index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
31,028
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/outcomes.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from enum import Enum
from azimuth.types import AliasModel
class OutcomeName(str, Enum):
CorrectAndPredicted = "CorrectAndPredicted"
CorrectAndRejected = "CorrectAndRejected"
IncorrectAndPredicted = "IncorrectAndPredicted"
IncorrectAndRejected = "IncorrectAndRejected"
ALL_OUTCOMES = [
OutcomeName.CorrectAndPredicted,
OutcomeName.CorrectAndRejected,
OutcomeName.IncorrectAndPredicted,
OutcomeName.IncorrectAndRejected,
]
class OutcomeResponse(AliasModel):
model_outcome: OutcomeName
postprocessed_outcome: OutcomeName
|
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"/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,029
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/plots.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from enum import Enum
from typing import List
from azimuth.types import DatasetSplitName
TITLE_FONT_SIZE = 20
AXIS_FONT_SIZE = 14
MARGIN = 32
PAPER_MARGINS = dict(t=54 + MARGIN, b=AXIS_FONT_SIZE + MARGIN, r=180, l=180)
X_LEFT_LEGEND = 1.037
X_RIGHT_LEGEND = 1.08
Y_LEGEND_LINE_1 = -17
Y_LEGEND_LINE_HEIGHT = -21
SANKEY_PALETTE = [
"#B8D8E9",
"#83A8BC",
"#FDCC8C",
"#FCB11F",
"#FCAEAD",
"#FF8271",
"#C1E5A1",
"#558A26",
"#FFFFB9",
"#FFEB38",
"#4C93C3",
"#1D5A93",
"#D5C1DE",
"#B873D8",
"#9DD196",
"#56D44D",
"#A0E8E3",
"#45BDB5",
]
class Colors(str, Enum):
Red = "#DB8F8F"
Red_transparent = "rgba(209, 86, 86, 0.7)"
Orange = "#ff9800"
DataViz2 = "#006DB3"
DataViz1 = "#63CCFF"
Axis = "rgba(0, 0, 0, 0.2)"
Gray_transparent = "rgba(41, 62, 64, 0.08)"
Blue = "#2196F3"
Text = "#2a3d40"
DATASET_SPLIT_COLORS = {
DatasetSplitName.eval: Colors.DataViz1,
DatasetSplitName.train: Colors.DataViz2,
}
DATASET_SPLIT_PRETTY_NAMES = {
DatasetSplitName.eval: "evaluation set",
DatasetSplitName.train: "training set",
}
DATASET_SPLIT_OFFSETS = {DatasetSplitName.eval: 0, DatasetSplitName.train: -0.4}
def plot_height_based_on_cls_count(cls_count):
return PAPER_MARGINS["t"] + PAPER_MARGINS["b"] + max(cls_count, 6) * 23
def fig_default(fig):
"""Basic layout applicable to all plots.
Args:
fig: Figure.
Returns:
Figure with layout modified.
"""
fig.update_layout(
colorway=[Colors.DataViz1, Colors.DataViz2],
plot_bgcolor="white",
title_x=0.5,
title_font_size=TITLE_FONT_SIZE,
font_family="Gilroy",
font_size=AXIS_FONT_SIZE,
font_color=Colors.Text,
margin=PAPER_MARGINS,
width=1000,
)
fig.update_xaxes(tickfont_size=AXIS_FONT_SIZE, fixedrange=True)
fig.update_yaxes(tickfont_size=AXIS_FONT_SIZE, fixedrange=True)
return fig
def shorten_cls_names(cls_names: List[str]) -> List[str]:
return [
class_name if len(class_name) < 24 else class_name[:20] + "..." for class_name in cls_names
]
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/types/utterance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/model_performance/utterance_count.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/validation.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/class_overlap.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/plots/sankey_plot.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/tests/test_modules/test_word_analysis/test_top_words.py": ["/azimuth/modules/word_analysis/top_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/tests/utils.py"], "/tests/test_modules/test_caching.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/exclude_fields_from_cache.py", "/tests/utils.py"], "/azimuth/app_loader.py": ["/azimuth/app.py", "/azimuth/config.py"], "/azimuth/routers/app.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/app.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/azimuth/modules/base_classes/indexable_module.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/validation.py"], "/azimuth/modules/pipeline_comparison/prediction_comparison.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_syntax_tagging.py": ["/azimuth/modules/dataset_analysis/syntax_tagging.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py"], "/azimuth/modules/dataset_analysis/syntax_tagging.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py", "/azimuth/utils/validation.py"], "/azimuth/dataset_split_manager.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/perturbation_test.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/model_performance/confusion_matrix.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/modules/model_performance/metrics.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/ece.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/config.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exclude_fields_from_cache.py", "/azimuth/utils/openapi.py"], "/tests/utils.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py"], "/azimuth/__init__.py": ["/azimuth/utils/logs.py"], "/tests/test_utils/test_dataset_filtering.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", 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"/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_app.py": ["/azimuth/routers/utterances.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/dataset_analysis/similarity_analysis.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/model_performance.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/utils/ml/ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/perturbation_testing/__init__.py": 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"/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", 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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", 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"/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], 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"/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,030
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_module.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import time
import pytest
from datasets import Dataset, DatasetDict
from distributed import Variable
from azimuth.config import CustomObject
from azimuth.modules.base_classes import AggregationModule, IndexableModule, Module
from azimuth.modules.model_contracts import HFTextClassificationModule
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod
def test_ds_loading(simple_text_config):
mod = Module(DatasetSplitName.eval, config=simple_text_config)
ds = mod.get_dataset_split()
assert isinstance(ds, Dataset)
assert len(ds) >= 1
project_hash = mod.config.get_project_hash()
assert len(mod.artifact_manager.dataset_split_managers_mapping[project_hash]) == 1
def test_model_loading(simple_text_config):
mod = HFTextClassificationModule(
DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Inputs, pipeline_index=0
),
)
hf_pipeline = mod.get_model()
assert callable(hf_pipeline)
def test_threshold(simple_text_config):
# by default, only ModelContractModules are affected by thresholds.
assert (
HFTextClassificationModule(
DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Inputs, pipeline_index=0
),
).get_threshold()
== 0.7
)
assert (
HFTextClassificationModule(
DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(
threshold=0.9, model_contract_method_name=SupportedMethod.Inputs, pipeline_index=0
),
).get_threshold()
== 0.9
)
def test_callback(simple_text_config, dask_client):
class MyModule(AggregationModule):
def compute_on_dataset_split(self):
time.sleep(1)
return [3]
def my_callback(fut, module, param):
time.sleep(2)
return fut.result()[0] + param
mod = MyModule(DatasetSplitName.eval, simple_text_config)
mod.start_task_on_dataset_split(dask_client)
# on_end added automatically
assert len(mod._callbacks) == 1
assert not mod.done()
mod.add_done_callback(my_callback, param=3)
assert len(mod._callbacks) == 2
assert not mod.done()
while not mod.done():
# Wait for everyone to finish
time.sleep(1)
assert all(m.done for m in mod._callbacks)
# 3 + 3, much wow
assert mod.result()[0] == 3
assert mod._callbacks[-1].result == 6
# Should be cached
new_mod = MyModule(DatasetSplitName.eval, simple_text_config)
assert new_mod.done() and len(new_mod._callbacks) == 0
class ModuleA(AggregationModule):
def compute_on_dataset_split(self):
# Awesome module that returns the result of a Variable.
a = Variable("my-variable")
val = a.get()
return [val]
class ModuleB(AggregationModule):
def compute_on_dataset_split(self):
# Awesome module that waits 3 seconds before setting a Variable
time.sleep(3)
a = Variable("my-variable")
a.set(123)
return [123]
def test_dependencies(simple_text_config, dask_client):
# Test that a module A can wait for a module B.
a = Variable("my-variable")
a.set(0)
modA = ModuleA(DatasetSplitName.eval, simple_text_config)
modB = ModuleB(DatasetSplitName.eval, simple_text_config)
assert not any(m.done() for m in [modA, modB])
modB.start_task_on_dataset_split(dask_client)
modA.start_task_on_dataset_split(dask_client, [modB])
# Wait and check that the modA is not done.
time.sleep(1)
assert not modA.done()
# We now wait for the results of all modules.
[resB] = modB.result()
[resA] = modA.result()
# We check that the values match, ie module A did not take the initial value (0).
assert resA == resB
# wait for events to be set
time.sleep(2)
assert modB.done_event.is_set()
assert modA.done_event.is_set()
def test_dependencies_failing(simple_text_config, dask_client):
# Test that we can't wait for a Module that is not started.
a = Variable("my-variable")
a.set(0)
modA = ModuleA(DatasetSplitName.eval, simple_text_config)
modB = ModuleB(DatasetSplitName.eval, simple_text_config)
assert not any(m.done() for m in [modA, modB])
with pytest.raises(ValueError) as excinfo:
modA.start_task_on_dataset_split(dask_client, dependencies=[modB])
assert "Can't wait for an unstarted Module" in str(excinfo.value)
def my_dataset_fn(valid):
ds = {
"train": Dataset.from_dict({"utterance": ["a", "b", "c"], "label": ["1", "2", "3"]}),
"test": Dataset.from_dict({"utterance": ["h", "i", "j"], "label": ["7", "8", "9"]}),
}
if valid:
ds["validation"] = Dataset.from_dict(
{"utterance": ["d", "e", "f"], "label": ["4", "5", "6"]}
)
return DatasetDict(ds).class_encode_column("label")
def test_validation_priority(simple_text_config):
new_simple_text_config = simple_text_config.copy(deep=True)
new_simple_text_config.dataset = CustomObject(
class_name="tests.test_modules.test_module.my_dataset_fn", kwargs={"valid": True}
)
mod = IndexableModule(
DatasetSplitName.eval,
config=new_simple_text_config,
mod_options=ModuleOptions(indices=[0, 1]),
)
ds = mod.get_dataset_split()
assert ds["utterance"] == ["d", "e"]
# Otherwise, we take validation
new_simple_text_config.dataset = CustomObject(
class_name="tests.test_modules.test_module.my_dataset_fn", kwargs={"valid": False}
)
mod = Module(DatasetSplitName.eval, config=new_simple_text_config)
ds = mod.get_dataset_split()
assert ds["utterance"] == ["h", "i", "j"]
def test_mod_options(tiny_text_config):
with pytest.raises(ValueError, match="Unexpected mod_options"):
IndexableModule(
DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0),
)
with pytest.raises(ValueError, match="HFTextClassificationModule requires"):
HFTextClassificationModule(DatasetSplitName.eval, config=tiny_text_config)
if __name__ == "__main__":
pytest.main()
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,031
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/task_mapping.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import azimuth.modules.model_performance.outcomes
from azimuth.modules import perturbation_testing
from azimuth.modules.dataset_analysis import (
class_overlap,
dataset_warnings,
similarity_analysis,
syntax_tagging,
)
from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping
from azimuth.modules.model_performance import (
confidence_binning,
confusion_matrix,
metrics,
outcome_count,
)
from azimuth.modules.pipeline_comparison import prediction_comparison
from azimuth.modules.validation import validation
from azimuth.modules.word_analysis import tokens_to_words, top_words
from azimuth.types import SupportedMethod, SupportedModule
# Uses raw indices and needs access to the model.
model_contract_methods = {
SupportedMethod.Inputs: model_contract_task_mapping,
SupportedMethod.Predictions: model_contract_task_mapping,
SupportedMethod.Saliency: model_contract_task_mapping,
SupportedMethod.PostProcess: model_contract_task_mapping,
}
# Black formats this in a way where the line is too long.
# fmt: off
modules = {
SupportedModule.ClassOverlap: class_overlap.ClassOverlapModule,
SupportedModule.ConfidenceBinIndex: confidence_binning.ConfidenceBinIndexModule,
SupportedModule.ConfidenceHistogram: confidence_binning.ConfidenceHistogramModule,
SupportedModule.ConfusionMatrix: confusion_matrix.ConfusionMatrixModule,
SupportedModule.DatasetWarnings: dataset_warnings.DatasetWarningsModule,
SupportedModule.FAISS: similarity_analysis.FAISSModule,
SupportedModule.Metrics: metrics.MetricsModule,
SupportedModule.MetricsPerFilter: metrics.MetricsPerFilterModule,
SupportedModule.NeighborsTagging: similarity_analysis.NeighborsTaggingModule,
SupportedModule.Outcome: azimuth.modules.model_performance.outcomes.OutcomesModule,
SupportedModule.OutcomeCountPerFilter: outcome_count.OutcomeCountPerFilterModule,
SupportedModule.OutcomeCountPerThreshold: outcome_count.OutcomeCountPerThresholdModule,
SupportedModule.PerturbationTesting: perturbation_testing.PerturbationTestingModule,
SupportedModule.PerturbationTestingMerged: perturbation_testing.PerturbationTestingMergedModule,
SupportedModule.PerturbationTestingSummary:
perturbation_testing.PerturbationTestingSummaryModule,
SupportedModule.PredictionComparison: prediction_comparison.PredictionComparisonModule,
SupportedModule.SyntaxTagging: syntax_tagging.SyntaxTaggingModule,
SupportedModule.TokensToWords: tokens_to_words.TokensToWordsModule,
SupportedModule.TopWords: top_words.TopWordsModule,
SupportedModule.Validation: validation.ValidationModule
}
# fmt: on
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,032
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/general/dataset.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from enum import Enum
class DatasetSplitName(str, Enum):
eval = "eval"
train = "train"
all = "all"
class DatasetColumn(str, Enum):
row_idx = "row_idx"
model_predictions = "model_predictions"
postprocessed_prediction = "postprocessed_prediction"
pipeline_steps = "pipeline_steps"
model_confidences = "model_confidences"
postprocessed_confidences = "postprocessed_confidences"
confidence_bin_idx = "confidence_bin_idx"
model_outcome = "model_outcome"
postprocessed_outcome = "postprocessed_outcome"
word_count = "word_count"
neighbors_train = f"neighbors_{DatasetSplitName.train}"
neighbors_eval = f"neighbors_{DatasetSplitName.eval}"
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,033
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/model_contracts/hf_text_classification.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List, Optional, Set
import numpy as np
import structlog
import torch
from datasets import Dataset
from azimuth.config import ModelContractConfig
from azimuth.modules.model_contracts.text_classification import TextClassificationModule
from azimuth.types import DatasetSplitName, ModuleOptions
from azimuth.types.general.module_arguments import GradientCalculation
from azimuth.types.task import PredictionResponse, SaliencyResponse
from azimuth.utils.ml.mc_dropout import MCDropout
from azimuth.utils.ml.saliency import (
find_word_embeddings_layer,
get_saliency,
register_embedding_gradient_hook,
register_embedding_list_hook,
)
log = structlog.get_logger(__file__)
EPSILON = 1e-6
ITERATIONS = 100
class HFTextClassificationModule(TextClassificationModule):
"""Handles text classification prediction and saliency."""
optional_mod_options: Set[str] = TextClassificationModule.optional_mod_options | {
"gradient_calculation",
"iterations",
"use_bma",
"filter_class",
}
def __init__(
self,
dataset_split_name: DatasetSplitName,
config: ModelContractConfig,
mod_options: Optional[ModuleOptions] = None,
):
super().__init__(dataset_split_name, config, mod_options)
gc_value = self.mod_options.gradient_calculation
self.saliency_layer = self.config.saliency_layer
self.gradient_calculation = GradientCalculation(gc_value)
def predict(self, batch: Dataset) -> List[PredictionResponse]:
"""Get predictions for a batch of utterances.
If more than one iteration is requested, this function will perform MC Dropout and
compute both the mean prediction and the epistemic uncertainty using BALD.
Args:
batch: batch of utterances.
Returns:
Predictions for the batch of utterances.
"""
utterances = batch[self.config.columns.text_input]
hf_pipeline = self.get_model()
use_bma = self.mod_options.use_bma and self.config.uncertainty.iterations > 1
if use_bma:
# TODO structlog warning issue tracked in BaaL #192
from baal.active.heuristics import BALD
with MCDropout(hf_pipeline.model):
n = self.config.uncertainty.iterations
n_predictions = np.stack(
[
self.extract_probs_from_output(
hf_pipeline(
utterances,
num_workers=0,
batch_size=self.config.batch_size,
truncation=True,
)
)
for _ in range(n)
],
axis=-1,
)
epistemic: List[float] = BALD().get_uncertainties(n_predictions)
predictions = n_predictions.mean(-1)
else:
epistemic = [0.0] * len(utterances)
predictions = hf_pipeline(
utterances, num_workers=0, batch_size=self.config.batch_size, truncation=True
)
(
model_output,
postprocessed_output,
preprocessing_steps,
postprocessing_steps,
) = self.get_postprocessed_output(batch, predictions)
return self._parse_prediction_output(
batch,
model_output,
postprocessed_output,
preprocessing_steps,
postprocessing_steps,
epistemic,
)
def saliency(self, batch: Dataset) -> List[SaliencyResponse]:
"""Get saliency maps for a batch of utterances using InputXGrad.
Saliency maps can be computed for the predicted class or for another class through the
`filter_class` mod_options.
Args:
batch: Batch of utterances.
Returns:
Saliency maps for a batch of utterances.
"""
if self.saliency_layer is None:
raise ValueError("This method should not be called when saliency_layer is not defined.")
hf_pipeline = self.get_model()
hf_model = hf_pipeline.model
inputs = hf_pipeline.tokenizer(
batch[self.config.columns.text_input],
return_tensors="pt",
padding=True,
truncation=True,
)
all_tokens = [hf_pipeline.tokenizer.convert_ids_to_tokens(i) for i in inputs["input_ids"]]
# Move to the same device as the pipeline
inputs["input_ids"] = inputs["input_ids"].to(hf_pipeline.device)
inputs["attention_mask"] = inputs["attention_mask"].to(hf_pipeline.device)
logits = hf_model(**inputs)[0]
output = torch.softmax(logits, dim=1).detach().cpu().numpy()
prediction = output.argmax(-1)
embedding_layer = (
hf_model.base_model.get_input_embeddings()
if self.saliency_layer == "auto"
else find_word_embeddings_layer(hf_model, self.saliency_layer)
)
embeddings_list: List[np.ndarray] = []
handle = register_embedding_list_hook(embeddings_list, embedding_layer)
embeddings_gradients: List[np.ndarray] = []
hook = register_embedding_gradient_hook(embeddings_gradients, embedding_layer)
filter_class = self.mod_options.filter_class
selected_classes = (
[filter_class] * len(prediction) if filter_class is not None else prediction
)
inputs["labels"] = torch.tensor(
selected_classes, dtype=torch.long, device=hf_pipeline.device
)
# Do backward pass to compute gradients
hf_model.zero_grad()
_loss = hf_model(**inputs)[0] # loss is at index 0 when passing labels
_loss.backward()
handle.remove()
hook.remove()
records = []
for eg, el, tokens in zip(embeddings_gradients[0], embeddings_list[0], all_tokens):
saliency_values = get_saliency(eg, el, self.gradient_calculation)
# Strip pad_token (ex. [PAD])
saliency_values, tokens = zip(
*list(
filter(
lambda tok: tok[1] != hf_pipeline.tokenizer.pad_token,
zip(saliency_values, tokens),
)
)
)
records.append(
SaliencyResponse(
saliency=saliency_values,
tokens=tokens,
)
)
return records
|
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"/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,034
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/routers/export.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import os
import time
from os.path import join as pjoin
from typing import Dict, Generator, List, Optional
import pandas as pd
from fastapi import APIRouter, Depends, HTTPException, Query
from fastapi.responses import FileResponse
from starlette.status import HTTP_404_NOT_FOUND
from azimuth.app import (
get_all_dataset_split_managers,
get_config,
get_dataset_split_manager,
get_task_manager,
)
from azimuth.config import AzimuthConfig
from azimuth.dataset_split_manager import DatasetSplitManager, PredictionTableKey
from azimuth.task_manager import TaskManager
from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions, SupportedModule
from azimuth.types.perturbation_testing import (
PerturbationTestSummary,
PerturbedUtteranceDetailedResult,
PerturbedUtteranceResult,
)
from azimuth.utils.conversion import orjson_dumps
from azimuth.utils.project import perturbation_testing_available
from azimuth.utils.routers import (
get_last_update,
get_standard_task_result,
query_pipeline_index,
require_available_model,
require_pipeline_index,
)
from azimuth.utils.validation import assert_not_none
router = APIRouter()
@router.get(
"/dataset_splits/{dataset_split_name}/utterances",
summary="Export dataset_split as csv.",
description="Export the dataset_split to a CSV file and returns it.",
response_class=FileResponse,
)
def export_dataset(
dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager),
pipeline_index: Optional[int] = Depends(query_pipeline_index),
config: AzimuthConfig = Depends(get_config),
use_bma: bool = Query(False, title="Use Bayesian Model Averaging for better estimation."),
) -> FileResponse:
table_key = (
None
if pipeline_index is None
else PredictionTableKey.from_pipeline_index(pipeline_index, config, use_bma=use_bma)
)
path = dataset_split_manager.save_csv(table_key=table_key)
filename = os.path.basename(path)
return FileResponse(path=path, filename=filename)
@router.get(
"/dataset_splits/{dataset_split_name}/proposed_actions",
summary="Export proposed actions as csv.",
description="Export proposed actions to a CSV file and returns it.",
response_class=FileResponse,
)
def export_proposed_actions(
dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager),
) -> FileResponse:
path = dataset_split_manager.save_proposed_actions_to_csv()
return FileResponse(path=path, filename=os.path.basename(path))
@router.get(
"/perturbation_testing_summary",
summary="Export perturbation testing summary as csv.",
description="Export the perturbation testing summary to a CSV file and returns it.",
response_class=FileResponse,
dependencies=[Depends(require_available_model)],
)
def get_export_perturbation_testing_summary(
task_manager: TaskManager = Depends(get_task_manager),
config: AzimuthConfig = Depends(get_config),
dataset_managers: Dict[DatasetSplitName, Optional[DatasetSplitManager]] = Depends(
get_all_dataset_split_managers
),
pipeline_index: int = Depends(require_pipeline_index),
) -> FileResponse:
if not perturbation_testing_available(config):
raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="No summary available.")
eval_dm = dataset_managers[DatasetSplitName.eval]
train_dm = dataset_managers[DatasetSplitName.train]
last_update = get_last_update([eval_dm, train_dm])
task_result: List[PerturbationTestSummary] = get_standard_task_result(
SupportedModule.PerturbationTestingSummary,
DatasetSplitName.all,
task_manager=task_manager,
last_update=last_update,
mod_options=ModuleOptions(pipeline_index=pipeline_index),
)[0].all_tests_summary
df = pd.DataFrame.from_records([t.dict() for t in task_result])
df["example"] = df["example"].apply(lambda i: i["perturbed_utterance"])
file_label = time.strftime("%Y%m%d_%H%M%S", time.localtime())
filename = f"azimuth_export_behavioral_testing_summary_{config.name}_{file_label}.csv"
path = pjoin(config.get_project_path(), filename)
df.to_csv(path, index=False)
return FileResponse(path=path, filename=filename)
@router.get(
"/dataset_splits/{dataset_split_name}/perturbed_utterances",
summary="Export perturbed dataset split as json.",
description="Export the perturbed dataset split (training or evaluation) to a JSON file and "
"returns it.",
response_class=FileResponse,
dependencies=[Depends(require_available_model)],
)
def get_export_perturbed_set(
dataset_split_name: DatasetSplitName,
task_manager: TaskManager = Depends(get_task_manager),
dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager),
pipeline_index: int = Depends(require_pipeline_index),
config: AzimuthConfig = Depends(get_config),
use_bma: bool = Query(False, title="Use Bayesian Model Averaging for better estimation."),
) -> FileResponse:
pipeline_index_not_null = assert_not_none(pipeline_index)
file_label = time.strftime("%Y%m%d_%H%M%S", time.localtime())
filename = f"azimuth_export_modified_set_{config.name}_{dataset_split_name}_{file_label}.json"
path = pjoin(config.get_project_path(), filename)
task_result: List[List[PerturbedUtteranceResult]] = get_standard_task_result(
SupportedModule.PerturbationTesting,
dataset_split_name,
task_manager,
mod_options=ModuleOptions(pipeline_index=pipeline_index_not_null),
)
output = list(
make_utterance_level_result(
dataset_split_manager,
task_result,
pipeline_index=pipeline_index_not_null,
config=config,
use_bma=use_bma,
)
)
with open(path, "w") as f:
f.write(orjson_dumps(output).decode())
return FileResponse(path=path, filename=filename)
def make_utterance_level_result(
dm: DatasetSplitManager,
results: List[List[PerturbedUtteranceResult]],
pipeline_index: int,
config: AzimuthConfig,
use_bma: bool,
) -> Generator[Dict, None, None]:
"""Massage perturbation testing results for the frontend.
Args:
dm: Current DatasetSplitManager.
results: Output of Perturbation Testing.
pipeline_index: Index of the pipeline that made the results.
config: Azimuth config
use_bma: Use Bayesian Model Averaging for better estimation.
Returns:
Generator that yield json-able object for the frontend.
"""
for idx, (utterance, test_results) in enumerate(
zip(
dm.get_dataset_split(
PredictionTableKey.from_pipeline_index(pipeline_index, config, use_bma=use_bma)
),
results,
)
):
for test in test_results:
yield PerturbedUtteranceDetailedResult(
original_prediction=dm.get_class_names()[
utterance[DatasetColumn.postprocessed_prediction]
],
original_utterance=utterance[dm.config.columns.text_input],
original_confidence=utterance[DatasetColumn.postprocessed_confidences][0],
label=dm.get_class_names()[utterance[dm.config.columns.label]],
**{**test.dict(), "prediction": dm.get_class_names()[test.prediction]},
).dict()
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/types/utterance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/model_performance/utterance_count.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/validation.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/class_overlap.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/plots/sankey_plot.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/tests/test_modules/test_word_analysis/test_top_words.py": ["/azimuth/modules/word_analysis/top_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/tests/utils.py"], "/tests/test_modules/test_caching.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/exclude_fields_from_cache.py", "/tests/utils.py"], "/azimuth/app_loader.py": ["/azimuth/app.py", "/azimuth/config.py"], "/azimuth/routers/app.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/app.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/azimuth/modules/base_classes/indexable_module.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/validation.py"], "/azimuth/modules/pipeline_comparison/prediction_comparison.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_syntax_tagging.py": ["/azimuth/modules/dataset_analysis/syntax_tagging.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py"], "/azimuth/modules/dataset_analysis/syntax_tagging.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py", "/azimuth/utils/validation.py"], "/azimuth/dataset_split_manager.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/perturbation_test.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/model_performance/confusion_matrix.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/modules/model_performance/metrics.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/ece.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/config.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exclude_fields_from_cache.py", "/azimuth/utils/openapi.py"], "/tests/utils.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py"], "/azimuth/__init__.py": ["/azimuth/utils/logs.py"], "/tests/test_utils/test_dataset_filtering.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/tests/utils.py"], "/azimuth/modules/base_classes/module.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exclude_fields_from_cache.py", "/azimuth/utils/validation.py"], "/azimuth/types/similarity_analysis.py": ["/azimuth/types/__init__.py", "/azimuth/types/utterance.py"], "/azimuth/modules/model_contracts/custom_classification.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/text_classification_no_saliency.py", "/azimuth/types/task.py"], "/tests/test_utils/test_exclude_from_cache.py": ["/azimuth/utils/exclude_fields_from_cache.py"], "/tests/test_startup.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/startup.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/model_contracts/file_based_text_classification.py": ["/azimuth/modules/model_contracts/text_classification_no_saliency.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/azimuth/modules/model_contracts/text_classification.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py", "/azimuth/utils/project.py"], "/azimuth/task_manager.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py"], "/tests/test_modules/test_perturbation_testing/test_perturbation_testing.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/tests/utils.py"], "/tests/test_documentation.py": ["/azimuth/__init__.py", "/azimuth/modules/base_classes/__init__.py"], "/azimuth/utils/object_loader.py": ["/azimuth/config.py"], "/azimuth/types/tag.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/custom_utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_config.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/perturbation_testing/perturbation_testing.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/azimuth/utils/ml/seeding.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_perturbation_testing/test_perturbation_testing_summary.py": ["/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/tests/test_task_manager.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/aggregation_module.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/azimuth_shr/loading_resources.py": ["/azimuth/config.py"], "/azimuth/routers/dataset_warnings.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_app.py": ["/azimuth/routers/utterances.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/dataset_analysis/similarity_analysis.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/model_performance.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/utils/ml/ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/perturbation_testing/__init__.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/outcomes.py": ["/azimuth/types/__init__.py"], "/azimuth/utils/plots.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/modules/task_mapping.py": ["/azimuth/modules/model_performance/outcomes.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py"], "/azimuth/modules/model_contracts/hf_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/mc_dropout.py", "/azimuth/utils/ml/saliency.py"], "/azimuth/routers/export.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,035
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/routers/config.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Dict, List
import structlog
from fastapi import APIRouter, Body, Depends, HTTPException, Query
from starlette.status import (
HTTP_400_BAD_REQUEST,
HTTP_403_FORBIDDEN,
HTTP_500_INTERNAL_SERVER_ERROR,
)
from azimuth.app import (
get_config,
get_task_manager,
initialize_managers,
require_editable_config,
run_startup_tasks,
)
from azimuth.config import (
AzimuthConfig,
AzimuthConfigHistoryWithHash,
AzimuthValidationError,
CustomObject,
PipelineDefinition,
SupportedLanguage,
)
from azimuth.task_manager import TaskManager
from azimuth.utils.cluster import default_cluster
from azimuth.utils.project import update_config
log = structlog.get_logger(__name__)
router = APIRouter()
REQUIRED = ""
@router.get(
"/default",
summary="Get default configuration",
description="Get the default configuration",
response_model=AzimuthConfig,
)
def get_config_default(
language: SupportedLanguage = Query(AzimuthConfig.__fields__["language"].default),
) -> AzimuthConfig:
return AzimuthConfig(
dataset=CustomObject(class_name=REQUIRED),
language=language,
pipelines=[PipelineDefinition(name=REQUIRED, model=CustomObject(class_name=REQUIRED))],
)
@router.get(
"/history",
summary="Get configuration history",
description="Get the history of the configuration",
response_model=List[AzimuthConfigHistoryWithHash],
)
def get_config_history(
config: AzimuthConfig = Depends(get_config),
) -> List[AzimuthConfigHistoryWithHash]:
return config.get_config_history()
@router.get(
"",
summary="Get configuration",
description="Get the current configuration",
response_model=AzimuthConfig,
)
def get_config_def(
config: AzimuthConfig = Depends(get_config),
) -> AzimuthConfig:
return config
@router.patch(
"/validate",
summary="Validate config",
description="Validate the given partial config update and return the complete config that would"
" result if this update was applied.",
response_model=AzimuthConfig,
dependencies=[Depends(require_editable_config)],
)
def validate_config(
config: AzimuthConfig = Depends(get_config),
partial_config: Dict = Body(...),
) -> AzimuthConfig:
new_config = update_config(old_config=config, partial_config=partial_config)
assert_permission_to_update_config(old_config=config, new_config=new_config)
return new_config
@router.patch(
"",
summary="Update config",
description="Update the config.",
response_model=AzimuthConfig,
dependencies=[Depends(require_editable_config)],
)
def patch_config(
task_manager: TaskManager = Depends(get_task_manager),
config: AzimuthConfig = Depends(get_config),
partial_config: Dict = Body(...),
) -> AzimuthConfig:
log.info(f"Validating config change with {partial_config}.")
new_config = update_config(old_config=config, partial_config=partial_config)
assert_permission_to_update_config(old_config=config, new_config=new_config)
if new_config.large_dask_cluster != config.large_dask_cluster:
cluster = default_cluster(new_config.large_dask_cluster)
else:
cluster = task_manager.cluster
try:
run_startup_tasks(new_config, cluster)
log.info(f"Config successfully updated with {partial_config}.")
except Exception as e:
log.error("Rollback config update due to error", exc_info=e)
new_config = config
initialize_managers(new_config, task_manager.cluster)
log.info("Config update cancelled.")
if isinstance(e, AzimuthValidationError):
raise HTTPException(HTTP_400_BAD_REQUEST, detail=str(e))
else:
raise HTTPException(
HTTP_500_INTERNAL_SERVER_ERROR, detail="Error when loading the new config."
)
return new_config
def assert_permission_to_update_config(*, old_config: AzimuthConfig, new_config: AzimuthConfig):
if old_config.artifact_path != new_config.artifact_path:
raise HTTPException(
HTTP_403_FORBIDDEN,
detail="Cannot edit artifact_path, otherwise config history would become inconsistent.",
)
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,036
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/word_analysis/top_words.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from collections import Counter
from typing import Dict, List, Tuple
import numpy as np
import spacy
from azimuth.config import TopWordsConfig
from azimuth.modules.base_classes import FilterableModule
from azimuth.modules.base_classes.dask_module import Worker
from azimuth.modules.task_execution import get_task_result
from azimuth.modules.word_analysis.tokens_to_words import TokensToWordsModule
from azimuth.types import ModuleOptions
from azimuth.types.word_analysis import (
TokensToWordsResponse,
TopWordsImportanceCriteria,
TopWordsResponse,
TopWordsResult,
)
from azimuth.utils.dataset_operations import get_predictions_from_ds
from azimuth.utils.project import saliency_available
from azimuth.utils.utterance import clean_utterance
MIN_SALIENCY = 0.01
class TopWordsModule(FilterableModule[TopWordsConfig]):
"""Returns the most important words in terms of their saliency value or frequency."""
optional_mod_options = FilterableModule.optional_mod_options | {
"top_x",
"th_importance",
"force_no_saliency",
}
worker = Worker.model
@staticmethod
def count_words(list_of_words: List[str], top_x: int) -> List[TopWordsResult]:
"""Count the number of words.
Args:
list_of_words: List of important words.
top_x: Number of top words to output.
Returns:
Top words and their occurrences.
"""
count_top_words: List[Tuple[str, int]] = Counter(list_of_words).most_common(top_x)
wordcount_list: List[TopWordsResult] = (
[TopWordsResult(word=str(x), count=y) for (x, y) in count_top_words]
if list_of_words
else []
)
return wordcount_list
def get_words_saliencies(self, indices: List[int]) -> List[TokensToWordsResponse]:
"""TokensToWordsModule results.
Args:
indices: Indices on which to get words and saliencies.
Returns:
List of Words and Saliencies per utterance.
"""
get_words_task = TokensToWordsModule(
self.dataset_split_name,
self.config,
mod_options=ModuleOptions(
pipeline_index=self.mod_options.pipeline_index, indices=indices
),
)
result = get_task_result(
task_module=get_words_task, result_type=List[TokensToWordsResponse]
)
return result
def compute_on_dataset_split(self) -> List[TopWordsResponse]: # type: ignore
"""
Compute most important words according to saliency maps, if available, or frequency for the
filtered dataset_split. It computes the most important words for all according predictions
(all), for predictions that were predicted right (right) and finally predictions that were
predicted wrongly (errors).
"""
importance_criteria = (
TopWordsImportanceCriteria.salient
if (saliency_available(self.config) and not self.mod_options.force_no_saliency)
else TopWordsImportanceCriteria.frequent
)
ds = self.get_dataset_split()
if len(ds) == 0:
return [
TopWordsResponse(
all=[],
right=[],
errors=[],
importance_criteria=importance_criteria,
)
]
important_words_per_idx: Dict[int, List[str]] = {}
if importance_criteria == TopWordsImportanceCriteria.salient:
words_saliencies = self.get_words_saliencies(self.get_indices())
tokenizer = self.get_model().tokenizer
for idx, record in enumerate(words_saliencies):
# Put everything to lower case and remove cls/sep tokens.
words, saliencies = zip(
*[
(word.lower(), saliency_value)
for word, saliency_value in zip(record.words, record.saliency)
if word not in [tokenizer.cls_token, tokenizer.sep_token]
]
)
if words:
importance_saliency = max(
self.mod_options.th_importance * max(record.saliency), MIN_SALIENCY
)
important_words_per_idx[idx] = [
word
for word, _ in filter(
lambda s: s[1] > importance_saliency, zip(words, saliencies)
)
]
# If saliency is not available, we proxy important words as any word that is neither
# punctuation nor a stop word.
else:
spacy_model = spacy.load(self.config.syntax.spacy_model)
utterances = ds[self.config.columns.text_input]
for idx, utterance in enumerate(utterances):
doc = spacy_model(clean_utterance(utterance))
important_words_per_idx[idx] = [
token.text for token in doc if not token.is_stop and not token.is_punct
]
is_error = np.array(
get_predictions_from_ds(ds, self.mod_options.without_postprocessing)
) != np.array(ds[self.config.columns.label])
important_words_all = []
important_words_errors = []
important_words_right = []
for idx, important_words in important_words_per_idx.items():
important_words_all.extend(important_words)
if is_error[idx]:
important_words_errors.extend(important_words)
else:
important_words_right.extend(important_words)
top_x = self.mod_options.top_x
return [
TopWordsResponse(
all=self.count_words(important_words_all, top_x),
right=self.count_words(important_words_right, top_x),
errors=self.count_words(important_words_errors, top_x),
importance_criteria=importance_criteria,
)
]
|
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"/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", 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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", 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"/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,037
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/conftest.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import threading
from copy import deepcopy
import pytest
from distributed import Client, LocalCluster
from azimuth.config import AzimuthConfig, SupportedLanguage
from azimuth.modules.base_classes import ArtifactManager
from azimuth.startup import START_UP_THREAD_NAME
from azimuth.task_manager import TaskManager
from azimuth.types import DatasetSplitName
from tests.utils import (
DATASET_CFG,
DATASET_CLINC150_CFG,
PIPELINE_CFG,
PIPELINE_GUSE_CFG,
SAMPLE_INTENT_TRAIN_FILE_NO_INTENT,
SAMPLE_PREDICTIONS_FILEPATH_TOP3,
SIMPLE_PERTURBATION_TESTING_CONFIG,
file_based_ds_from_paths,
file_based_pipeline_from,
generate_mocked_dm,
)
# General Fixtures
@pytest.fixture(autouse=True, scope="function")
def cleanup_class():
yield
ArtifactManager.clear_instance()
def kill_scheduler(dask_scheduler=None):
dask_scheduler.close()
def cleanup_workers():
import gc
ArtifactManager.clear_instance()
gc.collect()
@pytest.fixture()
def dask_client():
# Reduce memory print for tests, use a different port for less warnings.
cluster = LocalCluster(
n_workers=2, threads_per_worker=1, dashboard_address=None, memory_limit="2GB"
)
client = Client(cluster)
yield client
client.run(cleanup_workers)
client.run_on_scheduler(kill_scheduler)
cluster.close()
client.close()
@pytest.fixture
def apply_mocked_startup_task(simple_text_config):
def fn(config):
generate_mocked_dm(config, DatasetSplitName.eval)
generate_mocked_dm(config, DatasetSplitName.train)
fn(simple_text_config)
return fn
# Sentiment Analysis Fixtures
@pytest.fixture
def simple_text_config(tmp_path):
return AzimuthConfig(
name="sentiment-analysis",
dataset=DATASET_CFG,
pipelines=[PIPELINE_CFG],
artifact_path=str(tmp_path),
batch_size=10,
model_contract="hf_text_classification",
rejection_class=None,
behavioral_testing=SIMPLE_PERTURBATION_TESTING_CONFIG,
)
@pytest.fixture
def simple_text_config_multi_pipeline(simple_text_config):
pipeline_cfg_low = deepcopy(PIPELINE_CFG)
pipeline_cfg_low["name"] = "Low threshold Model"
pipeline_cfg_low["postprocessors"][-1]["threshold"] = 0.2
pipeline_cfg_high = deepcopy(PIPELINE_CFG)
pipeline_cfg_high["name"] = "High threshold Model"
pipeline_cfg_high["postprocessors"][-1]["threshold"] = 0.99
simple_multipipeline_text_config = simple_text_config.copy(
deep=True, update=dict(pipelines=[pipeline_cfg_low, pipeline_cfg_high])
)
return simple_multipipeline_text_config
@pytest.fixture
def tiny_text_config(simple_text_config) -> AzimuthConfig:
tiny_text_config = simple_text_config.copy(deep=True)
tiny_text_config.dataset.kwargs["max_dataset_len"] = 4
return tiny_text_config
@pytest.fixture(params=["train", "eval"])
def tiny_text_config_one_ds(tiny_text_config, request) -> AzimuthConfig:
tiny_text_config_one_ds = tiny_text_config.copy(deep=True)
tiny_text_config_one_ds.dataset.kwargs[request.param] = False
return tiny_text_config_one_ds
@pytest.fixture
def tiny_text_config_no_pipeline(tiny_text_config) -> AzimuthConfig:
tiny_text_config_no_pipeline = tiny_text_config.copy(deep=True)
tiny_text_config_no_pipeline.pipelines = None
return tiny_text_config_no_pipeline
@pytest.fixture
def tiny_text_config_no_postprocessor(tiny_text_config) -> AzimuthConfig:
pipeline_no_postprocessor = deepcopy(PIPELINE_CFG)
pipeline_no_postprocessor["postprocessors"] = None
tiny_text_config_no_postprocessor = tiny_text_config.copy(
deep=True, update=dict(pipelines=[pipeline_no_postprocessor])
)
return tiny_text_config_no_postprocessor
@pytest.fixture
def tiny_text_task_manager(tiny_text_config, dask_client):
task_manager = TaskManager(tiny_text_config, cluster=dask_client.cluster)
yield task_manager
task_manager.close()
# Close possible dangling threads that would never complete.
thread = threading.enumerate()
start_up_thread = [th for th in thread if th.name == START_UP_THREAD_NAME]
for th in start_up_thread:
th.join()
@pytest.fixture
def simple_text_config_french(tmp_path):
return AzimuthConfig(
name="sentiment-analysis-french",
dataset=DATASET_CFG,
pipelines=[PIPELINE_CFG],
artifact_path=str(tmp_path),
batch_size=10,
model_contract="hf_text_classification",
rejection_class=None,
language=SupportedLanguage.fr,
)
# File-Based Fixtures
@pytest.fixture
def file_text_config_top1(tmp_path):
ds_config = file_based_ds_from_paths()
return AzimuthConfig(
name="intent-test",
pipelines=[{"model": file_based_pipeline_from(ds_config)}],
dataset=ds_config,
artifact_path=str(tmp_path),
batch_size=10,
rejection_class="NO_INTENT",
model_contract="file_based_text_classification",
behavioral_testing=SIMPLE_PERTURBATION_TESTING_CONFIG,
)
@pytest.fixture
def file_text_config_top3(file_text_config_top1):
ds_config = file_based_ds_from_paths(test=SAMPLE_PREDICTIONS_FILEPATH_TOP3)
file_text_config_top3 = file_text_config_top1.copy(
deep=True,
update=dict(pipelines=[{"model": file_based_pipeline_from(ds_config)}], dataset=ds_config),
)
return file_text_config_top3
@pytest.fixture
def file_text_config_no_intent(file_text_config_top1):
ds_config = file_based_ds_from_paths(train=SAMPLE_INTENT_TRAIN_FILE_NO_INTENT)
file_text_config_no_intent = file_text_config_top1.copy(
deep=True,
update=dict(pipelines=[{"model": file_based_pipeline_from(ds_config)}], dataset=ds_config),
)
return file_text_config_no_intent
# Specific Dataset/Pipelines Fixtures
@pytest.fixture
def clinc_text_config(tmp_path):
clinc_text_config = AzimuthConfig(
name="clinc-test",
dataset=DATASET_CLINC150_CFG,
rejection_class="NO_INTENT",
pipelines=[PIPELINE_CFG],
artifact_path=str(tmp_path),
)
clinc_text_config.pipelines[0].postprocessors[0].temperature = 1.0
clinc_text_config.pipelines[0].postprocessors[0].kwargs["temperature"] = 1.0
clinc_text_config.pipelines[0].postprocessors[-1].threshold = 0.5
clinc_text_config.pipelines[0].postprocessors[-1].kwargs["threshold"] = 0.5
return clinc_text_config
@pytest.fixture
def guse_text_config(file_text_config_top1):
guse_text_config = file_text_config_top1.copy(
deep=True, update=dict(pipelines=[PIPELINE_GUSE_CFG])
)
guse_text_config.model_contract = "custom_text_classification"
return guse_text_config
|
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"/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,038
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/project.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Dict
import structlog
from datasets import DatasetDict
from azimuth.config import (
AzimuthConfig,
ModelContractConfig,
PerturbationTestingConfig,
SimilarityConfig,
)
from azimuth.types import DatasetSplitName, SupportedModelContract
from azimuth.utils.object_loader import load_custom_object
log = structlog.get_logger()
def load_dataset_from_config(azimuth_config: AzimuthConfig) -> DatasetDict:
"""Loads the dataset from the config and set the eval value that will be used in the app.
Args:
azimuth_config: Azimuth Config
Returns:
Dataset dict with all splits
Raises:
ValueError if neither validation or test exists in the dataset
"""
if azimuth_config.dataset is None:
return DatasetDict()
dataset_in_config: DatasetDict = load_custom_object(
azimuth_config.dataset, azimuth_config=azimuth_config
)
dataset_train_eval: DatasetDict = DatasetDict()
if "train" in dataset_in_config and len(dataset_in_config["train"]) > 0:
dataset_train_eval[DatasetSplitName.train] = dataset_in_config["train"]
if "validation" in dataset_in_config and len(dataset_in_config["validation"]) > 0:
dataset_train_eval[DatasetSplitName.eval] = dataset_in_config["validation"]
elif "test" in dataset_in_config and len(dataset_in_config["test"]) > 0:
dataset_train_eval[DatasetSplitName.eval] = dataset_in_config["test"]
if not dataset_train_eval:
raise ValueError(
"Unable to find a dataset split named train, validation or test"
f"Found {tuple(dataset_in_config.keys())}."
