Split (1)

train · 3.29M rows

index int64	repo_name string	branch_name string	path string	content string	import_graph string
30,908	ssh6189/2019.12.10	refs/heads/master	/연습.py	num = "" for i in range(10): if i <= 5 and (i % 2)==0: continue elif i is 7 or i is 10: continue else: num = str(i) + num print(num)	{"/test.py": ["/calc.py"]}
30,909	ssh6189/2019.12.10	refs/heads/master	/monster.py	import os class monster: def status(at, df): ats = at dfs = df mk = monster() mk.at = int(input("몬스터의 공격력을 입력하시오.")) mk.df = int(input("몬스터의 방어력을 입력하시오.")) os.system('cls') print(mk.at) print(mk.df)	{"/test.py": ["/calc.py"]}
30,910	ssh6189/2019.12.10	refs/heads/master	/구구단 계산기.py	import time import os n = int(input("구구단 몇단을(1~9)? 계산할까요?, 종료하고 싶으시면, 0을 입력하시오.")) while(n == 1): n = int(input("구구단 몇단을(1~9)? 계산할까요?, 종료하고 싶으시면, 0을 입력하시오.")) print("구구단 n단을 계산합니다") for i in range(1, 10): print(n, "x", i, "=", n*i) time.sleep(5) os.system('cls') print("프로그램을 종료합니다.")	{"/test.py": ["/calc.py"]}
30,911	ssh6189/2019.12.10	refs/heads/master	/이중 반복문.py	k = 1 st = " " for i in range(4): for j in range(3): st = st + " " + str(k) k = k + 1 print(st) st = " " print( )	{"/test.py": ["/calc.py"]}
30,912	ssh6189/2019.12.10	refs/heads/master	/문자열.py	문자열은 시퀀스 자료형 문자열과 메모리 공간 일반적으로 문자열을 저장하기 위해서는 영문자 한 글자당 1바이트의 메모리 공간을 사용한다, 다음과 같은 코드로 문자열이 저장된 공간의 크기를 확인할 수 있다. 컴퓨터공학자들은 이러한 문자를 처리하기 위해 이진수로 변환되는 표준규칙을 만들었다. 이를 인코딩이라고 한다. 1.컴퓨터는 문자를 직접 인식하지 못한다. 2.컴퓨터는 문자를 숫자로 변환하여 인식한다. 3.사람들은 문자를 숫자로 변환하기 위한 규칙을 만들었다. 4.일반적으로 이 규칙은 1개의 영문자를 1바이트, 즉, 2의 8승 정도의 공간에 저장할 수 있다. 기억만해두자 0 = 48 A = 65 a = 97 우리가 사용하는 모든 문자는 컴퓨터에 수로 저장되어있다. 문자열의 인덱싱 리스트처럼 글자 하나하나가 상대적인 주소를 가지는데, 이 주소를 사용해 할당된 값을 가져오는 인덱싱을 사용할 수 있다. 문자열의 슬라이싱 문자열의 주소값을 기반으로 문자열의 부분값을 지정한다. 문자열끼리 더하면 결합연산 곱하면 반복연산 문자열의 연산 다음과 같이 코드를 작성하면, 문자열과 정수형의 연산으로 인식하여 덧셈이 실행되지 않는다. 문자열의 연산 len() 문자열의 문자 개수 반환 upper() 문자로 변환 lower() 소문자로 변환 title() 각 단어의 앞글자만 대문자로 변환 capitalize() 첫 문자를 대문자로 변환 count('') '찾을 문자열'이 몇개 들어 있는지 개수 반환 find('') '찾을 문자열'의 인덱스를 앞에서부터 하나만 찾은 뒤 종료, 단어 입력시 첫글자의 인덱스 반한, 없으면 -1 반환 rfind('') '찾을 문자열'의 인덱스를 뒤에서부터 하나만 찾은 뒤 종료, 단어 입력시 첫글자의 인덱스 반한, 없으면 -1 반환 startswith('') 입력한 단어로 시작하는지 판단해서 맞으면 True, 아니면, False endswith('') 입력한 단어로 끝나는지 판단해서 맞으면 True, 아니면, False strip() 좌우공백삭제 rstrip() 오른쪽공백 삭제 lstrip() 왼쪽공백 삭제 split() 구분자를 기준으로 문자열을 잘라 리스트에 저장 isdigit() 해당 문자열이 숫자인지를 True 또는 False로 반환 islower() 모든 문자가 소문자인지 판단해서 맞으면 True 틀리면, False로 반환 isupper() 모든 문자가 대문자인지 판단해서 맞으면 True 틀리면, False로 반 "", '' 출력법 '""' "''" 두줄이상으로 작성시 """내용""" '''내용''' s = """I'm Okay I'm happy see you """	{"/test.py": ["/calc.py"]}
30,913	ssh6189/2019.12.10	refs/heads/master	/수 추측.py	import random import time import os n = -1 answer = random.randrange(1, 100) life = 4 while ((n != answer)): os.system('cls') print("life : ", life+1) n = int(input("추측하는 수를 입력하시오.")) if (life > 0): if (answer > n): print("더 큰 수를 입력하시오.") time.sleep(1) life = life - 1 elif (answer < n): print("더 작은 수를 입력하시오.") life = life - 1 time.sleep(1) else: print("정답입니다!") break else: print("라이프가 모두 소진되었습니다. 당신의 패배입니다.") break str(input())	{"/test.py": ["/calc.py"]}
30,914	ssh6189/2019.12.10	refs/heads/master	/튜플.py	# A tuple is a sequence of immutable Python objects. # tuple 생 tup1 = ('physics', 'chemistry', 1997, 2000) tup2 = (1, 2, 3, 4, 5 ) tup3 = "a", "b", "c", "d" print(tup3) list1=[] #empty list 생성? tup1 = () #error tup1 = (50,) #요소 1개인 tuple 생성할 때 반드시 ','와 함께... #리스트와 동일한 인덱싱 방식으로 요소에 접근, 슬라이싱 가능 print ("tup1[0]: ", tup1[0]) print ("tup2[1:5]: ", tup2[1:5]) tup1 = (12, 34.56) tup2 = ('abc', 'xyz') #tup1[0] = 100; #요소 변경은 불변객체이므로 error list1=[1] list2=[2] list1+list2 print(list1) tup3 = tup1 + tup2 # tuple이 결합되어 새로운 tuple 객체 생성 print (tup3) print(tup1)	{"/test.py": ["/calc.py"]}
30,915	ssh6189/2019.12.10	refs/heads/master	/십진수를 이진수로 변환.py	decimal = 10 result = '' while ((decimal > 10)): remainder = decimal %2 decimal = decimal // 2 result = str(remainder) + result print(result) str(input())	{"/test.py": ["/calc.py"]}
30,936	ServiceNow/azimuth	refs/heads/main	/tests/test_config.py	import os from datetime import datetime from glob import glob from os.path import join as pjoin import pytest from jsonlines import jsonlines from pydantic import ValidationError from azimuth.config import ( ArtifactsConfig, AzimuthConfig, AzimuthConfigHistoryWithHash, PipelineDefinition, SupportedLanguage, TemperatureScaling, ThresholdConfig, config_defaults_per_language, load_azimuth_config, ) from azimuth.utils.project import update_config CURR_PATH = os.path.dirname(os.path.dirname(__file__)) def test_loading_config(): config_search = pjoin(CURR_PATH, "config/*/.json") all_configs = glob(config_search, recursive=True) assert len(all_configs) > 0, f"No config found using {config_search}!" exceptions = [] for c in all_configs: try: AzimuthConfig.parse_file(c) except Exception as e: exceptions.append({"file": c, "exceptions": e}) if exceptions: raise RuntimeError(f"Found the following errors: {exceptions}") def test_config_shortcut(): mod = PipelineDefinition( name="MyPotato", model={"class_name": "potato"}, postprocessors=[{"temperature": 2}, {"threshold": 0.4}], ) assert isinstance(mod.postprocessors[0], TemperatureScaling) assert isinstance(mod.postprocessors[1], ThresholdConfig) mod = PipelineDefinition( name="MyPotato", model={"class_name": "potato"}, postprocessors=[ { "class_name": "my_cool_postpro", }, {"threshold": 0.4}, ], ) assert not isinstance(mod.postprocessors[0], TemperatureScaling) assert isinstance(mod.postprocessors[1], ThresholdConfig) MINIMAL_CONFIG = { "model_contract": "hf_text_classification", "dataset": {"class_name": "potato"}, } def test_pipeline_names(): # Happy path, no name anywhere cfg = AzimuthConfig( MINIMAL_CONFIG, pipelines=[ {"model": {"class_name": "model1"}}, {"model": {"class_name": "model2"}}, ], ) names = [pipeline.name for pipeline in cfg.pipelines] assert names == ["Pipeline_0", "Pipeline_1"] # One pipeline is named cfg = AzimuthConfig( MINIMAL_CONFIG, pipelines=[ {"name": "PotatoPipeline", "model": {"class_name": "model1"}}, {"model": {"class_name": "model2"}}, ], ) names = [pipeline.name for pipeline in cfg.pipelines] assert names == ["PotatoPipeline", "Pipeline_1"] # Test duplicates with pytest.raises(ValidationError): cfg = AzimuthConfig( MINIMAL_CONFIG, pipelines=[ {"name": "PotatoPipeline", "model": {"class_name": "model1"}}, {"name": "PotatoPipeline", "model": {"class_name": "model2"}}, ], name="SuperProject", ) # More duplicates with pytest.raises(ValidationError): cfg = AzimuthConfig( MINIMAL_CONFIG, pipelines=[ {"name": "Pipeline_1", "model": {"class_name": "model1"}}, {"model": {"class_name": "model2"}}, ], ) def test_french_defaults_and_override(): subj_tags_potatoes = ["russet", "yukon_gold"] cfg = AzimuthConfig( MINIMAL_CONFIG, language="fr", syntax={"subj_tags": subj_tags_potatoes}, ) assert ( cfg.syntax.subj_tags == subj_tags_potatoes ), "Config did not take user-provided values for subj_tags" assert ( cfg.syntax.spacy_model == config_defaults_per_language[SupportedLanguage.fr].spacy_model ), "Config did not take default French value for spacy_model" assert ( cfg.syntax.obj_tags == config_defaults_per_language[SupportedLanguage.fr].obj_tags ), "Config did not take default French value for spacy_model" assert ( cfg.similarity.faiss_encoder == config_defaults_per_language[SupportedLanguage.fr].faiss_encoder ), "Config did not take default French value for faiss encoder" assert ( cfg.behavioral_testing.neutral_token.suffix_list == config_defaults_per_language[SupportedLanguage.fr].suffix_list ), "Config did not take default French value for suffix list (neutral tokens)" assert ( cfg.behavioral_testing.neutral_token.prefix_list == config_defaults_per_language[SupportedLanguage.fr].prefix_list ), "Config did not take default French value for prefix list (neutral tokens)" def test_config_min_max_values(): AzimuthConfig( MINIMAL_CONFIG, pipelines=[{"model": {"class_name": "Cls"}, "postprocessors": [{"temperature": 0}]}], ) with pytest.raises(ValidationError): AzimuthConfig( MINIMAL_CONFIG, pipelines=[{"model": {"class_name": "Cls"}, "postprocessors": [{"temperature": -0.1}]}], ) AzimuthConfig( MINIMAL_CONFIG, pipelines=[{"model": {"class_name": "Cls"}, "postprocessors": [{"threshold": 0}]}], ) AzimuthConfig( MINIMAL_CONFIG, pipelines=[{"model": {"class_name": "Cls"}, "postprocessors": [{"threshold": 1}]}], ) with pytest.raises(ValidationError): AzimuthConfig( MINIMAL_CONFIG, pipelines=[{"model": {"class_name": "Cls"}, "postprocessors": [{"threshold": -0.1}]}], ) with pytest.raises(ValidationError): AzimuthConfig( MINIMAL_CONFIG, pipelines=[{"model": {"class_name": "Cls"}, "postprocessors": [{"threshold": 1.1}]}], ) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"min_num_per_class": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"min_num_per_class": 0}) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_class_imbalance": 0}) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_class_imbalance": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_class_imbalance": -0.1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_class_imbalance": 1.1}) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_representation": 0}) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_representation": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_representation": -0.1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_representation": 1.1}) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_mean_words": 0}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_mean_words": -0.1}) AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_std_words": 0}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, dataset_warnings={"max_delta_std_words": -0.1}) AzimuthConfig(MINIMAL_CONFIG, syntax={"short_utterance_max_word": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, syntax={"short_utterance_max_word": 0}) AzimuthConfig(MINIMAL_CONFIG, syntax={"long_utterance_min_word": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, syntax={"long_utterance_min_word": 0}) AzimuthConfig(MINIMAL_CONFIG, similarity={"conflicting_neighbors_threshold": 0}) AzimuthConfig(MINIMAL_CONFIG, similarity={"conflicting_neighbors_threshold": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, similarity={"conflicting_neighbors_threshold": -0.1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, similarity={"conflicting_neighbors_threshold": 1.1}) AzimuthConfig(MINIMAL_CONFIG, similarity={"no_close_threshold": -1}) AzimuthConfig(MINIMAL_CONFIG, similarity={"no_close_threshold": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, similarity={"no_close_threshold": -1.1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, similarity={"no_close_threshold": 1.1}) AzimuthConfig(MINIMAL_CONFIG, uncertainty={"iterations": 1}) with pytest.raises(ValidationError): AzimuthConfig(MINIMAL_CONFIG, uncertainty={"iterations": 0}) AzimuthConfig(MINIMAL_CONFIG, uncertainty={"high_epistemic_threshold": 0}) with pytest.raises(ValidationError): AzimuthConfig(**MINIMAL_CONFIG, uncertainty={"high_epistemic_threshold": -0.1}) def test_update_config(tiny_text_config, monkeypatch, dask_client): config_history_path = tiny_text_config.get_config_history_path() # Changing config for a first time partial_config = {"similarity": None} new_config = update_config(tiny_text_config, partial_config) assert not new_config.similarity new_config.save() with jsonlines.open(config_history_path, "r") as reader: all_configs = list(reader) assert len(all_configs) == 1 assert all_configs[0]["config"] == new_config # Changing config for a second time partial_config = {"dataset_warnings": {"min_num_per_class": 40}} new_config = update_config(new_config, partial_config) assert new_config.dataset_warnings.min_num_per_class == 40 new_config.save() with jsonlines.open(config_history_path, "r") as reader: all_configs = list(reader) assert len(all_configs) == 2 assert all_configs[-1]["config"] == new_config assert datetime.fromisoformat(all_configs[-1]["created_on"]) > datetime.fromisoformat( all_configs[0]["created_on"] ), "Second config should be created on after the first one." def test_load_from_config_history(tiny_text_config): # With no config history, the loaded config is the default, not the tiny_text_config. cfg = load_azimuth_config(config_path=None, load_config_history=True) assert cfg == AzimuthConfig() # With a config history, the loaded config is the last one from the config history. tiny_text_config.save() os.environ["ARTIFACT_PATH"] = tiny_text_config.artifact_path cfg = load_azimuth_config(config_path=None, load_config_history=True) assert cfg == tiny_text_config del os.environ["ARTIFACT_PATH"] def test_config_history_with_hash(): default_config = AzimuthConfigHistoryWithHash(config={}) specified_hash_ignored = AzimuthConfigHistoryWithHash(config={}, hash="Potato") assert specified_hash_ignored.hash == default_config.hash specified_default_value = AzimuthConfigHistoryWithHash(config={"name": "New project"}) assert specified_default_value.hash == default_config.hash specified_different_value = AzimuthConfigHistoryWithHash(config={"name": "Potato"}) assert specified_different_value.hash != default_config.hash # Make sure the hash doesn't cause an extra exception if the config has a validation error. with pytest.raises( ValidationError, match="1 validation error for AzimuthConfigHistoryWithHash\nconfig -> name" ): AzimuthConfigHistoryWithHash(config={"name": None}) def test_artifact_path_equality(): # This is important since we forbid updating the config if the artifact_path differs. default = ArtifactsConfig() assert ArtifactsConfig(artifact_path="cache/../cache") == default assert ArtifactsConfig(artifact_path=f"{os.getcwd()}/cache") == default	{"/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", 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30,937	ServiceNow/azimuth	refs/heads/main	/azimuth/types/general/module_arguments.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 Any, Dict, List, Optional from pydantic import Field from azimuth.types import AliasModel, SupportedMethod from azimuth.types.outcomes import OutcomeName from azimuth.types.tag import DataAction class AbsDatasetFilters(AliasModel): confidence_min: float = 0 confidence_max: float = 1 data_action: List[DataAction] = [] outcome: List[OutcomeName] = [] smart_tags: Dict[str, List[str]] = {} utterance: Optional[str] # Must contain this subset. class DatasetFilters(AbsDatasetFilters): label: List[int] = [] prediction: List[int] = [] class NamedDatasetFilters(AbsDatasetFilters): label: List[str] = [] prediction: List[str] = [] def to_dataset_filters(self, class_names: List[str]) -> DatasetFilters: return DatasetFilters( confidence_min=self.confidence_min, confidence_max=self.confidence_max, label=[class_names.index(cl) for cl in self.label], prediction=[class_names.index(cl) for cl in self.prediction], data_action=self.data_action, outcome=self.outcome, smart_tags=self.smart_tags, utterance=self.utterance, ) class GradientCalculation(str, Enum): """Denote the different ways to handle the gradient calculation.""" xMIN = "xMIN" # gradient x input, then choose dimension with lowest value # gradient x input, then choose dimension with highest absolute value xABS_MAX = "xABS_MAX" xSUM = "xSUM" # gradient x input, then sum across all dimensions xL2 = "xL2" # gradient x input, then compute L2 norm L2 = "L2" # vanilla gradient, then compute L2 norm class ModuleOptions(AliasModel): model_contract_method_name: Optional[SupportedMethod] = Field( None, title="Method to call on a ModelContractModule" ) filters: DatasetFilters = Field(DatasetFilters(), title="Filter the dataset") indices: Optional[List[int]] = Field(None, title="Indices on which to run the module.") pipeline_index: Optional[int] = Field( None, title="Pipeline Index", description="What is the pipeline we are requesting." ) without_postprocessing: bool = Field(False, title="Get predictions without postprocessing") gradient_calculation: GradientCalculation = Field( GradientCalculation.L2, title="Gradient calculation" ) threshold: Optional[float] = Field(None, title="Threshold") iterations: int = Field(1, title="MC Iterations") filter_class: Optional[int] = Field( None, title="Get saliency for another class then the " "prediction." ) top_x: int = Field(10, title="Number of word to count in top saliency") th_importance: float = Field( 0.6, title="Threshold for a token to be considered important in " "its utterance relative to the max saliency value.", ) perc_salient_eval: float = Field( 0.03, title="Threshold for a salient word to be considered important for a given class.", ) max_perc_train: float = Field( 0.1, title="Threshold for a salient word to be considered overlapping in the training set for " "a given class.", ) use_bma: bool = Field(False, title="Use Bayesian Model Averaging for better estimation.") force_no_saliency: bool = Field( False, title="Force Top Words to use frequency instead of " "saliency to determine important words.", ) x_ticks_count: int = Field( 21, title="Number of ticks on x axis.", ) cf_normalize: bool = Field(True, title="Normalize the confusion matrix.") cf_reorder_classes: bool = Field( True, title="Reorder classes based on reverse Cuthill-Mckee algorithm." ) class ModuleEffectiveArguments(AliasModel): """Config scope and module options affecting a module's computation.""" mod_options: Dict[str, Any] config_scope: Dict[str, Any]	{"/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"], 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"/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", 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["/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"], 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"/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"]}
30,938	ServiceNow/azimuth	refs/heads/main	/tests/test_dataset_manager.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 re from glob import glob from os.path import join as pjoin import numpy as np import pandas as pd import pytest from datasets import ClassLabel, Dataset, Features, Value from azimuth.config import AzimuthValidationError from azimuth.dataset_split_manager import DatasetSplitManager, PredictionTableKey from azimuth.types import DatasetColumn, DatasetSplitName from azimuth.types.tag import ALL_STANDARD_TAGS, ALL_TAGS from azimuth.utils.project import load_dataset_from_config from tests.test_loading_resources import load_sst2_dataset from tests.utils import generate_mocked_dm, get_table_key @pytest.fixture def a_text_dataset(): return load_sst2_dataset()["validation"] def test_dataset_manager_tags(a_text_dataset, simple_text_config): tags = ["red", "blue", "orange"] ds_mng = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=tags, dataset_split=a_text_dataset, ) assert os.path.exists(ds_mng._save_path) assert all(t in ds_mng.get_dataset_split().column_names for t in tags) assert not any(ds_mng.get_dataset_split()["red"]) # Utterances can be tagged tags_data = {1: {"red": True}, 2: {"red": True, "blue": True}} ds_mng.add_tags(tags_data) assert sum(ds_mng.get_dataset_split()["red"]) == 2 assert sum(ds_mng.get_dataset_split()["blue"]) == 1 # We can reload a dataset! ds_mng = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=tags, dataset_split=a_text_dataset, ) assert sum(ds_mng.get_dataset_split()["red"]) == 2 assert sum(ds_mng.get_dataset_split()["blue"]) == 1 # We can't tag garbage tags_data = {4: {"red": True}, 5: {"red": True, "garbage": True}} with pytest.raises(ValueError): ds_mng.add_tags(tags_data) assert "garbage" not in ds_mng.get_dataset_split().column_names # We can query all tags tags_data = ds_mng.get_tags() assert len(tags_data) == len(ds_mng.get_dataset_split()) assert len(tags_data[0]) == len(tags) assert not any(tags_data[0].values()) assert tags_data[1]["red"] and tags_data[2]["red"] and tags_data[2]["blue"] # We can query some tags tags_data = ds_mng.get_tags(indices=[1, 2]) assert len(tags_data) == 2 assert len(tags_data[1]) == len(tags) assert tags_data[1]["red"] and tags_data[2]["red"] and tags_data[2]["blue"] def test_dataset_manager_init(a_text_dataset, simple_text_config): tags = ["red", "blue", "orange"] with pytest.raises(ValueError): _ = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=tags, dataset_split=None ) with pytest.raises(AzimuthValidationError, match="potato"): cfg = simple_text_config.copy(update={"columns": {"text_input": "potato"}}, deep=True) _ = DatasetSplitManager( DatasetSplitName.eval, config=cfg, initial_tags=tags, dataset_split=a_text_dataset ) with pytest.raises(AzimuthValidationError, match="potato"): cfg = simple_text_config.copy(update={"columns": {"label": "potato"}}, deep=True) _ = DatasetSplitManager( DatasetSplitName.eval, config=cfg, initial_tags=tags, dataset_split=a_text_dataset ) def test_class_distribution(a_text_dataset, simple_text_config): num_classes = len(a_text_dataset.features["label"].names) tags = ["red", "blue", "orange"] ds_mng = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=tags, dataset_split=a_text_dataset, ) class_distribution = ds_mng.class_distribution(labels_only=True) assert len(class_distribution) == num_classes assert sum(class_distribution) == len(a_text_dataset) assert len(ds_mng.get_class_names(labels_only=True)) == num_classes # We add REJECTION_CLASS even if its not in the dataset. class_distribution = ds_mng.class_distribution(labels_only=False) assert len(class_distribution) == num_classes + 1 assert sum(class_distribution) == len(a_text_dataset) assert len(ds_mng.get_class_names(labels_only=False)) == num_classes + 1 def test_to_csv(simple_text_config): dm = generate_mocked_dm(simple_text_config) dm.config.name = "newName" path = dm.save_csv(get_table_key(simple_text_config)) assert os.path.exists(path) name = os.path.basename(path) assert re.match(r"azimuth_export_newName_eval_\d{8}_\d{6}\.csv", name) df = pd.read_csv(path) assert all(t in df.columns for t in ALL_TAGS) index = {c: i for i, c in enumerate(df.columns)} assert ( index[DatasetColumn.row_idx] == index[simple_text_config.columns.persistent_id] # By default persistent_id is row_idx < index[simple_text_config.columns.text_input] < index[simple_text_config.columns.label] < index[DatasetColumn.model_predictions] < index[DatasetColumn.postprocessed_prediction] < index[DatasetColumn.model_confidences] < index[DatasetColumn.postprocessed_confidences] < index[DatasetColumn.model_outcome] < index[DatasetColumn.postprocessed_outcome] < index[DatasetColumn.pipeline_steps] < index[DatasetColumn.confidence_bin_idx] < index[ALL_TAGS[0]] ), df.columns.tolist() assert df["label"][0] in dm.get_class_names() rejection_class_idx = dm.rejection_class_idx assert ( df[DatasetColumn.postprocessed_prediction][ dm._base_dataset_split[DatasetColumn.postprocessed_prediction].index( rejection_class_idx ) ] == "REJECTION_CLASS" ) def test_to_csv_no_model(simple_text_config): dm = generate_mocked_dm(simple_text_config) dm.config.name = "newName" pt = dm.save_csv(table_key=None) assert os.path.exists(pt) name = os.path.basename(pt) assert name.count("_") == 5 # azimuth_export_{name}_{dataset_split}_{date}_{time}.csv splitted = name.split("_") assert splitted[2] == "newName" and splitted[3] == "eval" df = pd.read_csv(pt) assert all(t in df.columns for t in ALL_STANDARD_TAGS) index = {c: i for i, c in enumerate(df.columns)} assert ( index[DatasetColumn.row_idx] == index[simple_text_config.columns.persistent_id] # By default persistent_id is row_idx < index[simple_text_config.columns.text_input] < index[simple_text_config.columns.label] < index[ALL_TAGS[0]] ), df.columns.tolist() assert df["label"][0] in dm.get_class_names() @pytest.mark.parametrize("column_name", ["failed_parsing", "malformed"]) def test_malformed(simple_text_config, column_name): simple_text_config.columns.failed_parsing_reason = column_name dataset = Dataset.from_dict( { simple_text_config.columns.text_input: ["hello", "a6fd", "potato", "09%198", "rock"], simple_text_config.columns.label: [1, 1, 0, 0, 0], simple_text_config.columns.failed_parsing_reason: [ "", "not a word", "", "not a word", "", ], }, features=Features( { simple_text_config.columns.text_input: Value("string"), simple_text_config.columns.failed_parsing_reason: Value("string"), simple_text_config.columns.label: ClassLabel(num_classes=2), } ), ) dm = DatasetSplitManager( name=DatasetSplitName.eval, config=simple_text_config, initial_tags=[], dataset_split=dataset, ) assert len(dm._base_dataset_split) == 3 assert len(dm._malformed_dataset) == 2 # Both dataset are saved! dm2 = DatasetSplitManager( name=DatasetSplitName.eval, config=simple_text_config, initial_tags=[], dataset_split=None ) # NOTE: Can't compare fingerprint as the column are sorted when we load the dataset. assert dm._base_dataset_split.shape == dm2._base_dataset_split.shape assert dm._malformed_dataset.shape == dm2._malformed_dataset.shape assert len(dm2._base_dataset_split) == 3 assert len(dm2._malformed_dataset) == 2 def test_multi_tables(a_text_dataset, simple_text_config): simple_table_key = get_table_key(simple_text_config) dm = DatasetSplitManager( name="potato", config=simple_text_config, initial_tags=["a", "b", "c"], dataset_split=a_text_dataset, ) ds = dm.get_dataset_split(simple_table_key) assert len(ds) == dm.num_rows assert DatasetColumn.row_idx in ds.column_names # Add a column to a non-existent table dm.add_column_to_prediction_table( "prediction", np.random.randn(len(ds), 10).tolist(), PredictionTableKey(threshold=0.1, temperature=21, use_bma=False, pipeline_index=0), ) # Check that the table exists assert ( PredictionTableKey(0.1, 21, False, pipeline_index=0) in dm._prediction_tables and "prediction" in dm._prediction_tables[PredictionTableKey(0.1, 21, False, pipeline_index=0)].column_names ) dm.add_column_to_prediction_table( "prediction2", np.random.randn(len(ds), 10).tolist(), simple_table_key, ) ds = dm.get_dataset_split(simple_table_key) # Check that this is a new dataset. assert "prediction2" in ds.column_names assert "prediction" not in ds.column_names # Check that the new column was not added to the previous dataset. ds = dm.dataset_split_with_predictions(PredictionTableKey(0.1, 21, False, 0)) assert "prediction" in ds.column_names assert "prediction2" not in ds.column_names # Can add a column normally dm.add_column("is_potato", [True] len(ds)) assert "is_potato" in dm._base_dataset_split.column_names assert "is_potato" in dm.get_dataset_split(simple_table_key).column_names # Check that there are 2 tables created. assert "HF_datasets" in os.listdir(dm._project_path) assert len(os.listdir(os.path.join(dm._hf_path, "prediction_tables"))) == 2 with pytest.raises(ValueError, match="Length mismatch"): dm.add_column("is_potato2", [True] * (len(ds) - 1)) with pytest.raises(ValueError, match="Length mismatch"): dm.add_column("is_potato2", [True] * (len(ds) + 1)) with pytest.raises(ValueError, match="Length mismatch"): dm.add_column("is_potato2", []) def test_caching(a_text_dataset, simple_text_config): simple_table_key = get_table_key(simple_text_config) dm1 = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=["red", "blue", "orange"], initial_prediction_tags=["potato"], dataset_split=a_text_dataset, ) pred_path = dm1._prediction_path(simple_table_key) assert len(glob(pjoin(pred_path, "version_.arrow"))) == 0, "Some preds were cached at init?" assert len(glob(pjoin(dm1._save_path, "version_.arrow"))) == 1, "Dataset not saved on disk" initial_last_update = dm1.last_update dm1.add_column("pink", list(range(len(a_text_dataset)))) assert ( len(glob(pjoin(pred_path, "version_.arrow"))) == 0 ), "Some preds were cached when adding a column on the base." assert ( len(glob(pjoin(dm1._save_path, "version_.arrow"))) == 2 ), "Dataset not saved on disk when added a column" modified_last_update = dm1.last_update assert initial_last_update < modified_last_update, "Last update should be updated." dm1.add_column_to_prediction_table( "apple", list(range(len(a_text_dataset))), table_key=simple_table_key ) # NOTE: We create 2 version, because we initialize a first one before adding the column. assert ( len(glob(pjoin(pred_path, "version_.arrow"))) == 2 ), "Preds were not cached when adding a column." assert ( len(glob(pjoin(dm1._save_path, "version_.arrow"))) == 2 ), "New version of based dataset detected when adding prediction" assert modified_last_update < dm1.last_update, "Last update should be updated." dm2 = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=["red", "blue", "orange"], initial_prediction_tags=["potato"], ) assert not any( {"apple", "pink"}.difference(dm2.get_dataset_split(simple_table_key).column_names) ), "Not latest version loaded." def test_rejection_class_check(simple_text_config, a_text_dataset): simple_text_config.rejection_class = None dm = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=[], dataset_split=a_text_dataset, ) assert dm.rejection_class_idx == 2 simple_text_config.rejection_class = "negative" dm = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=[], dataset_split=a_text_dataset, ) assert dm.rejection_class_idx == 0 # First class simple_text_config.rejection_class = "Potatoes" with pytest.raises(AzimuthValidationError): dm = DatasetSplitManager( DatasetSplitName.eval, config=simple_text_config, initial_tags=[], dataset_split=a_text_dataset, ) def test_default_persistent_id(clinc_text_config): dataset_split_name = DatasetSplitName.eval ds = load_dataset_from_config(clinc_text_config)[dataset_split_name] persistent_id = clinc_text_config.columns.persistent_id assert persistent_id not in ds.column_names, f"{persistent_id} should not be in clinc dataset." dm = DatasetSplitManager( dataset_split_name, clinc_text_config, initial_tags=ALL_STANDARD_TAGS, dataset_split=ds, ) assert ( persistent_id in dm.get_dataset_split().column_names ), f"{persistent_id} should be in clinc dataset after DatasetSplitManager creation." def test_custom_persistent_id(simple_text_config, a_text_dataset): simple_text_config_persistent_id = simple_text_config.copy() simple_text_config_persistent_id.columns.persistent_id = "idx" dm = DatasetSplitManager( DatasetSplitName.eval, simple_text_config_persistent_id, initial_tags=ALL_STANDARD_TAGS, dataset_split=a_text_dataset, ) assert ( simple_text_config_persistent_id.columns.persistent_id in dm.get_dataset_split().column_names ) simple_text_config_new_persistent_id = simple_text_config.copy() simple_text_config_new_persistent_id.columns.persistent_id = "yukongold" with pytest.raises(ValueError, match="not found in the dataset"): DatasetSplitManager( DatasetSplitName.eval, simple_text_config_new_persistent_id, initial_tags=ALL_STANDARD_TAGS, dataset_split=a_text_dataset, ) a_text_dataset_new_col = a_text_dataset.add_column( name="yukongold", column=[1] * len(a_text_dataset) ) with pytest.raises(ValueError, match="need to be unique"): DatasetSplitManager( DatasetSplitName.eval, simple_text_config_new_persistent_id, initial_tags=ALL_STANDARD_TAGS, dataset_split=a_text_dataset_new_col, ) def test_convert_persistent_id_to_row_idx(a_text_dataset, simple_text_config): simple_text_config_persistent_id = simple_text_config.copy() # Set persistent_id as the utterance (str). simple_text_config_persistent_id.columns.persistent_id = simple_text_config.columns.text_input dm = DatasetSplitManager( DatasetSplitName.eval, simple_text_config_persistent_id, initial_tags=ALL_STANDARD_TAGS, dataset_split=a_text_dataset, ) ds = dm.get_dataset_split() persistent_ids = list(reversed(ds[simple_text_config.columns.text_input])) row_indices = dm.get_row_indices_from_persistent_id(persistent_ids) assert row_indices == list(reversed(range(len(ds)))) random_rows = [10, 0] persistent_ids = ds.select(random_rows)[simple_text_config.columns.text_input] row_indices = dm.get_row_indices_from_persistent_id(persistent_ids) assert row_indices == random_rows def test_export_proposed_actions(simple_text_config, a_text_dataset): dm = DatasetSplitManager( DatasetSplitName.eval, simple_text_config, initial_tags=ALL_STANDARD_TAGS, dataset_split=a_text_dataset, ) proposed_actions = {0: {"remove": True}, 2: {"relabel": True}} dm.add_tags(proposed_actions) path = 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"/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"]}
