state of the DataManagerClassifier.

Usage

DataManagerClassifier$get_val_dataset()

Returns

Returns an object of class datasets.arrow_dataset.Dataset containing the requested kind of data along with all requested transformations for validation. The corresponding data set for training can be requested with get_dataset and the corresponding data set for testing with get_test_dataset.

Method get_test_dataset()

Method for requesting a data set for testing depending in the current state of the DataManagerClassifier.

Usage

DataManagerClassifier$get_test_dataset()

Returns

Returns an object of class datasets.arrow_dataset.Dataset containing the requested kind of data along with all requested transformations for validation. The corresponding data set for training can be requested with get_dataset and the corresponding data set for validation with get_val_dataset.

Method create_synthetic()

Method for generating synthetic data used during training. The process uses all labeled data belonging to the current state of the DataManagerClassifier.

Usage

DataManagerClassifier$create_synthetic(trace = TRUE, inc_pseudo_data = FALSE)

Arguments

Returns

This method does nothing return. It generates a new data set for synthetic cases which are stored as an object of class datasets.arrow_dataset.Dataset in the field datasets$data_labeled_synthetic. Please note that a call of this method will override an existing data set in the corresponding field.

Method add_replace_pseudo_data()

Method for adding data with pseudo labels generated by a classifier

Usage

DataManagerClassifier$add_replace_pseudo_data(inputs, labels)

Arguments

Returns

This method does nothing return. It generates a new data set for synthetic cases which are stored as an object of class datasets.arrow_dataset.Dataset in the field datasets$data_labeled_pseudo. Please note that a call of this method will override an existing data set in the corresponding field.

Method clone()

The objects of this class are cloneable with this method.

Usage

DataManagerClassifier$clone(deep = FALSE)

Arguments

See Also

Other R6 Classes for Developers: AIFEBaseModel, AIFEMaster, BaseModelCore, ClassifiersBasedOnTextEmbeddings, LargeDataSetBase, ModelsBasedOnTextEmbeddings, TEClassifiersBasedOnProtoNet, TEClassifiersBasedOnRegular, TokenizerBase

Description

Named list containing all available types of data sets as a string.

Usage

DataSetsIndex

Format

An object of class list of length 3.

See Also

Other Parameter Dictionary: BaseModelsIndex, TokenizerIndex, doc_formula(), get_TEClassifiers_class_names(), get_called_args(), get_depr_obj_names(), get_magnitude_values(), get_param_def(), get_param_dict(), get_param_doc_desc()

Description

Object of class R6 which stores the text embeddings generated by an object of class TextEmbeddingModel. The text embeddings are stored within memory/RAM. In the case of a high number of documents the data may not fit into memory/RAM. Thus, please use this object only for a small sample of texts. In general, it is recommended to use an object of class LargeDataSetForTextEmbeddings which can deal with any number of texts.

Value

Returns an object of class EmbeddedText. These objects are used for storing and managing the text embeddings created with objects of class TextEmbeddingModel. Objects of class EmbeddedText serve as input for objects of class TEClassifierRegular, TEClassifierProtoNet, and TEFeatureExtractor. The main aim of this class is to provide a structured link between embedding models and classifiers. Since objects of this class save information on the text embedding model that created the text embedding it ensures that only embedding generated with same embedding model are combined. Furthermore, the stored information allows objects to check if embeddings of the correct text embedding model are used for training and predicting.

Public fields

Methods

Public methods

• EmbeddedText$configure()

• EmbeddedText$save()

• EmbeddedText$is_configured()

• EmbeddedText$load_from_disk()

• EmbeddedText$get_model_info()

• EmbeddedText$get_model_label()

• EmbeddedText$get_times()

• EmbeddedText$get_features()

• EmbeddedText$get_original_features()

• EmbeddedText$get_pad_value()

• EmbeddedText$is_compressed()

• EmbeddedText$add_feature_extractor_info()

• EmbeddedText$get_feature_extractor_info()

• EmbeddedText$convert_to_LargeDataSetForTextEmbeddings()

• EmbeddedText$n_rows()

• EmbeddedText$get_all_fields()

• EmbeddedText$set_package_versions()

• EmbeddedText$get_package_versions()

• EmbeddedText$clone()

Method configure()

Creates a new object representing text embeddings.