)
return dataset_train_eval
def update_config(old_config: AzimuthConfig, partial_config: Dict) -> AzimuthConfig:
return old_config.copy(update=partial_config, deep=True)
def predictions_available(config: ModelContractConfig) -> bool:
return config.pipelines is not None
def similarity_available(config: SimilarityConfig) -> bool:
return config.similarity is not None
def perturbation_testing_available(config: PerturbationTestingConfig) -> bool:
correct_model_contract = config.model_contract in [
SupportedModelContract.hf_text_classification,
SupportedModelContract.custom_text_classification,
SupportedModelContract.file_based_text_classification,
]
is_enable = config.behavioral_testing is not None and predictions_available(config)
return correct_model_contract and is_enable
def postprocessing_known(config: ModelContractConfig, pipeline_index) -> bool:
# Sometimes the postprocessing cannot be edited but we still want show the values in the FE.
# Example: file-based
if config.pipelines is None:
return False
return config.pipelines[pipeline_index].postprocessors is not None
def postprocessing_editable(config: ModelContractConfig, pipeline_index) -> bool:
file_based = config.model_contract == SupportedModelContract.file_based_text_classification
postprocessing_unknown = not postprocessing_known(config, pipeline_index)
return not (file_based or postprocessing_unknown)
def saliency_available(config: ModelContractConfig):
return (
config.model_contract in [SupportedModelContract.hf_text_classification]
and predictions_available(config)
and config.saliency_layer is not None
)
|
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"/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", 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"/azimuth/utils/validation.py"], "/azimuth/routers/model_performance/utterance_count.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/validation.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/class_overlap.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/plots/sankey_plot.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/tests/test_modules/test_word_analysis/test_top_words.py": ["/azimuth/modules/word_analysis/top_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", 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"/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/ece.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/config.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exclude_fields_from_cache.py", "/azimuth/utils/openapi.py"], "/tests/utils.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py"], "/azimuth/__init__.py": ["/azimuth/utils/logs.py"], "/tests/test_utils/test_dataset_filtering.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", 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"/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", 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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", 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"/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,039
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from copy import deepcopy
from typing import Any, List
import numpy as np
import pytest
from datasets import ClassLabel
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.dataset_analysis.dataset_warnings import DatasetWarningsModule
from azimuth.types import DatasetSplitName, ModuleOptions
from tests.utils import generate_mocked_dm, get_tiny_text_config_one_ds_name
@pytest.mark.parametrize("remove_one_class", [True, False])
@pytest.mark.parametrize("rejection_class", [True, False])
def test_compute_on_dataset_split(
simple_text_config, apply_mocked_startup_task, rejection_class, remove_one_class, monkeypatch
):
mod = DatasetWarningsModule(
dataset_split_name=DatasetSplitName.all,
config=simple_text_config,
)
if rejection_class:
add_rejection_class(mod, monkeypatch=monkeypatch)
assert "NO_INTENT" in mod.get_dataset_split_manager(DatasetSplitName.eval).get_class_names(
labels_only=True
)
if remove_one_class:
original_length = len(mod.get_dataset_split(DatasetSplitName.eval))
remove_one_cls(mod)
assert original_length > len(mod.get_dataset_split(DatasetSplitName.eval))
output = mod.compute_on_dataset_split()[0].warning_groups
num_classes = mod.get_dataset_split_manager(DatasetSplitName.eval).get_num_classes(
labels_only=True
)
assert isinstance(output, List)
general_warnings: Any = output[0]
syntactic_warnings: Any = output[1]
assert general_warnings.name == "General Warnings"
assert syntactic_warnings.name == "Syntactic Warnings"
# Test general_warnings
assert len(general_warnings.warnings) == 3
# Test Number of samples per class
nb_samples_class = general_warnings.warnings[0]
nb_samples_class_train_values = [x.data[0].value for x in nb_samples_class.comparisons]
nb_samples_class_eval_values = [x.data[1].value for x in nb_samples_class.comparisons]
assert sum(nb_samples_class_train_values) == len(mod.get_dataset_split(DatasetSplitName.train))
assert sum(nb_samples_class_eval_values) == len(mod.get_dataset_split(DatasetSplitName.eval))
assert len(nb_samples_class.plots.overall.data) > 0
assert nb_samples_class.plots.overall.layout
# Test Class Imbalance
class_imbalance_warnings = general_warnings.warnings[0]
perc_values = [x.data[0].value for x in class_imbalance_warnings.comparisons]
# All percentage values should be higher than -100.
assert (np.array(perc_values) >= -100).all()
assert len(class_imbalance_warnings.plots.overall.data) > 0
assert class_imbalance_warnings.plots.overall.layout
# Test Class representation
class_repr = general_warnings.warnings[1]
class_repr_delta_values = [x.data[0].value for x in class_repr.comparisons]
assert (np.array(class_repr_delta_values) <= 1).all() and (
np.array(class_repr_delta_values) >= -1
).all()
assert len(class_repr.plots.overall.data) > 0
assert class_repr.plots.overall.layout
# Word Count warnings
word_count_warnings = syntactic_warnings.warnings[0]
assert len(word_count_warnings.comparisons) == num_classes
assert word_count_warnings.columns == ["mean", "std"]
assert len(word_count_warnings.plots.overall.data) > 0
assert word_count_warnings.plots.overall.layout
assert all([len(p.data) > 0 for cls_name, p in word_count_warnings.plots.per_class.items()])
assert all([p.layout for p in word_count_warnings.plots.per_class.values()])
def add_rejection_class(mod, monkeypatch):
# Adding a rejection class
dms = {
DatasetSplitName.eval: mod.get_dataset_split_manager(DatasetSplitName.eval),
DatasetSplitName.train: mod.get_dataset_split_manager(DatasetSplitName.train),
}
existing_classes = dms[DatasetSplitName.eval].get_class_names(labels_only=True)
class_label = ClassLabel(num_classes=3, names=existing_classes + ["NO_INTENT"])
for dm in dms.values():
dm._base_dataset_split.features["label"] = class_label
dms[DatasetSplitName.eval]._base_dataset_split = dms[
DatasetSplitName.eval
]._base_dataset_split.map(
lambda u, i: {"label": 2 if i % 10 == 0 else u["label"]}, with_indices=True
)
# Modifying the config reset the ArtifactManager, which we do not want.
monkeypatch.setattr(mod, "get_dataset_split_manager", lambda s: dms[s])
mod.config.rejection_class = "NO_INTENT"
def remove_one_cls(mod):
# Remove class 0 from eval set
eval_ds: DatasetSplitManager = mod.get_dataset_split_manager(DatasetSplitName.eval)
augmented = deepcopy(eval_ds._base_dataset_split.filter(lambda u: u["label"] != 0))
mod.get_dataset_split_manager(DatasetSplitName.eval)._base_dataset_split = augmented
def test_dataset_warnings_with_one_ds(tiny_text_config_one_ds):
split, _ = get_tiny_text_config_one_ds_name(tiny_text_config_one_ds)
generate_mocked_dm(tiny_text_config_one_ds, dataset_split_name=split) # Needed for word count.
mod = DatasetWarningsModule(
dataset_split_name=DatasetSplitName.all,
config=tiny_text_config_one_ds,
)
output = mod.compute_on_dataset_split()[0].warning_groups
general_warnings = output[0]
assert len(general_warnings.warnings) == 2, "Only 2 general warnings with one dataset split"
assert all(
len(comparison.data) == 1
for warning in general_warnings.warnings
for comparison in warning.comparisons
), "Only data for one split"
syntactic_warnings = output[1]
assert not any(
comparison.alert for comparison in syntactic_warnings.warnings[0].comparisons
), "No alert with one split"
def test_with_dataset_and_indices(simple_text_config):
with pytest.raises(ValueError):
DatasetWarningsModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config,
)
with pytest.raises(ValueError):
DatasetWarningsModule(
dataset_split_name=DatasetSplitName.all,
config=simple_text_config,
mod_options=ModuleOptions(indices=[1, 2]),
)
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,040
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/base_classes/expirable_mixin.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import abc
class ExpirableMixin(abc.ABC):
"""Module that can get expired based on changes in the dataset.
This class should be added to Modules which results can expire when the dataset is updated.
The most obvious examples are modules affected by filters, since data action tags can
change based on user actions.
"""
_time: float
def is_expired(self, compared_to: float):
# Check if this Module results are expired.
return self._time < compared_to
|
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"/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,041
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/validation/validation.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Any, List, Optional
import torch
from datasets import Dataset
from transformers import TextClassificationPipeline
from azimuth.config import ModelContractConfig
from azimuth.modules.base_classes import AggregationModule
from azimuth.modules.base_classes.dask_module import Worker
from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping
from azimuth.types import ModuleOptions, SupportedMethod, SupportedModelContract
from azimuth.types.validation import ValidationResponse
from azimuth.utils.logs import MultipleExceptions
from azimuth.utils.project import predictions_available, saliency_available
from azimuth.utils.validation import assert_not_none
class ExceptionGatherer:
"""Utilities to gather exception without raising."""
def __init__(self):
self.exceptions = []
def has_exception(self):
return len(self.exceptions) > 0
def try_calling_function(self, fn, *args, **kwargs) -> Optional[Any]:
try:
return fn(*args, **kwargs)
except Exception as e:
self.exceptions.append(e)
return None
class ValidationModule(AggregationModule[ModelContractConfig]):
optional_mod_options = {"pipeline_index"}
worker = Worker.model
def compute_on_dataset_split(self) -> List[ValidationResponse]: # type: ignore
cuda_available = torch.cuda.is_available()
exception_gatherer = ExceptionGatherer()
model = (
exception_gatherer.try_calling_function(self.get_model)
if predictions_available(self.config)
else None
)
can_load_model = model is not None
dataset: Optional[Dataset] = exception_gatherer.try_calling_function(self.get_dataset_split)
can_load_dataset = dataset is not None
if can_load_model:
model_has_correct_type = (
exception_gatherer.try_calling_function(self._validate_model_type, model)
is not None
)
else:
model_has_correct_type = False
if can_load_model and can_load_dataset and model_has_correct_type:
ds_object = assert_not_none(dataset)
batch = ds_object.select(range(0, min(2, len(ds_object))))
can_make_prediction = (
exception_gatherer.try_calling_function(self._validate_prediction, batch=batch)
is not None
)
if can_make_prediction and saliency_available(self.config):
can_make_saliency = (
exception_gatherer.try_calling_function(self._validate_saliency, batch=batch)
is not None
)
else:
can_make_saliency = False
else:
can_make_prediction = False
can_make_saliency = False
# Should we raise instead?
if exception_gatherer.exceptions:
raise MultipleExceptions(exceptions=exception_gatherer.exceptions)
return [
ValidationResponse(
is_cuda_available=cuda_available,
can_load_model=can_load_model,
can_load_dataset=can_load_dataset,
model_has_correct_type=model_has_correct_type,
can_make_prediction=can_make_prediction,
can_make_saliency=can_make_saliency,
)
]
def _validate_prediction(self, batch):
prediction_module = model_contract_task_mapping(
dataset_split_name=self.dataset_split_name,
config=self.config,
mod_options=ModuleOptions(
pipeline_index=self.mod_options.pipeline_index,
model_contract_method_name=SupportedMethod.Predictions,
),
)
return prediction_module.compute(batch)
def _validate_saliency(self, batch):
prediction_module = model_contract_task_mapping(
dataset_split_name=self.dataset_split_name,
config=self.config,
mod_options=ModuleOptions(
pipeline_index=self.mod_options.pipeline_index,
model_contract_method_name=SupportedMethod.Saliency,
),
)
return prediction_module.compute(batch)
def _validate_model_type(self, model):
if self.config.model_contract == SupportedModelContract.hf_text_classification:
if isinstance(model, TextClassificationPipeline):
model_has_correct_type = True
else:
raise ValueError(
f"Expected model to be of type "
f"transformers.TextClassificationPipeline, got {type(model)}"
)
else:
if callable(model):
model_has_correct_type = True
else:
raise ValueError(f"Expected model to be Callable, got {type(model)}")
return model_has_correct_type
|
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"/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,042
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/class_overlap.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Dict, List, Optional
from pydantic import Field
from azimuth.types import AliasModel, Array, PlotSpecification
class SimilarityArrays(AliasModel):
sample_probability: Array
sample_probability_norm: Array
class ClassOverlapResponse(AliasModel):
evals: Array
evecs: Array
difference: Array
s_matrix: Array
similarity_arrays: Dict[int, Dict[int, SimilarityArrays]]
class Config:
arbitrary_types_allowed = True
class ClassOverlapPlotResponse(AliasModel):
plot: PlotSpecification
default_overlap_threshold: float
class ClassOverlapTableClassPair(AliasModel):
source_class: str = Field(..., title="Source class")
target_class: str = Field(..., title="Target class")
overlap_score_train: float = Field(..., title="Overlap score on train")
pipeline_confusion_eval: Optional[int] = Field(
..., title="Pipeline confusion not normalized on eval", nullable=True
)
utterance_count_source_train: int = Field(
..., title="Utterance count for source training class"
)
utterance_count_source_eval: int = Field(..., title="Utterance count for source eval class")
utterance_count_with_overlap_train: int = Field(
..., title="Utterance count with overlap on train"
)
class ClassOverlapTableResponse(AliasModel):
class_pairs: List[ClassOverlapTableClassPair] = Field(..., title="Class pair overlap data")
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,043
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_routers/test_custom_utterances.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from fastapi import FastAPI
from starlette.status import HTTP_200_OK
from starlette.testclient import TestClient
def test_generate_perturbation_tests(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get(
"/custom_utterances/perturbed_utterances?utterances=hello, this is me"
+ "&utterances=I like potatoes."
)
assert resp.status_code == HTTP_200_OK, resp.text
assert resp.headers["content-type"] == "application/json"
assert int(resp.headers["content-length"]) > 0
assert "generate_perturbation_tests" in resp.headers["content-disposition"]
assert ".json" in resp.headers["content-disposition"]
|
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"/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,044
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/word_analysis.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from enum import Enum
from typing import List
from pydantic import Field
from azimuth.types import AliasModel, ModuleResponse
class TokensToWordsResponse(ModuleResponse):
words: List[str] = Field(..., title="Words")
saliency: List[float] = Field(..., title="Saliency")
class TopWordsResult(AliasModel):
word: str = Field(..., title="Word")
count: int = Field(..., title="Count")
class TopWordsImportanceCriteria(str, Enum):
salient = "salient"
frequent = "frequent"
class TopWordsResponse(ModuleResponse):
all: List[TopWordsResult] = Field(..., title="All")
right: List[TopWordsResult] = Field(..., title="Right")
errors: List[TopWordsResult] = Field(..., title="Errors")
importance_criteria: TopWordsImportanceCriteria = Field(..., title="Importance criteria")
|
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"/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/azimuth/utils/ml/seeding.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_perturbation_testing/test_perturbation_testing_summary.py": ["/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/tests/test_task_manager.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/aggregation_module.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/azimuth_shr/loading_resources.py": ["/azimuth/config.py"], "/azimuth/routers/dataset_warnings.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_app.py": ["/azimuth/routers/utterances.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/dataset_analysis/similarity_analysis.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/model_performance.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/utils/ml/ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/perturbation_testing/__init__.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/outcomes.py": ["/azimuth/types/__init__.py"], "/azimuth/utils/plots.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/modules/task_mapping.py": ["/azimuth/modules/model_performance/outcomes.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py"], "/azimuth/modules/model_contracts/hf_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/mc_dropout.py", "/azimuth/utils/ml/saliency.py"], "/azimuth/routers/export.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,045
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/plots/sankey_plot.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import json
import math
from itertools import cycle
from typing import List, Optional, Tuple
import numpy as np
from matplotlib.colors import to_rgb
from plotly import graph_objects as go
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.dataset_analysis.class_overlap import take
from azimuth.types import PlotSpecification
from azimuth.types.class_overlap import ClassOverlapPlotResponse, ClassOverlapResponse
from azimuth.utils.plots import AXIS_FONT_SIZE, SANKEY_PALETTE
def make_sankey_plot(
response: ClassOverlapResponse,
dataset_manager: DatasetSplitManager,
self_overlap: bool = False,
scale_by_class: bool = True,
overlap_threshold: Optional[float] = None,
) -> ClassOverlapPlotResponse:
"""
Plot the result of SpectralClusteringResponse (S-matrix) as a Sankey plot
with nodes ordered by S-matrix overlap.
Args:
response: Result of `compute_on_dataset_split`
dataset_manager: One dataset manager
self_overlap: Plot flow when class i == class j?
scale_by_class: Scale nodes/flow to size of class i?
overlap_threshold: Overlap threshold below which flow/overlap is not plotted. When set to
None, threshold is determined automatically to tenth highest overlap value.
Returns:
Sankey plot showing the similarity/overlap between classes
Overlap threshold used in plot (as input or calculated by default)
Default overlap threshold
"""
node_padding = 4
plot_height = 1000
top_margin = 4
bottom_margin = 4
def to_rgba_pl(col, alpha):
rgb = to_rgb(col)
r, g, b = [int(c * 255) for c in rgb]
return f"rgba({r},{g},{b},{alpha})"
source, target, value, similarity = [], [], [], []
sim: np.ndarray = response.s_matrix # similarity matrix
sim_shape: int = sim.shape[0]
class_names = dataset_manager.get_class_names(labels_only=True)[:sim_shape]
class_counts = dataset_manager.class_distribution()
colors = take(cycle(SANKEY_PALETTE), sim_shape)
sim_array = [(index[0], index[1], value) for index, value in np.ndenumerate(sim)]
sim_array_sorted: List[Tuple[int, int, float]] = sorted(
sim_array, key=lambda x: x[2], reverse=True
) # includes i==j
sim_array_nonself_sorted = [i for i in sim_array_sorted if i[0] != i[1]] # drops i==j
# Determine threshold algorithmically to show 10 overlapping pairs
pairs_to_show = min(len(sim_array_nonself_sorted), 10)
default_overlap_threshold = (
math.floor(sim_array_nonself_sorted[pairs_to_show - 1][2] * 100) / 100
)
if overlap_threshold is None:
overlap_threshold = default_overlap_threshold
sim_values = sim_array_sorted if self_overlap else sim_array_nonself_sorted
class_mass_i = np.zeros(sim_shape)
class_mass_j = np.zeros(sim_shape)
for i, j, sim_value in sim_values:
if sim_value < overlap_threshold:
continue
j_index = j + sim_shape
source.append(i)
target.append(j_index)
val = sim_value * class_counts[i] if scale_by_class else sim_value
value.append(val)
similarity.append(sim_value)
class_mass_i[i] += val
class_mass_j[j] += val
# Find y-positions for nodes, ordered by s-matrix overlap
# Order of nodes on plot (some nodes do not exist because sim_value < threshold)
# ordered set: https://www.peterbe.com/plog/fastest-way-to-uniquify-a-list-in-python-3.6
order_i_all = list(dict.fromkeys([item[0] for item in sim_array_nonself_sorted]))
order_j_all = list(dict.fromkeys([item[1] for item in sim_array_nonself_sorted]))
plotted_nodes_i = sorted(set(source)) # Ordered numerically
plotted_nodes_j = np.array(sorted(set(target))) - sim_shape # Ordered numerically
order_i = [c for c in order_i_all if c in plotted_nodes_i] # Reorder to plotted order
order_j = [c for c in order_j_all if c in plotted_nodes_j] # Reorder to plotted order
node_prop = (
plot_height - (top_margin + bottom_margin + node_padding * (len(order_i) - 1))
) / plot_height # Take plotted node spacing into account
# Class masses in plotted order (top to bottom); sum to node_prop_of_plot for finding positions
ordered_class_mass_norm_i = class_mass_i[order_i] / class_mass_i[order_i].sum() * node_prop
ordered_class_mass_norm_j = class_mass_j[order_j] / class_mass_j[order_j].sum() * node_prop
# Find positions for node centers; node bottom - half node height (plotted order) + padding
padding_i = (top_margin / plot_height) + np.array(range(len(order_i))) * (
node_padding / plot_height
)
padding_j = (top_margin / plot_height) + np.array(range(len(order_j))) * (
node_padding / plot_height
)
ordered_y_positions_i = (
np.cumsum(ordered_class_mass_norm_i) - ordered_class_mass_norm_i * 0.5 + padding_i
)
ordered_y_positions_j = (
np.cumsum(ordered_class_mass_norm_j) - ordered_class_mass_norm_j * 0.5 + padding_j
)
# Re-order to node index order (numerical)
positions_y_i = [ordered_y_positions_i[order_i.index(i)] for i in plotted_nodes_i]
positions_y_j = [ordered_y_positions_j[order_j.index(i)] for i in plotted_nodes_j]
positions_y = positions_y_i + positions_y_j
# x-positions like [0, 0, 0 ... 1, 1, 1]; Sankey plot needs values >0 and <1
positions_x = [0.001] * len(plotted_nodes_i) + [0.999] * len(plotted_nodes_j)
# For hover data (overlap info determined by threshold, so aligned with plotted data)
higher_than_thresh = sim >= overlap_threshold
np.fill_diagonal(higher_than_thresh, 0)
custom_data_i = higher_than_thresh.sum(-1).tolist()
custom_data_j = higher_than_thresh.sum(0).tolist()
custom_data = [
f"overlaps {i} class{'es' if i != 1 else ''} (over threshold)" for i in custom_data_i
] + [
f"is overlapped by {i} class{'es' if i != 1 else ''} (over threshold)"
for i in custom_data_j
]
fig = go.Figure(
data=[
go.Sankey(
arrangement="snap",
node=dict(
x=positions_x,
y=positions_y,
pad=node_padding,
thickness=20,
label=class_names + class_names,
color=colors + colors,
customdata=custom_data,
hovertemplate="%{label} %{customdata} <extra></extra>",
),
link=dict(
source=source, # indices correspond to labels, eg A1, A2, A2, B1, ...
target=target,
value=value,
color=[to_rgba_pl(colors[i], 0.5) for i in source],
customdata=similarity,
hovertemplate="%{source.label}<>%{target.label}:"
" %{customdata:.3f} <extra></extra>",
),
)
]
)
fig.update_layout(
font_size=AXIS_FONT_SIZE,
autosize=False,
width=700,
height=plot_height,
margin=dict(l=0, r=0, b=bottom_margin, t=top_margin, pad=node_padding),
)
return ClassOverlapPlotResponse(
plot=PlotSpecification(**json.loads(fig.to_json())),
default_overlap_threshold=default_overlap_threshold,
)
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,046
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_routers/test_model_performance/test_utterance_count.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from fastapi import FastAPI
from starlette.status import HTTP_200_OK
from starlette.testclient import TestClient
from azimuth.types.tag import SmartTag
def test_get_utterance_count_per_filter(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get("/dataset_splits/eval/utterance_count/per_filter?pipeline_index=0")
assert resp.status_code == HTTP_200_OK, resp.text
data = resp.json()
metrics = data.pop("countPerFilter")
assert "label" in metrics and len(metrics["label"]) == 3
assert "dissimilar" in metrics and len(metrics["dissimilar"]) == 5
assert "extremeLength" in metrics and len(metrics["extremeLength"]) == 4
assert metrics["extremeLength"][0]["filterValue"] == SmartTag.no_smart_tag
assert metrics["extremeLength"][0]["utteranceCount"] > 0
resp = client.get("/dataset_splits/eval/utterance_count/per_filter")
assert resp.status_code == HTTP_200_OK, resp.text
data = resp.json()
metrics = data.pop("countPerFilter")
assert "label" in metrics and len(metrics["label"]) == 3
assert "dissimilar" in metrics and len(metrics["dissimilar"]) == 5
assert "extremeLength" in metrics and len(metrics["extremeLength"]) == 4
assert "prediction" not in metrics and "outcome" not in metrics and "uncertain" not in metrics
resp = client.get(
"/dataset_splits/eval/utterance_count/per_filter?pipeline_index=0&label=positive"
)
assert resp.status_code == HTTP_200_OK, resp.text
data = resp.json()
metrics = data.pop("countPerFilter")
assert "label" in metrics and len(metrics["label"]) == 3
assert metrics["label"][0]["utteranceCount"] == 0 # Rejection class
assert metrics["label"][1]["utteranceCount"] == data["utteranceCount"] # Positive
assert metrics["label"][2]["utteranceCount"] == 0 # Negative
|
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["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,047
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/model_contracts/__init__.py
|
# Do not reorder
from azimuth.modules.model_contracts.hf_text_classification import HFTextClassificationModule
from azimuth.modules.model_contracts.custom_classification import CustomTextClassificationModule
from azimuth.modules.model_contracts.file_based_text_classification import (
FileBasedTextClassificationModule,
)
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,048
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_task_execution.py
|
from typing import List
from distributed import get_client
from azimuth.modules.base_classes import IndexableModule
from azimuth.modules.task_execution import get_task_result
from azimuth.types import DatasetSplitName, ModuleOptions, ModuleResponse
class Potato(ModuleResponse):
a: int
class MyModule(IndexableModule):
def compute_on_dataset_split(self) -> List[ModuleResponse]:
return [Potato(a=a) for a in self.get_indices()]
def test_get_task_result(dask_client, simple_text_config):
mod1 = MyModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(indices=[1, 2, 3]),
)
res = get_task_result(mod1, List[Potato])
assert len(res) == 3
mod1.wait()
assert mod1._check_cache(mod1.get_caching_indices())
def test_get_task_result_no_client(simple_text_config):
# We can also do that without a task client
try:
client = get_client()
client.close()
except Exception:
# Client already closed
pass
mod2 = MyModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(indices=[4, 5, 6, 7]),
)
res2 = get_task_result(mod2, List[Potato])
assert len(res2) == 4
mod2.wait()
assert mod2._check_cache(mod2.get_caching_indices())
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,049
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_word_analysis/test_tokens_to_words.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import numpy as np
from azimuth.modules.word_analysis.tokens_to_words import TokensToWordsModule
from azimuth.types import DatasetSplitName, ModuleOptions
from azimuth.types.task import SaliencyResponse
def fake_saliency_record(indices):
all_tokens = [
"hel",
"##lo",
",",
"my",
"na",
"##me",
"is",
"fred",
"[CLS]",
"the",
"sky",
"is",
"blue",
".",
"[SEP]",
"i",
"like",
"cof",
"##fee",
]
rng = np.random.RandomState(1338)
records = []
for i in indices:
records.append(
SaliencyResponse(saliency=rng.rand(19).tolist(), tokens=all_tokens) # 19 tokens
)
return records
def test_get_words(monkeypatch, simple_text_config, apply_mocked_startup_task):
mod = TokensToWordsModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(pipeline_index=0, indices=[0]),
)
monkeypatch.setattr(mod, "get_tokens_saliencies", fake_saliency_record)
assert mod is not None
json_output = mod.compute_on_dataset_split()
[record] = fake_saliency_record(indices=[0]) # Get mocked saliency values per token.
hello_saliency = record.saliency[0] + record.saliency[1] # Saliency associated to hello tokens.
assert json_output[0].words[0] == "hello" # Assert TokensToWordsModule concatenates hello.
# Assert TokensToWordsModule sums saliency values of tokens.
assert np.isclose(hello_saliency, json_output[0].saliency[0])
def test_top_words_get_tokens_saliencies(tiny_text_config):
mod = TokensToWordsModule(
DatasetSplitName.eval,
tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0),
)
rec = mod.get_tokens_saliencies([0, 1])
assert len(rec) == 2
|
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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,050
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_routers/test_top_words.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from fastapi import FastAPI
from starlette.status import HTTP_200_OK
from starlette.testclient import TestClient
def test_top_words(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get("/dataset_splits/eval/top_words?indices=1&indices=2&pipeline_index=0")
assert resp.status_code == HTTP_200_OK, resp.text
data = resp.json()
assert data == {
"all": [
{"count": 8, "word": "movie"},
{"count": 5, "word": "humor"},
{"count": 5, "word": "film"},
{"count": 3, "word": "music"},
{"count": 3, "word": "production"},
{"count": 3, "word": "look"},
{"count": 3, "word": "comedy"},
{"count": 3, "word": "high"},
{"count": 3, "word": "sense"},
{"count": 2, "word": "affecting"},
],
"errors": [
{"count": 7, "word": "movie"},
{"count": 5, "word": "film"},
{"count": 3, "word": "music"},
{"count": 3, "word": "production"},
{"count": 3, "word": "humor"},
{"count": 3, "word": "comedy"},
{"count": 3, "word": "high"},
{"count": 3, "word": "sense"},
{"count": 2, "word": "affecting"},
{"count": 2, "word": "slow"},
],
"importanceCriteria": "frequent",
"right": [
{"count": 2, "word": "humor"},
{"count": 1, "word": "root"},
{"count": 1, "word": "clara"},
{"count": 1, "word": "paul"},
{"count": 1, "word": "like"},
{"count": 1, "word": "emotion"},
{"count": 1, "word": "closer"},
{"count": 1, "word": "pity"},
{"count": 1, "word": "horror"},
{"count": 1, "word": "fans"},
],
}
def test_top_words_no_result(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get("/dataset_splits/eval/top_words?pipeline_index=0&utterance=no_result")
assert resp.status_code == HTTP_200_OK, resp.text
data = resp.json()
assert data == {
"all": [],
"errors": [],
"right": [],
"importanceCriteria": "frequent",
}
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,051
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_model_contracts/test_text_classification.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List, cast
import numpy as np
import pytest
import tensorflow as tf
import torch
from datasets import Dataset
from transformers.file_utils import ModelOutput
from azimuth.config import AzimuthConfig, PipelineDefinition
from azimuth.modules.model_contracts import (
FileBasedTextClassificationModule,
HFTextClassificationModule,
)
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod
from azimuth.types.task import PredictionResponse, SaliencyResponse
from azimuth.utils.ml.postprocessing import PostProcessingIO, TemperatureScaling
def test_create_dataset(simple_text_config):
task = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
assert task is not None
assert task.get_dataset_split() is not None
assert len(task.get_dataset_split()) > 0
assert len(task.get_dataset_split()[0]["utterance"]) > 0
@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires cuda enabled")
def test_cuda_support(simple_text_config):
batch = Dataset.from_dict(
{
"utterance": ["I love my life"],
}
)
task = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Saliency, pipeline_index=0
),
)
# Since we share one model across all instances, the device could still be CPU, so reset it
HFTextClassificationModule.pipeline = None
hf_pipeline = task.get_model()
# index 0 may not always be correct
assert hf_pipeline.device == torch.device("cuda", index=0)
json_output = cast(SaliencyResponse, task.compute(batch)[0])
# There must be a saliency value per token
assert len(json_output.saliency) == len(json_output.tokens)
def test_post_process(simple_text_config):
indices = [1, 2, 3]
mod_low = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
threshold=0.1,
model_contract_method_name=SupportedMethod.PostProcess,
pipeline_index=0,
indices=indices,
),
)
mod_high = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
threshold=0.9,
model_contract_method_name=SupportedMethod.PostProcess,
pipeline_index=0,
indices=indices,
),
)
res_low = cast(List[PredictionResponse], mod_low.compute_on_dataset_split())
# With a low threshold, the post-processing is not affecting the predicted class with a random
# model.
assert np.all([pred.model_output.preds == pred.postprocessed_output.preds for pred in res_low])
# The prediction of the last pipeline step should be the same as the postprocessed prediction.
assert all(
pred.postprocessed_output.preds == pred.postprocessing_steps[-1].output.preds
for pred in res_low
)
res_high = cast(List[PredictionResponse], mod_high.compute_on_dataset_split())
# With a high threshold, the post-processing is affecting all predicted classes with a random
# model.
assert np.all([pred.model_output.preds != pred.postprocessed_output.preds for pred in res_high])
# Assessing prediction is rejection_class
rejection_class_idx = mod_high.get_dataset_split_manager().rejection_class_idx
assert np.all([pred.postprocessed_output.preds == rejection_class_idx for pred in res_high])
# The prediction of the last pipeline step should be the same as the postprocessed prediction.
assert np.all(
[
pred.postprocessed_output.preds == pred.postprocessing_steps[-1].output.preds
for pred in res_high
]
)
# Try with threshold set at 0.
mod_none = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
threshold=0,
model_contract_method_name=SupportedMethod.PostProcess,
pipeline_index=0,
indices=indices,
),
)
res_none = cast(List[PredictionResponse], mod_none.compute_on_dataset_split())
rejection_class_idx = mod_none.get_dataset_split_manager().rejection_class_idx
# No prediction should be the rejection class.
assert np.all([pred.postprocessed_output.preds != rejection_class_idx for pred in res_none])
assert np.all(
[
pred.postprocessed_output.preds == pred.postprocessing_steps[-1].output.preds
for pred in res_none
]
)
def test_post_process_file_based(file_text_config_top1):
indices = [1, 2, 3]
mod_low = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top1,
mod_options=ModuleOptions(
threshold=0.1,
model_contract_method_name=SupportedMethod.PostProcess,
pipeline_index=0,
indices=indices,
),
)
mod_high = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top1,
mod_options=ModuleOptions(
threshold=0.9,
model_contract_method_name=SupportedMethod.PostProcess,
pipeline_index=0,
indices=indices,
),
)
res_low = cast(List[PredictionResponse], mod_low.compute_on_dataset_split())
res_high = cast(List[PredictionResponse], mod_high.compute_on_dataset_split())
# With file-based, the threshold should not affect the post-processed values
assert np.all(
[
pred_low.postprocessed_output.preds == pred_high.postprocessed_output.preds
for pred_low, pred_high in zip(res_low, res_high)
]
)
# No postprocessing steps for file-based.
assert np.all([len(pred.postprocessing_steps) == 0 for pred in res_low])
def test_post_process_no_postprocessors(tiny_text_config_no_postprocessor):
indices = [0, 1, 2]
mod = HFTextClassificationModule(
DatasetSplitName.eval,
tiny_text_config_no_postprocessor,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
indices=indices,
),
)
res = cast(List[PredictionResponse], mod.compute_on_dataset_split())
assert np.all(
[res[idx].model_output.probs == res[idx].postprocessed_output.probs for idx in indices]
)
def test_get_input(dask_client, simple_text_config):
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Inputs, pipeline_index=0, indices=[4, 25]
),
)
out = mod.compute_on_dataset_split()
assert len(out) == 2
def test_temperature_scaling(simple_text_config):
batch = Dataset.from_dict({"utterance": ["this is hell."], "label": [1]})
predictions = []
for temp in [1, 10]:
cfg = AzimuthConfig(
**{
**simple_text_config.dict(),
"pipelines": [
PipelineDefinition(
name="MyModel",
model=simple_text_config.pipelines[0].model,
postprocessors=[
{"temperature": temp},
{"threshold": simple_text_config.pipelines[-1].threshold},
],
)
],
}
)
predictions.append(
HFTextClassificationModule(
DatasetSplitName.eval,
cfg,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
).compute(batch)[0]
)
pred, pred_tempered = predictions
# Predictions should be the same before post-processing
assert np.allclose(pred.model_output.probs, pred_tempered.model_output.probs)
# Predictions should not be the same after post-processing
assert not np.allclose(
pred.postprocessed_output.probs, pred_tempered.postprocessed_output.probs
)
assert np.isclose(
pred.postprocessed_output.probs.sum(), pred_tempered.postprocessed_output.probs.sum()
)
# The tempered probs at step 0 in the postprocessing_steps should be
# the same as the postprocessed probs.
assert pred_tempered.postprocessing_steps[0].class_name == "TemperatureScaling"
assert np.allclose(
pred_tempered.postprocessed_output.probs, pred_tempered.postprocessing_steps[0].output.probs
)
# Verify that temp=1 is the original pred.
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
hf_pipeline = mod.get_model()
output = np.array([record["score"] for record in hf_pipeline(batch["utterance"])[0]])
assert np.allclose(output, pred.postprocessed_output.probs[0])
def test_temperature_scaling_multilabel(simple_text_config):
multiclass_input = np.array([[0.2, 0.8]])
multilabel_input = np.array([[0.3, 0.2]])
input_text = ["this is a sentence."]
temp = TemperatureScaling(temperature=1.0)
# Test on multiclass
out = temp(
PostProcessingIO(
texts=input_text,
probs=multiclass_input,
logits=np.log(multiclass_input),
preds=np.argmax(multiclass_input, -1),
)
).probs
assert np.isclose(out.sum(), 1.0)
assert np.allclose(out, multiclass_input)
# Test multilabel
temp_ml = TemperatureScaling(1.0)
out = temp_ml(
PostProcessingIO(
texts=input_text,
probs=multilabel_input,
logits=np.log(multilabel_input / (1 - multilabel_input)),
preds=np.argmax(multilabel_input, -1),
)
).probs
assert out.min() >= 0.0 and out.max() <= 1.0
assert np.allclose(out, multilabel_input)
temp.temperature = 8.0
# Test on multiclass
out = temp(
PostProcessingIO(
texts=input_text,
probs=multiclass_input,
logits=np.log(multiclass_input),
preds=np.argmax(multiclass_input, -1),
)
).probs
assert np.isclose(out.sum(), 1.0)
assert not np.allclose(out, multiclass_input)
# Test multilabel
temp_ml.temperature = 8.0
out = temp_ml(
PostProcessingIO(
texts=input_text,
probs=multilabel_input,
logits=np.log(multilabel_input / (1 - multilabel_input)),
preds=np.argmax(multilabel_input, -1),
)
).probs
assert out.min() >= 0.0 and out.max() <= 1.0
assert not np.allclose(out, multilabel_input)
def test_extract_output(simple_text_config):
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions,
indices=[4, 9, 3],
pipeline_index=0,
),
)
real_output = np.array([[0.1, 0.9]])
assert np.allclose(real_output, mod.extract_probs_from_output(real_output))
assert np.allclose(real_output, mod.extract_probs_from_output(torch.from_numpy(real_output)))
assert np.allclose(
real_output, mod.extract_probs_from_output(tf.convert_to_tensor(real_output))
)
assert np.allclose(
real_output,
mod.extract_probs_from_output([[{"score": i} for i in item] for item in real_output]),
)
assert np.allclose(
real_output,
mod.extract_probs_from_output(ModelOutput({"logits": torch.from_numpy(real_output).log()})),
)
with pytest.raises(ValueError, match="Can't handle"):
mod.extract_probs_from_output(real_output.tolist())
with pytest.raises(ValueError, match="Can't handle"):
mod.extract_probs_from_output(((0.1, 0.2),))
if __name__ == "__main__":
pytest.main()
|
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"/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", 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["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,052
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/pipeline_comparison.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from azimuth.types import AliasModel
class PredictionComparisonResponse(AliasModel):
pipeline_disagreement: bool
incorrect_for_all_pipelines: bool
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,053
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/word_analysis/tokens_to_words.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from functools import reduce
from typing import List, Tuple, cast
from datasets import Dataset
from azimuth.config import ModelContractConfig
from azimuth.modules.base_classes import IndexableModule
from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping
from azimuth.modules.task_execution import get_task_result
from azimuth.types import DatasetColumn, ModuleOptions, SupportedMethod
from azimuth.types.task import SaliencyResponse
from azimuth.types.word_analysis import TokensToWordsResponse
class TokensToWordsModule(IndexableModule[ModelContractConfig]):
"""Returns the words in an utterance with their saliency values."""
required_mod_options = {"pipeline_index"}
def get_tokens_saliencies(self, indices: List[int]) -> List[SaliencyResponse]:
"""
Get saliency values and tokens for the provided indices.
Args:
indices: Indices in the dataset_split for which to get saliency values and tokens.
Returns:
Saliency values for each token. Will be 0 if saliency maps are not available.
"""
saliency_task = model_contract_task_mapping(
self.dataset_split_name,
self.config,
ModuleOptions(
model_contract_method_name=SupportedMethod.Saliency,
pipeline_index=self.mod_options.pipeline_index,
indices=indices,
),
)
result = get_task_result(task_module=saliency_task, result_type=List[SaliencyResponse])
return result
def compute(self, batch: Dataset) -> List[TokensToWordsResponse]: # type: ignore
"""Get words and their saliencies for a given set of indices.
Args:
batch: Batch
Returns:
Words and their Saliencies for utterances associated to indices.
"""
TokenSaliency = Tuple[str, float]
def make_words(results: List[TokenSaliency], x: TokenSaliency) -> List[TokenSaliency]:
tok, sal = x
if tok.startswith("##"):
if len(results) == 0:
raise ValueError("First token is not a word.")
(l_tok, l_sal), rest = results[-1], results[:-1]
l_tok = l_tok + tok.lstrip("##")
l_sal = l_sal + sal
return rest + [(l_tok, l_sal)]
else:
return results + [x]
tokens_saliencies = self.get_tokens_saliencies(
cast(List[int], batch[DatasetColumn.row_idx])
)
get_words_responses: List[TokensToWordsResponse] = []
for idx, record in enumerate(tokens_saliencies):
tokens = record.tokens
saliencies = record.saliency
words_saliencies: List[TokenSaliency] = reduce(make_words, zip(tokens, saliencies), [])
if words_saliencies:
all_words, saliency_per_word = list(map(list, zip(*words_saliencies)))
else:
all_words, saliency_per_word = [], []
get_words_responses.append(
TokensToWordsResponse(words=all_words, saliency=saliency_per_word)
)
return get_words_responses
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,054
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/app.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Any, Dict, List, Optional
from pydantic import Field
from azimuth.types import AliasModel, DatasetSplitName, SupportedModelContract
from azimuth.types.perturbation_testing import PerturbationTestSummary
from azimuth.types.tag import DataAction, SmartTag
class StatusResponse(AliasModel):
startup_tasks_ready: bool = Field(..., title="Startup tasks ready")
startup_tasks_status: Dict[str, str] = Field(..., title="Startup tasks status")
class AvailableDatasetSplits(AliasModel):
train: bool
eval: bool
class UtteranceCountPerDatasetSplit(AliasModel):
train: Optional[int] = Field(..., nullable=True)
eval: Optional[int] = Field(..., nullable=True)
class DatasetInfoResponse(AliasModel):
project_name: str = Field(..., title="Name of the project")
data_actions: List[DataAction] = Field(..., title="Data action tags")
smart_tags: List[SmartTag] = Field(..., title="Smart tags")
startup_tasks: Dict[str, Any] = Field(..., title="Startup tasks status")
model_contract: SupportedModelContract = Field(..., title="Model Contract in the config.")
prediction_available: bool = Field(..., title="Indicator if prediction values are available.")
perturbation_testing_available: bool = Field(
..., title="Indicator if perturbation_tests are available."
)
available_dataset_splits: AvailableDatasetSplits = Field(
..., title="Which dataset splits are available."
)
utterance_count_per_dataset_split: UtteranceCountPerDatasetSplit = Field(
..., title="Utterance count per dataset split."
)
similarity_available: bool = Field(..., title="Indicator if similarities are available.")
postprocessing_editable: Optional[List[bool]] = Field(
..., title="Indicator if postprocessing can be overwritten.", nullable=True
)
class PerturbationTestingSummary(AliasModel):
all_tests_summary: List[PerturbationTestSummary]
failure_rates: Dict[DatasetSplitName, float]
|
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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,055
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/plots/dataset_warnings.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import json
from typing import Dict, List, Optional, cast
import numpy as np
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from azimuth.config import SyntaxOptions
from azimuth.types import DatasetSplitName, PlotSpecification
from azimuth.types.dataset_warnings import Agg, DatasetWarningPlots
from azimuth.utils.plots import (
AXIS_FONT_SIZE,
DATASET_SPLIT_COLORS,
DATASET_SPLIT_OFFSETS,
DATASET_SPLIT_PRETTY_NAMES,
X_LEFT_LEGEND,
X_RIGHT_LEGEND,
Y_LEGEND_LINE_1,
Y_LEGEND_LINE_HEIGHT,
Colors,
fig_default,
plot_height_based_on_cls_count,
shorten_cls_names,
)
def sample_count_plot(
count_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
alert_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
cls_names: List[str],
):
"""Generate the figure with the number of samples per class.
Args:
count_per_cls_per_split: Sample count per class per split.
alert_per_cls_per_split: Indicator if there is an alert per class per split.
cls_names: Class names.
Returns:
Plot.
"""
cls_names = shorten_cls_names(cls_names)
order_split = (
DatasetSplitName.eval
if DatasetSplitName.eval in count_per_cls_per_split
else DatasetSplitName.train
)
order = count_per_cls_per_split[order_split].argsort()
fig = go.Figure()
for split, count in sorted(count_per_cls_per_split.items()):
fig.add_bar(
y=[cls_names[x] for x in order],
x=count[order],
name=DATASET_SPLIT_PRETTY_NAMES[split],
offset=DATASET_SPLIT_OFFSETS[split],
marker=dict(color=DATASET_SPLIT_COLORS[split]),
)
fig.update_traces(orientation="h", width=0.4)
fig.update_layout(
title="Number of samples per class in both sets",
height=plot_height_based_on_cls_count(len(cls_names)),
)
fig.update_xaxes(
zeroline=True,
zerolinecolor=Colors.Axis,
zerolinewidth=1,
showgrid=True,
gridcolor=Colors.Gray_transparent,
)
fig.update_yaxes(ticks="outside", ticklen=24, tickcolor="white")
# Generate warning indicators on the left axis
alert_text_per_split = {DatasetSplitName.eval: "◓", DatasetSplitName.train: "◒"}
for split, alert in alert_per_cls_per_split.items():
for idx, cls in enumerate(order):
if alert[cls]:
fig.add_annotation(
text=alert_text_per_split[split],
y=idx,
xref="paper",
showarrow=False,
x=-0.037,
font=dict(color=Colors.Orange, size=AXIS_FONT_SIZE),
)
y_legend_1 = Y_LEGEND_LINE_1 + 3 * Y_LEGEND_LINE_HEIGHT
y_legend_2 = Y_LEGEND_LINE_1 + 4 * Y_LEGEND_LINE_HEIGHT
y_legend_3 = Y_LEGEND_LINE_1 + 5 * Y_LEGEND_LINE_HEIGHT
y_legend_4 = Y_LEGEND_LINE_1 + 6 * Y_LEGEND_LINE_HEIGHT
common_args = dict(
y=1,
xref="paper",
yref="paper",
xanchor="left",
yanchor="middle",
showarrow=False,
)
for y in [y_legend_2, y_legend_4]:
fig.add_annotation(
x=X_LEFT_LEGEND,
yshift=y,
text=alert_text_per_split[DatasetSplitName.eval],
font=dict(color=Colors.Orange),
**common_args,
)
for y in [y_legend_3, y_legend_4]:
fig.add_annotation(
x=X_LEFT_LEGEND,
yshift=y,
text=alert_text_per_split[DatasetSplitName.train],
font=dict(color=Colors.Orange),
**common_args,
)
fig.add_annotation(
x=X_LEFT_LEGEND,
yshift=y_legend_1,
text="Warning due to:",
**common_args,
)
for y, text in zip(
[y_legend_2, y_legend_3, y_legend_4],
[
DATASET_SPLIT_PRETTY_NAMES[DatasetSplitName.eval],
DATASET_SPLIT_PRETTY_NAMES[DatasetSplitName.train],
"both sets",
],
):
fig.add_annotation(
x=X_RIGHT_LEGEND,
yshift=y,
text=text,
**common_args,
)
fig = fig_default(fig)
return fig
def min_sample_count_plot(
count_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
threshold: int,
alert_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
cls_names: List[str],
) -> DatasetWarningPlots:
"""Generate the plot for the minimal number of samples per class.
Args:
count_per_cls_per_split: Sample count per class per split.
threshold: Threshold below which a class has an associated alert.
alert_per_cls_per_split: Indicator if there is an alert per class per split.
cls_names: Class names.
Returns:
Plot.
"""
fig = sample_count_plot(count_per_cls_per_split, alert_per_cls_per_split, cls_names)
fig.add_vline(x=threshold, line_width=1, line_dash="dot", line_color=Colors.Orange)
fig.add_annotation(
text=threshold,
x=threshold,
y=1.002,
yanchor="bottom",
yref="paper",
font=dict(color=Colors.Orange),
showarrow=False,
)
return DatasetWarningPlots(overall=json.loads(fig.to_json()), per_class=None)
def class_imbalance_plot(
count_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
mean_per_split: Dict[DatasetSplitName, float],
max_perc_delta: float,
alert_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
cls_names: List[str],
) -> DatasetWarningPlots:
"""Generate the plot for the class imbalance warnings.
Args:
count_per_cls_per_split: Sample count per class per split.
mean_per_split: Mean of the sample count per split.
max_perc_delta: Percentage around the mean where values need to be.
alert_per_cls_per_split: Indicator if there is an alert per class per split.
cls_names: Class names.
Returns:
Plot.
"""
fig = sample_count_plot(count_per_cls_per_split, alert_per_cls_per_split, cls_names)
common_args = dict(layer="below", line_width=1, y0=-1)
cls_count = len(cls_names)
y1_per_split = {DatasetSplitName.eval: cls_count + 2, DatasetSplitName.train: cls_count + 1}
for split, mean in sorted(mean_per_split.items()):
fig.add_shape(
x0=mean * (1 - max_perc_delta),
x1=mean * (1 + max_perc_delta),
y1=y1_per_split[split],
line_color=DATASET_SPLIT_COLORS[split],
fillcolor=DATASET_SPLIT_COLORS[split],
type="rect",
opacity=0.3,
**common_args,
)
fig.add_shape(
x0=mean,
x1=mean,
y1=y1_per_split[split],
line_color=DATASET_SPLIT_COLORS[split],
type="line",
line_dash="dot",
**common_args,
)
fig.add_annotation(
text=f"Mean in {split}: {mean:.2f}",
x=mean,
y=y1_per_split[split],
yanchor="top",
font=dict(color=Colors.Text),
showarrow=False,
)
return DatasetWarningPlots(overall=json.loads(fig.to_json()), per_class=None)
def class_representation(
count_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
count_norm_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
divergence_norm: np.ndarray,
threshold: float,
cls_names: List[str],
) -> DatasetWarningPlots:
"""Generate the plot for the minimal representation of samples per class.
