mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-27 19:00:35 +00:00
a59bca3661
* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
112 lines
3.7 KiB
Python
112 lines
3.7 KiB
Python
import pytest
|
|
|
|
from haystack.schema import Document
|
|
from haystack.nodes.document_classifier.base import BaseDocumentClassifier
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_document_classifier(document_classifier):
|
|
assert isinstance(document_classifier, BaseDocumentClassifier)
|
|
|
|
docs = [
|
|
Document(
|
|
content="""That's good. I like it.""" * 700, # extra long text to check truncation
|
|
meta={"name": "0"},
|
|
id="1",
|
|
),
|
|
Document(
|
|
content="""That's bad. I don't like it.""",
|
|
meta={"name": "1"},
|
|
id="2",
|
|
),
|
|
]
|
|
results = document_classifier.predict(documents=docs)
|
|
expected_labels = ["joy", "sadness"]
|
|
for i, doc in enumerate(results):
|
|
assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_zero_shot_document_classifier(zero_shot_document_classifier):
|
|
assert isinstance(zero_shot_document_classifier, BaseDocumentClassifier)
|
|
|
|
docs = [
|
|
Document(
|
|
content="""That's good. I like it.""" * 700, # extra long text to check truncation
|
|
meta={"name": "0"},
|
|
id="1",
|
|
),
|
|
Document(
|
|
content="""That's bad. I don't like it.""",
|
|
meta={"name": "1"},
|
|
id="2",
|
|
),
|
|
]
|
|
results = zero_shot_document_classifier.predict(documents=docs)
|
|
expected_labels = ["positive", "negative"]
|
|
for i, doc in enumerate(results):
|
|
assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_document_classifier_batch_size(batched_document_classifier):
|
|
assert isinstance(batched_document_classifier, BaseDocumentClassifier)
|
|
|
|
docs = [
|
|
Document(
|
|
content="""That's good. I like it.""" * 700, # extra long text to check truncation
|
|
meta={"name": "0"},
|
|
id="1",
|
|
),
|
|
Document(
|
|
content="""That's bad. I don't like it.""",
|
|
meta={"name": "1"},
|
|
id="2",
|
|
),
|
|
]
|
|
results = batched_document_classifier.predict(documents=docs)
|
|
expected_labels = ["joy", "sadness"]
|
|
for i, doc in enumerate(results):
|
|
assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_document_classifier_as_index_node(indexing_document_classifier):
|
|
assert isinstance(indexing_document_classifier, BaseDocumentClassifier)
|
|
|
|
docs = [
|
|
{
|
|
"content": """That's good. I like it.""" * 700, # extra long text to check truncation
|
|
"meta": {"name": "0"},
|
|
"id": "1",
|
|
"class_field": "That's bad.",
|
|
},
|
|
{"content": """That's bad. I like it.""", "meta": {"name": "1"}, "id": "2", "class_field": "That's good."},
|
|
]
|
|
output, output_name = indexing_document_classifier.run(documents=docs, root_node="File")
|
|
expected_labels = ["sadness", "joy"]
|
|
for i, doc in enumerate(output["documents"]):
|
|
assert doc["meta"]["classification"]["label"] == expected_labels[i]
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_document_classifier_as_query_node(document_classifier):
|
|
assert isinstance(document_classifier, BaseDocumentClassifier)
|
|
|
|
docs = [
|
|
Document(
|
|
content="""That's good. I like it.""" * 700, # extra long text to check truncation
|
|
meta={"name": "0"},
|
|
id="1",
|
|
),
|
|
Document(
|
|
content="""That's bad. I don't like it.""",
|
|
meta={"name": "1"},
|
|
id="2",
|
|
),
|
|
]
|
|
output, output_name = document_classifier.run(documents=docs, root_node="Query")
|
|
expected_labels = ["joy", "sadness"]
|
|
for i, doc in enumerate(output["documents"]):
|
|
assert doc.to_dict()["meta"]["classification"]["label"] == expected_labels[i]
|