30,939	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/base_classes/__init__.py	# Do not reorder from azimuth.modules.base_classes.artifact_manager import ArtifactManager from azimuth.modules.base_classes.dask_module import ConfigScope, DaskModule from azimuth.modules.base_classes.module import Module from azimuth.modules.base_classes.expirable_mixin import ExpirableMixin from azimuth.modules.base_classes.indexable_module import ( DatasetResultModule, IndexableModule, ModelContractModule, ) from azimuth.modules.base_classes.aggregation_module import ( AggregationModule, ComparisonModule, FilterableModule, )	{"/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", 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30,940	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/ml/postprocessing.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 from typing import List import numpy as np from scipy.special import expit, softmax from azimuth.types import AliasModel, Array from azimuth.types.outcomes import OutcomeName class PostProcessingIO(AliasModel): texts: List[str] logits: Array[float] preds: Array[int] probs: Array[float] @property def is_multiclass(self): return np.allclose(self.probs.sum(-1), 1.0) def __getitem__(self, item: int) -> "PostProcessingIO": """Get a single utterance result from a batch result.""" return PostProcessingIO( texts=[self.texts[item]], logits=self.logits[item][np.newaxis, ...], preds=self.preds[item][np.newaxis, ...], probs=self.probs[item][np.newaxis, ...], ) class PredictionDetails(AliasModel): """Details of the predictions. Args: predictions: Prediction class names, sorted by confidences prediction: Predicted class, which can be different than the first element of predictions, when thresholding for instance. confidences: Sorted confidences outcome: Outcome based on label and prediction """ predictions: List[str] prediction: str confidences: List[float] outcome: OutcomeName class PostprocessingStepAPIResponse(AliasModel): """Class for saving the results in the dataset and the routes.""" output: PredictionDetails class_name: str class PostprocessingStep(AliasModel): """Class received from the pipeline, and used in the Prediction Module.""" output: PostProcessingIO class_name: str def __getitem__(self, item: int) -> "PostprocessingStep": """Useful to get from a batch result to a single utterance result.""" return PostprocessingStep(output=self.output[item], class_name=self.class_name) class Postprocessing(abc.ABC): """Generic Functors for postprocessing. From a PostProcessingIO object returns a transformed PostProcessingIO. PostProcessingIO must be consistent across steps! If we modify the logits, we must recompute the confidences. """ @abc.abstractmethod def __call__(self, post_processing_io: PostProcessingIO) -> PostProcessingIO: ... def to_prob(self, post_processing_io: PostProcessingIO) -> PostProcessingIO: """Return an updated version of `post_processing_io` with the correct probabilities. Args: post_processing_io: Model output with logits computed. Returns: Updated post_processing_io with probs computed. """ return PostProcessingIO( texts=post_processing_io.texts, logits=post_processing_io.logits, preds=post_processing_io.preds, probs=softmax(post_processing_io.logits, axis=1) if post_processing_io.is_multiclass else expit(post_processing_io.logits), ) class TemperatureScaling(Postprocessing): """Postprocessing step to perform Temperature scaling. Temperature scaling is a simple approach to improve calibration. By dividing logits by a single number, the softmax-ed probability will be closer to a uniform distribution. Args: temperature: Which value to divide the logits by. """ def __init__(self, temperature): self.temperature = temperature def __call__(self, post_processing_io: PostProcessingIO) -> PostProcessingIO: new_logits = post_processing_io.logits / self.temperature confidences = ( softmax(new_logits, axis=1) if post_processing_io.is_multiclass else expit(new_logits) ) return PostProcessingIO( texts=post_processing_io.texts, logits=new_logits, preds=post_processing_io.preds, probs=confidences, ) class Thresholding(Postprocessing): """Postprocessing step to perform thresholding. If a prediction confidence falls below the threshold, we make no prediction. Args: threshold: Threshold at which we make a prediction. rejection_class_idx: What to return when the prediction is below a threshold. """ def __init__(self, threshold, rejection_class_idx=-1): self.threshold = threshold self.rejection_class_idx = rejection_class_idx def __call__(self, post_processing_io: PostProcessingIO) -> PostProcessingIO: threshold = self.threshold or 0.0 predictions = np.where( post_processing_io.probs.max(axis=-1) > threshold, np.argmax(post_processing_io.probs, -1), self.rejection_class_idx, ) return PostProcessingIO( texts=post_processing_io.texts, logits=post_processing_io.logits, preds=predictions, probs=post_processing_io.probs, )	{"/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"], 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30,941	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/task_execution.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, Type, TypeVar, cast import structlog from dask.context import thread_state from distributed import get_client, rejoin, secede from azimuth.modules.base_classes import Module from azimuth.types import ModuleResponse log = structlog.get_logger(__name__) U = TypeVar("U") def get_task_result(task_module: Module, result_type: Type[U]) -> U: """Schedule task if not done, and get result with any necessary secession. Args: task_module: The module to get the result from. result_type: Type for function/Module result. Returns: The result of the Module. """ if task_module.should_be_started(): try: task_module.start_task_on_dataset_split(get_client()) except Exception: log.debug("Can't get a Client, launching manually.") result = task_module.compute_on_dataset_split() task_module._store_data_in_cache( result=result, indices=task_module.get_caching_indices() ) return cast(U, result) result_from_future = cast(U, safe_get(task_module)) task_module.clear() return result_from_future def safe_get(future: Module) -> List[ModuleResponse]: """Will secede if possible before getting the result. Args: future: The module to get the result from. Returns: The result of the Module. """ with WorkerSecede(): future.wait() # We wait for everything to be done, callbacks included! res = future.result() return res class WorkerSecede: """Remove worker from the pool and re-add it when exiting.""" def __enter__(self): can_secede = hasattr(thread_state, "execution_state") if can_secede: # Inform the scheduler we are waiting secede() def __exit__(self, exc_type, exc_val, exc_tb): can_secede = hasattr(thread_state, "execution_state") if can_secede: # Ask for control again rejoin()	{"/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": 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"/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"]}
30,942	ServiceNow/azimuth	refs/heads/main	/runner.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 uvicorn from azimuth.config import parse_args if __name__ == "__main__": args = parse_args() kwargs = dict( app="azimuth.app_loader:app", host="0.0.0.0", port=int(args.port), reload=args.debug, debug=args.debug, workers=1, reload_dirs=["azimuth"], ) uvicorn.run(**kwargs)	{"/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"], 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"/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", 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30,943	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/routers.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 threading import Event from typing import Any, Dict, List, Optional from fastapi import Depends, HTTPException, Query from starlette.status import HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND, HTTP_503_SERVICE_UNAVAILABLE from azimuth.app import get_config, get_ready_flag, get_startup_tasks, get_task_manager from azimuth.config import AzimuthConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.modules.base_classes import Module from azimuth.task_manager import TaskManager from azimuth.types import ( DatasetSplitName, ModuleOptions, NamedDatasetFilters, PaginationParams, SupportedTask, ) from azimuth.types.outcomes import OutcomeName from azimuth.types.tag import DataAction, SmartTag, SmartTagFamily from azimuth.utils.project import predictions_available def get_last_update(dataset_split_managers: List[Optional[DatasetSplitManager]]) -> float: last_update = max([dsm.last_update if dsm else -1 for dsm in dataset_split_managers]) return last_update def build_named_dataset_filters( confidence_min: float = Query(0, title="Minimum confidence"), confidence_max: float = Query(1, title="Maximum confidence"), label: List[str] = Query([], title="Label"), prediction: List[str] = Query([], title="Prediction"), extreme_length: List[SmartTag] = Query([], title="Extreme length"), partial_syntax: List[SmartTag] = Query([], title="Partial syntax"), dissimilar: List[SmartTag] = Query([], title="Dissimilar"), almost_correct: List[SmartTag] = Query([], title="Almost correct"), behavioral_testing: List[SmartTag] = Query([], title="Behavioral testing"), pipeline_comparison: List[SmartTag] = Query([], title="Pipeline comparison"), uncertain: List[SmartTag] = Query([], title="Uncertain"), data_action: List[DataAction] = Query([], title="Data action tags"), outcome: List[OutcomeName] = Query([], title="Outcomes"), utterance: Optional[str] = Query(None, title="Utterance"), ) -> NamedDatasetFilters: """Build the named filter component. Intended as a FastAPI endpoint dependency. Args: confidence_min: The desired minimum confidence confidence_max: The desired maximum confidence label: The desired class labels prediction: The desired class predictions extreme_length: The desired `extreme_length` smart tags partial_syntax: The desired `partial_syntax` smart tags dissimilar: The desired `dissimilar` smart tags almost_correct: The desired `almost_correct` smart tags behavioral_testing: The desired `behavioral_testing` smart tags pipeline_comparison: The desired `pipeline_comparison` smart tags uncertain: The desired `uncertain` smart tags data_action: The desired data_action tags outcome: The desired outcomes utterance: The substring desired in each utterance Returns: The named dataset filter object """ return NamedDatasetFilters( confidence_min=confidence_min, confidence_max=confidence_max, label=label, prediction=prediction, smart_tags={ SmartTagFamily.extreme_length: extreme_length, SmartTagFamily.partial_syntax: partial_syntax, SmartTagFamily.dissimilar: dissimilar, SmartTagFamily.almost_correct: almost_correct, SmartTagFamily.behavioral_testing: behavioral_testing, SmartTagFamily.pipeline_comparison: pipeline_comparison, SmartTagFamily.uncertain: uncertain, }, data_action=data_action, utterance=utterance, outcome=outcome, ) def get_standard_task_result( task_name: SupportedTask, dataset_split_name: DatasetSplitName, task_manager: TaskManager, mod_options: Optional[ModuleOptions] = None, last_update: float = -1, ): """Generate the task object and get the result for standard tasks. Args: task_name: The task name e.g. ConfidenceHistogram dataset_split_name: e.g. DatasetSplitName.eval task_manager: The task manager mod_options: Module options to pass to the task launcher last_update: The last known update for this dataset_split, to know if we need to recompute. Returns: The task result Raises: HTTPException when the requested module doesn't exist. """ _, task = task_manager.get_task( task_name=task_name, dataset_split_name=dataset_split_name, mod_options=mod_options, last_update=last_update, ) if not task: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail=f"Task not found {task_name}") task_result = task.result() task.clear() return task_result def get_custom_task_result( task_name: SupportedTask, task_manager: TaskManager, custom_query: Dict[str, Any], mod_options: Optional[ModuleOptions] = None, ): """Generate the task object and get the result for custom tasks. Args: task_name: The task name e.g. ConfidenceHistogram task_manager: The task manager custom_query: A dictionary specifying the custom query mod_options: Options to pass to the task launcher Returns: The task result Raises: HTTPException when the requested module doesn't exist. """ _, task = task_manager.get_custom_task( task_name=task_name, custom_query=custom_query, mod_options=mod_options ) if not task: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Aggregation not found") return task.result() def require_pipeline_index( pipeline_index: int = Query(..., title="Pipeline index", ge=0), config: AzimuthConfig = Depends(get_config), ): return query_pipeline_index(pipeline_index, config) def query_pipeline_index( pipeline_index: Optional[int] = Query(None, title="Pipeline index", ge=0), config: AzimuthConfig = Depends(get_config), ) -> Optional[int]: """Get and validate the pipeline index from query parameters. Args: pipeline_index: Which pipeline to select in the config. config: App config Returns: Validated pipeline_index. Raises: HTTPException(400) on validation error. """ if pipeline_index is None: return pipeline_index elif config.pipelines is None: raise HTTPException( HTTP_400_BAD_REQUEST, detail=f"Current config has no pipeline specified," f" but pipeline index {pipeline_index} was requested.", ) elif len(config.pipelines) < pipeline_index: raise HTTPException( HTTP_400_BAD_REQUEST, detail=f"Current config has {len(config.pipelines)} models specified," f" but pipeline index {pipeline_index} was requested.", ) return pipeline_index def require_available_model( config: AzimuthConfig = Depends(get_config), pipeline_index: int = Depends(require_pipeline_index), ): if not predictions_available(config) or pipeline_index is None: raise HTTPException( 400, detail="This route requires a pipeline, but none was provided in the config." ) def require_application_ready( startup_tasks: Optional[Dict[str, Module]] = Depends(get_startup_tasks), ready_flag: Optional[Event] = Depends(get_ready_flag), task_manager: TaskManager = Depends(get_task_manager), ) -> bool: """Check if the application is ready. If all startup tasks are done, the app is ready. This is costly to check so we have `ready_flag` to skip future checks. Args: startup_tasks: All startup tasks. ready_flag: Event that if set, means the app is already ready. task_manager: Task Manager. Returns: True when the application is ready. Raises: HTTPException: When the startup tasks are not completed. """ def startup_tasks_completed( startup_tasks: Optional[Dict[str, Module]], task_manager: TaskManager ): startup_task_ready = startup_tasks is not None and all( st.done() or st.status() == "error" for st in startup_tasks.values() ) return startup_task_ready and not task_manager.is_locked if ready_flag is not None and ready_flag.is_set(): # Skip verification return True if not startup_tasks_completed(startup_tasks, task_manager): # 503 Not available raise HTTPException( status_code=HTTP_503_SERVICE_UNAVAILABLE, detail="Application is not ready, try again later.", ) if ready_flag is not None: # The app is ready, we set a flag to speedup checks in the future. ready_flag.set() return True def get_pagination( limit: Optional[int] = Query(None, title="Limit", ge=1), offset: Optional[int] = Query(None, title="Offset", ge=0), ) -> Optional[PaginationParams]: """Get the pagination parameters if available. Args: limit: How many items to return. offset: Starting point. Raises: HTTPException if not all params are supplied. Returns: If limit and offset are provided, returns a PaginationParams otherwise None. """ if (limit is None) ^ (offset is None): raise HTTPException( HTTP_400_BAD_REQUEST, detail=f"`limit` and `offset` must be all set or all null. Got {limit} and {offset}", ) if (limit is None) and (offset is None): return None else: return PaginationParams(limit=limit, offset=offset)	{"/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": 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30,944	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_performance/confusion_matrix.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 import numpy as np from datasets import Dataset from scipy.sparse import csr_matrix from scipy.sparse.csgraph import reverse_cuthill_mckee from sklearn.metrics import confusion_matrix from azimuth.config import ModelContractConfig from azimuth.modules.base_classes import FilterableModule from azimuth.types.model_performance import ConfusionMatrixResponse from azimuth.utils.dataset_operations import get_predictions_from_ds from azimuth.utils.validation import assert_not_none MIN_CONFUSION_CUTHILL_MCKEE = 0.1 class ConfusionMatrixModule(FilterableModule[ModelContractConfig]): """Computes the confusion matrix on the specified dataset split.""" optional_mod_options = FilterableModule.optional_mod_options \| { "cf_normalize", "cf_reorder_classes", } def compute_on_dataset_split(self) -> List[ConfusionMatrixResponse]: # type: ignore """Computes confusion matrix from sklearn. Returns: Confusion Matrix according to current filters """ ds: Dataset = assert_not_none(self.get_dataset_split()) predictions, labels = ( get_predictions_from_ds(ds, self.mod_options.without_postprocessing), ds[self.config.columns.label], ) ds_mng = self.get_dataset_split_manager() num_classes = ds_mng.get_num_classes() class_ids = list(range(num_classes)) class_names = ds_mng.get_class_names() rejection_idx = ds_mng.rejection_class_idx rejection_class = class_names[rejection_idx] cf = confusion_matrix( y_true=labels, y_pred=predictions, labels=class_ids, normalize="true" if self.mod_options.cf_normalize else None, ) # Reorder rows and columns so the bandwidth of the matrix is smaller if self.mod_options.cf_reorder_classes: # Get a normalized confusion matrix if not already computed if not self.mod_options.cf_normalize: cf_normalized = confusion_matrix( y_true=labels, y_pred=predictions, labels=class_ids, normalize="true" ) else: cf_normalized = cf # Remove the rejection class so it doesn't influence the algorithm cf_no_rejection = np.delete( np.delete(cf_normalized, rejection_idx, 0), rejection_idx, 1 ) # Get order based on reverse_cuthill_mckee algorithm order_no_rejection = reverse_cuthill_mckee( csr_matrix(cf_no_rejection >= MIN_CONFUSION_CUTHILL_MCKEE) ) # Get class indices by reduced-bandwidth order and add rejection_idx at the end. classes_no_rejection = np.delete(class_ids, rejection_idx) order = np.append(classes_no_rejection[order_no_rejection], rejection_idx) # Re-order matrix cf = cf[np.ix_(order, order)] class_names = [ds_mng.get_class_names()[i] for i in order] return [ ConfusionMatrixResponse( confusion_matrix=cf, class_names=class_names, normalize=self.mod_options.cf_normalize, reorder_classes=self.mod_options.cf_reorder_classes, rejection_class=rejection_class, ) ]	{"/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", 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30,945	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_model_performance/test_confusion_matrix.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_performance.confusion_matrix import ConfusionMatrixModule from azimuth.types import DatasetFilters, DatasetSplitName, ModuleOptions from tests.utils import save_predictions def test_confusion_matrix(tiny_text_config): save_predictions(tiny_text_config) # Basic confusion matrix mod = ConfusionMatrixModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0), ) [json_output] = mod.compute_on_dataset_split() dm = mod.get_dataset_split_manager() num_classes = dm.get_num_classes() assert json_output.confusion_matrix.shape == (num_classes, num_classes) # All row sums to one except the last one because it is rejection class assert np.allclose(json_output.confusion_matrix.sum(-1)[:-1], 1.0) assert json_output.class_names[-1] == json_output.rejection_class == "REJECTION_CLASS" # Filtered confusion matrix mod_filter = ConfusionMatrixModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(filters=DatasetFilters(label=[0]), pipeline_index=0), ) [json_output_filter] = mod_filter.compute_on_dataset_split() # A row sums to 0. assert any(json_output_filter.confusion_matrix.sum(-1) == 0) assert json_output_filter.class_names[-1] == "REJECTION_CLASS" # Confusion matrix without postprocessing mod_without_postprocessing = ConfusionMatrixModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0, without_postprocessing=True), ) [json_output_without_postprocessing] = mod_without_postprocessing.compute_on_dataset_split() # Last column (REJECTION_CLASS) should be not empty by default, but empty without postprocessing assert json_output.confusion_matrix.sum(0)[2] > 0 assert json_output_without_postprocessing.confusion_matrix.sum(0)[2] == 0 # When not normalized, we get the predictions. mod_not_normalized = ConfusionMatrixModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0, cf_normalize=False), ) [json_output_not_normalized] = mod_not_normalized.compute_on_dataset_split() assert json_output_not_normalized.confusion_matrix.sum() == dm.num_rows # Preserving the class order from the dataset mod_preserve_order = ConfusionMatrixModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0, cf_reorder_classes=False), ) [json_output_preserve_order] = mod_preserve_order.compute_on_dataset_split() assert json_output_preserve_order.class_names == dm.get_class_names() assert not json_output_preserve_order.reorder_classes def test_confusion_matrix_reorder_rejection_class(guse_text_config): # Setting high threshold to make sure everything gets predicted as NO_INTENT guse_text_config.pipelines[0].postprocessors[-1].threshold = 1 save_predictions(guse_text_config) # Basic confusion matrix mod = ConfusionMatrixModule( DatasetSplitName.eval, guse_text_config, mod_options=ModuleOptions(pipeline_index=0), ) [json_output] = mod.compute_on_dataset_split() dm = mod.get_dataset_split_manager() # Assert that the rejection class is not the last one already num_classes = dm.get_num_classes() assert dm.rejection_class_idx != num_classes - 1 # Assert that the confusion matrix is empty for all columns except the last one. assert sum(sum(json_output.confusion_matrix[:, :-1])) == 0 assert sum(json_output.confusion_matrix[:, -1]) > 0 # Assert the rejection class is last in the class names rejection_class = dm.get_class_names()[dm.rejection_class_idx] assert json_output.class_names[-1] == json_output.rejection_class == rejection_class # Preserving the class order from the dataset mod_preserve_order = ConfusionMatrixModule( DatasetSplitName.eval, guse_text_config, mod_options=ModuleOptions(pipeline_index=0, cf_reorder_classes=False), ) [json_output_preserve_order] = mod_preserve_order.compute_on_dataset_split() assert json_output_preserve_order.class_names == dm.get_class_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", 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30,946	ServiceNow/azimuth	refs/heads/main	/azimuth/plots/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 typing import List import numpy as np from plotly import graph_objs as go from plotly.subplots import make_subplots from azimuth.utils.plots import Colors def make_ece_figure( accuracy: np.ndarray, expected: np.ndarray, ece: float, count: List[int] ) -> go.Figure: """Create an ECE Figure from the computed data. Notes: The expected accuracy in this plot does not exactly match the ECE formula, which uses the mean confidence. It instead uses the mid confidence per bin, which gives an idea of the expected accuracy even for empty bins. Args: accuracy: Accuracy for each bin expected: Expected accuracy for each bin ece: ECE value. count: Count for each bin. Returns: Plotly figure of the ECE. """ accuracy_filtered = [None if count[i] == 0 else acc for i, acc in enumerate(accuracy)] fig = make_subplots( rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, specs=[[{"type": "scatter"}], [{"type": "scatter"}]], row_width=[0.2, 0.8], ) fig.add_bar(x=expected, y=count, name="Count", row=2, col=1) fig.add_scatter( x=expected, y=expected, name="Expected", row=1, col=1, mode="lines+markers", connectgaps=True, ) fig.add_scatter( x=expected, y=accuracy_filtered, name="Accuracy", row=1, col=1, mode="lines+markers", connectgaps=True, ) fig.update_layout( yaxis_title="Accuracy", barmode="stack", xaxis_tickmode="linear", xaxis_tick0=0.0, xaxis_dtick=(1.0 / len(expected)), margin=dict(l=20, r=20, t=30, b=20), title=dict(text=f"ECE={ece:.3f}", x=0.5), legend=dict(x=0.03, y=1, traceorder="reversed", bgcolor="rgba(0,0,0,0)"), colorway=[Colors.Blue, Colors.Gray_transparent, Colors.DataViz2], plot_bgcolor="white", ) fig.update_yaxes(title_text="Count", row=2, col=1) fig.update_xaxes(title_text="Confidence", row=2, col=1) return fig	{"/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"], 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30,947	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_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, HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND from starlette.testclient import TestClient from azimuth.types.tag import DataAction from tests.utils import get_enum_validation_error_msg, is_sorted UTTERANCE_COUNT = 42 def test_get_similar(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances/1/similar_utterances?limit=2").json() resp = resp["utterances"] # We are not our own neighbors assert 1 not in [r["index"] for r in resp] assert len(resp) == 2 resp = client.get( "/dataset_splits/eval/utterances/1/similar_utterances?limit=2&neighbors_dataset_split_name" "=eval" ).json()["utterances"] # We are not our own neighbors assert 1 not in [r["index"] for r in resp] assert len(resp) == 2 resp = client.get( "/dataset_splits/eval/utterances/1/similar_utterances?limit=2&neighbors_dataset_split_name" "=train" ).json()["utterances"] assert len(resp) == 2 def test_get_utterances(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances").json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted([u["index"] for u in resp["utterances"]]) for utterance in resp["utterances"]: assert utterance["persistentId"] == utterance["index"] def test_get_utterances_sort_confidence(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances?pipeline_index=0&sort=confidence").json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted( [u["modelPrediction"]["postprocessedConfidences"][0] for u in resp["utterances"]] ) resp = client.get( "/dataset_splits/eval/utterances?pipeline_index=0&sort=confidence" "&without_postprocessing=true" ).json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted([u["modelPrediction"]["modelConfidences"][0] for u in resp["utterances"]]) def test_get_utterances_sort_prediction(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances?pipeline_index=0&sort=prediction").json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted([u["modelPrediction"]["postprocessedPrediction"] for u in resp["utterances"]]) resp = client.get( "/dataset_splits/eval/utterances?pipeline_index=0&sort=prediction" "&without_postprocessing=true" ).json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted([u["modelPrediction"]["modelPredictions"][0] for u in resp["utterances"]]) def test_get_utterances_sort_descending(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances?pipeline_index=0&descending=true").json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted([u["index"] for u in resp["utterances"]], descending=True) def test_get_utterances_sort_unavailable_column(app: FastAPI): client = TestClient(app) # The `sort=confidence` is ignored because no pipeline is specified resp = client.get("/dataset_splits/eval/utterances?sort=confidence").json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert resp["utteranceCount"] == UTTERANCE_COUNT assert is_sorted([u["index"] for u in resp["utterances"]]) def test_get_utterances_pagination(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances?limit=0&offset=0") assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text assert resp.json()["detail"] == ( "query parameter limit=0: ensure this value is greater than or equal to 1" ) resp = client.get("/dataset_splits/eval/utterances?limit=10&offset=-1") assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text assert resp.json()["detail"] == ( "query parameter offset=-1: ensure this value is greater than or equal to 0" ) resp = client.get("/dataset_splits/eval/utterances?limit=10&offset=10").json() assert len(resp["utterances"]) == 10 assert resp["utteranceCount"] == UTTERANCE_COUNT assert resp["utterances"][0]["index"] == 10 resp = client.get("/dataset_splits/eval/utterances?limit=10&offset=100").json() assert len(resp["utterances"]) == 0 assert resp["utteranceCount"] == UTTERANCE_COUNT resp = client.get("/dataset_splits/eval/utterances?limit=3") assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text resp = client.get("/dataset_splits/eval/utterances?offset=3") assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text def test_get_utterances_filtering_and_indexing(app: FastAPI): client = TestClient(app) # Filtering on label resp = client.get("/dataset_splits/eval/utterances?label=negative").json() assert len(resp["utterances"]) == 22 assert resp["utteranceCount"] == 22 assert all(u["label"] == "negative" for u in resp["utterances"]) # Filtering on prediction resp = client.get("/dataset_splits/eval/utterances?pipeline_index=0&prediction=negative").json() assert len(resp["utterances"]) == 3 assert resp["utteranceCount"] == 3 assert all( u["modelPrediction"]["postprocessedPrediction"] == "negative" for u in resp["utterances"] ) # Filtering on index resp = client.get("/dataset_splits/eval/utterances?indices=3").json() assert len(resp["utterances"]) == 1 assert resp["utteranceCount"] == 1 assert resp["utterances"][0]["index"] == 3 # Filtering on indices resp = client.get("/dataset_splits/eval/utterances?indices=3&indices=4").json() assert len(resp["utterances"]) == 2 assert resp["utteranceCount"] == 2 assert [res["index"] for res in resp["utterances"]] == [3, 4] # Filtering on label and index (not empty) resp = client.get("/dataset_splits/eval/utterances?label=positive&indices=3").json() assert len(resp["utterances"]) == 1 assert resp["utteranceCount"] == 1 assert resp["utterances"][0]["label"] == "positive" assert resp["utterances"][0]["index"] == 3 # Filtering on label and index (empty) resp = client.get("/dataset_splits/eval/utterances?label=negative&indices=3").json() assert len(resp["utterances"]) == 0 assert resp["utteranceCount"] == 0 def test_get_utterances_empty_filters(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances?utterance=yukongold").json() assert len(resp["utterances"]) == 0 # This used to fail when we were filtering on an empty dataset resp = client.get( "/dataset_splits/eval/utterances?utterance=yukongold&data_action=relabel" ).json() assert len(resp["utterances"]) == 0 def test_get_utterances_no_pipeline(app: FastAPI, monkeypatch): import azimuth.routers.utterances as utt_module monkeypatch.setattr(utt_module, "saliency_available", lambda x: True) client = TestClient(app) resp = client.get("/dataset_splits/eval/utterances").json() assert len(resp["utterances"]) == UTTERANCE_COUNT assert all( item["modelPrediction"] is None and item["modelSaliency"] is None for item in resp["utterances"] ) def test_perturbed_utterances(app: FastAPI, monkeypatch): client = TestClient(app) resp = client.get( "/dataset_splits/eval/utterances/1/perturbed_utterances?pipeline_index=0" ).json() # Utterance 1 has 11 perturbation tests assert len(resp) == 11 def test_patch_utterances(app: FastAPI) -> None: client = TestClient(app) request = [{"dataAction": "potato"}] resp = client.patch("/dataset_splits/eval/utterances", json=request) assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text assert resp.json()["detail"] == ( "Request['body'][0]['persistentId']: field required\n" f"Request['body'][0]['dataAction']: {get_enum_validation_error_msg(DataAction)}" ) request = [{"persistentId": 0, "dataAction": "remove"}] resp = client.patch("/dataset_splits/eval/utterances", json=request) assert resp.status_code == HTTP_200_OK, resp.text assert resp.json() == request # Set tag of a non-existent persistent-id (raise) request = [ {"persistentId": "potato", "dataAction": "augment_with_similar"}, {"persistentId": 0, "dataAction": "relabel"}, ] resp = client.patch("/dataset_splits/eval/utterances", json=request) assert resp.status_code == HTTP_404_NOT_FOUND, resp.text # Set tag of a non-existent persistent-id (ignore) resp = client.patch("/dataset_splits/eval/utterances?ignore_not_found=true", json=request) assert resp.status_code == HTTP_200_OK, resp.text assert resp.json() == [request[1]] # Only the second requested element was applied. # Reset tag to NO_ACTION request = [{"persistentId": 0, "dataAction": "NO_ACTION"}] resp = client.patch("/dataset_splits/eval/utterances", json=request) assert resp.status_code == HTTP_200_OK, resp.text assert resp.json() == 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30,948	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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 typing import List from fastapi import APIRouter, Depends, HTTPException, Query from starlette.status import HTTP_400_BAD_REQUEST from azimuth.app import get_dataset_split_manager, get_task_manager from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, NamedDatasetFilters, SupportedModule from azimuth.types.model_performance import ( OutcomeCountPerFilterResponse, OutcomeCountPerThresholdResponse, ) from azimuth.utils.routers import ( build_named_dataset_filters, get_standard_task_result, require_pipeline_index, ) router = APIRouter() @router.get( "/per_threshold", summary="Get outcome count for multiple thresholds.", description="Get prediction count per outcome for multiple thresholds.", response_model=OutcomeCountPerThresholdResponse, ) def get_outcome_count_per_threshold( 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), ) -> OutcomeCountPerThresholdResponse: mod_options = ModuleOptions( pipeline_index=pipeline_index, ) try: task_result: List[OutcomeCountPerThresholdResponse] = get_standard_task_result( SupportedModule.OutcomeCountPerThreshold, dataset_split_name, task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, ) except ValueError as e: raise HTTPException(HTTP_400_BAD_REQUEST, detail=str(e)) return task_result[0] @router.get( "/per_filter", summary="Get outcome count for each filter.", description="Get outcome count for each filter based on the current filtering.", response_model=OutcomeCountPerFilterResponse, ) def get_outcome_count_per_filter( dataset_split_name: DatasetSplitName, named_filters: NamedDatasetFilters = Depends(build_named_dataset_filters), task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), pipeline_index: int = Depends(require_pipeline_index), without_postprocessing: bool = Query(False, title="Without Postprocessing"), ) -> OutcomeCountPerFilterResponse: mod_options = ModuleOptions( filters=named_filters.to_dataset_filters(dataset_split_manager.get_class_names()), pipeline_index=pipeline_index, without_postprocessing=without_postprocessing, ) task_result: OutcomeCountPerFilterResponse = get_standard_task_result( SupportedModule.OutcomeCountPerFilter, dataset_split_name, task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, )[0] return task_result	{"/tests/test_config.py": ["/azimuth/config.py", 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30,949	ServiceNow/azimuth	refs/heads/main	/tests/test_utils/test_api_dependency_injection.py	import pytest from fastapi import HTTPException from azimuth import app from azimuth.app import get_all_dataset_split_managers, get_dataset_split_manager from azimuth.types import DatasetSplitName eval_split = "eval_split" train_split = "train_split" mock_dataset_split_mapping_with_only_eval = {DatasetSplitName.eval: eval_split} mock_dataset_split_mapping_with_both = { DatasetSplitName.eval: eval_split, DatasetSplitName.train: train_split, } def test_get_dataset_split_manager(monkeypatch): monkeypatch.setattr( app, "_dataset_split_managers", mock_dataset_split_mapping_with_only_eval, raising=True ) with pytest.raises(HTTPException) as excinfo: get_dataset_split_manager(DatasetSplitName.train) assert excinfo.value.status_code == 404 r = get_dataset_split_manager(DatasetSplitName.eval) assert r == eval_split def test_get_all_dataset_split_managers_with_both_dataset_splits(monkeypatch): monkeypatch.setattr( app, "_dataset_split_managers", mock_dataset_split_mapping_with_both, raising=True ) r = get_all_dataset_split_managers() assert r == mock_dataset_split_mapping_with_both def test_get_all_dataset_split_managers_with_only_eval_split(monkeypatch): monkeypatch.setattr( app, "_dataset_split_managers", mock_dataset_split_mapping_with_only_eval, raising=True ) with pytest.raises(HTTPException) as excinfo: get_all_dataset_split_managers() assert excinfo.value.status_code == 404	{"/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": 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30,950	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_artifact_manager.