From 591c0adb2eaa6258041d7b370be61de287d1babf Mon Sep 17 00:00:00 2001
From: matt-bernstein <60152561+matt-bernstein@users.noreply.github.com>
Date: Thu, 7 Nov 2024 10:48:01 -0500
Subject: [PATCH] fix: DIA-1523: test coverage for LabelStudioSkill (#247)
---
adala/skills/collection/entity_extraction.py | 27 +
adala/skills/collection/label_studio.py | 18 +-
..._label_studio_skill_valid_predictions.yaml | 66416 ++++++++++++++++
tests/test_label_studio_skill.py | 315 +-
4 files changed, 66744 insertions(+), 32 deletions(-)
create mode 100644 tests/cassettes/test_label_studio_skill/test_label_studio_skill_valid_predictions.yaml
diff --git a/adala/skills/collection/entity_extraction.py b/adala/skills/collection/entity_extraction.py
index 202716dc..f08f215e 100644
--- a/adala/skills/collection/entity_extraction.py
+++ b/adala/skills/collection/entity_extraction.py
@@ -13,6 +13,33 @@
logger = logging.getLogger(__name__)
+def validate_output_format_for_ner_tag(df: InternalDataFrame, input_field_name: str, output_field_name: str):
+ '''
+ The output format for Labels is:
+ {
+ "start": start_idx,
+ "end": end_idx,
+ "text": text,
+ "labels": [label1, label2, ...]
+ }
+ Sometimes the model cannot populate "text" correctly, but this can be fixed deterministically.
+ '''
+ for i, row in df.iterrows():
+ if row.get("_adala_error"):
+ logger.warning(f"Error in row {i}: {row['_adala_message']}")
+ continue
+ text = row[input_field_name]
+ entities = row[output_field_name]
+ for entity in entities:
+ corrected_text = text[entity["start"]:entity["end"]]
+ if entity.get("text") is None:
+ entity["text"] = corrected_text
+ elif entity["text"] != corrected_text:
+ # this seems to happen rarely if at all in testing, but could lead to invalid predictions
+ logger.warning(f"text and indices disagree for a predicted entity")
+ return df
+
+
def extract_indices(
df,
input_field_name,
diff --git a/adala/skills/collection/label_studio.py b/adala/skills/collection/label_studio.py
index 20518511..8097350a 100644
--- a/adala/skills/collection/label_studio.py
+++ b/adala/skills/collection/label_studio.py
@@ -1,6 +1,6 @@
import logging
import pandas as pd
-from typing import Optional, Type
+from typing import Type, Iterator
from functools import cached_property
from adala.skills._base import TransformSkill
from pydantic import BaseModel, Field, model_validator
@@ -12,7 +12,7 @@
from label_studio_sdk.label_interface.control_tags import ControlTag
from label_studio_sdk._extensions.label_studio_tools.core.utils.json_schema import json_schema_to_pydantic
-from .entity_extraction import extract_indices
+from .entity_extraction import extract_indices, validate_output_format_for_ner_tag
logger = logging.getLogger(__name__)
@@ -29,13 +29,14 @@ class LabelStudioSkill(TransformSkill):
# TODO: implement postprocessing to verify Taxonomy
- def has_ner_tag(self) -> Optional[ControlTag]:
+ def ner_tags(self) -> Iterator[ControlTag]:
# check if the input config has NER tag ( + ), and return its `from_name` and `to_name`
interface = LabelInterface(self.label_config)
for tag in interface.controls:
+ #TODO: don't need to check object tag because at this point, unusable control tags should have been stripped out of the label config, but confirm this - maybe move this logic to LSE
if tag.tag == 'Labels':
- return tag
-
+ yield tag
+
@model_validator(mode='after')
def validate_response_model(self):
@@ -79,10 +80,11 @@ async def aapply(
instructions_template=self.instructions,
response_model=ResponseModel,
)
- ner_tag = self.has_ner_tag()
- if ner_tag:
+ for ner_tag in self.ner_tags():
input_field_name = ner_tag.objects[0].value.lstrip('$')
output_field_name = ner_tag.name
quote_string_field_name = 'text'
