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57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import spacy
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from spacy.util import minibatch, compounding
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def test_issue4030():
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""" Test whether textcat works fine with empty doc """
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unique_classes = ["offensive", "inoffensive"]
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x_train = [
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"This is an offensive text",
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"This is the second offensive text",
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"inoff",
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]
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y_train = ["offensive", "offensive", "inoffensive"]
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# preparing the data
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pos_cats = list()
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for train_instance in y_train:
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pos_cats.append({label: label == train_instance for label in unique_classes})
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train_data = list(zip(x_train, [{"cats": cats} for cats in pos_cats]))
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# set up the spacy model with a text categorizer component
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nlp = spacy.blank("en")
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textcat = nlp.create_pipe(
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"textcat",
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config={"exclusive_classes": True, "architecture": "bow", "ngram_size": 2},
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)
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for label in unique_classes:
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textcat.add_label(label)
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nlp.add_pipe(textcat, last=True)
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# training the network
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with nlp.disable_pipes([p for p in nlp.pipe_names if p != "textcat"]):
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optimizer = nlp.begin_training()
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for i in range(3):
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losses = {}
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batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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texts, annotations = zip(*batch)
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nlp.update(
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docs=texts,
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golds=annotations,
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sgd=optimizer,
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drop=0.1,
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losses=losses,
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)
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# processing of an empty doc should result in 0.0 for all categories
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doc = nlp("")
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assert doc.cats["offensive"] == 0.0
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assert doc.cats["inoffensive"] == 0.0
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