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.modules.base_classes import ArtifactManager, Module from azimuth.types import DatasetSplitName def test_artifact_manager(simple_text_config, file_text_config_top1): am = ArtifactManager.instance() # ArtifactManager is empty assert len(am.dataset_dict_mapping) == 0 # Load 2 dataset splits am.get_dataset_split_manager(simple_text_config, DatasetSplitName.eval) am.get_dataset_split_manager(file_text_config_top1, DatasetSplitName.eval) assert len(am.dataset_dict_mapping) == 2 assert len(am.dataset_split_managers_mapping) == 2 # Assert Dataset Split Managers are different assert ( am.dataset_split_managers_mapping[simple_text_config.get_project_hash()][ DatasetSplitName.eval ].num_rows != am.dataset_split_managers_mapping[file_text_config_top1.get_project_hash()][ DatasetSplitName.eval ].num_rows ) new_am = ArtifactManager.instance() assert id(am) == id(new_am) def test_artifact_manager_from_module(simple_text_config, file_text_config_top1): mod = Module(DatasetSplitName.eval, simple_text_config) mod.get_dataset_split() # Make sure the first module have access to only one dataset split manager. dm_mapping = mod.artifact_manager.dataset_split_managers_mapping assert len(dm_mapping) == 1 # Only one config is saved. assert len(dm_mapping[mod.config.get_project_hash()]) == 1 # New Module with another DatasetSplit mod2 = Module(DatasetSplitName.train, simple_text_config) mod2.get_dataset_split() # Make sure the first module now have access to two dataset split managers. dm_mapping = mod.artifact_manager.dataset_split_managers_mapping assert len(dm_mapping[mod2.config.get_project_hash()]) == 2 # New Module with another Config mod3 = Module(DatasetSplitName.eval, file_text_config_top1) mod3.get_dataset_split() # Make sure the first module now have access to two configs. dm_mapping = mod.artifact_manager.dataset_split_managers_mapping assert len(dm_mapping) == 2 assert len(dm_mapping[mod3.config.get_project_hash()]) == 1	{"/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", 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30,951	ServiceNow/azimuth	refs/heads/main	/azimuth/types/task.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 pydantic import Field from azimuth.types import AliasModel, ModuleResponse from azimuth.utils.ml.postprocessing import PostProcessingIO, PostprocessingStep from azimuth.utils.ml.preprocessing import PreprocessingStep class Prediction(AliasModel): class_index: int confidence: float class PredictionResponse(ModuleResponse): entropy: float = Field(..., title="Entropy") epistemic: float = Field(..., title="Epistemic") label: int = Field(..., title="Label") model_output: PostProcessingIO = Field(..., title="Output before post-processing") postprocessed_output: PostProcessingIO = Field(..., title="Output after post-processing") preprocessing_steps: List[PreprocessingStep] = Field(..., title="Preprocessing Steps") postprocessing_steps: List[PostprocessingStep] = Field(..., title="Postprocessing Steps") class SaliencyResponse(ModuleResponse): saliency: List[float] = Field(..., title="Saliency") tokens: List[str] = Field(..., title="Tokens")	{"/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", 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30,952	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_model_performance/test_confusion_matrix.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 fastapi import FastAPI from starlette.status import HTTP_200_OK from starlette.testclient import TestClient def test_get_confusion_matrix(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/confusion_matrix?pipeline_index=0") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert np.allclose( data["confusionMatrix"], [[0.05, 0.05, 0.9], [0.136, 0.090, 0.772], [0.0, 0.0, 0.0]], atol=1e-2, ) # not normalized resp = client.get("/dataset_splits/eval/confusion_matrix?pipeline_index=0&normalize=false") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert np.allclose( data["confusionMatrix"], [[1.0, 1.0, 18.0], [3.0, 2.0, 17.0], [0.0, 0.0, 0.0]], atol=1e-2, ) # preserve class order resp = client.get( "/dataset_splits/eval/confusion_matrix?pipeline_index=0&reorder_classes=false" ) assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert np.allclose( data["confusionMatrix"], [[0.090, 0.136, 0.772], [0.05, 0.05, 0.9], [0.0, 0.0, 0.0]], atol=1e-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", 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30,953	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_utilities/test_validation.py	import pytest from azimuth.config import CustomObject from azimuth.modules.validation.validation import ValidationModule from azimuth.types import DatasetSplitName, ModuleOptions from azimuth.utils.logs import MultipleExceptions class ExceptionRaiserOnInit: def __init__(self): raise ValueError("Can't load!") class ExceptionRaiserOnCall: def __call__(self, batch): raise ValueError("Can't call!") def test_validation_module_happy_path_text_classif(simple_text_config): validation = ValidationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=0), ) [result] = validation.compute_on_dataset_split() assert result.can_load_model assert result.can_load_dataset assert result.model_has_correct_type assert result.can_make_prediction assert result.can_make_saliency def test_validation_module_happy_path_custom_text_classification(guse_text_config): validation = ValidationModule( dataset_split_name=DatasetSplitName.eval, config=guse_text_config, mod_options=ModuleOptions(pipeline_index=0), ) [result] = validation.compute_on_dataset_split() assert result.can_load_model assert result.can_load_dataset assert result.model_has_correct_type assert result.can_make_prediction assert not result.can_make_saliency def test_validation_module_cant_load_model(simple_text_config): simple_text_config.pipelines[0].model = CustomObject( class_name="tests.test_modules.test_utilities." "test_validation.ExceptionRaiserOnInit" ) validation = ValidationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=0), ) with pytest.raises(MultipleExceptions) as match: validation.compute_on_dataset_split() assert len(match.value.exceptions) == 1 and "Can't load!" in str(match.value.exceptions[0]) def test_validation_module_cant_load_dataset(simple_text_config): simple_text_config.dataset = CustomObject( class_name="tests.test_modules.test_utilities." "test_validation.ExceptionRaiserOnInit" ) validation = ValidationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=0), ) with pytest.raises(MultipleExceptions) as match: validation.compute_on_dataset_split() assert len(match.value.exceptions) == 1 and "Can't load!" in str(match.value.exceptions[0]) def test_validation_module_cant_predict_utterance_embedding(guse_text_config): guse_text_config.pipelines[0].model = CustomObject( class_name="tests.test_modules.test_utilities." "test_validation.ExceptionRaiserOnCall" ) validation = ValidationModule( dataset_split_name=DatasetSplitName.eval, config=guse_text_config, mod_options=ModuleOptions(pipeline_index=0), ) with pytest.raises(MultipleExceptions) as match: validation.compute_on_dataset_split() assert len(match.value.exceptions) == 1 and "Can't call!" in str( match.value.exceptions[0] ), match.value.exceptions def test_validation_module_no_model(simple_text_config): simple_text_config.pipelines = None validation = ValidationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=None), ) [result] = validation.compute_on_dataset_split() assert not result.can_load_model assert result.can_load_dataset assert not result.model_has_correct_type assert not result.can_make_prediction assert not result.can_make_saliency	{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": 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30,954	ServiceNow/azimuth	refs/heads/main	/azimuth/types/__init__.py	# Do not reorder from azimuth.types.general.alias_model import ( AliasModel, InputResponse, ModuleResponse, PaginationParams, PlotSpecification, ) from azimuth.types.general.array_type import Array from azimuth.types.general.dataset import DatasetColumn, DatasetSplitName from azimuth.types.general.modules import ( SupportedMethod, SupportedModelContract, SupportedModule, SupportedTask, ) from azimuth.types.general.module_arguments import ( DatasetFilters, ModuleOptions, NamedDatasetFilters, )	{"/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", 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30,955	ServiceNow/azimuth	refs/heads/main	/tests/test_performance.py	import time from azimuth.modules.model_contracts import HFTextClassificationModule from azimuth.types import DatasetFilters, DatasetSplitName, ModuleOptions, SupportedMethod from azimuth.utils.dataset_operations import filter_dataset_split def test_dataset_processing_speed(simple_text_config): mod = HFTextClassificationModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0 ), ) mod.compute_on_dataset_split() # Loading the ds start = time.perf_counter() ds = mod.get_dataset_split() stop = time.perf_counter() assert (stop - start) <= 0.02 # Filtering on the prediction start = time.perf_counter() filter_dataset_split( ds, DatasetFilters(prediction=[0]), config=simple_text_config, ) stop = time.perf_counter() assert (stop - start) <= 0.0003	{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], 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30,956	ServiceNow/azimuth	refs/heads/main	/tests/test_utils/test_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. import numpy as np import pytest from pydantic import BaseModel, ValidationError from pydantic.schema import schema from azimuth.types import Array class MyModel(BaseModel): class Config: # Might not be needed one day see # https://github.com/samuelcolvin/pydantic/issues/951#issuecomment-547920677 json_encoders = {np.ndarray: lambda x: x.tolist()} arr: Array[float] def test_array_init(): # Check that we validate the schema correctly. sch = schema([MyModel]) assert "Array" in str(sch) x = np.array([1, 2, 3]) mod = MyModel(arr=x) # The field is a numpy array assert np.allclose(mod.arr + 1, [2, 3, 4]) # We can json encode the type. out = mod.json() assert "[1.0, 2.0, 3.0]" in out # We can instantiate using a list. _ = MyModel(arr=[1, 2, 3]) def test_bad_values(): class MyModel(BaseModel): arr: Array[float, (3,)] with pytest.raises(ValidationError): _ = MyModel(arr=[1, 2, 3, 4]) with pytest.raises(ValidationError): _ = MyModel(arr=["a", "b", "c"])	{"/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", 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30,957	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_model_contracts/test_model_contract_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. from typing import List, cast from unittest.mock import MagicMock import numpy as np import pytest from datasets import Dataset from azimuth.config import PipelineDefinition from azimuth.modules.model_contracts import ( CustomTextClassificationModule, HFTextClassificationModule, ) from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions, SupportedMethod from azimuth.types.tag import SmartTag from azimuth.types.task import PredictionResponse from azimuth.utils.ml.postprocessing import PostProcessingIO from tests.utils import get_table_key def test_save_result(simple_text_config): mod = HFTextClassificationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0 ), ) dm = mod.get_dataset_split_manager(DatasetSplitName.eval) mod.save_result(mod.compute_on_dataset_split(), dm) # Testing that there is more no intent in the column "postprocessed_prediction" # than in "model_predictions[0]". rejection_class_idx = dm.rejection_class_idx ds = dm.get_dataset_split(mod._get_table_key()) assert len( ds.filter(lambda x: x[DatasetColumn.postprocessed_prediction] == rejection_class_idx) ) > len(ds.filter(lambda x: x[DatasetColumn.model_predictions][0] == rejection_class_idx)) def test_pipeline_steps(simple_text_config): mod = HFTextClassificationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0 ), ) dm = mod.get_dataset_split_manager(DatasetSplitName.eval) mod.save_result(mod.compute_on_dataset_split(), dm) ds = dm.get_dataset_split(mod._get_table_key()) # Two postprocessing_steps are set by default, Temperature Scaling and Thresholding assert np.all( [len(res["postprocessing_steps"]) == 2 for res in ds[DatasetColumn.pipeline_steps]] ) for order, class_name in enumerate(("TemperatureScaling", "Thresholding")): assert ( ds[DatasetColumn.pipeline_steps][0]["postprocessing_steps"][order]["class_name"] == class_name ) # The prediction of the last pipeline step should be the same as the postprocessed prediction. class_names = dm.get_class_names() assert [ res["postprocessing_steps"][-1]["output"]["prediction"] for res in ds[DatasetColumn.pipeline_steps] ] == [class_names[idx] for idx in ds[DatasetColumn.postprocessed_prediction]] # Same for confidences assert [ res["postprocessing_steps"][-1]["output"]["confidences"] for res in ds[DatasetColumn.pipeline_steps] ] == ds[DatasetColumn.postprocessed_confidences] # Pre-processing Steps should be empty for HF pipelines assert np.all( [len(res["preprocessing_steps"]) == 0 for res in ds[DatasetColumn.pipeline_steps]] ) def test_high_epistemic_tag(simple_text_config): simple_table_key_with_bma = get_table_key(simple_text_config, use_bma=True) mod = HFTextClassificationModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0, indices=[4, 9, 3], ), ) dm = mod.get_dataset_split_manager(DatasetSplitName.eval) # 10 high epistemic thing. epistemic = [0.2] * 10 + [0.01] * (dm.num_rows - 10) mocked_output = PostProcessingIO( texts=["this is a sentence."], preds=np.zeros(1), probs=np.zeros((1, dm.get_num_classes())), logits=np.zeros((1, dm.get_num_classes())), ) predictions = [ PredictionResponse( entropy=0.0, epistemic=epis, model_output=mocked_output, postprocessed_output=mocked_output, label=1, preprocessing_steps=[], postprocessing_steps=[], ) for epis in epistemic ] # If `use_bma` is not set in the module, the `simple_table_key_with_bma` table is untouched # and nothing is tagged in it. mod.save_result(predictions, dm) assert ( sum(dm.get_dataset_split(simple_table_key_with_bma)[SmartTag.high_epistemic_uncertainty]) == 0 ) # Set BMA mod.config.uncertainty.iterations = 20 mod.mod_options.use_bma = True mod.save_result(predictions, dm) # If BMA is set, we expect 10 tagged, and those tags to be found in the prediction table # with the regular key, so they can be retrieved like the other tags. simple_table_key = get_table_key(simple_text_config) assert sum(dm.get_dataset_split(simple_table_key)[SmartTag.high_epistemic_uncertainty]) == 10 def test_pred_smart_tags(clinc_text_config): mod = HFTextClassificationModule( DatasetSplitName.eval, clinc_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0 ), ) dm = mod.get_dataset_split_manager(DatasetSplitName.eval) ds = mod.get_dataset_split() label_list = ds["label"] # [9, 9, 13, 15, 15] random_label = 0 # Not in label_list. Top prediction for incorrect examples below. correct_probs = np.zeros(dm.get_num_classes()) correct_probs[label_list[0]] = 0.9 correct_top_3_probs = np.zeros(dm.get_num_classes()) correct_top_3_probs[random_label] = 0.9 correct_top_3_probs[label_list[1]] = 0.1 correct_low_conf_probs = np.zeros(dm.get_num_classes()) correct_low_conf_probs[label_list[2]] = 0.2 # Incorrect prediction; label NO_INTENT; NO_INTENT IN TOP3 incorrect_no_intent_probs = np.zeros(dm.get_num_classes()) incorrect_no_intent_probs[random_label] = 0.9 incorrect_no_intent_probs[label_list[3]] = 0.1 # Model is wrong, but prediction after threshold becomes NO_INTENT; label NO_INTENT correct_no_intent_probs = np.zeros(dm.get_num_classes()) correct_no_intent_probs[random_label] = 0.2 model_output = np.array( ( correct_probs, correct_top_3_probs, correct_low_conf_probs, incorrect_no_intent_probs, correct_no_intent_probs, ) ) batch = Dataset.from_dict( { mod.config.columns.text_input: [""] * len(ds), mod.config.columns.label: label_list, } ) model_output, postprocessed_output, _, _ = mod.get_postprocessed_output(batch, model_output) res = mod._parse_prediction_output( batch, model_output, postprocessed_output, [], [], np.random.random(len(ds)) ) mod.save_result(res, dm) table_key = get_table_key(clinc_text_config) ds = dm.get_dataset_split(table_key) assert SmartTag.correct_top_3 and SmartTag.correct_low_conf in ds.column_names assert ds[SmartTag.correct_top_3] == [ False, True, False, True, False, ], "Problem with correct_top_3 smart tag" assert ds[SmartTag.correct_low_conf] == [ False, False, True, False, False, ], "Problem with correct_low_conf smart tag" def test_pred_smart_tags_2class(simple_text_config): simple_table_key = get_table_key(simple_text_config) mod = HFTextClassificationModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0 ), ) dm = mod.get_dataset_split_manager() res = mod.compute_on_dataset_split() mod.save_result(res, dm) assert SmartTag.correct_top_3 in dm.get_dataset_split(simple_table_key).column_names assert SmartTag.correct_low_conf in dm.get_dataset_split(simple_table_key).column_names assert not np.any( dm.get_dataset_split(simple_table_key)[SmartTag.correct_top_3] ), "Problem with correct_top_3 smart tag" @pytest.mark.parametrize( "model_contract", [HFTextClassificationModule, CustomTextClassificationModule] ) def test_structured_output_text_classification(simple_text_config, model_contract): simple_text_config.pipelines[0] = PipelineDefinition( name="StructuredOutputModel", model={ "class_name": "tests.test_loading_resources.config_structured_output", "kwargs": {"num_classes": 2}, }, postprocessors=None, ) mod = model_contract( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=0 ), ) preds = cast(List[PredictionResponse], mod.compute_on_dataset_split()) #### # Postprocessing ### mod_postpro = model_contract( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.PostProcess, pipeline_index=0 ), ) mod_postpro.run_postprocessing = MagicMock() # Check that PostProcessed did not modify the probs. postprocessed_result = cast(List[PredictionResponse], mod_postpro.compute_on_dataset_split()) assert ( postprocessed_result[0].postprocessed_output.probs == preds[0].postprocessed_output.probs ).all() mod_postpro.run_postprocessing.assert_not_called()	{"/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", 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30,958	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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 APIRouter, Depends, Query from azimuth.app import get_dataset_split_manager, get_task_manager from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, NamedDatasetFilters, SupportedModule from azimuth.types.word_analysis import TopWordsResponse from azimuth.utils.routers import ( build_named_dataset_filters, get_standard_task_result, require_pipeline_index, ) router = APIRouter() @router.get( "", summary="Get most important words.", description="Get most important words for right predictions and errors based on filters.", response_model=TopWordsResponse, ) def get_top_words( dataset_split_name: DatasetSplitName, named_filters: NamedDatasetFilters = Depends(build_named_dataset_filters), task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), pipeline_index: int = Depends(require_pipeline_index), without_postprocessing: bool = Query(False, title="Without Postprocessing"), ) -> TopWordsResponse: mod_options = ModuleOptions( filters=named_filters.to_dataset_filters(dataset_split_manager.get_class_names()), pipeline_index=pipeline_index, force_no_saliency=pipeline_index is None, without_postprocessing=without_postprocessing, ) task_result: TopWordsResponse = get_standard_task_result( SupportedModule.TopWords, dataset_split_name, task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, )[0] return task_result	{"/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", 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30,959	ServiceNow/azimuth	refs/heads/main	/azimuth/types/general/alias_model.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 numpy as np import orjson from pydantic import BaseModel, Extra, Field from azimuth.utils.conversion import orjson_dumps from azimuth.utils.openapi import fix_union_types from azimuth.utils.pydantic_utils import create_model def to_camel_case(string: str) -> str: """Takes a snake_case string and transforms it to camelCase. Args: string: The snake_case string to transform. Returns: The camelCase result. """ words = string.split("_") for idx in range(1, len(words)): words[idx] = words[idx].capitalize() return "".join(words) class AliasModel(BaseModel): """This model should be used as the base for any model that defines aliases to ensure that all fields are represented correctly. """ @classmethod def with_fields(cls, name, bases, field_definitions): return create_model(name, __base__=(cls, bases), **field_definitions) class Config: allow_population_by_field_name = True alias_generator = to_camel_case extra = Extra.forbid json_encoders = {np.ndarray: lambda x: x.tolist()} json_loads = orjson.loads json_dumps = orjson_dumps schema_extra = fix_union_types class ModuleResponse(AliasModel): ... class InputResponse(ModuleResponse): input: str class PaginationParams(AliasModel): limit: int = Field(..., title="Limit") offset: int = Field(..., title="Offset") class PlotSpecification(AliasModel): data: List layout: 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", 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30,960	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_performance/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 typing import List import numpy as np from numpy import ndarray from azimuth.config import ModelContractConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.modules.base_classes import DatasetResultModule from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping from azimuth.types import DatasetColumn, SupportedMethod from azimuth.types.outcomes import OutcomeName, OutcomeResponse from azimuth.utils.ml.model_performance import compute_outcome from azimuth.utils.validation import assert_not_none class OutcomesModule(DatasetResultModule[ModelContractConfig]): """Computes the outcome for each utterance in the dataset split.""" required_mod_options = {"pipeline_index"} optional_mod_options = DatasetResultModule.optional_mod_options \| {"threshold"} def _get_predictions(self, without_postprocessing: bool) -> ndarray: mod_options = self.mod_options.copy(deep=True) mod_options.model_contract_method_name = SupportedMethod.PostProcess mod_options.indices = self.get_indices() prediction_task = model_contract_task_mapping( dataset_split_name=self.dataset_split_name, config=self.config, mod_options=mod_options, ) pred_result = prediction_task.compute_on_dataset_split() predictions = np.zeros(len(mod_options.indices)) for idx, pred in enumerate(pred_result): if without_postprocessing: predictions[idx] = pred.model_output.preds else: predictions[idx] = pred.postprocessed_output.preds return predictions def compute_on_dataset_split(self) -> List[OutcomeResponse]: # type: ignore """Compute outcomes for a set of predictions, both with and without postprocessing. Returns: List of tuple for each utterance with the model outcome and the postprocessed outcome. """ dm = self.get_dataset_split_manager() ds = assert_not_none(self.get_dataset_split()) labels = ds[self.config.columns.label] model_predictions = self._get_predictions(without_postprocessing=True) model_outcomes: List[OutcomeName] = [ compute_outcome(y_pred, label, dm.rejection_class_idx) for y_pred, label in zip(model_predictions, labels) ] postprocessed_predictions = self._get_predictions(without_postprocessing=False) postprocessed_outcomes: List[OutcomeName] = [ compute_outcome(y_pred, label, dm.rejection_class_idx) for y_pred, label in zip(postprocessed_predictions, labels) ] return [ OutcomeResponse( model_outcome=model_outcome, postprocessed_outcome=postprocessed_outcome ) for model_outcome, postprocessed_outcome in zip(model_outcomes, postprocessed_outcomes) ] def _save_result(self, res: List[OutcomeResponse], dm: DatasetSplitManager): # type: ignore """Save the outcomes in the dataset_split. Args: res: Results from `compute_on_dataset_split` dm: the dataset_split manager used to get `res`. """ table_key = assert_not_none(self._get_table_key()) dm.add_column_to_prediction_table( DatasetColumn.model_outcome, [outcomes.model_outcome for outcomes in res], table_key=table_key, ) dm.add_column_to_prediction_table( DatasetColumn.postprocessed_outcome, [outcomes.postprocessed_outcome for outcomes in res], table_key=table_key, )	{"/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", 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30,961	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/perturbation_testing/perturbation_testing_summary.