- output = extract_indices(pd.concat([input, output], axis=1), input_field_name, output_field_name, quote_string_field_name)
+ df = pd.concat([input, output], axis=1)
+ output = validate_output_format_for_ner_tag(df, input_field_name, output_field_name)
+ output = extract_indices(output, input_field_name, output_field_name, quote_string_field_name)
return output
diff --git a/tests/cassettes/test_label_studio_skill/test_label_studio_skill_valid_predictions.yaml b/tests/cassettes/test_label_studio_skill/test_label_studio_skill_valid_predictions.yaml
new file mode 100644
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diff --git a/tests/test_label_studio_skill.py b/tests/test_label_studio_skill.py
index b608b45a..72a5ae15 100644
--- a/tests/test_label_studio_skill.py
+++ b/tests/test_label_studio_skill.py
@@ -2,17 +2,23 @@
import os
import pandas as pd
from adala.agents import Agent
-
+from label_studio_sdk.label_interface import LabelInterface
+from label_studio_sdk.label_interface.objects import PredictionValue
@pytest.mark.asyncio
@pytest.mark.vcr
async def test_label_studio_skill():
- df = pd.DataFrame([
- {"title": "I can't login", "description": "I can't login to the platform"},
- {"title": "Support new file types", "description": "It would be great if we could upload files of type .docx"},
- ])
+ df = pd.DataFrame(
+ [
+ {"title": "I can't login", "description": "I can't login to the platform"},
+ {
+ "title": "Support new file types",
+ "description": "It would be great if we could upload files of type .docx",
+ },
+ ]
+ )
agent_payload = {
"runtimes": {
@@ -31,11 +37,11 @@ async def test_label_studio_skill():
{
"type": "LabelStudioSkill",
"name": "AnnotationResult",
- "input_template": '''
+ "input_template": """
Given the github issue title:\n{title}\n and the description:\n{description}\n,
classify the issue. Provide a rationale for your classification.
Evaluate the final classification on a Likert scale from 1 to 5,
- where 1 is "Completely irrelevant" and 5 is "Completely relevant".''',
+ where 1 is "Completely irrelevant" and 5 is "Completely relevant".""",
"label_config": """
@@ -52,7 +58,7 @@ async def test_label_studio_skill():
- """
+ """,
}
],
}
@@ -64,7 +70,7 @@ async def test_label_studio_skill():
assert predictions.evaluation.tolist() == [5, 5]
assert predictions.rationale.tolist() == [
"The issue clearly indicates a problem with the login functionality of the platform, which is a critical feature. Users are unable to access their accounts, suggesting a potential bug that needs to be addressed.",
- "The issue is requesting the addition of support for a new file type (.docx), which indicates a desire for new functionality in the system. This aligns with the definition of a feature request, as it seeks to enhance the capabilities of the application."
+ "The issue is requesting the addition of support for a new file type (.docx), which indicates a desire for new functionality in the system. This aligns with the definition of a feature request, as it seeks to enhance the capabilities of the application.",
]
@@ -107,7 +113,7 @@ async def test_label_studio_skill_with_ner():
{
"type": "LabelStudioSkill",
"name": "AnnotationResult",
- "input_template": 'Extract entities from the input text:\n{text}',
+ "input_template": "Extract entities from the input text:\n{text}",
"label_config": """
@@ -117,7 +123,7 @@ async def test_label_studio_skill_with_ner():
- """
+ """,
}
],
}
@@ -127,27 +133,288 @@ async def test_label_studio_skill_with_ner():
expected_predictions = [
[
- {'start': 0, 'end': 10, 'labels': ['Organization'], 'text': 'Apple Inc.'},
- {'start': 17, 'end': 58, 'labels': ['Organization'], 'text': 'American multinational technology company'}
+ {"start": 0, "end": 10, "labels": ["Organization"], "text": "Apple Inc."},
+ {
+ "start": 17,
+ "end": 58,
