/
test_log_for_token_classification.py
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test_log_for_token_classification.py
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import pytest
import rubrix
from rubrix import TokenClassificationRecord
def test_log_with_empty_text(mocked_client):
dataset = "test_log_with_empty_text"
text = " "
rubrix.delete(dataset)
with pytest.raises(Exception, match="No text or empty text provided"):
rubrix.log(
TokenClassificationRecord(id=0, text=text, tokens=["a", "b", "c"]),
name=dataset,
)
def test_log_with_empty_tokens_list(mocked_client):
dataset = "test_log_with_empty_text"
text = "The text"
rubrix.delete(dataset)
with pytest.raises(
Exception,
match="At least one token should be provided",
):
rubrix.log(
TokenClassificationRecord(id=0, text=text, tokens=[]),
name=dataset,
)
def test_log_record_that_makes_me_cry(mocked_client):
dataset = "test_log_record_that_makes_me_cry"
record = TokenClassificationRecord(
text="'Secret Story : Última hora' debuta con un pobre 8.7% en el access de Telecinco.. . "
"PROGRAMAS CON MEJOR CUOTA DEL LUNES (POR CADENAS). . ",
tokens=[
"'",
"Secret",
"Story",
":",
"Última",
"hora",
"'",
"debuta",
"con",
"un",
"pobre",
"8.7%",
"en",
"el",
"access",
"de",
"Telecinco",
"..",
".",
"PROGRAMAS",
"CON",
"MEJOR",
"CUOTA",
"DEL",
"LUNES",
"(",
"POR",
"CADENAS",
")",
".",
".",
],
prediction=[("ENG", 60, 66)],
annotation=None,
prediction_agent=None,
annotation_agent=None,
id=None,
metadata={"section": "television", "newspaper": "eldiario"},
status="Default",
event_timestamp=None,
)
rubrix.delete(dataset)
rubrix.log(record, name=dataset)
records = rubrix.load(dataset, as_pandas=False)
assert len(records) == 1
assert records[0].text == record.text
assert records[0].tokens == record.tokens
assert records[0].metrics == {
"annotated": {"mentions": [], "tags": []},
"predicted": {
"mentions": [
{
"capitalness": "LOWER",
"chars_length": 6,
"density": 0.03225806451612903,
"label": "ENG",
"score": 1.0,
"tokens_length": 1,
"value": "access",
}
],
"tags": [
{"tag": "O", "value": "'"},
{"tag": "O", "value": "Secret"},
{"tag": "O", "value": "Story"},
{"tag": "O", "value": ":"},
{"tag": "O", "value": "Última"},
{"tag": "O", "value": "hora"},
{"tag": "O", "value": "'"},
{"tag": "O", "value": "debuta"},
{"tag": "O", "value": "con"},
{"tag": "O", "value": "un"},
{"tag": "O", "value": "pobre"},
{"tag": "O", "value": "8.7%"},
{"tag": "O", "value": "en"},
{"tag": "O", "value": "el"},
{"tag": "B-ENG", "value": "access"},
{"tag": "O", "value": "de"},
{"tag": "O", "value": "Telecinco"},
{"tag": "O", "value": ".."},
{"tag": "O", "value": "."},
{"tag": "O", "value": "PROGRAMAS"},
{"tag": "O", "value": "CON"},
{"tag": "O", "value": "MEJOR"},
{"tag": "O", "value": "CUOTA"},
{"tag": "O", "value": "DEL"},
{"tag": "O", "value": "LUNES"},
{"tag": "O", "value": "("},
{"tag": "O", "value": "POR"},
{"tag": "O", "value": "CADENAS"},
{"tag": "O", "value": ")"},
{"tag": "O", "value": "."},
{"tag": "O", "value": "."},
],
},
"text_length": 137,
"tokens": [
{
"capitalness": "UPPER",
"char_end": 0,
"char_start": 0,
"custom": None,
"idx": 0,
"length": 1,
"score": None,
"tag": "O",
"value": "'",
},
{
"capitalness": "FIRST",
"char_end": 6,
"char_start": 1,
"custom": None,
"idx": 1,
"length": 6,
"score": None,
"tag": "O",
"value": "Secret",
},
{
"capitalness": "FIRST",
"char_end": 12,
"char_start": 8,
"custom": None,
"idx": 2,
"length": 5,
"score": None,
"tag": "O",
"value": "Story",
},
{
"capitalness": "UPPER",
"char_end": 14,
"char_start": 14,
"custom": None,
"idx": 3,
"length": 1,
"score": None,
"tag": "O",
"value": ":",
},
{
"capitalness": "FIRST",
"char_end": 21,
"char_start": 16,
"custom": None,
"idx": 4,
"length": 6,
"score": None,
"tag": "O",
"value": "Última",
},
{
"capitalness": "LOWER",
"char_end": 26,
"char_start": 23,
"custom": None,
"idx": 5,
"length": 4,
"score": None,
"tag": "O",
"value": "hora",
},
{
"capitalness": "UPPER",
"char_end": 27,
"char_start": 27,
"custom": None,
"idx": 6,
"length": 1,
"score": None,
"tag": "O",
"value": "'",
},
{
"capitalness": "LOWER",
"char_end": 34,
"char_start": 29,
"custom": None,
