TextClassification

MicroTC(
    config::MicroTC_Config,
    train_corpus::AbstractVector,
    train_y;
    textconfig=config.textconfig,
    verbose=true)
MicroTC(config::MicroTC_Config, textmodel::TextModel, train_X::AbstractVector{S}, train_y; verbose=true) where {S<:SVEC}

Creates a MicroTC model on the given dataset and configuration

predict(tc::MicroTC, text)
predict(tc::MicroTC, vec::SVEC)

Predicts the label of the given input

accuracy_score(gold, predicted)

Computes the accuracy score between the gold and the predicted sets

classification_scores(gold, predicted; labelnames=nothing)

Computes several scores for the given gold-standard and predictions, namely: precision, recall, and f1 scores, for global and per-class granularity. If labelnames is given, then it is an array of label names.

f1_score(gold, predicted; weight=:macro)::Float64

It computes the F1 score between the gold dataset and the list of predictions predicted

It applies the desired weighting scheme for binary and multiclass problems

• :macro performs a uniform weigth to each class
• :weigthed the weight of each class is proportional to its population in gold
• :micro returns the global F1, without distinguishing among classes

isqerror(X::AbstractVector{F}, Y::AbstractVector{F}) where {F <: AbstractFloat}

Negative squared error (to be used for maximizing algorithms)

pearson(X::AbstractVector{F}, Y::AbstractVector{F}) where {F <: AbstractFloat}

Pearson correlation score

precision_recall(gold::AbstractVector, predicted::AbstractVector)

Computes the global and per-class precision and recall values between the gold standard and the predicted set

precision_score(gold, predicted; weight=:macro)::Float64

It computes the precision between the gold dataset and the list of predictions predict

It applies the desired weighting scheme for binary and multiclass problems

• :macro performs a uniform weigth to each class
• :weigthed the weight of each class is proportional to its population in gold
• :micro returns the global precision, without distinguishing among classes

recall_score(gold, predicted; weight=:macro)::Float64

It computes the recall between the gold dataset and the list of predictions predict

It applies the desired weighting scheme for binary and multiclass problems

• :macro performs a uniform weigth to each class
• :weigthed the weight of each class is proportional to its population in gold
• :micro returns the global recall, without distinguishing among classes

spearman(X::AbstractVector{F}, Y::AbstractVector{F}) where {F <: AbstractFloat}

Spearman rank correleation score

vectorize(tc::MicroTC, text; bow=BOW(), textconfig=tc.textconfig, normalize=true)
vectorize(tc::MicroTC, bow::BOW; normalize=true)

Creates a weighted vector using the model. The input text can be a string or an array of strings; it also can be an already computed bag of words.