Args:
count_per_cls_per_split: Sample count per class per split.
count_norm_per_cls_per_split: Normalized sample count per class per split.
divergence_norm: Difference in percentage per class between train and eval.
threshold: Threshold below which a class has an associated alert.
cls_names: Class names.
Returns:
Plot.
"""
order = divergence_norm.argsort()
cls_names = shorten_cls_names(cls_names)
fig = make_subplots(rows=1, cols=2, column_widths=[0.3, 0.7])
for split, c_norm in sorted(count_norm_per_cls_per_split.items()):
fig.add_bar(
y=[cls_names[x] for x in order],
x=c_norm,
text=count_per_cls_per_split[split][order],
name=DATASET_SPLIT_PRETTY_NAMES[split],
xaxis="x2",
hoverinfo="x+y+text",
width=0.4,
offset=DATASET_SPLIT_OFFSETS[split],
marker=dict(color=DATASET_SPLIT_COLORS[split]),
)
fig.add_bar(
y=[cls_names[x] for x in order],
x=divergence_norm[order],
marker=dict(
color=[
Colors.Axis if (x <= threshold) and (x >= -threshold) else Colors.Orange
for x in divergence_norm[order]
]
),
name="delta",
xaxis="x1",
)
for mult in [-1, 1]:
fig.add_vline(
x=mult * threshold,
line_width=1,
line_dash="dot",
line_color=Colors.Orange,
col=1,
row=1,
)
# These transparent lines make sure that the left plot is centered and with a min. range.
fig.add_vline(x=mult * 0.2, line_color="rgba(0,0,0,0)", col=1, row=1)
fig.add_annotation(
text=f"±{100 * threshold}%",
x=0,
y=1.002,
yanchor="bottom",
yref="paper",
font=dict(color=Colors.Orange),
showarrow=False,
)
for idx, cls in enumerate(order):
if divergence_norm[cls] > threshold or divergence_norm[cls] < -threshold:
fig.add_annotation(
text="⬤",
font=dict(color=Colors.Orange, size=AXIS_FONT_SIZE - 3),
x=-0.02,
xref="paper",
y=idx,
showarrow=False,
)
y_legend = Y_LEGEND_LINE_1 + 3 * Y_LEGEND_LINE_HEIGHT
common_args = dict(
y=1,
xref="paper",
yref="paper",
xanchor="left",
yanchor="middle",
showarrow=False,
yshift=y_legend,
)
fig.add_annotation(
x=X_LEFT_LEGEND,
text="⬤",
font=dict(color=Colors.Orange, size=AXIS_FONT_SIZE - 3),
**common_args,
)
fig.add_annotation(
x=X_RIGHT_LEGEND,
text="warning",
font=dict(color=Colors.Text, size=AXIS_FONT_SIZE),
**common_args,
)
fig.update_traces(orientation="h")
fig.update_layout(
title="Delta in class representation between both sets",
height=plot_height_based_on_cls_count(len(cls_names)),
)
fig.update_xaxes(
tickformat=",.0%",
zeroline=True,
zerolinecolor=Colors.Axis,
zerolinewidth=1,
showgrid=True,
gridcolor=Colors.Gray_transparent,
)
fig = fig_default(fig)
fig.update_yaxes(ticks="outside", ticklen=15, tickcolor="white") # Override default
return DatasetWarningPlots(overall=json.loads(fig.to_json()), per_class=None)
def create_histogram_mean_std(
hist_per_split: Dict[DatasetSplitName, np.ndarray],
value_per_agg_per_split: Dict[DatasetSplitName, Dict[Agg, float]],
syntax_options: SyntaxOptions,
divergence_per_agg: Optional[Dict[Agg, float]] = None,
) -> PlotSpecification:
"""Create the histogram traces and annotations for each class (and "all").
Args:
hist_per_split: Histogram values for the selected class per split.
value_per_agg_per_split: Mean and std dev for the selected class per split.
divergence_per_agg: Difference in the mean and std dev. None means that we are generating
the plot for 'all' classes.
syntax_options: SyntaxOptions from config.
Returns:
Plot for one class.
"""
fig = go.Figure()
hist_normalized = {
split: hist / sum(hist) for split, hist in hist_per_split.items() if any(hist)
}
# Helps position the error bars.
max_y = max((value.max() for value in hist_normalized.values()), default=0)
if len(hist_normalized) == 0:
fig.add_annotation(
x=0.5,
y=0.5,
text="No utterances for this class",
font=dict(color=Colors.Text, size=AXIS_FONT_SIZE),
xref="paper",
xanchor="center",
yref="paper",
yanchor="middle",
showarrow=False,
)
else:
y_per_split = {DatasetSplitName.eval: 1.14, DatasetSplitName.train: 1.07}
opacity_per_split = {DatasetSplitName.eval: 1, DatasetSplitName.train: 0.5}
# sorted because the color overlay looks better when eval is first
for split, hist in sorted(hist_normalized.items()):
fig.add_bar(
x=list(range(1, len(hist) + 1)),
y=hist,
name=DATASET_SPLIT_PRETTY_NAMES[split],
marker=dict(color=DATASET_SPLIT_COLORS[split], opacity=opacity_per_split[split]),
)
fig.add_scatter(
x=[np.round(value_per_agg_per_split[split][Agg.mean], 2)],
y=[max_y * y_per_split[split]],
name=f"{split}_mean_std",
error_x=dict(
type="constant", value=np.round(value_per_agg_per_split[split][Agg.std], 2)
),
hoverinfo="x",
marker=dict(color=DATASET_SPLIT_COLORS[split]),
)
if divergence_per_agg and len(hist_normalized) == 2:
fig.add_annotation(
x=0,
y=max_y * 1.2,
text=f"Delta of {divergence_per_agg[Agg.mean]:.2f}±"
f"{divergence_per_agg[Agg.std]:.2f} tokens",
font=dict(color=Colors.Text),
xref="paper",
xanchor="left",
showarrow=False,
)
# Add thresholds for short and long utterances.
max_x = max((len(hist) for hist in hist_normalized.values()), default=0)
for threshold, edge, pretty_name, xanchor in (
(syntax_options.short_utterance_max_word + 0.5, 0, "Short utterances", "right"),
(syntax_options.long_utterance_min_word - 0.5, max_x + 1, "Long utterances", "left"),
):
common_args = dict(layer="below", y0=0, y1=max_y)
fig.add_shape(
type="rect",
x0=threshold,
x1=edge,
line_color="rgba(0,0,0,0)",
fillcolor=Colors.Gray_transparent,
**common_args,
)
fig.add_shape(
type="line",
x0=threshold,
x1=threshold,
line_color=Colors.Axis,
line_width=1,
**common_args,
)
fig.add_annotation(
text=pretty_name,
x=threshold,
xshift=-4 if xanchor == "right" else 4,
y=max_y,
yanchor="top",
yshift=-4,
xanchor=xanchor,
font=dict(color=Colors.Text),
showarrow=False,
)
fig.update_layout(
barmode="overlay",
title="Histogram of utterances per word count",
height=500,
)
fig.update_yaxes(
tickformat=",.0%",
zerolinecolor=Colors.Axis,
zerolinewidth=1,
showgrid=True,
gridcolor=Colors.Gray_transparent,
tickmode="array",
tickvals=np.arange(0, max_y, 0.05),
)
fig.update_xaxes(zeroline=True, zerolinecolor=Colors.Axis, zerolinewidth=1)
fig = fig_default(fig)
fig.update_layout(margin=dict(l=60)) # Overwriting default for this plot
return cast(PlotSpecification, json.loads(fig.to_json()))
def word_count_plot(
hist_per_cls_per_split: Dict[DatasetSplitName, np.ndarray],
value_per_agg_per_split: Dict[DatasetSplitName, Dict[Agg, float]],
value_per_cls_per_agg_per_split: Dict[DatasetSplitName, Dict[Agg, np.ndarray]],
divergence_per_cls_per_agg: Dict[Agg, np.ndarray],
cls_names: List[str],
syntax_options: SyntaxOptions,
) -> DatasetWarningPlots:
"""Create the plot with the dropdown for all classes.
Args:
hist_per_cls_per_split: Histogram of word count per class per split.
value_per_agg_per_split: Mean and std dev per split.
value_per_cls_per_agg_per_split: Mean and std dev per class per split.
divergence_per_cls_per_agg: Alert value for the mean and std dev per class.
cls_names: List of class names.
syntax_options: SyntaxOptions from config.
Returns:
Plot for all classes combined, and plot per class.
"""
# Sanitize values for the plot.
sanitized_value_per_cls_per_agg_per_split = {
split: {agg: np.nan_to_num(value) for agg, value in per_split_value.items()}
for split, per_split_value in value_per_cls_per_agg_per_split.items()
}
hist_per_split = {split: h.sum(axis=0) for split, h in hist_per_cls_per_split.items()}
# Traces and Annotations across all classes
fig_all = create_histogram_mean_std(hist_per_split, value_per_agg_per_split, syntax_options)
# Traces and Annotations for each class
figs_dict = {}
for cls_id in np.arange(len(cls_names)):
figs_dict[cls_names[cls_id]] = create_histogram_mean_std(
{split: value[cls_id] for split, value in hist_per_cls_per_split.items()},
{
split: {agg: value[cls_id] for agg, value in per_split_value.items()}
for split, per_split_value in sanitized_value_per_cls_per_agg_per_split.items()
},
syntax_options,
{agg: value[cls_id] for agg, value in divergence_per_cls_per_agg.items()},
)
return DatasetWarningPlots(overall=fig_all, per_class=figs_dict)
|
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"/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,056
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/app.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import logging
from threading import Event
from typing import Dict, Optional
import structlog
from distributed import SpecCluster
from fastapi import APIRouter, Depends, FastAPI, HTTPException
from fastapi.exceptions import RequestValidationError
from pydantic import BaseModel, ValidationError
from starlette.middleware.cors import CORSMiddleware
from starlette.status import (
HTTP_400_BAD_REQUEST,
HTTP_401_UNAUTHORIZED,
HTTP_403_FORBIDDEN,
HTTP_404_NOT_FOUND,
HTTP_422_UNPROCESSABLE_ENTITY,
HTTP_503_SERVICE_UNAVAILABLE,
)
from azimuth.config import AzimuthConfig, load_azimuth_config
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.base_classes import ArtifactManager, DaskModule
from azimuth.startup import startup_tasks
from azimuth.task_manager import TaskManager
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedModule
from azimuth.utils.cluster import default_cluster
from azimuth.utils.conversion import JSONResponseIgnoreNan
from azimuth.utils.exception_handlers import handle_validation_error
from azimuth.utils.logs import set_logger_config
from azimuth.utils.validation import assert_not_none
_dataset_split_managers: Dict[DatasetSplitName, Optional[DatasetSplitManager]] = {}
_task_manager: Optional[TaskManager] = None
_startup_tasks: Optional[Dict[str, DaskModule]] = None
_azimuth_config: Optional[AzimuthConfig] = None
_ready_flag: Optional[Event] = None
COMMON_HTTP_ERROR_CODES = (
HTTP_400_BAD_REQUEST,
HTTP_401_UNAUTHORIZED,
HTTP_403_FORBIDDEN,
HTTP_404_NOT_FOUND,
# The default handler for RequestValidationError was returning an HTTP_422_UNPROCESSABLE_ENTITY.
# We overwrite that handler with the following handle_validation_error(), which returns more
# conventional HTTP codes.
# This overwrites the default ValidationError response for 422 in the OpenAPI spec.
HTTP_422_UNPROCESSABLE_ENTITY,
HTTP_503_SERVICE_UNAVAILABLE,
)
class HTTPExceptionModel(BaseModel):
detail: str
def get_dataset_split_manager_mapping() -> Dict[DatasetSplitName, Optional[DatasetSplitManager]]:
return _dataset_split_managers
def get_all_dataset_split_managers() -> Dict[DatasetSplitName, Optional[DatasetSplitManager]]:
dm_eval = _dataset_split_managers.get(DatasetSplitName.eval)
dm_train = _dataset_split_managers.get(DatasetSplitName.train)
if dm_train is None or dm_eval is None:
raise HTTPException(HTTP_404_NOT_FOUND, detail="Dataset split not found.")
return _dataset_split_managers
def get_dataset_split_manager(dataset_split_name: DatasetSplitName) -> DatasetSplitManager:
dm = _dataset_split_managers.get(dataset_split_name)
if dm is None:
raise HTTPException(HTTP_404_NOT_FOUND, detail="Dataset split not found.")
return dm
def get_task_manager() -> Optional[TaskManager]:
return _task_manager
def get_startup_tasks() -> Optional[Dict[str, DaskModule]]:
return _startup_tasks
def get_ready_flag() -> Optional[Event]:
return _ready_flag
def get_config() -> Optional[AzimuthConfig]:
return _azimuth_config
def require_editable_config(config: AzimuthConfig = Depends(get_config)):
if config.read_only_config:
raise HTTPException(HTTP_403_FORBIDDEN, detail="The Azimuth config is currently read-only.")
def start_app(config_path: Optional[str], load_config_history: bool, debug: bool) -> FastAPI:
"""Launch the application's API.
Args:
config_path: path to the config
load_config_history: Load the last config from history, or if empty, default to config_path.
debug: Debug flag
Returns:
API.
Raises:
ValueError: If no dataset_split in config.
"""
global _dataset_split_managers, _task_manager, _startup_tasks, _azimuth_config, _ready_flag
level = logging.DEBUG if debug else logging.INFO
set_logger_config(level)
log = structlog.get_logger(__name__)
log.info("🔭 Azimuth starting 🔭")
azimuth_config = load_azimuth_config(config_path, load_config_history)
local_cluster = default_cluster(large=azimuth_config.large_dask_cluster)
run_startup_tasks(azimuth_config, local_cluster)
task_manager = assert_not_none(_task_manager)
task_manager.client.run(set_logger_config, level)
app = create_app()
log.info("All routes added to router.")
if debug:
log.debug(f"See Dask dashboard at {task_manager.client.dashboard_link}.")
for r in app.router.routes:
log.debug("Route", methods=r.__dict__.get("methods"), path=r.__dict__["path"])
@app.on_event("shutdown")
def shutdown_event():
if _task_manager:
_task_manager.close()
_task_manager.cluster.close()
return app
def create_app() -> FastAPI:
"""Create the FastAPI.
Returns:
FastAPI.
"""
api = FastAPI(
title="Azimuth API",
description="Azimuth API",
version="1.0",
default_response_class=JSONResponseIgnoreNan,
responses={code: {"model": HTTPExceptionModel} for code in COMMON_HTTP_ERROR_CODES},
exception_handlers={
ValidationError: handle_validation_error, # for PATCH "/config",
# where we call old_config.copy(update=partial_config, deep=True) ourselves.
RequestValidationError: handle_validation_error,
},
root_path=".", # Tells Swagger UI and ReDoc to fetch the OpenAPI spec from ./openapi.json
# (relative) so it works through the front-end proxy.
)
# Setup routes
from azimuth.routers import (
app,
class_overlap,
config,
custom_utterances,
dataset_warnings,
export,
top_words,
utterances,
)
from azimuth.routers.model_performance import (
confidence_histogram,
confusion_matrix,
metrics,
outcome_count,
utterance_count,
)
from azimuth.utils.routers import require_application_ready, require_available_model
api_router = APIRouter()
api_router.include_router(app.router, prefix="", tags=["App"])
api_router.include_router(config.router, prefix="/config", tags=["Config"])
api_router.include_router(
class_overlap.router,
prefix="/dataset_splits/{dataset_split_name}/class_overlap",
tags=["Class Overlap"],
dependencies=[Depends(require_application_ready)],
)
api_router.include_router(
confidence_histogram.router,
prefix="/dataset_splits/{dataset_split_name}/confidence_histogram",
tags=["Confidence Histogram"],
dependencies=[Depends(require_application_ready), Depends(require_available_model)],
)
api_router.include_router(
dataset_warnings.router,
prefix="/dataset_warnings",
tags=["Dataset Warnings"],
dependencies=[Depends(require_application_ready)],
)
api_router.include_router(
metrics.router,
prefix="/dataset_splits/{dataset_split_name}/metrics",
tags=["Metrics"],
dependencies=[Depends(require_application_ready), Depends(require_available_model)],
)
api_router.include_router(
outcome_count.router,
prefix="/dataset_splits/{dataset_split_name}/outcome_count",
tags=["Outcome Count"],
dependencies=[Depends(require_application_ready), Depends(require_available_model)],
)
api_router.include_router(
utterance_count.router,
prefix="/dataset_splits/{dataset_split_name}/utterance_count",
tags=["Utterance Count"],
dependencies=[Depends(require_application_ready)],
)
api_router.include_router(
utterances.router,
prefix="/dataset_splits/{dataset_split_name}/utterances",
tags=["Utterances"],
dependencies=[Depends(require_application_ready)],
)
api_router.include_router(
export.router,
prefix="/export",
tags=["Export"],
dependencies=[Depends(require_application_ready)],
)
api_router.include_router(
custom_utterances.router,
prefix="/custom_utterances",
tags=["Custom Utterances"],
dependencies=[Depends(require_application_ready)],
)
api_router.include_router(
top_words.router,
prefix="/dataset_splits/{dataset_split_name}/top_words",
tags=["Top Words"],
dependencies=[Depends(require_application_ready), Depends(require_available_model)],
)
api_router.include_router(
confusion_matrix.router,
prefix="/dataset_splits/{dataset_split_name}/confusion_matrix",
tags=["Confusion Matrix"],
dependencies=[Depends(require_application_ready), Depends(require_available_model)],
)
api.include_router(api_router)
api.add_middleware(
CORSMiddleware,
allow_methods=["*"],
allow_headers=["*"],
)
return api
def load_dataset_split_managers_from_config(
azimuth_config: AzimuthConfig,
) -> Dict[DatasetSplitName, Optional[DatasetSplitManager]]:
"""
Load all dataset splits for the application.
Args:
azimuth_config: Azimuth Configuration.
Returns:
For all DatasetSplitName, None or a dataset_split manager.
"""
artifact_manager = ArtifactManager.instance()
dataset = artifact_manager.get_dataset_dict(azimuth_config)
return {
dataset_split_name: None
if dataset_split_name not in dataset
else artifact_manager.get_dataset_split_manager(
azimuth_config, DatasetSplitName[dataset_split_name]
)
for dataset_split_name in [DatasetSplitName.eval, DatasetSplitName.train]
}
def initialize_managers(azimuth_config: AzimuthConfig, cluster: SpecCluster):
"""Initialize DatasetSplitManagers and TaskManagers.
Args:
azimuth_config: Configuration
cluster: Dask cluster to use.
"""
global _task_manager, _dataset_split_managers, _azimuth_config
_azimuth_config = azimuth_config
if _task_manager is not None:
task_history = _task_manager.current_tasks
else:
task_history = {}
_task_manager = TaskManager(azimuth_config, cluster=cluster)
_task_manager.current_tasks = task_history
_dataset_split_managers = load_dataset_split_managers_from_config(azimuth_config)
def run_validation(
dataset_split: DatasetSplitName, task_manager: TaskManager, config: AzimuthConfig
):
"""Run validation module on the specified split.
Args:
dataset_split: Which split to run on.
task_manager: Task Manager to use
config: Current config.
Raises:
MultipleException if the validation failed.
"""
def run_validation_module(pipeline_index=None):
_, task = task_manager.get_task(
task_name=SupportedModule.Validation,
dataset_split_name=dataset_split,
mod_options=ModuleOptions(pipeline_index=pipeline_index),
)
# Will raise exceptions as needed.
task.result()
if config.pipelines is None:
run_validation_module()
else:
for pipeline_index in range(len(config.pipelines)):
run_validation_module(pipeline_index)
def run_startup_tasks(azimuth_config: AzimuthConfig, cluster: SpecCluster):
"""Initialize managers, run validation and startup tasks.
Args:
azimuth_config: Config
cluster: Cluster
"""
initialize_managers(azimuth_config, cluster)
task_manager = assert_not_none(get_task_manager())
# Validate that everything is in order **before** the startup tasks.
if _dataset_split_managers.get(DatasetSplitName.train):
run_validation(DatasetSplitName.train, task_manager, azimuth_config)
if _dataset_split_managers.get(DatasetSplitName.eval):
run_validation(DatasetSplitName.eval, task_manager, azimuth_config)
azimuth_config.save() # Save only after the validation modules ran successfully
global _startup_tasks, _ready_flag
_startup_tasks = startup_tasks(_dataset_split_managers, task_manager)
_ready_flag = Event()
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,057
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_utils/test_ml/test_ece.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from azimuth.types.model_performance import ConfidenceBinDetails
from azimuth.types.outcomes import OutcomeName
from azimuth.utils.ml.ece import compute_ece_from_bins
def test_ece():
# perfect case
bins = [
ConfidenceBinDetails(
bin_index=18,
bin_confidence=0.95,
mean_bin_confidence=0.94,
outcome_count={
OutcomeName.IncorrectAndPredicted: 5,
OutcomeName.IncorrectAndRejected: 1,
OutcomeName.CorrectAndPredicted: 90,
OutcomeName.CorrectAndRejected: 5,
},
),
ConfidenceBinDetails(
bin_index=19,
bin_confidence=0.975,
mean_bin_confidence=0.978,
outcome_count={
OutcomeName.IncorrectAndPredicted: 2,
OutcomeName.IncorrectAndRejected: 1,
OutcomeName.CorrectAndPredicted: 95,
OutcomeName.CorrectAndRejected: 2,
},
),
]
ece = compute_ece_from_bins(bins)[0]
assert ece < 0.01
# far from perfect case
bins = [
ConfidenceBinDetails(
bin_index=18,
bin_confidence=0.95,
mean_bin_confidence=0.94,
outcome_count={
OutcomeName.IncorrectAndPredicted: 10,
OutcomeName.IncorrectAndRejected: 10,
OutcomeName.CorrectAndPredicted: 75,
OutcomeName.CorrectAndRejected: 5,
},
),
ConfidenceBinDetails(
bin_index=19,
bin_confidence=0.975,
mean_bin_confidence=0.978,
outcome_count={
OutcomeName.IncorrectAndPredicted: 10,
OutcomeName.IncorrectAndRejected: 10,
OutcomeName.CorrectAndPredicted: 75,
OutcomeName.CorrectAndRejected: 5,
},
),
]
ece = compute_ece_from_bins(bins)[0]
assert 0.1 < ece < 0.2
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,058
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/openapi.py
|
from typing import Dict
from pydantic import BaseSettings
def fix_union_types(schema: Dict):
"""Replace oneOf with anyOf, which openapi-typescript understands better."""
for field in schema["properties"].values():
field = field.get("additionalProperties", field) # Dict values (if applicable)
field = field.get("items", field) # List items (if applicable)
if "anyOf" in field:
field["oneOf"] = field.pop("anyOf")
def make_all_properties_required(schema: Dict):
"""pydantic considers fields with default values to be optional, but when the API returns an
object, all the default values are set, so the fields are always present."""
schema["required"] = list(schema["properties"].keys())
class AzimuthBaseSettings(BaseSettings):
class Config:
@staticmethod
def schema_extra(schema):
fix_union_types(schema)
make_all_properties_required(schema)
|
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"/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/azimuth/utils/ml/seeding.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_perturbation_testing/test_perturbation_testing_summary.py": ["/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/tests/test_task_manager.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/aggregation_module.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/azimuth_shr/loading_resources.py": ["/azimuth/config.py"], "/azimuth/routers/dataset_warnings.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_app.py": ["/azimuth/routers/utterances.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/dataset_analysis/similarity_analysis.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/model_performance.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/utils/ml/ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/perturbation_testing/__init__.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", 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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,059
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/pydantic_utils.py
|
# Taken from https://github.com/samuelcolvin/pydantic/pull/3946/files
# TODO When we update pydantic >= v1.10, we can remove this file and use their create_model()
import warnings
from types import prepare_class, resolve_bases
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar, Union, cast
from pydantic import BaseConfig, BaseModel, ConfigError
from pydantic.main import inherit_config
from pydantic.utils import is_valid_field
if TYPE_CHECKING:
from pydantic.typing import AnyClassMethod, DictStrAny # type: ignore
Model = TypeVar("Model", bound="BaseModel")
def create_model(
__model_name: str,
*,
__config__: Optional[Type[BaseConfig]] = None,
__base__: Union[None, Type["Model"], Tuple[Type["Model"], ...]] = None,
__module__: str = __name__,
__validators__: Dict[str, "AnyClassMethod"] = None,
**field_definitions: Any,
) -> Type["Model"]:
"""
Dynamically create a model.
Args:
__model_name: name of the created model
__config__: config class to use for the new model
__base__: base class for the new model to inherit from
__module__: module of the created model
__validators__: a dict of method names and @validator class methods
field_definitions: fields of the model (or extra fields if a base is supplied)
in the format `<name>=(<type>, <default default>)` or `<name>=<default value>, e.g.
`foobar=(str, ...)` or `foobar=123`, or, for complex use-cases, in the format
`<name>=<FieldInfo>`, e.g. `foo=Field(default_factory=datetime.utcnow, alias='bar')`
Returns:
New Model type
Raises:
ConfigError when arguments are not tuples.
"""
if __base__ is not None:
if __config__ is not None:
raise ConfigError("to avoid confusion __config__ and __base__ cannot be used together")
if not isinstance(__base__, tuple):
__base__ = (__base__,)
else:
__base__ = (cast(Type["Model"], BaseModel),)
fields = {}
annotations = {}
for f_name, f_def in field_definitions.items():
if not is_valid_field(f_name):
warnings.warn(
f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning
)
if isinstance(f_def, tuple):
try:
f_annotation, f_value = f_def
except ValueError as e:
raise ConfigError(
"field definitions should either be a tuple of (<type>, <default>) or just a "
"default value, unfortunately this means tuples as "
"default values are not allowed"
) from e
else:
f_annotation, f_value = None, f_def
if f_annotation:
annotations[f_name] = f_annotation
fields[f_name] = f_value
namespace: "DictStrAny" = {"__annotations__": annotations, "__module__": __module__}
if __validators__:
namespace.update(__validators__)
namespace.update(fields)
if __config__:
namespace["Config"] = inherit_config(__config__, BaseConfig)
resolved_bases = resolve_bases(__base__)
meta, ns, kwds = prepare_class(__model_name, resolved_bases)
if resolved_bases is not __base__:
ns["__orig_bases__"] = __base__
namespace.update(ns)
return meta(__model_name, resolved_bases, namespace, **kwds) # type: ignore
|
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"/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,060
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/dataset_operations.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List, Union, cast
from datasets import Dataset
from azimuth.config import ProjectConfig
from azimuth.types import DatasetColumn, DatasetFilters, NamedDatasetFilters
from azimuth.types.outcomes import OutcomeName
from azimuth.types.tag import (
ALL_DATA_ACTIONS,
SMART_TAGS_FAMILY_MAPPING,
DataAction,
SmartTag,
SmartTagFamily,
)
from azimuth.utils.utterance import clean_utterance
def filter_dataset_split(
dataset_split: Dataset,
filters: Union[DatasetFilters, NamedDatasetFilters],
config: ProjectConfig,
without_postprocessing: bool = False,
) -> Dataset:
"""Filter dataset_split according to a filter component.
Args:
dataset_split: examples to filter.
filters: On what to filter on.
config: Azimuth Config.
without_postprocessing: Filter on columns without_postprocessing (model)
Returns:
Filtered dataset_split.
Raises:
ValueError, if required column to filter by is not in the dataset_split's columns.
"""
if filters.confidence_min > 0 or filters.confidence_max < 1:
confidence_column = (
DatasetColumn.model_confidences
if without_postprocessing
else DatasetColumn.postprocessed_confidences
)
if confidence_column in dataset_split.column_names:
dataset_split = dataset_split.filter(
lambda x: filters.confidence_min <= x[0] <= filters.confidence_max,
input_columns=confidence_column,
)
if len(filters.label) > 0 and dataset_split.num_rows != 0:
dataset_split = dataset_split.filter(
lambda x: x in filters.label, input_columns=config.columns.label
)
if filters.utterance is not None and dataset_split.num_rows != 0:
cleaned_utterance = clean_utterance(filters.utterance)
# Filter in utterances or if string matches a known row_idx or persistent_id
dataset_split = dataset_split.filter(
lambda text, idx, persistent_id: cleaned_utterance in clean_utterance(text)
or filters.utterance == str(idx)
or filters.utterance == str(persistent_id),
input_columns=[
config.columns.text_input,
DatasetColumn.row_idx,
config.columns.persistent_id,
],
)
if len(filters.prediction) > 0 and dataset_split.num_rows != 0:
prediction_column = (
DatasetColumn.model_predictions
if without_postprocessing
else DatasetColumn.postprocessed_prediction
)
if prediction_column in dataset_split.column_names:
if without_postprocessing:
dataset_split = dataset_split.filter(
lambda x: x[0] in filters.prediction,
input_columns=DatasetColumn.model_predictions,
)
else:
dataset_split = dataset_split.filter(
lambda x: x in filters.prediction,
input_columns=DatasetColumn.postprocessed_prediction,
)
if len(filters.data_action) > 0 and dataset_split.num_rows != 0:
# We do OR for data_action tags.
dataset_split = dataset_split.filter(
lambda x: any(
((not any(x[v] for v in ALL_DATA_ACTIONS)) if v == DataAction.no_action else x[v])
for v in filters.data_action
)
)
if len(filters.outcome) > 0 and dataset_split.num_rows != 0:
outcome_column = (
DatasetColumn.model_outcome
if without_postprocessing
else DatasetColumn.postprocessed_outcome
)
if outcome_column in dataset_split.column_names:
# We do OR for outcomes.
dataset_split = dataset_split.filter(
lambda x: x in filters.outcome, input_columns=outcome_column
)
for key, tags_in_family in filters.smart_tags.items():
# For each smart tag family, we do OR, but AND between families
# If NO_SMART_TAGS, it is none of them.
family = cast(SmartTagFamily, key)
if (
len(tags_in_family) > 0
and all(tag in dataset_split.column_names for tag in SMART_TAGS_FAMILY_MAPPING[family])
and dataset_split.num_rows != 0
):
dataset_split = dataset_split.filter(
lambda x: any(
(
(not any(x[tag] for tag in SMART_TAGS_FAMILY_MAPPING[family]))
if tag_f == SmartTag.no_smart_tag
else x[tag_f]
)
for tag_f in tags_in_family
)
)
return dataset_split
def get_predictions_from_ds(ds: Dataset, without_postprocessing: bool = False) -> List[int]:
"""Get predicted classes, with or without postprocessing.
Args:
ds: Dataset Split for which to get predictions.
without_postprocessing: Determine which column to use.
Returns: List of Predictions
"""
if without_postprocessing:
return cast(List[int], [preds[0] for preds in ds[DatasetColumn.model_predictions]])
else:
return cast(List[int], ds[DatasetColumn.postprocessed_prediction])
def get_confidences_from_ds(ds: Dataset, without_postprocessing: bool = False) -> List[List[float]]:
"""Get confidences, with or without postprocessing.
Notes: Confidences are sorted according to their values (not the class id).
Args:
ds: Dataset Split for which to get confidences.
without_postprocessing: Determine which column to use.
Returns: List of Confidences
"""
confidences = (
ds[DatasetColumn.model_confidences]
if without_postprocessing
else ds[DatasetColumn.postprocessed_confidences]
)
return cast(List[List[float]], confidences)
def get_outcomes_from_ds(ds: Dataset, without_postprocessing: bool = False) -> List[OutcomeName]:
"""Get outcomes, with or without postprocessing.
Args:
ds: Dataset Split for which to get outcomes.
without_postprocessing: Determine which column to use.
Returns: List of Outcomes
"""
outcomes = (
ds[DatasetColumn.model_outcome]
if without_postprocessing
else ds[DatasetColumn.postprocessed_outcome]
)
return cast(List[OutcomeName], outcomes)
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", 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["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/outcomes.py": ["/azimuth/types/__init__.py"], "/azimuth/utils/plots.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/modules/task_mapping.py": ["/azimuth/modules/model_performance/outcomes.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py"], "/azimuth/modules/model_contracts/hf_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/mc_dropout.py", "/azimuth/utils/ml/saliency.py"], "/azimuth/routers/export.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", 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"/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,061
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_loading_resources.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import string
from dataclasses import dataclass
from typing import Callable, Dict, List
import numpy as np
import torch
from datasets import Dataset, DatasetDict, load_dataset
from scipy.special import softmax
from tensorflow import keras
from tensorflow.keras import layers
from transformers import (
DistilBertConfig,
DistilBertForSequenceClassification,
DistilBertModel,
DistilBertTokenizer,
Pipeline,
TextClassificationPipeline,
)
from azimuth.config import AzimuthConfig
from azimuth.utils.ml.seeding import RandomContext
from azimuth_shr.loading_resources import align_labels
_CACHE_DIR = "/tmp/azimuth_test_models"
_MAX_DATASET_LEN = 42
def _should_use_cuda(azimuth_config: AzimuthConfig):
if azimuth_config.use_cuda == "auto":
use_cuda = torch.cuda.is_available()
else:
use_cuda = azimuth_config.use_cuda
return use_cuda
def load_hf_text_classif_pipeline(checkpoint_path: str, azimuth_config: AzimuthConfig) -> Pipeline:
# The first time, we load a random model, save it for subsequent loads
use_cuda = _should_use_cuda(azimuth_config)
try:
model = DistilBertForSequenceClassification.from_pretrained(_CACHE_DIR)
except OSError:
with RandomContext(seed=2022):
model = DistilBertForSequenceClassification(DistilBertConfig())
model.init_weights()
model.save_pretrained(_CACHE_DIR)
tokenizer = DistilBertTokenizer.from_pretrained(checkpoint_path)
device = 0 if use_cuda else -1
# We set return_all_scores=True to get all softmax outputs
return TextClassificationPipeline(
model=model, tokenizer=tokenizer, device=device, return_all_scores=True
)
def load_sst2_dataset(
train: bool = True, eval: bool = True, max_dataset_len: int = _MAX_DATASET_LEN
) -> DatasetDict:
datasets = load_dataset("glue", "sst2")
ds = {}
if eval:
# Test set has no label
datasets["validation"] = datasets["validation"].rename_column("sentence", "utterance")
ds["validation"] = datasets["validation"].select(np.arange(max_dataset_len))
if train:
datasets["train"] = datasets["train"].rename_column("sentence", "utterance")
ds["train"] = datasets["train"].select(np.arange(max_dataset_len))
return DatasetDict(ds)
def load_intent_data(train_path, test_path, python_loader) -> Dataset:
return load_dataset(python_loader, data_files={"train": train_path, "test": test_path})
def load_file_dataset(data_files: Dict[str, str], azimuth_config) -> DatasetDict:
# Load a file dataset and cast the label column as a ClassLabel.
# Train and test need to be loaded separately because they don't always share the same columns.
# Train sometimes doesn't have predictions. HF will complain if we load both together.
ds_dict = load_dataset("csv", data_files={"train": data_files["train"]})
ds_dict_test = load_dataset("csv", data_files={"test": data_files["test"]})
ds_dict.update(ds_dict_test)
return align_labels(ds_dict, azimuth_config)
def load_CLINC150_data(full_path, python_loader) -> Dataset:
"""This dataset contains train, validation and test in one file."""
return load_dataset(python_loader, data_files={"full": full_path})
def load_tf_model(checkpoint_path: str) -> Callable:
# Not using checkpoint_path here as we just want to create a dummy model
inputs = layers.Input(shape=(768,))
# Fix number of output neurons to 4
logits = layers.Dense(4, activation="softmax")(inputs)
model_dummy = keras.Model(inputs=inputs, outputs=logits)
# The first time, we load a random model, save it for subsequent loads
try:
model = DistilBertModel.from_pretrained(_CACHE_DIR)
except OSError:
model = DistilBertModel(DistilBertConfig())
model.save_pretrained(_CACHE_DIR)
tokenizer = DistilBertTokenizer.from_pretrained(checkpoint_path, cache_dir=_CACHE_DIR)
# Create embedder from tokenizer + model
def embedder(utterances):
tokens = tokenizer(utterances, return_tensors="pt", padding=True)
outputs = model(**tokens)
last_hidden_states = outputs.last_hidden_state
embeddings = last_hidden_states.detach()[:, 0, :] # Only take CLS token for each utterance
return model_dummy(np.array(embeddings))
return embedder
def config_structured_output(num_classes, threshold=0.8):
from azimuth.utils.ml.postprocessing import PostProcessingIO
@dataclass
class MyOutputFormat:
model_output: PostProcessingIO
postprocessor_output: PostProcessingIO
class StructuredOutput:
def __init__(self, num_classes, threshold, no_prediction_idx):
self.rng = np.random.RandomState(2022)
self.num_classes = num_classes
self.threshold = threshold
self.no_prediction_idx = no_prediction_idx
def __call__(
self, utterances: List[str], num_workers=0, batch_size=32, truncation=True
) -> MyOutputFormat:
# Random logits based on the first letter
initial_logits = np.stack(
[np.random.RandomState(ord(s[0])).randn(self.num_classes) * 2 for s in utterances]
)
initial_preds = PostProcessingIO(
texts=utterances,
logits=initial_logits,
preds=np.argmax(initial_logits, -1)[:, None],
probs=softmax(initial_logits, -1),
)
# Our particular postprocessing is very cool,
# we apply temp scaling=5 and threshold
postprocessed_logits = initial_logits / 5
postprocessed_probs = softmax(postprocessed_logits, -1)
preds = np.where(
postprocessed_logits.max(-1) > self.threshold,
postprocessed_probs.argmax(-1),
self.no_prediction_idx,
)
postprocessed = PostProcessingIO(
texts=utterances,
logits=postprocessed_logits,
preds=preds[:, None],
probs=postprocessed_probs,
)
return MyOutputFormat(model_output=initial_preds, postprocessor_output=postprocessed)
return StructuredOutput(num_classes, threshold, no_prediction_idx=num_classes)
def get_custom_dataset(length, num_classes, azimuth_config) -> DatasetDict:
text_column = azimuth_config.columns.text_input
label_column = azimuth_config.columns.label
rng = np.random.RandomState(2022)
def gen_text():
return "".join(rng.choice(list(string.ascii_uppercase + string.digits), size=10))
classes = {i: f"class_{i}" for i in range(num_classes)}
inverse_mapping = {v: k for k, v in classes.items()}
ds_dict = DatasetDict(
train=Dataset.from_dict(
{
text_column: [gen_text() for _ in range(length)],
label_column: [classes[i % num_classes] for i in range(length)],
}
),
validation=Dataset.from_dict(
{
text_column: [gen_text() for _ in range(length)],
label_column: [classes[i % num_classes] for i in range(length)],
}
),
)
ds_dict = ds_dict.class_encode_column(label_column)
ds_dict.align_labels_with_mapping(inverse_mapping, label_column=label_column)
return ds_dict
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,062
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/ml/preprocessing.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List
from azimuth.types import AliasModel
class PreprocessingStepAPIResponse(AliasModel):
"""Class for saving the results in the dataset and the routes."""
text: str
class_name: str
class PreprocessingStep(AliasModel):
"""Class received from the pipeline, and used in the Prediction Module."""
text: List[str]
class_name: str
def __getitem__(self, item: int) -> "PreprocessingStep":
"""Useful to get from a batch result to a single utterance result."""
return PreprocessingStep(text=[self.text[item]], class_name=self.class_name)
|
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"/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,063
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/perturbation_testing.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from enum import Enum
from typing import List, Optional
from pydantic import Field
from azimuth.types import AliasModel, ModuleResponse
class PerturbationTestType(str, Enum):
# From the Checklist paper, what type of test is it.
invariant = "invariant"
directional = "directional"
mft = "mft"
class PerturbationTestFamily(str, Enum):
fuzzy_matching = "Fuzzy Matching"
punctuation = "Punctuation"
class PerturbationTestClass(str, Enum):
# From the CheckList paper, what is the class of this test.
vocabulary = "vocabulary"
robustness = "robustness"
temporal = "temporal"
fairness = "fairness"
negation = "negation"
class PerturbationTestName(str, Enum):
neutral_token = "Neutral Token"
question_mark = "Question Mark"
ending_period = "Ending Period"
inner_comma = "Inner Comma"
inner_period = "Inner Period"
typos = "Typos"
contractions = "Contractions"
class PerturbationType(str, Enum):
PreInsertion = "PreInsertion"
PostInsertion = "PostInsertion"
Deletion = "Deletion"
Insertion = "Insertion"
Replacement = "Replacement"
Swap = "Swap"
Expansion = "Expansion"
Contraction = "Contraction"
PRETTY_PERTURBATION_TYPES = {
PerturbationType.Deletion: "Delete",
PerturbationType.Insertion: "Insert",
PerturbationType.PostInsertion: "Append",
PerturbationType.PreInsertion: "Prepend",
PerturbationType.Replacement: "Replace",
PerturbationType.Swap: "Swap",
PerturbationType.Contraction: "Contract",
PerturbationType.Expansion: "Expand",
}
class PerturbationTestFailureReason(str, Enum):
PredClass = "Different predicted class."
PredConfThreshold = "Confidence too far from original."
NA = "NA"
class PerturbedUtteranceDetails(AliasModel):
perturbed_utterance: str
perturbation_type: PerturbationType
perturbations: List[str]
class PerturbedUtteranceExample(AliasModel):
utterance: str
perturbed_utterance: str
class PerturbationTestSummary(AliasModel):
name: PerturbationTestName
description: str
family: PerturbationTestFamily
perturbation_type: PerturbationType
eval_failure_rate: float
eval_count: int
eval_failed_count: int
eval_confidence_delta: float
train_failure_rate: float
train_count: int
train_failed_count: int
train_confidence_delta: float
example: PerturbedUtteranceExample
class PerturbationTestingSummaryResponse(ModuleResponse):
all_tests_summary: List[PerturbationTestSummary]
class PerturbationTestingMergedResponse(ModuleResponse):
eval_failure_rate: float
train_failure_rate: float
class PerturbedUtteranceAbstract(AliasModel):
name: PerturbationTestName
description: str
family: PerturbationTestFamily
perturbed_utterance: str
perturbations: List[str]
perturbation_type: PerturbationType
confidence: float
confidence_delta: Optional[float] = Field(..., nullable=True)
failed: bool
failure_reason: PerturbationTestFailureReason
class PerturbedUtteranceResult(PerturbedUtteranceAbstract):
prediction: int
class PerturbedUtteranceWithClassNames(PerturbedUtteranceAbstract):
prediction: str
class PerturbedUtteranceDetailedResult(PerturbedUtteranceAbstract):
prediction: str
original_prediction: str
original_confidence: float
label: str
original_utterance: str
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/types/utterance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,064
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/general/array_type.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Generic, TypeVar
import numpy as np
"""
Module that handles numpy arrays in Pydantic models.
Code from: https://github.com/samuelcolvin/pydantic/issues/380
"""
T = TypeVar("T")
class _ArrayMeta(type):
def __getitem__(self, t):
"""Handles when we do Array[float]"""
return type("Array", (Array,), {"__dtype__": t})
class Array(np.ndarray, Generic[T], metaclass=_ArrayMeta):
"""Class that allow numpy arrays in Pydantic models."""
@classmethod
def __get_validators__(cls):
yield cls.validate_type
@classmethod
def validate_type(cls, val):
"""Validate whether `val` is a numpy array."""
dtype = getattr(cls, "__dtype__", None)
if isinstance(dtype, tuple):
dtype, shape = dtype
else:
shape = tuple()
result = np.array(val, dtype=dtype, copy=False, ndmin=len(shape))
assert not shape or len(shape) == len(result.shape) # ndmin guarantees this
if any((shape[i] != -1 and shape[i] != result.shape[i]) for i in range(len(shape))):
result = result.reshape(shape)
return result
@classmethod
def __modify_schema__(cls, field_schema):
# __modify_schema__ should mutate the dict it receives in place,
field_schema.update(
# simplified regex here for brevity, see the wikipedia link above
type="Array",
# some example postcodes
examples=[[1, 2, 3], [1, 2, 3]],
)
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,065
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_utils/test_ml/test_mc_dropout.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import numpy as np
from azimuth.modules.model_contracts import HFTextClassificationModule
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod
from azimuth.utils.ml.mc_dropout import MCDropout
def test_mc_dropout_context(simple_text_config):
mod = HFTextClassificationModule(
config=simple_text_config,
dataset_split_name=DatasetSplitName.eval,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
hf_pipeline = mod.get_model()
hf_pipeline.model.eval()
# Get a batch
batch = mod.get_dataset_split()[[1, 2, 3]]
# Model is deterministic.
preds = [
[[r["score"] for r in res] for res in hf_pipeline(batch["utterance"])] for _ in range(10)
]
assert all(np.allclose(preds[0], p) for p in preds)
with MCDropout(hf_pipeline.model):
# The model is now not deterministic.
preds = [
[[r["score"] for r in res] for res in hf_pipeline(batch["utterance"])]
for _ in range(10)
]
assert not all(np.allclose(preds[0], p) for p in preds)
# When out of the MCDropout context, the model is deterministic again.
preds = [
[[r["score"] for r in res] for res in hf_pipeline(batch["utterance"])] for _ in range(10)
]
assert all(np.allclose(preds[0], p) for p in preds)
|
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|
31,066
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import numpy as np
from sklearn.preprocessing import normalize
import azimuth.modules.dataset_analysis.similarity_analysis as faiss_mod
from azimuth.app import load_dataset_split_managers_from_config
from azimuth.dataset_split_manager import FEATURE_FAISS
from azimuth.modules.dataset_analysis.similarity_analysis import NeighborsTaggingModule
from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions
from azimuth.types.tag import SmartTag
from tests.utils import get_table_key, get_tiny_text_config_one_ds_name
IDX = 3
class MockedTransformer:
def __init__(self, name, num_features: int = 123):
self.num_features = num_features
def encode(self, x, *args, **kwargs):
return normalize(np.random.rand(len(x), self.num_features), axis=1, norm="l1")
def test_neighbors(simple_text_config, dask_client, monkeypatch):
monkeypatch.setattr(faiss_mod, "SentenceTransformer", MockedTransformer)
# Mock SentenceTransformer on all workers.
dask_client.run(
lambda: monkeypatch.setattr(faiss_mod, "SentenceTransformer", MockedTransformer)
)
simple_text_config.similarity.conflicting_neighbors_threshold = 0.1
simple_table_key = get_table_key(simple_text_config)
mod = NeighborsTaggingModule(DatasetSplitName.eval, simple_text_config)
res = mod.compute_on_dataset_split()
dm = mod.get_dataset_split_manager()
# Confirm correct length; indices=None should return the result on all indices
assert (
len(res) == dm.num_rows
), f"Length of result ({len(res)}) does not match length of full dataset"
mod.save_result(res, dm)
ds = dm.dataset_split_with_index(simple_table_key)
embd = np.array(ds["features"][IDX]).reshape([1, -1]).astype(np.float32)
# The FAISS exists!
scores, examples = ds.get_nearest_examples(FEATURE_FAISS, embd, k=5)
# Check that the first item is indeed the real index
assert IDX in examples[DatasetColumn.row_idx]
assert any(mod.get_dataset_split()[SmartTag.conflicting_neighbors_train])
assert any(mod.get_dataset_split()[SmartTag.conflicting_neighbors_eval])
assert "neighbors_eval" in mod.get_dataset_split().column_names
assert "no_close_train" in mod.get_dataset_split().column_names
# Reloading the dataset_split, FAISS is still there.
dm = load_dataset_split_managers_from_config(simple_text_config)[DatasetSplitName.eval]
ds = dm.dataset_split_with_index(simple_table_key)
_ = ds.get_nearest_examples(FEATURE_FAISS, embd, k=5)
# Confirm that module works with subset of indices
indices_subset = [1, 2, 3]
mod_idx_subset = NeighborsTaggingModule(
DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions(indices=indices_subset)
)
res = mod_idx_subset.compute_on_dataset_split()
assert len(res) == len(
indices_subset
), f"Length of result ({len(res)}) does not match length of indices ({len(indices_subset)})"
# Would currently fail on mod.save_result() because of indices (length) mismatch
def test_neighbors_one_ds(tiny_text_config_one_ds, dask_client, monkeypatch):
monkeypatch.setattr(faiss_mod, "SentenceTransformer", MockedTransformer)
# Mock SentenceTransformer on all workers.
dask_client.run(
lambda: monkeypatch.setattr(faiss_mod, "SentenceTransformer", MockedTransformer)
)
tiny_text_config_one_ds.similarity.conflicting_neighbors_threshold = 0.1
ds_name, other_ds_name = get_tiny_text_config_one_ds_name(tiny_text_config_one_ds)
mod = NeighborsTaggingModule(ds_name, tiny_text_config_one_ds)
res = mod.compute_on_dataset_split()
assert not any([r.tags[f"conflicting_neighbors_{other_ds_name}"] for r in res])
assert not any([r.tags[f"no_close_{other_ds_name}"] for r in res])
assert not any([r.adds[f"neighbors_{other_ds_name}"] for r in res])
assert any([r.adds[f"neighbors_{ds_name}"] for r in res])
|
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|
31,067
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/ml/saliency.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import Any, List
import numpy as np
import structlog
from torch.nn import Embedding
from azimuth.types.general.module_arguments import GradientCalculation
log = structlog.get_logger()
def find_word_embeddings_layer(model: Any, layer_name: str) -> Any:
"""
Find the word embedding layer according to the name set in the config.