Usage

EmbeddedText$configure(
  embeddings,
  model_name = NA,
  model_label = NA,
  model_date = NA,
  model_method = NA,
  model_version = NA,
  model_language = NA,
  param_seq_length = NA,
  param_chunks = NULL,
  param_features = NULL,
  param_overlap = NULL,
  param_emb_layer_min = NULL,
  param_emb_layer_max = NULL,
  param_emb_pool_type = NULL,
  param_aggregation = NULL,
  param_pad_value = -100L
)

Arguments

Returns

Returns an object of class EmbeddedText which stores the text embeddings produced by an objects of class TextEmbeddingModel.

Method save()

Saves a data set to disk.

Usage

EmbeddedText$save(dir_path, folder_name, create_dir = TRUE)

Arguments

Returns

Method does not return anything. It write the data set to disk.

Method is_configured()

Method for checking if the model was successfully configured. An object can only be used if this value is TRUE.

Usage

EmbeddedText$is_configured()

Returns

bool TRUE if the model is fully configured. FALSE if not.

Method load_from_disk()

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

Usage

EmbeddedText$load_from_disk(dir_path)

Arguments

Returns

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

Method get_model_info()

Method for retrieving information about the model that generated this embedding.

Usage

EmbeddedText$get_model_info()

Returns

list contains all saved information about the underlying text embedding model.

Method get_model_label()

Method for retrieving the label of the model that generated this embedding.

Usage

EmbeddedText$get_model_label()

Returns

string Label of the corresponding text embedding model

Method get_times()

Number of chunks/times of the text embeddings.

Usage

EmbeddedText$get_times()

Returns

Returns an int describing the number of chunks/times of the text embeddings.

Method get_features()

Number of actual features/dimensions of the text embeddings.In the case a feature extractor was used the number of features is smaller as the original number of features. To receive the original number of features (the number of features before applying a feature extractor) you can use the method get_original_features of this class.

Usage

EmbeddedText$get_features()

Returns

Returns an int describing the number of features/dimensions of the text embeddings.

Method get_original_features()

Number of original features/dimensions of the text embeddings.

Usage

EmbeddedText$get_original_features()

Returns

Returns an int describing the number of features/dimensions if no feature extractor) is used or before a feature extractor) is applied.

Method get_pad_value()

Value for indicating padding.

Usage

EmbeddedText$get_pad_value()

Returns

Returns an int describing the value used for padding.

Method is_compressed()

Checks if the text embedding were reduced by a feature extractor.

Usage

EmbeddedText$is_compressed()

Returns

Returns TRUE if the number of dimensions was reduced by a feature extractor. If not return FALSE.

Method add_feature_extractor_info()

Method setting information on the feature extractor that was used to reduce the number of dimensions of the text embeddings. This information should only be used if a feature extractor was applied.

Usage

EmbeddedText$add_feature_extractor_info(
  model_name,
  model_label = NA,
  features = NA,
  method = NA,
  noise_factor = NA,
  optimizer = NA
)

Arguments

Returns

Method does nothing return. It sets information on a feature extractor.

Method get_feature_extractor_info()

Method for receiving information on the feature extractor that was used to reduce the number of dimensions of the text embeddings.

Usage

EmbeddedText$get_feature_extractor_info()

Returns

Returns a list with information on the feature extractor. If no feature extractor was used it returns NULL.

Method convert_to_LargeDataSetForTextEmbeddings()

Method for converting this object to an object of class LargeDataSetForTextEmbeddings.

