Class: FastText::Classifier
- Inherits:
-
Model
- Object
- Model
- FastText::Classifier
show all
- Defined in:
- lib/fasttext/classifier.rb
Constant Summary
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- DEFAULT_OPTIONS =
{
lr: 0.1,
lr_update_rate: 100,
dim: 100,
ws: 5,
epoch: 5,
min_count: 1,
min_count_label: 0,
neg: 5,
word_ngrams: 1,
loss: "softmax",
model: "supervised",
bucket: 2000000,
minn: 0,
maxn: 0,
thread: 3,
t: 0.0001,
label_prefix: "__label__",
verbose: 2,
pretrained_vectors: "",
save_output: false,
seed: 0,
autotune_validation_file: "",
autotune_metric: "f1",
autotune_predictions: 1,
autotune_duration: 60 * 5,
autotune_model_size: ""
}
Instance Method Summary
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Methods inherited from Model
#dimension, #initialize, #quantized?, #save_model, #sentence_vector, #subword_id, #subwords, #word_id, #word_vector, #words
Instance Method Details
#fit(x, y = nil, autotune_set: nil) ⇒ Object
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# File 'lib/fasttext/classifier.rb', line 32
def fit(x, y = nil, autotune_set: nil)
input, _ref = input_path(x, y)
@m ||= Ext::Model.new
a = build_args(DEFAULT_OPTIONS)
a.input = input
a.model = "supervised"
if autotune_set
x, y = autotune_set
a.autotune_validation_file, _autotune_ref = input_path(x, y)
end
m.train(a)
end
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#labels(include_freq: false) ⇒ Object
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# File 'lib/fasttext/classifier.rb', line 76
def labels(include_freq: false)
labels, freqs = m.labels
labels.map! { |v| remove_prefix(v) }
if include_freq
labels.zip(freqs).to_h
else
labels
end
end
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#predict(text, k: 1, threshold: 0.0) ⇒ Object
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# File 'lib/fasttext/classifier.rb', line 45
def predict(text, k: 1, threshold: 0.0)
multiple = text.is_a?(Array)
text = [text] unless multiple
result =
text.map do |t|
m.predict(prep_text(t), k, threshold).to_h do |v|
[remove_prefix(v[1]), v[0]]
end
end
multiple ? result : result.first
end
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#quantize ⇒ Object
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# File 'lib/fasttext/classifier.rb', line 71
def quantize
a = Ext::Args.new
m.quantize(a)
end
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#test(x, y = nil, k: 1) ⇒ Object
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# File 'lib/fasttext/classifier.rb', line 60
def test(x, y = nil, k: 1)
input, _ref = input_path(x, y)
res = m.test(input, k)
{
examples: res[0],
precision: res[1],
recall: res[2]
}
end
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