Args:
model: Model
layer_name: Layer Name
Returns:
Module of the word embedding layer.
Raises:
ValueError if searchable_str not a valid layer in the model.
"""
searchable_str = layer_name.lower().strip()
for name, layer in model.named_modules():
if name == searchable_str:
return layer
raise ValueError(f"{searchable_str} layer not found in model")
def register_embedding_list_hook(
embeddings_list: List[np.ndarray], embedding_layer: Embedding
) -> Any:
"""Register hook to get the embedding values from model.
Args:
embeddings_list: Variable to save values.
embedding_layer: Embedding layer on which to compute the saliency map.
Returns:
Hook.
"""
def forward_hook(module, inputs, output):
embeddings_list.append(output.detach().cpu().clone().numpy())
return embedding_layer.register_forward_hook(forward_hook)
def register_embedding_gradient_hook(
embeddings_gradients: List[np.ndarray], embedding_layer: Embedding
) -> Any:
"""Register hook to get the gradient values from the embedding layer.
Args:
embeddings_gradients: Variable to save values.
embedding_layer: Embedding layer on which to compute the saliency map.
Returns:
Hook.
"""
def hook_layers(module, grad_in, grad_out):
embeddings_gradients.append(grad_out[0].detach().cpu().clone().numpy())
return embedding_layer.register_full_backward_hook(hook_layers)
def get_saliency(
saliency_grad: np.ndarray,
embeddings: np.ndarray,
gradient_calc: GradientCalculation = GradientCalculation.L2,
) -> List[float]:
"""Return the values corresponding to a saliency map.
Args:
saliency_grad: Gradient of the batch
embeddings: Embedding of the batch
gradient_calc: How to compute the saliency.
Returns:
Saliency values.
Raises:
ValueError: provided gradient_calc is invalid.
"""
assert saliency_grad.shape == embeddings.shape # num_tokens x 768
# in order to have token importance be positive, multiply the gradient if need be
multiplier = -1
if gradient_calc == GradientCalculation.xABS_MAX:
# Take the maximum absolute value across all embedding (768) dimensions
saliency_grad = np.amax(np.abs(saliency_grad * embeddings), axis=1)
multiplier = 1
elif gradient_calc == GradientCalculation.xL2:
# motivated by https://arxiv.org/abs/2009.13295, take the L2 norm across all dimensions
saliency_grad = np.linalg.norm(saliency_grad * embeddings, ord=2, axis=1)
multiplier = 1
elif gradient_calc == GradientCalculation.L2:
# motivated by https://arxiv.org/abs/2009.13295, take the L2 norm across all dimensions
saliency_grad = np.linalg.norm(saliency_grad, ord=2, axis=1)
multiplier = 1
elif gradient_calc == GradientCalculation.xMIN:
# Take the minimum value across all embedding (768) dimensions because the lowest
# negative gradient for loss means the highest positive influence on class score
saliency_grad = np.amin(saliency_grad * embeddings, axis=1)
elif gradient_calc == GradientCalculation.xSUM:
# Multiply gradient x embedding, then sum across rows (sum all dimensions for each token)
saliency_grad = np.sum(saliency_grad * embeddings, axis=1)
else:
raise ValueError("Invalid way to get saliency")
# Get the L1 norm to normalize across all tokens
norm = np.linalg.norm(saliency_grad, ord=1)
# negative gradient for loss means positive influence on decision
saliency_values: List[float] = ((saliency_grad / norm) * multiplier).tolist()
return saliency_values
|
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"/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,068
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/ml/third_parties/transformations_types.py
|
"""
Code from https://github.com/GEM-benchmark/NL-Augmenter/blob/
26343c2b9116c8886eaaf8aa904e412e6fc64b17/interfaces/SentenceOperation.py#L10
MIT License
Copyright (c) 2021 GEM-benchmark
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Contractions and Expansions List(s) borrowed from https://github.com/marcotcr/checklist/
blob/master/checklist/perturb.py
"""
from typing import List, Tuple
class Operation(object):
languages = None
tasks = None
seed = 0
heavy = False
max_outputs = 1
def __init__(self, seed=0, verbose=False, max_outputs=1):
self.seed = seed
self.verbose = verbose
self.max_outputs = max_outputs
if self.verbose:
print(f"Loading Operation {self.name()}")
@classmethod
def compare(self, raw: object, pt: List[object]) -> Tuple[int, int]:
successful_pt = 0
failed_pt = 0
for pt_example in pt:
if pt_example == raw:
failed_pt += 1
else:
successful_pt += 1
return successful_pt, failed_pt
@classmethod
def is_heavy(cls):
return cls.heavy
@classmethod
def domain(cls):
return cls.tasks, cls.languages
@classmethod
def name(cls):
return cls.__name__
class SentenceOperation(Operation):
"""
The base class for implementing utterance-level perturbations and transformations.
"tasks" :: The tasks for which this perturbation is applicable. All the list of tasks are
given in tasks.TaskType.
"languages" :: The locales and/or languages for which this perturbation is applicable.
eg. "es", "mr",
"en_IN"
"""
def generate(self, sentence: str) -> List[str]:
raise NotImplementedError
def filter(self, sentence: str) -> bool:
raise NotImplementedError
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", 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"/azimuth/utils/validation.py"], "/azimuth/routers/model_performance/utterance_count.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/validation.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/class_overlap.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/plots/sankey_plot.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/tests/test_modules/test_word_analysis/test_top_words.py": ["/azimuth/modules/word_analysis/top_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,069
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/ml/mc_dropout.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import warnings
from typing import Optional
# TODO structlog warning issue tracked in BaaL #192
with warnings.catch_warnings():
warnings.simplefilter("ignore")
from baal.bayesian import Dropout
from baal.bayesian.dropout import Dropout2d, patch_module
from torch import nn
def unpatch_module(module: nn.Module) -> bool:
"""
Recursively iterate over the children of a module and replace them if
they are a BaaL dropout layer. This function operates in-place.
Args:
module: Module to unpatch dropout layers.
Returns:
Flag indicating if a layer was modified.
"""
changed = False
for name, child in module.named_children():
new_module: Optional[nn.Module] = None
if isinstance(child, Dropout):
new_module = nn.Dropout(p=child.p, inplace=child.inplace)
elif isinstance(child, Dropout2d):
new_module = nn.Dropout2d(p=child.p, inplace=child.inplace)
if new_module is not None:
changed = True
module.add_module(name, new_module)
# recursively apply to child
changed |= unpatch_module(child)
return changed
class MCDropout:
"""
Context manager that will modify
Dropout layer and revert at the end.
Args:
mod: Pytorch Module to modify
"""
def __init__(self, mod: nn.Module):
self.mod = patch_module(mod)
def __enter__(self):
return self.mod
def __exit__(self, type, value, traceback):
unpatch_module(self.mod)
# Need to call eval again because Module are train=True by default.
self.mod.eval()
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", 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|
31,070
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/model_performance/confidence_binning.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from collections import defaultdict
from typing import List, cast
import numpy as np
from datasets import Dataset
from azimuth.config import ModelContractConfig
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.base_classes import DatasetResultModule, FilterableModule
from azimuth.types import DatasetColumn
from azimuth.types.model_performance import ConfidenceBinDetails, ConfidenceHistogramResponse
from azimuth.types.outcomes import ALL_OUTCOMES, OutcomeName
from azimuth.utils.dataset_operations import get_confidences_from_ds, get_outcomes_from_ds
from azimuth.utils.validation import assert_not_none
CONFIDENCE_BINS_COUNT = 20
class ConfidenceHistogramModule(FilterableModule[ModelContractConfig]):
"""Return a confidence histogram of the predictions."""
@staticmethod
def get_outcome_mask(
ds, outcome: OutcomeName, without_postprocessing: bool = False
) -> List[bool]:
return [
utterance_outcome == outcome
for utterance_outcome in get_outcomes_from_ds(ds, without_postprocessing)
]
@classmethod
def get_bins(
cls, ds: Dataset, without_postprocessing: bool = False
) -> List[ConfidenceBinDetails]:
"""Compute the bins on the specified dataset split.
Note: This lives outside of `compute_on_dataset_split()` so that it can be called without
going through calling the module and filtering the dataset.
Args:
ds: Dataset Split on which to compute bins
without_postprocessing: Whether to use outcomes and confidences without pipeline
postprocessing
Returns:
List of the confidence bins with their confidence and the outcome count.
"""
bins = np.linspace(0, 1, CONFIDENCE_BINS_COUNT + 1)
if len(ds) > 0:
# Get the bin index for each prediction.
confidences = np.max(get_confidences_from_ds(ds, without_postprocessing), axis=1)
bin_indices = np.minimum(
np.floor(confidences * CONFIDENCE_BINS_COUNT),
CONFIDENCE_BINS_COUNT - 1, # So that 100% falls in the last bin
)
# Create the records. We drop the last bin as it's the maximum.
result = []
for bin_index, bin_min_value in enumerate(bins[:-1]):
bin_mask = bin_indices == bin_index
outcome_count = defaultdict(int)
for outcome in ALL_OUTCOMES:
outcome_count[outcome] = np.logical_and(
bin_mask, cls.get_outcome_mask(ds, outcome, without_postprocessing)
).sum()
mean_conf = (
0 if bin_mask.sum() == 0 else np.nan_to_num(confidences[bin_mask].mean())
)
result.append(
ConfidenceBinDetails(
bin_index=bin_index,
bin_confidence=bin_min_value + bins[1] / 2,
mean_bin_confidence=mean_conf,
outcome_count=outcome_count,
)
)
else:
# Create empty bins
result = [
ConfidenceBinDetails(
bin_index=bin_index,
bin_confidence=0,
mean_bin_confidence=0,
outcome_count={outcome: 0 for outcome in ALL_OUTCOMES},
)
for bin_index, bin_min_value in enumerate(bins[:-1])
]
return result
def compute_on_dataset_split(self) -> List[ConfidenceHistogramResponse]: # type: ignore
"""Compute the confidence histogram with CONFIDENCE_BINS_COUNT bins on the dataset split.
Returns:
Confidence bins and threshold.
"""
ds: Dataset = assert_not_none(self.get_dataset_split())
return [
ConfidenceHistogramResponse(
bins=self.get_bins(ds, self.mod_options.without_postprocessing),
confidence_threshold=self.get_threshold(),
)
]
class ConfidenceBinIndexModule(DatasetResultModule[ModelContractConfig]):
"""Return confidence bin indices for the selected dataset split."""
required_mod_options = {"pipeline_index"}
optional_mod_options = DatasetResultModule.optional_mod_options | {"threshold"}
def compute_on_dataset_split(self) -> List[int]: # type: ignore
"""Get the bin indices for each utterance in the dataset split.
Returns:
List of bin indices for all utterances.
"""
ds = assert_not_none(self.get_dataset_split())
postprocessed_confidences = cast(
List[List[float]], ds[DatasetColumn.postprocessed_confidences]
)
bin_indices: List[int] = (
np.floor(np.max(postprocessed_confidences, axis=1) * CONFIDENCE_BINS_COUNT)
.astype("int")
.tolist()
)
return bin_indices
def _save_result(self, res: List[int], dm: DatasetSplitManager): # type: ignore
dm.add_column_to_prediction_table(
DatasetColumn.confidence_bin_idx, res, table_key=assert_not_none(self._get_table_key())
)
|
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"/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,071
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth_shr/data/CLINC150.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import json
from pathlib import Path
import datasets
_DESCRIPTION = """\
Subset of CLINC150 Dataset (https://github.com/clinc/oos-eval).
"""
# Restrict the dataset to the following intents, including out-of-scope (NO_INTENT)
SUBSET_INTENTS = [
"freeze_account",
"routing",
"pin_change",
"bill_due",
"pay_bill",
"account_blocked",
"interest_rate",
"min_payment",
"bill_balance",
"transfer",
"order_checks",
"balance",
"spending_history",
"transactions",
"report_fraud",
"NO_INTENT", # In the dataset this is called oos but we call it NO_INTENT
]
class CLINC150Config(datasets.BuilderConfig):
"""BuilderConfig for subset of CLINC 150 dataset."""
def __init__(self, **kwargs):
"""BuilderConfig for subset of CLINC 150.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CLINC150Config, self).__init__(**kwargs)
class CLINC150Dataset(datasets.GeneratorBasedBuilder):
"""Clinc150 Dataset."""
BUILDER_CONFIGS = [
CLINC150Config(
name="CLINC150",
version=datasets.Version("1.0.0"),
description=_DESCRIPTION,
),
]
def __init__(self, *args, writer_batch_size=None, **kwargs):
super(CLINC150Dataset, self).__init__(*args, **kwargs)
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"utterance": datasets.Value("string"),
"label": datasets.ClassLabel(
num_classes=len(SUBSET_INTENTS), names=SUBSET_INTENTS
),
}
),
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": self.config.data_files["full"][0], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": self.config.data_files["full"][0], "split": "val"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": self.config.data_files["full"][0], "split": "test"},
),
]
def _generate_examples(self, filepath: str, split: str):
"""Generate intent examples."""
filepath_suffix = Path(filepath).suffix.lower()
if filepath_suffix == ".json":
return self._read_json(filepath, split)
else:
raise ValueError(f"Invalid file extension: {filepath}")
def _read_json(self, filepath, split: str):
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
sample_idx = 0
for sample in data[split]:
# First element contains the utterance and the second contains the intent name
intent_name = sample[1].lower()
if intent_name in SUBSET_INTENTS:
yield sample_idx, {
"utterance": sample[0].lower(),
"label": intent_name,
}
sample_idx += 1
# Handle oos -> NO_INTENT separately
for sample in data["oos_" + split]:
yield sample_idx, {
"utterance": sample[0].lower(),
"label": "NO_INTENT",
}
sample_idx += 1
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,072
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_model_contracts/test_custom_text_classification.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import pytest
from azimuth.modules.model_contracts import CustomTextClassificationModule
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod
def test_loading_model(guse_text_config):
task = CustomTextClassificationModule(
DatasetSplitName.eval,
guse_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
# Test that we get the model
assert task.get_model() is not None
# The model in this case consists of a callable
assert callable(task.get_model())
def test_prediction(guse_text_config):
task = CustomTextClassificationModule(
DatasetSplitName.eval,
guse_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
indices=[0, 1, 2],
),
)
assert task is not None
result = task.compute_on_dataset_split()
# We just test that the prediction is not broken as we are using a dummy model
assert len(result) == len(task.get_indices())
def test_saliency(guse_text_config):
with pytest.raises(NotImplementedError):
task = CustomTextClassificationModule(
DatasetSplitName.eval,
guse_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Saliency,
pipeline_index=0,
indices=[0, 1, 2],
),
)
task.compute_on_dataset_split()
|
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|
31,073
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/ml/perturbation_functions.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import contextlib
import re
from itertools import zip_longest
from typing import List
import nlpaug.augmenter.char as nac
from azimuth.config import PerturbationTestingConfig
from azimuth.types.perturbation_testing import PerturbationType, PerturbedUtteranceDetails
from azimuth.utils.ml.third_parties.contractions import ContractionExpansions
from azimuth.utils.validation import assert_not_none
"""
This file contains functions that perturb an utterance, and can be used by
PerturbationTestingModule.
"""
re_punctuation = re.compile(r"(\w+)")
def add_neutral_token(
original: str, position: str, neutral_tokens: List[str]
) -> List[PerturbedUtteranceDetails]:
"""Add neutral tokens to an utterance at the position indicated.
Args:
original: Original utterance.
position: prefix or suffix.
neutral_tokens: list of neutral tokens to add.
Returns:
List of perturbed utterance details.
Raises:
ValueError: if position is invalid.
"""
results = []
for neutral_token in neutral_tokens:
# Create a perturbed_utterance with a new token as prefix and get the predictions.
if position == "prefix":
utterance = f"{neutral_token} {original}"
perturbation_type = PerturbationType.PreInsertion
elif position == "suffix":
utterance = f"{original} {neutral_token}"
perturbation_type = PerturbationType.PostInsertion
else:
raise ValueError("Position not supported.")
results.append(
PerturbedUtteranceDetails(
perturbed_utterance=utterance,
perturbation_type=perturbation_type,
perturbations=[neutral_token],
)
)
return results
def neutral_prefix(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Add a neutral prefix to an utterance.
Args:
original: Original utterance.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
behavioral_testing_config = assert_not_none(config.behavioral_testing)
prefix = behavioral_testing_config.neutral_token.prefix_list
return add_neutral_token(original, "prefix", prefix)
def neutral_suffix(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Add a neutral suffix to an utterance.
Args:
original: Original utterance.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
behavioral_testing_config = assert_not_none(config.behavioral_testing)
suffixes = behavioral_testing_config.neutral_token.suffix_list
return add_neutral_token(original, "suffix", suffixes)
def add_all_neutral_tokens(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Add neutral tokens at the beginning and the end of the utterance.
Args:
original: Utterance to perturb.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
return neutral_prefix(original, config) + neutral_suffix(original, config)
def remove_or_add_final_punctuation(
original: str, punctuation_sign: str
) -> List[PerturbedUtteranceDetails]:
"""Remove or add the specified punctuation sign at the end of an utterance.
If an utterance's last character is the specified punctuation, it removes it. Otherwise,
it either replaces the last character (if it's a period, exclamation mark, question mark or
coma) or adds it.
Args:
original: Utterance to perturb.
punctuation_sign: Punctuation sign to remove or add.
Returns:
List of perturbed utterance details.
"""
last_char = original[-1]
# Delete punctuation if last character is the specified punctuation sign.
if last_char == punctuation_sign:
perturb_utt = original[:-1]
perturbation_type = PerturbationType.Deletion
# Replace with specified punctuation if last character is another punctuation sign.
elif last_char in [".", "!", "?", ","]:
perturb_utt = original[:-1].rstrip() + punctuation_sign
perturbation_type = PerturbationType.Replacement
# Add specified punctuation otherwise.
else:
perturb_utt = original + punctuation_sign
perturbation_type = PerturbationType.PostInsertion
return [
PerturbedUtteranceDetails(
perturbed_utterance=perturb_utt,
perturbation_type=perturbation_type,
perturbations=[punctuation_sign],
)
]
def remove_or_add_final_period(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Remove or add period at the end of an utterance.
Args:
original: Utterance to perturb.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
return remove_or_add_final_punctuation(original, ".")
def remove_or_add_final_question_mark(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Remove or add question mark at the end of an perturbed_utterance.
Args:
original: Utterance to perturb.
config: Azimuth Config
Returns:
List of perturbed utterance details.
"""
return remove_or_add_final_punctuation(original, "?")
def remove_or_add_inside_punctuation(
original: str, punctuation_sign: str
) -> List[PerturbedUtteranceDetails]:
"""Remove or add a specified punctuation sign inside an utterance.
It will not touch the punctuation at the end, except if there is just one word in the
utterance.
Args:
original: Utterance to perturb.
punctuation_sign: Punctuation sign to add or remove.
Returns:
List of perturbed utterance details.
"""
# If specified punctuation sign is in any position apart from the last, remove it.
if punctuation_sign in original[:-1]:
perturb_utt = (
original[:-1].translate(str.maketrans("", "", punctuation_sign)) + original[-1]
)
perturbation_type = PerturbationType.Deletion
# Add the specified punctuation sign in the middle of the utterance (or after if there is
# only one word. This can produce odd results, such as 2 punctuation signs
# one beside the other.
else:
all_words = original.split()
# Position of the added punctuation
punctuation_position = (len(all_words) - 1) // 2
perturb_utt = " ".join(
word + punctuation_sign if pos == punctuation_position else word
for pos, word in enumerate(all_words)
)
perturbation_type = PerturbationType.Insertion
return [
PerturbedUtteranceDetails(
perturbed_utterance=perturb_utt,
perturbation_type=perturbation_type,
perturbations=[punctuation_sign],
)
]
def remove_or_add_inside_comma(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Remove or add a comma inside an utterance (not at the end).
Args:
original: Utterance to perturb.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
return remove_or_add_inside_punctuation(original, ",")
def remove_or_add_inside_period(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Remove or add a period inside an utterance (not at the end).
Args:
original: Utterance to perturb.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
return remove_or_add_inside_punctuation(original, ".")
def get_utterances_diff(original_utterance: str, perturbed_utterance: str) -> List[str]:
return list(set(perturbed_utterance.split()).difference(set(original_utterance.split())))
def typo(original: str, config: PerturbationTestingConfig) -> List[PerturbedUtteranceDetails]:
"""Create different types of typos in an utterance.
It uses the nlpaug package for this test and introduce mistakes raised by OCR, keyboard typo,
or common mistakes such as missing a char or swapping two chars. This test will generate
several perturbed utterances per original utterance, but we limit the number of generated
augmentations to one per type of augmentation.
Args:
original: Utterance to perturb.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
augmentations = []
perturbation_types = []
behavioral_testing_config = assert_not_none(config.behavioral_testing)
# Special chars limited for all augs to facilitate cleaning up nac spacing/punctuation below
for nb_typo in range(1, behavioral_testing_config.typo.nb_typos_per_utterance + 1):
augmentations.extend(
[
nac.KeyboardAug(
min_char=4,
aug_word_max=nb_typo,
aug_char_p=0.1 * nb_typo,
include_special_char=False,
),
nac.RandomCharAug(
action="swap",
min_char=4,
aug_word_max=nb_typo,
aug_char_p=0.1 * nb_typo,
spec_char="_", # Breaks/includes others if min_char=1; nlpaug issue #315
),
nac.RandomCharAug(
action="delete",
min_char=4,
aug_word_max=nb_typo,
aug_char_p=0.1 * nb_typo,
spec_char="_", # Breaks/includes others if min_char=1; nlpaug issue #315
),
]
)
perturbation_types.extend(
[PerturbationType.Replacement, PerturbationType.Swap, PerturbationType.Deletion]
)
results = []
for aug, perturbation_type in zip(augmentations, perturbation_types):
with contextlib.redirect_stdout(None):
# While nlpaug fixes their useless print, we ignore it.
perturbed_utterance = aug.augment(original, n=1)
# nac alters spacing around punctuation (e.g., around '; after $; before ?) and replaces
# apostrophes with quotes; revert to original. (See nlpaug issue #313)
punctuations = re_punctuation.split(original)[::2]
words = re_punctuation.split(perturbed_utterance)[1::2]
perturbed_utterance = "".join(
punctuation + word
for punctuation, word in zip_longest(punctuations, words, fillvalue="")
)
perturbations = get_utterances_diff(original, perturbed_utterance)
results.append(
PerturbedUtteranceDetails(
perturbed_utterance=perturbed_utterance,
perturbation_type=perturbation_type,
perturbations=perturbations,
)
)
return results
def remove_or_add_contractions(
original: str, config: PerturbationTestingConfig
) -> List[PerturbedUtteranceDetails]:
"""Expand or contract relevant expressions when present in the utterance.
Args:
original: Utterance to perturb.
config: Azimuth Config.
Returns:
List of perturbed utterance details.
"""
contract_exp = ContractionExpansions()
results = []
def check_original(new_utt, ori_utt, perturbation_type):
if perturb_utt != ori_utt:
perturbations = get_utterances_diff(ori_utt, new_utt)
return PerturbedUtteranceDetails(
perturbed_utterance=new_utt,
perturbation_type=perturbation_type,
perturbations=perturbations,
)
else:
return None
perturb_utt = contract_exp.expand_contractions(original)
result = check_original(perturb_utt, original, PerturbationType.Expansion)
if result:
results.append(result)
perturb_utt = contract_exp.contract(original)
result = check_original(perturb_utt, original, PerturbationType.Contraction)
if result:
results.append(result)
return results
|
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["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/outcomes.py": ["/azimuth/types/__init__.py"], "/azimuth/utils/plots.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/modules/task_mapping.py": ["/azimuth/modules/model_performance/outcomes.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py"], "/azimuth/modules/model_contracts/hf_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/mc_dropout.py", "/azimuth/utils/ml/saliency.py"], "/azimuth/routers/export.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,074
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/dataset_analysis/dataset_warnings.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from collections import defaultdict
from functools import partial
from typing import Dict, List, Set
import numpy as np
from azimuth.config import DatasetWarningConfig
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.base_classes import ComparisonModule
from azimuth.plots.dataset_warnings import (
class_imbalance_plot,
class_representation,
min_sample_count_plot,
word_count_plot,
)
from azimuth.types import DatasetColumn, DatasetSplitName
from azimuth.types.dataset_warnings import (
Agg,
DatasetDistributionComparison,
DatasetDistributionComparisonValue,
DatasetWarning,
DatasetWarningGroup,
DatasetWarningsResponse,
FormatType,
)
class DatasetWarningsModule(ComparisonModule[DatasetWarningConfig]):
"""Compute warnings related to difference between the training set and the evaluation set."""
optional_mod_options: Set[str] = set()
def compute_on_dataset_split(self) -> List[DatasetWarningsResponse]: # type: ignore
"""
Analysis of the difference between the training set and evaluation set on different aspects,
such as semantic differences, syntactic ones or general ones related
to the class distribution.
Returns:
The warnings grouped by type.
"""
dm_dict = {
split: self.get_dataset_split_manager(split) for split in self.available_dataset_splits
}
return [
DatasetWarningsResponse(
warning_groups=[
DatasetWarningGroup(
name="General Warnings",
warnings=self.get_general_warnings(dm_dict),
),
DatasetWarningGroup(
name="Syntactic Warnings",
warnings=self.get_syntactic_warnings(dm_dict),
),
]
)
]
def get_general_warnings(
self, dm_dict: Dict[DatasetSplitName, DatasetSplitManager]
) -> List[DatasetWarning]:
"""Generate different alerts based on sample count in each dataset split.
Args:
dm_dict: Dict with all dataset split managers.
Returns:
General warnings
"""
first_dm = next(iter(dm_dict.values()))
cls_names = first_dm.get_class_names(labels_only=True)
count_per_cls_per_split = {
s: dm.class_distribution(labels_only=True) for s, dm in dm_dict.items()
}
# Missing samples warning
min_count_per_cls = self.config.dataset_warnings.min_num_per_class
alert_min_per_cls_per_split = {
split: count < min_count_per_cls for split, count in count_per_cls_per_split.items()
}
alert_min_per_cls = np.logical_or.reduce(tuple(alert_min_per_cls_per_split.values()))
warnings = [
DatasetWarning(
name=f"Missing samples (<{min_count_per_cls})",
description=f"Nb of samples per class in the training or evaluation set is below "
f"{min_count_per_cls}.",
columns=list(reversed(sorted(count_per_cls_per_split.keys()))),
format=FormatType.Integer,
comparisons=[
DatasetDistributionComparison(
name=cls_name,
alert=alert_min_per_cls[i],
data=[
DatasetDistributionComparisonValue(
value=int(count[i]), alert=alert_min_per_cls_per_split[s][i]
)
# Training set needs to be first
for s, count in reversed(sorted(count_per_cls_per_split.items()))
],
)
for i, cls_name in enumerate(cls_names)
],
plots=min_sample_count_plot(
count_per_cls_per_split,
min_count_per_cls,
alert_min_per_cls_per_split,
cls_names,
),
)
]
# Class imbalance warning
mean_per_split = {}
imb_per_cls_per_split = {}
for split, count in count_per_cls_per_split.items():
mean_per_split[split] = np.mean(count)
imb_per_cls_per_split[split] = count / mean_per_split[split] - 1
max_delta_imb = self.config.dataset_warnings.max_delta_class_imbalance
alert_imb_per_cls_per_split = {
s: np.abs(imb) > max_delta_imb for s, imb in imb_per_cls_per_split.items()
}
alert_imb_per_cls = np.logical_or.reduce(tuple(alert_imb_per_cls_per_split.values()))
warnings.append(
DatasetWarning(
name=f"Class imbalance" f" (>{max_delta_imb * 100:.0f}%)",
description=f"Relative difference between the number of "
f"samples per class and the mean in each dataset split is above "
f"{max_delta_imb * 100:.0f}%.",
columns=list(reversed(sorted(imb_per_cls_per_split.keys()))),
format=FormatType.Percentage,
comparisons=[
DatasetDistributionComparison(
name=cls_name,
alert=alert_imb_per_cls[i],
data=[
DatasetDistributionComparisonValue(
value=imb[i], alert=alert_imb_per_cls_per_split[s][i]
)
# Training set needs to be first
for s, imb in reversed(sorted(imb_per_cls_per_split.items()))
],
)
for i, cls_name in enumerate(cls_names)
],
plots=class_imbalance_plot(
count_per_cls_per_split,
mean_per_split,
max_delta_imb,
alert_imb_per_cls_per_split,
cls_names,
),
)
)
# Class representation warning - only useful with 2 dataset splits
if len(self.available_dataset_splits) == 2:
count_norm_per_cls_per_split = {
s: c / sum(c) for s, c in count_per_cls_per_split.items()
}
divergence_norm_per_cls = (
count_norm_per_cls_per_split[DatasetSplitName.eval]
- count_norm_per_cls_per_split[DatasetSplitName.train]
)
max_delta_representation = self.config.dataset_warnings.max_delta_representation
alert_norm_per_cls = np.abs(divergence_norm_per_cls) > max_delta_representation
warnings.append(
DatasetWarning(
name=f"Representation mismatch " f"(>{100 * max_delta_representation:.0f}%)",
description=f"Absolute difference between the proportion of a given class in "
f"the training set vs the evaluation set is above "
f"{100 * max_delta_representation:.0f}%.",
columns=["abs. diff."],
format=FormatType.Percentage,
comparisons=[
DatasetDistributionComparison(
name=cls_name,
alert=alert_norm_per_cls[i],
data=[
DatasetDistributionComparisonValue(
value=float(np.abs(divergence_norm_per_cls[i])),
alert=alert_norm_per_cls[i],
)
],
)
for i, cls_name in enumerate(cls_names)
],
plots=class_representation(
count_per_cls_per_split,
count_norm_per_cls_per_split,
divergence_norm_per_cls,
max_delta_representation,
cls_names,
),
)
)
return warnings
def get_syntactic_warnings(
self, dm_dict: Dict[DatasetSplitName, DatasetSplitManager]
) -> List[DatasetWarning]:
"""Generate warnings related to the syntax of the utterances.
Args:
dm_dict: Dict with all dataset split managers.
Returns:
List of syntactic warnings.
"""
first_dm = next(iter(dm_dict.values()))
cls_indices = np.arange(first_dm.get_num_classes(labels_only=True))
value_per_agg_per_split: Dict[DatasetSplitName, Dict[Agg, float]] = defaultdict(dict)
value_per_cls_per_agg_per_split: Dict[
DatasetSplitName, Dict[Agg, np.ndarray]
] = defaultdict(dict)
hist_per_cls_per_split = {}
label_col = self.config.columns.label
for split in self.available_dataset_splits:
ds = self.get_dataset_split(split)
df = ds.remove_columns(
list(set(ds.column_names) - {DatasetColumn.word_count, label_col})
).to_pandas()
value_per_agg_per_split[split][Agg.mean] = df["word_count"].mean()
value_per_agg_per_split[split][Agg.std] = df["word_count"].std()
# Get mean and std word count per class
stats_per_cls = df.groupby(label_col).agg(list(Agg)).reindex(cls_indices)
for agg in Agg:
value_per_cls_per_agg_per_split[split][agg] = stats_per_cls["word_count"][
agg
].to_numpy()
# Get word count histogram per class. Columns are word counts and rows are classes.
hist_per_cls_per_split[split] = (
df.groupby([label_col, "word_count"])
.size()
.unstack(fill_value=0)
.reindex(cls_indices, fill_value=0)
)
# Compute divergence and alerts
divergence_per_cls_per_agg: Dict[Agg, np.ndarray] = defaultdict(
partial(np.full, shape=len(cls_indices), fill_value=np.nan)
)
alert_per_cls_per_agg: Dict[Agg, np.ndarray] = defaultdict(
partial(np.full, shape=len(cls_indices), fill_value=False)
)
threshold_per_agg = {
Agg.mean: self.config.dataset_warnings.max_delta_mean_words,
Agg.std: self.config.dataset_warnings.max_delta_std_words,
}
if len(self.available_dataset_splits) == 2:
for agg, threshold in threshold_per_agg.items():
divergence_per_cls_per_agg[agg] = np.abs(
value_per_cls_per_agg_per_split[DatasetSplitName.train][agg]
- value_per_cls_per_agg_per_split[DatasetSplitName.eval][agg]
)
alert_per_cls_per_agg[agg] = divergence_per_cls_per_agg[agg] > threshold
alert_per_cls = np.any(
[alert_per_cls_per_agg[Agg.mean], alert_per_cls_per_agg[Agg.std]], axis=0
)
# Fill columns so both splits have the same values from 1 to N.
N = max(max(hist.columns) for hist in hist_per_cls_per_split.values())
hist_filled_per_cls_per_split = {}
for split, hist_per_cls in hist_per_cls_per_split.items():
hist_filled_per_cls_per_split[split] = np.zeros([len(hist_per_cls), N], dtype=int)
# Fill with word count (-1 because columns start at 1)
hist_filled_per_cls_per_split[split][
:, np.array(hist_per_cls.columns) - 1
] = hist_per_cls.to_numpy()
cls_names = first_dm.get_class_names(labels_only=True)
return [
DatasetWarning(
name=f"Length mismatch "
f"(>{threshold_per_agg[Agg.mean]}±{threshold_per_agg[Agg.std]} words)",
description=f"Delta between the number of words per utterance for a "
f"given class in the evaluation set vs the train set is "
f"above {threshold_per_agg[Agg.mean]}±{threshold_per_agg[Agg.std]}.",
columns=list(Agg),
format=FormatType.Decimal,
comparisons=[
DatasetDistributionComparison(
name=cls_names[i],
alert=alert_per_cls[i],
data=[
DatasetDistributionComparisonValue(
value=divergence_per_cls_per_agg[agg][i],
alert=alert_per_cls_per_agg[agg][i],
)
for agg in Agg
],
)
for i in cls_indices
],
plots=word_count_plot(
hist_filled_per_cls_per_split,
value_per_agg_per_split,
value_per_cls_per_agg_per_split,
divergence_per_cls_per_agg,
cls_names,
self.config.syntax,
),
)
]
|
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"/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,075
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_model_performance/test_metrics.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from collections import defaultdict
from typing import Dict
import datasets
import numpy as np
import pytest
from azimuth.config import MetricDefinition
from azimuth.modules.model_performance.metrics import MetricsModule, MetricsPerFilterModule
from azimuth.modules.model_performance.outcome_count import (
OutcomeCountPerFilterModule,
OutcomeCountPerThresholdModule,
)
from azimuth.modules.model_performance.outcomes import OutcomesModule
from azimuth.plots.ece import make_ece_figure
from azimuth.types import DatasetFilters, DatasetSplitName, ModuleOptions
from azimuth.types.outcomes import OutcomeName, OutcomeResponse
from azimuth.types.tag import SMART_TAGS_FAMILY_MAPPING, DataAction, SmartTag
from tests.utils import save_outcomes, save_predictions
def test_metrics(tiny_text_config):
save_predictions(tiny_text_config)
save_outcomes(tiny_text_config)
metrics_mod = MetricsModule(
dataset_split_name=DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0),
)
ds = metrics_mod.get_dataset_split()
[metrics_res] = metrics_mod.compute_on_dataset_split()
assert 0.0 <= metrics_res.ece <= 1.0
assert "data" in make_ece_figure(*metrics_res.ece_plot_args)
assert metrics_res.utterance_count == len(ds)
# Check that outcome count match utterance count
assert sum(metrics_res.outcome_count.values()) == len(ds)
# Check that outcome and accuracy are close.
assert np.isclose(
metrics_res.custom_metrics["Accuracy"],
metrics_res.outcome_count[OutcomeName.CorrectAndPredicted]
+ metrics_res.outcome_count[OutcomeName.CorrectAndRejected] / len(ds),
)
# Changing the filters changes the values.
metrics_mod_filters = MetricsModule(
DatasetSplitName.eval,
tiny_text_config,
mod_options=ModuleOptions(filters=DatasetFilters(prediction=[1]), pipeline_index=0),
)
[metrics_res_filters] = metrics_mod_filters.compute_on_dataset_split()
ds = metrics_mod_filters.get_dataset_split()
assert metrics_res_filters.utterance_count == len(ds)
# Disabling postprocessing changes the values
metrics_mod_post = MetricsModule(
DatasetSplitName.eval,
tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0, without_postprocessing=True),
)
[metrics_res_post] = metrics_mod_post.compute_on_dataset_split()
assert metrics_res_post.ece != metrics_res.ece
assert metrics_res_post.outcome_count.values() != metrics_res.outcome_count.values()
assert metrics_res_post.custom_metrics["Precision"] != metrics_res.custom_metrics["Precision"]
assert metrics_res_post.custom_metrics["Recall"] != metrics_res.custom_metrics["Recall"]
def test_outcomes(file_text_config_no_intent):
# Using file_text_config_no_intent since we know the predicted classes for each utterance.
mod = OutcomesModule(
DatasetSplitName.eval,
file_text_config_no_intent,
mod_options=ModuleOptions(pipeline_index=0),
)
res = mod.compute_on_dataset_split()
# Outcome determined from the values in sample_predictions_top1.csv
# Results are the same with and without postprocessing with File-based.
assert res == [
OutcomeResponse(
model_outcome=OutcomeName.CorrectAndRejected,
postprocessed_outcome=OutcomeName.CorrectAndRejected,
),
OutcomeResponse(
model_outcome=OutcomeName.CorrectAndRejected,
postprocessed_outcome=OutcomeName.CorrectAndRejected,
),
OutcomeResponse(
model_outcome=OutcomeName.CorrectAndPredicted,
postprocessed_outcome=OutcomeName.CorrectAndPredicted,
),
OutcomeResponse(
model_outcome=OutcomeName.IncorrectAndPredicted,
postprocessed_outcome=OutcomeName.IncorrectAndPredicted,
),
OutcomeResponse(
model_outcome=OutcomeName.CorrectAndRejected,
postprocessed_outcome=OutcomeName.CorrectAndRejected,
),
OutcomeResponse(
model_outcome=OutcomeName.IncorrectAndRejected,
postprocessed_outcome=OutcomeName.IncorrectAndRejected,
),
]
def test_empty_ds(tiny_text_config):
mod = MetricsModule(
DatasetSplitName.eval,
tiny_text_config,
mod_options=ModuleOptions(filters=DatasetFilters(label=[42]), pipeline_index=0),
)
_ = mod.get_dataset_split()
[json_output] = mod.compute_on_dataset_split()
# Nothing is there, but the response works.
assert json_output.ece == 0
assert json_output.utterance_count == 0
def test_outcome_count_per_threshold(tiny_text_config):
x_ticks_count = 4
mod = OutcomeCountPerThresholdModule(
dataset_split_name=DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(x_ticks_count=x_ticks_count, pipeline_index=0),
)
num_rows = mod.get_dataset_split().num_rows
[result] = mod.compute_on_dataset_split()
assert len(result.outcome_count_per_threshold) == x_ticks_count
assert all(
sum(rg_per_th.outcome_count.values()) == num_rows
for rg_per_th in result.outcome_count_per_threshold
)
# Check that outcomes change by threshold
outcomes_for_all_threshold = defaultdict(list)
for rg_per_th in result.outcome_count_per_threshold:
outcomes_for_all_threshold[OutcomeName.IncorrectAndRejected].append(
rg_per_th.outcome_count.get(OutcomeName.IncorrectAndRejected, 0)
)
outcomes_for_all_threshold[OutcomeName.IncorrectAndPredicted].append(
rg_per_th.outcome_count.get(OutcomeName.IncorrectAndPredicted, 0)
)
outcomes_for_all_threshold[OutcomeName.CorrectAndPredicted].append(
rg_per_th.outcome_count.get(OutcomeName.CorrectAndPredicted, 0)
)
outcomes_for_all_threshold[OutcomeName.CorrectAndRejected].append(
rg_per_th.outcome_count.get(OutcomeName.CorrectAndRejected, 0)
)
assert not all(len(set(counts)) == 1 for counts in outcomes_for_all_threshold.values())
def test_outcome_count_per_filter(tiny_text_config):
save_predictions(tiny_text_config)
save_outcomes(tiny_text_config)
mod = OutcomeCountPerFilterModule(
DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0),
)
[res] = mod.compute_on_dataset_split()
mod_filter_0 = OutcomeCountPerFilterModule(
DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(filters=DatasetFilters(label=[0]), pipeline_index=0),
)
[res_filter_0] = mod_filter_0.compute_on_dataset_split()
dm = mod.get_dataset_split_manager()
# Assert the class name associated with rejection_class
# is first (in this case, it will be one of the two classes)
assert (
res.count_per_filter.label[0].filter_value
== res_filter_0.count_per_filter.label[0].filter_value
== dm.get_class_names()[dm.rejection_class_idx]
)
assert (
res.count_per_filter.prediction[0].filter_value
== res_filter_0.count_per_filter.prediction[0].filter_value
== dm.get_class_names()[dm.rejection_class_idx]
)
assert (
res.count_per_filter.extreme_length[0].filter_value
== res_filter_0.count_per_filter.extreme_length[0].filter_value
== SmartTag.no_smart_tag
)
assert (
res.count_per_filter.data_action[0].filter_value
== res_filter_0.count_per_filter.data_action[0].filter_value
== DataAction.no_action
)
# Assert that for one of the 2 classes, the total will be the same in both results.
assert any(
res.count_per_filter.label[x].utterance_count
== res_filter_0.count_per_filter.label[x].utterance_count
for x in [0, 1]
)
# Assert that for one of the 2 classes, the total will be 0 for the second results.
assert any(res_filter_0.count_per_filter.label[x].utterance_count == 0 for x in [0, 1])
mod_post = OutcomeCountPerFilterModule(
DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0, without_postprocessing=True),
)
[res_post] = mod_post.compute_on_dataset_split()
# Assert results are different without postprocessing
assert res_post.count_per_filter.data_action != res.count_per_filter.data_action
assert res_post.count_per_filter.label != res.count_per_filter.label
assert res_post.count_per_filter.outcome != res.count_per_filter.outcome
assert res_post.count_per_filter.prediction != res.count_per_filter.prediction
assert res_post.count_per_filter.extreme_length != res.count_per_filter.extreme_length
def test_outcome_count_per_filter_without_postprocessing(tiny_text_config):
save_predictions(tiny_text_config)
save_outcomes(tiny_text_config)
[res] = OutcomeCountPerFilterModule(
DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(
filters=DatasetFilters(outcome=[OutcomeName.IncorrectAndRejected]), pipeline_index=0
),
).compute_on_dataset_split()
for per_outcome in res.count_per_filter.outcome:
if per_outcome.filter_value != OutcomeName.IncorrectAndRejected:
assert per_outcome.utterance_count == 0, f"expected no {per_outcome.filter_value}"
else:
assert per_outcome.utterance_count > 0, (
f"expected some {per_outcome.filter_value},"
"otherwise the following test doesn't test much"
)
# Regression test for bug https://github.com/ServiceNow/azimuth/issues/195
# If we pass without_postprocessing to the modules, but we forget to propagate it in
# FilterableModule.get_dataset_split(), utterances are mistakenly filtered based on their
# post-processed outcome, but then the response is counted using the model outcome. Here, some
# utterances that were IncorrectAndRejected with post-processing would be counted as a different
# model outcome. We verify that the counts are still 0 for the other outcomes.
[res_without_postprocessing] = OutcomeCountPerFilterModule(
DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(
filters=DatasetFilters(outcome=[OutcomeName.IncorrectAndRejected]),
pipeline_index=0,
without_postprocessing=True,
),
).compute_on_dataset_split()
for per_outcome in res_without_postprocessing.count_per_filter.outcome:
if per_outcome.filter_value != OutcomeName.IncorrectAndRejected:
assert per_outcome.utterance_count == 0, f"expected no {per_outcome.filter_value}"
def test_metrics_per_filter(tiny_text_config, apply_mocked_startup_task):
apply_mocked_startup_task(tiny_text_config)
mf_module = MetricsPerFilterModule(
dataset_split_name=DatasetSplitName.eval,
config=tiny_text_config,
mod_options=ModuleOptions(pipeline_index=0),
)
[result] = mf_module.compute_on_dataset_split()
ds_len = len(mf_module.get_dataset_split())
num_classes = mf_module.get_dataset_split_manager().get_num_classes()
assert result.utterance_count == ds_len
prediction_metrics = result.metrics_per_filter.prediction
assert sum([mf_v.utterance_count for mf_v in prediction_metrics]) == ds_len
assert len(prediction_metrics) == num_classes
label_metrics = result.metrics_per_filter.label
assert sum([mf_v.utterance_count for mf_v in label_metrics]) == ds_len
assert len(label_metrics) == num_classes
for family, smart_tags in SMART_TAGS_FAMILY_MAPPING.items():
smart_tag_metrics = getattr(result.metrics_per_filter, family.value)
assert sum([mf_v.utterance_count for mf_v in smart_tag_metrics]) == ds_len
assert len(smart_tag_metrics) == len(smart_tags) + 1
_CITATION = """\
"""
_DESCRIPTION = """
Very cool metric that returns the ratio of high confidence items.
"""
_KWARGS_DESCRIPTION = """
Args:
predictions: Predicted classes [num_samples].
references: Targets to be matched [num_samples].
sum: Whether to sum the per-class perc deflected metric or not.
