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# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2022
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hundreds of individual components, each of which was
# individually copyrighted. Each Cloudera open source product is a
# collective work under U.S. Copyright Law. Your license to use the
# collective work is as provided in your written agreement with
# Cloudera. Used apart from the collective work, this file is
# licensed for your use pursuant to the open source license
# identified above.
#
# This code is provided to you pursuant a written agreement with
# (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
# this code. If you do not have a written agreement with Cloudera nor
# with an authorized and properly licensed third party, you do not
# have any rights to access nor to use this code.
#
# Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
# contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
# KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
# WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
# IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
# AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
# ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
# OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
# CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
# RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
import pytest
import transformers
from src.style_transfer import StyleTransfer
from src.style_classification import StyleIntensityClassifier
from src.content_preservation import ContentPreservationScorer
from src.transformer_interpretability import InterpretTransformer
@pytest.fixture
def subjectivity_example_data():
examples = [
"""there is an iconic roadhouse, named "spud's roadhouse", which sells fuel and general shop items , has great meals and has accommodation.""",
"chemical abstracts service (cas), a prominent division of the american chemical society, is the world's leading source of chemical information.",
"the most serious scandal was the iran-contra affair.",
"another strikingly elegant four-door saloon for the s3 continental came from james young.",
"other ambassadors also sent their messages of condolence following her passing.",
]
ground_truth = [
'there is a roadhouse, named "spud\'s roadhouse", which sells fuel and general shop items and has accommodation.',
"chemical abstracts service (cas), a division of the american chemical society, is a source of chemical information.",
"one controversy was the iran-contra affair.",
"another four-door saloon for the s3 continental came from james young.",
"other ambassadors also sent their messages of condolence following her death.",
]
return {"examples": examples, "ground_truth": ground_truth}
@pytest.fixture
def subjectivity_styletransfer():
MODEL_PATH = "cffl/bart-base-styletransfer-subjective-to-neutral"
return StyleTransfer(model_identifier=MODEL_PATH, max_gen_length=200)
@pytest.fixture
def subjectivity_styleintensityclassifier():
CLS_MODEL_PATH = "cffl/bert-base-styleclassification-subjective-neutral"
return StyleIntensityClassifier(model_identifier=CLS_MODEL_PATH)
@pytest.fixture
def subjectivity_contentpreservationscorer():
CLS_MODEL_PATH = "cffl/bert-base-styleclassification-subjective-neutral"
SBERT_MODEL_PATH = "sentence-transformers/all-MiniLM-L6-v2"
return ContentPreservationScorer(
cls_model_identifier=CLS_MODEL_PATH, sbert_model_identifier=SBERT_MODEL_PATH
)
@pytest.fixture
def subjectivity_interprettransformer():
CLS_MODEL_PATH = "cffl/bert-base-styleclassification-subjective-neutral"
return InterpretTransformer(cls_model_identifier=CLS_MODEL_PATH)
# test class initialization
def test_StyleTransfer_init(subjectivity_styletransfer):
assert isinstance(
subjectivity_styletransfer.pipeline,
transformers.pipelines.text2text_generation.Text2TextGenerationPipeline,
)
def test_StyleIntensityClassifier_init(subjectivity_styleintensityclassifier):
assert isinstance(
subjectivity_styleintensityclassifier.pipeline,
transformers.pipelines.text_classification.TextClassificationPipeline,
)
def test_ContentPreservationScorer_init(subjectivity_contentpreservationscorer):
assert isinstance(
subjectivity_contentpreservationscorer.cls_model,
transformers.models.bert.modeling_bert.BertForSequenceClassification,
)
assert isinstance(
subjectivity_contentpreservationscorer.sbert_model,
transformers.models.bert.modeling_bert.BertModel,
)
def test_InterpretTransformer_init(subjectivity_interprettransformer):
assert isinstance(
subjectivity_interprettransformer.cls_model,
transformers.models.bert.modeling_bert.BertForSequenceClassification,
)
# test class functionality
def test_StyleTransfer_transfer(subjectivity_styletransfer, subjectivity_example_data):
assert subjectivity_example_data[
"ground_truth"
] == subjectivity_styletransfer.transfer(subjectivity_example_data["examples"])
def test_StyleIntensityClassifier_calculate_transfer_intensity_fraction(
subjectivity_styleintensityclassifier, subjectivity_example_data
):
sti_frac = (
subjectivity_styleintensityclassifier.calculate_transfer_intensity_fraction(
input_text=subjectivity_example_data["examples"],
output_text=subjectivity_example_data["ground_truth"],
)
)
assert sti_frac == [
0.9891820847234861,
0.9808499743983614,
0.8070009460737938,
0.9913705583756346,
0.9611679711017459,
]
def test_ContentPreservationScorer_calculate_content_preservation_score(
subjectivity_contentpreservationscorer, subjectivity_example_data
):
cps = subjectivity_contentpreservationscorer.calculate_content_preservation_score(
input_text=subjectivity_example_data["examples"],
output_text=subjectivity_example_data["ground_truth"],
mask_type="none",
)
assert cps == [0.9369, 0.9856, 0.7328, 0.9718, 0.9709]
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