Usage

EmbeddedText$convert_to_LargeDataSetForTextEmbeddings()

Returns

Returns an object of class LargeDataSetForTextEmbeddings which uses memory mapping allowing to work with large data sets.

Method n_rows()

Number of rows.

Usage

EmbeddedText$n_rows()

Returns

Returns the number of rows of the text embeddings which represent the number of cases.

Method get_all_fields()

Return all fields.

Usage

EmbeddedText$get_all_fields()

Returns

Method returns a list containing all public and private fields of the object.

Method set_package_versions()

Method for setting the package version for 'aifeducation', 'reticulate', 'torch', and 'numpy' to the currently used versions.

Usage

EmbeddedText$set_package_versions()

Returns

Method does not return anything. It is used to set the private fields fo package versions.

Method get_package_versions()

Method for requesting a summary of the R and python packages' versions used for creating the model.

Usage

EmbeddedText$get_package_versions()

Returns

Returns a list containing the versions of the relevant R and python packages.

Method clone()

The objects of this class are cloneable with this method.

Usage

EmbeddedText$clone(deep = FALSE)

Arguments

See Also

Other Data Management: LargeDataSetForText, LargeDataSetForTextEmbeddings

Description

Function extracts the content of a column from a python data set in order to allow further operations in R.

Usage

extract_column_from_py_dataset(py_dataset, column_name, format = "R")

Arguments

Value

Returns a vector, matrix or array for format="R". In all other cases the requested format is returned.

See Also

Other Utils Python Data Management Developers: class_vector_to_py_dataset(), create_py_dataset_cache_file_path(), data.frame_to_py_dataset(), get_batches_index(), prepare_r_array_for_dataset(), py_dataset_to_embeddings(), reduce_to_unique(), tensor_list_to_numpy(), tensor_to_numpy()

Description

This function calculates Fleiss' Kappa.

Usage

fleiss_kappa(rater_one, rater_two, additional_raters = NULL)

Arguments

Value

Returns the value for Fleiss' Kappa.

References

Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378–382. https://doi.org/10.1037/h0031619

See Also

Other performance measures: calc_standard_classification_measures(), cohens_kappa(), get_coder_metrics(), gwet_ac(), kendalls_w(), kripp_alpha()

Description

Function generates a specific number of combinations for a method. These are used for automating tests of objects.

Usage

generate_args_for_tests(
  object_name,
  method,
  var_objects = list(),
  necessary_objects = list(),
  var_override = list()
)

Arguments

Value

Returns a list with combinations of arguments.

Note

var_objects, necessary_objects, and var_override the names must exactly match the name of the parameter. Otherwise they are not applied. Names of arguments which are not part a a method are ignored. #'

See Also

Other Utils TestThat Developers: check_adjust_n_samples_on_CI(), generate_embeddings(), generate_tensors(), get_current_args_for_print(), get_fixed_test_tensor(), get_test_data_for_classifiers(), monitor_test_time_on_CI(), random_bool_on_CI()

Description

Functions generates a random test embedding that can be used for testing methods and functions. The embeddings have the shape (Batch, Times,Features).

Usage

generate_embeddings(times, features, seq_len, pad_value)

Arguments

Value

Returns an array with dim ⁠(length(seq_len),times,features)⁠.

Note

To generate a 'PyTorch' object please use generate_tensors.

See Also

Other Utils TestThat Developers: check_adjust_n_samples_on_CI(), generate_args_for_tests(), generate_tensors(), get_current_args_for_print(), get_fixed_test_tensor(), get_test_data_for_classifiers(), monitor_test_time_on_CI(), random_bool_on_CI()

Description

Function for generating an ID suffix for objects of class TextEmbeddingModel, TEClassifierRegular, and TEClassifierProtoNet.

Usage

generate_id(length = 16L)

Arguments

Value

Returns a string of the requested length.