Returns:
'value':
"""
class MyMetric(datasets.Metric):
def _info(self) -> datasets.MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Value("int64"),
"references": datasets.Value("int64"),
}
),
)
def _compute(
self,
predictions: np.ndarray,
references: np.ndarray,
probabilities: np.ndarray,
threshold: float = 0.8,
) -> Dict[str, float]:
return {"value": (probabilities.max(-1) > threshold).sum() / len(probabilities)}
@pytest.mark.parametrize("threshold", [0.8, 0.99])
def test_custom_metrics(simple_text_config, apply_mocked_startup_task, threshold):
simple_text_config.metrics["MyMetric"] = MetricDefinition(
class_name="tests.test_modules.test_model_performance.test_metrics.MyMetric",
additional_kwargs={"threshold": threshold},
)
metric_module = MetricsModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(pipeline_index=0),
)
out = metric_module.compute_on_dataset_split()[0]
confidences = np.array(metric_module.get_dataset_split()["postprocessed_confidences"])
ratio_high_confidence = (confidences.max(-1) > threshold).sum() / len(confidences)
assert out.custom_metrics["MyMetric"] == ratio_high_confidence
|
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"/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], 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31,076
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_routers/test_utils.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from fastapi import FastAPI
from starlette.status import HTTP_200_OK, HTTP_503_SERVICE_UNAVAILABLE
from starlette.testclient import TestClient
def test_blocked_state(app_not_started: FastAPI) -> None:
client = TestClient(app_not_started)
# Can reach status
resp = client.get("/status")
assert resp.status_code == HTTP_200_OK, resp.text
# Can't start a task
resp = client.get("/dataset_warnings")
assert resp.status_code == HTTP_503_SERVICE_UNAVAILABLE, resp.text
|
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"/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,077
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/model_performance/outcome_count.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from collections import Counter, defaultdict
from typing import Dict, List, Tuple
import numpy as np
from datasets import Dataset
from tqdm import tqdm
from azimuth.config import ModelContractConfig
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.base_classes import AggregationModule, FilterableModule
from azimuth.modules.model_performance.outcomes import OutcomesModule
from azimuth.types import DatasetColumn, ModuleOptions
from azimuth.types.model_performance import (
OutcomeCountPerFilter,
OutcomeCountPerFilterResponse,
OutcomeCountPerFilterValue,
OutcomeCountPerThresholdResponse,
OutcomeCountPerThresholdValue,
)
from azimuth.types.outcomes import ALL_OUTCOMES, OutcomeName
from azimuth.types.tag import ALL_DATA_ACTION_FILTERS, SMART_TAGS_FAMILY_MAPPING, SmartTag
from azimuth.utils.dataset_operations import get_outcomes_from_ds
from azimuth.utils.ml.model_performance import (
sorted_by_utterance_count,
sorted_by_utterance_count_with_last,
)
from azimuth.utils.project import postprocessing_editable
from azimuth.utils.validation import assert_is_list
class OutcomeCountPerFilterModule(FilterableModule[ModelContractConfig]):
"""Computes the outcome count for each filter."""
def get_outcome_count_per_class(
self, dm: DatasetSplitManager, ds: Dataset, dataset_column: str
) -> List[OutcomeCountPerFilterValue]:
"""Get outcome count for a specific class name, either in label or prediction.
Args:
dm: DatasetSplitManager
ds: Dataset Split.
dataset_column: Column on which to compute the outcome count (prediction or label)
Returns:
Outcome count per class name.
"""
outcome_count_per_class: Dict[Tuple[str, OutcomeName], int] = defaultdict(int)
for utterance_class, outcome in zip(
ds[dataset_column], get_outcomes_from_ds(ds, self.mod_options.without_postprocessing)
):
outcome_count_per_class[(dm.get_class_names()[utterance_class], outcome)] += 1
return sorted_by_utterance_count_with_last(
self.get_outcome_count(outcome_count_per_class, dm.get_class_names()),
dm.rejection_class_idx,
)
def get_outcome_count_per_tag(
self, dm: DatasetSplitManager, ds: Dataset, filters: List[str]
) -> List[OutcomeCountPerFilterValue]:
"""Get outcome count for a list of tag filter options.
Args:
dm: DatasetSplitManager.
ds: Dataset Split.
filters: Tag filter options, assuming the last item (-1) is NO_ACTION/NO_SMART_TAGS.
Returns:
Outcome count per tag.
"""
outcome_count_per_tag: Dict[Tuple[str, OutcomeName], int] = defaultdict(int)
all_tags = dm.get_tags(
indices=assert_is_list(ds[DatasetColumn.row_idx]), table_key=self._get_table_key()
)
for utterance_tags, outcome in zip(
all_tags.values(), get_outcomes_from_ds(ds, self.mod_options.without_postprocessing)
):
no_tag = True
for filter_, tagged in utterance_tags.items():
if tagged and filter_ in filters[:-1]:
outcome_count_per_tag[(filter_, outcome)] += 1
no_tag = False
if no_tag:
outcome_count_per_tag[(filters[-1], outcome)] += 1
return sorted_by_utterance_count_with_last(
self.get_outcome_count(outcome_count_per_tag, filters), -1
)
def get_outcome_count_per_outcome(self, ds: Dataset) -> List[OutcomeCountPerFilterValue]:
"""Compute outcome count per outcome.
Args:
ds: Dataset Split.
Returns:
List of Outcome Count for each outcome.
"""
outcome_count = defaultdict(
int, Counter(get_outcomes_from_ds(ds, self.mod_options.without_postprocessing))
)
empty_outcome_count = {outcome: 0 for outcome in OutcomeName}
metrics = [
OutcomeCountPerFilterValue(
outcome_count={**empty_outcome_count, outcome: outcome_count[outcome]},
utterance_count=outcome_count[outcome],
filter_value=outcome,
)
for outcome in OutcomeName
]
return sorted_by_utterance_count(metrics)
@classmethod
def get_outcome_count(
cls,
outcome_count_per_filter: Dict[Tuple[str, OutcomeName], int],
filters: List[str],
) -> List[OutcomeCountPerFilterValue]:
"""Compute outcome count and total amount for a given set of filters.
Args:
outcome_count_per_filter: Outcome count per filter.
filters: List of filters.
Returns:
List of outcome counts for the set of filters.
"""
return [
OutcomeCountPerFilterValue(
outcome_count={
outcome: outcome_count_per_filter[(filter_value, outcome)]
for outcome in ALL_OUTCOMES
},
utterance_count=sum(
outcome_count_per_filter[(filter_value, outcome)] for outcome in ALL_OUTCOMES
),
filter_value=filter_value,
)
for filter_value in filters
]
def compute_on_dataset_split(self) -> List[OutcomeCountPerFilterResponse]: # type: ignore
dm = self.get_dataset_split_manager()
ds = self.get_dataset_split()
return [
OutcomeCountPerFilterResponse(
count_per_filter=OutcomeCountPerFilter(
label=self.get_outcome_count_per_class(dm, ds, dm.config.columns.label),
prediction=self.get_outcome_count_per_class(
dm, ds, DatasetColumn.postprocessed_prediction
),
data_action=self.get_outcome_count_per_tag(dm, ds, ALL_DATA_ACTION_FILTERS),
outcome=self.get_outcome_count_per_outcome(ds),
**{
family.value: self.get_outcome_count_per_tag(
dm, ds, tags + [SmartTag.no_smart_tag]
)
for family, tags in SMART_TAGS_FAMILY_MAPPING.items()
},
),
utterance_count=len(ds),
)
]
class OutcomeCountPerThresholdModule(AggregationModule[ModelContractConfig]):
"""Compute the outcome count per threshold."""
required_mod_options = {"pipeline_index"}
optional_mod_options = {"x_ticks_count"}
def compute_on_dataset_split(self) -> List[OutcomeCountPerThresholdResponse]: # type: ignore
if not postprocessing_editable(self.config, self.mod_options.pipeline_index):
raise ValueError("Postprocessing is not editable")
x_ticks_count = self.mod_options.x_ticks_count
ths = np.linspace(0, 1, x_ticks_count)
result = []
for th in tqdm(
ths,
desc=f"{self.task_name} on {self.dataset_split_name} set "
f"for pipeline {self.mod_options.pipeline_index}",
):
outcomes_mod = OutcomesModule(
dataset_split_name=self.dataset_split_name,
config=self.config,
mod_options=ModuleOptions(
# Convert to float instead of numpy.float64
threshold=float(th),
pipeline_index=self.mod_options.pipeline_index,
),
)
outcomes = outcomes_mod.compute_on_dataset_split()
postprocessed_outcomes = [outcome.postprocessed_outcome for outcome in outcomes]
result.append(
OutcomeCountPerThresholdValue(
threshold=th,
outcome_count=Counter(postprocessed_outcomes),
)
)
return [
OutcomeCountPerThresholdResponse(
outcome_count_per_threshold=result,
confidence_threshold=self.get_threshold(),
utterance_count=len(self.get_dataset_split()),
)
]
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,078
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/cluster.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import os
import tempfile
from os.path import join as pjoin
import dask
import distributed
import structlog
from distributed import SpecCluster
log = structlog.get_logger()
def default_cluster(large=False) -> SpecCluster:
"""Create a default Dask cluster for scheduling.
Args:
large: Whether workers have 12Gb of mem or 6Gb.
Notes:
1. We start workers as non-daemon as we need to spawn workers in the workers.
2. If one wants to make their own cluster please see:
https://docs.dask.org/en/latest/how-to/deploy-dask-clusters.html
Returns:
A LocalCluster with 2 workers.
"""
dask_envs = {
"DASK_DISTRIBUTED__WORKER__DAEMON": "False",
"DASK_DISTRIBUTED__WORKER__MULTIPROCESSING_METHOD": "fork",
}
os.environ.update(dask_envs)
# Start the cluster locally
memory_limit = "12GB" if large else "6GB"
log.info(f"Starting cluster with 2 workers of {memory_limit} of memory!")
tmp_file = pjoin(str(tempfile.mkdtemp()), "dask-worker-space")
with dask.config.set({"distributed.worker.daemon": False}):
cluster = distributed.LocalCluster(
n_workers=2,
local_directory=tmp_file,
threads_per_worker=1,
memory_limit=memory_limit, # "auto" doesnt work well.
)
return cluster
|
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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,079
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_routers/test_export.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from fastapi import FastAPI
from starlette.status import HTTP_200_OK, HTTP_400_BAD_REQUEST
from starlette.testclient import TestClient
def test_get_export(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get("/export/dataset_splits/eval/utterances")
assert resp.status_code == HTTP_200_OK, resp.text
assert resp.headers["content-type"] == "text/csv; charset=utf-8"
assert int(resp.headers["content-length"]) > 0
assert "azimuth_export_sentiment-analysis_eval" in resp.headers["content-disposition"]
assert resp.text.startswith("row_idx,utterance,label") # snake_case
def test_get_report(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get("/export/perturbation_testing_summary")
assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text
assert resp.json()["detail"] == "query parameter pipeline_index: field required"
resp = client.get("/export/perturbation_testing_summary?pipeline_index=-10")
assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text
assert resp.json()["detail"] == (
"query parameter pipeline_index=-10: ensure this value is greater than or equal to 0"
)
resp = client.get("/export/perturbation_testing_summary?pipeline_index=0")
assert resp.status_code == HTTP_200_OK, resp.text
assert resp.headers["content-type"] == "text/csv; charset=utf-8"
assert int(resp.headers["content-length"]) > 0
assert "behavioral_testing_summary" in resp.headers["content-disposition"]
assert ".csv" in resp.headers["content-disposition"]
assert resp.text.startswith("name,description,family,perturbation_type") # snake_case
def test_get_utterances(app: FastAPI) -> None:
client = TestClient(app)
resp = client.get("/export/dataset_splits/eval/perturbed_utterances?pipeline_index=0")
assert resp.status_code == HTTP_200_OK, resp.text
assert resp.headers["content-type"] == "application/json"
assert int(resp.headers["content-length"]) > 0
assert "modified_set" in resp.headers["content-disposition"]
assert ".json" in resp.headers["content-disposition"]
def test_get_proposed_actions(app: FastAPI) -> None:
client = TestClient(app)
request = [
{"persistent_id": 0, "data_action": "remove"},
{"persistent_id": 1, "data_action": "relabel"},
]
resp = client.patch("/dataset_splits/eval/utterances", json=request)
assert resp.status_code == HTTP_200_OK, resp.text
resp = client.get("/export/dataset_splits/eval/proposed_actions")
assert resp.status_code == HTTP_200_OK, resp.text
assert resp.headers["content-type"] == "text/csv; charset=utf-8"
assert int(resp.headers["content-length"]) > 0
assert "proposed_actions" in resp.headers["content-disposition"]
assert ".csv" in resp.headers["content-disposition"]
assert resp.text.startswith("row_idx,proposed_action") # snake_case
|
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"/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,080
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_dataset_analysis/test_class_overlap.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import numpy as np
from sklearn.preprocessing import normalize
import azimuth.modules.dataset_analysis.similarity_analysis as faiss_mod
from azimuth.modules.dataset_analysis.class_overlap import ClassOverlapModule
from azimuth.types import DatasetSplitName
from azimuth.types.class_overlap import ClassOverlapResponse
class MockedTransformer:
def __init__(self, name, num_features: int = 123):
self.num_features = num_features
def encode(self, x, *args, **kwargs):
return normalize(np.random.rand(len(x), self.num_features), axis=1, norm="l1")
def test_incomplete_class_set(clinc_text_config, dask_client, monkeypatch):
monkeypatch.setattr(faiss_mod, "SentenceTransformer", MockedTransformer)
# Mock SentenceTransformer on all workers.
dask_client.run(
lambda: monkeypatch.setattr(faiss_mod, "SentenceTransformer", MockedTransformer)
)
mod = ClassOverlapModule(dataset_split_name=DatasetSplitName.train, config=clinc_text_config)
result: ClassOverlapResponse = mod.compute_on_dataset_split()
class_ids = set(mod.get_dataset_split_manager().get_dataset_split()["label"])
s_matrix = result[0].s_matrix
matrix_size = s_matrix.shape[0]
indices_with_overlap = [
(i, j) for i in range(matrix_size) for j in range(matrix_size) if s_matrix[i, j] > 0
]
assert all(
[val in class_ids for tup in indices_with_overlap for val in tup]
), "Overlap value > 0 for nonexistent class index"
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,081
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/general/__init__.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], 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31,082
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/base_classes/caching.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import abc
import json
import os
import time
from typing import List, Optional
import h5py
import structlog
from filelock import FileLock
from retrying import RetryError, retry
from azimuth.types import ModuleResponse
from azimuth.utils.conversion import from_pickle_bytes, to_pickle_bytes
log = structlog.get_logger(__name__)
class HDF5FileOpenerWithRetry:
"""Open an HDF5 file with multiple retries.
Args:
args: Forwarded to h5py.File
n_retry: Number of tries to open the file.
sleep: Sleep time between tries in second.
kwargs: Forwarded to h5py.File.
"""
def __init__(
self,
*args,
n_retry=5,
sleep=1,
**kwargs,
):
self.n_retry = n_retry
self.sleep = sleep
self.file = None
for i in range(self.n_retry):
try:
self.file = h5py.File(*args, **kwargs)
break
except OSError as e:
log.warning(f"Issue opening HDF5: {e}. Retrying... {i}/{self.n_retry}")
time.sleep(self.sleep)
else:
raise TimeoutError("Can't open that file.")
def __enter__(self):
return self.file
def __exit__(self, exc_type, exc_val, exc_tb):
if self.file is not None:
self.file.close()
class CachingMechanism(abc.ABC):
"""Parent module for caching mechanism in the application."""
@abc.abstractmethod
def _store_data_in_cache(self, result: List[ModuleResponse], indices: List[int]):
...
@abc.abstractmethod
def _check_cache(self, indices: Optional[List[int]]):
...
@abc.abstractmethod
def _get_cache(self, indices: List[int]):
...
class HDF5CacheMixin(CachingMechanism):
"""HDF5 caching functions to cache the results of modules."""
name: str
_cache_lock: str # Lockfile path
_cache_file: str # Cache file path
_cache_effective_arguments: str # Effective arguments cache file
def get_effective_arguments(self):
raise NotImplementedError
def _store_data_in_cache(self, result: List[ModuleResponse], indices: List[int]):
"""Store `results` in `handle` for some `indices`.
Args:
result (List[Dict]): A list of records to store.
indices (List[int]): A list of indices to map result to.
"""
if len(indices) != len(result):
raise ValueError(
f"Expecting same length for `indices`({len(indices)}) and `results`({len(result)}."
)
with FileLock(self._cache_lock), HDF5FileOpenerWithRetry(
self._cache_file, "w", libver="latest"
) as handle:
handle.swmr_mode = True
for idx, res in zip(indices, result):
arr = to_pickle_bytes(res)
ds = handle.require_dataset(f"{self.name}/{idx}", shape=arr.shape, dtype=arr.dtype)
ds[()] = arr
handle.flush()
# Save all that affects caching so it can be used for identification and debugging.
with open(os.path.join(self._cache_effective_arguments), "w") as f:
json.dump(self.get_effective_arguments().dict(), f, indent=2)
@retry(stop_max_attempt_number=5, wait_fixed=0.5)
def _check_cache_internal(self, indices: Optional[List[int]]):
"""Try to read the cache up to 5 times (in case there are errors)
and see if we have all the indices.
Notes:
Should not be called directly, please refer to _check_cache instead.
Args:
indices: Optional set of indices to verify.
"""
if indices is None or not os.path.exists(self._cache_file):
# Not a cacheable query
return False
with h5py.File(self._cache_file, "r", libver="latest", swmr=True) as handle:
if self.name not in handle:
return False
task_grp = handle[self.name]
return all(str(i) in task_grp for i in indices)
def _check_cache(self, indices: Optional[List[int]]):
"""Check if all indices are in the cache.
This is used to know if we already have the result of a request.
Args:
indices: Optional set of indices to verify.
"""
try:
return self._check_cache_internal(
indices,
)
except (OSError, KeyError, RuntimeError, RetryError) as e:
# NOTE: The file might be corrupted
# so the key can be there, but not the values.
print(f"Error accessing cache in {self.name}", e)
return False
@retry(stop_max_attempt_number=5, wait_fixed=0.5)
def _get_cache(self, indices: List[int]):
"""
Will gather the results of this tasks on some indices.
Note: we fail if indices are not there.
Args:
indices (List[int]): A list of indices to map result to.
Returns:
List[Dict], a list of records for the indices.
"""
with h5py.File(self._cache_file, "r", libver="latest", swmr=True) as handle:
result = []
log.debug(f"Get cache from {self._cache_file} for {len(indices)} key(s)")
for i in indices:
grp = handle[f"{self.name}/{i}"]
result.append(from_pickle_bytes(grp[()]))
return result
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,083
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py
|
import pytest
from azimuth.dataset_split_manager import PredictionTableKey
from azimuth.modules.pipeline_comparison.prediction_comparison import PredictionComparisonModule
from azimuth.types import DatasetSplitName
from azimuth.types.pipeline_comparison import PredictionComparisonResponse
from azimuth.types.tag import SmartTag
def test_response(simple_text_config_multi_pipeline):
module = PredictionComparisonModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config_multi_pipeline,
)
result = module.compute_on_dataset_split()
predictions_0 = module._get_predictions(0)
predictions_1 = module._get_predictions(1)
assert any(tags.pipeline_disagreement for tags in result)
assert any(tags.incorrect_for_all_pipelines for tags in result)
for pred_0, pred_1, tags in zip(predictions_0, predictions_1, result):
pred_cls_0, pred_cls_1 = (
pred_0.postprocessed_output.preds.item(),
pred_1.postprocessed_output.preds.item(),
)
assert tags.pipeline_disagreement == (pred_cls_0 != pred_cls_1), (pred_cls_0, pred_cls_1)
assert tags.incorrect_for_all_pipelines == (
pred_cls_0 != pred_0.label and pred_cls_1 != pred_1.label
)
def test_less_than_two_pipeline(simple_text_config, tiny_text_config_no_pipeline):
for cfg in (simple_text_config, tiny_text_config_no_pipeline):
module = PredictionComparisonModule(
dataset_split_name=DatasetSplitName.eval,
config=cfg,
)
with pytest.raises(ValueError, match="less than two pipelines"):
module.compute_on_dataset_split()
def test_save_results(simple_text_config_multi_pipeline):
module = PredictionComparisonModule(
dataset_split_name=DatasetSplitName.eval, config=simple_text_config_multi_pipeline
)
dm = module.get_dataset_split_manager()
preds = [
PredictionComparisonResponse(
pipeline_disagreement=i < 10, incorrect_for_all_pipelines=i < 15
)
for i in range(dm.num_rows)
]
module.save_result(preds, dm)
assert SmartTag.pipeline_disagreement not in dm.get_dataset_split(None)
assert SmartTag.incorrect_for_all_pipelines not in dm.get_dataset_split(None)
# Check that the tag is applied to all prediction tables.
for pipeline_index in range(len(simple_text_config_multi_pipeline.pipelines)):
table_key = PredictionTableKey.from_pipeline_index(
pipeline_index, simple_text_config_multi_pipeline, use_bma=False
)
ds = dm.get_dataset_split(table_key)
assert (
sum(ds[SmartTag.pipeline_disagreement]) == 10
and sum(ds[SmartTag.incorrect_for_all_pipelines]) == 15
)
|
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"/azimuth/modules/model_contracts/file_based_text_classification.py": ["/azimuth/modules/model_contracts/text_classification_no_saliency.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/azimuth/modules/model_contracts/text_classification.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py", "/azimuth/utils/project.py"], "/azimuth/task_manager.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py"], "/tests/test_modules/test_perturbation_testing/test_perturbation_testing.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/tests/utils.py"], "/tests/test_documentation.py": ["/azimuth/__init__.py", "/azimuth/modules/base_classes/__init__.py"], "/azimuth/utils/object_loader.py": ["/azimuth/config.py"], "/azimuth/types/tag.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/custom_utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_config.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/perturbation_testing/perturbation_testing.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", 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"/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_app.py": ["/azimuth/routers/utterances.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/dataset_analysis/similarity_analysis.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/model_performance.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/utils/ml/ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/perturbation_testing/__init__.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/class_overlap.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/similarity_analysis.py"], "/azimuth/types/model_performance.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/azimuth/types/utterance.py": ["/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/azimuth/routers/model_performance/metrics.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", 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"/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], "/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,084
|
ServiceNow/azimuth
|
refs/heads/main
|
/openapi.py
|
import json
import sys
from fastapi.openapi.utils import get_openapi
from azimuth.app import create_app
if __name__ == "__main__":
app = create_app()
openapi = get_openapi(
title=app.title,
version=app.version,
openapi_version=app.openapi_version,
description=app.description,
routes=app.routes,
tags=app.openapi_tags,
servers=app.servers,
)
json.dump(openapi, sys.stdout)
|
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"/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", 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31,085
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/startup.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import threading
import time
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import structlog
from distributed import Future
from azimuth.config import AzimuthConfig
from azimuth.dataset_split_manager import DatasetSplitManager
from azimuth.modules.base_classes import DaskModule, DatasetResultModule
from azimuth.task_manager import TaskManager
from azimuth.types import (
DatasetSplitName,
ModuleOptions,
SupportedMethod,
SupportedModule,
SupportedTask,
)
from azimuth.utils.project import (
perturbation_testing_available,
postprocessing_editable,
predictions_available,
saliency_available,
similarity_available,
)
from azimuth.utils.validation import assert_not_none
log = structlog.get_logger()
@dataclass
class Startup:
name: str # TODO Improve: A name cannot be a substring of any other names.
module: SupportedTask
mod_options: Dict = field(default_factory=dict)
# The names of other start-up tasks to wait for.
dependency_names: List[str] = field(default_factory=list)
run_on_all_pipelines: bool = False
dataset_split_names: List[DatasetSplitName] = field(
default_factory=lambda: [DatasetSplitName.train, DatasetSplitName.eval]
)
START_UP_THREAD_NAME = "Azimuth_Startup"
SIMILARITY_TASKS = [
Startup("faiss", SupportedModule.FAISS),
Startup("neighbors_tags", SupportedModule.NeighborsTagging, dependency_names=["faiss"]),
Startup(
"class_overlap",
SupportedModule.ClassOverlap,
dependency_names=["neighbors_tags"],
dataset_split_names=[DatasetSplitName.train],
),
]
BMA_PREDICTION_TASKS = [
Startup(
"prediction_bma",
SupportedMethod.Predictions,
mod_options={"use_bma": True},
run_on_all_pipelines=True,
)
]
PERTURBATION_TESTING_TASKS = [
Startup(
"perturbation_testing",
SupportedModule.PerturbationTesting,
dependency_names=["prediction"],
run_on_all_pipelines=True,
),
Startup(
"perturbation_testing_summary",
SupportedModule.PerturbationTestingSummary,
dependency_names=["perturbation_testing"],
run_on_all_pipelines=True,
dataset_split_names=[DatasetSplitName.all],
),
]
PIPELINE_COMPARISON_TASKS = [
Startup(
"prediction_comparison",
SupportedModule.PredictionComparison,
dependency_names=["prediction"],
)
]
POSTPROCESSING_TASKS = [
Startup(
"outcome_count_per_threshold",
SupportedModule.OutcomeCountPerThreshold,
dependency_names=["prediction", "outcome"],
run_on_all_pipelines=True,
dataset_split_names=[DatasetSplitName.eval],
)
]
SALIENCY_TASKS = [
Startup("saliency", SupportedMethod.Saliency, run_on_all_pipelines=True),
]
BASE_PREDICTION_TASKS = [
Startup("predictions", SupportedMethod.Predictions, run_on_all_pipelines=True),
Startup(
"outcomes",
SupportedModule.Outcome,
dependency_names=["predictions"],
run_on_all_pipelines=True,
),
Startup(
"confidence_bins",
SupportedModule.ConfidenceBinIndex,
dependency_names=["predictions"],
run_on_all_pipelines=True,
),
]
PER_FILTER_TASKS = [
Startup(
"metrics_by_filter",
SupportedModule.MetricsPerFilter,
dependency_names=[
"predictions",
"outcomes",
"prediction_comparison",
"perturbation_testing",
"neighbors_tags",
"prediction_bma",
"syntax_tags",
],
run_on_all_pipelines=True,
)
]
def on_end(fut: Future, module: DaskModule, dm: DatasetSplitManager, task_manager: TaskManager):
"""This is a callback that will be run at the end of the computation.
Args:
fut: Future of the module.
module: DaskModule.
dm: DatasetSplitManager in which to save the results.
task_manager: Task Manager.
"""
if fut.status == "finished":
# Task is done, save the result.
if isinstance(module, DatasetResultModule):
module.save_result(module.result(), dm)
else:
log.exception("Error in", module=module, fut=fut, exc_info=fut.exception())
def make_startup_tasks(
dataset_split_managers: Dict[DatasetSplitName, Optional[DatasetSplitManager]],
task_manager: TaskManager,
supported_module: SupportedTask,
mod_options: Dict,
dependencies: List[DaskModule],
pipeline_index: Optional[int],
dataset_split_names: List[DatasetSplitName],
) -> Dict[DatasetSplitName, DaskModule]:
"""
Apply `module` on all indices of all dataset split names and call `save_results` at the end.
Args:
dataset_split_managers: loaded dataset_split_managers.
task_manager: Initialized task managers.
supported_module: A Module to instantiate.
mod_options: Special kwargs for the Module.
dependencies: Which modules to include as dependency.
pipeline_index: On which pipeline to run the startup task.
dataset_split_names: List of DatasetSplitName on which to run the task.
Returns:
Named Modules with the tasks.
"""
available_dms = {k: v for k, v in dataset_split_managers.items() if v is not None}
available_dms_with_all: Dict[DatasetSplitName, Optional[DatasetSplitManager]] = {
**available_dms,
DatasetSplitName.all: None,
}
tasks: Dict[DatasetSplitName, DaskModule] = {}
for dataset_split_name, dm in available_dms_with_all.items():
if dataset_split_name not in dataset_split_names:
continue
_, maybe_task = task_manager.get_task(
task_name=supported_module,
dataset_split_name=dataset_split_name,
dependencies=dependencies,
mod_options=ModuleOptions(pipeline_index=pipeline_index, **mod_options),
)
task = assert_not_none(maybe_task)
task_launched = task.future is not None
if task_launched:
task.add_done_callback(on_end, dm=dm, task_manager=task_manager)
# If the task is not launched (because it is cached), we save the module cached results in
# the dataset, so that it contains the relevant data associated with the config.
elif isinstance(task, DatasetResultModule):
task.save_result(task.result(), assert_not_none(dm))
tasks[dataset_split_name] = task
return tasks
def get_modules(module_objects: Dict[str, DaskModule], deps_name: List[str]):
# Get all modules where the name is a substring of any dependency.
return [v for k, v in module_objects.items() if any(d in k for d in deps_name)]
def startup_tasks(
dataset_split_managers: Dict[DatasetSplitName, Optional[DatasetSplitManager]],
task_manager: TaskManager,
) -> Dict[str, DaskModule]:
"""Create and launch all startup tasks.
Args:
dataset_split_managers: Dataset Managers.
task_manager: Task Manager.
Returns:
Modules with their names.
"""
config = task_manager.config
if config.dataset is None:
return {}
# The order in start_up_tasks matters; a task needs to be added after its dependencies.
# TODO Refactor so the startup can be robust to the order in start_up_tasks.
start_up_tasks = [
Startup("syntax_tags", SupportedModule.SyntaxTagging),
]
if similarity_available(config):
start_up_tasks += SIMILARITY_TASKS
if predictions_available(config):
start_up_tasks += BASE_PREDICTION_TASKS
if perturbation_testing_available(config):
start_up_tasks += PERTURBATION_TESTING_TASKS
if config.uncertainty.iterations > 1:
start_up_tasks += BMA_PREDICTION_TASKS
if config.pipelines is not None and len(config.pipelines) > 1:
start_up_tasks += PIPELINE_COMPARISON_TASKS
# TODO We only check pipeline_index=0, but we should check all pipelines.
if postprocessing_editable(config, 0):
start_up_tasks += POSTPROCESSING_TASKS
if saliency_available(config):
start_up_tasks += SALIENCY_TASKS
start_up_tasks += PER_FILTER_TASKS
mods = start_tasks_for_dms(config, dataset_split_managers, task_manager, start_up_tasks)
startup_ready = all(m.done() for m in mods.values())
if startup_ready:
log.info("Loading the application from cache. It should be accessible now.")
else:
# Start a thread to monitor the status.
th = threading.Thread(
target=wait_for_startup, args=(mods, task_manager), name=START_UP_THREAD_NAME
)
th.setDaemon(True)
th.start()
return mods
def start_tasks_for_dms(
config: AzimuthConfig,
dataset_split_managers: Dict[DatasetSplitName, Optional[DatasetSplitManager]],
task_manager: TaskManager,
tasks: List[Startup],
) -> Dict[str, DaskModule]:
"""Start `tasks` for all `dataset_splits_managers`.
Args:
config: App config
dataset_split_managers: DatasetSplitManager to run the module on.
task_manager: TaskManager to launch tasks with.
tasks: Which tasks to run.
Returns:
Dict[name, DaskModule] for all tasks.
"""
mods: Dict[str, DaskModule] = {}
for startup in tasks:
if startup.run_on_all_pipelines and config.pipelines is None:
raise ValueError(f"{startup.name} requires pipelines, but none provided in config.")
if startup.run_on_all_pipelines:
config_pipelines = assert_not_none(config.pipelines)
pipeline_indices: List[Optional[int]] = list(range(len(config_pipelines)))
else:
pipeline_indices = [None]
for pipeline_index in pipeline_indices:
dep_mods = get_modules(mods, startup.dependency_names)
mods.update(
{
f"{startup.name}_{k.value}_{pipeline_index or ''}".rstrip("_"): v
for k, v in make_startup_tasks(
dataset_split_managers,
task_manager,
startup.module,
mod_options=startup.mod_options,
dependencies=dep_mods,
pipeline_index=pipeline_index,
dataset_split_names=startup.dataset_split_names,
).items()
}
)
return mods
def wait_for_startup(startup_mods: Dict[str, DaskModule], task_manager: TaskManager):
"""Wait for all startup tasks and restart the Cluster after.
Notes:
We lock the TaskManager because if we start a new Module and then restart the
TaskManager, this Module will be cancelled and lost. To prevent this, we wont allow new
Module till the TaskManager is restarted.
Args:
startup_mods: Key-value pair of the named Modules.
task_manager: Current TaskManager.
"""
start_time = time.time()
task_manager.lock() # Lock the TaskManager to prevent new tasks.
done = False
def log_progress():
last_per_status = None
while not done:
time.sleep(5) # to avoid spamming the user with logs.
per_status = defaultdict(list)
for name, mod in startup_mods.items():
status = "saving" if mod.status() == "finished" and not mod.done() else mod.status()
per_status[status].append(name)
if per_status == last_per_status:
continue
last_per_status = per_status
logs = [
f"Startup tasks statuses: {len(per_status['finished'])}/{len(startup_mods)}",
*(
f"{status} ({len(per_status[status])}): {', '.join(per_status[status])}"
for status in ("not_started", "pending", "lost", "error", "saving", "finished")
if status in per_status
),
]
log.info("\n\t".join(logs))
thread_log_progress = threading.Thread(target=log_progress, daemon=True)
thread_log_progress.start()
for mod in startup_mods.values():
mod.result()
done = True
log.info("Startup task completed. The application should be accessible now.")
log.debug(f"Startup took {time.time() - start_time}.")
if errored_modules := [
name for name, module in startup_mods.items() if module.status() == "error"
]:
log.error(
f"Error(s) at startup! The application could be unstable {errored_modules}."
f" You may try to relaunch the app."
)
# Cleaning stuff up otherwise we hold too much in memory.
task_manager.restart()
# After restarting, it is safe to unlock the task manager.
task_manager.unlock()
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/types/utterance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/model_performance/utterance_count.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/validation.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/class_overlap.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/plots/sankey_plot.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/tests/test_modules/test_word_analysis/test_top_words.py": ["/azimuth/modules/word_analysis/top_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/tests/utils.py"], "/tests/test_modules/test_caching.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/exclude_fields_from_cache.py", "/tests/utils.py"], "/azimuth/app_loader.py": ["/azimuth/app.py", "/azimuth/config.py"], "/azimuth/routers/app.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/app.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/azimuth/modules/base_classes/indexable_module.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", 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"/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/ece.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/config.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exclude_fields_from_cache.py", "/azimuth/utils/openapi.py"], "/tests/utils.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py"], "/azimuth/__init__.py": ["/azimuth/utils/logs.py"], "/tests/test_utils/test_dataset_filtering.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,086
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/model_contracts/text_classification_no_saliency.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List
from datasets import Dataset
from azimuth.modules.model_contracts.text_classification import TextClassificationModule
from azimuth.types.task import SaliencyResponse
class TextClassificationNoSaliencyModule(TextClassificationModule):
def saliency(self, batch: Dataset) -> List[SaliencyResponse]:
raise NotImplementedError
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,087
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/types/dataset_warnings.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from enum import Enum
from typing import Dict, List, Optional, Union
from pydantic import Field
from pydantic.types import StrictFloat, StrictInt
from azimuth.types import AliasModel, PlotSpecification
class Agg(str, Enum):
mean = "mean"
std = "std"
class FormatType(str, Enum):
Integer = "Integer"
Percentage = "Percentage"
Decimal = "Decimal"
class DatasetDistributionComparisonValue(AliasModel):
value: Optional[Union[StrictInt, StrictFloat]] = Field(..., nullable=True)
alert: bool
class DatasetDistributionComparison(AliasModel):
name: str
alert: bool
data: List[DatasetDistributionComparisonValue]
class DatasetWarningPlots(AliasModel):
overall: PlotSpecification
per_class: Optional[Dict[str, PlotSpecification]] = Field(..., nullable=True)
class DatasetWarning(AliasModel):
name: str
description: str
columns: List[str]
format: FormatType
comparisons: List[DatasetDistributionComparison]
plots: DatasetWarningPlots
class DatasetWarningGroup(AliasModel):
name: str
warnings: List[DatasetWarning]
class DatasetWarningsResponse(AliasModel):
warning_groups: List[DatasetWarningGroup]
|
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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,088
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/utils/utterance.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import re
re_irregular_apostrophes = re.compile("[’`]")
def clean_utterance(utterance: str) -> str:
"""Converts characters as appropriate for downstream functions (e.g., search, spaCy).
Args:
utterance: Utterance to be cleaned.
Returns:
utterance_clean: Cleaned utterance.
"""
utterance_clean = re_irregular_apostrophes.sub("'", utterance).lower()
return utterance_clean
|
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"/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,089
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_utils/test_task_running.py
|
from unittest.mock import MagicMock
import pytest
from azimuth.types import DatasetFilters, DatasetSplitName, ModuleOptions, SupportedModule
from azimuth.utils.routers import get_custom_task_result, get_standard_task_result
standard_task_result = "task_result"
custom_task_result = "custom_task_result"
task_name = SupportedModule.PerturbationTestingSummary
dataset_split_name = DatasetSplitName.train
indices = [1, 2, 3]
mod_options = ModuleOptions(
threshold=0.6, filters=DatasetFilters(label=[0]), pipeline_index=0
) # example
custom_query = {"custom_key1": 1, "custom_key2": "two"}
last_update = 11
@pytest.fixture
def mock_task_result():
fake_task_response = MagicMock()
fake_task_response.result = MagicMock(return_value=standard_task_result)
fake_custom_task_response = MagicMock()
fake_custom_task_response.result = MagicMock(return_value=custom_task_result)
fake_task_manager = MagicMock()
fake_task_manager.get_task = MagicMock(return_value=("DISCARD", fake_task_response))
fake_task_manager.get_custom_task = MagicMock(
return_value=("DISCARD", fake_custom_task_response)
)
return fake_task_manager
def test_run_standard_task(mock_task_result):
result = get_standard_task_result(
task_name, dataset_split_name, mock_task_result, mod_options, last_update
)
mock_task_result.get_task.assert_called_with(
task_name=task_name,
dataset_split_name=dataset_split_name,
mod_options=mod_options,
last_update=last_update,
)
assert result == standard_task_result
def test_run_custom_task(mock_task_result):
result = get_custom_task_result(task_name, mock_task_result, custom_query, mod_options)
mock_task_result.get_custom_task.assert_called_with(
task_name=task_name,
custom_query=custom_query,
mod_options=mod_options,
)
assert result == custom_task_result
|
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"/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/tests/utils.py"], "/tests/test_documentation.py": ["/azimuth/__init__.py", "/azimuth/modules/base_classes/__init__.py"], "/azimuth/utils/object_loader.py": ["/azimuth/config.py"], "/azimuth/types/tag.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/custom_utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_config.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/perturbation_testing/perturbation_testing.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/conversion.py", "/azimuth/utils/ml/perturbation_functions.py", "/azimuth/utils/ml/perturbation_test.py", "/azimuth/utils/ml/seeding.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_perturbation_testing/test_perturbation_testing_summary.py": ["/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/tests/test_task_manager.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/aggregation_module.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/azimuth_shr/loading_resources.py": ["/azimuth/config.py"], "/azimuth/routers/dataset_warnings.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/routers.py"], "/tests/test_routers/test_app.py": ["/azimuth/routers/utterances.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/dataset_analysis/similarity_analysis.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/utils/validation.py"], "/azimuth/utils/ml/model_performance.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/utils/ml/ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/perturbation_testing/__init__.py": ["/azimuth/modules/perturbation_testing/perturbation_testing.py", "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py"], "/tests/test_modules/conftest.py": ["/azimuth/modules/base_classes/__init__.py"], "/azimuth/modules/model_contract_task_mapping.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_model_performance/test_confidence_binning.py": ["/azimuth/modules/model_performance/confidence_binning.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/tests/utils.py"], "/azimuth_shr/models/file_based.py": ["/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/azimuth/modules/base_classes/artifact_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/object_loader.py", 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"/azimuth/utils/routers.py"], "/azimuth/types/outcomes.py": ["/azimuth/types/__init__.py"], "/azimuth/utils/plots.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/modules/task_mapping.py": ["/azimuth/modules/model_performance/outcomes.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py"], "/azimuth/modules/model_contracts/hf_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/mc_dropout.py", "/azimuth/utils/ml/saliency.py"], "/azimuth/routers/export.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/conversion.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/config.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/task_manager.py", "/azimuth/utils/cluster.py", "/azimuth/utils/project.py"], "/azimuth/modules/word_analysis/top_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/task_execution.py", "/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/utterance.py"], "/tests/conftest.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/utils/project.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/utils/object_loader.py"], "/tests/test_modules/test_dataset_analysis/test_dataset_warnings.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/dataset_warnings.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/modules/validation/validation.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/validation.py", "/azimuth/utils/logs.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/types/class_overlap.py": ["/azimuth/types/__init__.py"], "/azimuth/types/word_analysis.py": ["/azimuth/types/__init__.py"], "/azimuth/plots/sankey_plot.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/utils/plots.py"], 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"/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_ml/test_ece.py": ["/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/ece.py"], "/azimuth/utils/dataset_operations.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/utterance.py"], "/tests/test_loading_resources.py": ["/azimuth/config.py", "/azimuth/utils/ml/seeding.py", "/azimuth_shr/loading_resources.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/utils/ml/preprocessing.py": ["/azimuth/types/__init__.py"], "/azimuth/types/perturbation_testing.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_ml/test_mc_dropout.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/ml/mc_dropout.py"], "/tests/test_modules/test_dataset_analysis/test_similarity_analysis.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/utils/ml/saliency.py": ["/azimuth/types/general/module_arguments.py"], "/azimuth/modules/model_performance/confidence_binning.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_contracts/test_custom_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/utils/ml/perturbation_functions.py": ["/azimuth/config.py", "/azimuth/types/perturbation_testing.py", "/azimuth/utils/validation.py"], "/azimuth/modules/dataset_analysis/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/plots/dataset_warnings.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": 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"/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,090
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/profiling_debugging.py
|
"""
This file help profiling Module. Change the constants and run the file.
"""
import tempfile
import threading
import time
import psutil
from pyinstrument import Profiler
from tqdm import tqdm
from azimuth.app import load_dataset_split_managers_from_config
from azimuth.config import AzimuthConfig
from azimuth.modules.base_classes import DatasetResultModule
from azimuth.modules.model_performance.metrics import MetricsPerFilterModule
from azimuth.task_manager import TaskManager
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod, SupportedModule
CFG_FILE = "../local_configs/development/clinc/conf.json"
MODULE = MetricsPerFilterModule
DATASET_SPLIT = DatasetSplitName.eval
DEPENDENCIES = [SupportedMethod.Predictions, SupportedModule.Outcome]
DEPS_MOD_OPTIONS = ModuleOptions(pipeline_index=0)
MOD_OPTIONS = ModuleOptions(pipeline_index=0)
def print_memory():
def fn():
while True:
print(f"Memory used: {psutil.virtual_memory().used / 1024 / 1024}")
time.sleep(2)
threading.Thread(target=fn).start()
def main():
print_memory()
config = AzimuthConfig.parse_file(CFG_FILE).copy(
update={"artifact_path": str(tempfile.mkdtemp())}
)
_ = load_dataset_split_managers_from_config(config)
task_manager = TaskManager(config)
for dep in tqdm(DEPENDENCIES, desc="Running dependencies..."):
_, mod = task_manager.get_task(
dep, dataset_split_name=DATASET_SPLIT, mod_options=DEPS_MOD_OPTIONS
)
if isinstance(mod, DatasetResultModule):
mod.save_result(mod.result(), mod.get_dataset_split_manager())
print("Starting profiler!")
with Profiler() as p:
mod = MODULE(dataset_split_name=DATASET_SPLIT, config=config, mod_options=MOD_OPTIONS)
res = mod.compute_on_dataset_split()
file_name = "logs.txt"
with open(file_name, "w") as f:
p.print(file=f)
print(res)
if __name__ == "__main__":
main()
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", 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["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,091
|
ServiceNow/azimuth
|
refs/heads/main
|
/azimuth/modules/base_classes/dask_module.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import abc
import os
import threading
import time
import uuid
from enum import IntEnum
from functools import partial
from os.path import join as pjoin
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, cast
import structlog
from datasets import Dataset
from distributed import Client, Event, Future, rejoin, secede
from azimuth.config import CommonFieldsConfig
from azimuth.modules.base_classes.caching import HDF5CacheMixin
from azimuth.types import DatasetSplitName, ModuleResponse
from azimuth.utils.logs import TimerLogging
log = structlog.get_logger()
ConfigScope = TypeVar("ConfigScope", bound=CommonFieldsConfig)
class Worker(IntEnum):
model = 0
encoder = 0
class DaskModule(HDF5CacheMixin, Generic[ConfigScope]):
"""Abstract class that define an item of work to be computed on the cluster.
Notes:
For now, we expect one result per index stored as a record.
Args:
dataset_split_name: Which dataset_split to run the Module on.
config: Configuration of the application
"""
allowed_splits = {DatasetSplitName.train, DatasetSplitName.eval}
worker: Optional[Worker] = None
def __init__(
self,
dataset_split_name: DatasetSplitName,
config: ConfigScope,
):
if dataset_split_name not in self.allowed_splits:
raise ValueError(
f"{dataset_split_name} not allowed for this Module: {self.allowed_splits}"
)
self.dataset_split_name = dataset_split_name
self.config = self._get_config_scope(config)
self._name = self._get_name()
self._callbacks: List[ModuleCallback] = []
# A Future is the async task that is run on the Dask cluster.
# It is a "promise" that it will hold a value sometime in the future.
self.future: Optional[Future] = None
self.done_event: Optional[Event] = None
# We cache the result in a HDF5 file and we have a FileLock.
self.cache_dir = pjoin(self.config.get_project_path(), self.__class__.__name__)
os.makedirs(self.cache_dir, exist_ok=True)
self._cache_file = pjoin(self.cache_dir, f"{self.name}.h5")
self._cache_lock = pjoin(self.cache_dir, f"{self.name}.h5.lock")
self._cache_effective_arguments = pjoin(self.cache_dir, f"{self.name}.json")
self._status = "not_started"
self._time = time.time() # Only used for expirable modules
@property
def name(self):
return self._name
@abc.abstractmethod
def _get_name(self) -> str:
raise NotImplementedError
@abc.abstractmethod
def get_caching_indices(self) -> List[int]:
raise NotImplementedError
@property
def task_id(self) -> str:
return f"{self.name}_{hash(tuple(self.get_caching_indices()))}"
def _get_config_scope(self, config) -> ConfigScope:
"""Get the current config scope from full/partial config."""
# TODO: This is hacky, should be a better to do get the config scope.
base = self.__orig_bases__[0] # type: ignore
if not hasattr(base, "__args__") or isinstance(base.__args__[0], TypeVar):
log.warning("Can't get internal type. Applying soft-cast only!")
scoped_config = config.__class__
else:
scoped_config = base.__args__[0]
return cast(ConfigScope, scoped_config.parse_obj(config.dict()))
def start_task_on_dataset_split(
self, client: Client, dependencies: List["DaskModule"] = None
) -> "DaskModule":
"""Will schedule the task on the Cluster with a `Client`.
Args:
client: A Dask client.
dependencies: Optional list of Modules to wait for.
Returns:
self.
