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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 | |
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| # 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 | |
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| # AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS | |
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| # 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 | |
| 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} | |
| def subjectivity_styletransfer(): | |
| MODEL_PATH = "cffl/bart-base-styletransfer-subjective-to-neutral" | |
| return StyleTransfer(model_identifier=MODEL_PATH, max_gen_length=200) | |
| def subjectivity_styleintensityclassifier(): | |
| CLS_MODEL_PATH = "cffl/bert-base-styleclassification-subjective-neutral" | |
| return StyleIntensityClassifier(model_identifier=CLS_MODEL_PATH) | |
| 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 | |
| ) | |
| 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] | |