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 dataclasses import dataclass from typing import List, Optional import pandas as pd from azimuth.config import PerturbationTestingConfig from azimuth.modules.base_classes import ComparisonModule from azimuth.modules.perturbation_testing import PerturbationTestingModule from azimuth.modules.task_execution import get_task_result from azimuth.types import DatasetSplitName, ModuleOptions from azimuth.types.perturbation_testing import ( PerturbationTestFailureReason, PerturbationTestFamily, PerturbationTestingMergedResponse, PerturbationTestingSummaryResponse, PerturbationTestName, PerturbationTestSummary, PerturbationType, PerturbedUtteranceExample, PerturbedUtteranceResult, ) @dataclass class PerturbedUtteranceNeededInfo: name: PerturbationTestName description: str family: PerturbationTestFamily perturbation_type: PerturbationType original_utterance: str perturbed_utterance: str confidence_delta: Optional[float] dataset_split_name: DatasetSplitName is_failed: bool failure_reason: PerturbationTestFailureReason PERTURBATION_TEST_GROUPING = ["family", "name", "perturbation_type", "dataset_split_name"] class PerturbationTestingSummaryModule(ComparisonModule[PerturbationTestingConfig]): """Summary of perturbation tests per dataset split.""" required_mod_options = {"pipeline_index"} def compute_on_dataset_split( # type: ignore self, ) -> List[PerturbationTestingSummaryResponse]: # Get all the test and make them into a DataFrame all_tests = self.get_all_tests() df = pd.DataFrame(all_tests) grouped = df.groupby(PERTURBATION_TEST_GROUPING) # We select the first example as the test example. examples = grouped.first().reset_index() # Compute failure rate per dataset_split. grouped_with_set = grouped["is_failed"] grouped_with_set_count = grouped_with_set.count().reset_index() grouped_with_set_failed_count = grouped_with_set.sum().reset_index() test_ratio = (grouped_with_set.sum() / grouped_with_set.count()).reset_index() # Average confidence delta, we filter the -1 first not_failed_grouped = df[df["confidence_delta"].notnull()].groupby( PERTURBATION_TEST_GROUPING ) average_delta = not_failed_grouped["confidence_delta"].mean().reset_index() # Merge everything. merged_df = pd.merge( examples, test_ratio, on=PERTURBATION_TEST_GROUPING, suffixes=("", "_ratio") ) merged_df = pd.merge( merged_df, grouped_with_set_count, on=PERTURBATION_TEST_GROUPING, suffixes=("", "_total_count"), ) merged_df = pd.merge( merged_df, grouped_with_set_failed_count, on=PERTURBATION_TEST_GROUPING, suffixes=("", "_count"), ) # Merge left because average_delta can be empty. merged_df = pd.merge( merged_df, average_delta, how="left", on=PERTURBATION_TEST_GROUPING, suffixes=("", "_computed"), ) test_types_info = merged_df.groupby(PERTURBATION_TEST_GROUPING[:-1]) result = [] for (family, name, perturbation_type), group in test_types_info: record = group.to_dict(orient="records") records_by_ds = {r["dataset_split_name"]: r for r in record} original_utterance = record[0]["original_utterance"] perturbed_utterance = record[0]["perturbed_utterance"] description = record[0]["description"] def get_stats(dataset_split_name): if dataset_split_name in records_by_ds.keys(): set_r = records_by_ds[dataset_split_name] failure_rate = set_r["is_failed_ratio"] count = set_r["is_failed_total_count"] failed_count = set_r["is_failed_count"] confidence_delta = set_r["confidence_delta_computed"] else: failure_rate, count, failed_count, confidence_delta = (0, 0, 0, 0) return failure_rate, count, failed_count, confidence_delta test_failure_rate, test_count, test_failed_count, test_confidence_delta = get_stats( DatasetSplitName.eval ) train_failure_rate, train_count, train_failed_count, train_confidence_delta = get_stats( DatasetSplitName.train ) result.append( PerturbationTestSummary( description=description, name=name, example=PerturbedUtteranceExample( utterance=original_utterance, perturbed_utterance=perturbed_utterance ), family=family, perturbation_type=perturbation_type, eval_failure_rate=test_failure_rate, eval_count=test_count, eval_failed_count=test_failed_count, eval_confidence_delta=test_confidence_delta, train_failure_rate=train_failure_rate, train_count=train_count, train_failed_count=train_failed_count, train_confidence_delta=train_confidence_delta, ) ) return [PerturbationTestingSummaryResponse(all_tests_summary=result)] def get_all_tests(self) -> List[PerturbedUtteranceNeededInfo]: """Get all CheckList test for all indices of all dataset splits. Returns: A list with all the tests. """ accumulator = [] for dataset_split_name in self.available_dataset_splits: all_tests = PerturbationTestingModule( dataset_split_name=dataset_split_name, config=self.config, mod_options=ModuleOptions(pipeline_index=self.mod_options.pipeline_index), ) result = get_task_result( task_module=all_tests, result_type=List[List[PerturbedUtteranceResult]] ) result_with_utterances = [ [ PerturbedUtteranceNeededInfo( name=perturbed_utterance_result.name, description=perturbed_utterance_result.description, family=perturbed_utterance_result.family, original_utterance=original_utterance, perturbed_utterance=perturbed_utterance_result.perturbed_utterance, perturbation_type=perturbed_utterance_result.perturbation_type, confidence_delta=perturbed_utterance_result.confidence_delta, dataset_split_name=dataset_split_name, is_failed=perturbed_utterance_result.failed, failure_reason=perturbed_utterance_result.failure_reason, ) for perturbed_utterance_result in perturbed_utterance_results ] for original_utterance, perturbed_utterance_results in zip( self.get_dataset_split(dataset_split_name)[self.config.columns.text_input], result, ) ] # Merge all test merged_result: List[PerturbedUtteranceNeededInfo] = sum(result_with_utterances, []) accumulator += merged_result return accumulator class PerturbationTestingMergedModule(PerturbationTestingSummaryModule): """Failure rate per dataset split for perturbation tests.""" def compute_on_dataset_split(self) -> List[PerturbationTestingMergedResponse]: # type: ignore all_tests = self.get_all_tests() df = pd.DataFrame(all_tests) # Compute failure rate per dataset_split. grouped_with_set = df.groupby("dataset_split_name")["is_failed"] test_ratio = (grouped_with_set.sum() / grouped_with_set.count()).reset_index() eval_fr = next( iter( test_ratio[test_ratio["dataset_split_name"] == DatasetSplitName.eval]["is_failed"] ), 0.0, ) train_fr = next( iter( test_ratio[test_ratio["dataset_split_name"] == DatasetSplitName.train]["is_failed"] ), 0.0, ) 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30,962	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/ml/seeding.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 random import numpy as np class RandomContext: """ Set seed for `random` and `numpy` and when exiting set the original back. Args: seed: seed for the random generator. """ def __init__(self, seed): self.seed = seed self.rnd_ctx = random.getstate() self.np_ctx = np.random.get_state() def __enter__(self): random.seed(self.seed) np.random.seed(self.seed) def __exit__(self, exc_type, exc_val, exc_tb): random.setstate(self.rnd_ctx) np.random.set_state(self.np_ctx)	{"/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", 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30,963	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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 copy import copy from enum import Enum from typing import Dict, List, Optional from fastapi import APIRouter, Body, Depends, HTTPException, Query from starlette.status import HTTP_404_NOT_FOUND from azimuth.app import ( get_config, get_dataset_split_manager, get_dataset_split_manager_mapping, 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, NamedDatasetFilters, PaginationParams, SupportedMethod, SupportedModule, ) from azimuth.types.perturbation_testing import ( PerturbedUtteranceResult, PerturbedUtteranceWithClassNames, ) from azimuth.types.similarity_analysis import SimilarUtterance, SimilarUtterancesResponse from azimuth.types.tag import ( ALL_DATA_ACTIONS, DATASET_SMART_TAG_FAMILIES, PIPELINE_SMART_TAG_FAMILIES, SMART_TAGS_FAMILY_MAPPING, DataAction, ) from azimuth.types.utterance import ( GetUtterancesResponse, ModelPrediction, ModelSaliency, Utterance, UtterancePatch, ) from azimuth.utils.dataset_operations import filter_dataset_split from azimuth.utils.project import ( perturbation_testing_available, postprocessing_known, predictions_available, saliency_available, similarity_available, ) from azimuth.utils.routers import ( build_named_dataset_filters, get_pagination, get_standard_task_result, query_pipeline_index, require_available_model, require_pipeline_index, ) from azimuth.utils.validation import assert_not_none router = APIRouter() class UtterancesSortableColumn(str, Enum): index = "index" # type: ignore utterance = "utterance" label = "label" prediction = "prediction" confidence = "confidence" @router.get( "", summary="Get utterances table view", description="Get a table view of the utterances according to filters.", response_model=GetUtterancesResponse, ) def get_utterances( dataset_split_name: DatasetSplitName, indices: List[int] = Query(None, title="Indices"), named_filters: NamedDatasetFilters = Depends(build_named_dataset_filters), sort_by: UtterancesSortableColumn = Query( UtterancesSortableColumn.index, title="Column to sort utterances by", alias="sort" ), descending: bool = Query(False, title="Descending"), task_manager: TaskManager = Depends(get_task_manager), config: AzimuthConfig = Depends(get_config), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), pipeline_index: Optional[int] = Depends(query_pipeline_index), use_bma: bool = Query(False, title="Use Bayesian Model Averaging for better estimation."), pagination: Optional[PaginationParams] = Depends(get_pagination), without_postprocessing: bool = Query(False, title="Without Postprocessing"), ) -> GetUtterancesResponse: if predictions_available(config) and pipeline_index is not None: threshold = ( assert_not_none(config.pipelines)[pipeline_index].threshold if postprocessing_known(config, pipeline_index) else None ) table_key = PredictionTableKey.from_pipeline_index(pipeline_index, config, use_bma=use_bma) else: threshold, table_key = None, None ds = dataset_split_manager.get_dataset_split(table_key) if indices is not None: ds = ds.select(indices) ds = filter_dataset_split( ds, named_filters.to_dataset_filters(dataset_split_manager.get_class_names()), config, without_postprocessing, ) ds = dataset_split_manager.get_dataset_split_with_class_names(table_key=table_key).select( ds[DatasetColumn.row_idx] ) # We create _top_conf and _top_prediction because we can't sort on columns made of lists. # They start with an underscore to emphasize that they are not saved and that therefore they # should not be used anywhere else. if sort_by == UtterancesSortableColumn.confidence and pipeline_index is not None: sort_by_column = "_top_conf" column = ( DatasetColumn.model_confidences if without_postprocessing else DatasetColumn.postprocessed_confidences ) ds = ds.map(lambda i: {sort_by_column: i[column][0]}) elif sort_by == UtterancesSortableColumn.prediction and pipeline_index is not None: if without_postprocessing: sort_by_column = "_top_prediction" ds = ds.map(lambda i: {sort_by_column: i[DatasetColumn.model_predictions][0]}) else: sort_by_column = DatasetColumn.postprocessed_prediction elif sort_by == UtterancesSortableColumn.utterance: sort_by_column = config.columns.text_input elif sort_by == UtterancesSortableColumn.label: sort_by_column = config.columns.label else: sort_by_column = DatasetColumn.row_idx ds = ds.sort(sort_by_column, reverse=descending) utterance_count = len(ds) # Before pagination to get the full length. if pagination is not None: indices = ds[DatasetColumn.row_idx][ pagination.offset : pagination.offset + pagination.limit ] ds = ds.filter(lambda i: i in indices, input_columns=DatasetColumn.row_idx) if utterance_count == 0: # No utterances, empty response. return GetUtterancesResponse( utterances=[], utterance_count=utterance_count, confidence_threshold=threshold ) if saliency_available(config) and pipeline_index is not None: saliency_results = get_standard_task_result( SupportedMethod.Saliency, dataset_split_name, task_manager, last_update=dataset_split_manager.last_update, mod_options=ModuleOptions( pipeline_index=pipeline_index, indices=ds[DatasetColumn.row_idx] ), ) model_saliencies: List[Optional[ModelSaliency]] = [ ModelSaliency( tokens=saliency_result.tokens, saliencies=saliency_result.saliency, ) for saliency_result in saliency_results ] else: model_saliencies = [None] * len(ds) available_families = copy(DATASET_SMART_TAG_FAMILIES) if pipeline_index is not None: available_families += PIPELINE_SMART_TAG_FAMILIES utterances = [ Utterance( index=data[DatasetColumn.row_idx], persistent_id=data[config.columns.persistent_id], data_action=next((t for t in ALL_DATA_ACTIONS if data[t]), DataAction.no_action), label=data[config.columns.label], utterance=data[config.columns.text_input], model_prediction=ModelPrediction( model_predictions=data[DatasetColumn.model_predictions], postprocessed_prediction=data[DatasetColumn.postprocessed_prediction], model_confidences=data[DatasetColumn.model_confidences], postprocessed_confidences=data[DatasetColumn.postprocessed_confidences], model_outcome=data[DatasetColumn.model_outcome], postprocessed_outcome=data[DatasetColumn.postprocessed_outcome], preprocessing_steps=data[DatasetColumn.pipeline_steps]["preprocessing_steps"], postprocessing_steps=data[DatasetColumn.pipeline_steps]["postprocessing_steps"], ) if predictions_available(config) and pipeline_index is not None else None, model_saliency=model_saliency, # Smart tags families { family.value: [t for t in tags_in_family if data[t]] if family in available_families else [] for family, tags_in_family in SMART_TAGS_FAMILY_MAPPING.items() }, ) for data, model_saliency in zip(ds, model_saliencies) ] return GetUtterancesResponse( utterances=utterances, utterance_count=utterance_count, confidence_threshold=threshold ) @router.patch( "", summary="Patch utterances", description="Patch utterances, such as updating proposed actions.", response_model=List[UtterancePatch], ) def patch_utterances( utterances: List[UtterancePatch] = Body(...), config: AzimuthConfig = Depends(get_config), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), ignore_not_found: bool = Query(False), ) -> List[UtterancePatch]: if ignore_not_found: all_persistent_ids = dataset_split_manager.get_dataset_split()[config.columns.persistent_id] utterances = [u for u in utterances if u.persistent_id in all_persistent_ids] persistent_ids = [utterance.persistent_id for utterance in utterances] try: row_indices = dataset_split_manager.get_row_indices_from_persistent_id(persistent_ids) except ValueError as e: raise HTTPException(HTTP_404_NOT_FOUND, detail=f"Persistent id not found: {e}.") data_actions = {} for row_idx, utterance in zip(row_indices, utterances): data_actions[row_idx] = {data_action: False for data_action in ALL_DATA_ACTIONS} if utterance.data_action != DataAction.no_action: data_actions[row_idx][utterance.data_action] = True dataset_split_manager.add_tags(data_actions) updated_tags = dataset_split_manager.get_tags(row_indices) return [ UtterancePatch( persistent_id=persistent_id, data_action=next( (tag for tag, value in tags.items() if tag in ALL_DATA_ACTIONS and value), DataAction.no_action, ), ) for persistent_id, tags in zip(persistent_ids, updated_tags.values()) ] @router.get( "/{index}/perturbed_utterances", summary="Get a perturbed utterances for a single utterance.", description="Get a perturbed utterances for a single utterance.", response_model=List[PerturbedUtteranceWithClassNames], dependencies=[Depends(require_available_model)], ) def get_perturbed_utterances( dataset_split_name: DatasetSplitName, index: int, task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), pipeline_index: int = Depends(require_pipeline_index), ) -> List[PerturbedUtteranceWithClassNames]: """ Perturbation tests are meant to be requested per utterance. The user is requesting more details on perturbation tests for an utterance based on the results in the perturbation_testing_summary. Therefore only a GET request is available for a provided idx. For endpoints that support per index request, we will not return a list of result. """ if not perturbation_testing_available(task_manager.config): return [] response: List[PerturbedUtteranceResult] = get_standard_task_result( SupportedModule.PerturbationTesting, dataset_split_name, task_manager, last_update=dataset_split_manager.last_update, mod_options=ModuleOptions(pipeline_index=pipeline_index, indices=[index]), )[0] named_response = [ PerturbedUtteranceWithClassNames( prediction=dataset_split_manager.get_class_names()[r.prediction], r.dict(exclude={"prediction"}), ) for r in response ] return named_response @router.get( "/{index}/similar_utterances", summary="Get similar examples to a query", description="Get similar examples using a KNN approach.", response_model=SimilarUtterancesResponse, ) def get_similar( dataset_split_name: DatasetSplitName, index: int, limit: int = Query(20, title="Limit", ge=1), neighbors_dataset_split_name: Optional[DatasetSplitName] = Query( None, title="Neighbors dataset split" ), # the source dataset dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), dataset_split_managers: Dict[DatasetSplitName, Optional[DatasetSplitManager]] = Depends( get_dataset_split_manager_mapping ), config: AzimuthConfig = Depends(get_config), pipeline_index: Optional[int] = Depends(query_pipeline_index), use_bma: bool = Query(False, title="Use Bayesian Model Averaging for better estimation."), ) -> SimilarUtterancesResponse: if not similarity_available(config): return SimilarUtterancesResponse(utterances=[]) # Set same split if not specified. neighbors_dataset_split_name = neighbors_dataset_split_name or dataset_split_name neighbors_ds = dataset_split_managers.get(neighbors_dataset_split_name) if neighbors_ds is None: raise HTTPException(HTTP_404_NOT_FOUND, detail="Dataset split not found.") table_key = ( PredictionTableKey.from_pipeline_index(pipeline_index, config, use_bma=use_bma) if pipeline_index is not None else None ) neighbors_ds_with_class_names = neighbors_ds.get_dataset_split_with_class_names(table_key) # We need to get the pipeline_index for full output with predicted class and confidence. source_ds = dataset_split_manager.get_dataset_split(table_key=table_key) # Get a list of indices and scores. item_scores = dict( source_ds.select([index])[f"neighbors_{neighbors_dataset_split_name}"][0][:limit] ) items: Dict[int, Dict] = { idx: neighbors_ds_with_class_names[int(idx)] for idx in item_scores.keys() } # Build utterances from `items` similar_utterances = [ SimilarUtterance( index=data[DatasetColumn.row_idx], persistent_id=data[config.columns.persistent_id], utterance=data[config.columns.text_input], label=data[config.columns.label], postprocessed_prediction=data.get(DatasetColumn.postprocessed_prediction, None), postprocessed_confidence=data.get(DatasetColumn.postprocessed_confidences, [None])[0], similarity=item_scores[idx], ) for idx, data in items.items() ] return 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30,964	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/model_performance/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 collections import Counter from typing import Dict, List from fastapi import APIRouter, Depends from azimuth.app import get_config, get_dataset_split_manager from azimuth.config import AzimuthConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.types import NamedDatasetFilters from azimuth.types.model_performance import ( UtteranceCountPerFilter, UtteranceCountPerFilterResponse, UtteranceCountPerFilterValue, ) from azimuth.types.tag import ( ALL_DATA_ACTIONS, DATASET_SMART_TAG_FAMILIES, SMART_TAGS_FAMILY_MAPPING, SmartTag, SmartTagFamily, ) from azimuth.utils.conversion import merge_counters from azimuth.utils.dataset_operations import filter_dataset_split from azimuth.utils.ml.model_performance import sorted_by_utterance_count_with_last from azimuth.utils.routers import build_named_dataset_filters router = APIRouter() def get_default_counter(value_list) -> Counter: return Counter({value: 0 for value in value_list}) def counter_to_count_per_filter_value(counter: Counter) -> List[UtteranceCountPerFilterValue]: return [ UtteranceCountPerFilterValue(filter_value=filter_value, utterance_count=utt_count) for filter_value, utt_count in counter.items() ] @router.get( "/per_filter", summary="Get count for each filter.", description="Get count for each filter based on the current filtering.", response_model=UtteranceCountPerFilterResponse, ) def get_count_per_filter( named_filters: NamedDatasetFilters = Depends(build_named_dataset_filters), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), config: AzimuthConfig = Depends(get_config), ) -> UtteranceCountPerFilterResponse: full_ds = dataset_split_manager.get_dataset_split_with_class_names() ds = filter_dataset_split(full_ds, named_filters, config) class_names = dataset_split_manager.get_class_names() label_counter = merge_counters( get_default_counter(class_names), Counter(ds[config.columns.label]) ) smart_tag_counter: Dict[SmartTagFamily, Counter] = { tag_family: Counter( { SmartTag.no_smart_tag.value: sum( ds.to_pandas()[SMART_TAGS_FAMILY_MAPPING[tag_family]] # type: ignore[index] .to_numpy() .sum(1) == 0 ) }, { t: sum(ds[t]) if t in ds.column_names else 0 for t in SMART_TAGS_FAMILY_MAPPING[tag_family] }, ) for tag_family in DATASET_SMART_TAG_FAMILIES } data_action_counter: Counter = Counter({t: sum(ds[t]) for t in ALL_DATA_ACTIONS}) return UtteranceCountPerFilterResponse( count_per_filter=UtteranceCountPerFilter( label=sorted_by_utterance_count_with_last( counter_to_count_per_filter_value(label_counter), dataset_split_manager.rejection_class_idx, ), **{ family.value: sorted_by_utterance_count_with_last( counter_to_count_per_filter_value(counters), 0 ) for family, counters in smart_tag_counter.items() }, data_action=sorted_by_utterance_count_with_last( counter_to_count_per_filter_value(data_action_counter), -1, ), ), utterance_count=len(ds), )	{"/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", 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30,965	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/logs.py	""" Note - This is a copied from the another internal repo. Also inspired by https://gist.github.com/tboquet/588955d846c03de66b87c8bca0fb99cb """ import collections import inspect import logging import os import threading import time from typing import List import structlog from structlog.dev import ConsoleRenderer from structlog.processors import StackInfoRenderer, TimeStamper, format_exc_info from structlog.stdlib import add_log_level log = structlog.get_logger(__name__) def set_logger_config(level=logging.INFO): """Set log configuration to our standard. Args: level: Logging level to use """ structlog.configure( processors=[ structlog.stdlib.PositionalArgumentsFormatter(), StackInfoRenderer(), format_exc_info, structlog.processors.UnicodeDecoder(), TimeStamper(fmt="iso", utc=True), add_log_level, add_caller_info, order_keys, add_pid_thread, BetterConsoleRenderer(), ], context_class=structlog.threadlocal.wrap_dict(dict), wrapper_class=structlog.make_filtering_bound_logger(level), cache_logger_on_first_use=True, ) def _foreground_color(color): return "\x1b[" + str(color) + "m" # pylint: disable=W0612 def _level_styles(): """Set color for different levels. Returns: Dict with color for each level. """ red = 31 green = 32 yellow = 33 blue = 34 light_gray = 37 return { "critical": _foreground_color(red), "exception": _foreground_color(red), "error": _foreground_color(red), "warning": _foreground_color(yellow), "info": _foreground_color(green), "debug": _foreground_color(blue), "notset": _foreground_color(light_gray), } def add_pid_thread(_, __, event_dict): pid = os.getpid() thread = threading.current_thread().getName() event_dict["pid_thread"] = f"{pid}-{thread}" return event_dict def add_caller_info(logger, method_name, event_dict): """Add source info of the log to get the function name. Args: logger: ... method_name: ... event_dict: ... Returns: event log updated with `module` key. """ # Typically skipped funcs: _add_caller_info, _process_event, _proxy_to_logger, _proxy_to_logger frame = inspect.currentframe() while frame: frame = frame.f_back module = frame.f_globals["__name__"] if module.startswith("structlog."): continue event_dict["module"] = module return event_dict def order_keys(logger, method_name, event_dict): return collections.OrderedDict( sorted(event_dict.items(), key=lambda item: (item[0] != "event", item)) ) class BetterConsoleRenderer: """Renderer with timestamp, pid and module name.""" def __init__(self): self._worse_console_renderer = ConsoleRenderer(level_styles=_level_styles()) def __call__(self, logger, log_method, event_dict): pid = event_dict.pop("pid_thread", None) module = event_dict.pop("module", None) # Format timestamp to remove milliseconds event_dict["timestamp"] = event_dict.get("timestamp", "")[:-8] if pid is not None: pid_thread = "[{}] ".format(str(pid).ljust(16)) else: pid_thread = "" mod_func_line = "[{}] ".format(module) return ( pid_thread + mod_func_line + self._worse_console_renderer(logger, log_method, event_dict) ) class TimerLogging: """ Small Context manager to time things. Args: name: How to name the Timer. Examples: ``` with TimerLogging("potato"): time.sleep(10) # prints: "Complete duration=10 name=potato" ``` """ def __init__(self, name: str): self.start = None self.name = name def __enter__(self): self.start = time.time() def __exit__(self, exc_type, exc_val, exc_tb): if self.start is not None: duration = int(time.time() - self.start) log.debug("Complete", name=self.name, duration=duration) class MultipleExceptions(Exception): def __init__(self, exceptions: List[Exception], message="We found the following exceptions: "): super().__init__(exceptions, message) self.exceptions = exceptions self.message = message def __str__(self): return f"{self.message} \n\t" + "\n".join(map(str, self.exceptions))	{"/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", 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30,966	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/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 fastapi import FastAPI from starlette.status import HTTP_200_OK from starlette.testclient import TestClient def test_dataset_warnings(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_warnings") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() for warning_type in data: for warning in warning_type["warnings"]: warning.pop("plots") assert data == [ { "name": "General Warnings", "warnings": [ { "columns": ["train", "eval"], "comparisons": [ { "alert": False, "data": [{"alert": False, "value": 23}, {"alert": False, "value": 22}], "name": "negative", }, { "alert": True, "data": [{"alert": True, "value": 19}, {"alert": False, "value": 20}], "name": "positive", }, ], "description": "Nb of samples per class in the training or " "evaluation set is below 20.", "format": "Integer", "name": "Missing samples (<20)", }, { "columns": ["train", "eval"], "comparisons": [ { "alert": False, "data": [ {"alert": False, "value": 0.09523809523809534}, {"alert": False, "value": 0.04761904761904767}, ], "name": "negative", }, { "alert": False, "data": [ {"alert": False, "value": -0.09523809523809523}, {"alert": False, "value": -0.04761904761904767}, ], "name": "positive", }, ], "description": "Relative difference between the number of " "samples per class and the mean in each dataset " "split is above 50%.", "format": "Percentage", "name": "Class imbalance (>50%)", }, { "columns": ["abs. diff."], "comparisons": [ { "alert": False, "data": [{"alert": False, "value": 0.023809523809523836}], "name": "negative", }, { "alert": False, "data": [{"alert": False, "value": 0.02380952380952378}], "name": "positive", }, ], "description": "Absolute difference between the proportion of a given class " "in the training set vs the evaluation set is " "above 5%.", "format": "Percentage", "name": "Representation mismatch (>5%)", }, ], }, { "name": "Syntactic Warnings", "warnings": [ { "columns": ["mean", "std"], "comparisons": [ { "alert": True, "data": [ {"alert": True, "value": 7.92292490118577}, {"alert": True, "value": 3.5343478830359656}, ], "name": "negative", }, { "alert": True, "data": [ {"alert": True, "value": 7.547368421052633}, {"alert": True, "value": 3.475890077806949}, ], "name": "positive", }, ], "description": "Delta between the number of words per utterance for a given " "class in the evaluation set vs the train set " "is above 3.0±3.0.", "format": "Decimal", "name": "Length mismatch (>3.0±3.0 words)", } ], }, ]	{"/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": 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30,967	ServiceNow/azimuth	refs/heads/main	/azimuth/types/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 azimuth.types import AliasModel class ValidationResponse(AliasModel): is_cuda_available: bool can_load_model: bool can_load_dataset: bool model_has_correct_type: bool can_make_prediction: bool can_make_saliency: bool	{"/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": 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30,968	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/conversion.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 hashlib import pickle from collections import Counter from typing import Any, List import numpy as np import orjson from starlette.responses import JSONResponse def flatten(lst: List[List]) -> List: return sum(lst, []) def md5_hash(d): """Hash any structure! No Security because we use it only to get a unique id. """ return hashlib.md5(orjson_dumps(d)).hexdigest() # nosec def to_pickle_bytes(arr): # Used for serialization only. pickled_str = pickle.dumps(arr) # nosec return np.array(pickled_str) def from_pickle_bytes(arr): # Used for serialization only. return pickle.loads(arr) # nosec def orjson_dumps(v, , default=...): return orjson.dumps( v, default=default, option=orjson.OPT_NON_STR_KEYS \| orjson.OPT_SERIALIZE_NUMPY ) class JSONResponseIgnoreNan(JSONResponse): """Copy of `starlette.JSONResponse`, but using `orjson.dumps` instead of `json.dumps`. It is faster and serializes nan as null. """ def render(self, content: Any) -> Any: return orjson_dumps(content) def merge_counters(counter1, counter2) -> Counter: """Merge two counters. We can't use c1 + c2 as it removes 0. Instead, we create a new Counter. Args: counter1: first counter counter2: second counter Returns: Merged counter with 0 still there. 