"""
log.info(f"Starting {self.name}")
deps = [d.done_event for d in dependencies] if dependencies else []
if not all(deps):
raise ValueError("Can't wait for an unstarted Module.")
self.done_event = Event(name=self.task_id, client=client)
# pure=false to be sure that everything is rerun.
self.future = client.submit(
self._compute_on_dataset_split_with_deps,
pure=False,
dependencies=deps,
key=f"{self.task_id}_{uuid.uuid4()}", # Unique identifier
workers=self.worker,
)
# Tell that this future is used on which indices.
self.future.indices = self.get_caching_indices()
self.future.is_custom = False
# Create an event for its completion for logging.
self.add_done_callback(self.on_end)
# Register an event that will await all callbacks
th = threading.Thread(target=self._wait_for_completion)
th.setDaemon(True)
th.start()
return self
def custom_query_task_id(self, custom_query):
# Using self.name as we don't have indices
return f"{self.name}_{hash(str(custom_query))}"
def start_task(self, client: Client, custom_query: Dict[str, Any]) -> "DaskModule":
"""
Will schedule the task on the Cluster with a `Client`.
Args:
client: A Dask client.
custom_query: Custom query to process.
Returns:
self
"""
log.info(f"Starting custom query {self.name}")
# pure=false to be sure that everything is rerun.
self.future = client.submit(
self.compute,
custom_query,
key=self.custom_query_task_id(custom_query),
pure=False,
workers=self.worker,
)
# Tell that this future is for custom use only.
self.future.is_custom = True
self.add_done_callback(self.on_end)
return self
def _compute_on_dataset_split_with_deps(self, dependencies: Optional[List[Event]] = None):
"""Will wait for dependencies to be completed before computing.
Args:
dependencies: Set of Module.done_event to wait for.
Returns:
Result of `self.compute_on_dataset_split`.
"""
if dependencies:
secede()
for event in dependencies:
try:
event.wait()
except AttributeError:
log.error(
"Error while waiting on dependencies,"
" will launch the task now and hope for the best.",
event_name=event.name,
module=self.name,
)
rejoin()
with TimerLogging(self.name):
return self.compute_on_dataset_split()
@abc.abstractmethod
def compute_on_dataset_split(self) -> List[ModuleResponse]:
raise NotImplementedError
@abc.abstractmethod
def compute(self, batch: Dataset) -> List[ModuleResponse]:
"""Method that computes the actual task result.
Args:
batch: input to be processed.
"""
raise NotImplementedError
def add_done_callback(self, fn: Callable, **kwargs):
self._callbacks.append(ModuleCallback.create_callback(fn, module=self, **kwargs))
def on_end(self, fut, module):
"""Callback that handles errors.
Args:
fut: The dask scheduled task.
module: Not used, current module.
"""
self._status = fut.status
if fut.status == "error":
e = fut.exception()
log.exception(f"Exception in {self.name}", exc_info=e)
elif fut.status == "lost":
log.warning(f"Future is lost in {self.name}! Retrying")
fut.retry()
elif fut.status == "finished" and not fut.is_custom:
# Store the result in cache
self._store_data_in_cache(fut.result(), fut.indices)
log.info(f"{self.name} completed and stored in cache", status=fut.status)
def clear(self):
"""Useful to make sure the task is not kept in memory."""
self.future = None
def _wait_for_completion(self):
"""Internal function that loop till completion and then set `self.done_event`."""
while not self.done(allow_cache=False):
time.sleep(1)
if self.done_event is not None:
self.done_event.set()
def wait(self):
"""Client function that wait on the event till completion."""
if self.done_event is not None:
self.done_event.wait()
def should_be_started(self):
return self.status() not in {"pending", "finished"} and not self.done()
def status(self):
"""Return the status of the future.
Returns:
One of {'not_started', 'pending', 'lost', 'error', 'finished'}.
If the task was not launched, `not_started` will be returned by default.
"""
if self.future:
return self.future.status
return self._status
def done(self, allow_cache: bool = True):
"""Check if this task and its callbacks are completed.
Args:
allow_cache: If true, allow to go look on disk versus
only looking at the `future` and callbacks status.
Notes:
When allow_cache=True, it require to load the datasets.
Do not do this in the main thread.
Returns:
If this module is completed.
"""
in_cache = self._check_cache(self.get_caching_indices()) if allow_cache else False
future_done = self.future is not None and self.future.done()
cbk_dones = all(c.done for c in self._callbacks)
return (in_cache or future_done) and cbk_dones
def result(self) -> List[ModuleResponse]:
"""Get the result of the future and save it to cache.
Returns:
result: The dask task response.
Raises:
If the Dask Future is empty.
"""
result: List[ModuleResponse]
in_cache = self._check_cache(self.get_caching_indices())
if self.future is not None:
# Result not in cache, need to get them.
if self.status() == "lost":
log.warning("Future is lost! Retrying")
self.future.retry()
# Wait for the result
result = self.future.result()
return result
elif in_cache:
# Fetch the result in cache.
result = self._get_cache(self.get_caching_indices())
else:
raise ValueError("Cant get result from an unlaunched tasks!")
return result
class ModuleCallback:
"""Add callbacks to modules.
Args:
fn: callback.
"""
def __init__(self, fn: Callable):
self.fn = fn
self.done = False
self.result = None
@classmethod
def create_callback(cls, fn, module, **kwargs) -> "ModuleCallback":
if module.future is None:
raise ValueError("Module has no Future")
cbk = cls(fn)
module.future.add_done_callback(partial(cbk._run, module=module, **kwargs))
return cbk
def _run(self, fut, module, **kwargs):
try:
self.result = self.fn(fut, module, **kwargs)
finally:
self.done = True
|
{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py"], "/tests/test_dataset_manager.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py", "/tests/test_loading_resources.py", "/tests/utils.py"], "/azimuth/modules/base_classes/__init__.py": ["/azimuth/modules/base_classes/artifact_manager.py", "/azimuth/modules/base_classes/dask_module.py", "/azimuth/modules/base_classes/module.py", "/azimuth/modules/base_classes/expirable_mixin.py", "/azimuth/modules/base_classes/indexable_module.py", "/azimuth/modules/base_classes/aggregation_module.py"], "/azimuth/utils/ml/postprocessing.py": ["/azimuth/types/__init__.py", "/azimuth/types/outcomes.py"], "/azimuth/modules/task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/runner.py": ["/azimuth/config.py"], "/azimuth/utils/routers.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/project.py"], "/azimuth/modules/model_performance/confusion_matrix.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_model_performance/test_confusion_matrix.py": ["/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/types/__init__.py", "/tests/utils.py"], "/azimuth/plots/ece.py": ["/azimuth/utils/plots.py"], "/tests/test_routers/test_utterances.py": ["/azimuth/types/tag.py", "/tests/utils.py", "/azimuth/routers/utterances.py"], "/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/validation.py"], "/azimuth/modules/perturbation_testing/perturbation_testing_summary.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/perturbation_testing/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py"], "/azimuth/routers/utterances.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/similarity_analysis.py", "/azimuth/types/tag.py", "/azimuth/types/utterance.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py", "/azimuth/utils/validation.py"], "/azimuth/routers/model_performance/utterance_count.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/tag.py", "/azimuth/utils/conversion.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/routers.py"], "/azimuth/types/validation.py": ["/azimuth/types/__init__.py"], "/azimuth/routers/class_overlap.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/plots/sankey_plot.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py", "/azimuth/types/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/tests/test_modules/test_word_analysis/test_top_words.py": ["/azimuth/modules/word_analysis/top_words.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/tests/utils.py"], "/tests/test_modules/test_caching.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/modules/model_performance/confusion_matrix.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/exclude_fields_from_cache.py", "/tests/utils.py"], "/azimuth/app_loader.py": ["/azimuth/app.py", "/azimuth/config.py"], "/azimuth/routers/app.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/app.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py", "/azimuth/utils/object_loader.py", "/azimuth/utils/project.py", "/azimuth/utils/routers.py"], "/azimuth/modules/base_classes/indexable_module.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,092
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_model_contracts/test_hf_text_classification.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List, cast
import numpy as np
import pytest
import torch
from datasets import Dataset
from azimuth.modules.model_contracts import HFTextClassificationModule
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod
from azimuth.types.general.module_arguments import GradientCalculation
from azimuth.types.task import PredictionResponse, SaliencyResponse
from azimuth.utils.ml.saliency import find_word_embeddings_layer
def test_create_sentiment_pipeline(simple_text_config):
task = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
assert task is not None
# By default, use L2 gradient calculation
assert task.gradient_calculation == GradientCalculation.L2
_ = task.get_model()
# Test a different gradient calculation option
task = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
gradient_calculation="xSUM",
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
),
)
assert task is not None
assert task.gradient_calculation == GradientCalculation.xSUM
# Test invalid gradient calculation option
with pytest.raises(ValueError):
task = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
gradient_calculation="meh",
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
),
)
# Test MIN option
task = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
gradient_calculation="xMIN",
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
),
)
assert task.gradient_calculation == GradientCalculation.xMIN
def test_prediction(simple_text_config):
indices = [1, 2, 3]
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
indices=indices,
),
)
res = cast(List[PredictionResponse], mod.compute_on_dataset_split())
assert len(res) == len(indices)
pred0 = res[0]
assert len(pred0.model_output.probs[0]) == 2 and len(pred0.postprocessed_output.probs[0]) == 2
assert np.allclose(np.sum(pred0.model_output.probs, axis=1), 1.0) # 2 classes
assert pred0.label in [0, 1] # 2 classes
assert pred0.model_output.preds in [0, 1] # 2 classes
assert pred0.entropy > 0 # Entropy > 0
batch = Dataset.from_dict(
{
simple_text_config.columns.text_input: ["red", "blue", "green"],
simple_text_config.columns.label: [1, 0, 1],
}
)
res = cast(List[PredictionResponse], mod.compute(batch))
assert len(res) == 3
for label, pred_res in zip(batch[simple_text_config.columns.label], res):
assert label == pred_res.label
assert pred_res.postprocessed_output.probs.shape == (1, 2)
def test_rejection_class(simple_text_config):
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
indices=list(range(10)),
),
)
out = cast(List[PredictionResponse], mod.compute_on_dataset_split())
# At least 1 out of the 10 predictions will have confidence below the threshold.
rejected_items = [
item for item in out if item.postprocessed_output.preds[0] != item.model_output.preds[0]
]
assert len(rejected_items) > 0
assert all(
np.max(pred_res.postprocessed_output.probs[0]) <= simple_text_config.pipelines[0].threshold
for pred_res in rejected_items
), rejected_items
def test_mc_dropout(simple_text_config):
batch = Dataset.from_dict(
{
"utterance": ["this is hell.", "hello new york", "I like potatoes."],
"label": [1, 2, 3],
}
)
simple_text_config.uncertainty.iterations = 5
p1, p2 = [
HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
use_bma=use_bma,
model_contract_method_name=SupportedMethod.Predictions,
pipeline_index=0,
indices=[4, 9, 3],
),
).compute(batch)[0]
for use_bma in [False, True]
]
assert not np.allclose(p1.model_output.probs[0], p2.model_output.probs[0])
assert p1.epistemic == 0 and p2.epistemic > 0
def test_find_layer_by_name(simple_text_config):
task = HFTextClassificationModule(
dataset_split_name=DatasetSplitName.eval,
config=simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Saliency, pipeline_index=0
),
)
model = task.get_model().model
layer = find_word_embeddings_layer(model, "distilbert.embeddings.word_embeddings")
assert isinstance(layer, torch.nn.Module)
with pytest.raises(ValueError):
find_word_embeddings_layer(model, "word_embeddings")
def test_saliency(dask_client, simple_text_config):
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Saliency, pipeline_index=0, indices=[4, 25]
),
)
out = cast(List[SaliencyResponse], mod.compute_on_dataset_split())
hf_pipeline = mod.get_model()
assert len(out) == 2
assert not any(any(tok == hf_pipeline.tokenizer.pad_token for tok in rec.tokens) for rec in out)
def test_custom_class_saliency(simple_text_config):
# Does not work on L2. TODO Verify this behaviour is as expected.
gc = "xSUM"
all_options = [
ModuleOptions(
gradient_calculation=gc,
model_contract_method_name=SupportedMethod.Saliency,
pipeline_index=0,
),
ModuleOptions(
gradient_calculation=gc,
filter_class=0,
model_contract_method_name=SupportedMethod.Saliency,
pipeline_index=0,
),
ModuleOptions(
gradient_calculation=gc,
filter_class=1,
model_contract_method_name=SupportedMethod.Saliency,
pipeline_index=0,
),
]
batch = Dataset.from_dict({"utterance": ["this is hell."]})
result = [
cast(
List[SaliencyResponse],
HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=mod_options,
).compute(batch),
)
for mod_options in all_options
]
# Check that None matches with one of the other
assert any(np.allclose(result[0][0].saliency, result[i][0].saliency) for i in [1, 2])
# Check 0, 1 do not match.
assert not np.allclose(result[1][0].saliency, result[2][0].saliency)
def test_load_CLINC150_dataset(clinc_text_config):
task = HFTextClassificationModule(
DatasetSplitName.train,
clinc_text_config,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
ds = task.get_dataset_split()
assert ds is not None
labels_str = [ds.features["label"].int2str(label) for label in ds["label"]]
# We replaced the out-of-scope label with NO_INTENT
assert "NO_INTENT" in labels_str and "oos" not in labels_str
# only 3 classes in the subset: "transfer", "transactions" and "NO_INTENT"
assert len(ds) == 6
ds = task.get_dataset_split(DatasetSplitName.eval)
assert ds is not None
assert "NO_INTENT" in labels_str and "oos" not in labels_str
# only 3 classes in the subset: "transfer", "transactions" and "NO_INTENT"
assert len(ds) == 5
def test_long_utterances_truncated(simple_text_config):
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
pipeline_index=0, model_contract_method_name=SupportedMethod.Predictions
),
)
batch = Dataset.from_dict(
{
simple_text_config.columns.text_input: ["Hello " * 1000],
simple_text_config.columns.label: [0],
}
)
_ = mod.compute(batch)
mod = HFTextClassificationModule(
DatasetSplitName.eval,
simple_text_config,
mod_options=ModuleOptions(
pipeline_index=0, model_contract_method_name=SupportedMethod.Saliency
),
)
res = mod.compute(batch)[0]
assert (
len(res.saliency) == len(res.tokens) == mod.get_model().model.config.max_position_embeddings
)
|
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"/tests/test_routers/test_model_performance/test_utterance_count.py": ["/azimuth/types/tag.py"], "/azimuth/modules/model_contracts/__init__.py": ["/azimuth/modules/model_contracts/hf_text_classification.py", "/azimuth/modules/model_contracts/custom_classification.py", "/azimuth/modules/model_contracts/file_based_text_classification.py"], "/tests/test_modules/test_task_execution.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_word_analysis/test_tokens_to_words.py": ["/azimuth/modules/word_analysis/tokens_to_words.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"], "/tests/test_modules/test_model_contracts/test_text_classification.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py"], "/azimuth/types/pipeline_comparison.py": ["/azimuth/types/__init__.py"], "/azimuth/modules/word_analysis/tokens_to_words.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_contract_task_mapping.py", "/azimuth/modules/task_execution.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py", "/azimuth/types/word_analysis.py"], "/azimuth/types/app.py": ["/azimuth/types/__init__.py", "/azimuth/types/perturbation_testing.py", "/azimuth/types/tag.py"], "/azimuth/plots/dataset_warnings.py": ["/azimuth/config.py", "/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py", "/azimuth/utils/plots.py"], "/azimuth/app.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/startup.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/cluster.py", "/azimuth/utils/conversion.py", "/azimuth/utils/exception_handlers.py", "/azimuth/utils/logs.py", "/azimuth/utils/validation.py", "/azimuth/utils/routers.py"], 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,093
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_routers/conftest.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
import json
import time
import pytest
from fastapi import FastAPI
from starlette.testclient import TestClient
from azimuth.app import get_ready_flag, get_startup_tasks, get_task_manager, start_app
from azimuth.config import AzimuthConfig
from tests.utils import DATASET_CFG, SIMPLE_PERTURBATION_TESTING_CONFIG
def mock_ready_flag_false():
class MockedEvent:
def is_set(self):
return False
return MockedEvent()
def create_test_app(config) -> FastAPI:
json.dump(config.dict(), open("/tmp/config.json", "w"))
return start_app("/tmp/config.json", load_config_history=False, debug=False)
FAST_TEST_CFG = {
"model": {
"class_name": "tests.test_loading_resources.config_structured_output",
"kwargs": {"num_classes": 2, "threshold": 0.4},
},
"postprocessors": None,
}
@pytest.fixture
def wait_for_startup_after(app):
yield
client = TestClient(app)
resp = client.get("/status")
while resp.json()["startupTasksReady"] is not True:
time.sleep(1)
resp = client.get("/status")
task_manager = get_task_manager()
while task_manager.is_locked:
time.sleep(1)
@pytest.fixture(scope="session")
def app() -> FastAPI:
router_config = AzimuthConfig(
name="sentiment-analysis",
dataset=DATASET_CFG,
pipelines=[FAST_TEST_CFG],
artifact_path="/tmp/azimuth_test_cache",
batch_size=16,
use_cuda=False,
model_contract="custom_text_classification",
rejection_class=None,
behavioral_testing=SIMPLE_PERTURBATION_TESTING_CONFIG,
)
_app = create_test_app(router_config)
# Wait for app to be ready
client = TestClient(_app)
resp = client.get("/status")
while resp.json()["startupTasksReady"] is not True:
time.sleep(1)
resp = client.get("/status")
task_manager = get_task_manager()
while task_manager.is_locked:
time.sleep(1)
yield _app
@pytest.fixture(scope="function")
def app_not_started(app) -> FastAPI:
startup_tasks = get_startup_tasks()
class ModuleThatWillNeverEnd:
def status(self):
return "pending"
def done(self):
return False
# Replace real tasks by fake tasks
_real_task = {k: v for k, v in startup_tasks.items()}
_real_event = get_ready_flag()
for name in startup_tasks:
startup_tasks[name] = ModuleThatWillNeverEnd()
app.dependency_overrides[get_ready_flag] = mock_ready_flag_false
yield app
# Replace to the original values.
for name in startup_tasks:
startup_tasks[name] = _real_task[name]
app.dependency_overrides[get_ready_flag] = lambda: _real_event
|
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"/azimuth/routers/model_performance/outcome_count.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/utils/routers.py"], "/tests/test_utils/test_api_dependency_injection.py": ["/azimuth/__init__.py", "/azimuth/app.py", "/azimuth/types/__init__.py"], "/tests/test_modules/test_artifact_manager.py": ["/azimuth/modules/base_classes/__init__.py", "/azimuth/types/__init__.py"], "/azimuth/types/task.py": ["/azimuth/types/__init__.py", "/azimuth/utils/ml/postprocessing.py", "/azimuth/utils/ml/preprocessing.py"], "/tests/test_modules/test_utilities/test_validation.py": ["/azimuth/config.py", "/azimuth/modules/validation/validation.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/azimuth/types/__init__.py": ["/azimuth/types/general/alias_model.py", "/azimuth/types/general/array_type.py", "/azimuth/types/general/dataset.py", "/azimuth/types/general/modules.py", "/azimuth/types/general/module_arguments.py"], "/tests/test_performance.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/utils/dataset_operations.py"], "/tests/test_utils/test_array_type.py": ["/azimuth/types/__init__.py"], "/tests/test_modules/test_model_contracts/test_model_contract_module.py": ["/azimuth/config.py", "/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/tag.py", "/azimuth/types/task.py", "/azimuth/utils/ml/postprocessing.py", "/tests/utils.py"], "/azimuth/routers/top_words.py": ["/azimuth/app.py", "/azimuth/dataset_split_manager.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/types/word_analysis.py", "/azimuth/utils/routers.py"], "/azimuth/types/general/alias_model.py": ["/azimuth/utils/conversion.py", "/azimuth/utils/openapi.py", "/azimuth/utils/pydantic_utils.py"], "/azimuth/modules/model_performance/outcomes.py": ["/azimuth/config.py", 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"/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,094
|
ServiceNow/azimuth
|
refs/heads/main
|
/tests/test_modules/test_model_contracts/test_file_based_text_classification.py
|
# Copyright ServiceNow, Inc. 2021 – 2022
# This source code is licensed under the Apache 2.0 license found in the LICENSE file
# in the root directory of this source tree.
from typing import List, cast
import numpy as np
import pytest
from azimuth.modules.model_contracts import FileBasedTextClassificationModule
from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod
from azimuth.types.task import PredictionResponse
def test_loading_dataset(file_text_config_top3):
eval_task = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top3,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
assert eval_task.get_model() is not None and callable(eval_task.get_model())
ds = eval_task.get_dataset_split()
assert ds is not None
# We know our eval predictions file has only 4 entries
assert len(ds) == 4
train_task = FileBasedTextClassificationModule(
DatasetSplitName.train,
file_text_config_top3,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
# Make sure the training set is read as usual
assert len(train_task.get_dataset_split()) == 8
def test_predictions_top3(file_text_config_top3):
task = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top3,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
result = cast(List[PredictionResponse], task.compute_on_dataset_split())
assert len(result) == 4
for prediction_entry in result:
# The model used in the fixture predictions file got it all correct
assert prediction_entry.label == prediction_entry.postprocessed_output.preds[0]
# Make sure we are predicting in the expected order, as defined in the fixture
assert np.isclose(np.max(result[0].postprocessed_output.probs[0]), 0.999999881)
assert np.isclose(np.max(result[1].postprocessed_output.probs[0]), 0.811522245)
assert np.isclose(np.max(result[2].postprocessed_output.probs[0]), 0.986969113)
assert np.isclose(np.max(result[3].postprocessed_output.probs[0]), 0.9999999913300003)
# First 2 examples are no intent and last is class 3
assert result[0].label == result[1].label == 2
assert result[2].label == 3
assert result[0].postprocessed_output.preds[0] == 2
assert result[1].postprocessed_output.preds[0] == 2
assert result[2].postprocessed_output.preds[0] == 3
# Example where the sum of softmax outputs > 1
assert np.isfinite(result[3].entropy)
def test_saliency_top3(file_text_config_top3):
with pytest.raises(NotImplementedError):
task = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top3,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Saliency, pipeline_index=0
),
)
task.compute_on_dataset_split()
def test_predictions_top1(file_text_config_top1):
task = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top1,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
ds = task.get_dataset_split()
assert ds is not None
# We know our top-1 predictions file has 6 entries
assert len(ds) == 6
result = cast(List[PredictionResponse], task.compute_on_dataset_split())
assert len(result) == 6
for prediction_entry in result:
# sum of softmax outputs <= 1
assert np.isfinite(prediction_entry.entropy)
# Make sure we are predicting in the expected order, as defined in the fixture
assert np.isclose(np.max(result[0].postprocessed_output.probs[0]), 0.987)
assert np.isclose(np.max(result[1].postprocessed_output.probs[0]), 0.811)
assert np.isclose(np.max(result[2].postprocessed_output.probs[0]), 0.986)
# First 2 examples are no intent and last is class 3
assert result[0].label == result[1].label == 2
assert result[2].label == 3
assert result[0].postprocessed_output.preds[0] == 2
assert result[1].postprocessed_output.preds[0] == 2
assert result[2].postprocessed_output.preds[0] == 3
def test_dataset_with_nointent(file_text_config_no_intent):
task = FileBasedTextClassificationModule(
DatasetSplitName.train,
file_text_config_no_intent,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
ds = task.get_dataset_split()
# In this case, eval there is a no intent class and that it was not added at the end
class_names = ds.features["label"].names
assert "NO_INTENT" in class_names
assert class_names.index("NO_INTENT") != (len(class_names) - 1)
assert len(class_names) == 5
ds_eval = task.get_dataset_split(DatasetSplitName.eval)
assert ds_eval is not None
# We know our top-1 predictions evaluation file has 5 entries
assert len(ds_eval) == 6
def test_empty_no_intent(file_text_config_top1):
task = FileBasedTextClassificationModule(
DatasetSplitName.eval,
file_text_config_top1,
mod_options=ModuleOptions(
model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0
),
)
ds = task.get_dataset_split()
assert ds is not None
# We know our top-1 predictions file has 6 entries, and 2 as NO_INTENT
assert len(ds) == 6
ds_train = task.get_dataset_split(DatasetSplitName.train)
class_names = ds_train.features["label"].names
# Ensure we read empty string as NO_INTENT
assert ds[3]["label"] == class_names.index("NO_INTENT")
assert ds[4]["label"] == class_names.index("NO_INTENT")
result = cast(List[PredictionResponse], task.compute_on_dataset_split())
# Ensure we read empty string as NO_INTENT in the predictions
assert result[3].postprocessed_output.preds[0] != class_names.index("NO_INTENT")
assert result[4].postprocessed_output.preds[0] == class_names.index("NO_INTENT")
# If we read NO_INTENT as prediction, then we put all the mass there
assert np.max(result[4].postprocessed_output.probs[0]) == 1.0
|
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"/azimuth/types/__init__.py", "/azimuth/types/dataset_warnings.py"], "/tests/test_modules/test_model_performance/test_metrics.py": ["/azimuth/config.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/modules/model_performance/outcome_count.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/plots/ece.py", "/azimuth/types/__init__.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/tests/utils.py"], "/azimuth/modules/model_performance/outcome_count.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/outcomes.py", "/azimuth/types/__init__.py", "/azimuth/types/model_performance.py", "/azimuth/types/outcomes.py", "/azimuth/types/tag.py", "/azimuth/utils/dataset_operations.py", "/azimuth/utils/ml/model_performance.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/tests/test_modules/test_dataset_analysis/test_class_overlap.py": ["/azimuth/modules/dataset_analysis/similarity_analysis.py", "/azimuth/modules/dataset_analysis/class_overlap.py", "/azimuth/types/__init__.py", "/azimuth/types/class_overlap.py"], "/azimuth/modules/base_classes/caching.py": ["/azimuth/types/__init__.py", "/azimuth/utils/conversion.py"], "/tests/test_modules/test_pipeline_comparison/test_prediction_comparison.py": ["/azimuth/dataset_split_manager.py", "/azimuth/modules/pipeline_comparison/prediction_comparison.py", "/azimuth/types/__init__.py", "/azimuth/types/pipeline_comparison.py", "/azimuth/types/tag.py"], "/openapi.py": ["/azimuth/app.py"], "/azimuth/startup.py": ["/azimuth/config.py", "/azimuth/dataset_split_manager.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py", "/azimuth/utils/project.py", "/azimuth/utils/validation.py"], "/azimuth/modules/model_contracts/text_classification_no_saliency.py": ["/azimuth/modules/model_contracts/text_classification.py", "/azimuth/types/task.py"], "/azimuth/types/dataset_warnings.py": ["/azimuth/types/__init__.py"], "/tests/test_utils/test_task_running.py": ["/azimuth/types/__init__.py", "/azimuth/utils/routers.py"], "/tests/profiling_debugging.py": ["/azimuth/app.py", "/azimuth/config.py", "/azimuth/modules/base_classes/__init__.py", "/azimuth/modules/model_performance/metrics.py", "/azimuth/task_manager.py", "/azimuth/types/__init__.py"], "/azimuth/modules/base_classes/dask_module.py": ["/azimuth/config.py", "/azimuth/modules/base_classes/caching.py", "/azimuth/types/__init__.py", "/azimuth/utils/logs.py"], "/tests/test_modules/test_model_contracts/test_hf_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/general/module_arguments.py", "/azimuth/types/task.py", "/azimuth/utils/ml/saliency.py"], "/tests/test_routers/conftest.py": ["/azimuth/app.py", "/azimuth/config.py", "/tests/utils.py"], "/tests/test_modules/test_model_contracts/test_file_based_text_classification.py": ["/azimuth/modules/model_contracts/__init__.py", "/azimuth/types/__init__.py", "/azimuth/types/task.py"]}
|
31,107
|
croisez/invaderStub
|
refs/heads/master
|
/config.py
|
# ===========
# invaderStub
# ===========
#
# This is a kind of simple python replacement for the incredible PixelInvaders Java application
Panels = ['ROTATE_180_FLIPPEDY', 'ROTATE_180_FLIPPEDX']
#Panels = ['NO_ROTATE','NO_ROTATE','NO_ROTATE']
USE_TCPIP = 1
TCP_IP = "192.168.99.187"
TCP_PORT = 5333
#SERIAL_PORT = 'COM45'
BRIGHTNESS = 50
|
{"/ledwall.py": ["/invaderStub.py"]}
|
31,108
|
croisez/invaderStub
|
refs/heads/master
|
/invaderStub.py
|
# ===========
# invaderStub
# ===========
#
# This is a kind of simple python replacement for the incredible PixelInvaders Java application
#
# This program, invaderStub, is released as Free Software.
#
# First written by LM Croisez, on 5 Aug 2014
#
# Programme d'interfaçage avec des panneaux du type PixelInvaders
# This program makes the itf between 2d xy plan and PixelInvaders-type panels.
#
# The frame can be either sent by TCPIP, or by serial port direct connection.
# If using TCPIP, you must setup a ser2net daemon to make the via.
# See www.pixelinvaders.ch for more info on this setup.
#
# GENERAL TIPS:
# You must use the p(x,y) plan to put there the pixels where you want, and in the 15bit color you want.
# Once this is done, you must use the refreshPanels() function to latch your picture to the panels.
#
# The following functions are available:
#
# clear
# convert24To15Bit(r,g,b): convert 24bit color to 15bit color
# UpdatePanels
# ScrollPanelsLeft
# ScrollPanelsRight
# ScrollPanelsUp
# ScrollPanelsDown
# Circle(xc,yc,radius,r,g,b,[max_x],[max_y])
# Sprite(x,y,data,r,g,b)
# Text(x, y, buf, r, g, b, w=4): use Sprite() to display the buf string at (x,y) coordinates, using rgb color, each letter being spaced by w pixels
# LoadImage(name): load an image inside the virtualplan.
#
# For the sprite, a dictionnary has been initiated, containing a big part of the alphabet.
import socket
import serial
from time import sleep
import random
import math
from PIL import Image # https://www.pkimber.net/howto/python/modules/pillow.html
import config
BRIGHTNESS = config.BRIGHTNESS;
dico = { # 4x6 matrix sprite dictionnary
'A': 'AEAA40', 'B': 'CACAC0', 'C': '688860', 'D': 'CAAAC0', 'E': 'E8C8E0', 'F': '88C8E0', 'G': '6B8860',
'H': 'AAEAA0', 'I': '444440', 'J': 'CC4440', 'K': '9ACA90', 'L': 'E88880', 'M': 'AAAEA0', 'N': '99BD90',
'O': 'EAAAE0', 'P': '88EAE0', 'Q': '3EAAE0', 'R': 'AACAE0', 'S': 'C24860', 'T': '4444E0', 'U': 'EAAAA0',
'V': '4AAAA0', 'W': 'AEAAA0', 'X': 'AA4AA0', 'Y': '44AAA0', 'Z': 'E842E0',
'0': '4AAA40', '1': '444C40', '2': 'F42A40', '3': 'C2C2C0', '4': '22EAA0',
'5': 'C2C8E0', '6': '4AC860', '7': '8E22E0', '8': 'EA4AE0', '9': 'E2EAE0',
' ': '000000', '.': '660000', ',': '866000', ';': '866066', '!': '606666', '?': '4042A4'
}
TPM2NET_HEADER_SIZE = 4
TPM2NET_HEADER_SIMULATE = 0x74
TPM2NET_HEADER_IDENT = 0x9c
TPM2NET_CMD_DATAFRAME = 0xda
TPM2NET_CMD_COMMAND = 0xc0
TPM2NET_CMD_ANSWER = 0xaa
TPM2NET_FOOTER_IDENT = 0x36
NUM_PANEL = len(config.Panels)
if (config.USE_TCPIP):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((config.TCP_IP, config.TCP_PORT))
else:
s = serial.Serial(
port=config.SERIAL_PORT,
baudrate=115200,
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=8
)
#Array creation
VIRTPLAN_MULT_SIZE = 3
p = [[] for _ in range(8*NUM_PANEL*VIRTPLAN_MULT_SIZE)]
for x in range(8*NUM_PANEL*VIRTPLAN_MULT_SIZE):
for y in range(8*VIRTPLAN_MULT_SIZE):
p[x].append(0)
q = [[] for _ in range(8*NUM_PANEL)]
for x in range(8*NUM_PANEL):
for y in range(8):
q[x].append(0)
#Returns the height of a plan (either p or q, for example)
def height(o):
return len(o[0])
#Returns the width of a plan (either p or q, for example)
def width(o):
return len(o)
def clear():
for x in range(width(p)):
for y in range(height(p)):
p[x][y]=0
UpdatePanels()
def sendFrame(buf):
if (config.USE_TCPIP):
s.send(buf)
else:
s.write(buf)
#Conversion of a 24 bit (3x1 byte) color to a 15 bit (2bytes) color
#Beware that each input rgb color components must already be shrinked to 5bit, meaning values from 0 to 31 only.
def convert24To15Bit(r,g,b):
# BBBBBBBB BBBBBBBB
# 11111111 22222222
# -bbbbbgg gggrrrrr
b = int(b * BRIGHTNESS / 100)
g = int(g * BRIGHTNESS / 100)
r = int(r * BRIGHTNESS / 100)
B1=(b<<2) + ((g&~0b111)>>3)
B2=((g&~0b11000)<<5) + r
return B1*256 + B2
#Validate if given coordinates is inside the virtualplan
def is_pcoord_valid(x, y):
if x < width(p) and x >= 0 and y < height(p) and y >= 0:
return 1
#print(x,y)
return 0
#Plan transformation. p(x,y) => q(x,y). Note that this is q(x,y) which is latched to the panels
def transformPanel(panel, transform, vx=0, vy=0):
# Note: descriptions des transformations:
# flippedy: x <= x , y <= 7-y
# flippedx: x <= 7-x, y <= y
# vx & vy allows to displace the viewport inside the virtualplan p(x,y),
# which is useful for scrolling in a big picture map, for example.
###############################################################
if (transform == 'NO_ROTATE'):
#retourner lignes impaires
for x in range(0, 8, 2):
for y in range(8):
if is_pcoord_valid(vx+x+panel*8, vy+y):
q[x + panel*8][y] = p[vx+x+panel*8][vy+y]
else:
q[x + panel*8][y] = 0
for x in range(1, 8, 2):
for y in range(8):
if is_pcoord_valid(vx+x+panel*8,vy+7-y):
q[x + panel*8][y] = p[vx+x+panel*8][vy+7-y]
else:
q[x + panel*8][y] = 0
###############################################################
if (transform == 'ROTATE_90'):
pass
###############################################################
if (transform == 'ROTATE_90_FLIPPEDY'):
pass
###############################################################
if (transform == 'ROTATE_180'):
pass
###############################################################
if (transform == 'ROTATE_180_FLIPPEDY'):
for x in range(0, 8, 2):
for y in range(8):
if is_pcoord_valid(vx+7-y+panel*8, vy+7-x):
q[x + panel*8][y] = p[vx+7-y+panel*8][vy+7-x]
else:
q[x + panel*8][y] = 0
for x in range(1, 8, 2):
for y in range(8):
if is_pcoord_valid(vx+y+panel*8, vy+7-x):
q[x + panel*8][y] = p[vx+y+panel*8][vy+7-x]
else:
q[x + panel*8][y] = 0
###############################################################
if (transform == 'ROTATE_180_FLIPPEDX'):
#retourner lignes impaires et symetrie sur la diagonale
for x in range(0, 8, 2):
for y in range(8):
if is_pcoord_valid(vx+y+panel*8, vy+x):
q[x + panel*8][y] = p[vx+y+panel*8][vy+x]
else:
q[x + panel*8][y] = 0
for x in range(1, 8, 2):
for y in range(8):
if is_pcoord_valid(vx+7-y+panel*8, vy+x):
q[x + panel*8][y] = p[vx+7-y+panel*8][vy+x]
else:
q[x + panel*8][y] = 0
###############################################################
if (transform == 'ROTATE_270'):
pass
###############################################################
#A panel update.
def UpdatePanel(panel, transform, virtplan_offset_x=0, virtplan_offset_y=0):
frame = bytearray()
frame.append(TPM2NET_HEADER_IDENT)
frame.append(TPM2NET_CMD_DATAFRAME)
frame.append(0) #hsb
frame.append(128) #lsb
frame.append(panel)
frame.append(1)
transformPanel(panel, transform, virtplan_offset_x, virtplan_offset_y)
for x in range(panel*8, panel*8 + 8):
for y in range(8):
B1 = (q[x][y]) >> 8
B2 = (q[x][y]) % 256
frame.append(B1)
frame.append(B2)
frame.append(TPM2NET_FOOTER_IDENT)
sendFrame(frame)
#Update of all panels
def UpdatePanels(virtplan_offset_x=0, virtplan_offset_y=0):
for i in range(len(config.Panels)):
UpdatePanel(i,config.Panels[i], virtplan_offset_x, virtplan_offset_y)
sleep(0.006) #delay needed for the Teensy Arduino board to digest the received information
def ScrollPanelsLeft():
for x in range(1, 8*NUM_PANEL):
for y in range(8):
p[x-1][y]=p[x][y]
def ScrollPanelsRight():
x=8*NUM_PANEL-1
while (x>0):
x=x-1
for y in range(8):
p[x+1][y]=p[x][y]
def ScrollPanelsUp():
y=7
while (y>0):
y=y-1
for x in range(8*NUM_PANEL):
p[x][y+1]=p[x][y]
def ScrollPanelsDown():
for y in range(1,8):
for x in range(8*NUM_PANEL):
p[x][y-1]=p[x][y]
def Circle(xc,yc,radius, r, g, b, max_x=8*NUM_PANEL, max_y=8):
k = 0
while (k <= 2*math.pi):
k = k + .1
x = int(math.floor(math.sin(k) * radius + xc))
y = int(math.floor(math.cos(k) * radius + yc))
if x >= 0 and x < max_x and y >= 0 and y < max_y:
p[x][y] = convert24To15Bit(r,g,b)
def Sprite(x,y,data, r,g,b):
x0 = x
y0 = y
for i in range(len(data)):
bindata = "{0:04b}".format(int(data[i],16))
for j in range(4):
c = convert24To15Bit(r,g,b)
if bindata[j] == '0':
if is_pcoord_valid(x,y):
c = p[x][y] #if nothing is displayed, we leave the former content of the pixel
else:
c = 0
if x >= 0 and x < width(p) and y >= 0 and y < height(p):
if is_pcoord_valid(x,y):
p[x][y] = c
x = x + 1
y = y + 1
x = x0
#Function that displays a text, which bottom-left coordinates are (x,y), with (rgb) as color.
#w is the spacing between each letters, which is equal to 4 by default.
#Beware that there is no check for the existence of an entry in the dictionnary.
def Text(x, y, buf, r, g, b, w=4):
x0 = x
for i in range(len(buf)):
Sprite(x0,y, dico[buf[i]], r, g, b)
x0 = x0 + w
def map(x, in_min, in_max, out_min, out_max):
return math.floor((x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min);
def LoadImage(name):
im = Image.open(name)
pix = im.load()
#redefine p to fit the image size and load pixels into virtualplan
global p
p = []
for x in range(im.size[0]):
p.append([])
for y in range(im.size[1]):
r = map(pix[x,im.size[1]-y-1][0], 0, 255, 0, 31)
g = map(pix[x,im.size[1]-y-1][1], 0, 255, 0, 31)
b = map(pix[x,im.size[1]-y-1][2], 0, 255, 0, 31)
c = convert24To15Bit(r,g,b)
p[x].append(c)
def DoAnimationRandPanels():
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for x in range(8*NUM_PANEL):
for y in range(8):
p[x][y] = convert24To15Bit(r,g,b)
UpdatePanels()
sleep(0.2)
def DoAnimationRandPixels():
for x in range(8*NUM_PANEL):
for y in range(8):
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
p[x][y] = convert24To15Bit(r,g,b)
UpdatePanels()
sleep(0.2)
def DoAnimationRandHLINE():
for y in range(8):
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for x in range(8*NUM_PANEL):
p[x][y] = convert24To15Bit(r,g,b)
UpdatePanels()
sleep(0.2)
def DoAnimationRandVLINE():
for x in range(8*NUM_PANEL):
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for y in range(0,8):
p[x][y] = convert24To15Bit(r,g,b)
UpdatePanels()
sleep(0.2)
def DoAnimationRandVLineScrollLeft():
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for y in range(8):
p[8*NUM_PANEL-1][y] = convert24To15Bit(r,g,b)
ScrollPanelsLeft();
UpdatePanels()
sleep(0.1)
def DoAnimationRandVLineScrollRight():
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for y in range(8):
p[0][y] = convert24To15Bit(r,g,b)
ScrollPanelsRight();
UpdatePanels()
sleep(0.1)
def DoAnimationRandHLineScrollUp():
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for x in range(8*NUM_PANEL):
p[x][0] = convert24To15Bit(r,g,b)
ScrollPanelsUp();
UpdatePanels()
sleep(0.1)
def DoAnimationRandHLineScrollDown():
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
for x in range(8*NUM_PANEL):
p[x][7] = convert24To15Bit(r,g,b)
ScrollPanelsDown();
UpdatePanels()
sleep(0.1)
def DoAnimationCircle():
x = random.randint(0,8*NUM_PANEL)
y = random.randint(0,8)
radius = random.randint(1,6)
r = random.randint(0,31)
g = random.randint(0,31)
b = random.randint(0,31)
Circle( x,y,radius, r,g,b )
UpdatePanels()
sleep(0.3)
def DoAnimationSprite(scroll):
clear()
r = random.randint(0,31); g = random.randint(0,31); b = random.randint(0,31); Sprite(1,2, dico['C'], r,g,b)
r = random.randint(0,31); g = random.randint(0,31); b = random.randint(0,31); Sprite(4,2, dico['E'], r,g,b)
r = random.randint(0,31); g = random.randint(0,31); b = random.randint(0,31); Sprite(7,2, dico['T'], r,g,b)
r = random.randint(0,31); g = random.randint(0,31); b = random.randint(0,31); Sprite(9,2, dico['I'], r,g,b)
r = random.randint(0,31); g = random.randint(0,31); b = random.randint(0,31); Sprite(11,2, dico['C'], r,g,b)
UpdatePanels()
sleep(0.2)
if scroll=="Left":
for j in range(8*NUM_PANEL):
ScrollPanelsLeft()
UpdatePanels()
sleep(0.1)
if scroll=="Right":
for j in range(8*NUM_PANEL):
ScrollPanelsRight()
UpdatePanels()
sleep(0.1)
if scroll=="Up":
for j in range(8):
ScrollPanelsUp()
UpdatePanels()
sleep(0.1)
if scroll=="Down":
for j in range(8):
ScrollPanelsDown()
UpdatePanels()
sleep(0.1)
sleep(0.1)
def DoAnimationVirtualPlan():
global p
p = [[] for _ in range(8*NUM_PANEL*VIRTPLAN_MULT_SIZE)]
for x in range(8*NUM_PANEL*VIRTPLAN_MULT_SIZE):
for y in range(8*VIRTPLAN_MULT_SIZE):
p[x].append(0)
clear()
#Put some text in the virtualplan
Text(1,2, "CETIC", random.randint(0,31),random.randint(0,31),random.randint(0,31) ,3)
Text(21,2, "ECS LAB", random.randint(0,31),random.randint(0,31),random.randint(0,31), 4)
Text(1,12, "CETIC", random.randint(0,31),random.randint(0,31),random.randint(0,31) ,3)
Text(21,12, "SST LAB", random.randint(0,31),random.randint(0,31),random.randint(0,31), 4)
doIt = 200
posx = 0; posy = 0; incx = 1; incy = 1;
while (doIt):
posx = posx + incx
posy = posy + incy
if posx < 0 or posx > width(p) - 8*NUM_PANEL:
incx = -incx
posx = posx + incx
if posy < 0 or posy > height(p) - 8:
incy = -incy
poxy = posy + incy
UpdatePanels(posx, posy); sleep(0.3)
doIt = doIt - 1
def DoAnimationLoadImage():
LoadImage("rainbow.png")
#LoadImage("flower.jpg")
#LoadImage("rainbow.jpg")
#LoadImage("cetic_logo.png")
doIt = 5000
posx = 0; posy = 0;
incx = 1; incy = 1;
#incx = random.randint(1,2); incy = random.randint(1,2);
while (doIt):
posx = posx + incx
posy = posy + incy
if posx < 0 or posx > width(p) - 8*NUM_PANEL:
incx = -incx
posx = posx + incx
if posy < 0 or posy > height(p) - 8:
incy = -incy
poxy = posy + incy
UpdatePanels(posx, posy); sleep(0.05)
doIt = doIt - 1
def DoAnimationAll():
LengthOfEachDemo = 30
while True:
for i in range(LengthOfEachDemo):
clear()
DoAnimationLoadImage()
for i in range(LengthOfEachDemo):
clear()
DoAnimationVirtualPlan()
for i in range(LengthOfEachDemo):
DoAnimationSprite("Left")
DoAnimationSprite("Right")
DoAnimationSprite("Down")
DoAnimationSprite("Up")
for i in range(LengthOfEachDemo):
DoAnimationRandVLineScrollLeft()
for i in range(LengthOfEachDemo):
DoAnimationRandVLineScrollRight()
for i in range(LengthOfEachDemo):
DoAnimationRandHLineScrollUp()
for i in range(LengthOfEachDemo):
DoAnimationRandHLineScrollDown()
for i in range(LengthOfEachDemo):
DoAnimationRandPanels()
for i in range(LengthOfEachDemo):
DoAnimationRandPixels()
for i in range(LengthOfEachDemo):
DoAnimationRandHLINE()
for i in range(LengthOfEachDemo):
DoAnimationRandVLINE()
for i in range(LengthOfEachDemo):
DoAnimationCircle()
if __name__ == "__main__":
clear()
DoAnimationAll()
|
{"/ledwall.py": ["/invaderStub.py"]}
|
31,109
|
croisez/invaderStub
|
refs/heads/master
|
/ledwall.py
|
from invaderStub import *
clear()
DoAnimationAll()
|
{"/ledwall.py": ["/invaderStub.py"]}
|
31,110
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/crawler/cron.py
|
from django_cron import CronJobBase, Schedule
from barylka_django.crawler import micro_crawler
class CrawlerCronJob(CronJobBase):
RUN_EVERY_MINS = 1
schedule = Schedule(run_every_mins=RUN_EVERY_MINS)
code = 'crawler.crawler_cron_job' # a unique code
def do(self):
micro_crawler.crawl(None)
print "cron done!"
