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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="ensure this value has at least 1 items",
):
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 == {
"tokens": [
{
"idx": 0,
"value": "'",
"char_start": 0,
"char_end": 0,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 1,
"value": "Secret",
"char_start": 1,
"char_end": 6,
"length": 6,
"capitalness": "FIRST",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 2,
"value": "Story",
"char_start": 8,
"char_end": 12,
"length": 5,
"capitalness": "FIRST",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 3,
"value": ":",
"char_start": 14,
"char_end": 14,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 4,
"value": "Última",
"char_start": 16,
"char_end": 21,
"length": 6,
"capitalness": "FIRST",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 5,
"value": "hora",
"char_start": 23,
"char_end": 26,
"length": 4,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 6,
"value": "'",
"char_start": 27,
"char_end": 27,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 7,
"value": "debuta",
"char_start": 29,
"char_end": 34,
"length": 6,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 8,
"value": "con",
"char_start": 36,
"char_end": 38,
"length": 3,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 9,
"value": "un",
"char_start": 40,
"char_end": 41,
"length": 2,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 10,
"value": "pobre",
"char_start": 43,
"char_end": 47,
"length": 5,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 11,
"value": "8.7%",
"char_start": 49,
"char_end": 52,
"length": 4,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 12,
"value": "en",
"char_start": 54,
"char_end": 55,
"length": 2,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 13,
"value": "el",
"char_start": 57,
"char_end": 58,
"length": 2,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 14,
"value": "access",
"char_start": 60,
"char_end": 65,
"length": 6,
"capitalness": "LOWER",
"score": None,
"tag": "B-ENG",
"custom": None,
},
{
"idx": 15,
"value": "de",
"char_start": 67,
"char_end": 68,
"length": 2,
"capitalness": "LOWER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 16,
"value": "Telecinco",
"char_start": 70,
"char_end": 78,
"length": 9,
"capitalness": "FIRST",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 17,
"value": "..",
"char_start": 79,
"char_end": 80,
"length": 2,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 18,
"value": ".",
"char_start": 82,
"char_end": 82,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 19,
"value": "PROGRAMAS",
"char_start": 84,
"char_end": 92,
"length": 9,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 20,
"value": "CON",
"char_start": 94,
"char_end": 96,
"length": 3,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 21,
"value": "MEJOR",
"char_start": 98,
"char_end": 102,
"length": 5,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 22,
"value": "CUOTA",
"char_start": 104,
"char_end": 108,
"length": 5,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 23,
"value": "DEL",
"char_start": 110,
"char_end": 112,
"length": 3,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 24,
"value": "LUNES",
"char_start": 114,
"char_end": 118,
"length": 5,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 25,
"value": "(",
"char_start": 120,
"char_end": 120,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 26,
"value": "POR",
"char_start": 121,
"char_end": 123,
"length": 3,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 27,
"value": "CADENAS",
"char_start": 125,
"char_end": 131,
"length": 7,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 28,
"value": ")",
"char_start": 132,
"char_end": 132,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 29,
"value": ".",
"char_start": 133,
"char_end": 133,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
{
"idx": 30,
"value": ".",
"char_start": 135,
"char_end": 135,
"length": 1,
"capitalness": "UPPER",
"score": None,
"tag": "O",
"custom": None,
},
],
"tokens_length": 31,
"predicted": {
"mentions": [
{
"value": "access",
"label": "ENG",
"score": 1.0,
"capitalness": "LOWER",
"density": 0.03225806451612903,
"tokens_length": 1,
"chars_length": 6,
}
]
},
"annotated": {"mentions": []},
}
def test_search_keywords(mocked_client):
dataset = "test_search_keywords"
from datasets import load_dataset
dataset_ds = load_dataset("rubrix/gutenberg_spacy-ner", 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