+ "labels": ["Organization"],
+ "text": "American multinational technology company",
+ },
],
[
- {'start': 4, 'end': 13, 'labels': ['Product'], 'text': 'iPhone 14'},
- {'start': 44, 'end': 53, 'labels': ['Organization'], 'text': 'Apple Inc'}
+ {"start": 4, "end": 13, "labels": ["Product"], "text": "iPhone 14"},
+ {"start": 44, "end": 53, "labels": ["Organization"], "text": "Apple Inc"},
],
[
- {'start': 4, 'end': 15, 'labels': ['Product'], 'text': 'MacBook Pro'},
- {'start': 88, 'end': 98, 'labels': ['Organization'], 'text': 'Apple Inc.'},
- {'start': 29, 'end': 38, 'labels': ['Product'], 'text': 'Macintosh'}
+ {"start": 4, "end": 15, "labels": ["Product"], "text": "MacBook Pro"},
+ {"start": 88, "end": 98, "labels": ["Organization"], "text": "Apple Inc."},
+ {"start": 29, "end": 38, "labels": ["Product"], "text": "Macintosh"},
],
[
- {'start': 4, 'end': 15, 'labels': ['Product'], 'text': 'Apple Watch'},
- {'start': 54, 'end': 63, 'labels': ['Organization'], 'text': 'Apple Inc'}
+ {"start": 4, "end": 15, "labels": ["Product"], "text": "Apple Watch"},
+ {"start": 54, "end": 63, "labels": ["Organization"], "text": "Apple Inc"},
],
[
- {'start': 4, 'end': 8, 'labels': ['Product'], 'text': 'iPad'},
- {'start': 76, 'end': 85, 'labels': ['Organization'], 'text': 'Apple Inc'}
- ]
+ {"start": 4, "end": 8, "labels": ["Product"], "text": "iPad"},
+ {"start": 76, "end": 85, "labels": ["Organization"], "text": "Apple Inc"},
+ ],
]
assert predictions.entities.tolist() == expected_predictions
-
\ No newline at end of file
+
+
+@pytest.mark.vcr
+@pytest.mark.asyncio
+async def test_label_studio_skill_valid_predictions():
+ """
+ Fuzz test matrix of text input tags x control tags x models
+ """
+
+ ALLOWED_OBJECT_TAGS = {"Text", "HyperText"}
+ # ALLOWED_CONTROL_TAGS = {'Choices', 'Labels', 'TextArea', 'Rating', 'Number', 'Pairwise'}
+ MODELS = {"gpt-4o-mini", "gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"}
+ RUNS_PER_MODEL = 5
+
+ sample_text = """
+
Product Review: iPhone 14 Pro
+
+ The new iPhone 14 Pro represents a significant upgrade over previous models. The build quality is exceptional, with premium materials used throughout. Key features include:
+
+
+ Dynamic Island display integration
+ 48MP main camera
+ A16 Bionic chip
+
+
+ Battery life is impressive, lasting a full day of heavy use. The camera system produces stunning photos in both daylight and low-light conditions.
+
+ Overall, while expensive at $999, this device delivers excellent value for power users and photography enthusiasts.
+ """
+
+ choices_label_configs = [
+ # test defaults
+ """
+
+
+
+
+
+
+
+
+ """,
+ # test required=false and choices=multiple
+ """
+
+
+
+
+
+
+
+
+ """,
+ # test nested choices
+ """
+
+
+
+
+
+
+
+
+
+
+
+
+ """,
+ # TODO: test value=$task_column
+ # TODO: perRegion is not supported
+ # TODO: perItem is not supported?
+ ]
+
+ labels_label_configs = [
+ # test basic NER
+ """
+
+
+
+
+
+
+
+
+ """,
+ # test alias and preselection
+ """
+
+
+
+
+
+
+
+
+ """,
+ # test multiple selection
+ """
+
+
+
+
+
+
+
+
+ """,
+ # test max usages
+ """
+
+
+
+
+
+
+
+
+ """,
+ # test granularity
+ """
+
+
+
+
+
+
+
+
+ """,
+ # TODO: test value=$task_column
+ ]
+
+ textarea_label_configs = [
+ # test basic textarea
+ """
+
+
+
+
+ """,
+ # test required
+ """
+
+
+
+
+ """,
+ # test max submissions and duplicates
+ # TODO are these meaningful without perRegion?
+ """
+
+
+
+
+ """,
+ # test default value
+ """
+
+
+
+
+ """,
+ # TODO text perRegion
+ ]
+
+ all_label_configs = (
+ choices_label_configs + labels_label_configs + textarea_label_configs
+ )
+
+ # add configs for object tags besides Text
+ for label_config in all_label_configs.copy():
+ for tag in ALLOWED_OBJECT_TAGS - {"Text"}:
+ new_config = label_config.replace("