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,111
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/web/templatetags/commontags.py
|
from django import template
register = template.Library()
@register.filter
def key(d, key_name):
return d[key_name]
@register.filter
def toInt(d):
return int(d)
key = register.filter('key', key)
toInt = register.filter('toInt', toInt)
@register.filter
def sort_by_keys(value, keys):
transformed = []
for key in keys.split(','):
factor = 1
if key.startswith('-'):
key = key.replace('-', '')
factor = -1
transformed.append((key, factor))
def compare(item1, item2):
for (key, factor) in transformed:
result = factor * cmp(getattr(item1, key), getattr(item2, key))
if result != 0:
return result
return 0
return sorted(list(value), cmp=compare)
sort_by_key = register.filter('sort_by_key', sort_by_keys)
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,112
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/web/models.py
|
from django.db import models
from django.contrib import admin
# Create your models here.
BLOOD_TYPE = (
('A-', 'A Rh-'),
('A+', 'A Rh+'),
('B-', 'B Rh-'),
('B+', 'B Rh+'),
('0-', '0 Rh-'),
('0+', '0 Rh+'),
('AB-', 'AB Rh-'),
('AB+', 'AB Rh+'),
)
GENDER_CHOICES = (
('M', 'Male'),
('F', 'Female'),
)
DONATION_TYPE = (
('Blood', 450),
('Platelets', 500),
('Plasma', 200),
)
class Edition(models.Model):
number = models.IntegerField(primary_key=True)
capacity = models.IntegerField()
start_date = models.DateField()
end_date = models.DateField(null=True)
admin.site.register(Edition)
class User(models.Model):
name = models.CharField(max_length=120, primary_key=True)
corrector = models.BooleanField(default=False)
blood_type = models.CharField(max_length=3, choices=BLOOD_TYPE, null=True)
gender = models.CharField(max_length=1, choices=GENDER_CHOICES, null=True)
def __unicode__( self ):
return u"@{0}".format(self.name)
admin.site.register(User)
class DonationEntry(models.Model):
date = models.DateTimeField()
micro_id = models.IntegerField(primary_key=True)
author = models.ForeignKey(User, related_name="wrote")
corrector = models.ForeignKey(User, related_name="corrected", null=True)
msg = models.CharField(max_length=4098)
def __unicode__( self ):
return u"{2} #{3} - {0}, {1}".format(self.author, self.corrector, self.date, self.micro_id)
admin.site.register(DonationEntry)
class Donation(models.Model):
donor = models.ForeignKey(User, related_name="donate")
date = models.DateTimeField()
type = models.CharField(max_length=10, choices=DONATION_TYPE)
value = models.IntegerField()
entry = models.ForeignKey(DonationEntry)
stamp_img_url = models.CharField(max_length=2048, blank=True, null=True)
barylka_edition = models.IntegerField()
def __unicode__( self ):
return u"{0} ml - {1}, {2} - @{3}".format(
self.value, self.date, self.type, self.entry.author.name
)
admin.site.register(Donation)
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,113
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/crawler/micro_crawler.py
|
# -*- coding: utf-8 -*-
import os
import re
import wykop
from django.db.models import DateField
from django.conf import settings
from barylka_django.web.models import *
from unidecode import unidecode
def read_entry(body, donations):
body = unidecode(body.lower())
equation = re.search('(\d+)(?:\s*ml)*((?:\s*-\s*\d+(?:\s*ml)*)*)\s*=\s*(\d+)(?:\s*ml)*', body.replace(" ", ""), re.MULTILINE|re.DOTALL)
dates = re.search('dat[ay](?: donacji)?:\s*(\d{4}[-\.]\d{1,2}[-\.]\d{1,2}(?:\s*,\s*\d{4}[-\.]\d{1,2}[-\.]\d{1,2})*)', body, re.MULTILINE|re.DOTALL)
types = re.search('skladniki?:\s*((?:krew|plytki|osocze)(?:\s*,\s*(?:krew|plytki|osocze))*)', body, re.MULTILINE|re.DOTALL)
if equation:
base = int(equation.group(1))
ds = equation.group(2).replace("ml", "").strip("- ").split("-")
donation_values = [int(dv.strip()) for dv in ds]
result = int(equation.group(3))
print "%d - %d = %d" % (base, sum(donation_values), result)
if base - sum(donation_values) != result:
print "Error"
if not donations:
for dv in donation_values:
donations.append({"value":dv})
else:
for dv, i in zip(donation_values, range(len(donation_values))):
if len(donations) <= i:
donations.append({"value":dv})
else:
donations[i]["value"] = dv
for donation in donations:
for type, ml in DONATION_TYPE:
if donation["value"] == ml:
donation["type"] = type
if dates:
donation_dates = dates.group(1).split(",")
for date, i in zip(donation_dates, range(len(donation_dates))):
date = date.replace(".", "-")
donations[i]["date"] = str(DateField().to_python(date.strip()))
if types:
trl = {"krew":"Blood", "plytki":"Platelets", "osocze":"Plasma"}
donation_types = types.group(1).split(",")
for type, i in zip(donation_types, range(len(donation_types))):
donations[i]["type"] = trl[type.strip()]
for donation in donations:
print str(donation)
def crawl(test):
api = wykop.WykopAPI(appkey=os.environ['BARYLKA_WYKOP_API_KEY'], secretkey=os.environ['BARYLKA_WYKOP_SECRET_KEY'],
login=os.environ['BARYLKA_WYKOP_LOGIN'], accountkey=os.environ['BARYLKA_WYKOP_ACCOUNT_KEY'])
entries = api.tag("barylkakrwi")
for entry in reversed(entries['items']):
if not DonationEntry.objects.filter(micro_id=entry.id) and entry.type == 'entry' and '#ignoruj' not in entry.body:
try:
donations=[]
read_entry(entry.body, donations)
for donation in donations:
print str(donation)
for comment in entry.comments:
if '#korekta' in comment.body:
try:
user = User.objects.get(name=comment.author)
if user.corrector:
read_entry(comment.body, donations)
if donations:
api.vote_entry_comment(entry.id, comment.id)
except User.DoesNotExist:
print 'User is not corrector'
if donations:
user, created = User.objects.get_or_create(name=entry.author)
de = DonationEntry.objects.create(micro_id=entry.id, date=entry.date, author=user, msg=entry.body)
api.vote_entry(entry.id)
for donation in donations:
Donation.objects.create(donor=user, date=donation.get('date', de.date),
type=donation.get('type', 'Blood'), value=donation['value'], entry=de,
barylka_edition=settings.CURRENT_BARYLKA_EDITION)
except:
import sys, traceback
traceback.print_exc()
from django.http import HttpResponse
return HttpResponse("ok")
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,114
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/web/migrations/0001_initial.py
|
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Edition'
db.create_table(u'web_edition', (
('number', self.gf('django.db.models.fields.IntegerField')(primary_key=True)),
('capacity', self.gf('django.db.models.fields.IntegerField')()),
('start_date', self.gf('django.db.models.fields.DateField')()),
('end_date', self.gf('django.db.models.fields.DateField')(null=True)),
))
db.send_create_signal(u'web', ['Edition'])
# Adding model 'User'
db.create_table(u'web_user', (
('name', self.gf('django.db.models.fields.CharField')(max_length=120, primary_key=True)),
('corrector', self.gf('django.db.models.fields.BooleanField')(default=False)),
('blood_type', self.gf('django.db.models.fields.CharField')(max_length=3, null=True)),
('gender', self.gf('django.db.models.fields.CharField')(max_length=1, null=True)),
))
db.send_create_signal(u'web', ['User'])
# Adding model 'DonationEntry'
db.create_table(u'web_donationentry', (
('date', self.gf('django.db.models.fields.DateTimeField')()),
('micro_id', self.gf('django.db.models.fields.IntegerField')(primary_key=True)),
('author', self.gf('django.db.models.fields.related.ForeignKey')(related_name='wrote', to=orm['web.User'])),
('corrector', self.gf('django.db.models.fields.related.ForeignKey')(related_name='corrected', null=True, to=orm['web.User'])),
('msg', self.gf('django.db.models.fields.CharField')(max_length=4098)),
))
db.send_create_signal(u'web', ['DonationEntry'])
# Adding model 'Donation'
db.create_table(u'web_donation', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('donor', self.gf('django.db.models.fields.related.ForeignKey')(related_name='donate', to=orm['web.User'])),
('date', self.gf('django.db.models.fields.DateTimeField')()),
('type', self.gf('django.db.models.fields.CharField')(max_length=10)),
('value', self.gf('django.db.models.fields.IntegerField')()),
('entry', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['web.DonationEntry'])),
('stamp_img_url', self.gf('django.db.models.fields.CharField')(max_length=2048)),
('barylka_edition', self.gf('django.db.models.fields.IntegerField')()),
))
db.send_create_signal(u'web', ['Donation'])
def backwards(self, orm):
# Deleting model 'Edition'
db.delete_table(u'web_edition')
# Deleting model 'User'
db.delete_table(u'web_user')
# Deleting model 'DonationEntry'
db.delete_table(u'web_donationentry')
# Deleting model 'Donation'
db.delete_table(u'web_donation')
models = {
u'web.donation': {
'Meta': {'object_name': 'Donation'},
'barylka_edition': ('django.db.models.fields.IntegerField', [], {}),
'date': ('django.db.models.fields.DateTimeField', [], {}),
'donor': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'donate'", 'to': u"orm['web.User']"}),
'entry': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.DonationEntry']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'stamp_img_url': ('django.db.models.fields.CharField', [], {'max_length': '2048'}),
'type': ('django.db.models.fields.CharField', [], {'max_length': '10'}),
'value': ('django.db.models.fields.IntegerField', [], {})
},
u'web.donationentry': {
'Meta': {'object_name': 'DonationEntry'},
'author': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'wrote'", 'to': u"orm['web.User']"}),
'corrector': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'corrected'", 'null': 'True', 'to': u"orm['web.User']"}),
'date': ('django.db.models.fields.DateTimeField', [], {}),
'micro_id': ('django.db.models.fields.IntegerField', [], {'primary_key': 'True'}),
'msg': ('django.db.models.fields.CharField', [], {'max_length': '4098'})
},
u'web.edition': {
'Meta': {'object_name': 'Edition'},
'capacity': ('django.db.models.fields.IntegerField', [], {}),
'end_date': ('django.db.models.fields.DateField', [], {'null': 'True'}),
'number': ('django.db.models.fields.IntegerField', [], {'primary_key': 'True'}),
'start_date': ('django.db.models.fields.DateField', [], {})
},
u'web.user': {
'Meta': {'object_name': 'User'},
'blood_type': ('django.db.models.fields.CharField', [], {'max_length': '3', 'null': 'True'}),
'corrector': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'gender': ('django.db.models.fields.CharField', [], {'max_length': '1', 'null': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '120', 'primary_key': 'True'})
}
}
complete_apps = ['web']
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,115
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/web/templatetags/__init__.py
|
__author__ = 'work'
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,116
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/web/views.py
|
# -*- coding: utf-8 -*-
import datetime
from django.shortcuts import render
from barylka_django.settings import CURRENT_BARYLKA_EDITION
from barylka_django.web.models import *
from django.db.models import Sum, Min
def index(request, edition=CURRENT_BARYLKA_EDITION):
editon_obj = Edition.objects.filter(number=edition)[0]
c = {'edycja':editon_obj}
c["total"] = Donation.objects.filter(barylka_edition=edition).aggregate(Sum('value'))["value__sum"]
c["procent_zapelnienia"] = float("%.2f" % ((c["total"]/float(editon_obj.capacity))*100))
c["rank"] = Donation.objects.filter(barylka_edition=edition).values("donor", "donor__blood_type").annotate(total=Sum("value")).order_by("-total")
c["last"] = DonationEntry.objects.all().order_by("-date")[:5]
for e in c["last"]:
e.total = Donation.objects.filter(entry=e).values("entry").annotate(total=Sum("value"))[0]["total"]
c["new_users"] = Donation.objects.filter(barylka_edition=edition).values("donor").annotate(join_date=Min("date")).order_by("-join_date")[:5]
c["minirank"]=[]
for blood_type in ["A-", "A+", "B-", "B+", "0-", "0+", "AB-", "AB+"]:
c["minirank"].append(
(
blood_type,
Donation.objects.filter(barylka_edition=edition).values("donor", "donor__blood_type").filter(donor__blood_type=blood_type).annotate(total=Sum("value")).order_by("-total")[:5]
)
)
c["genderrank"]=[]
for gender, gender_pl in [("M", u"Mężczyźni"), ("F", u"Kobiety")]:
c["genderrank"].append((gender_pl, Donation.objects.filter(barylka_edition=edition).values("donor", "donor__gender", "donor__blood_type").filter(
donor__gender=gender).annotate(total=Sum("value")).order_by("-total")[:5]))
c["all_users"]=User.objects.all().order_by("name").values()
return render(request, "index.html", c)
def rank(request, edition=CURRENT_BARYLKA_EDITION):
editon_obj = Edition.objects.filter(number=edition)[0]
c = {'edycja':editon_obj}
c['total'] = Donation.objects.filter(barylka_edition=edition).aggregate(Sum('value'))['value__sum']
c['procent_zapelnienia'] = float('%.2f' % ((c['total']/float(editon_obj.capacity))*100))
rank = Donation.objects.filter(barylka_edition=edition).values('donor', 'donor__blood_type').annotate(total=Sum('value')).order_by('-total')
rank = [rank[x:x+20] for x in range(0, len(rank),20)]
c['rank'] = [rank[x:x+3] for x in range(0, len(rank),3)]
rr={}
user_stat={}
for type, ml in DONATION_TYPE:
rr['type'] = Donation.objects.filter(barylka_edition=edition).filter(type=type).values('donor').annotate(**{'total_'+type:Sum('value')}).order_by('-total_'+type)
for dic in rr['type']:
if dic['donor'] not in user_stat:
user_stat[dic['donor']]={}
user_stat[dic['donor']]['total_'+type] = dic['total_'+type]
for nick in user_stat.keys():
for type, ml in DONATION_TYPE:
if 'total_'+type not in user_stat[nick]:
user_stat[nick]['total_'+type]=0
c['user_stat'] = user_stat
c['total_by_type'] = {'max':0}
for type, ml in DONATION_TYPE:
c['total_by_type'][type] = Donation.objects.filter(barylka_edition=edition).filter(type=type).values('donor', 'value').annotate(total=Sum('value')).order_by('-total')
if c['total_by_type'][type]:
c['total_by_type'][type] = c['total_by_type'][type][0]['total']
if c['total_by_type'][type] > c['total_by_type']['max']:
c['total_by_type']['max'] = c['total_by_type'][type]
else:
c['total_by_type'][type]=0
return render(request, 'ranking.html', c)
def rank_all_time(request):
c = {
'total': Donation.objects.aggregate(Sum('value'))['value__sum']
}
rank = Donation.objects.exclude(
barylka_edition=0
).values(
'donor', 'donor__blood_type'
).annotate(total=Sum('value')).order_by('-total')
rank = [rank[x:x+20] for x in range(0, len(rank), 20)]
c['rank'] = [rank[x:x+3] for x in range(0, len(rank), 3)]
rr = {}
user_stat = {}
for _type, ml in DONATION_TYPE:
rr['type'] = Donation.objects.exclude(
barylka_edition=0
).filter(
type=_type
).values(
'donor'
).annotate(
**{'total_'+_type: Sum('value')}
).order_by('-total_'+_type)
for dic in rr['type']:
if dic['donor'] not in user_stat:
user_stat[dic['donor']] = {}
user_stat[dic['donor']]['total_'+_type] = dic['total_'+_type]
for nick in user_stat.keys():
for _type, ml in DONATION_TYPE:
if 'total_'+_type not in user_stat[nick]:
user_stat[nick]['total_'+_type] = 0
c['user_stat'] = user_stat
c['total_by_type'] = {'max': 0}
for _type, ml in DONATION_TYPE:
c['total_by_type'][_type] = Donation.objects.exclude(
barylka_edition=0
).filter(
type=_type
).values(
'donor', 'value'
).annotate(
total=Sum('value')
).order_by('-total')
if c['total_by_type'][_type]:
c['total_by_type'][_type] = c['total_by_type'][_type][0]['total']
if c['total_by_type'][_type] > c['total_by_type']['max']:
c['total_by_type']['max'] = c['total_by_type'][_type]
else:
c['total_by_type'][_type] = 0
return render(request, 'ranking_all_time.html', c)
def get_user_data(user_name, edition):
d = Donation.objects.filter(
barylka_edition=edition
).values(
'donor', 'donor__name'
).filter(donor__name=user_name)
if not d:
return
c = d.annotate(total=Sum('value'))[0]
c['donations'] = d.order_by('date').values()
donations = c['donations']
grouped_donations = [[donations[0]]]
for i, donation in enumerate(donations[1:]):
if donation['date'] == donations[i]['date'] and \
donation['date'].strftime('%M:%S') == '00:00':
grouped_donations[-1].append(donation)
grouped_donations[-1].sort(key=lambda x: x['value'], reverse=True)
else:
grouped_donations.append([donation])
c['donations'] = grouped_donations
for _type, ml in DONATION_TYPE:
dic = Donation.objects.filter(
barylka_edition=edition,
type=_type,
donor=user_name
).values(
'donor'
).annotate(
**{'total_'+_type: Sum('value')}
).order_by('-total_' + _type)
if dic:
c['total_'+_type] = dic[0]['total_'+_type]
c['donation_type_tr'] = {
'Blood': u'krew',
'Platelets': u'płytki',
'Plasma': u'osocze',
}
return c
def user(request, user_name):
c = {
'edition': {},
'total': 0,
}
for edition in [0, 1, 2]:
c['edition'][edition] = get_user_data(user_name, edition)
if c['edition'][edition]:
c['total'] += c['edition'][edition]['total']
c.update(User.objects.filter(name=user_name).values()[0])
c['blood_type'] = c['blood_type'][:-1] + ' Rh' + c['blood_type'][-1:] if c['blood_type'] else ''
c["join_date"] = Donation.objects.filter(donor=user_name).values("donor").annotate(join_date=Min("date"))[0]["join_date"]
return render(request, "user.html", c)
def tos(request):
return render(request, "tos.html")
def tag_browser(request, edition=CURRENT_BARYLKA_EDITION):
edition = int(edition)
barylka_edition = Edition.objects.get(number=edition)
end_date = barylka_edition.end_date or datetime.datetime.now()
entries = DonationEntry.objects.all(
).filter(
date__gte = barylka_edition.start_date,
date__lte = end_date
).order_by('-date')
c = {
'donationEntries': entries,
'donation_type_tr': {
'Blood': u'krew',
'Platelets': u'płytki',
'Plasma': u'osocze',
},
'edition': edition,
}
return render(request, 'tag_browser.html', c)
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,117
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/crawler/tests.py
|
# -*- coding: utf-8 -*-
"""
This file demonstrates writing tests using the unittest module. These will pass
when you run "manage.py test".
Replace this with more appropriate tests for your application.
"""
from django.test import TestCase
from .micro_crawler import *
class MicroCrawlerTest(TestCase):
def run_read_entry(self, tpl_list):
donations=[]
for body, result in tpl_list:
read_entry(body, donations)
self.assertEqual(donations, result)
def test_read_entry_1(self):
arg = [
(
"325950 - 950 = 325000",
[{"value":950}]
),
(
"""Będzie możliwe rozdzielenie wpisów i przydzielenie im stosownych dat.
325950 - 450 - 500 = 325000
daty: 2014-03-09, 2014-03-28
#korekta
jeżeli wartości donacji są typowe (450-krew, 500-plytki, 200-osocze), automatycznie zostanie przydzielony odpowiedni typ donacji.""",
[{"value":450, "date":"2014-03-09", "type":"Blood"}, {"value":500, "date":"2014-03-28", "type":"Platelets"}]
),
]
self.run_read_entry(arg)
def test_read_entry_2(self):
arg = [
(
"""328000 - 450 = 327550
data donacji: 2014-03-01""",
[{"value":450, "date":"2014-03-01", "type":"Blood"}]
),
]
self.run_read_entry(arg)
def test_read_entry_3(self):
arg = [
(
"""328000 - 450 = 327550
data donacji: 2014-3-1""",
[{"value":450, "date":"2014-03-01", "type":"Blood"}]
),
]
self.run_read_entry(arg)
def test_read_entry_4(self):
arg = [
(
"""327550 - 500 = 327050
data: 2014.3.5
#testowywpis #test""",
[{"value":500, "date":"2014-03-05", "type":"Platelets"}]
),
]
self.run_read_entry(arg)
def test_read_entry_5(self):
arg = [
(
"""327050 - 450 - 500 = 326100
#testowywpis #test""",
[{"value":450, "type":"Blood"}, {"value":500, "type":"Platelets"}]
),
(
"""daty donacji: 2014-03-07, 2014-03-27
#korekta""",
[{"value":450, "type":"Blood", "date":"2014-03-07"}, {"value":500, "type":"Platelets", "date":"2014-03-27"}]
),
]
self.run_read_entry(arg)
def test_read_entry_6(self):
arg = [
(
"""326100 - 150 = 325850
#testowywpis #test""",
[{"value":150}]
),
(
"""@noisy:
326100 - 150 = 325850
data: 2014-03-28
skladnik: krew
#korekta""",
[{"value":150, "type":"Blood", "date":"2014-03-28"}]
),
(
"""@noisy:
326100 - 150 = 325950
#korekta""",
[{"value":150, "type":"Blood", "date":"2014-03-28"}]
),
]
self.run_read_entry(arg)
def test_read_entry_7(self):
arg = [
(
"""325850 - 233 = 325617
#testowywpis #test""",
[{"value":233}]
),
(
u"""@noisy:
data: 2014.3.17
skladnik: płytki
#korekta""",
[{"value":233, "type":"Platelets", "date":"2014-03-17"}]
),
]
self.run_read_entry(arg)
def test_read_entry_8(self):
arg = [
(
"""327050 ml - 450 ml - 500 ml = 326100 ml
#testowywpis #test""",
[{"value":450, "type":"Blood"}, {"value":500, "type":"Platelets"}]
),
]
self.run_read_entry(arg)
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,118
|
noisy/barylkakrwi
|
refs/heads/master
|
/barylka_django/urls.py
|
from django.conf import settings
from django.conf.urls import patterns, include, url
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
from django.contrib import admin
from django.views.generic.base import RedirectView
admin.autodiscover()
urlpatterns = patterns('',
url(r'^$', 'barylka_django.web.views.index', name='index'),
url(r'^edycja/'+ str(settings.CURRENT_BARYLKA_EDITION) + r'/$', RedirectView.as_view(url='/', permanent=False)),
url(r'^edycja/(\d)/$', 'barylka_django.web.views.index', name='index'),
url(r'^ranking/$', 'barylka_django.web.views.rank', name='rank'),
url(r'^ranking/'+ str(settings.CURRENT_BARYLKA_EDITION) + r'/$', RedirectView.as_view(url='/ranking/', permanent=False)),
url(r'^ranking/(\d)/$', 'barylka_django.web.views.rank', name='rank'),
url(r'^ranking/wszystkie-edycje/$', 'barylka_django.web.views.rank_all_time', name='rank'),
url(r'^wpisy/$', 'barylka_django.web.views.tag_browser', name='tag_browser'),
url(r'^wpisy/(\d)/$', 'barylka_django.web.views.tag_browser', name='tag_browser'),
url(r'^wpisy/'+ str(settings.CURRENT_BARYLKA_EDITION) + r'/$', RedirectView.as_view(url='/wpisy/', permanent=False)),
url(r'^ludzie/([\w-]*)/$', 'barylka_django.web.views.user', name='user'),
url(r'^crawl$', 'barylka_django.crawler.micro_crawler.crawl', name='crawl'),
url(r'^regulamin/', 'barylka_django.web.views.tos', name='tos'),
url(r'^admin/doc/', include('django.contrib.admindocs.urls')),
url(r'^admin/', include(admin.site.urls)),
)
urlpatterns += staticfiles_urlpatterns()
|
{"/barylka_django/web/views.py": ["/barylka_django/web/models.py"], "/barylka_django/crawler/tests.py": ["/barylka_django/crawler/micro_crawler.py"]}
|
31,136
|
netrunners/genclass
|
refs/heads/master
|
/generators/__init__.py
|
from .monguo import Monguo
from .reactComponent import ReactComponent
from .reactList import ReactList
from .torgHandler import TorgHandler
from .wtform import WtForm
all = ['Mongo','ReactComponent', 'ReactList', 'TorgHandler', 'WtForm']
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,137
|
netrunners/genclass
|
refs/heads/master
|
/generators/reactComponent.py
|
def ReactComponent(_,o):
import pprint
definition = _.definitions['reactComponent']
imports = definition['imports']
field = definition['input']
typedmembers = _._getmembers(o)
members = [m[0] for m in _._getmembers(o)] #
indent = ' '
print(members)
fields = {
'name':o.name,
'membersdict' : ',\n'.join([ (indent + "%s:null") % m for m in members ]),
'memberStateDict' : ',\n'.join(["{0}:this.state.{0}".format(m) for m in members ]),
'memberslist' : ', '.join(members),
'andmembers' : ' && '.join(members),
'inputs' : (indent+'\n').join( [ field.format(name=m[0],type=m[1][0]) for m in typedmembers ] )
}
pprint.pprint(fields)
Class = definition['class'].format(**fields)
out = "/*\n%s*/\n%s\n%s" % ( _._header(o), imports, Class)
_._write(out,o.name,definition['file'].format(o.name))
return out
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,138
|
netrunners/genclass
|
refs/heads/master
|
/generators/utils.py
|
# upper first letter (classNames)
def upper(s): return s[0].upper() + s[1:]
# lower first letter (files)
def lower(s): return s[0].lower() + s[1:]
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,139
|
netrunners/genclass
|
refs/heads/master
|
/generators/torgHandler.py
|
from .utils import upper, lower
def TorgHandler(_,o):
definition = _.definitions['TorgHandler']
urlpattern = definition['urlpattern']
members = _._getmembers(o)
CRUD = 'Create Read Update Delete List FullList'.split()
docdict = '\n'.join(["%s%s%s" % (
3*' ',
definition['member'].format(name=x[0]),
"," ) for x in members])
dform = { # those use memberlist
'Create':True,
'Update':True
}
h = "\'''\n%s\n'''\n" % _._header(o)
imports = "%s\n" % definition['imports'].format(
name=lower(o.name),
Name=upper(o.name))
if o.cat.checkState():
imports += "%s\n" % definition['import_cat'].format(
name=lower(o.name),
Name=upper(o.name))
cls = ''
for handler in CRUD :
print(handler)
Cls = definition[handler]
docmembers = docdict if dform.get(handler) else ''
# humhum, hence the presence of '''docmembers''' in every handlers
cls += Cls.format(name=o.name,
docmembers=docmembers)
urls = '\n '.join(
[ urlpattern.format(
name=lower(o.name),
crud=lower(handler),
Crud=handler,
Name=upper(o.name)) for handler in CRUD ])
localimports = "from .{0}_h import {1}".format(
upper(o.name),
','.join([ o.name + crud + '_H' for crud in CRUD])
)
urls = "%s\nurlpattern = (\n %s\n)\n" % (localimports,urls)
_._write(urls,o.name,"%s/%s" % (o.name,"urlmap.py"))
out = "%s%s%s" % (h,imports,cls)
_._write(out,o.name,definition['file'].format(o.name))
return out
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,140
|
netrunners/genclass
|
refs/heads/master
|
/Genclass.py
|
'''
Genclass
'''
from PyQt5.QtWidgets import (qApp,
QApplication,QWidget,
QMessageBox, QFileDialog,
QPushButton,
QTableView,QTableWidget,QTableWidgetItem,
QSizePolicy, QHeaderView,
QComboBox, QCheckBox, QLineEdit,
QMainWindow,QDesktopWidget,
QVBoxLayout, QHBoxLayout, QGridLayout)
from PyQt5.QtCore import (Qt, pyqtSignal, QEvent)
from PyQt5.QtGui import QIcon
# from TableWidgetDragRows import TableWidgetDragRows # dysfunct
import sys
import yaml
import yml
import gui
from Struct import Struct
import Generators
from generators import *
Generators.Generators._load([Monguo,WtForm,ReactComponent,ReactList,TorgHandler])
icon = "./icon.png"
KnownDefinitions = yml.load("templates.yaml")
classTables = []
dictTables = []
types = None
typeTable = None
DefaultGenerator = Generators.Generators(KnownDefinitions)
DefaultGenerator.dir = '../torg/api/'
class Table(QTableWidget):
Change = pyqtSignal(QTableWidgetItem)
Add = pyqtSignal(QTableWidgetItem)
Del = pyqtSignal(QTableWidgetItem)
def __init__(_,parent):
super().__init__(parent)
_.center()
def center(_):
qr = _.frameGeometry()
cp = QDesktopWidget().availableGeometry().center()
qr.moveCenter(cp)
_.move(qr.topLeft())
class TypesTable(Table):
def __init__(_, types=[], name=None, generators=DefaultGenerator, parent=None):
super().__init__(parent)
_.name = name
_.setColumnCount(1)
_.setRowCount(len(types))
_.setColumnWidth(0,200)
_.adjustSize()
#_.setBaseSize(500,200)
_.load(types)
#_.itemChanged.connect(_.TyChange.emit)
def load(_,types):
_.clear()
_.setRowCount(len(types))
for x in range(len(types)):
if types[x] in _.names():
#print(types[x],'already in')
pass
else:
it = QTableWidgetItem()
it.setText(types[x])
_.setItem(x,0,it)
def types(_):
for x in range(_.rowCount()):
yield _.item(x,0)
def names(_) :
return [i.text() if i else '' for i in _.types()]
def getItemNamed(_,n):
i = _.names().index(n)
#print("get item named",n,i)
return _.item(i,0)
def newType(_,s=''):
if s in _.names():
#print('already in', s)
i = _.names().index(s)
return _.itemAt(i,0)
else:
lt = _.rowCount()
it = QTableWidgetItem()
it.setText(s)
_.setRowCount(lt+1)
_.setItem(lt,0,it)
_.Add.emit(it)
return it
def addType(_):
'''
add a new empty item and broadcast it.
when the name is set the chgtype event is triggered
'''
if '' not in _.names():
lt = _.rowCount()
_.setRowCount(lt+1)
it = QTableWidgetItem()
_.setItem(lt,0,it)
_.editItem(it)
_.Add.emit(it)
def clearType(_):
i = _.currentRow()
if i > -1:
it = _.takeItem(i,0)
if it:
_.Del.emit(it)
def delType(_):
i = _.currentRow()
if i > -1:
it = _.takeItem(i,0)
if it:
_.removeRow(i)
_.Del.emit(it)
def keyPressEvent(_,event) :
if _.state() != QTableView.EditingState:
if event.key() == Qt.Key_Backspace:
_.delType()
if event.key() == Qt.Key_Delete:
_.clearType()
elif event.key() == Qt.Key_Return:
_.addType()
elif event.key() == Qt.Key_Space:
_.load(['0','1','2'])
else :
super().keyPressEvent(event)
def generate(_):
return "%s: %s" % (_.name,"[%s]" % ",".join(_.names()))
def save(_):
return {
'name':_.name,
'names':_.names(),
}
class DictTable(Table):
def __init__(_, D, name=None, parent=None):
super().__init__(parent)
_.name = name
_.dict = D
_.setColumnCount(2)
_.setRowCount(len(D))
#_.setColumnWidth(0,200)
i = 0
for k,v in D.items():
ik = QTableWidgetItem(k)
iv = QTableWidgetItem(str(v))
_.setItem(i,0,ik)
_.setItem(i,1,iv)
i += 1
#_.itemChanged.connect(_.TyChange.emit)
def name(_):
for x in range(_.rowCount()): yield _.item(x,0)
def value(_):
for x in range(_.rowCount()): yield _.item(x,1)
def names(_) :
return [i.text() if i else '' for i in _.name()]
def values(_) :
return [i.text() if i else '' for i in _.value()]
def add(_):
if '' not in _.names():
lt = _.rowCount()
_.setRowCount(lt+1)
it = QTableWidgetItem()
_.setItem(lt,0,it)
_.editItem(it)
_.Add.emit(it)
def rm(_):
i = _.currentRow()
if i > -1:
it = _.takeItem(i,0)
if it:
_.Del.emit(it)
def keyPressEvent(_,event) :
if _.state() != QTableView.EditingState:
if event.key() == Qt.Key_Backspace: _.rm()
elif event.key() == Qt.Key_Return: _.add()
else :
super().keyPressEvent(event)
def save(_):
yaml.dump(_.dict)
def generate(_) :
return yaml.dump(_.dict)
class MembersTable(Table):
def __init__(_, types, members, parent=None):
super().__init__(parent)
_.setColumnCount(4)
_.setRowCount(len(members))
_.setHorizontalHeaderLabels(['name','type','R','L'])
for i in range(_.rowCount()):
it = QTableWidgetItem(members[i][0])
_.setItem(i,0,it)
combo = QComboBox()
combo.addItems(types.names())
#print(types.names())
idx = types.names().index(members[i][1][0])
combo.setCurrentIndex(idx)
_.setCellWidget(i,1,combo)
cb = QCheckBox()
cb.setTristate(True)
cb.setCheckState(members[i][1][1])
_.setCellWidget(i,2,cb)
cb = QCheckBox()
cb.setTristate(True)
if len(members[i][1]) == 2:
members[i][1].append(0)
cb.setCheckState(members[i][1][2])
_.setCellWidget(i,3,cb)
_.horizontalHeader().setSectionResizeMode(QHeaderView.ResizeToContents)
def rowcount(_):
return range(_.rowCount())
def combo(_,x):
return _.cellWidget(x,1)
def combos(_):
for x in _.rowcount(): yield _.combo(x)
def members(_) :
for x in _.rowcount(): yield _.name(x)
def name(_,x):
return _.item(x,0).text()
def cb(_,x):
return _.cellWidget(x,2)
def cb2(_,x):
return _.cellWidget(x,3)
def dict(_) :
return [
{
_.name(x): [
_.combo(x).currentText(),
int(_.cb(x).checkState()),
int(_.cb2(x).checkState())
]
} for x in range(_.rowCount())]
def names(_) :
return [ i if i else '' for i in _.members()]
def fields(_) :
''' return '''
return [
( _.item(x,0).text(),
_.combo(x).currentText(),
_.cb(x).checkState(),
_.cb2(x).checkState()
)
for x in range(_.rowCount())
]
def addTy(_,tableitem):
'''
update the types. new combos
if the set type still exist in the new list, keep it
'''
[co.addItem("") for co in _.combos() ]
#[_.combo(x).addItem("undefined") for x in range(_.rowCount()) ]
'''
for index in range(_.rowCount()):
combo = _.cellWidget(index,1)
combo.addItem("undefined") # or "None" :) ?
'''
def chgTy(_,it):
[co.setItemText(it.row(),it.text()) for co in _.combos() ]
'''
update the types. new combos
if the set type still exist in the new list, keep it
'''
'''
for index in range(_.rowCount()):
combo = _.cellWidget(index,1)
combo.addItem("undefined") # or "None" :) ?
'''
def setTypesTable(_,types):
'''
update the types. new combos
if the set type still exist in the new list, keep it
'''
for x in range(_.rowCount()):
combo = QComboBox()
combo.addItems(types)
v = _.cellWidget(x,1)
_.setCellWidget(x,1,combo)
if v in types:
i = types.index(v)
_.cellWidget(x,1).setCurrentIndex(i)
def keyPressEvent(_,event) :
if _.state() != QTableView.EditingState:
if event.key() == Qt.Key_Return:
if '' not in [n for n in _.names()]:
lt = _.rowCount()
_.setRowCount(lt+1)
it = QTableWidgetItem()
_.setItem(lt,0,it)
_.editItem(it)
_.Add.emit(it)
combo = QComboBox()
combo.addItems(types.names())
_.setCellWidget(lt,1,combo)
cb = QCheckBox()
cb.setTristate(True)
_.setCellWidget(lt,2,cb)
cb2 = QCheckBox()
cb2.setTristate(True)
_.setCellWidget(lt,3,cb2)
elif event.key() == Qt.Key_Delete:
i = _.currentRow()
if i > -1:
it = _.takeItem(i,0)
_.Del.emit(it)
elif event.key() == Qt.Key_Backspace:
i = _.currentRow()
if i > -1:
it = _.takeItem(i,0)
if it:
_.Del.emit(it)
_.removeRow(i)
else :
super().keyPressEvent(event)
class ClassTable(QWidget):
'''
remember that types can either be primitive types or come from a ClassTable
hence a generic TypeName
don't elucubrate on subset heritance, this is volatile data.
'''
boundGenerators = False
activateLock = False
def __init__(
_, typesTable, data=[], name='undefined',
generators=DefaultGenerator, parent=None):
'''
pop out a ClassTable types is Typetable
'''
super().__init__(parent)
#_.setWindowFlags(Qt.Tool)
_.name = name
_.generators = generators
_.typesTable = typeTable
_.nameWidget = QLineEdit()
_.nameWidget.setText(name)
_.nameWidget.textEdited.connect(_.setName)
_.cat = QCheckBox()
_.cat.setTristate(True)
_.generateButton = QPushButton("g")
_.copyButton = QPushButton("c")
btnSize = (20,20)
maxsize = (QSizePolicy.Fixed,QSizePolicy.Fixed)
_.generateButton.setMaximumSize(*btnSize)
_.generateButton.setSizePolicy(*maxsize)
_.copyButton.setMaximumSize(*btnSize)
_.copyButton.setSizePolicy(*maxsize)
_.selectGenerator = QComboBox()
_.selectGenerator.addItems(generators._availables())
_.selectGenerator.addItem('ALL')
_.selectGenerator.currentIndexChanged.connect(_.generatorChanged)
_.generateButton.clicked.connect(_.generate)
_.copyButton.clicked.connect(_.copy)
vbox = QVBoxLayout()
hbox = QHBoxLayout()
grid = QGridLayout()
hbox.addWidget(_.nameWidget)
hbox.addWidget(_.cat)
hbox.addWidget(_.selectGenerator)
hbox.addWidget(_.generateButton)
hbox.addWidget(_.copyButton)
_.setLayout(grid)
grid.addLayout(hbox,0,0)
grid.addLayout(vbox,1,0)
_.members = MembersTable(types,data)
typesTable.Add.connect(_.members.addTy)
typesTable.itemChanged.connect(_.members.chgTy)
vbox.addWidget(_.members)
_.typeitem = typesTable.newType()
_.setName(name)
def setName(_,s):
_.name = s
if s in _.typesTable.table.names():
it = _.typesTable.table.getItemNamed(s)
#print(s,'already',it.text())
_.typeitem = it
_.typeitem.setText(s)
_.setWindowTitle(s)
def generate(_):
gen = _.selectGenerator
if gen.currentText() == "ALL" :
gens = [ gen.itemText(x) for x in range(gen.count())]
gens.remove('ALL')
[ _.__generate(g) for g in gens ]
else :
_.__generate(gen.currentText())
def __generate(_,gen):
G = _.generators._get(gen)
# add the class list to the generator, so it can differenciate
# types and primitive types from Objects
_.generators.classes = [ c.name for c in classTables]
res = G(_.generators,_) # method is called externally as functor
print(res)
def copy(_):
name = _.name + str(len(classTables))
if name in types.names():
QMessageBox.warning(None,'woops','''
There's already a class named %s''' % name,QMessageBox.Ok)
else:
m = [list(m.items())[0] for m in _.members.dict()]
C = ClassTable(
typesTable=types,
data=m,
name=name)
classTables.append(C)
C.show()
C.move(_.x()+_.width(),_.y()+20)
def save(_):
return {
_.name : {
'position':[ _.x(), _.y()],
'size' : [_.width(), _.height()],
'generator': _.selectGenerator.currentIndex(),
'members':[ x for x in _.members.dict() ]
}
}
def generatorChanged(_,i):
if ClassTable.boundGenerators :
[ c.selectGenerator.setCurrentIndex(i) for c in classTables]
class GenTable(QWidget): # superflu
def __init__(_, table, name='undefined', parent=None):
super().__init__(parent)
grid = QGridLayout()
hbox = QHBoxLayout()
vbox = QVBoxLayout()
_.name = name
_.table = table
_.nameEdit = QLineEdit()
_.generateButton = QPushButton("generate")
_.nameEdit.textEdited.connect(_.setName)
_.generateButton.clicked.connect(_.generate)
vbox.addWidget(table)
hbox.addWidget(_.nameEdit)
hbox.addWidget(_.generateButton)
grid.addLayout(hbox,0,0)
grid.addLayout(vbox,1,0)
_.setLayout(grid)
_.nameEdit.setText(name)
_.setWindowTitle(name)
def setName(_,s):
_.name = s
_.table.name = s
def generate(_):
r = _.table.generate()
return r
def yml(_):
return "%s: %s" % (_.name,"[%s]" % ",".join(_.table.names()))
def save(_):
T = _.table.save()
T.update({
'position':[_.x(),_.y()],
'size':[_.width(),_.height()]
})
return T
class MainWindow(QMainWindow):
'''
need an intermediate central widget?
'''
def __init__(_):
super().__init__()
_.app = app
_.setWindowTitle('Genclass')
_.setWindowIcon(QIcon(icon))
_.forceclose = True
_.lastsavefile = None
menubar = _.menuBar()
Gui = gui.GUI(qApp, _)
G = dict()
for k, a in Gui['actions'].items():
G[k] = gui.buildAction(_, k, *a)
for menu in Gui['Menus']:
m = menubar.addMenu(menu[0])
for entry in menu[1]:
m.addAction(G[entry])
_.center()
def center(_):
qr = _.frameGeometry()
cp = QDesktopWidget().availableGeometry().center()
qr.moveCenter(cp)
_.move(qr.topLeft())
def keyPressEvent(_,event):
if event.key() == Qt.Key_Escape:
sys.exit()
def closeEvent(_, event):
if not _.forceclose :
event.accept()
reply = QMessageBox.question(_, 'Message',
"Are you sure to quit?",
QMessageBox.Yes | QMessageBox.No,
QMessageBox.No )
if reply == QMessageBox.Yes :
#_.clean_at_exit()
return event.accept()
else: event.ignore()
else:
sys.exit()
def changeEvent(_,event):
if event.type() == QEvent.ActivationChange:
if _.isActiveWindow():
[ c.raise_() for c in classTables ]
def save(_):
if not _.lastsavefile:
_.saveAs()
else:
t = yaml.dump({'types':typeTable.save()})
classes = {'classes':[x.save() for x in classTables]}
ya = yaml.dump(classes)
print(ya)
with open(_.lastsavefile,'w') as file:
file.write(t)
file.write(ya)
def saveAs(_):
fileName = QFileDialog.getSaveFileName(
_,"Save File",
"genclass.yml",
"yaml (*.yml)")
if fileName[0]:
with open(fileName[0],'w') as file:
file.write(yaml.dump({'types':typeTable.save()}))
classes = {'classes':[x.save() for x in classTables]}
ya = yaml.dump(classes)
print(ya)
file.write(ya)
_.lastsavefile = fileName[0]
print('saved')
def open(_):
fileName = QFileDialog.getOpenFileName(
_,"Save File",
"genclass.yml",
"yaml (*.yml)")
if fileName[0]:
_.load(fileName[0])
def load(_,file):
S = yml.load(file)
[ x.close() for x in classTables ]
classTables.clear()
t = Struct(S.types)
typeTable.nameEdit.setText(t.name)
typeTable.setName(t.name)
typeTable.table.load(t.names)
typeTable.show()
typeTable.move(*t.position)
typeTable.resize(*t.size)
for x in S.classes:
k = list(x.keys())[0]
klass = x[k]
print('loading %s' % k)
C = ClassTable(
typeTable.table,
[list(m.items())[0] for m in klass['members']],
k)
C.selectGenerator.setCurrentIndex(klass['generator'])
C.show()
C.move(*klass['position'])
C.resize(*klass['size'])
classTables.append(C)
_.lastsavefile = file
def newclass(_):
if '' in types.names():
QMessageBox.warning(None,'woops','''
There's already a class without a name,
and only one nameless class is allowed at a time.