See Also

Other Utils Developers: auto_n_cores(), create_object(), create_synthetic_units_from_matrix(), get_n_chunks(), get_synthetic_cases_from_matrix(), get_time_stamp(), matrix_to_array_c(), tensor_to_matrix_c(), to_categorical_c()

Description

Functions generates a random test tensor that can be used for testing methods and functions based on 'PyTorch'. The tensors have the shape (Batch, Times,Features).

Usage

generate_tensors(times, features, seq_len, pad_value)

Arguments

Value

Returns an object of class Tensor from 'PyTorch'.

Note

To request a R array please use generate_embeddings.

See Also

Other Utils TestThat Developers: check_adjust_n_samples_on_CI(), generate_args_for_tests(), generate_embeddings(), get_current_args_for_print(), get_fixed_test_tensor(), get_test_data_for_classifiers(), monitor_test_time_on_CI(), random_bool_on_CI()

Description

Function for requesting a vector containing the alpha-3 codes for most countries.

Usage

get_alpha_3_codes()

Value

Returns a vector containing the alpha-3 codes for most countries.

See Also

Other Utils Sustainability Developers: summarize_tracked_sustainability()

Description

Function groups cases into batches.

Usage

get_batches_index(number_rows, batch_size, zero_based = FALSE)

Arguments

Value

Returns a list of batches. Each entry in the list contains a vector of int representing the cases belonging to that batch.

See Also

Other Utils Python Data Management Developers: class_vector_to_py_dataset(), create_py_dataset_cache_file_path(), data.frame_to_py_dataset(), extract_column_from_py_dataset(), prepare_r_array_for_dataset(), py_dataset_to_embeddings(), reduce_to_unique(), tensor_list_to_numpy(), tensor_to_numpy()

Description

Function for receiving all arguments that were called by a method or function.

Usage

get_called_args(n = 1L)

Arguments

Value

Returns a named list of all arguments and their values.

See Also

Other Parameter Dictionary: BaseModelsIndex, DataSetsIndex, TokenizerIndex, doc_formula(), get_TEClassifiers_class_names(), get_depr_obj_names(), get_magnitude_values(), get_param_def(), get_param_dict(), get_param_doc_desc()

Description

This function calculates different reliability measures which are based on the empirical research method of content analysis.

Usage

get_coder_metrics(
  true_values = NULL,
  predicted_values = NULL,
  return_names_only = FALSE
)

Arguments

Value

If return_names_only = FALSE returns a vector with the following reliability measures:

• iota_index: Iota Index from the Iota Reliability Concept Version 2.

• min_iota2: Minimal Iota from Iota Reliability Concept Version 2.

• avg_iota2: Average Iota from Iota Reliability Concept Version 2.

• max_iota2: Maximum Iota from Iota Reliability Concept Version 2.

• min_alpha: Minmal Alpha Reliability from Iota Reliability Concept Version 2.

• avg_alpha: Average Alpha Reliability from Iota Reliability Concept Version 2.

• max_alpha: Maximum Alpha Reliability from Iota Reliability Concept Version 2.

• static_iota_index: Static Iota Index from Iota Reliability Concept Version 2.

• dynamic_iota_index: Dynamic Iota Index Iota Reliability Concept Version 2.

• kalpha_nominal: Krippendorff's Alpha for nominal variables.

• kalpha_ordinal: Krippendorff's Alpha for ordinal variables.

• kendall: Kendall's coefficient of concordance W with correction for ties.

• c_kappa_unweighted: Cohen's Kappa unweighted.

• c_kappa_linear: Weighted Cohen's Kappa with linear increasing weights.

• c_kappa_squared: Weighted Cohen's Kappa with quadratic increasing weights.

• kappa_fleiss: Fleiss' Kappa for multiple raters without exact estimation.

• percentage_agreement: Percentage Agreement.

• balanced_accuracy: Average accuracy within each class.

• gwet_ac1_nominal: Gwet's Agreement Coefficient 1 (AC1) for nominal data which is unweighted.

