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"Apache License 2.0" authors = ["Amazon FMEval Team "] @@ -14,40 +14,42 @@ classifiers=[ "Natural Language :: English", "Programming Language :: Python", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", ] [tool.poetry.dependencies] python = "^3.10" -urllib3 = ">=2.2.2" -ray = "2.23.0" +urllib3 = ">=2.3.0" +ray = "2.40.0" semantic-version = "2.10.0" pyarrow = "*" -pyfunctional = "1.4.3" -torch = ">=2.0.0, !=2.0.1, !=2.1.0" -matplotlib = "^3.8.3" +pyfunctional = "1.5.0" +torch = ">=2.5.0" +matplotlib = "^3.10.0" # https://discuss.ray.io/t/pandas-importerror-with-ray-data-dataset-show/13486 -pandas = "2.1.4" +pandas = "2.2.3" nltk = "^3.9.0" markdown = "*" IPython = "*" evaluate = "^0.4.0" rouge-score = "^0.1.2" bert-score = "^0.3.13" -scikit-learn = "^1.3.1" -jiwer = "^3.0.3" -transformers = "^4.36.0" -sagemaker = "^2.225.0" +scikit-learn = "^1.6.0" +jiwer = "^3.0.5" +transformers = "^4.47.0" +sagemaker = "^2.237.1" testbook = "^0.4.2" -ipykernel = "^6.26.0" -mypy-boto3-bedrock = "^1.33.2" -grpcio = "^1.60.0" -aiohttp = "^3.9.2" +ipykernel = "^6.29.5" +mypy-boto3-bedrock = "^1.35.75" +grpcio = "^1.68.1" +aiohttp = "^3.11.11" [tool.poetry.group.dev.dependencies] fire = "*" -black = "24.3.0" -pre-commit = "3.3.3" +black = "24.10.0" +pre-commit = "^4.0.0" pytest = "*" pytest-pspec = "*" flake8 = "*" @@ -61,7 +63,7 @@ conventional-pre-commit = "*" optional = true [tool.poetry.group.docs.dependencies] -pdoc = "^14.4.0" +pdoc = "^15.0.1" [build-system] requires = ["poetry-core"] diff --git a/test/unit/eval_algorithms/test_classification_accuracy.py b/test/unit/eval_algorithms/test_classification_accuracy.py index df6429a3..03d68830 100644 --- a/test/unit/eval_algorithms/test_classification_accuracy.py +++ b/test/unit/eval_algorithms/test_classification_accuracy.py @@ -60,24 +60,26 @@ CLASSIFICATION_DATASET_WITHOUT_MODEL_OUTPUT = CLASSIFICATION_DATASET.drop_columns( - DatasetColumns.MODEL_OUTPUT.value.name + cols=[DatasetColumns.MODEL_OUTPUT.value.name] ) -CLASSIFICATION_DATASET_WITHOUT_MODEL_INPUT = CLASSIFICATION_DATASET.drop_columns(DatasetColumns.MODEL_INPUT.value.name) +CLASSIFICATION_DATASET_WITHOUT_MODEL_INPUT = CLASSIFICATION_DATASET.drop_columns( + cols=[DatasetColumns.MODEL_INPUT.value.name] +) CLASSIFICATION_DATASET_WITHOUT_MODEL_INPUT_OR_MODEL_OUTPUT = CLASSIFICATION_DATASET_WITHOUT_MODEL_INPUT.drop_columns( - DatasetColumns.MODEL_OUTPUT.value.name + cols=[DatasetColumns.MODEL_OUTPUT.value.name] ) CLASSIFICATION_DATASET_WITHOUT_TARGET_OUTPUT = CLASSIFICATION_DATASET.drop_columns( - DatasetColumns.TARGET_OUTPUT.value.name + cols=[DatasetColumns.TARGET_OUTPUT.value.name] ) CLASSIFICATION_DATASET_WITHOUT_CATEGORY_WITHOUT_MODEL_OUTPUT = CLASSIFICATION_DATASET_WITHOUT_MODEL_OUTPUT.drop_columns( - DatasetColumns.CATEGORY.value.name + cols=[DatasetColumns.CATEGORY.value.name] ) -CLASSIFICATION_DATASET_WITHOUT_CATEGORY = CLASSIFICATION_DATASET.drop_columns(DatasetColumns.CATEGORY.value.name) +CLASSIFICATION_DATASET_WITHOUT_CATEGORY = CLASSIFICATION_DATASET.drop_columns(cols=[DatasetColumns.CATEGORY.value.name]) DATASET_SCORES = [ EvalScore(name=CLASSIFICATION_ACCURACY_SCORE, value=2 / 3), diff --git a/test/unit/eval_algorithms/test_classification_accuracy_semantic_robustness.py b/test/unit/eval_algorithms/test_classification_accuracy_semantic_robustness.py index 6cbbcc04..40288062 100644 --- a/test/unit/eval_algorithms/test_classification_accuracy_semantic_robustness.py +++ b/test/unit/eval_algorithms/test_classification_accuracy_semantic_robustness.py @@ -60,7 +60,6 @@ DATASET_WITHOUT_MODEL_INPUT = DATASET.drop_columns(cols=[DatasetColumns.MODEL_INPUT.value.name]) - CATEGORY_SCORES = [ CategoryScore( name="brownie", diff --git a/test/unit/eval_algorithms/test_general_semantic_robustness.py b/test/unit/eval_algorithms/test_general_semantic_robustness.py index 13c83363..cb889875 100644 --- a/test/unit/eval_algorithms/test_general_semantic_robustness.py +++ b/test/unit/eval_algorithms/test_general_semantic_robustness.py @@ -59,15 +59,17 @@ ] ) -DATASET_WITH_ONLY_BERT_SCORE = DATASET_WITH_SCORES.drop_columns(cols=WER_SCORE) +DATASET_WITH_ONLY_BERT_SCORE = DATASET_WITH_SCORES.drop_columns(cols=[WER_SCORE]) -DATASET_WITH_MODEL_OUTPUT = DATASET_WITH_ONLY_BERT_SCORE.drop_columns(cols=BERT_SCORE_DISSIMILARITY) +DATASET_WITH_MODEL_OUTPUT = DATASET_WITH_ONLY_BERT_SCORE.drop_columns(cols=[BERT_SCORE_DISSIMILARITY]) -DATASET = DATASET_WITH_MODEL_OUTPUT.drop_columns(cols=DatasetColumns.MODEL_OUTPUT.value.name) +DATASET = DATASET_WITH_MODEL_OUTPUT.drop_columns(cols=[DatasetColumns.MODEL_OUTPUT.value.name]) -DATASET_NO_CATEGORY = DATASET.drop_columns(cols=DatasetColumns.CATEGORY.value.name) +DATASET_NO_CATEGORY = DATASET.drop_columns(cols=[DatasetColumns.CATEGORY.value.name]) -DATASET_WITH_MODEL_OUTPUT_NO_CATEGORY = DATASET_WITH_MODEL_OUTPUT.drop_columns(cols=DatasetColumns.CATEGORY.value.name) +DATASET_WITH_MODEL_OUTPUT_NO_CATEGORY = DATASET_WITH_MODEL_OUTPUT.drop_columns( + cols=[DatasetColumns.CATEGORY.value.name] +) class ConstantModel(ModelRunner): diff --git a/test/unit/eval_algorithms/test_qa_accuracy.py b/test/unit/eval_algorithms/test_qa_accuracy.py index b437b24b..66f2b057 100644 --- a/test/unit/eval_algorithms/test_qa_accuracy.py +++ b/test/unit/eval_algorithms/test_qa_accuracy.py @@ -96,16 +96,16 @@ ] ) -QA_DATASET_WITHOUT_MODEL_OUTPUT = QA_DATASET.drop_columns(DatasetColumns.MODEL_OUTPUT.value.name) +QA_DATASET_WITHOUT_MODEL_OUTPUT = QA_DATASET.drop_columns(cols=[DatasetColumns.MODEL_OUTPUT.value.name]) -QA_DATASET_WITHOUT_MODEL_INPUT = QA_DATASET.drop_columns(DatasetColumns.MODEL_INPUT.value.name) +QA_DATASET_WITHOUT_MODEL_INPUT = QA_DATASET.drop_columns(cols=[DatasetColumns.MODEL_INPUT.value.name]) -QA_DATASET_WITHOUT_TARGET_OUTPUT = QA_DATASET.drop_columns(DatasetColumns.TARGET_OUTPUT.value.name) +QA_DATASET_WITHOUT_TARGET_OUTPUT = QA_DATASET.drop_columns(cols=[DatasetColumns.TARGET_OUTPUT.value.name]) -QA_DATASET_WITHOUT_CATEGORY = QA_DATASET.drop_columns(DatasetColumns.CATEGORY.value.name) +QA_DATASET_WITHOUT_CATEGORY = QA_DATASET.drop_columns(cols=[DatasetColumns.CATEGORY.value.name]) QA_DATASET_WITHOUT_CATEGORY_WITHOUT_MODEL_OUTPUT = QA_DATASET_WITHOUT_CATEGORY.drop_columns( - DatasetColumns.MODEL_OUTPUT.value.name + cols=[DatasetColumns.MODEL_OUTPUT.value.name] ) CATEGORY_SCORES = [ diff --git a/test/unit/reporting/test_cells.py b/test/unit/reporting/test_cells.py index 3de41be8..760f68c8 100644 --- a/test/unit/reporting/test_cells.py +++ b/test/unit/reporting/test_cells.py @@ -30,8 +30,8 @@ def get_num_table_rows(html: str) -> int: class TestCell: # Hard-coded base64 strings corresponding to different test cases - FIGURE_CELL_NO_ARGS = "

" - FIGURE_CELL_WITH_ARGS = "

" + FIGURE_CELL_NO_ARGS = "

" + FIGURE_CELL_WITH_ARGS = "

" class TestCaseMarkdownInit(NamedTuple): args: List[Union[str, MarkdownCell]]