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https://github.com/deepset-ai/haystack.git
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dde9d59271
* fix pip backtracking issue * restrict azure-core version * Remove the trailing comma * Add skip_magic_trailing_comma in pyproject.toml for pydoc compatibility * Pin pydoc-markdown _again_ Co-authored-by: Sara Zan <sarazanzo94@gmail.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
import pytest
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from haystack.schema import Document
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from haystack.nodes.document_classifier.base import BaseDocumentClassifier
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@pytest.mark.slow
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def test_document_classifier(document_classifier):
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assert isinstance(document_classifier, BaseDocumentClassifier)
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docs = [
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Document(
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content="""That's good. I like it.""" * 700, # extra long text to check truncation
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meta={"name": "0"},
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id="1",
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),
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Document(content="""That's bad. I don't like it.""", meta={"name": "1"}, id="2"),
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]
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results = document_classifier.predict(documents=docs)
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expected_labels = ["joy", "sadness"]
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for i, doc in enumerate(results):
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assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
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@pytest.mark.slow
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def test_zero_shot_document_classifier(zero_shot_document_classifier):
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assert isinstance(zero_shot_document_classifier, BaseDocumentClassifier)
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docs = [
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Document(
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content="""That's good. I like it.""" * 700, # extra long text to check truncation
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meta={"name": "0"},
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id="1",
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),
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Document(content="""That's bad. I don't like it.""", meta={"name": "1"}, id="2"),
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]
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results = zero_shot_document_classifier.predict(documents=docs)
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expected_labels = ["positive", "negative"]
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for i, doc in enumerate(results):
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assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
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@pytest.mark.slow
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def test_document_classifier_batch_size(batched_document_classifier):
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assert isinstance(batched_document_classifier, BaseDocumentClassifier)
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docs = [
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Document(
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content="""That's good. I like it.""" * 700, # extra long text to check truncation
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meta={"name": "0"},
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id="1",
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),
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Document(content="""That's bad. I don't like it.""", meta={"name": "1"}, id="2"),
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]
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results = batched_document_classifier.predict(documents=docs)
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expected_labels = ["joy", "sadness"]
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for i, doc in enumerate(results):
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assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
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@pytest.mark.slow
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def test_document_classifier_as_index_node(indexing_document_classifier):
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assert isinstance(indexing_document_classifier, BaseDocumentClassifier)
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docs = [
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{
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"content": """That's good. I like it.""" * 700, # extra long text to check truncation
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"meta": {"name": "0"},
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"id": "1",
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"class_field": "That's bad.",
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},
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{"content": """That's bad. I like it.""", "meta": {"name": "1"}, "id": "2", "class_field": "That's good."},
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]
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output, output_name = indexing_document_classifier.run(documents=docs, root_node="File")
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expected_labels = ["sadness", "joy"]
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for i, doc in enumerate(output["documents"]):
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assert doc["meta"]["classification"]["label"] == expected_labels[i]
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@pytest.mark.slow
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def test_document_classifier_as_query_node(document_classifier):
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assert isinstance(document_classifier, BaseDocumentClassifier)
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docs = [
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Document(
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content="""That's good. I like it.""" * 700, # extra long text to check truncation
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meta={"name": "0"},
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id="1",
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),
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Document(content="""That's bad. I don't like it.""", meta={"name": "1"}, id="2"),
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]
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output, output_name = document_classifier.run(documents=docs, root_node="Query")
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expected_labels = ["joy", "sadness"]
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for i, doc in enumerate(output["documents"]):
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assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
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