• gwet_ac2_linear: Gwet's Agreement Coefficient 2 (AC2) for ordinal data with linear weights.

• gwet_ac2_quadratic: Gwet's Agreement Coefficient 2 (AC2) for ordinal data with quadratic weights.

If return_names_only = TRUE returns only the names of the vector elements.

See Also

Other performance measures: calc_standard_classification_measures(), cohens_kappa(), fleiss_kappa(), gwet_ac(), kendalls_w(), kripp_alpha()

Description

Functions prints the used arguments. The aim of this function is to print the arguments to the console that resulted in a failed test.

Usage

get_current_args_for_print(arg_list)

Arguments

Value

Function does nothing return.

See Also

Other Utils TestThat Developers: check_adjust_n_samples_on_CI(), generate_args_for_tests(), generate_embeddings(), generate_tensors(), get_fixed_test_tensor(), get_test_data_for_classifiers(), monitor_test_time_on_CI(), random_bool_on_CI()

Description

Function returns the names of all objects that are deprecated.

Usage

get_depr_obj_names()

Value

Returns a vector containing the names.

See Also

Other Parameter Dictionary: BaseModelsIndex, DataSetsIndex, TokenizerIndex, doc_formula(), get_TEClassifiers_class_names(), get_called_args(), get_magnitude_values(), get_param_def(), get_param_dict(), get_param_doc_desc()

Description

Function for generating the documentation of a specific core model.

Usage

get_desc_for_core_model_architecture(
  name,
  title_format = "bold",
  inc_img = FALSE
)

Arguments

Value

Returns a string containing the description written in rmarkdown.

See Also

Other Utils Documentation: build_documentation_for_model(), build_layer_stack_documentation_for_vignette(), get_dict_cls_type(), get_dict_core_models(), get_dict_input_types(), get_layer_dict(), get_layer_documentation(), get_parameter_documentation()

Description

Function for requesting the file extension

Usage

get_file_extension(file_path)

Arguments

Value

Returns the extension of a file as a string.

See Also

Other Utils File Management Developers: create_dir()

Description

Function generates a static test tensor which is always the same.

Usage

get_fixed_test_tensor(pad_value)

Arguments

Value

Returns an object of class Tensor which is always the same except padding. Shape (5,3,7).

See Also

Other Utils TestThat Developers: check_adjust_n_samples_on_CI(), generate_args_for_tests(), generate_embeddings(), generate_tensors(), get_current_args_for_print(), get_test_data_for_classifiers(), monitor_test_time_on_CI(), random_bool_on_CI()

Description

Function for generating the documentation of a specific layer.

Usage

get_layer_documentation(
  layer_name,
  title_format = "bold",
  subtitle_format = "italic",
  inc_img = FALSE,
  inc_params = FALSE,
  inc_references = FALSE
)

Arguments

Value

Returns a string containing the description written in rmarkdown.

See Also

Other Utils Documentation: build_documentation_for_model(), build_layer_stack_documentation_for_vignette(), get_desc_for_core_model_architecture(), get_dict_cls_type(), get_dict_core_models(), get_dict_input_types(), get_layer_dict(), get_parameter_documentation()

Description

Function calculates different magnitude for a numeric argument.

Usage

get_magnitude_values(magnitude, n_elements = 9L, max = NULL, min = NULL)

Arguments

Value

Returns a numeric vector with the generated values. The values are calculated with the following formula: max * magnitude^i for i=1,...,n_elements. Only values equal or greater min are returned.

See Also

Other Parameter Dictionary: BaseModelsIndex, DataSetsIndex, TokenizerIndex, doc_formula(), get_TEClassifiers_class_names(), get_called_args(), get_depr_obj_names(), get_param_def(), get_param_dict(), get_param_doc_desc()

Description

Function for calculating the number of chunks/sequences for every case.

Usage

get_n_chunks(text_embeddings, features, times, pad_value = -100L)

Arguments