so set its name first before creating a new one''',QMessageBox.Ok)
else:
C = ClassTable(typesTable=types, data=[('a',['int',0])])
classTables.append(C)
C.show()
C.move(_.x()+_.width(),_.y())
def newdict(_):
D = DictTable({1:1})
classTables.append(D)
D.show()
D.move(_.x()+_.width(),_.y())
def bindGenerators(_):
ClassTable.boundGenerators = not ClassTable.boundGenerators
def generateAll(_):
[ c.generate() for c in classTables]
print('done')
if __name__ == '__main__':
app = QApplication(sys.argv)
types = TypesTable(KnownDefinitions.types, 'types')
typeTable = GenTable(types,'types')
typeTable.show()
m = MainWindow()
m.show()
#m.load('genclass.yml')
'''
D = DictTable({'A':1})
D1 = GenTable(D)
D1.show()
D1.move(700,700)
'''
app.exec_()
sys.exit()
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,141
|
netrunners/genclass
|
refs/heads/master
|
/generators/reactList.py
|
from .utils import upper, lower
def ReactList(_,o):
definition = _.definitions['reactList']
imports = definition['imports']
th = definition['th']
td = definition['td']
members = [m[0] for m in _._getmembers(o)] #
fields = {
'name': lower(o.name),
'Name': upper(o.name),
'th' : '\n'.join([th.format(m) for m in members]),
'td' : '\n'.join([td.format(m) for m in members]),
}
Class = definition['class'].format(**fields)
out = "/*\n%s*/\n%s\n%s" % ( _._header(o), imports, Class)
_._write(out,o.name,definition['file'].format(o.name))
return out
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,142
|
netrunners/genclass
|
refs/heads/master
|
/scaff.py
|
import importlib
import imp
from importlib import import_module
import ast, types, sys, time
from inspect import signature, getmembers, isclass, ismethod, _empty
from monguo import Field,StringField,IntegerField, ReferenceField
import torg.debuglog
log = torg.debuglog.get(__name__)
def GetSubList(obj):
if isclass(type(obj)):
if type(obj) is list or type(obj) is tuple:
# set the name as the indice in the list
# i stays as integer for holder[i] access
members = [(i, o) for i, o in enumerate(obj)]
return members
else:
try:
members = getmembers(
obj, lambda x: not isinstance(x, types.ModuleType))
# discardedFields = [ '__builtins__','__cached__','__doc__','__file__','__loader__','__name__','__package__','__spec__']
# __dict__,__dir__,__eq__,__format__,__ge__,__getattribute__,__gt__,__hash__,__init__,__le__,__lt__,__module__,__ne__,__new__,__reduce__,__reduce_ex__,__repr__,__setattr__,__sizeof__,__str__,__subclasshook__,__weakref__
#print(type(members))
#[ logger.debug(x + ' ' + str(o)) for(x,o) in members]
#[ print(x + ' ' + str(o)) for(x,o) in members]
members = [(name, o)
for (name, o) in members if not name.startswith('__')
]
#members.append(('__init__', obj.__init__))
return members
except Exception as e:
log.exception(e)
return []
def generate(Obj):
for name, o in GetSubList(Obj):
print(name)
if isinstance(o,Field) :
print(name, o.required)
if isinstance(o,StringField) :
print(name, o.required)
["{cls}{t}".format(cls="a", t=x) for x in [1,2]]
def forms(cls, Op):
return '''
class {cls}{Op}(Form):
{{fields}}
'''.format(cls=cls, Op=Op)
def generateForms(Cls, meth):
members = getmembers( Cls, lambda x: isinstance(x, Field))
for name, o in members:
validators = ''
if o.required :
validators += '''
InputRequired(message=T("%s")),
''' % name+' can''t be empty'
print(''' %s = %s(validators=[%s]) ''' % (
name,
o.__class__.__name__,
validators
))
if isinstance(o,Field) :
print(name, o.required)
if isinstance(o,StringField) :
print(name, o.required)
for m in meth:
form = forms(Obj.__name__,m)
def main():
# x = import_module("%s.%s" % (package,module))
#x = import_module(module,package)
fp, pathname, description = imp.find_module("torg/api/db/Debat")
module = imp.load_module("Debat.Debat", fp, pathname, description)
print(module)
generate(module)
from torg.api.db.Debat import Debat
if __name__ == '__main__':
meth = "Create Read Update Delete"
generateForms(Debat,meth.split())
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,143
|
netrunners/genclass
|
refs/heads/master
|
/Generators.py
|
import yaml,os
from generators.utils import lower
from PyQt5.QtWidgets import QMessageBox
class Generators:
'''
The code generators
the Generators are functions in this class not starting with a _
(ie considered private in some conventions)
'''
def __init__(_,definitions):
_.definitions = definitions.classes
def _load(gens):
for g in gens:
setattr(Generators,g.__name__,g)
def _classdict(_):
return _.__class__.__dict__
def _availables(_) :
L = [ x for x in _._classdict() if x[0] != '_' ]
L.sort(reverse=False)
implemented = L
'''
definitions = set(_.definitions.keys())
ind = implemented.difference(definitions)
dni = definitions.difference(implemented)
if ind :
print(ind, "an implemention exists but no template")
if dni :
print(dni, "template exists but not implemented ")
'''
return implemented
def _write(_,content,d,f):
dirName = lower(d)
fileName = lower(f)
gdir = os.path.join(_.dir,dirName)
print(gdir,dirName,fileName)
try:
if not os.path.exists(gdir):
os.mkdir(gdir)
with open(os.path.join(_.dir,fileName),'w') as file:
file.write(content)
except FileNotFoundError as fnfe:
QMessageBox.critical(None,'woops',str(fnfe), QMessageBox.Ok)
def _get(_,x):
return _._classdict()[x]
def _header(_,o):
return _.definitions['_header'].format(name=o.name)
def _Yaml(_,o):
return yaml.dump({
o.name : o.members.dict()
})
def _getmembers(_,o):
return [ list(x.items())[0] for x in o.members.dict() ]
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,144
|
netrunners/genclass
|
refs/heads/master
|
/yml.py
|
import yaml
def load(file):
class Struct:
def __init__(self,rawdat) :
self.__dict__ = rawdat
with open(file) as f:
rawDic = yaml.safe_load(f)
return Struct(rawDic)
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,145
|
netrunners/genclass
|
refs/heads/master
|
/generators/wtform.py
|
from .utils import upper
def WtForm(_,o):
# retrieve class template
definition = _.definitions['WtForm']
imports = definition['imports']
Cls = definition['class']
# generate head
# generate class with head
fields = []
for x in _._getmembers(o): # write members
name = x[0]
f = upper(x[1][0])
fieldType = upper(f[0]) + f[1:]
req = x[1][1]
isList = x[1][2]
validators = 'validators=[InputRequired()]' if req else ''
'''
if f not in _.classes and f not in fieldsImported and f != 'Oid':
fieldsImported.append(fieldType + 'Field')
'''
if isList :
if f in _.classes:
field = "ListField(OidField(),%s)" % (validators)
else:
field = "ListField(%sField(),%s)" % (fieldType,validators)
else :
if f in _.classes:
field = "OidField()" # required
else:
field = "%sField(%s)" % (fieldType,validators)
fields.append(" %s = %s" % (name,field))
#importFields = 'from wtforms.fields import (%s)' % ','.join(fieldsImported)
out = '\n'.join([ imports, Cls.format(name=o.name+'CreateForm') ])
out += '\n'.join(fields)
out += '\n' + Cls.format(name=o.name+'UpdateForm')
out += '\n'.join(fields)
out += '\n' + Cls.format(name=o.name+'DeleteForm') + ' _id = ListField(OidField())' + '\n'
_._write(out,o.name,definition['file'].format(o.name))
return out
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,146
|
netrunners/genclass
|
refs/heads/master
|
/Struct.py
|
class Struct:
def __init__(self,rawdat) :
self.__dict__ = rawdat
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,147
|
netrunners/genclass
|
refs/heads/master
|
/gui.py
|
from collections import OrderedDict
from PyQt5.QtWidgets import QAction
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import QState, QStateMachine
from PyQt5.QtWidgets import QBoxLayout
Icons = './icons/'
win = None
def GUI(qApp,window):
global win
win = window
return {
'Menus': [
['File' ,[ 'Open','Save','Save As','Exit' ]],
['Tables' ,[ 'New Class', 'New Dict', 'Bind Generators', 'Generate all']],
],
'actions': OrderedDict([
# key statusText shortcut, icon func checkable
('Open' ,['Open file' ,'Ctrl+o' , None, window.open ]),
('Save' ,['Save file' ,'Ctrl+s' , None, window.save ]),
('Save As' ,['Save file' ,'Ctrl+shift+s', None, window.saveAs ]),
('Exit' ,['get out' ,'Ctrl+q' , None, window.close ]),
('New Class' ,['new class table', '' , None, window.newclass ]),
('New Dict' ,['new dict table' , '' , None, window.newdict ]),
('Bind Generators' ,['bind them' , '' , None, window.bindGenerators,True]),
('Generate all' ,['generate' , '' , None, window.generateAll ]),
]),
}
def buildAction(
parent,key,statusText=None,shortcut=None, icon=None,func=None,checkable=False
):
a = QAction(QIcon(Icons + ('' if icon is None else icon)), '&'+key, parent)
a.setStatusTip(statusText)
a.setShortcut(shortcut)
a.setCheckable(checkable)
a.triggered.connect(func)
return a
def twoStateButton(name,stateMachine,action,*states):
st = QStateMachine()
a1,a2 = states
s1,s2 = QState(),QState()
icon1 = QIcon(Icons + a1[1])
icon2 = QIcon(Icons + a2[1])
s1.setObjectName(a1[0])
s2.setObjectName(a2[0])
s1.assignProperty(action, "icon", icon1)
s2.assignProperty(action, "icon", icon2)
s1.assignProperty(action, "text", a1[0])
s2.assignProperty(action, "text", a2[0])
s1.addTransition(action.triggered, s2)
s2.addTransition(action.triggered, s1)
st.addState(s1)
st.addState(s2)
st.setInitialState(s1)
stateMachine[name] = st
st.start()
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,148
|
netrunners/genclass
|
refs/heads/master
|
/generators/monguo.py
|
from .utils import upper
def Monguo(_,o):
definition = _.definitions['Monguo']
out = '\n'.join([
"\'''",
_._header(o),
"\'''\n",
definition['imports'],
definition['document'].format(name=o.name)
])
for x in _._getmembers(o): # write members
name = x[0]
f = x[1][0]
req = x[1][1]
isList = x[1][2]
fieldType = upper(f[0]) + f[1:]
req = 'required=True' if req else ''
if isList :
if f in _.classes:
field = "ListField(ReferenceField('%s'),%s)" % (fieldType,req)
else:
field = "ListField(%sField(),%s)" % (fieldType,req)
else :
if f in _.classes:
field = "ReferenceField('%s',%s)" % (fieldType,req)
else:
field = "%sField(%s)" % (fieldType,req)
f = "%s = %s" % (name,field)
out += ' ' + f + '\n'
if o.cat.checkState():
out += 2*'\n' + definition['makeCategory'].format(o.name)
_._write(out,o.name,definition['file'].format(o.name))
return out
|
{"/generators/__init__.py": ["/generators/monguo.py", "/generators/reactComponent.py", "/generators/reactList.py", "/generators/torgHandler.py", "/generators/wtform.py"], "/generators/torgHandler.py": ["/generators/utils.py"], "/Genclass.py": ["/yml.py", "/gui.py", "/Struct.py", "/Generators.py", "/generators/__init__.py"], "/generators/reactList.py": ["/generators/utils.py"], "/Generators.py": ["/generators/utils.py"], "/generators/wtform.py": ["/generators/utils.py"], "/generators/monguo.py": ["/generators/utils.py"]}
|
31,158
|
timdvd/python_videoplayer
|
refs/heads/main
|
/main.py
|
import sys
from PyQt5.QtWidgets import QApplication
from mediaPlayer import MediaWindow
app = QApplication(sys.argv)
mediaPlayerWindow = MediaWindow()
sys.exit(app.exec_())
|
{"/main.py": ["/mediaPlayer.py"], "/mediaPlayer.py": ["/ui_mediaPlayer.py"]}
|
31,159
|
timdvd/python_videoplayer
|
refs/heads/main
|
/mediaPlayer.py
|
from PyQt5 import QtWidgets
from ui_mediaPlayer import Ui_MediaPlayer
from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QHBoxLayout, QVBoxLayout, QLabel, \
QSlider, QStyle, QSizePolicy, QFileDialog, QMessageBox
import sys
from PyQt5.QtMultimedia import QMediaPlayer, QMediaContent
from PyQt5.QtMultimediaWidgets import QVideoWidget
from PyQt5.QtGui import QIcon, QPalette
from PyQt5.QtCore import Qt, QUrl
class MediaWindow(QtWidgets.QMainWindow, Ui_MediaPlayer):
def __init__(self):
super().__init__()
self.setupUi(self)
self.show()
# Functions connecting
self.actionAdd_File.triggered.connect(self.open_file)
self.actionQuit.triggered.connect(self.closeEvent)
self.actionHelp.triggered.connect(self.aboutEvent)
self.playBtn.clicked.connect(self.play_video)
self.movieLengthSlider.sliderMoved.connect(self.set_pos)
self.volumeSlider.sliderMoved.connect(self.set_vol)
self.volumeBtn.clicked.connect(self.switch_vol)
self.mediaPlayer.stateChanged.connect(self.mediaPlayOrStop)
self.mediaPlayer.positionChanged.connect(self.position_changed)
self.mediaPlayer.durationChanged.connect(self.duration_changed)
# Functions defining
def hhmmss(self, ms):
# s = 1000
# m = 60000
# h = 360000
h, r = divmod(ms, 360000)
m, r = divmod(r, 60000)
s, _ = divmod(r, 1000)
return ("%02d:%02d:%02d" % (h, m, s)) if h else ("%d:%02d" % (m, s))
# Open file for 'Open File' button in menuBar
def open_file(self):
filename, _ = QFileDialog.getOpenFileName(self, "Open Video")
if filename != '':
self.mediaPlayer.setMedia(QMediaContent(QUrl.fromLocalFile(filename)))
self.playBtn.setEnabled(True)
self.volumeBtn.setEnabled(True)
# Close function for quit button in menuBar
def closeEvent(self, event):
self.close()
def aboutEvent(self, event):
about = QMessageBox.about(self, 'About', "This my first program that was written with pyqt5\n Thanks for using it")
# Play function
def play_video(self):
if self.mediaPlayer.state() == QMediaPlayer.PlayingState:
self.mediaPlayer.pause()
else:
self.mediaPlayer.play()
# Change pause and play buttons
def mediaPlayOrStop(self, state):
if self.mediaPlayer.state() == QMediaPlayer.PlayingState:
self.playBtn.setIcon(QIcon("images/pause.png"))
else:
self.playBtn.setIcon(QIcon("images/play.png"))
def position_changed(self, position):
self.movieLengthSlider.setValue(position)
if position >= 0:
self.startLabel.setText(self.hhmmss(position))
def duration_changed(self, duration):
self.movieLengthSlider.setMaximum(duration)
if duration >= 0:
self.fullLabel.setText(self.hhmmss(duration))
def set_pos(self, pos):
self.mediaPlayer.setPosition(pos)
# Volume sliding
def set_vol(self, vol):
self.mediaPlayer.setVolume(vol)
self.volumeLabel.setText(str(vol))
if vol >= 60:
self.volumeBtn.setIcon(QIcon("images/volume100.png"))
elif vol >= 30 and vol <= 59:
self.volumeBtn.setIcon(QIcon("images/volume60.png"))
elif vol >= 1 and vol <= 30:
self.volumeBtn.setIcon(QIcon("images/volume30.png"))
elif vol == 0:
self.volumeBtn.setIcon(QIcon("images/novolume.png"))
# Mute and unmute the video
def switch_vol(self):
if self.mediaPlayer.volume() == 0:
self.set_vol(50)
self.volumeSlider.setValue(50)
self.volumeLabel.setText(str(50))
elif self.mediaPlayer.volume() != 0:
self.set_vol(0)
self.volumeSlider.setValue(0)
self.volumeLabel.setText(str(0))
|
{"/main.py": ["/mediaPlayer.py"], "/mediaPlayer.py": ["/ui_mediaPlayer.py"]}
|
31,160
|
timdvd/python_videoplayer
|
refs/heads/main
|
/ui_mediaPlayer.py
|
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'mediaPlayer.ui'
#
# Created by: PyQt5 UI code generator 5.13.0
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import Qt, QUrl, QSize
# PyQt5 Multimedia imports for media playing
from PyQt5.QtMultimedia import QMediaPlayer, QMediaContent
from PyQt5.QtMultimediaWidgets import QVideoWidget
# GUI media class
class Ui_MediaPlayer(object):
def setupUi(self, MediaPlayer):
MediaPlayer.setObjectName("MediaPlayer")
MediaPlayer.setMinimumSize(QSize(720,480))
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(14)
MediaPlayer.setFont(font)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap("images/player.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
MediaPlayer.setWindowIcon(icon)
MediaPlayer.setStyleSheet("background-color: #EDEDED;")
self.centralwidget = QtWidgets.QWidget(MediaPlayer)
self.centralwidget.setObjectName("centralwidget")
MediaPlayer.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MediaPlayer)
self.menubar.setGeometry(QtCore.QRect(0, 0, 762, 27))
font = QtGui.QFont()
font.setPointSize(12)
self.menubar.setFont(font)
self.menubar.setStyleSheet("border-bottom: 1px solid black;")
self.menubar.setObjectName("menubar")
self.menuFile = QtWidgets.QMenu(self.menubar)
self.menuFile.setObjectName("menuFile")
self.menuApp = QtWidgets.QMenu(self.menubar)
self.menuApp.setObjectName("menuApp")
MediaPlayer.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MediaPlayer)
self.statusbar.setObjectName("statusbar")
MediaPlayer.setStatusBar(self.statusbar)
self.actionAdd_File = QtWidgets.QAction(MediaPlayer)
self.actionAdd_File.setObjectName("actionAdd_File")
self.actionHelp = QtWidgets.QAction(MediaPlayer)
self.actionHelp.setObjectName("actionHelp")
self.actionQuit = QtWidgets.QAction(MediaPlayer)
self.actionQuit.setObjectName("actionQuit")
self.menuFile.addAction(self.actionAdd_File)
self.menuApp.addAction(self.actionHelp)
self.menuApp.addAction(self.actionQuit)
self.menubar.addAction(self.menuApp.menuAction())
self.menubar.addAction(self.menuFile.menuAction())
self.mediaPlayer = QMediaPlayer(None, QMediaPlayer.VideoSurface)
# create videowidget object
videowidget = QVideoWidget()
# Play button
self.playBtn = QtWidgets.QPushButton()
self.playBtn.setEnabled(False)
self.playBtn.setIcon(QIcon("images/play.png"))
self.playBtn.setIconSize(QtCore.QSize(50, 50))
self.playBtn.setStyleSheet("QPushButton {\n"
" background-color: transparent;\n"
" border-radius: 50%;\n"
" color: orange;\n"
" border: none;\n"
" width: 50px;\n"
" height: 50px;\n"
"}\n")
# Start Label time
self.startLabel = QtWidgets.QLabel()
self.startLabel.setText('00:00')
labelfont = QtGui.QFont()
labelfont.setFamily("Arial")
labelfont.setPointSize(16)
self.startLabel.setFont(labelfont)
# Movie length slider
self.movieLengthSlider = QtWidgets.QSlider(Qt.Horizontal)
self.movieLengthSlider.setRange(0,0)
# Full length of the movie
self.fullLabel = QtWidgets.QLabel()
self.fullLabel.setText('00:00')
self.fullLabel.setFont(labelfont)
# Volume button
self.volumeBtn = QtWidgets.QPushButton()
self.volumeBtn.setEnabled(False)
self.volumeBtn.setIcon(QIcon("images/volume60.png"))
self.volumeBtn.setIconSize(QtCore.QSize(50, 50))
self.volumeBtn.setStyleSheet("QPushButton {\n"
" background-color: transparent;\n"
" border-radius: 50%;\n"
" color: rgb(0, 0, 0);\n"
" border: none;\n"
" width: 50px;\n"
" height: 50px;\n"
"}\n")
# Movie volume slider
self.volumeSlider = QtWidgets.QSlider(Qt.Horizontal)
self.volumeSlider.setRange(0, 0)
self.volumeSlider.setFocusPolicy(Qt.StrongFocus)
self.volumeSlider.setTickPosition(QtWidgets.QSlider.TicksBothSides)
self.volumeSlider.setSingleStep(1)
self.volumeSlider.setMinimum(0)
self.volumeSlider.setMaximum(100)
self.volumeSlider.setValue(50)
self.volumeSlider.setStyleSheet("QSlider {\n"
" width: 50px;\n"
" height: 30px;\n"
"}\n")
self.volumeSlider.setMaximumSize(100, 30)
self.volumeLabel = QtWidgets.QLabel()
self.volumeLabel.setText('50')
self.volumeLabel.setFont(labelfont)
self.volumeLabel.setStyleSheet("QPushButton {\n"
" background-color: transparent;\n"
" color: rgb(0, 0, 0);\n"
" border: none;\n"
" width: 30px;\n"
" height: 50px;\n"
"}\n")
# Horizontal box for navigation
self.hboxLayout = QtWidgets.QHBoxLayout()
self.hboxLayout.setContentsMargins(0, 0, 0, 0)
self.hboxLayout.addWidget(self.playBtn)
self.hboxLayout.addWidget(self.startLabel)
self.hboxLayout.addWidget(self.movieLengthSlider)
self.hboxLayout.addWidget(self.fullLabel)
self.hboxLayout.addWidget(self.volumeBtn)
self.hboxLayout.addWidget(self.volumeSlider)
# Vertical widget for all media
self.vboxLayout = QtWidgets.QVBoxLayout(self.centralwidget)
self.vboxLayout.addWidget(videowidget)
self.vboxLayout.addLayout(self.hboxLayout)
self.setLayout(self.vboxLayout)
self.mediaPlayer.setVideoOutput(videowidget)
self.retranslateUi(MediaPlayer)
QtCore.QMetaObject.connectSlotsByName(MediaPlayer)
def retranslateUi(self, MediaPlayer):
_translate = QtCore.QCoreApplication.translate
MediaPlayer.setWindowTitle(_translate("MediaPlayer", "MediaPlayer"))
self.menuFile.setTitle(_translate("MediaPlayer", "File"))
self.menuApp.setTitle(_translate("MediaPlayer", "App"))
self.actionAdd_File.setText(_translate("MediaPlayer", "Open File"))
self.actionHelp.setText(_translate("MediaPlayer", "About"))
self.actionQuit.setText(_translate("MediaPlayer", "Quit"))
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
MediaPlayer = QtWidgets.QMainWindow()
ui = Ui_MediaPlayer()
ui.setupUi(MediaPlayer)
MediaPlayer.show()
sys.exit(app.exec_())
|
{"/main.py": ["/mediaPlayer.py"], "/mediaPlayer.py": ["/ui_mediaPlayer.py"]}
|
31,324
|
oldsix-learn/Passenger-Flow-Forecast
|
refs/heads/master
|
/export.py
|
import datetime
import os
import re
import time
import warnings
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import feature_selection
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
path = '' # csv文件所在目录
for root, dirs, files in os.walk('a9'):
if len(files) > 0:
path = 'a9/'
else:
path = 'http://114.55.125.234:1111/a9/'
dic = {
'晴': 0,
'多云': 1,
'阴': 2,
'小雨': 3,
'中雨': 4,
'大雨': 5,
'暴雨': 6,
'雷阵雨': 7
}
warnings.filterwarnings('ignore')
class MyModel(object):
def __init__(self, date, text_day=0, route=None, station=None, all=False):
self.over = False
self.data = pd.read_csv(path + 'trips.csv', encoding='gbk')[['进站名称', '进站时间', '出站名称']]
_date = datetime.datetime.strptime(date, '%Y-%m-%d').strftime('%Y-%m-%d')
if _date > datetime.datetime.strptime("2020-7-15", '%Y-%m-%d').strftime('%Y-%m-%d'):
self.over = True
if self.over:
self.end = datetime.datetime.strptime('2020-7-15', '%Y-%m-%d').strftime('%Y-%m-%d')
self.true_end = datetime.datetime.strptime(date, '%Y-%m-%d').strftime('%Y-%m-%d')
self.begin = (datetime.datetime.strptime('2020-7-15', '%Y-%m-%d') - datetime.timedelta(
days=text_day + 10)).strftime(
'%Y-%m-%d')
else:
self.end = datetime.datetime.strptime(date, '%Y-%m-%d').strftime('%Y-%m-%d')
self.true_end = self.end
self.begin = (
datetime.datetime.strptime(date, '%Y-%m-%d') - datetime.timedelta(days=text_day + 10)).strftime(
'%Y-%m-%d')
self.data.set_index('进站时间', inplace=True, drop=True)
self.data.index = pd.DatetimeIndex(self.data.index)
if not all:
self.data = self.data.loc[self.begin:self.end]
else:
self.data = self.data.loc[:self.end]
self.data.reset_index(inplace=True)
self.station = station
self.route = route
self.text_day = text_day
self.fea = ['昨日客流量', 'month', '标注', '上周客流量', 'MA5', 'MA10', '最高气温', '最低气温', 'morning', 'afternoon']
# self.fea = []
def clean(self):
station = pd.read_csv(path + 'test_station.csv', encoding='gbk')[['line_name', 'station_name']]
station.drop(station.loc[~(station['line_name'] == self.route)].index.values, inplace=True)
self.data.drop(self.data.loc[(self.data['出站名称'] == self.data['进站名称'])].index.values, inplace=True)
self.data.drop(self.data.loc[~self.data['进站名称'].isin(station['station_name']) & ~self.data['出站名称'].isin(
station['station_name'])].index.values, inplace=True)
self.data.rename(columns={'进站时间': '日期'}, inplace=True)
self.data = self.data.groupby(pd.to_datetime(self.data['日期']).dt.to_period('D')).size().reset_index(
name='客流量').set_index('日期')
self.data.fillna(0, inplace=True)
self.data = self.data['客流量'].to_frame()
def _clean(self):
data1 = self.data.copy()
self.data.drop(self.data.loc[self.data['进站名称'] != self.station].index.values, inplace=True)
data1.drop(data1.loc[data1['出站名称'] != self.station].index.values, inplace=True)
self.data = pd.merge(self.data, data1, how='outer')
self.data.drop(self.data.loc[(self.data['出站名称'] == self.data['进站名称'])].index.values, inplace=True)
self.data.rename(columns={'进站时间': '日期'}, inplace=True)
self.data = self.data.groupby(pd.to_datetime(self.data['日期']).dt.to_period('D')).size().reset_index(
name='客流量').set_index('日期')
self.data.fillna(0, inplace=True)
self.data = self.data['客流量'].to_frame()
def add(self):
# if self.over:
weather = pd.read_csv(path + 'city.csv')
weather['日期'] = weather['日期'].apply(lambda x: datetime.datetime.strptime(x, '%Y年%m月%d日').strftime('%Y-%m-%d'))
weather['最高气温'] = weather['最高气温'].apply(lambda x: re.findall('\d+', x)[0]).astype(int)
weather['最低气温'] = weather['最低气温'].apply(lambda x: re.findall('\d+', x)[0]).astype(int)
weather['morning'] = weather['天气状况'].apply(lambda x: dic[re.findall('(.*) /(.*)', x)[0][0]])
weather['afternoon'] = weather['天气状况'].apply(lambda x: dic[re.findall('(.*) /(.*)', x)[0][1]])
weather.drop('天气状况', axis=1, inplace=True)
weather.set_index('日期', drop=True, inplace=True)
weather.index = pd.DatetimeIndex(weather.index)
days = pd.read_csv(path + '2020.csv', encoding='gbk', parse_dates=['日期'], index_col='日期')
days = pd.concat([weather, days], axis=1)
days = days.to_period("D")
self.data = pd.concat([self.data, days], axis=1)
self.data.reset_index(inplace=True)
self.data['上周客流量'] = self.data['客流量'].shift(7)
self.data['昨日客流量'] = self.data['客流量'].shift(1)
self.data['MA5'] = self.data['客流量'].rolling(5).mean()
self.data['MA10'] = self.data['客流量'].rolling(10).mean()
# print(self.data)
self.data['month'] = self.data['日期'].map(lambda x: x.month)
self.data.set_index('日期', drop=True, inplace=True)
if self.over:
# self.pre_data = self.data.loc[datetime.datetime.strptime('2020-7-6', '%Y-%m-%d').strftime('%Y-%m-%d'):self.true_end]
self.pre_data = self.data.loc[:self.true_end]
self.pre_data.reset_index(inplace=True)
else:
self.pre_data = None
self.data.reset_index(inplace=True)
self.data.dropna(inplace=True)
# self.data.drop(self.data.loc[(self.data['标注'] == 3)].index.values, inplace=True)
# exit()
# print(self.data)
def test_model(self):
time.sleep(2)
self.dat = self.data.tail(1)
self.data.drop(self.data.tail(1).index.values, inplace=True)
self.X = self.data[self.fea]
self.y = self.data['客流量']
self.X.index = range(self.X.shape[0])
self.X_length = self.X.shape[0]
split = int(self.X_length * 0.9)
self.X_train, self.X_test = self.X[:split], self.X[split:]
self.y_train, self.y_test = self.y[:split], self.y[split:]
self.date = self.data['日期'][split:]
Regressors = [["RandomForest", RandomForestRegressor()]
, ["DecisionTree", DecisionTreeRegressor()]
, ["Lasso", Lasso()]
, ["AdaBoostRegressor", AdaBoostRegressor()]
, ["GradientBoostingRegressor", GradientBoostingRegressor()]
, ["XGB", XGBRegressor()]
]
reg_result = []
names = []
prediction = []
for name, reg in Regressors:
reg = reg.fit(self.X_train, self.y_train)
y_pred = reg.predict(self.X_test)
# 回归评估
mae = mean_absolute_error(self.y_test, y_pred)
mse = mean_squared_error(self.y_test, y_pred)
r2 = r2_score(self.y_test, y_pred)
class_eva = pd.DataFrame([mae, mse, r2])
reg_result.append(class_eva)
name = pd.Series(name)
names.append(name)
y_pred = pd.Series(y_pred)
prediction.append(y_pred)
names = pd.DataFrame(names)
names = names[0].tolist()
result = pd.concat(reg_result, axis=1)
result.columns = names
result.index = ["mae", "mse", "r2"]
# print(result.head())
a = result.loc['r2']
if max(a.tolist()) < 0:
print('error')
return self.data['客流量'].head(30).mean()
func = getattr(self, "_%s" % a.idxmax())
return func()
def feature(self):
self.X = self.data[self.fea]
self.y = self.data['客流量']
# self.data.loc[(self.data['标注'] == 3)]
fv, pv = feature_selection.f_regression(self.X, self.y)
# data = [fv, pv]
df = pd.DataFrame()
df['feature'] = self.X.columns
df['fv'] = fv
df['pv'] = pv
print(df)
# self.fea = df.loc[df['fv'] > 0.5]['feature'].tolist()
self.fea = df.sort_values(by='fv')['feature'].head(7).tolist()
# print(df)
print(self.fea)
# print(df.loc['fv'].reset_index(inplace=True))
# print(df.loc[df.loc['fv'] > 1])
# feature = self.data.drop(['客流量'], axis=1)
# corr = feature.corr()
# plt.figure(figsize=(15, 6))
# ax = sns.heatmap(corr, xticklabels=corr.columns,
# yticklabels=corr.columns, linewidths=0.2, cmap="RdYlGn", annot=True)
# plt.title("Correlation between variables")
# plt.show()
# df_onehot = pd.get_dummies(self.data)
# plt.figure(figsize=(15, 6))
# df_onehot.corr()['客流量'].sort_values(ascending=False).plot(kind='bar')
# plt.title('Correlation between 客流量 and variables')
# plt.show()
def importance(self, features, reg, name):
impor = pd.DataFrame([*zip(features, reg.feature_importances_)])
impor.columns = ['feature', 'importance']
impor.sort_values(by='importance', inplace=True)
plt.barh(impor['feature'], height=0.5, width=impor['importance'])
plt.title("%s选择特征重要性" % name)
plt.show()
def pre_route(self):
# 清洗数据
self.clean()
# print('2运行时间：', int(time.time() - be), 'sec')
# print(self.data)
# print(self.data['日期'])
# 增加特征
self.add()
# print('3运行时间：', int(time.time() - be), 'sec')
# 特征工程
# self.feature()
# 选择最好模型
return self.test_model()
def pre_station(self):
self._clean()
# 增加特征
self.add()
# 特征工程
# self.feature()
# 选择最好模型
return self.test_model()
def prin(self, y_pred2, y_test=None):
print(self.end, '-------------')
print('预测值：', y_pred2[0])
if y_test:
print('真实值：', y_test.tolist()[0])
def date_over(self, y_pred2, model):
end = self.end
while end < self.true_end:
end = (datetime.datetime.strptime(end, '%Y-%m-%d') + datetime.timedelta(days=1)).strftime('%Y-%m-%d')
self.pre_data.loc[self.pre_data['日期'] == end, '客流量'] = y_pred2
self.update()
X_test = self.pre_data.loc[self.pre_data['日期'] == end]
X_test = X_test[self.fea]
y_pred2 = model.predict(X_test)
return y_pred2[0]
def _Lasso(self):
# rfc = Lasso()
# rfc = rfc.fit(self.X_train, self.y_train)
# y_pred = rfc.predict(self.X_test)
reg = Lasso()
reg = reg.fit(self.X, self.y)
X_test = self.dat[self.fea]
# y_test = self.dat['客流量']
y_pred2 = reg.predict(X_test)
# self.prin(y_pred2, y_test)
# self.importance(X.columns, reg, 'Lasso')
# self.draw(y_pred, self.y_test, 'Lasso')
return self.date_over(y_pred2, reg)
def _RandomForest(self):
reg = RandomForestRegressor(n_estimators=30
, random_state=123
, bootstrap=True
, oob_score=True
)
reg = reg.fit(self.X, self.y)
X_test = self.dat[self.fea]
# y_test = self.dat['客流量']
y_pred2 = reg.predict(X_test)
# self.prin(y_pred2, y_test)
return self.date_over(y_pred2, reg)
def _DecisionTree(self):
model = DecisionTreeRegressor(max_depth=3, random_state=123)
model.fit(self.X, self.y)
X_test = self.dat[self.fea]
y_pred2 = model.predict(X_test)
# y_test = self.dat['客流量']
# self.prin(y_pred2, y_test)
return self.date_over(y_pred2, model)
def _AdaBoostRegressor(self):
model = AdaBoostRegressor(random_state=123)
# model.fit(self.X_train, self.y_train)
# y_pred = model.predict(self.X_test)
model.fit(self.X, self.y)
X_test = self.dat[self.fea]
y_pred2 = model.predict(X_test)
# print(y_test, y_pred2)
# self.draw(y_pred, self.y_test, 'AdaBoostRegressor')
return self.date_over(y_pred2, model)
def _GradientBoostingRegressor(self):
model = GradientBoostingRegressor(random_state=123)
# model.fit(self.X_train, self.y_train)
# y_pred = model.predict(self.X_test)
model.fit(self.X, self.y)
X_test = self.dat[self.fea]
y_pred2 = model.predict(X_test)
# y_test = self.dat['客流量']
# self.prin(y_pred2, y_test)
# self.draw(y_pred, self.y_test, 'GradientBoostingRegressor')
return self.date_over(y_pred2, model)
def _XGB(self):
model = XGBRegressor()
# model.fit(self.X_train, self.y_train)
# y_pred = model.predict(self.X_test)
model.fit(self.X, self.y)
X_test = self.dat[self.fea]
y_pred2 = model.predict(X_test)
# y_test = self.dat['客流量']
# self.prin(y_pred2, y_test)
# self.draw(y_pred, self.y_test, 'XGB')
return self.date_over(y_pred2, model)
def draw(self, y_pred, y_test, name):
date = []
for i in self.date.tolist():
# print(type(i.to_timestamp().strftime('%Y-%m-%d')), i)
date.append(i.to_timestamp().strftime('%Y-%m-%d'))
plt.figure(figsize=(15, 6))
plt.title(name + '预测结果图')
plt.plot(date, y_test.ravel(), label='真实值')
plt.plot(date, y_pred, label='预测值')
plt.xticks()
plt.legend()
plt.show()
def update(self):
self.pre_data.loc[:, '上周客流量'] = self.pre_data['客流量'].shift(7)
self.pre_data.loc[:, '昨日客流量'] = self.pre_data['客流量'].shift(1)
self.pre_data.loc[:, 'MA5'] = self.pre_data['客流量'].rolling(5).mean()
self.pre_data.loc[:, 'MA10'] = self.pre_data['客流量'].rolling(10).mean()
if __name__ == '__main__':
print('Hello')
# 控制显示， 生产环境无关
pd.set_option('display.max_columns', None) # 显示完整的列
pd.set_option('display.max_rows', None) # 显示完整的行
pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.max_columns', 1000) # 对齐
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
pd.set_option('display.unicode.ambiguous_as_wide', True)
pd.set_option('display.unicode.east_asian_width', True)
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# path = '../a9/' # csv文件所在目录
"""
all = True ==> 所有数据
text_day 测试数据
date 预测日
over 预测日期超过7月15日， 就要True
"""
be = time.time()
# pre = MyModel(date='2020-7-15', text_day=90, route='1号线')
# a = pre.pre_route()
# print(a)
# print('1运行时间：', int(time.time()-be), 'sec')
pre2 = MyModel(date='2020-7-16', text_day=120, station='Sta97', all=True)
a = pre2.pre_station()
print(a)
print('2运行时间：', int(time.time() - be), 'sec')
|
{"/example.py": ["/export.py"], "/app.py": ["/export.py"]}
|
31,325
|
oldsix-learn/Passenger-Flow-Forecast
|
refs/heads/master
|
/example.py
|
from export import MyModel
import time
def pre_station(date, station):
pre = MyModel(date=date, text_day=120, station=station, all=True)
return pre.pre_station()
def pre_route(date, route):
pre = MyModel(date=date, text_day=90, route=route)
return pre.pre_route()
if __name__ == '__main__':
be = time.time()
print(pre_station('2020-8-15', 'Sta126'))
# print('1运行时间：', int(time.time() - be), 'sec')
print(pre_route('2020-8-15', '2号线'))
# print('2运行时间：', int(time.time() - be), 'sec')
|
{"/example.py": ["/export.py"], "/app.py": ["/export.py"]}
|
31,326
|
oldsix-learn/Passenger-Flow-Forecast
|
refs/heads/master
|
/total.py
|
import json
import os
import pandas as pd
import datetime as dt
path = ''
for root, dirs, files in os.walk('a9'):
if len(files) > 0:
path = 'init_data/'
else:
path = 'http://114.55.125.234:1111/init_data/'
trips = pd.read_csv(os.path.join(path, 'clean_data.csv'), encoding='gbk') # 读取
user = pd.read_csv(os.path.join(path, 'users.csv'), encoding='gbk')
days = pd.read_csv(os.path.join(path, 'workdays2020.csv'), encoding='gbk')
days.columns = ['date', 'type']
days['date'] = pd.to_datetime(days['date'], format='%Y%m%d')
station_path = os.path.join(path, 'test_station.csv')
station = pd.read_csv(station_path, encoding='gbk')
def my_write(data, name):
"""
将数据写成json格式
:param data: 数据
:param name: 文件名
:return:
"""
with open(os.path.join(os.path.join('static', 'data'), name), mode='w', encoding='utf-8') as f:
f.write(json.dumps(data, sort_keys=True, indent=4, separators=(',', ': '),
ensure_ascii=False))
def line_group():
stations = station.copy()
route_list = stations['line_name'].unique()
return route_list # 返回线路表
def station_group():
stations = station.copy()
station_list = stations['station_name'].unique()
return station_list # 返回线路表
def count_bymonth(): # 统计单月客流
entertime = pd.to_datetime(trips['in_time']).to_frame() # 转换为时间
data_month1 = entertime.groupby(
[entertime['in_time'].dt.year.rename('year'), entertime['in_time'].dt.month.rename('month')],
as_index=False).size() # 按年和月分组
data_month1 = data_month1[data_month1['year'] == 2020]
ans = data_month1['size'].tolist()
date = []
for y, m in zip(data_month1['year'], data_month1['month']):
date.append((str(y) + '-' + str(m)))
my_write({'counts':ans,'date':date},'a.json')
# return date, ans
def weekdays_or_weekends_flow():
trip = trips.copy()
trip.rename(columns={'in_time': 'date'}, inplace=True)
trips_date = trip['date'].astype(str).str[0:10]
trips_date = pd.to_datetime(trips_date)
ans1 = []
ans2 = []
for i in range(7):
temp = pd.merge(trips_date, days, on='date')
temp = temp[temp['date'].dt.month == (i + 1)]
ans = temp.groupby([temp['type'], temp['date'].dt.day], as_index=False).size()
temp1 = ans.loc[ans['type'] == 1]
temp2 = ans.loc[ans['type'] == 2]
x = temp1['size'].tolist();
y = temp2['size'].tolist();
ans1.append(int(sum(x) / len(x)))
ans2.append(int(sum(y) / len(y)))
my_write({'workday':ans2,'weekday':ans1},'b.json')
# return ans1, ans2
def inflow_allyear(): # 入站客流 top10
trips_data = trips.copy()
trips_data['year'] = pd.to_datetime(trips_data['in_time']).dt.year
trips_data['month'] = pd.to_datetime(trips_data['in_time']).dt.month
trips_data = trips_data.loc[trips_data['year'] == 2020]
count_data = trips_data.groupby(['in_name'], as_index=False).size()
count_data = count_data.sort_values(by='size', ascending=False)
top_list = count_data.iloc[0:10, 0].tolist()
result = [[]] * 10
for i in range(len(top_list)):
trips_temp = trips_data.loc[trips_data['in_name'] == top_list[i]]
ans = trips_temp.groupby(['month'], as_index=False).size()
list1 = ans['month'].tolist()
list2 = ans['size'].tolist()
if 2 not in list1:
list1.insert(1, 2)
list2.insert(1, 0)
result[i] = list2
ans = {}
temp2 = [[]] * 7
for i in range(7):
temp = []
for j in range(10):
temp.append(result[j][i])
temp2[i] = temp
for i in range(7):
ans['2020-' + str(i + 1)] = temp2[i]
my_write([top_list,ans],'111.json')
# return top_list, ans
def outflow_allyear(): # 出站客流 top10
trips_data = trips.copy()
trips_data['year'] = pd.to_datetime(trips_data['out_time']).dt.year
trips_data['month'] = pd.to_datetime(trips_data['out_time']).dt.month
trips_data = trips_data.loc[trips_data['year'] == 2020]
count_data = trips_data.groupby(['out_name'], as_index=False).size()
count_data = count_data.sort_values(by='size', ascending=False)
top_list = count_data.iloc[0:10, 0].tolist()
result = [[]] * 10
for i in range(len(top_list)):
trips_temp = trips_data.loc[trips_data['out_name'] == top_list[i]]
ans = trips_temp.groupby(['month'], as_index=False).size()
list1 = ans['month'].tolist()
list2 = ans['size'].tolist()
if 2 not in list1:
list1.insert(1, 2)
list2.insert(1, 0)
result[i] = list2
ans = {}
temp2 = [[]] * 7
for i in range(7):
temp = []
for j in range(10):
temp.append(result[j][i])
temp2[i] = temp
for i in range(7):
ans['2020-' + str(i + 1)] = temp2[i]
my_write([top_list, ans], 'c_out.json')
# return top_list, ans
def old(): # 乘客年龄结构
user_data = user.copy()
user_data['age'] = dt.datetime.today().year - user_data['出生年份']
age_0_count = len(user_data.loc[user_data.age < 20])
age_20_count = len(user_data.loc[(user_data.age < 30) & (user_data.age >= 20)])
age_30_count = len(user_data.loc[(user_data.age < 40) & (user_data.age >= 30)])
age_40_count = len(user_data.loc[(user_data.age < 60) & (user_data.age >= 40)])
age_60_count = len(user_data.loc[user_data.age >= 60])
ans = ['20岁以下', '20~30岁', '30~40岁', '40~60岁', '60岁以上']
ans2 = [age_0_count, age_20_count, age_30_count, age_40_count, age_60_count]
result = dict(zip(ans, ans2))
my_write(result,'d.json')
# return result
def peak_flow():
trips_data = trips.copy()
trips_data['in_time'] = pd.to_datetime(trips_data['in_time'])
trips_data['year'] = pd.to_datetime(trips_data['in_time']).dt.year
trips_data['month'] = pd.to_datetime(trips_data['in_time']).dt.month
trips_data['day'] = pd.to_datetime(trips_data['in_time']).dt.day
trips_data['hour'] = trips_data['in_time'].dt.hour
trips_data = trips_data.loc[trips_data['year'] == 2020]
early = trips_data.loc[(trips_data['hour'] >= 7) & (trips_data['hour'] <= 9)] # 根据站统计
evening = trips_data.loc[(trips_data['hour'] >= 17) & (trips_data['hour'] <= 19)]
ans1 = early.groupby(['month', 'day'], as_index=False).size()
ans2 = evening.groupby(['month', 'day'], as_index=False).size()
ans1['size2'] = ans2['size']
ans = []
for a, b in ans1.iterrows():
temp = {
'date': "2020-{}-{}".format(b['month'].astype(str), b['day'].astype(str)),
'early': b['size'].astype(str),
'evening': b['size2'].astype(str)
}
ans.append(temp)
my_write(ans,'ear_peak.json')
# return ans
def _od():
trip = trips.copy()
link = trip.groupby(['in_name', 'out_name']).size().reset_index(name='客流量')
dic = []
for name, i in link.iterrows():
di = {'source': "{}_0".format(i['in_name']),
'target': "{}_1".format(i['out_name']),
'value': i['客流量'],
}
dic.append(di)
# with open('data111.json', 'w') as f:
# f.write(json.dumps(dic))
return dic
def od2():
lines=station.copy()
lines.drop(columns=['id','district','sequence'],inplace=True)
in_station=lines.copy()
out_station=lines.copy()
in_station.columns=['in_name','in_line']
out_station.columns = ['out_name', 'out_line']
trip_data = trips.copy()
trip_data = pd.merge(trip_data, in_station, on='in_name', how='left')
trip_data = pd.merge(trip_data, out_station, on='out_name', how='left')
link=trip_data.groupby(['in_line','out_line'],as_index=False).size()
dic=[]
for name, i in link.iterrows():
di = {'source': "{}_上行".format(i['in_line']),
'target': "{}_下行".format(i['out_line']),
'value': i['size'],
}
dic.append(di)
my_write(dic,'line-to-line_od.json')
# return dic
def line_section_flow():
data = trips.copy()
station_data = station.copy()
station_in = data.groupby('in_name')['price'].count().reset_index(name='in_count')
station_out = data.groupby('out_name')['price'].count().reset_index(name='out_count')
station_in_and_out = pd.merge(station_in, station_out, left_on='in_name', right_on='out_name')
line_station = station_data.groupby(['line_name', 'station_name'])['id'].count().reset_index(name='id')
result = {}
for line in line_station['line_name'].unique():
result[line] = []
line_sta = [s for s in line_station.loc[line_station['line_name'] == line]['station_name']]
result[line].append(line_sta)
in_count = [in_c for sta in line_sta for in_c in
station_in_and_out.loc[station_in_and_out['in_name'] == sta]['in_count']]
result[line].append(in_count)
out_count = [out_c for sta in line_sta for out_c in
station_in_and_out.loc[station_in_and_out['out_name'] == sta]['out_count']]
result[line].append(out_count)
my_write(result,'line_section.json')
# return result
# print(line_group())
# print(station_group())
#以下全部直接返回json数据
# count_bymonth() # a-单月统计
# weekdays_or_weekends_flow() # b-工作日和周末
# inflow_allyear() # c_in入站top10
# outflow_allyear() # c_out出站top10
# old() # d 年龄结构2
# peak_flow() # e 早晚高峰
od2() #f 线路to线路客流
# line_section_flow() g 线路断面客流
|
{"/example.py": ["/export.py"], "/app.py": ["/export.py"]}
|
31,327
|
oldsix-learn/Passenger-Flow-Forecast
|
refs/heads/master
|
/app.py
|
import filecmp
import json
import os
import sys
import time
from threading import Thread
from flask import Flask, render_template
from flask_sqlalchemy import SQLAlchemy
from export import MyModel
app = Flask(__name__)
app.secret_key = os.getenv('SECRET_KEY', 'secret string')
app.jinja_env.trim_blocks = True
app.jinja_env.lstrip_blocks = True
app.config['SQLALCHEMY_DATABASE_URI'] = "mysql+pymysql://admin:root@114.55.125.234:3306/passenger-flow"
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
DB_CACHE = 'db_cache'
def pre_station(date, station):
pre = MyModel(date=date, text_day=120, station=station, all=True)
return pre.pre_station()
def pre_route(date, route):
pre = MyModel(date=date, text_day=90, route=route)
return pre.pre_route()
def removeSame(name):
"""
首先移除所有包含name的空文件，然后在剩余文件中选择时间最近的文件
:param name: 部分文件名
:return: 无返回值
"""
path = os.path.join(sys.path[0], DB_CACHE)
files_list = []
last_time = '0000_00_00_00_00_00'
for root, dirs, files in os.walk(path):
for file in files:
if file.find(name) != -1:
if os.path.getsize(os.path.join(root, file)) == 0:
os.remove(os.path.join(root, file))
continue
files_list.append(file)
file_time = file[:19]
if file_time > last_time:
last_time = file_time
flag = True
for i in range(len(files_list)):
for j in range(len(files_list) - 1):
j = j + 1
if not filecmp.cmp(os.path.join(sys.path[0], DB_CACHE, files_list[i]),
os.path.join(sys.path[0], DB_CACHE, files_list[j])):
flag = False
break
file = files_list[i]
if file.find(last_time) == -1 and os.path.exists(os.path.join(path, file)):
os.remove(os.path.join(path, file))
return flag
def readDB():
db = SQLAlchemy(app)
db.reflect()
all_table = {table_obj.name: table_obj for table_obj in db.get_tables_for_bind()}
db_tables = list(all_table.keys())
flag = True
for name in db_tables:
now_time = time.strftime("%Y_%m_%d_%H_%M_%S")
data = [dict(zip(result.keys(), result)) for result in db.session.query(all_table[name]).all()]
data = json.dumps(data, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False)
print(name)
with open(os.path.join(sys.path[0], DB_CACHE, '{}_{}_data.json'.format(now_time, name)), mode='w') as f:
f.write(data)
flag = removeSame(name)
if not flag:
return flag
return flag
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict/<select>/<pre_time>/<name>')
def predict(select, pre_time, name):
print(pre_time, name)
if select == 'station':
data = pre_station(pre_time, name)
return '{}'.format(int(data))
elif select == 'route':
data = pre_route(pre_time, name)
return '{}'.format(int(data))
class readDBThread(Thread):
def __init__(self):
Thread.__init__(self)
def run(self) -> None:
while True:
print(time.ctime(), '开始读取数据库')
readDB()
print(time.ctime(), '读取成功')
time.sleep(10)
readDBThread().start()
|
{"/example.py": ["/export.py"], "/app.py": ["/export.py"]}
|
31,328
|
oldsix-learn/Passenger-Flow-Forecast
|
refs/heads/master
|
/run.py
|
import os
import requests
data = ['a9/2020.csv', 'a9/city.csv', 'a9/test_station.csv', 'a9/trips.csv', 'init_data/clean_data.csv',
'init_data/test_station.csv', 'init_data/users.csv', 'init_data/workdays2020.csv']
# def get_all_name():
# data_dirs = ['a9', 'init_data']
# result = []
# for dir_item in data_dirs:
# for root, dirs, files in os.walk(dir_item):
# for file in files:
# result.append('{}/{}'.format(dir_item, file))
# print(result)
if not os.path.exists('a9'):
os.mkdir('a9')
if not os.path.exists('init_data'):
os.mkdir('init_data')
print(os.path.exists(data[0]))
for item in data:
if not os.path.exists(item):
print('开始下载 {}'.format(item))
with open(item, mode='wb') as f:
f.write(requests.get('http://114.55.125.234:1111/{}'.format(item)).content)
print('下载成功 {}'.format(item))
print('Success')
# get_all_name()
|
{"/example.py": ["/export.py"], "/app.py": ["/export.py"]}
|
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