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30,969	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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, Optional import numpy as np from fastapi import APIRouter, Depends, HTTPException, Query from azimuth.app import ( get_config, get_dataset_split_manager, get_dataset_split_manager_mapping, get_task_manager, ) from azimuth.config import AzimuthConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.plots.sankey_plot import make_sankey_plot from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, SupportedModule from azimuth.types.class_overlap import ( ClassOverlapPlotResponse, ClassOverlapResponse, ClassOverlapTableClassPair, ClassOverlapTableResponse, ) from azimuth.types.model_performance import ConfusionMatrixResponse from azimuth.utils.project import similarity_available from azimuth.utils.routers import get_standard_task_result, query_pipeline_index router = APIRouter() EPSILON = 1e-4 @router.get( "/plot", summary="Get class overlap plot.", description="Get a plot of class overlap using Spectral clustering and Monte-Carlo sampling " "(currently set to all samples).", response_model=ClassOverlapPlotResponse, ) def get_class_overlap_plot( dataset_split_name: DatasetSplitName, task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), config: AzimuthConfig = Depends(get_config), self_overlap: bool = Query( False, title="SelfOverlap", description="Whether to include overlap of a class with itself.", ), scale_by_class: bool = Query( True, title="ScaleByClass", description="Whether to scale overlap values by class sample counts.", ), overlap_threshold: Optional[float] = Query( None, title="OverlapThreshold", description="Plot overlap greater than this value (`None` sets threshold to show ~10 " "class pairs).", ), ) -> ClassOverlapPlotResponse: if not similarity_available(config): raise HTTPException( 400, detail="Please enable similarity in the configuration to enable this route." ) task_result: ClassOverlapResponse = get_standard_task_result( SupportedModule.ClassOverlap, dataset_split_name=dataset_split_name, task_manager=task_manager, last_update=-1, )[0] class_overlap_plot_response: ClassOverlapPlotResponse = make_sankey_plot( task_result, dataset_split_manager, self_overlap=self_overlap, scale_by_class=scale_by_class, overlap_threshold=overlap_threshold, ) return class_overlap_plot_response @router.get( "", summary="Get class overlap table.", description="Get data for class overlap, confusion, and related utterance counts.", response_model=ClassOverlapTableResponse, ) def get_class_overlap( dataset_split_name: DatasetSplitName, task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), dataset_split_managers: Dict[DatasetSplitName, DatasetSplitManager] = Depends( get_dataset_split_manager_mapping ), pipeline_index: Optional[int] = Depends(query_pipeline_index), ) -> ClassOverlapTableResponse: eval_dm = dataset_split_managers.get(DatasetSplitName.eval) class_overlap_result: ClassOverlapResponse = get_standard_task_result( SupportedModule.ClassOverlap, dataset_split_name=dataset_split_name, task_manager=task_manager, last_update=-1, )[0] dataset_class_count = class_overlap_result.s_matrix.shape[0] # Compute number of utterances that are overlapping. overlapping_count = np.zeros_like(class_overlap_result.s_matrix) for class_idx, sim_arrays in class_overlap_result.similarity_arrays.items(): norm_probs = np.stack([arrays.sample_probability_norm for arrays in sim_arrays.values()]) overlapping_count[class_idx] = (norm_probs > EPSILON).sum(0) confusion_result: Optional[ConfusionMatrixResponse] = ( get_standard_task_result( SupportedModule.ConfusionMatrix, DatasetSplitName.eval, task_manager=task_manager, mod_options=ModuleOptions( pipeline_index=pipeline_index, cf_normalize=False, cf_reorder_classes=False ), last_update=eval_dm.last_update, )[0] if pipeline_index is not None and eval_dm else None ) class_counts_train = dataset_split_manager.class_distribution() class_counts_eval = ( eval_dm.class_distribution() if eval_dm else np.zeros(dataset_split_manager.get_num_classes()) ) class_names = dataset_split_manager.get_class_names(labels_only=True) class_pairs_list = [ ClassOverlapTableClassPair( source_class=class_names[i], target_class=class_names[j], overlap_score_train=class_overlap_result.s_matrix[i, j], pipeline_confusion_eval=confusion_result and confusion_result.confusion_matrix[i, j], utterance_count_source_train=class_counts_train[i], utterance_count_source_eval=class_counts_eval[i], utterance_count_with_overlap_train=overlapping_count[i, j], ) for i in range(dataset_class_count) for j in range(dataset_class_count) if i != j and ( class_overlap_result.s_matrix[i, j] > 0 or (confusion_result and confusion_result.confusion_matrix[i, j] > 0) ) ] api_result = ClassOverlapTableResponse(class_pairs=class_pairs_list) return api_result	{"/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", 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30,970	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_word_analysis/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. import numpy as np from azimuth.modules.word_analysis.top_words import TopWordsModule from azimuth.types import DatasetSplitName, ModuleOptions from azimuth.types.word_analysis import TokensToWordsResponse from tests.utils import save_predictions ALL_WORDS = [ "hello", ",", "my", "name", "is", "fred", "[CLS]", "the", "sky", "is", "blue", ".", "[SEP]", "i", "like", "coffee", ] def fake_get_words_saliencies(indices): rng = np.random.RandomState(1338) records = [] for i in indices: records.append( TokensToWordsResponse( saliency=rng.rand(16).tolist(), # 16 words words=ALL_WORDS, ) ) return records def test_top_words_with_saliency(simple_text_config, apply_mocked_startup_task, monkeypatch): mod = TopWordsModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(top_x=4, pipeline_index=0), ) monkeypatch.setattr(mod, "get_words_saliencies", fake_get_words_saliencies) assert mod is not None [json_output] = mod.compute_on_dataset_split() assert len(json_output.all) > 0 assert len(json_output.right) > 0 or len(json_output.errors) > 0 def test_top_words_without_saliency(file_text_config_top1): save_predictions(file_text_config_top1) mod = TopWordsModule( dataset_split_name=DatasetSplitName.eval, config=file_text_config_top1, mod_options=ModuleOptions(top_x=16, pipeline_index=0), ) [json_output] = mod.compute_on_dataset_split() top_word_all = {top_words_result.word for top_words_result in json_output.all} top_word_right = {top_words_result.word for top_words_result in json_output.right} top_word_errors = {top_words_result.word for top_words_result in json_output.errors} assert {"phone", "potato", "pizza", "forest", "park", "turning"}.issubset(top_word_all) 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30,971	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_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 json from copy import deepcopy from typing import List import h5py import numpy as np import pytest from azimuth.config import ModelContractConfig, PerturbationTestingConfig from azimuth.modules.base_classes import ( AggregationModule, FilterableModule, IndexableModule, Module, ) from azimuth.modules.base_classes.caching import HDF5FileOpenerWithRetry from azimuth.modules.model_performance.confusion_matrix import ConfusionMatrixModule from azimuth.modules.task_execution import get_task_result from azimuth.types import DatasetFilters, DatasetSplitName, ModuleOptions, ModuleResponse from azimuth.types.model_performance import ConfusionMatrixResponse from azimuth.utils.exclude_fields_from_cache import exclude_fields_from_cache from tests.utils import save_outcomes, save_predictions @pytest.mark.parametrize( "data, indices", [ ([{"a": 1}], [1]), # Simple ([{"a": 1}, {"a": 2}], [1, 5]), # Multiple indices ([{"a": {"b": 32}}], [1]), # Complex structure ([{"a": [1, 4, 6]}], [1]), # Complex structure ([{"a": [1, 4, 6]}, {"a": [1, 4, 5]}], [1, 7]), # Multi indices, complex ], ) def test_hdf5_caching(data, indices, simple_text_config): mod = IndexableModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions(indices=indices) ) assert not mod._check_cache(indices) # File doesn't exist with pytest.raises(OSError): mod._get_cache(indices) mod._store_data_in_cache(data, indices) assert mod._check_cache(indices) data_retrieved = mod._get_cache(indices) # Cache doesn't exist with pytest.raises(KeyError): mod._get_cache([43, 35, 56]) # Should be jsonable in any case. assert json.dumps(data) == json.dumps(data_retrieved) # Verify that if changing the threshold, the module name will be different, which implies a # new cache. modified_th_config = deepcopy(simple_text_config) # We modify the threshold at both places to be consistent, # but in practice only the `kwargs` is used. modified_th_config.pipelines[0].postprocessors[-1].kwargs["threshold"] = 0.8 modified_th_config.pipelines[0].postprocessors[-1].threshold = 0.8 modified_th_mod = IndexableModule( DatasetSplitName.eval, modified_th_config, mod_options=ModuleOptions(indices=indices) ) assert mod.name != modified_th_mod.name def test_module_cache_with_options(simple_text_config): # Testing FilterableModule which should be affected by changing the filters original_mod = FilterableModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions(filters=DatasetFilters(label=[0]), pipeline_index=0), ) modified_mod = FilterableModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions(filters=DatasetFilters(label=[1]), pipeline_index=0), ) assert original_mod.name != modified_mod.name # Testing regular modules cannot be affected by filters. with pytest.raises(ValueError): Module( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions(filters=DatasetFilters(label=[0]), pipeline_index=0), ) class MyFileThatIsOpened: n = 0 def __init__(self, args, kwargs): self.open = False if MyFileThatIsOpened.n < 5: MyFileThatIsOpened.n += 1 raise OSError("Can't open") self.open = True def close(self): self.open = False def test_hdf5_retry_success(monkeypatch): MyFileThatIsOpened.n = 0 monkeypatch.setattr(h5py, "File", MyFileThatIsOpened) with HDF5FileOpenerWithRetry("my_file", n_retry=6) as f: assert isinstance(f, MyFileThatIsOpened) assert f.open assert not f.open def test_hdf5_retry_fail(monkeypatch): MyFileThatIsOpened.n = 0 monkeypatch.setattr(h5py, "File", MyFileThatIsOpened) with pytest.raises(TimeoutError, match="Can't open"): with HDF5FileOpenerWithRetry("my_file", n_retry=1) as _: pass class Potato(ModuleResponse): a: int b: float def test_wrong_aggregation_module(simple_text_config): class MyAggregationModule(AggregationModule): pass with pytest.raises(ValueError): MyAggregationModule( dataset_split_name=DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(indices=[1, 2, 3]), ) class MyPerturbationModule(Module[PerturbationTestingConfig]): def compute_on_dataset_split(self): return [{"potato": i} for i in self.get_indices()] def test_scoped_caching_different(simple_text_config, dask_client): my_perturbation_module = MyPerturbationModule( DatasetSplitName.eval, config=simple_text_config, ) assert not my_perturbation_module.done() res = my_perturbation_module.compute_on_dataset_split() my_perturbation_module._store_data_in_cache(res, my_perturbation_module.get_indices()) new_config = simple_text_config.copy(update={"similarity": None}) new_perturbation_module = MyPerturbationModule(DatasetSplitName.eval, config=new_config) assert new_perturbation_module.name == my_perturbation_module.name assert new_perturbation_module.done() def test_scoped_caching_inner(simple_text_config, dask_client): my_perturbation_module = MyPerturbationModule(DatasetSplitName.eval, config=simple_text_config) assert not my_perturbation_module.done() res = my_perturbation_module.compute_on_dataset_split() my_perturbation_module._store_data_in_cache(res, my_perturbation_module.get_indices()) new_config = simple_text_config.copy(update={"saliency_layer": "potato"}) new_perturbation_module = MyPerturbationModule(DatasetSplitName.eval, config=new_config) assert new_perturbation_module.name != my_perturbation_module.name assert not new_perturbation_module.done() def test_scoped_caching_common(simple_text_config, dask_client): my_perturbation_module = MyPerturbationModule(DatasetSplitName.eval, config=simple_text_config) assert not my_perturbation_module.done() res = my_perturbation_module.compute_on_dataset_split() my_perturbation_module._store_data_in_cache(res, my_perturbation_module.get_indices()) new_config = simple_text_config.copy(update={"batch_size": 32}) new_perturbation_module = MyPerturbationModule(DatasetSplitName.eval, config=new_config) assert new_perturbation_module.name == my_perturbation_module.name assert new_perturbation_module.done() def test_effective_arguments(tiny_text_config): save_predictions(tiny_text_config) save_outcomes(tiny_text_config) mod = ConfusionMatrixModule( config=tiny_text_config, dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(pipeline_index=0, cf_normalize=False), ) result = get_task_result(mod, List[ConfusionMatrixResponse])[0] with open(mod._cache_effective_arguments, "r") as f: effective_arguments = json.load(f) mod_2 = ConfusionMatrixModule( config=tiny_text_config, dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(effective_arguments["mod_options"]), ) result_2 = get_task_result(mod_2, List[ConfusionMatrixResponse])[0] assert np.array_equal(result.confusion_matrix, result_2.confusion_matrix) assert not result_2.normalize # Saved config attributes should match mod_2.config, when excluding fields not affecting cache. loaded_cfg = ModelContractConfig(*effective_arguments["config_scope"]) loaded_cfg_exclude_fields_from_cache = loaded_cfg.dict( exclude=exclude_fields_from_cache(loaded_cfg) ) mod_2_exclude_fields_from_cache = mod_2.config.dict( exclude=exclude_fields_from_cache(mod_2.config) ) assert mod_2_exclude_fields_from_cache == loaded_cfg_exclude_fields_from_cache 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"], 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"/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"]}
30,972	ServiceNow/azimuth	refs/heads/main	/azimuth/app_loader.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.app import start_app from azimuth.config import parse_args args = parse_args() app = start_app( config_path=args.config_path, load_config_history=args.load_config_history, debug=args.debug )	{"/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", 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30,973	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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 Dict from datasets import Metric, MetricInfo from fastapi import APIRouter, Depends, HTTPException from azimuth.app import ( get_config, get_dataset_split_manager_mapping, get_ready_flag, get_startup_tasks, get_task_manager, ) from azimuth.config import AzimuthConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.modules.base_classes import Module from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, SupportedModule from azimuth.types.app import ( AvailableDatasetSplits, DatasetInfoResponse, PerturbationTestingSummary, StatusResponse, UtteranceCountPerDatasetSplit, ) from azimuth.types.perturbation_testing import ( PerturbationTestingMergedResponse, PerturbationTestingSummaryResponse, ) from azimuth.types.tag import ALL_DATA_ACTION_FILTERS, ALL_SMART_TAG_FILTERS from azimuth.utils.object_loader import load_custom_object from azimuth.utils.project import ( perturbation_testing_available, postprocessing_editable, predictions_available, similarity_available, ) from azimuth.utils.routers import ( get_last_update, get_standard_task_result, require_application_ready, require_available_model, require_pipeline_index, ) router = APIRouter() @router.get( "/status", summary="Get status", description="Get the status of the app", response_model=StatusResponse, ) def get_status( startup_tasks=Depends(get_startup_tasks), ready_flag=Depends(get_ready_flag), task_manager: TaskManager = Depends(get_task_manager), ) -> StatusResponse: try: is_ready = require_application_ready(startup_tasks, ready_flag, task_manager) except HTTPException: is_ready = False status_response = StatusResponse( startup_tasks_ready=is_ready, startup_tasks_status={name: mod.status() for name, mod in startup_tasks.items()}, ) return status_response @router.get( "/dataset_info", summary="Get dataset info", description="Get the current dataset info", response_model=DatasetInfoResponse, ) def get_dataset_info( dataset_split_managers: Dict[DatasetSplitName, DatasetSplitManager] = Depends( get_dataset_split_manager_mapping ), startup_tasks: Dict[str, Module] = Depends(get_startup_tasks), config: AzimuthConfig = Depends(get_config), ): eval_dm = dataset_split_managers.get(DatasetSplitName.eval) training_dm = dataset_split_managers.get(DatasetSplitName.train) return DatasetInfoResponse( project_name=config.name, data_actions=ALL_DATA_ACTION_FILTERS, smart_tags=ALL_SMART_TAG_FILTERS, startup_tasks={k: v.status() for k, v in startup_tasks.items()}, model_contract=config.model_contract, prediction_available=predictions_available(config), perturbation_testing_available=perturbation_testing_available(config), available_dataset_splits=AvailableDatasetSplits( eval=eval_dm is not None, train=training_dm is not None ), utterance_count_per_dataset_split=UtteranceCountPerDatasetSplit( eval=eval_dm and eval_dm.num_rows, train=training_dm and training_dm.num_rows ), similarity_available=similarity_available(config), postprocessing_editable=None if config.pipelines is None else [postprocessing_editable(config, idx) for idx in range(len(config.pipelines))], ) @router.get( "/custom_metrics_info", summary="Gives information about custom metrics.", description="Gives the description of all custom metrics.", response_model=Dict[str, MetricInfo], ) def custom_metrics_info( dataset_split_managers=Depends(get_dataset_split_manager_mapping), config: AzimuthConfig = Depends(get_config), ) -> Dict[str, MetricInfo]: result = {} dm = dataset_split_managers.get(DatasetSplitName.eval) or dataset_split_managers.get( DatasetSplitName.train ) for metric_name, metric_obj_def in config.metrics.items(): met: Metric = load_custom_object( metric_obj_def, label_list=dm.get_class_names(), rejection_class_idx=dm.rejection_class_idx, force_kwargs=True, # Set True here as load_metrics has **kwargs. ) result[metric_name] = met.info return result @router.get( "/perturbation_testing_summary", summary="Get the perturbation testing summary.", description="Get a failure rate per dataset split and per test as well.", response_model=PerturbationTestingSummary, dependencies=[Depends(require_application_ready), Depends(require_available_model)], ) def get_perturbation_testing_summary( dataset_split_managers: Dict[DatasetSplitName, DatasetSplitManager] = Depends( get_dataset_split_manager_mapping ), task_manager: TaskManager = Depends(get_task_manager), config: AzimuthConfig = Depends(get_config), pipeline_index: int = Depends(require_pipeline_index), ): eval_dm = dataset_split_managers.get(DatasetSplitName.eval) training_dm = dataset_split_managers.get(DatasetSplitName.train) last_update = get_last_update([training_dm, eval_dm]) if perturbation_testing_available(config) and pipeline_index is not None: perturbation_testing_result: PerturbationTestingMergedResponse = get_standard_task_result( SupportedModule.PerturbationTestingMerged, dataset_split_name=DatasetSplitName.all, task_manager=task_manager, last_update=last_update, mod_options=ModuleOptions(pipeline_index=pipeline_index), )[0] failure_rates = { DatasetSplitName.eval: perturbation_testing_result.eval_failure_rate, DatasetSplitName.train: perturbation_testing_result.train_failure_rate, } else: failure_rates = {} response: PerturbationTestingSummaryResponse = get_standard_task_result( SupportedModule.PerturbationTestingSummary, dataset_split_name=DatasetSplitName.all, task_manager=task_manager, mod_options=ModuleOptions(pipeline_index=pipeline_index), )[0] return PerturbationTestingSummary( all_tests_summary=response.all_tests_summary, failure_rates=failure_rates )	{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], 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30,974	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/base_classes/indexable_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 dataclasses from abc import ABC, abstractmethod from typing import Callable, Dict, List, Optional, Set, 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 ConfigScope, Module from azimuth.modules.base_classes.dask_module import Worker from azimuth.types import ( DatasetColumn, DatasetSplitName, InputResponse, ModuleResponse, SupportedMethod, ) from azimuth.types.tag import SmartTag from azimuth.types.task import PredictionResponse, SaliencyResponse from azimuth.utils.ml.model_performance import compute_outcome from azimuth.utils.ml.postprocessing import ( PostProcessingIO, PostprocessingStep, PostprocessingStepAPIResponse, PredictionDetails, ) from azimuth.utils.ml.preprocessing import PreprocessingStepAPIResponse from azimuth.utils.object_loader import load_custom_object from azimuth.utils.validation import assert_not_none class IndexableModule(Module[ConfigScope], ABC): optional_mod_options: Set[str] = {"indices"} def get_dataset_split(self, name: Optional[DatasetSplitName] = None) -> Dataset: """Get the specified dataset_split, according to module indices. If indices are None, it gets the full dataset. Args: name: Which dataset_split to select. Returns: The loaded dataset_split. """ dataset_split = super().get_dataset_split(name) indices = self.mod_options.indices if indices: dataset_split = dataset_split.select(indices) return dataset_split class DatasetResultModule(IndexableModule[ConfigScope], ABC): @abstractmethod def _save_result(self, res: List[ModuleResponse], dm: DatasetSplitManager): raise NotImplementedError def save_result(self, res: List[ModuleResponse], dm: DatasetSplitManager): """Save results in a DatasetSplitManager or anywhere else. Args: res: Results from `compute_on_dataset_split`. dm: the dataset_split manager used to get `res`. """ if len(res) != dm.num_rows: raise ValueError("The results length don't match the dataset size.") return self._save_result(res, dm) class ModelContractModule(DatasetResultModule[ModelContractConfig], abc.ABC): required_mod_options: Set[str] = {"pipeline_index", "model_contract_method_name"} optional_mod_options: Set[str] = DatasetResultModule.optional_mod_options \| {"threshold"} worker = Worker.model def compute(self, batch: Dataset) -> List[ModuleResponse]: my_func = self.route_request(assert_not_none(self.model_contract_method_name)) if my_func: res: List[ModuleResponse] = my_func(batch) return res def route_request(self, method_name: SupportedMethod) -> Callable: """Route the method_name to the correct fn. Args: method_name: Name of the supported method. Returns: Function associated to the method_name. Raises: Exception: If method_name is not a valid SupportedMethod. """ if method_name == SupportedMethod.Inputs: return self.get_input elif method_name == SupportedMethod.Predictions: return self.predict elif method_name == SupportedMethod.Saliency: return self.saliency elif method_name == SupportedMethod.PostProcess: return self.post_process else: raise Exception("Method is not found") @abc.abstractmethod def get_input(self, batch: Dataset) -> List[InputResponse]: # Get the input to show properly in the application. ... @abc.abstractmethod def predict(self, batch: Dataset) -> List[PredictionResponse]: # Compute prediction on the batch. ... @abc.abstractmethod def post_process(self, batch: Dataset) -> List[PredictionResponse]: # Recompute post-processing on the batch based on new config attributes or mod_options. ... @abc.abstractmethod def saliency(self, batch: Dataset) -> List[SaliencyResponse]: # Compute saliency on the batch. ... def run_postprocessing(self, output: PostProcessingIO, kwargs) -> List[PostprocessingStep]: """ Run all postprocessors defined in `self.config` on a batch. Args: output: output of the model as a numpy array. kwargs: Additional arguments to the postprocessors. Returns: postprocessed output. Raises: ValueError when pipeline_index is not valid. """ if self.mod_options.pipeline_index is None or self.config.pipelines is None: raise ValueError( f"Expected non-null Pipeline index (got {self.mod_options.pipeline_index})" f" and pipeline definitions (got {self.config.pipelines})." ) postprocessors = self.config.pipelines[self.mod_options.pipeline_index].postprocessors postprocessing_steps = [] if postprocessors is not None: for post in postprocessors: # Q: Why do we reload the postprocessors all the time? # A: We should memoize it based on the module options, but that can get complicated. # It is less burdensome to just reload it. fn = load_custom_object(post, kwargs) output = fn(output) class_name = post.class_name.removeprefix(f"{PostprocessingStep.__module__}.") postprocessing_steps.append( PostprocessingStep( class_name=class_name, output=output, ) ) return postprocessing_steps def _save_result(self, res, dm): if isinstance(res[0], PredictionResponse): # Save result in a DatasetSplitManager table_key = self._get_table_key() class_names = dm.get_class_names() res_casted = cast(List[PredictionResponse], res) dm.add_column_to_prediction_table( key=DatasetColumn.model_predictions, features=[ list(np.argsort(pred_res.model_output.probs[0])[::-1]) for pred_res in res_casted ], table_key=table_key, ) dm.add_column_to_prediction_table( key=DatasetColumn.model_confidences, features=[ list(np.sort(pred_res.model_output.probs[0])[::-1]) for pred_res in res_casted ], table_key=table_key, ) dm.add_column_to_prediction_table( key=DatasetColumn.postprocessed_prediction, features=[int(pred_res.postprocessed_output.preds[0]) for pred_res in res_casted], table_key=table_key, ), dm.add_column_to_prediction_table( key=DatasetColumn.postprocessed_confidences, features=[ list(np.sort(pred_res.postprocessed_output.probs[0])[::-1]) for pred_res in res_casted ], table_key=table_key, ) dm.add_column_to_prediction_table( key=DatasetColumn.pipeline_steps, features=[ { "preprocessing_steps": [ PreprocessingStepAPIResponse( class_name=step.class_name, text=step.text[0] ).dict() for step in pred_res.preprocessing_steps ], "postprocessing_steps": [ PostprocessingStepAPIResponse( class_name=step.class_name, output=PredictionDetails( predictions=[ class_names[cl_idx] for cl_idx in np.argsort(step.output.probs[0])[::-1] ], prediction=class_names[step.output.preds[0]], confidences=list(np.sort(step.output.probs[0])[::-1]), outcome=compute_outcome( step.output.preds[0], pred_res.label, dm.rejection_class_idx ), ), ).dict() for step in pred_res.postprocessing_steps ], } for pred_res in res_casted ], table_key=table_key, ) if table_key.use_bma: # Epistemic uncertainty is only computed when use_bma is set to True, hence why the # smart tag is only computed on that condition. high_epistemic = { idx: { SmartTag.high_epistemic_uncertainty: r.epistemic > self.config.uncertainty.high_epistemic_threshold } for idx, r in enumerate(res_casted) } # We save the smart tag with use_bma=False so the smart tag can be retrieved in the # prediction table with the regular key, as the other tags. non_bma_table_key = dataclasses.replace(table_key, use_bma=False) dm.add_tags(high_epistemic, table_key=non_bma_table_key) else: # These tags only need to be computed for the prediction table with the regular key # (use_bma=False) pred_tags = self.get_pred_tags( label=dm.get_dataset_split(table_key=table_key)[self.config.columns.label], model_predictions=dm.get_dataset_split(table_key=table_key)[ DatasetColumn.model_predictions ], postprocessed_prediction=dm.get_dataset_split(table_key=table_key)[ DatasetColumn.postprocessed_prediction ], ) dm.add_tags( {idx: pred_tag_row for idx, pred_tag_row in enumerate(pred_tags)}, table_key=table_key, ) @staticmethod def get_pred_tags( label: List[int], model_predictions: List[List[int]], postprocessed_prediction: List[int], ) -> List[Dict[SmartTag, bool]]: """Compute the `correct_low_conf` and `correct_top_3` tags. `correct_low_conf` is set when the top prediction is correct but its confidence is below the threshold, so the prediction is set to the rejection class. `correct_top_3` is set when the top prediction is incorrect, but the label is in the top 3 model_predictions (i.e., second or third). Args: label: List of labels per index. model_predictions: List of lists of all model_predictions per index. postprocessed_prediction: List of predictions after post-processing. Returns: tags: List (length equal to number of indices) of dictionaries where key is smart tag name and value is smart tag value. """ tags = [] for row_lab, row_preds, row_postproc_pred in zip( label, model_predictions, postprocessed_prediction ): correct_low_conf = correct_top_3 = False if row_postproc_pred != row_lab: correct_low_conf = row_preds[0] == row_lab correct_top_3 = (len(row_preds) > 3) and (row_lab in row_preds[1:3]) tags.append( {SmartTag.correct_low_conf: correct_low_conf, SmartTag.correct_top_3: correct_top_3} ) return tags	{"/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", 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30,975	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/pipeline_comparison/prediction_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 typing import List from azimuth.config import ModelContractConfig from azimuth.dataset_split_manager import DatasetSplitManager, PredictionTableKey from azimuth.modules.base_classes import DatasetResultModule 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 ModuleOptions, SupportedMethod from azimuth.types.pipeline_comparison import PredictionComparisonResponse from azimuth.types.tag import SmartTag from azimuth.types.task import PredictionResponse from azimuth.utils.validation import assert_not_none class PredictionComparisonModule(DatasetResultModule[ModelContractConfig]): def compute_on_dataset_split(self) -> List[PredictionComparisonResponse]: # type: ignore self._validate_config() num_pipelines = len(assert_not_none(self.config.pipelines)) preds_per_pipeline = [ self._get_predictions(pipeline_idx=pipeline_idx) for pipeline_idx in range(num_pipelines) ] result = [] for predictions in zip(*preds_per_pipeline): incorrect_for_all_pipelines = all( pred.postprocessed_output.preds.item() != pred.label for pred in predictions ) pipeline_disagreement = ( len(set(pred.postprocessed_output.preds.item() for pred in predictions)) > 1 ) result.append( PredictionComparisonResponse( pipeline_disagreement=pipeline_disagreement, incorrect_for_all_pipelines=incorrect_for_all_pipelines, ) ) return result def _get_predictions(self, pipeline_idx: int) -> List[PredictionResponse]: predict_task = model_contract_task_mapping( dataset_split_name=self.dataset_split_name, config=self.config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=pipeline_idx, ), ) return get_task_result(task_module=predict_task, result_type=List[PredictionResponse]) def _get_table_key_for_pipeline_index(self, pipeline_index): if self.config.pipelines is None or len(self.config.pipelines) < pipeline_index: raise ValueError(f"Can't find pipeline {pipeline_index} from {self.config.pipelines}") current_pipeline = self.config.pipelines[pipeline_index] use_bma = self.mod_options.use_bma table_key = PredictionTableKey( temperature=current_pipeline.temperature, threshold=current_pipeline.threshold, use_bma=use_bma, pipeline_index=pipeline_index, ) return table_key def _save_result( # type: ignore self, res: List[PredictionComparisonResponse], dm: DatasetSplitManager ): self._validate_config() for pipeline_idx in range(len(assert_not_none(self.config.pipelines))): table_key = self._get_table_key_for_pipeline_index(pipeline_index=pipeline_idx) dm.add_tags( { i: { SmartTag.pipeline_disagreement: response.pipeline_disagreement, SmartTag.incorrect_for_all_pipelines: response.incorrect_for_all_pipelines, } for i, response in enumerate(res) }, table_key=table_key, ) def _validate_config(self): if self.config.pipelines is None or len(self.config.pipelines) < 2: raise ValueError( "Can't compute prediction comparison with less than two pipelines got" f" {0 if self.config.pipelines is None else len(self.config.pipelines)}" )	{"/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", 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30,976	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_dataset_analysis/test_syntax_tagging.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 itertools import zip_longest from azimuth.modules.dataset_analysis.syntax_tagging import SyntaxTaggingModule from azimuth.types import DatasetColumn, DatasetSplitName from azimuth.types.tag import SmartTag def verify_syntax_results(json_output, smart_tags_per_idx, token_count_per_idx): for res, smart_tags, token_count in zip_longest( json_output, smart_tags_per_idx, token_count_per_idx ): assert list(res.tags.values()) == [k in smart_tags for k in res.tags.keys()] assert res.adds[DatasetColumn.word_count] == token_count def test_syntax_tagging(tiny_text_config): batch = { "utterance": [ "detect files.", "this is hell. It is terribly horrible for me to write this test but I am having fun.", "I am looking for", "sugar and cookies!", ], "label": [0, 1, 0, 1], } mod = SyntaxTaggingModule( DatasetSplitName.eval, tiny_text_config, ) json_output = mod.compute(batch) assert len(json_output) == 4 smart_tags_per_idx = [ [SmartTag.short, SmartTag.no_subj], [SmartTag.multi_sent, SmartTag.long], [SmartTag.no_obj], [SmartTag.no_obj, SmartTag.no_verb, SmartTag.no_subj, SmartTag.short], ] token_count_per_idx = [2, 18, 4, 3] verify_syntax_results(json_output, smart_tags_per_idx, token_count_per_idx) # Edit config values tiny_text_config_edited = tiny_text_config.copy(deep=True) tiny_text_config_edited.syntax.short_utterance_max_word = 4 tiny_text_config_edited.syntax.long_utterance_min_word = 3 mod = SyntaxTaggingModule( DatasetSplitName.eval, tiny_text_config_edited, ) json_output = mod.compute(batch) # Tags should change for utterances below, based on the new config values for idx in [2, 3]: assert json_output[idx].tags[SmartTag.long] and json_output[idx].tags[SmartTag.short] json_output_all = mod.compute_on_dataset_split() ds = mod.get_dataset_split() assert len(json_output_all) == len(ds) def test_syntax_tagging_french(simple_text_config_french): batch = { "utterance": [ "adore les biscuits!", "c'est terrible. C'est horrible pour moi d'écrire ce test, mais je m'amuse bien.", f"J{chr(8217)}aimerais aller", # Single quote; sm model struggled with j'aimerais subj "le sucre et les biscuits!", # As currently implemented, no subject or object "Indiquez-moi l'état de mes demandes", # First word (a) capitalized and (b) verb ], "label": [0, 1, 0, 1, 0], } mod = SyntaxTaggingModule( DatasetSplitName.eval, simple_text_config_french, ) json_output = mod.compute(batch) assert len(json_output) == 5 smart_tags_per_idx = [ [SmartTag.short, SmartTag.no_subj], [SmartTag.long, SmartTag.multi_sent], [SmartTag.short, SmartTag.no_obj], [SmartTag.no_obj, SmartTag.no_verb, SmartTag.no_subj], [SmartTag.no_subj], ] token_count_per_idx = [3, 17, 3, 5, 7] verify_syntax_results(json_output, smart_tags_per_idx, token_count_per_idx)	{"/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", 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30,977	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/dataset_analysis/syntax_tagging.