"idx": 7,
"length": 6,
"score": None,
"tag": "O",
"value": "debuta",
},
{
"capitalness": "LOWER",
"char_end": 38,
"char_start": 36,
"custom": None,
"idx": 8,
"length": 3,
"score": None,
"tag": "O",
"value": "con",
},
{
"capitalness": "LOWER",
"char_end": 41,
"char_start": 40,
"custom": None,
"idx": 9,
"length": 2,
"score": None,
"tag": "O",
"value": "un",
},
{
"capitalness": "LOWER",
"char_end": 47,
"char_start": 43,
"custom": None,
"idx": 10,
"length": 5,
"score": None,
"tag": "O",
"value": "pobre",
},
{
"capitalness": "UPPER",
"char_end": 52,
"char_start": 49,
"custom": None,
"idx": 11,
"length": 4,
"score": None,
"tag": "O",
"value": "8.7%",
},
{
"capitalness": "LOWER",
"char_end": 55,
"char_start": 54,
"custom": None,
"idx": 12,
"length": 2,
"score": None,
"tag": "O",
"value": "en",
},
{
"capitalness": "LOWER",
"char_end": 58,
"char_start": 57,
"custom": None,
"idx": 13,
"length": 2,
"score": None,
"tag": "O",
"value": "el",
},
{
"capitalness": "LOWER",
"char_end": 65,
"char_start": 60,
"custom": None,
"idx": 14,
"length": 6,
"score": None,
"tag": "B-ENG",
"value": "access",
},
{
"capitalness": "LOWER",
"char_end": 68,
"char_start": 67,
"custom": None,
"idx": 15,
"length": 2,
"score": None,
"tag": "O",
"value": "de",
},
{
"capitalness": "FIRST",
"char_end": 78,
"char_start": 70,
"custom": None,
"idx": 16,
"length": 9,
"score": None,
"tag": "O",
"value": "Telecinco",
},
{
"capitalness": "UPPER",
"char_end": 80,
"char_start": 79,
"custom": None,
"idx": 17,
"length": 2,
"score": None,
"tag": "O",
"value": "..",
},
{
"capitalness": "UPPER",
"char_end": 82,
"char_start": 82,
"custom": None,
"idx": 18,
"length": 1,
"score": None,
"tag": "O",
"value": ".",
},
{
"capitalness": "UPPER",
"char_end": 92,
"char_start": 84,
"custom": None,
"idx": 19,
"length": 9,
"score": None,
"tag": "O",
"value": "PROGRAMAS",
},
{
"capitalness": "UPPER",
"char_end": 96,
"char_start": 94,
"custom": None,
"idx": 20,
"length": 3,
"score": None,
"tag": "O",
"value": "CON",
},
{
"capitalness": "UPPER",
"char_end": 102,
"char_start": 98,
"custom": None,
"idx": 21,
"length": 5,
"score": None,
"tag": "O",
"value": "MEJOR",
},
{
"capitalness": "UPPER",
"char_end": 108,
"char_start": 104,
"custom": None,
"idx": 22,
"length": 5,
"score": None,
"tag": "O",
"value": "CUOTA",
},
{
"capitalness": "UPPER",
"char_end": 112,
"char_start": 110,
"custom": None,
"idx": 23,
"length": 3,
"score": None,
"tag": "O",
"value": "DEL",
},
{
"capitalness": "UPPER",
"char_end": 118,
"char_start": 114,
"custom": None,
"idx": 24,
"length": 5,
"score": None,
"tag": "O",
"value": "LUNES",
},
{
"capitalness": "UPPER",
"char_end": 120,
"char_start": 120,
"custom": None,
"idx": 25,
"length": 1,
"score": None,
"tag": "O",
"value": "(",
},
{
"capitalness": "UPPER",
"char_end": 123,
"char_start": 121,
"custom": None,
"idx": 26,
"length": 3,
"score": None,
"tag": "O",
"value": "POR",
},
{
"capitalness": "UPPER",
"char_end": 131,
"char_start": 125,
"custom": None,
"idx": 27,
"length": 7,
"score": None,
"tag": "O",
"value": "CADENAS",
},
{
"capitalness": "UPPER",
"char_end": 132,
"char_start": 132,
"custom": None,
"idx": 28,
"length": 1,
"score": None,
"tag": "O",
"value": ")",
},
{
"capitalness": "UPPER",
"char_end": 133,
"char_start": 133,
"custom": None,
"idx": 29,
"length": 1,
"score": None,
"tag": "O",
"value": ".",
},
{
"capitalness": "UPPER",
"char_end": 135,
"char_start": 135,
"custom": None,
"idx": 30,
"length": 1,
"score": None,
"tag": "O",
"value": ".",
},
],
"tokens_length": 31,
}
def test_search_keywords(mocked_client):
dataset = "test_search_keywords"
from datasets import load_dataset
dataset_ds = load_dataset("rubrix/gutenberg_spacy-ner_sm", split="train")
dataset_rb = rubrix.read_datasets(dataset_ds, task="TokenClassification")
rubrix.delete(dataset)
rubrix.log(name=dataset, records=dataset_rb)
df = rubrix.load(dataset, query="lis*")
assert not df.empty
assert "search_keywords" in df.columns
top_keywords = set(
[
keyword
for keywords in df.search_keywords.value_counts(sort=True, ascending=False)
.index[:3]
.tolist()
for keyword in keywords
]
)
assert {"listened", "listen"} == top_keywords, top_keywords