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Text embedding classifier with a ProtoNet

Source: R/obj_TEClassifierProtoNet.R
TEClassifierProtoNet.Rd

Abstract class for neural nets with 'pytorch'.

This class is deprecated. Please use an Object of class TEClassifierSequentialPrototype instead.

This object represents in implementation of a prototypical network for few-shot learning as described by Snell, Swersky, and Zemel (2017). The network uses a multi way contrastive loss described by Zhang et al. (2019). The network learns to scale the metric as described by Oreshkin, Rodriguez, and Lacoste (2018)

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 and a factor 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. The factor contains the classes/categories for every text. Missing values (unlabeled cases) are supported. For predictions an object of class EmbeddedText or LargeDataSetForTextEmbeddings has to be used which was created with the same TextEmbeddingModel as for training.

Note

This model requires pad_value=0. If this condition is not met the padding value is switched automatically.

References

Oreshkin, B. N., Rodriguez, P. & Lacoste, A. (2018). TADAM: Task dependent adaptive metric for improved few-shot learning. https://doi.org/10.48550/arXiv.1805.10123

Snell, J., Swersky, K. & Zemel, R. S. (2017). Prototypical Networks for Few-shot Learning. https://doi.org/10.48550/arXiv.1703.05175

Zhang, X., Nie, J., Zong, L., Yu, H. & Liang, W. (2019). One Shot Learning with Margin. In Q. Yang, Z.-H. Zhou, Z. Gong, M.-L. Zhang & S.-J. Huang (Eds.), Lecture Notes in Computer Science. Advances in Knowledge Discovery and Data Mining (Vol. 11440, pp. 305–317). Springer International Publishing. https://doi.org/10.1007/978-3-030-16145-3_24

See also

Other Classification: TEClassifierParallel, TEClassifierParallelPrototype, TEClassifierRegular, TEClassifierSequential, TEClassifierSequentialPrototype

Super classes

aifeducation::AIFEBaseModel -> aifeducation::ModelsBasedOnTextEmbeddings -> aifeducation::ClassifiersBasedOnTextEmbeddings -> aifeducation::TEClassifiersBasedOnProtoNet -> TEClassifierProtoNet

Methods

Public methods

Inherited methods

Method new()

Creating a new instance of this class.

Usage

TEClassifierProtoNet$new()

Returns

Returns an object of class TEClassifierProtoNet which is ready for configuration.

Method configure()

Creating a new instance of this class.

Usage

TEClassifierProtoNet$configure(
  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,
  embedding_dim = 2,
  self_attention_heads = 0,
  intermediate_size = NULL,
  attention_type = "Fourier",
  add_pos_embedding = TRUE,
  act_fct = "ELU",
  parametrizations = "None",
  rec_dropout = 0.1,
  repeat_encoder = 1,
  dense_dropout = 0.4,
  encoder_dropout = 0.1
)

Arguments

name

string Name of the new model. Please refer to common name conventions. Free text can be used with parameter label. If set to NULL a unique ID is generated automatically. Allowed values: any

label

string Label for the new model. Here you can use free text. Allowed values: any

text_embeddings

EmbeddedText, LargeDataSetForTextEmbeddings Object of class EmbeddedText or LargeDataSetForTextEmbeddings.

feature_extractor

TEFeatureExtractor 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 note 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. Allowed values: 1 <= x

dense_layers

int Number of dense layers. Allowed values: 0 <= x

rec_size

int Number of neurons for each recurrent layer. Allowed values: 1 <= x

rec_layers

int Number of recurrent layers. Allowed values: 0 <= x

rec_type

string Type of the recurrent layers. rec_type='GRU' for Gated Recurrent Unit and rec_type='LSTM' for Long Short-Term Memory. Allowed values: 'GRU', 'LSTM'

rec_bidirectional

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

embedding_dim

int determining the number of dimensions for the embedding. Allowed values: 2 <= x

self_attention_heads

int determining the number of attention heads for a self-attention layer. Only relevant if attention_type='multihead' Allowed values: 0 <= x

intermediate_size

int determining the size of the projection layer within a each transformer encoder. Allowed values: 1 <= x

attention_type

string Choose the attention type. Allowed values: 'Fourier', 'MultiHead'

add_pos_embedding

bool TRUE if positional embedding should be used.

act_fct

string Activation function for all layers. Allowed values: 'ELU', 'LeakyReLU', 'ReLU', 'GELU', 'Sigmoid', 'Tanh', 'PReLU'

parametrizations

string Re-Parametrizations of the weights of layers. Allowed values: 'None', 'OrthogonalWeights', 'WeightNorm', 'SpectralNorm'

rec_dropout

double determining the dropout between recurrent layers. Allowed values: 0 <= x <= 0.6

repeat_encoder

int determining how many times the encoder should be added to the network. Allowed values: 0 <= x

dense_dropout

double determining the dropout between dense layers. Allowed values: 0 <= x <= 0.6

encoder_dropout

double determining the dropout for the dense projection within the transformer encoder layers. Allowed values: 0 <= x <= 0.6

bias

bool If TRUE a bias term is added to all layers. If FALSE no bias term is added to the layers.

Method embed()

Method for embedding documents. Please do not confuse this type of embeddings with the embeddings of texts created by an object of class TextEmbeddingModel. These embeddings embed documents according to their similarity to specific classes.

Usage

TEClassifierProtoNet$embed(embeddings_q = NULL, batch_size = 32)

Arguments

embeddings_q

Object of class EmbeddedText or LargeDataSetForTextEmbeddings containing the text embeddings for all cases which should be embedded into the classification space.

batch_size

int batch size.

Returns

Returns a list containing the following elements

  • embeddings_q: embeddings for the cases (query sample).

  • embeddings_prototypes: embeddings of the prototypes which were learned during training. They represents the center for the different classes.

Method plot_embeddings()

Method for creating a plot to visualize embeddings and their corresponding centers (prototypes).

Usage

TEClassifierProtoNet$plot_embeddings(
  embeddings_q,
  classes_q = NULL,
  batch_size = 12,
  alpha = 0.5,
  size_points = 3,
  size_points_prototypes = 8,
  inc_unlabeled = TRUE
)

Arguments

embeddings_q

Object of class EmbeddedText or LargeDataSetForTextEmbeddings containing the text embeddings for all cases which should be embedded into the classification space.

classes_q

Named factor containg the true classes for every case. Please note that the names must match the names/ids in embeddings_q.

batch_size

int batch size.

alpha

float Value indicating how transparent the points should be (important if many points overlap). Does not apply to points representing prototypes.

size_points

int Size of the points excluding the points for prototypes.

size_points_prototypes

int Size of points representing prototypes.

inc_unlabeled

bool If TRUE plot includes unlabeled cases as data points.

Returns

Returns a plot of class ggplotvisualizing embeddings.

Method clone()

The objects of this class are cloneable with this method.

Usage

TEClassifierProtoNet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.