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 itertools from typing import Dict, List, cast import spacy from datasets import Dataset from spacy.lang.en import English from azimuth.config import SyntaxConfig, SyntaxOptions from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.modules.base_classes import DatasetResultModule from azimuth.types import DatasetColumn, ModuleResponse from azimuth.types.tag import ( SMART_TAGS_FAMILY_MAPPING, SmartTag, SmartTagFamily, Tag, TaggingResponse, ) from azimuth.utils.utterance import clean_utterance from azimuth.utils.validation import assert_not_none class SyntaxTaggingModule(DatasetResultModule[SyntaxConfig]): """Calculate smart tags related to syntax.""" spacy_sentencizer_en = English() spacy_sentencizer_en.add_pipe("sentencizer") # we use pos_ for verb since it is simpler and more reliable than dep_ verb_tags = ["VERB", "AUX"] def compute(self, batch: Dataset) -> List[TaggingResponse]: # type: ignore """Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the smart tags. Returns: tags: Newly calculated tags. """ syntax_options: SyntaxOptions = assert_not_none(self.config.syntax) spacy_model = spacy.load(syntax_options.spacy_model) utterances = batch[self.config.columns.text_input] records: List[TaggingResponse] = [] for utterance in utterances: tag: Dict[Tag, bool] = { smart_tag: False for family in [SmartTagFamily.extreme_length, SmartTagFamily.partial_syntax] for smart_tag in SMART_TAGS_FAMILY_MAPPING[family] } doc = spacy_model(clean_utterance(utterance)) # Remove punctuation for word count and smart tags tokens = [token.text for token in doc if not token.is_punct] if len(tokens) >= syntax_options.long_utterance_min_word: tag[SmartTag.long] = True if len(tokens) <= syntax_options.short_utterance_max_word: tag[SmartTag.short] = True sub_toks = [tok for tok in doc if (tok.dep_ in syntax_options.subj_tags)] obj_toks = [tok for tok in doc if (tok.dep_ in syntax_options.obj_tags)] vrb_toks = [tok for tok in doc if (tok.pos_ in self.verb_tags)] if not sub_toks: tag[SmartTag.no_subj] = True if not obj_toks: tag[SmartTag.no_obj] = True if not vrb_toks: tag[SmartTag.no_verb] = True # Some issues occur with other languages such as french if using doc.sents directly. # Hence, we use an English sentencizer that seems to work better for similar languages. doc_sentencizer_en = self.spacy_sentencizer_en(clean_utterance(utterance)) sentence_count = len(list(doc_sentencizer_en.sents)) if sentence_count > 1: tag[SmartTag.multi_sent] = True adds = {DatasetColumn.word_count: len(tokens)} records.append(TaggingResponse(tags=tag, adds=adds)) return records def _save_result(self, res: List[ModuleResponse], dm: DatasetSplitManager): """Save tags in a DatasetSplitManager. Args: res: Results from `compute_on_dataset_split`. dm: the dataset_split manager used to get `res`. 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30,978	ServiceNow/azimuth	refs/heads/main	/azimuth/dataset_split_manager.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 collections import OrderedDict, defaultdict from copy import copy, deepcopy from dataclasses import asdict, dataclass from glob import glob from os.path import join as pjoin from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import faiss import numpy as np import pandas as pd import structlog from datasets import ClassLabel, Dataset, concatenate_datasets from filelock import FileLock from azimuth.config import AzimuthConfig, AzimuthValidationError, CommonFieldsConfig from azimuth.types import DatasetColumn, DatasetFilters, DatasetSplitName from azimuth.types.tag import ALL_DATA_ACTIONS, Tag from azimuth.utils.dataset_operations import filter_dataset_split from azimuth.utils.validation import assert_not_none REJECTION_CLASS = "REJECTION_CLASS" log = structlog.get_logger("DatasetSplitManager") FEATURES = "features" FEATURE_FAISS = "features_faiss" Time = float @dataclass(eq=True, frozen=True) # Generates __hash__ class PredictionTableKey: threshold: Optional[float] temperature: Optional[float] use_bma: bool pipeline_index: Optional[int] @classmethod def from_pipeline_index(cls, index: int, config: AzimuthConfig, , use_bma: bool): pipelines = assert_not_none(config.pipelines) return PredictionTableKey( threshold=pipelines[index].threshold, temperature=pipelines[index].temperature, use_bma=use_bma, pipeline_index=index, ) def to_class_name(class_idx, class_names): if isinstance(class_idx, Sequence): return [to_class_name(v, class_names) for v in class_idx] return class_names[class_idx] class DatasetSplitManager: """Manage the dataset_split state, add/filter tags. Args: name: Name of the dataset_split, used in the filename. config: Application configuration. initial_tags: List of tags to initialize with. initial_prediction_tags: List of tags related to predictions. dataset_split: Dataset split to used, None if loading from cache. (The workers do that) """ def __init__( self, name: DatasetSplitName, config: CommonFieldsConfig, initial_tags: List[Tag], initial_prediction_tags: Optional[List[Tag]] = None, dataset_split: Optional[Dataset] = None, ): self.name = name self._tags = initial_tags self._prediction_tags = initial_prediction_tags or [] self.config = config self._project_path = config.get_project_path() self._hf_path = pjoin(self._project_path, "HF_datasets", self.name) self._base_dataset_path = pjoin(self._hf_path, "base_tables") os.makedirs(self._base_dataset_path, exist_ok=True) self._save_path = pjoin(self._base_dataset_path, "cache_ds.arrow") self._malformed_path = pjoin(self._base_dataset_path, "malformed_ds.arrow") self._index_path = pjoin(self._base_dataset_path, "index.faiss") self._features_path = pjoin(self._base_dataset_path, "features.faiss.npy") self._file_lock = pjoin(self._hf_path, f"{name}.lock") # Load the dataset_split from disk. self._base_dataset_split_last_update: Time = -1 cached_base_dataset_split = self._load_latest_base_dataset_split() if cached_base_dataset_split is None: if dataset_split is None: raise ValueError("No dataset_split cached, can't initialize.") log.info("Initializing tags", tags=initial_tags) self._base_dataset_split, self._malformed_dataset = self._process_base_dataset_split( dataset_split ) self._save_base_dataset_split() else: self._base_dataset_split, self._malformed_dataset = cached_base_dataset_split self._prediction_tables: Dict[PredictionTableKey, Dataset] = {} self._prediction_tables_last_update: Dict[PredictionTableKey, Time] = defaultdict(float) self._validate_columns() @property def last_update(self) -> Time: return max( (self._base_dataset_split_last_update, self._prediction_tables_last_update.values()) ) def get_dataset_split(self, table_key: Optional[PredictionTableKey] = None) -> Dataset: """Return a dataset_split concatenated with the config predictions. Args: table_key: Which pipeline to gather preds from. Returns: Dataset with predictions if available. """ current_last_update = self._base_dataset_split_last_update latest_base_ds, last_update = self.load_latest_cache(self._save_path, current_last_update) if latest_base_ds: self._base_dataset_split = latest_base_ds self._base_dataset_split_last_update = last_update if table_key is None: return self._base_dataset_split return self.dataset_split_with_predictions(table_key=table_key) def dataset_split_with_predictions(self, table_key: PredictionTableKey) -> Dataset: """Return dataset_split concatenated with the prediction table for the specified values. Args: table_key: Key to the table. Returns: Dataset with predictions if possible. """ prediction_table = self._get_prediction_table(table_key) ds: Dataset = concatenate_datasets([self._base_dataset_split, prediction_table], axis=1) return ds @property def num_rows(self): return len(self._base_dataset_split) @property def classification_columns(self): """Get columns that refer to classes in some way.""" return { self.config.columns.label, DatasetColumn.model_predictions, DatasetColumn.postprocessed_prediction, } def _load_latest_base_dataset_split(self) -> Optional[Tuple[Dataset, Dataset]]: if os.path.exists(self._save_path): current_update = self._base_dataset_split_last_update base_ds, last_update = self.load_latest_cache(self._save_path, current_update) malformed, _ = self.load_latest_cache(self._malformed_path, current_update) self._base_dataset_split_last_update = last_update return assert_not_none(base_ds), assert_not_none(malformed) return None def _process_base_dataset_split(self, dataset_split) -> Tuple[Dataset, Dataset]: base_dataset_split, malformed_dataset = self._split_malformed(dataset_split) # Checking if a persistent id was provided. persistent_id = self.config.columns.persistent_id if persistent_id != DatasetColumn.row_idx: # Default value if persistent_id not in base_dataset_split.column_names: raise ValueError(f"Persistent id named {persistent_id} not found in the dataset.") all_persistent_ids = base_dataset_split[persistent_id] if len(all_persistent_ids) > len(set(all_persistent_ids)): raise ValueError(f"Persistent ids in {persistent_id} column need to be unique.") base_dataset_split = self._init_dataset_split(base_dataset_split, self._tags) return base_dataset_split, malformed_dataset def _save_base_dataset_split(self): # NOTE: We should not have the Index in `self.dataset_split`. with FileLock(self._file_lock): version_path, last_update = self._get_new_version_path(self._save_path) self._base_dataset_split.save_to_disk(version_path) malformed, _ = self._get_new_version_path(self._malformed_path) self._malformed_dataset.save_to_disk(malformed) self._base_dataset_split_last_update = last_update log.debug("Base dataset split saved.", path=version_path) def get_dataset_split_with_class_names( self, table_key: Optional[PredictionTableKey] = None ) -> Dataset: """Get a copy of the dataset_split, with class names instead of ids. Args: table_key: If provided, which pipeline predictions to return. Returns: Dataset with class indices replaces for their names. """ # Not using self.dataset_split.features["label"].int2str() as it throws on -1, # which is possible in postprocessed_prediction # if a rejection class is missing from the classes. class_names = [ self.get_class_names(), self.config.rejection_class or REJECTION_CLASS, ] # so class_names[-1] returns "REJECTION_CLASS" ds = self.get_dataset_split(table_key=table_key) col_names = ds.column_names class_columns = {c for c in self.classification_columns if c in col_names} ds_with_names: Dataset = ds.map( lambda r: {c: to_class_name(r[c], class_names) for c in class_columns}, batched=True ) return ds_with_names def save_csv(self, table_key=None) -> str: """Save the dataset_split and return the path. Args: table_key: If provided, which prediction table to select. Returns: Local path to the csv. """ log.info("Saving dataset_split as csv.", path=self._project_path) file_label = time.strftime("%Y%m%d_%H%M%S", time.localtime()) pt = pjoin( self._project_path, f"azimuth_export_{self.config.name}_{self.name}_{file_label}.csv", ) order = [ DatasetColumn.row_idx, self.config.columns.persistent_id, self.config.columns.raw_text_input, self.config.columns.text_input, self.config.columns.label, DatasetColumn.model_predictions, DatasetColumn.postprocessed_prediction, DatasetColumn.model_confidences, DatasetColumn.postprocessed_confidences, DatasetColumn.model_outcome, DatasetColumn.postprocessed_outcome, DatasetColumn.pipeline_steps, DatasetColumn.confidence_bin_idx, DatasetColumn.word_count, DatasetColumn.neighbors_train, DatasetColumn.neighbors_eval, self._tags, ] available_columns = self.get_dataset_split(table_key).column_names order = [c for c in order if c in available_columns] # This available_columns allows for new or extra columns to end up here automatically, # instead of being lost if we were to hardcode the whole list. # The dict.fromkeys() avoids duplicates. columns = list(dict.fromkeys([order, available_columns])) # pd.to_csv() instead of HF version to avoid unintended type conversions (list to array) df = pd.DataFrame(self.get_dataset_split_with_class_names(table_key)).reindex( columns=columns ) df.to_csv(path_or_buf=pt, index=False) log.info("Dataset saved as CSV.", path=pt) return pt def _init_dataset_split(self, dataset_split: Dataset, tags) -> Dataset: dataset_split = dataset_split.map(lambda r: {k: False for k in tags}) # Our own column that maps to the row index to preserve idx. dataset_split = dataset_split.map( lambda u, i: {DatasetColumn.row_idx: i}, with_indices=True ) return dataset_split def get_row_indices_from_persistent_id(self, persistent_ids: List[Union[int, str]]): ds = self.get_dataset_split() all_persistent_ids = ds[self.config.columns.persistent_id] indices = [all_persistent_ids.index(persistent_id) for persistent_id in persistent_ids] return indices def add_tags( self, tags: Dict[int, Dict[Tag, bool]], table_key: Optional[PredictionTableKey] = None, ): """Add Tags to the dataset. Args: tags: Dict where the keys are the row_idx, and the values are tags to be updated. table_key: If tags are related to a table, which table is it. """ base_tags: Dict[int, Dict[Tag, bool]] = defaultdict(dict) pred_tags: Dict[int, Dict[Tag, bool]] = defaultdict(dict) for idx, tag_values in tags.items(): for tag, value in tag_values.items(): if tag in self._tags: base_tags[idx][tag] = value elif tag in self._prediction_tags: if not table_key: raise ValueError(f"No table key supplied for prediction tags {pred_tags}.") pred_tags[idx][tag] = value else: raise ValueError(f"Unknown tag {tag}") # Process base tags base_tags_present = any(len(tag_values) for tag_values in base_tags.values()) if base_tags_present: self._base_dataset_split = self._base_dataset_split.map( lambda u, i: base_tags[i], with_indices=True, desc="Set base tag" ) self._save_base_dataset_split() # Process prediction tags if table_key: non_null_table_key = assert_not_none(table_key) pred_table = self._get_prediction_table(table_key=non_null_table_key) self._prediction_tables[non_null_table_key] = pred_table.map( lambda u, i: pred_tags[i], with_indices=True, desc="Set pipeline tag" ) self.save_prediction_table(non_null_table_key) def get_tags( self, indices: Optional[List[int]] = None, table_key: Optional[PredictionTableKey] = None ) -> Dict[int, Dict[Tag, bool]]: """Get tags from the dataset split. Args: indices: Set of indices to select. table_key: Predictions table to gather prediction tags. Returns: Value of tags per row_idx. """ ds = self.get_dataset_split(table_key) if indices is not None and len(indices) > 0: ds = ds.select(indices) available_tags = self._tags if table_key is None else self._tags + self._prediction_tags return {row[DatasetColumn.row_idx]: {tag: row[tag] for tag in available_tags} for row in ds} def class_distribution(self, labels_only=False): """Compute the class distribution for a dataset_split. Args: labels_only: If true, will filter out REJECTION_CLASS if it was added by Azimuth. Returns: Array[int], count for each class. """ class_distribution = np.unique( self._base_dataset_split[self.config.columns.label], return_counts=True ) class_distribution_dict = dict(zip(class_distribution[0], class_distribution[1])) for x in range(self.get_num_classes(labels_only=labels_only)): if x not in class_distribution_dict: class_distribution_dict[x] = 0 class_distribution_ordered = OrderedDict(sorted(class_distribution_dict.items())) return np.array(list(class_distribution_ordered.values())) def add_column(self, key, features, save=True, kwargs): """Add a column to the base dataset_split. Notes: This doesn't fail if the column is already there compared to Dataset.add_column. Args: key: Name of the column features: List of features to set. save: Whether to hard save the dataset_split or not. kwargs: kwargs sent to Dataset.Map Raises: ValueError when length of features is not the number of rows. """ if len(features) != self.num_rows: raise ValueError( f"Length mismatch, expected {self.num_rows} (`len(dataset)`), got {len(features)}." ) self._base_dataset_split = self._base_dataset_split.map( lambda u, i: {key: features[i]}, with_indices=True, kwargs, desc=f"Add column {key}" ) if save: self._save_base_dataset_split() def add_faiss_index(self, features): """Add a FAISS index to the dataset_split. To get the dataset_split with index, call `DatasetSplitManager.dataset_split_with_index`. Notes: We drop the index because you can't interact easily with a HF dataset that has an index. Args: features: Set of features to compute the index. """ with FileLock(self._file_lock): # We take the lock because we are doing many manipulation to the dataset_split. self.add_column(FEATURES, features, save=False) self._base_dataset_split.add_faiss_index( FEATURES, index_name=FEATURE_FAISS, metric_type=faiss.METRIC_INNER_PRODUCT, string_factory="Flat", ) self._base_dataset_split.save_faiss_index(FEATURE_FAISS, file=self._index_path) np.save(self._features_path, self._base_dataset_split[FEATURES], allow_pickle=False) self._base_dataset_split.drop_index(FEATURE_FAISS) self._base_dataset_split = self._base_dataset_split.remove_columns(FEATURES) def dataset_split_with_index(self, table_key: Optional[PredictionTableKey] = None) -> Dataset: """ Get the dataset split with the FAISS index loaded. Args: table_key: If provided, with return the dataset with the predictions. Returns: Dataset split with FEATURES_FAISS and FEATURES loaded. """ ds: Dataset = deepcopy(self.get_dataset_split(table_key)) ds = ds.add_column(FEATURES, np.load(self._features_path, allow_pickle=False).tolist()) ds.load_faiss_index(FEATURE_FAISS, self._index_path) return ds def get_class_names(self, labels_only=False): """ Get class names. Args: labels_only: If true, will filter out REJECTION_CLASS if it was added by Azimuth. Returns: List of class names. """ cls_names = copy(self._base_dataset_split.features[self.config.columns.label].names) if self.config.rejection_class is None and not labels_only: cls_names.append(REJECTION_CLASS) return cls_names def get_num_classes(self, labels_only=False): """ Get number of classes. Args: labels_only: If true, will filter out REJECTION_CLASS if it was added by Azimuth. Returns: Number of classes. """ return len(self.get_class_names(labels_only=labels_only)) @property def rejection_class_idx(self): if self.config.rejection_class is None: # This is a dataset without a rejection class in the classes, like SST2. return self.get_class_names().index(REJECTION_CLASS) if self.config.rejection_class not in self.get_class_names(labels_only=True): raise AzimuthValidationError( f"Expected {self.config.rejection_class} in" f" {self.get_class_names(labels_only=True)}." f" If your dataset does not have rejection class," f" you can set `rejection_class=None` in the configuration." f" Otherwise set `rejection_class` to the class associated with no prediction." ) return self.get_class_names().index(self.config.rejection_class) def _get_prediction_table(self, table_key: PredictionTableKey) -> Dataset: """Return the prediction table associated with `table_key`. Args: table_key: Key to the table. Returns: A table for this key, will create one if it doesn't exists. """ pred_path = self._prediction_path(table_key=table_key) if os.path.exists(pred_path): current_last_update = self._prediction_tables_last_update[table_key] newest_pred_ds, last_update = self.load_latest_cache(pred_path, current_last_update) if newest_pred_ds: self._prediction_tables[table_key] = newest_pred_ds self._prediction_tables_last_update[table_key] = last_update else: empty_ds = Dataset.from_dict({"pred_row_idx": list(range(self.num_rows))}) self._prediction_tables[table_key] = self._init_dataset_split( empty_ds, self._prediction_tags ).remove_columns([DatasetColumn.row_idx]) self.save_prediction_table(table_key) return self._prediction_tables[table_key] def _prediction_path(self, table_key: PredictionTableKey) -> str: """Path to table file.""" folder = pjoin(self._hf_path, "prediction_tables") table_name = "_".join( f"{k}={v:.2f}" if type(v) is float else f"{k}={v}" for k, v in asdict(table_key).items() ) os.makedirs(folder, exist_ok=True) return pjoin(folder, f"{table_name}_cache_ds.arrow") def save_prediction_table(self, table_key: PredictionTableKey): """Save the prediction to disk.""" with FileLock(self._file_lock): pred_path = self._prediction_path(table_key=table_key) version_path, last_update = self._get_new_version_path(pred_path) self._prediction_tables[table_key].save_to_disk(version_path) self._prediction_tables_last_update[table_key] = last_update log.debug("Prediction dataset split saved.", path=version_path) def add_column_to_prediction_table( self, key: str, features: List[Any], table_key: PredictionTableKey, kwargs ): """ Add a column to the prediction table. Notes: This doesnt fail if the column is already there compared to Dataset.add_column. Args: key: Name of the column features: List of features to set. table_key: Key to the prediction table. kwargs: kwargs sent to Dataset.Map Raises: ValueError if the features don't match the dataset length. """ ds = self._get_prediction_table(table_key=table_key) if len(features) != len(ds): raise ValueError( f"Can't add a column of {len(features)} in a dataset of {len(ds)} rows." ) ds = ds.map( lambda u, i: {key: features[i]}, with_indices=True, kwargs, desc=f"Add column {key}", ) self._prediction_tables[table_key] = ds self.save_prediction_table(table_key) def _split_malformed(self, dataset: Dataset) -> Tuple[Dataset, Dataset]: # Split dataset between malformed and correctly formed. malformed = dataset.filter( lambda u: u.get(self.config.columns.failed_parsing_reason, "") != "" ) dataset = dataset.filter( lambda u: u.get(self.config.columns.failed_parsing_reason, "") == "" ) return dataset, malformed def _validate_columns(self): if diff := {self.config.columns.text_input, self.config.columns.label}.difference( self._base_dataset_split.column_names ): raise AzimuthValidationError(f"{' and '.join(diff)} missing from dataset {self.name}") if not isinstance(self._base_dataset_split.features[self.config.columns.label], ClassLabel): raise AzimuthValidationError( f"Expected column {self.config.columns.label}" f" to have type `datasets.ClassLabel`." f" You can call `Dataset.class_encode_column` to cast it automatically." ) # Check that `rejection_class_idx` is valid. # It would throw an error if that wasn't the case. _ = self.rejection_class_idx if len(self._base_dataset_split) == 0: raise AzimuthValidationError(f"No rows found from dataset {self.name}") def load_latest_cache( self, folder: str, current_last_update: Time ) -> Tuple[Optional[Dataset], Time]: """Load the latest dataset saved in a given folder. Notes: Only load when the dataset is more recent than what is current saved in the DM. Args: folder: Where to look for. current_last_update: Current version saved in the DM. Returns: The cached dataset, if more recent. Otherwise None. Raises: FileNotFoundError if no cache found. """ def get_time_from_file_name(file_path): return float(file_path.split("_")[-1][:-6]) try: cache_files = glob(f"{folder}/version.arrow") cache_file = next(iter(sorted(cache_files, key=get_time_from_file_name, reverse=True))) except StopIteration: raise FileNotFoundError(f"No previously saved dataset in {folder}") last_update = get_time_from_file_name(cache_file) if current_last_update >= last_update: return None, -1 with FileLock(self._file_lock): log.debug("Loading latest dataset in cache.", path=cache_file) return Dataset.load_from_disk(cache_file), last_update @staticmethod def _get_new_version_path(directory) -> Tuple[str, Time]: now = time.time() return pjoin(directory, f"version_{now}.arrow"), now def save_proposed_actions_to_csv(self) -> str: """Save proposed actions to a csv file. Returns: Path to the csv file. """ log.info("Saving proposed actions as csv.", path=self._project_path) timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) path = pjoin( self._project_path, f"azimuth_export_{self.config.name}_{self.name}_proposed_actions_{timestamp}.csv", ) ds_with_proposed_actions = filter_dataset_split( self.get_dataset_split(), filters=DatasetFilters(data_action=ALL_DATA_ACTIONS), config=self.config, ) persistent_id = self.config.columns.persistent_id ds_with_proposed_actions = ds_with_proposed_actions.remove_columns( list(set(ds_with_proposed_actions.column_names) - {*ALL_DATA_ACTIONS, persistent_id}) ) df: pd.DataFrame = ds_with_proposed_actions.to_pandas() # Return column name with max value for each row. df = df.set_index(persistent_id).idxmax(axis=1).reset_index() df.columns = [persistent_id, "proposed_action"] df.to_csv(path_or_buf=path, index=False) log.info("Proposed Actions saved as CSV.", path=path) return path	{"/tests/test_config.py": ["/azimuth/config.py", 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30,979	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/exception_handlers.py	from pydantic import ValidationError from starlette.requests import Request from starlette.responses import JSONResponse from starlette.status import HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND async def handle_validation_error(request: Request, exception: ValidationError): """Handle ValidationError. Args: request: Request exception: ValidationError Returns: A JSONResponse with the appropriate error status code and content. """ def pretty(location): """Get a pretty representation of the error's location. Args: location: e.g. ["query", "outcome", 0] or ["uncertainty", "iterations"] Returns: e.g. "query parameter outcome=potato" or "AzimuthConfig['uncertainty']['iterations']" """ if exception.model.__name__ == "Request" and location[0] in {"path", "query"}: param_type, param, *index = location params = getattr(request, f"{param_type}_params") value = params.getlist(param)[index[0]] if index else params.get(param) return f"{param_type} parameter {param}{'' if value is None else f'={value}'}" return exception.model.__name__ + "".join(f"[{repr(p)}]" for p in location) detail = "\n".join(dict.fromkeys(f'{pretty(e["loc"])}: {e["msg"]}' for e in exception.errors())) return JSONResponse( status_code=HTTP_404_NOT_FOUND # for errors in paths, e.g., /dataset_splits/potato if "path" in (error["loc"][0] for error in exception.errors()) else HTTP_400_BAD_REQUEST, # for other errors like in query params, e.g., pipeline_index=-1 content={"detail": detail}, )	{"/tests/test_config.py": 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30,980	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/ml/perturbation_test.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 dataclasses import dataclass from typing import Callable, List, Optional, Tuple from azimuth.config import PerturbationTestingConfig from azimuth.types.perturbation_testing import ( PerturbationTestClass, PerturbationTestFailureReason, PerturbationTestFamily, PerturbationTestName, PerturbationTestType, PerturbedUtteranceDetails, ) @dataclass class PerturbationTest: """Used in PerturbationTestingModule to create different perturbations on the dataset_split.""" name: PerturbationTestName family: PerturbationTestFamily test_fn: Callable[[str, PerturbationTestingConfig], List[PerturbedUtteranceDetails]] test_type: PerturbationTestType test_class: PerturbationTestClass conf_delta_threshold: float # Delta in confidence which will fail the test. expect_flip: bool # Whether we expect the prediction to flip or not. description: str = "No Description" def is_failed( self, new_pred: Tuple[int, float], original_pred: Tuple[int, float] ) -> Tuple[bool, PerturbationTestFailureReason, Optional[float]]: """ Check if a test has failed given the original and modified predictions. Args: new_pred: Predicted class and conf of the perturbed utterance. original_pred: Predicted class and conf of the original utterance. Returns: [is_failed, failure_reason, confidence delta] """ # Check if the test failed according to a threshold. new_pred_class, new_conf = new_pred ori_pred_class, ori_conf = original_pred delta_conf = round(abs(new_conf - ori_conf), 4) if not self.expect_flip and new_pred_class != ori_pred_class: # if the delta of confidence is not applicable we report None return True, PerturbationTestFailureReason.PredClass, None elif self.expect_flip and new_pred_class == ori_pred_class: # if the delta of confidence is not applicable we report None return True, PerturbationTestFailureReason.PredClass, None elif delta_conf > self.conf_delta_threshold: return True, PerturbationTestFailureReason.PredConfThreshold, delta_conf else: # if the delta of confidence is not applicable we report the conf. return False, PerturbationTestFailureReason.NA, delta_conf	{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": 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30,981	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/model_performance/confusion_matrix.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 APIRouter, Depends, Query from azimuth.app import get_dataset_split_manager, get_task_manager from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, NamedDatasetFilters, SupportedModule from azimuth.types.model_performance import ConfusionMatrixResponse from azimuth.utils.routers import ( build_named_dataset_filters, get_standard_task_result, require_pipeline_index, ) router = APIRouter() @router.get( "", summary="Get confusion matrix.", description="Get confusion matrix on specified filters.", response_model=ConfusionMatrixResponse, ) def get_confusion_matrix( dataset_split_name: DatasetSplitName, named_filters: NamedDatasetFilters = Depends(build_named_dataset_filters), task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), pipeline_index: int = Depends(require_pipeline_index), without_postprocessing: bool = Query(False, title="Without Postprocessing"), normalize: bool = Query(True, title="Normalize"), reorder_classes: bool = Query(True, title="Reorder Classes"), ) -> ConfusionMatrixResponse: mod_options = ModuleOptions( filters=named_filters.to_dataset_filters(dataset_split_manager.get_class_names()), pipeline_index=pipeline_index, without_postprocessing=without_postprocessing, cf_normalize=normalize, cf_reorder_classes=reorder_classes, ) task_result: ConfusionMatrixResponse = get_standard_task_result( SupportedModule.ConfusionMatrix, dataset_split_name, task_manager=task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, )[0] return 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30,982	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_performance/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. import inspect import json import warnings from collections import Counter from typing import Dict, List, Optional import numpy as np from datasets import Dataset, Metric from sklearn.exceptions import UndefinedMetricWarning from tqdm import tqdm from azimuth.config import MetricsConfig, MetricsPerFilterConfig from azimuth.modules.base_classes import AggregationModule, FilterableModule from azimuth.modules.model_performance.confidence_binning import ConfidenceHistogramModule from azimuth.plots.ece import make_ece_figure from azimuth.types import DatasetColumn, DatasetFilters from azimuth.types.model_performance import ( MetricsAPIResponse, MetricsModuleResponse, MetricsPerFilter, MetricsPerFilterModuleResponse, MetricsPerFilterValue, ) from azimuth.types.outcomes import ALL_OUTCOMES from azimuth.types.tag import SMART_TAGS_FAMILY_MAPPING, SmartTag, SmartTagFamily from azimuth.utils.dataset_operations import ( filter_dataset_split, get_confidences_from_ds, get_outcomes_from_ds, get_predictions_from_ds, ) from azimuth.utils.ml.ece import compute_ece_from_bins from azimuth.utils.ml.model_performance import sorted_by_utterance_count_with_last from azimuth.utils.validation import assert_not_none MAX_PRED = 3 BASE_RESPONSE = MetricsModuleResponse( outcome_count={outcome: 0 for outcome in ALL_OUTCOMES}, ece=0.0, ece_plot_args=None, utterance_count=0, custom_metrics={}, ) def first_value(di: Optional[Dict]) -> Optional[float]: if di is None: return None # Get first value of a dict. return next(iter(di.values()), None) class MetricsModule(FilterableModule[MetricsConfig]): """Computes different metrics on each dataset split.""" def compute_metrics(self, ds: Dataset) -> List[MetricsModuleResponse]: """Compute all metrics 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 for which to compute metrics. Returns: MetricsModuleResponse with all metrics. """ if len(ds) == 0: # Nothing to do, we return an empty response. return [BASE_RESPONSE] else: utterance_count = len(ds) outcome_count = Counter( get_outcomes_from_ds(ds, self.mod_options.without_postprocessing) ) outcome_count.update({outcome: 0 for outcome in ALL_OUTCOMES}) # Compute ECE bins = ConfidenceHistogramModule.get_bins(ds, self.mod_options.without_postprocessing) ece, acc, expected = compute_ece_from_bins(bins) count_per_bin = [sum(b.outcome_count.values()) for b in bins] metric_values = {} dm = self.get_dataset_split_manager() for metric_name, metric_obj_def in self.config.metrics.items(): met: Metric = self.artifact_manager.get_metric( self.config, metric_name, label_list=dm.get_class_names(), rejection_class_idx=dm.rejection_class_idx, force_kwargs=True, # Set True here as load_metrics has kwargs. ) accept_probabilities = "probabilities" in inspect.signature(met._compute).parameters extra_kwargs = ( dict(probabilities=self.make_probabilities()) if accept_probabilities else {} ) extra_kwargs.update(metric_obj_def.additional_kwargs) with warnings.catch_warnings(): # Ignore warnings such as # UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 warnings.simplefilter("ignore", category=UndefinedMetricWarning) metric_values[metric_name] = assert_not_none( first_value( met.compute( predictions=get_predictions_from_ds( ds, self.mod_options.without_postprocessing ), references=ds[self.config.columns.label], extra_kwargs, ) ) ) return [ MetricsModuleResponse( outcome_count=outcome_count, ece=ece, ece_plot_args=(acc, expected, ece, count_per_bin), utterance_count=utterance_count, custom_metrics=metric_values, ) ] def compute_on_dataset_split(self) -> List[MetricsModuleResponse]: # type: ignore """Computes different metrics according to the specified module options.""" ds: Dataset = assert_not_none(self.get_dataset_split()) return self.compute_metrics(ds) @staticmethod def module_to_api_response(res: List[MetricsModuleResponse]) -> List[MetricsAPIResponse]: """Converts the module response in what the API route needs to return. In this module, the module response contains the plot args which are used to build the plot. The plot is returned in the API. Args: res: Module response. Returns: API response. """ metrics_res = res[0] plot_args = metrics_res.ece_plot_args fig = plot_args and json.loads(make_ece_figure(plot_args).to_json()) res_with_plot = MetricsAPIResponse( outcome_count=metrics_res.outcome_count, utterance_count=metrics_res.utterance_count, custom_metrics=metrics_res.custom_metrics, ece=metrics_res.ece, ece_plot=fig, ) return [res_with_plot] def make_probabilities(self) -> np.ndarray: """Make probabilities from dataset columns. Returns: Array with shape [len(dataset), num_classes] with probabilities. """ ds = self.get_dataset_split() num_classes = self.get_dataset_split_manager().get_num_classes(labels_only=True) probs = np.zeros([len(ds), num_classes]) for idx, (confidences, predictions) in enumerate( zip( get_confidences_from_ds(ds, self.mod_options.without_postprocessing), ds[DatasetColumn.model_predictions], ) ): probs[idx] = np.array(confidences)[predictions] return probs class MetricsPerFilterModule(AggregationModule[MetricsPerFilterConfig]): """Computes the metrics for each filter. Note: This module should be recomputed if smart tags are changed when the config is modified. It is not an expirable module because it is too long to compute. As such, it cannot be affected by data actions. """ required_mod_options = {"pipeline_index"} def get_metrics_for_filter( self, filters_dict: Dict[str, DatasetFilters] ) -> List[MetricsPerFilterValue]: """Get metrics for a list of filters. Args: filters_dict: Dictionary with filter values as keys and the corresponding dataset filters as values. Returns: Metrics for labels, predictions and smart tags. """ ds = self.get_dataset_split() accumulator = [] for filter_value, filters in filters_dict.items(): ds_filtered = filter_dataset_split(ds, filters, config=self.config) metrics = MetricsModule( dataset_split_name=self.dataset_split_name, config=self.config, mod_options=self.mod_options, ).compute_metrics(ds_filtered)[0] accumulator.append( MetricsPerFilterValue( outcome_count=metrics.outcome_count, utterance_count=metrics.utterance_count, custom_metrics=metrics.custom_metrics, ece=metrics.ece, filter_value=filter_value, ) ) return accumulator def compute_on_dataset_split(self) -> List[MetricsPerFilterModuleResponse]: # type: ignore dm = self.get_dataset_split_manager() ds = self.get_dataset_split() with tqdm(total=3) as pbar: pbar.set_description( f"MetricsPerFilter on {self.dataset_split_name} " f"set for pipeline {self.mod_options.pipeline_index}" ) label_filters = { class_name: self.edit_filter(self.mod_options.filters, label=i) for i, class_name in enumerate(dm.get_class_names()) } metrics_per_label = sorted_by_utterance_count_with_last( self.get_metrics_for_filter(label_filters), dm.rejection_class_idx ) pbar.update() prediction_filters = { class_name: self.edit_filter(self.mod_options.filters, prediction=i) for i, class_name in enumerate(dm.get_class_names()) } metrics_per_prediction = sorted_by_utterance_count_with_last( self.get_metrics_for_filter(prediction_filters), dm.rejection_class_idx, ) pbar.update() smart_tag_filters: Dict[SmartTagFamily, Dict[str, DatasetFilters]] = { tag_family: { smart_tag: self.edit_filter( self.mod_options.filters, smart_tag={tag_family: [smart_tag]} ) for smart_tag in tags + [SmartTag.no_smart_tag] } for tag_family, tags in SMART_TAGS_FAMILY_MAPPING.items() } metrics_per_smart_tag = { tag_family.value: sorted_by_utterance_count_with_last( self.get_metrics_for_filter(filters_for_family), -1 ) for tag_family, filters_for_family in smart_tag_filters.items() } pbar.update() return [ MetricsPerFilterModuleResponse( metrics_per_filter=MetricsPerFilter( label=metrics_per_label, prediction=metrics_per_prediction, *metrics_per_smart_tag, ), utterance_count=len(ds), ) ] def edit_filter( self, initial_filter: DatasetFilters, label=None, prediction=None, data_action=None, outcome=None, smart_tag=None, ): """Edit filters to include an update set. Args: initial_filter: Filter to edit. label: label to add to the filters prediction: prediction to add to the filters data_action: data_action to add to the filters outcome: outcome to add to the filters smart_tag: smart_tag to add to the filters Returns: An updated version of the filter. """ filter_copy = initial_filter.copy(deep=True) if label is not None: filter_copy.label.append(label) if prediction is not None: filter_copy.prediction.append(prediction) if data_action is not None: filter_copy.data_action.append(data_action) if outcome is not None: filter_copy.outcome.append(outcome) if smart_tag is not None: filter_copy.smart_tags.update(smart_tag) return filter_copy	{"/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", 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"/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"]}
30,983	ServiceNow/azimuth	refs/heads/main	/azimuth/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. import argparse import os from datetime import datetime, timezone from enum import Enum from os.path import join as pjoin from typing import Any, Dict, List, Literal, Optional, TypeVar, Union import structlog from jsonlines import jsonlines from pydantic import Extra, Field, ValidationError, root_validator, validator from azimuth.types import AliasModel, DatasetColumn, SupportedModelContract from azimuth.utils.conversion import md5_hash from azimuth.utils.exclude_fields_from_cache import exclude_fields_from_cache from azimuth.utils.openapi import AzimuthBaseSettings log = structlog.get_logger(__file__) T = TypeVar("T", bound="ProjectConfig") REQUIRED_EXT = {"csv", "json"} class SupportedLanguage(str, Enum): en = "en" fr = "fr" # spaCy models must be in pyproject.toml to be loaded by Azimuth class SupportedSpacyModels(str, Enum): use_default = "" en_core_web_sm = "en_core_web_sm" fr_core_news_md = "fr_core_news_md" # To see all dep tag options for a spacy model: spacy.load(SPACY_MODEL).get_pipe("parser").labels class LanguageDefaultValues(AliasModel): suffix_list: List[str] prefix_list: List[str] spacy_model: SupportedSpacyModels subj_tags: List[str] obj_tags: List[str] faiss_encoder: str config_defaults_per_language: Dict[SupportedLanguage, LanguageDefaultValues] = { SupportedLanguage.en: LanguageDefaultValues( suffix_list=["pls", "please", "thank you", "appreciated"], prefix_list=["pls", "please", "hello", "greetings"], spacy_model="en_core_web_sm", subj_tags=["nsubj", "nsubjpass"], obj_tags=["dobj", "pobj", "obj"], faiss_encoder="all-MiniLM-L12-v2", ), SupportedLanguage.fr: LanguageDefaultValues( suffix_list=["svp", "s'il vous plaît", "merci", "super"], prefix_list=["svp", "s'il vous plaît", "bonjour", "allô"], spacy_model="fr_core_news_md", subj_tags=["nsubj", "nsubj:pass"], obj_tags=["obj", "iobj", "obl:arg", "obl:agent", "obl:mod"], faiss_encoder="distiluse-base-multilingual-cased-v1", ), } def parse_args(): """Parse CLI args. Returns: argparse Namespace """ parser = argparse.ArgumentParser() parser.add_argument("config_path", default=None, nargs="?") parser.add_argument( "--load-config-history", action="store_true", help="Load the last config from history, or if empty, default to config_path.", ) parser.add_argument("--port", default=8091, help="Port to serve the API.") parser.add_argument("--debug", action="store_true") return parser.parse_args() class AzimuthValidationError(Exception): pass class CustomObject(AzimuthBaseSettings): class_name: str = Field( ..., title="Class name to load.", description="Name of the function or class that is located in `remote`." "`args` and `kwargs` will be sent to the function/class.", ) args: List[Any] = [] kwargs: Dict[str, Any] = {} remote: Optional[str] = Field( None, description="Absolute path to class. `class_name` needs to be accessible from this path.", nullable=True, ) CustomObject.update_forward_refs() class MetricDefinition(CustomObject): additional_kwargs: Dict = Field( default_factory=dict, title="Additional kwargs", description="Keyword arguments supplied to `compute`.", ) class TemperatureScaling(CustomObject): class_name: Literal[ "azimuth.utils.ml.postprocessing.TemperatureScaling" ] = "azimuth.utils.ml.postprocessing.TemperatureScaling" temperature: float = Field(1, ge=0, env="TEMP") @root_validator def _check_temps(cls, values): kwargs = values.get("kwargs", {}) if "temperature" not in kwargs: kwargs["temperature"] = values.get("temperature", 1) values["kwargs"] = kwargs return values class ThresholdConfig(CustomObject): class_name: Literal[ "azimuth.utils.ml.postprocessing.Thresholding" ] = "azimuth.utils.ml.postprocessing.Thresholding" threshold: float = Field(0.5, ge=0, le=1, env="TH") @root_validator def _check_threshold(cls, values): kwargs = values.get("kwargs", {}) if "threshold" not in kwargs: kwargs["threshold"] = values.get("threshold", 0.5) values["kwargs"] = kwargs return values class PipelineDefinition(AzimuthBaseSettings): name: str = Field(..., exclude_from_cache=True) model: CustomObject postprocessors: Optional[ List[Union[TemperatureScaling, ThresholdConfig, CustomObject]] ] = Field([ThresholdConfig(threshold=0.5)], nullable=True) @property def threshold(self) -> Optional[float]: if self.postprocessors is None: return None thresholding = next( (post for post in self.postprocessors if isinstance(post, ThresholdConfig)), None ) if thresholding: return thresholding.threshold return None @property def temperature(self) -> Optional[float]: if self.postprocessors is None: return None temp_scaling = next( (post for post in self.postprocessors if isinstance(post, TemperatureScaling)), None ) if temp_scaling: return temp_scaling.temperature return None class DatasetWarningsOptions(AzimuthBaseSettings): min_num_per_class: int = Field(20, ge=1) max_delta_class_imbalance: float = Field(0.5, ge=0, le=1) max_delta_representation: float = Field(0.05, ge=0, le=1) max_delta_mean_words: float = Field(3, ge=0) max_delta_std_words: float = Field(3, ge=0) class SyntaxOptions(AzimuthBaseSettings): short_utterance_max_word: int = Field(3, ge=1) long_utterance_min_word: int = Field(12, ge=1) spacy_model: SupportedSpacyModels = SupportedSpacyModels.use_default # Language-based default subj_tags: List[str] = [] # Language-based dynamic default value obj_tags: List[str] = [] # Language-based dynamic default value class NeutralTokenOptions(AzimuthBaseSettings): threshold: float = Field(1, ge=0, le=1) suffix_list: List[str] = [] # Language-based default value prefix_list: List[str] = [] # Language-based default value class PunctuationTestOptions(AzimuthBaseSettings): threshold: float = Field(1, ge=0, le=1) class FuzzyMatchingTestOptions(AzimuthBaseSettings): threshold: float = Field(1, ge=0, le=1) class TypoTestOptions(AzimuthBaseSettings): threshold: float = Field(1, ge=0, le=1) nb_typos_per_utterance: int = Field( 1, ge=1, description="For example, the value 2 would create both tests with 1 typo and with 2 typos " "per utterance.", ) class BehavioralTestingOptions(AzimuthBaseSettings): neutral_token: NeutralTokenOptions = NeutralTokenOptions() punctuation: PunctuationTestOptions = PunctuationTestOptions() fuzzy_matching: FuzzyMatchingTestOptions = FuzzyMatchingTestOptions() typo: TypoTestOptions = TypoTestOptions() # should be accessible via UI seed: int = 300 class SimilarityOptions(AzimuthBaseSettings): faiss_encoder: str = Field("", description="Language-based dynamic default value.") conflicting_neighbors_threshold: float = Field( 0.9, ge=0, le=1, description="Threshold to use when finding conflicting neighbors." ) no_close_threshold: float = Field( 0.5, ge=-1, le=1, description="Threshold to determine whether there are close neighbors." ) class UncertaintyOptions(AzimuthBaseSettings): iterations: int = Field(1, ge=1, description="Number of MC sampling to do. 1 disables BMA.") high_epistemic_threshold: float = Field( 0.1, ge=0, description="Threshold to determine high epistemic items." ) class ColumnConfiguration(AzimuthBaseSettings): # Column for the preprocessed text input text_input: str = "utterance" # Column for the raw text input raw_text_input: str = "utterance_raw" # Features column for the label label: str = "label" # Optional column to specify whether an example has failed preprocessing. failed_parsing_reason: str = "failed_parsing_reason" # Unique identifier for every example persistent_id: str = DatasetColumn.row_idx class ProjectConfig(AzimuthBaseSettings): # Name of the current project. name: str = Field("New project", exclude_from_cache=True) # Dataset object definition. dataset: Optional[CustomObject] = Field(None, nullable=True) # Column names config in dataset columns: ColumnConfiguration = ColumnConfiguration() # Name of the rejection class. rejection_class: Optional[str] = Field("REJECTION_CLASS", nullable=True) def copy(self: T, , validate: bool = True, kwargs: Any) -> T: copy: T = super().copy(kwargs) if validate: return self.validate( dict(copy._iter(to_dict=False, by_alias=False, exclude_unset=True)) ) return copy def get_project_hash(self): return md5_hash( self.dict( include=ProjectConfig.__fields__.keys(), exclude=exclude_fields_from_cache(self), ) ) class ArtifactsConfig(AzimuthBaseSettings, extra=Extra.ignore): artifact_path: str = Field( default_factory=lambda: os.path.abspath("cache"), description="Where to store artifacts (Azimuth config history, HDF5 files, HF datasets).", exclude_from_cache=True, ) @validator("artifact_path") def validate_artifact_path(cls, artifact_path): return os.path.abspath(artifact_path) def get_config_history_path(self): return f"{self.artifact_path}/config_history.jsonl" class CommonFieldsConfig(ArtifactsConfig, ProjectConfig, extra=Extra.ignore): """Fields that can be modified without affecting caching.""" # Batch size to use during inference. batch_size: int = Field(32, exclude_from_cache=True) # Will use CUDA and will need GPUs if set to True. # If "auto" we check if CUDA is available. use_cuda: Union[Literal["auto"], bool] = Field("auto", exclude_from_cache=True) # Memory of the dask cluster. Regular is 6GB, Large is 12GB. # For bigger models, large might be needed. large_dask_cluster: bool = Field(False, exclude_from_cache=True) # Disable configuration changes read_only_config: bool = Field(False, exclude_from_cache=True) def get_project_path(self) -> str: """Generate a path for caching. The path contains the project name and a subset of a hash of the project config. Additional fields in the config won't result in a different hash. Returns: Path to a folder where it is safe to store data. """ path = pjoin(self.artifact_path, f"{self.name}_{self.get_project_hash()[:5]}") os.makedirs(path, exist_ok=True) return path class ModelContractConfig(CommonFieldsConfig): # Which model_contract the application is using. model_contract: SupportedModelContract = SupportedModelContract.hf_text_classification # Model object definition. pipelines: Optional[List[PipelineDefinition]] = Field(None, nullable=True) # Uncertainty configuration uncertainty: UncertaintyOptions = UncertaintyOptions() # Layer name where to calculate the gradients, normally the word embeddings layer. saliency_layer: Union[Literal["auto"], str, None] = Field("auto", nullable=True) @validator("pipelines", pre=True) def _check_pipeline_names(cls, pipeline_definitions): # We support both [] and None (null in JSON), and we standardize it to None. if not pipeline_definitions: return None pipeline_definitions = [ pipeline_def.dict() if isinstance(pipeline_def, PipelineDefinition) else pipeline_def for pipeline_def in pipeline_definitions ] for pipeline_idx, pipeline_def in enumerate(pipeline_definitions): if "name" not in pipeline_def or pipeline_def["name"] == "": # Default value is the project name + idx. pipeline_def["name"] = f"Pipeline_{pipeline_idx}" pipeline_names = set(pipeline_def["name"] for pipeline_def in pipeline_definitions) if len(pipeline_definitions) != len(pipeline_names): raise ValueError(f"Duplicated pipeline names {pipeline_names}.") return pipeline_definitions def get_model_contract_hash(self): """Hash for fields related to model contract only (excluding fields from the parents).""" return md5_hash( self.dict( include=ModelContractConfig.__fields__.keys() - CommonFieldsConfig.__fields__.keys(), ) ) class MetricsConfig(ModelContractConfig): # Custom HuggingFace metrics metrics: Dict[str, MetricDefinition] = { "Accuracy": MetricDefinition( class_name="datasets.load_metric", kwargs={"path": "accuracy"} ), "Precision": MetricDefinition( class_name="datasets.load_metric", kwargs={"path": "precision"}, additional_kwargs={"average": "weighted"}, ), "Recall": MetricDefinition( class_name="datasets.load_metric", kwargs={"path": "recall"}, additional_kwargs={"average": "weighted"}, ), "F1": MetricDefinition( class_name="datasets.load_metric", kwargs={"path": "f1"}, additional_kwargs={"average": "weighted"}, ), } class LanguageConfig(CommonFieldsConfig): # Language config sets multiple config values; see `config_defaults_per_language` for details # Language should only determine other config values and not be referenced in modules. # The default `language` environment variable was conflicting on certain machines. language: SupportedLanguage = Field(SupportedLanguage.en, env=[]) class PerturbationTestingConfig(ModelContractConfig): # Perturbation Testing configuration to define which test and with which params to run. behavioral_testing: Optional[BehavioralTestingOptions] = Field( BehavioralTestingOptions(), nullable=True ) class SimilarityConfig(CommonFieldsConfig): # Similarity configuration to define the encoder and the similarity threshold. similarity: Optional[SimilarityOptions] = Field(SimilarityOptions(), nullable=True) class SyntaxConfig(CommonFieldsConfig): # Syntax configuration to change thresholds that determine short and long utterances. syntax: SyntaxOptions = SyntaxOptions() class DatasetWarningConfig(SyntaxConfig): # Dataset warnings configuration to change thresholds that trigger warnings dataset_warnings: DatasetWarningsOptions = DatasetWarningsOptions() class TopWordsConfig(SyntaxConfig, ModelContractConfig): pass class MetricsPerFilterConfig( PerturbationTestingConfig, SimilarityConfig, SyntaxConfig, LanguageConfig, MetricsConfig ): pass class AzimuthConfig( MetricsPerFilterConfig, TopWordsConfig, DatasetWarningConfig, extra=Extra.forbid, ): # Reminder: If a module depends on an attribute in AzimuthConfig, the module will be forced to # include all other configs in its scope. @root_validator def _dynamic_language_config_values(cls, values): defaults = config_defaults_per_language[values["language"]] if behavioral_testing := values.get("behavioral_testing"): neutral_token = behavioral_testing.neutral_token neutral_token.prefix_list = neutral_token.prefix_list or defaults.prefix_list neutral_token.suffix_list = neutral_token.suffix_list or defaults.suffix_list if syntax := values.get("syntax"): syntax.spacy_model = syntax.spacy_model or defaults.spacy_model syntax.subj_tags = syntax.subj_tags or defaults.subj_tags syntax.obj_tags = syntax.obj_tags or defaults.obj_tags if similarity := values.get("similarity"): similarity.faiss_encoder = similarity.faiss_encoder or defaults.faiss_encoder return values @classmethod def load(cls, config_path: Optional[str], load_config_history: bool) -> "AzimuthConfig": if load_config_history: # Load artifact_path from config_path if specified, or else from env var ARTIFACT_PATH. cfg = ArtifactsConfig.parse_file(config_path) if config_path else ArtifactsConfig() config_history_path = cfg.get_config_history_path() last_config = cls.load_last_from_config_history(config_history_path) if last_config: log.info(f"Loading latest config from {config_history_path}.") return last_config log.info("Empty or invalid config history.") # Load config from config_path if specified, or else from env vars only. return cls.parse_file(config_path) if config_path else cls() @classmethod def load_last_from_config_history(cls, config_history_path: str) -> Optional["AzimuthConfig"]: try: with jsonlines.open(config_history_path, mode="r") as config_history: _, last_config = config_history except (FileNotFoundError, ValueError): return None else: return AzimuthConfigHistory.parse_obj(last_config).config def get_config_history(self) -> List["AzimuthConfigHistoryWithHash"]: config_history = [] try: with jsonlines.open(self.get_config_history_path(), mode="r") as reader: for item in reader: try: config_history.append(AzimuthConfigHistoryWithHash.parse_obj(item)) except ValidationError: pass except (FileNotFoundError, ValueError): pass return config_history def log_info(self): log.info(f"Config loaded for {self.name} with {self.model_contract} as a model contract.") if self.dataset: remote_mention = "" if not self.dataset.remote else f"from {self.dataset.remote} " log.info( f"Dataset will be loaded with {self.dataset.class_name} " + remote_mention + f"with the following args and kwargs: {self.dataset.args} {self.dataset.kwargs}." ) if self.pipelines: for pipeline_idx, pipeline in enumerate(self.pipelines): remote_pipeline = self.pipelines[pipeline_idx].model.remote remote_mention = "" if not remote_pipeline else f"from {remote_pipeline} " log.info( f"Pipeline {pipeline_idx} " f"will be loaded with {self.pipelines[pipeline_idx].model.class_name} " + remote_mention + f"with the following args: {self.pipelines[pipeline_idx].model.kwargs}. " f"Processors are set to {self.pipelines[pipeline_idx].postprocessors}." ) not_default_config_values = self.dict( exclude_defaults=True, exclude={"name", "model_contract", "dataset", "pipelines"}, exclude_unset=True, ) log.info(f"The following additional fields were set: {not_default_config_values}") def save(self): """Append config to config_history.jsonl to retrieve past configs.""" if self == self.load_last_from_config_history(self.get_config_history_path()): return # TODO https://stackoverflow.com/questions/2333872/ # how-to-make-file-creation-an-atomic-operation with jsonlines.open(self.get_config_history_path(), mode="a") as f: f.write(AzimuthConfigHistory(config=self).dict()) class AzimuthConfigHistory(AzimuthBaseSettings): config: AzimuthConfig created_on: str = Field(default_factory=lambda: str(datetime.now(timezone.utc))) class AzimuthConfigHistoryWithHash(AzimuthConfigHistory): hash: str = "" @root_validator(skip_on_failure=True) def _set_hash(cls, values): return {**values, "hash": md5_hash(values["config"].dict())} def load_azimuth_config(config_path: Optional[str], load_config_history: bool) -> AzimuthConfig: log.info("-------------Loading Config--------------") cfg = AzimuthConfig.load(config_path, load_config_history) cfg.log_info() log.info("-------------Config loaded--------------") return cfg	{"/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", 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30,984	ServiceNow/azimuth	refs/heads/main	/tests/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 enum import Enum from pathlib import Path from typing import List, Type import numpy as np from azimuth.config import BehavioralTestingOptions, NeutralTokenOptions, TypoTestOptions from azimuth.dataset_split_manager import DatasetSplitManager, PredictionTableKey from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping from azimuth.modules.model_performance.outcomes import OutcomesModule from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions, SupportedMethod from azimuth.types.tag import ( ALL_DATA_ACTIONS, ALL_PREDICTION_TAGS, ALL_SMART_TAGS, ALL_STANDARD_TAGS, ) from azimuth.utils.ml.model_performance import compute_outcome from azimuth.utils.project import load_dataset_from_config _AZ_ROOT = Path(__file__).parents[1].resolve() _CURRENT_DIR = Path(__file__).parent.resolve() _SAMPLE_DATA_DIR = _CURRENT_DIR / "fixtures" _SAMPLE_VOCAB_DIR = _SAMPLE_DATA_DIR / "distilbert-tokenizer-files" _CHECKPOINT_PATH = str(_SAMPLE_VOCAB_DIR) SIMPLE_PERTURBATION_TESTING_CONFIG = BehavioralTestingOptions( neutral_token=NeutralTokenOptions(suffix_list=["pls", "thanks"], prefix_list=["pls", "hello"]), typo=TypoTestOptions(threshold=0.005), ) # Sentiment Analysis PIPELINE_CFG = { "name": "Default Model", "model": { "class_name": "tests.test_loading_resources.load_hf_text_classif_pipeline", "kwargs": {"checkpoint_path": _CHECKPOINT_PATH}, }, "postprocessors": [{"temperature": 3}, {"threshold": 0.7}], } DATASET_CFG = { "class_name": "tests.test_loading_resources.load_sst2_dataset", "kwargs": {}, } # Intent Classification _SAMPLE_INTENT_DIR = _SAMPLE_DATA_DIR / "intent" _SAMPLE_INTENT_TRAIN_FILE = str(_SAMPLE_INTENT_DIR / "sample_train_file.csv") _SAMPLE_PREDICTIONS_FILEPATH_TOP1 = str(_SAMPLE_INTENT_DIR / "sample_predictions_top1.csv") _SAMPLE_CLINC150 = str(_SAMPLE_DATA_DIR / "sample_CLINC150.json") SAMPLE_INTENT_TRAIN_FILE_NO_INTENT = str(_SAMPLE_INTENT_DIR / "sample_train_file_wnointent.csv") SAMPLE_PREDICTIONS_FILEPATH_TOP3 = str(_SAMPLE_INTENT_DIR / "sample_predictions_top3.csv") DATASET_CLINC150_CFG = { "class_name": "tests.test_loading_resources.load_CLINC150_data", "kwargs": { "python_loader": str(_AZ_ROOT / "azimuth_shr/data/CLINC150.py"), "full_path": _SAMPLE_CLINC150, }, } PIPELINE_GUSE_CFG = { "model": { "class_name": "tests.test_loading_resources.load_tf_model", "kwargs": {"checkpoint_path": _CHECKPOINT_PATH}, } } def file_based_ds_from_paths( train=_SAMPLE_INTENT_TRAIN_FILE, test=_SAMPLE_PREDICTIONS_FILEPATH_TOP1 ): return { "class_name": "tests.test_loading_resources.load_file_dataset", "kwargs": { "data_files": { "train": train, "test": test, }, }, } def file_based_pipeline_from(ds_config): return { "class_name": "models.file_based.FileBasedModel", "remote": str(_AZ_ROOT / "azimuth_shr"), "kwargs": { "test_path": ds_config["kwargs"]["data_files"]["test"], }, } def save_predictions(config, ds_split_name=DatasetSplitName.eval): pred_mod = model_contract_task_mapping( dataset_split_name=ds_split_name, config=config, mod_options=ModuleOptions( pipeline_index=0, model_contract_method_name=SupportedMethod.Predictions ), ) pred_res = pred_mod.compute_on_dataset_split() dm = pred_mod.get_dataset_split_manager() pred_mod.save_result(pred_res, dm) def save_outcomes(config, ds_split_name=DatasetSplitName.eval): outcome_mod = OutcomesModule( dataset_split_name=ds_split_name, config=config, mod_options=ModuleOptions(pipeline_index=0), ) outcome_res = outcome_mod.compute_on_dataset_split() dm = outcome_mod.get_dataset_split_manager() outcome_mod.save_result(outcome_res, dm) def get_table_key(config, use_bma=False): return PredictionTableKey.from_pipeline_index(0, config, use_bma=use_bma) def generate_mocked_dm(config, dataset_split_name=DatasetSplitName.eval): ds = load_dataset_from_config(config)[dataset_split_name] ds = ds.map( lambda x, i: { DatasetColumn.model_predictions: [i % 2, 1 - i % 2], DatasetColumn.model_confidences: [i / len(ds), 1 - i / len(ds)], # 0.9 simulates temperature scaling DatasetColumn.postprocessed_confidences: [0.9 * (i / len(ds)), 1 - 0.9 * (i / len(ds))], DatasetColumn.pipeline_steps: [], DatasetColumn.confidence_bin_idx: np.random.randint(1, 20), }, with_indices=True, ) base_class_names = ds.features[config.columns.label].names try: rejection_class_idx = base_class_names.index(config.rejection_class) except ValueError: rejection_class_idx = len(base_class_names) ds = ds.map( lambda x: { DatasetColumn.postprocessed_prediction: x[DatasetColumn.model_predictions][0] if x[DatasetColumn.postprocessed_confidences][0] > config.pipelines[0].threshold else rejection_class_idx, } ) ds = ds.map( lambda x: { DatasetColumn.model_outcome: compute_outcome( x[DatasetColumn.model_predictions][0], x["label"], rejection_class_idx ), DatasetColumn.postprocessed_outcome: compute_outcome( x[DatasetColumn.postprocessed_prediction], x["label"], rejection_class_idx ), DatasetColumn.neighbors_train: [ [np.random.randint(1, 1000), np.random.rand()] for i in range(0, 20) ], DatasetColumn.neighbors_eval: [ [np.random.randint(1, 1000), np.random.rand()] for i in range(0, 20) ], DatasetColumn.word_count: np.random.randint(5, 12), } ) dm = DatasetSplitManager( dataset_split_name, config, initial_tags=ALL_STANDARD_TAGS, initial_prediction_tags=ALL_PREDICTION_TAGS, dataset_split=ds, ) add_tags(dm) faiss_features = lambda: np.random.randn(786) dm.add_faiss_index([faiss_features() for _ in range(dm.num_rows)]) return dm def add_tags(dm): def add_all_tags_once(all_tags): for idx, t in enumerate(all_tags): tags = {idx: {t: True}} dm.add_tags(tags, table_key) table_key = get_table_key(dm.config) add_all_tags_once(ALL_DATA_ACTIONS) add_all_tags_once(ALL_SMART_TAGS) def get_tiny_text_config_one_ds_name(config): ds_name = DatasetSplitName.eval if "train" in config.dataset.kwargs else DatasetSplitName.train other_ds_name = ( DatasetSplitName.train if ds_name == DatasetSplitName.eval else DatasetSplitName.eval ) return ds_name, other_ds_name def get_enum_validation_error_msg(enum: Type[Enum]): permitted = ", ".join(repr(v.value) for v in enum) return f"value is not a valid enumeration member; permitted: {permitted}" def is_sorted(numbers: List[float], descending=False): return all(a >= b if descending else a <= b for a, b in zip(numbers[:-1], numbers[1:]))	{"/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", 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30,985	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_model_performance/test_confidence_histogram.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 fastapi import FastAPI from starlette.status import HTTP_200_OK, HTTP_404_NOT_FOUND from starlette.testclient import TestClient def test_get_confidence_histogram_wrong_dataset(app: FastAPI) -> None: client = TestClient(app) invalid_dataset_split_name = "potato" resp = client.get(f"/dataset_splits/{invalid_dataset_split_name}/confidence_histogram") assert resp.status_code == HTTP_404_NOT_FOUND, resp.text def test_get_confidence_histogram(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/confidence_histogram?bins=0&bins=1&pipeline_index=0") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert data == { "bins": [ { "binConfidence": pytest.approx(0.025), "binIndex": 0, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.07500000000000001), "binIndex": 1, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.125), "binIndex": 2, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.17500000000000002), "binIndex": 3, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.225), "binIndex": 4, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.275), "binIndex": 5, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.32500000000000007), "binIndex": 6, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.37500000000000006), "binIndex": 7, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.42500000000000004), "binIndex": 8, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.47500000000000003), "binIndex": 9, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.525), "binIndex": 10, "meanBinConfidence": pytest.approx(0.5304411773021637), "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 22, }, }, { "binConfidence": pytest.approx(0.5750000000000001), "binIndex": 11, "meanBinConfidence": pytest.approx(0.5642743974538343), "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 3, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.6250000000000001), "binIndex": 12, "meanBinConfidence": pytest.approx(0.6288018955444876), "outcomeCount": { "CorrectAndPredicted": 1, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 12, }, }, { "binConfidence": pytest.approx(0.675), "binIndex": 13, "meanBinConfidence": pytest.approx(0.6812974707353887), "outcomeCount": { "CorrectAndPredicted": 1, "CorrectAndRejected": 0, "IncorrectAndPredicted": 1, "IncorrectAndRejected": 1, }, }, { "binConfidence": pytest.approx(0.7250000000000001), "binIndex": 14, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.775), "binIndex": 15, "meanBinConfidence": pytest.approx(0.7535185332847896), "outcomeCount": { "CorrectAndPredicted": 1, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.8250000000000001), "binIndex": 16, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.8750000000000001), "binIndex": 17, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.925), "binIndex": 18, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, { "binConfidence": pytest.approx(0.9750000000000001), "binIndex": 19, "meanBinConfidence": 0.0, "outcomeCount": { "CorrectAndPredicted": 0, "CorrectAndRejected": 0, "IncorrectAndPredicted": 0, "IncorrectAndRejected": 0, }, }, ], "confidenceThreshold": None, }	{"/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", 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30,986	ServiceNow/azimuth	refs/heads/main	/azimuth/__init__.py	import os from pathlib import Path from datasets import disable_progress_bar from datasets.utils.logging import set_verbosity_error from azimuth.utils.logs import set_logger_config PROJECT_ROOT = str(Path(__file__).parent) set_verbosity_error() disable_progress_bar() set_logger_config() os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1" # INFO messages from TensorFlow are not printed	{"/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", 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30,987	ServiceNow/azimuth	refs/heads/main	/tests/test_utils/test_dataset_filtering.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.types import DatasetColumn, DatasetFilters from azimuth.types.outcomes import OutcomeName from azimuth.types.tag import ( ALL_SMART_TAGS, DATASET_SMART_TAG_FAMILIES, PIPELINE_SMART_TAG_FAMILIES, SMART_TAGS_FAMILY_MAPPING, DataAction, SmartTag, SmartTagFamily, ) from azimuth.utils.dataset_operations import filter_dataset_split from tests.utils import generate_mocked_dm, get_table_key def test_all_smart_tags_have_a_family(): assert sorted( tag.value for family in SMART_TAGS_FAMILY_MAPPING.values() for tag in family ) == sorted(ALL_SMART_TAGS) def test_all_smart_tag_families_are_dataset_or_pipeline(): assert sorted(SMART_TAGS_FAMILY_MAPPING.keys()) == sorted( DATASET_SMART_TAG_FAMILIES + PIPELINE_SMART_TAG_FAMILIES ) def test_dataset_filtering(simple_text_config): dm = generate_mocked_dm(simple_text_config) ds = dm.get_dataset_split(get_table_key(simple_text_config)) ds_len = {} def filtered_len(tf: DatasetFilters) -> int: ds_filtered = filter_dataset_split(ds, tf, config=dm.config) ds_len[(tuple(tf.label), tuple(tf.prediction))] = len(ds_filtered) return len(ds_filtered) assert filtered_len(DatasetFilters()) == len(ds) assert filtered_len(DatasetFilters(confidence_min=0.5)) == 0.9 * len(ds) // 2 assert filtered_len(DatasetFilters(confidence_max=0.5)) == len(ds) - 0.9 * len(ds) // 2 assert filtered_len(DatasetFilters(label=[0])) == 22 assert filtered_len(DatasetFilters(data_action=[DataAction.relabel])) == 1 assert filtered_len(DatasetFilters(outcome=[OutcomeName.IncorrectAndRejected])) == 33 assert ( filtered_len(DatasetFilters(smart_tags={SmartTagFamily.extreme_length: [SmartTag.short]})) == 1 ) assert filtered_len(DatasetFilters(utterance="some")) == 2 assert filtered_len(DatasetFilters(utterance="32")) == 1, "Utterance with index 32" assert filtered_len(DatasetFilters(prediction=[1])) == 5 # We can filter by combinations of filter combination_len = filtered_len(DatasetFilters(label=[0], prediction=[1])) assert combination_len == 3 assert combination_len < min(ds_len[((), (1,))], ds_len[((0,), ())]) def test_dataset_filtering_columns_not_in_dataset(simple_text_config): dm = generate_mocked_dm(simple_text_config) ds = dm.get_dataset_split(get_table_key(simple_text_config)) ds = ds.rename_column(DatasetColumn.postprocessed_prediction, "col1") ds = ds.rename_column(DatasetColumn.postprocessed_outcome, "col4") ds = ds.rename_column(DatasetColumn.postprocessed_confidences, "col5") ds_filtered = filter_dataset_split(ds, DatasetFilters(prediction=[0]), config=dm.config) assert len(ds_filtered) == len(ds) outcome = [OutcomeName.IncorrectAndRejected] ds_filtered = filter_dataset_split(ds, DatasetFilters(outcome=outcome), config=dm.config) assert len(ds_filtered) == len(ds) ds_filtered = filter_dataset_split(ds, DatasetFilters(confidence_max=0.5), config=dm.config) assert len(ds_filtered) == len(ds) def test_dataset_filtering_multi(simple_text_config): simple_table_key = get_table_key(simple_text_config) dm = generate_mocked_dm(simple_text_config) ds = dm.get_dataset_split(simple_table_key) # We can filter by nothing! filters = DatasetFilters() ds_filtered = filter_dataset_split(ds, filters, config=dm.config) assert len(ds_filtered) == len(ds) # We can filter by multiple targets ie does nothing in this case. ds_filtered = filter_dataset_split(ds, DatasetFilters(label=[0, 1]), config=dm.config) assert len(ds) == len(ds_filtered) # We can filter by multiple preds, will remove the rejection class from preds ds_filtered = filter_dataset_split(ds, DatasetFilters(prediction=[0, 1]), config=dm.config) num_rejection_class = sum( p != dm.rejection_class_idx for p in dm.get_dataset_split(simple_table_key)[DatasetColumn.postprocessed_prediction] ) assert num_rejection_class == len(ds_filtered) @pytest.mark.parametrize( "family", [ SmartTagFamily.extreme_length, SmartTagFamily.partial_syntax, SmartTagFamily.dissimilar, SmartTagFamily.behavioral_testing, SmartTagFamily.almost_correct, SmartTagFamily.pipeline_comparison, SmartTagFamily.uncertain, ], ) def test_dataset_filtering_no_smart_tag(simple_text_config, family): dm = generate_mocked_dm(simple_text_config) simple_table_key = get_table_key(simple_text_config) # Reset all tags to False for the first utterance, to ensure that at least one row is # associated with no smart tags. [ dm.add_tags( {0: {smart_tag: False}}, table_key=simple_table_key, ) for smart_tag in ALL_SMART_TAGS ] ds_filtered = filter_dataset_split( dm.get_dataset_split(simple_table_key), DatasetFilters(smart_tags={family: ["NO_SMART_TAGS"]}), config=dm.config, ) assert len(ds_filtered) > 0 assert ( len(ds_filtered.filter(lambda x: any(x[v] for v in SMART_TAGS_FAMILY_MAPPING[family]))) == 0 ) def test_dataset_filtering_confidence(simple_text_config): dm = generate_mocked_dm(simple_text_config) ds = dm.get_dataset_split(get_table_key(simple_text_config)) ds_filtered = filter_dataset_split( ds, DatasetFilters(confidence_min=0.4, confidence_max=0.6), config=dm.config ) assert len(ds_filtered) == 10 assert ( len( ds_filtered.filter( lambda x: not 0.4 <= x[DatasetColumn.postprocessed_confidences][0] <= 0.6 ) ) == 0 ) def test_dataset_filtering_smart_tags_uses_or_within_family(simple_text_config): dm = generate_mocked_dm(simple_text_config) ds = dm.get_dataset_split(get_table_key(simple_text_config)) ds_filtered_long_utterance = filter_dataset_split( ds, DatasetFilters(smart_tags={SmartTagFamily.extreme_length: ["long_utterance"]}), config=dm.config, ) ds_filtered_short_utterance = filter_dataset_split( ds, DatasetFilters(smart_tags={SmartTagFamily.extreme_length: ["short_utterance"]}), config=dm.config, ) ds_filtered = filter_dataset_split( ds, DatasetFilters( smart_tags={SmartTagFamily.extreme_length: ["long_utterance", "short_utterance"]} ), config=dm.config, ) assert len(ds_filtered) == len(ds_filtered_long_utterance) + len(ds_filtered_short_utterance) def test_dataset_filtering_without_postprocessing(simple_text_config): dm = generate_mocked_dm(simple_text_config) ds = dm.get_dataset_split(get_table_key(simple_text_config)) ds_filtered_with_postprocessing = filter_dataset_split( ds, DatasetFilters(prediction=[0]), config=dm.config, ) ds_filtered_without_postprocessing = filter_dataset_split( ds, 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30,988	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/base_classes/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. from typing import List, Optional, Set, cast import numpy as np from datasets import Dataset from tqdm import tqdm from azimuth.config import ModelContractConfig, PipelineDefinition from azimuth.dataset_split_manager import DatasetSplitManager, PredictionTableKey from azimuth.modules.base_classes import ArtifactManager, ConfigScope, DaskModule from azimuth.modules.base_classes.dask_module import Worker from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions, ModuleResponse from azimuth.types.general.module_arguments import ModuleEffectiveArguments from azimuth.utils.conversion import md5_hash from azimuth.utils.exclude_fields_from_cache import exclude_fields_from_cache from azimuth.utils.validation import assert_not_none class Module(DaskModule[ConfigScope]): """Abstract class to define functions used by all modules for computing results and interfacing with the dataset and the pipelines.""" required_mod_options: Set[str] = set() optional_mod_options: Set[str] = set() def __init__( self, dataset_split_name: DatasetSplitName, config: ConfigScope, mod_options: Optional[ModuleOptions] = None, ): mod_options = mod_options or ModuleOptions() self.mod_options = mod_options defined_mod_options = set(self.mod_options.dict(exclude_defaults=True).keys()) if not self.required_mod_options.issubset(defined_mod_options): raise ValueError(f"{self.__class__.__name__} requires {self.required_mod_options}.") if diff := (defined_mod_options - self.required_mod_options - self.optional_mod_options): raise ValueError(f"Unexpected mod_options {diff} for {self.__class__.__name__}.") self.model_contract_method_name = mod_options.model_contract_method_name self.task_name = self.model_contract_method_name or self.__class__.__name__ super().__init__(dataset_split_name, config) def get_effective_arguments(self) -> ModuleEffectiveArguments: """Retrieve arguments affecting the Module, i.e. module options and config scope. Returns: Module's Effective Arguments """ # indices are excluded, since the cache for all indices should be in the same file. # model_contract_method_name are excluded too because it's already in the task_name. return ModuleEffectiveArguments( mod_options=self.mod_options.dict( exclude={"indices", "model_contract_method_name"}, include=self.required_mod_options \| self.optional_mod_options, ), config_scope=self.config.dict( exclude=exclude_fields_from_cache(self.config), ), ) def _get_name(self) -> str: effective_arguments = self.get_effective_arguments() hash_options = md5_hash(effective_arguments.mod_options)[:5] hash_attributes = md5_hash(effective_arguments.config_scope)[:5] return f"{self.task_name}_{self.dataset_split_name}_{hash_options}_{hash_attributes}" def get_caching_indices(self) -> List[int]: return self.mod_options.indices or self.get_indices() def get_indices(self, name: Optional[DatasetSplitName] = None) -> List[int]: """Get indices from dataset_split.""" return cast(List[int], self.get_dataset_split(name)[DatasetColumn.row_idx]) @property def artifact_manager(self): """This is set as a property so the Module always have access to the current version of the ArtifactManager on the worker.""" return ArtifactManager.instance() @property def available_dataset_splits(self) -> Set[DatasetSplitName]: dataset_dict = self.artifact_manager.get_dataset_dict(self.config) return set(dataset_dict.keys()).intersection( [DatasetSplitName.eval, DatasetSplitName.train] ) def compute_on_dataset_split(self) -> List[ModuleResponse]: """Method that iterates over a dataset_split and call `compute`""" ds: Dataset = self.get_dataset_split() batch_size = self.config.batch_size num_rows = list(range(len(ds))) batches = [num_rows[i : i + batch_size] for i in range(0, len(ds), batch_size)] result = [] for batch in tqdm( batches, desc=f"{self.task_name} on {self.dataset_split_name} set " f"for pipeline {self.mod_options.pipeline_index}", ): result += self.compute(ds.select(batch)) return result def compute(self, batch: Dataset) -> List[ModuleResponse]: raise NotImplementedError def get_dataset_split(self, name: Optional[DatasetSplitName] = None) -> Dataset: """Get the specified dataset_split. For regular modules, not filtering or indexing is done. Args: name: Which dataset_split to get. Returns: The loaded dataset_split. Raises: ValueError: If no dataset_split name is provided or defined in the module. """ if self.dataset_split_name is None and name is None: raise ValueError( "No dataset_split_name defined for this module. Specify what dataset_split to get." ) dataset_split_name = name or self.dataset_split_name dm = self.get_dataset_split_manager(dataset_split_name) dataset_split: Dataset = dm.get_dataset_split(self._get_table_key()) return dataset_split def get_dataset_split_manager( self, name: Optional[DatasetSplitName] = None ) -> DatasetSplitManager: """ Get the DatasetSplitManager object that the module is working with. Args: name: Which dataset_split to get DatasetSplitManager for. Returns: DatasetSplitManager instance. Raises: If there is no dataset defined in the config. Or if the split is not defined. """ dataset_split_name = name or self.dataset_split_name dataset_split_manager: DatasetSplitManager = ( self.artifact_manager.get_dataset_split_manager(self.config, dataset_split_name) ) return dataset_split_manager def get_model(self): """Load the model according to the config and module options. This will invoke the Python method supplied in the config. See our API Doc for more details. It can return a HF Pipeline, a Callable (which can be just a model or a custom pipeline) and a file prediction reader. Returns: Loaded model according to user spec. Raises: ValueError if no valid pipeline exists. """ if self.worker != Worker.model: raise RuntimeError("This module cannot load the model. Modify self.worker.") _ = self.get_pipeline_definition() # Validate current pipeline exists return self.artifact_manager.get_model(self.config, self.mod_options.pipeline_index) def _get_table_key(self) -> Optional[PredictionTableKey]: """Get the Table key for the current module. Returns: PredictionTableKey to use with DatasetSplitManager. """ if self.mod_options.pipeline_index is None or not isinstance( self.config, ModelContractConfig ): return None return PredictionTableKey( temperature=self.get_pipeline_definition().temperature, threshold=self.get_threshold(), use_bma=self.mod_options.use_bma, pipeline_index=self.mod_options.pipeline_index, ) def get_threshold(self) -> Optional[float]: # The default is None so we have to handle it this way. thresh = self.mod_options.threshold if thresh is None: thresh = self.get_pipeline_definition().threshold return thresh def get_pipeline_definition(self) -> PipelineDefinition: """Get the pipeline definition from the config according to the pipeline_index. Returns: Definition of the pipeline from the config. Raises: ValueError if no valid pipeline exists. 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30,989	ServiceNow/azimuth	refs/heads/main	/azimuth/types/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. from typing import List, Optional, Tuple from pydantic import Field from azimuth.types import AliasModel, Array, ModuleResponse from azimuth.types.utterance import BaseUtterance class SimilarUtterance(BaseUtterance): postprocessed_prediction: Optional[str] = Field(..., nullable=True) postprocessed_confidence: Optional[float] = Field(..., nullable=True) similarity: float class SimilarUtterancesResponse(AliasModel): utterances: List[SimilarUtterance] = Field(..., title="Closest utterances") class FAISSResponse(ModuleResponse): features: Array[float] class NeighborsResponse(ModuleResponse): train_neighbors: List[Tuple[int, float]] eval_neighbors: List[Tuple[int, float]]	{"/tests/test_config.py": ["/azimuth/config.py", 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30,990	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_contracts/custom_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 Callable, List import structlog from datasets import Dataset from azimuth.modules.base_classes import ModelContractModule from azimuth.modules.model_contracts.text_classification_no_saliency import ( TextClassificationNoSaliencyModule, ) from azimuth.types.task import PredictionResponse log = structlog.get_logger(__file__) class CustomTextClassificationModule(TextClassificationNoSaliencyModule): """Handles text classification prediction where we are not using HF as the underlying framework. Notes: This should be able to handle any framework/models, but it doesn't support saliency. The model should be a callable. """ optional_mod_options = ModelContractModule.optional_mod_options \| {"iterations", "use_bma"} def predict(self, batch: Dataset) -> List[PredictionResponse]: """Predict the bath of utterances. The epistemic uncertainty is dummy. Args: batch: Batch of utterances. Returns: Predictions for the batch. 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"/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"]}
30,991	ServiceNow/azimuth	refs/heads/main	/tests/test_utils/test_exclude_from_cache.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, Union from pydantic import BaseModel, Field from azimuth.utils.exclude_fields_from_cache import exclude_fields_from_cache class Sub(BaseModel): field: int class SubWithExcludedField(BaseModel): excluded_field: int = Field(..., exclude_from_cache=True) field: int class Model(BaseModel): sub: Sub excluded_sub_with_excluded_field: SubWithExcludedField = Field(..., exclude_from_cache=True) sub_with_excluded_field: SubWithExcludedField list_of_sub_with_excluded_field: List[SubWithExcludedField] any: Any list_of_any: List[Any] list_of_union: List[Union[SubWithExcludedField, Sub, int]] dict_of_union: Dict[str, Union[SubWithExcludedField, Sub, int]] def test_exclude_from_cache(): model = Model( sub=Sub(field=9), excluded_sub_with_excluded_field=SubWithExcludedField(excluded_field=1, field=7), sub_with_excluded_field=SubWithExcludedField(excluded_field=2, field=8), list_of_sub_with_excluded_field=[SubWithExcludedField(excluded_field=3, field=5)], any=SubWithExcludedField(excluded_field=-1, field=-2), list_of_any=[SubWithExcludedField(excluded_field=4, field=6), Sub(field=10), 11, "hi!"], list_of_union=[SubWithExcludedField(excluded_field=4, field=6), Sub(field=10), 11], dict_of_union={ "a": SubWithExcludedField(excluded_field=4, field=6), "b": Sub(field=10), "c": 11, }, ) exclude = exclude_fields_from_cache(model) assert exclude == { "excluded_sub_with_excluded_field": ..., "sub_with_excluded_field": {"excluded_field": ...}, "list_of_sub_with_excluded_field": {0: {"excluded_field": ...}}, "any": {"excluded_field": ...}, "list_of_any": {0: {"excluded_field": ...}}, "list_of_union": {0: {"excluded_field": ...}}, "dict_of_union": {"a": {"excluded_field": ...}}, } assert model.dict(exclude=exclude) == { "sub": {"field": 9}, "sub_with_excluded_field": {"field": 8}, "list_of_sub_with_excluded_field": [{"field": 5}], "any": {"field": -2}, "list_of_any": [{"field": 6}, {"field": 10}, 11, "hi!"], "list_of_union": [{"field": 6}, {"field": 10}, 11], "dict_of_union": {"a": {"field": 6}, "b": {"field": 10}, "c": 11}, }	{"/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", 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30,992	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/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. import pytest from fastapi import FastAPI from starlette.status import HTTP_200_OK from starlette.testclient import TestClient common_metric_response = { "customMetrics": pytest.approx( { "Accuracy": 0.07142857142857142, "F1": 0.12349206349206351, "Precision": 0.4682539682539682, "Recall": 0.07142857142857142, } ), "ece": pytest.approx(0.5196982765240196), "outcomeCount": { "CorrectAndPredicted": 3, "CorrectAndRejected": 0, "IncorrectAndPredicted": 4, "IncorrectAndRejected": 35, }, "utteranceCount": 42, } def test_get_metrics(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/metrics?pipeline_index=0") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() _ = data.pop("ecePlot") assert data == common_metric_response def test_get_metrics_per_filter(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/metrics/per_filter?pipeline_index=0") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert data["metricsOverall"][0] == dict(**common_metric_response, filterValue="overall") metrics_per_filter = data.pop("metricsPerFilter") assert "label" in metrics_per_filter and len(metrics_per_filter["label"]) == 3	{"/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": 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30,993	ServiceNow/azimuth	refs/heads/main	/tests/test_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. from unittest.mock import Mock import pytest from datasets import Dataset, DatasetDict from azimuth.app import get_ready_flag, load_dataset_split_managers_from_config, run_startup_tasks from azimuth.config import CustomObject from azimuth.modules.model_contracts import HFTextClassificationModule from azimuth.startup import on_end, startup_tasks from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod, SupportedModule from tests.utils import get_table_key, get_tiny_text_config_one_ds_name def test_startup_task(tiny_text_config, tiny_text_task_manager): dms = load_dataset_split_managers_from_config(tiny_text_config) mods = startup_tasks(dms, tiny_text_task_manager) one_mod = mods["syntax_tags_eval"] # We lock the task manager assert tiny_text_task_manager.is_locked assert not one_mod.done() assert all("train" in k or "eval" in k or "all" in k for k in mods.keys()) assert all( on_end in [cbk.fn for cbk in mod._callbacks] for mod in mods.values() ), "Some modules don't have callbacks!" assert len(mods) == 21 def test_startup_task_fast(tiny_text_config, tiny_text_task_manager): tiny_text_config.behavioral_testing = None tiny_text_config.similarity = None dms = load_dataset_split_managers_from_config(tiny_text_config) mods = startup_tasks(dms, tiny_text_task_manager) assert not any( mod.task_name in (SupportedModule.PerturbationTesting, SupportedModule.NeighborsTagging) for mod in mods.values() ) def test_on_end(tiny_text_config): dms = load_dataset_split_managers_from_config(tiny_text_config) # Test that the dataset_split manager is called. mod = HFTextClassificationModule( dataset_split_name=DatasetSplitName.eval, config=tiny_text_config, mod_options=ModuleOptions( pipeline_index=0, model_contract_method_name=SupportedMethod.Predictions, indices=[0, 1], ), ) res = mod.compute_on_dataset_split() mod = Mock(HFTextClassificationModule) mod.result = Mock(return_value=res) mocked_fut = Mock() mocked_fut.status = "finished" task_manager = Mock() on_end(mocked_fut, mod, dm=dms[DatasetSplitName.eval], task_manager=task_manager) mod.save_result.assert_called_once() def test_startup_task_one_ds(tiny_text_config_one_ds, tiny_text_task_manager): dms = load_dataset_split_managers_from_config(tiny_text_config_one_ds) assert DatasetSplitName.eval in dms and DatasetSplitName.train in dms mods = startup_tasks(dms, tiny_text_task_manager) ds_name, other_ds_name = get_tiny_text_config_one_ds_name(tiny_text_config_one_ds) assert all( (DatasetSplitName.all in k or ds_name in k) and other_ds_name not in k for k in mods.keys() ) assert all( on_end in [cbk.fn for cbk in mod._callbacks] for mod in mods.values() ), "Some modules don't have callbacks!" @pytest.mark.parametrize("iterations", [20, 1]) def test_startup_task_bma(tiny_text_config, tiny_text_task_manager, iterations): tiny_text_config.uncertainty.iterations = iterations dms = load_dataset_split_managers_from_config(tiny_text_config) mods = startup_tasks(dms, tiny_text_task_manager) # Check that we have BMA at some point if iterations > 1 if iterations == 1: assert all("bma" not in mod_name for mod_name in mods.keys()) else: assert any("bma" in mod_name for mod_name in mods.keys()) def test_initialize_backbone(tiny_text_config, dask_client): from azimuth.app import ( get_config, get_dataset_split_manager, get_startup_tasks, get_task_manager, ) event = get_ready_flag() assert event is None assert get_startup_tasks() is None run_startup_tasks(tiny_text_config, cluster=dask_client.cluster) assert get_task_manager().cluster is dask_client.cluster assert get_config() is tiny_text_config assert get_dataset_split_manager(DatasetSplitName.train).config is tiny_text_config assert get_dataset_split_manager(DatasetSplitName.eval).config is tiny_text_config assert len(get_startup_tasks()) > 0 assert not get_ready_flag().is_set() 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): simple_text_config.dataset = CustomObject( class_name="tests.test_modules.test_module.my_dataset_fn", kwargs={"valid": True} ) simple_table_key = get_table_key(simple_text_config) ds = load_dataset_split_managers_from_config(simple_text_config)[ DatasetSplitName.eval ].get_dataset_split(simple_table_key) assert ds["utterance"] == ["d", "e", "f"] # Otherwise, we take validation simple_text_config.dataset = CustomObject( 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30,994	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_contracts/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 from datasets import Dataset from azimuth.modules.model_contracts.text_classification_no_saliency import ( TextClassificationNoSaliencyModule, ) from azimuth.modules.task_execution import get_task_result from azimuth.types import DatasetColumn, ModuleOptions, SupportedMethod from azimuth.types.task import PredictionResponse class FileBasedTextClassificationModule(TextClassificationNoSaliencyModule): """Module that does not require a model. To get predictions, read them from the same file that contains the test examples (csv). """ def predict(self, batch: Dataset) -> List[PredictionResponse]: """Predict the bath of utterances. For utterances in the test set, predictions will be read from the prediction file. For utterances in the training set, the predictions will be hard-coded to the labels. Args: batch: Utterances. Returns: Predictions for the batch. """ # We only have predictions for the test set prediction_reader = self.get_model() predictions = prediction_reader(batch, self.dataset_split_name) # Ignore postprocessing as we expect file-based to be postprocessed already. raw, _, _, _ = self.get_postprocessed_output(batch, predictions) epistemic = [0.0] * len(batch[DatasetColumn.row_idx]) return self._parse_prediction_output(batch, raw, raw, [], [], epistemic) def post_process(self, batch: Dataset) -> List[PredictionResponse]: """In File-Based, predictions from the file are already post-processed. As such, we just retrieve the predictions from the cache. We cannot change the post-processors. Args: batch: Utterances. Returns: Predictions for the batch. 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30,995	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_contracts/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 abc from typing import Any, Dict, List, Protocol, Tuple, Union, cast, runtime_checkable import numpy as np import tensorflow import torch import transformers from datasets import Dataset from scipy.special import softmax from scipy.stats import entropy from transformers.file_utils import ModelOutput from azimuth.modules.base_classes import ModelContractModule from azimuth.modules.task_execution import get_task_result from azimuth.types import DatasetColumn, InputResponse, ModuleOptions, SupportedMethod from azimuth.types.task import PredictionResponse, SaliencyResponse from azimuth.utils.ml.postprocessing import PostProcessingIO, PostprocessingStep from azimuth.utils.ml.preprocessing import PreprocessingStep from azimuth.utils.project import postprocessing_editable EPSILON = 1e-6 ITERATIONS = 100 @runtime_checkable class PipelineOutputProtocol(Protocol): """Class containing result of a batch""" # model output without passing through post-processing stage: texts, logits, probs, preds model_output: PostProcessingIO # output after passing through post-processing: pre-processed texts, logits, probs, preds postprocessor_output: PostProcessingIO @runtime_checkable class PipelineOutputProtocolV2(PipelineOutputProtocol, Protocol): """Class containing result of a batch with pre and postprocessing steps""" preprocessing_steps: List[Dict[str, Union[str, List[str]]]] postprocessing_steps: List[Dict[str, Union[str, PostProcessingIO]]] SupportedOutput = Union[ PipelineOutputProtocol, np.ndarray, # Shape [N, num_classes] torch.Tensor, # Shape [N, num_classes] tensorflow.Tensor, transformers.file_utils.ModelOutput, ] class TextClassificationModule(ModelContractModule, abc.ABC): """Handles text classification prediction and saliency.""" def get_input(self, batch: Dataset) -> List[InputResponse]: return [InputResponse(input=x) for x in batch[self.config.columns.text_input]] @abc.abstractmethod def predict(self, batch: Dataset) -> List[PredictionResponse]: """Get predictions for a batch of utterances. Args: batch: batch of utterances. Returns: Predictions for the batch of utterances. """ raise NotImplementedError @abc.abstractmethod 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. """ raise NotImplementedError def post_process(self, batch: Dataset) -> List[PredictionResponse]: """This function recompute post-processing, without recomputing predictions from the model. This can be useful when another module needs to compute information based on different post-processors values. Ex: threshold, temperature. These values can be changed in the config, or set through module options. Args: batch: batch of utterances Returns: Predictions, with the updated postprocess values. """ # Get cached predictions predictions_task = type(self)( dataset_split_name=self.dataset_split_name, config=self.config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=self.mod_options.pipeline_index, indices=cast(List[int], batch[DatasetColumn.row_idx]), ), ) predictions = get_task_result( task_module=predictions_task, result_type=List[PredictionResponse] ) if not postprocessing_editable(self.config, pipeline_index=self.mod_options.pipeline_index): return predictions # Reconstruct pipeline_out and all_epistemic dm = self.get_dataset_split_manager() pipeline_out = np.zeros( (len(batch[DatasetColumn.row_idx]), dm.get_num_classes(labels_only=True)) ) epistemic_all = [] for idx, pred in enumerate(predictions): pipeline_out[idx, :] = pred.model_output.probs epistemic_all.append(pred.epistemic) # Call post_processing and reconstruct product output ( model_output, postprocessed_output, preprocessing_steps, postprocessing_steps, ) = self.get_postprocessed_output(batch, pipeline_out) return self._parse_prediction_output( batch, model_output, postprocessed_output, preprocessing_steps, postprocessing_steps, epistemic_all, ) def extract_probs_from_output(self, model_out: Any) -> np.ndarray: """Extract probabilities from model output. Args: model_out: Model's output following one of our conventions. Can be an array, tensor, output of HF pipeline or transformers.ModelOutput. Returns: Array as probabilities. """ if isinstance(model_out, np.ndarray): out = model_out elif isinstance(model_out, torch.Tensor): out = model_out.numpy() elif isinstance(model_out, tensorflow.Tensor): out = np.array(model_out) elif ( isinstance(model_out, list) and isinstance(model_out[0], list) and model_out[0] and isinstance(model_out[0][0], dict) and "score" in model_out[0][0] ): out = np.array([[cls_out["score"] for cls_out in record] for record in model_out]) elif isinstance(model_out, ModelOutput): out = softmax(model_out["logits"].cpu().numpy(), -1) elif hasattr(model_out, "model_output"): # NOTE: One can give this in some cases. out = model_out.model_output.probs else: raise ValueError( "Can't handle this type of Model output," f" expecting {SupportedOutput}." f" Got {type(model_out)}, {model_out}" ) return out def _parse_prediction_output( self, input_batch: Dataset, model_output: PostProcessingIO, postprocessed_output: PostProcessingIO, preprocessing_steps: List[PreprocessingStep], postprocessing_steps: List[PostprocessingStep], epistemic: List[float], ) -> List[PredictionResponse]: """Take the pipeline output and return a PredictionResponse. Args: input_batch: Input batch. model_output: Output of the model postprocessed_output: Postprocessed output of the model. epistemic: Epistemic uncertainty for each element of the batch. Returns: List of PredictionResponse for each element. """ json_output = [] labels = input_batch[self.config.columns.label] for idx, (label, epis) in enumerate(zip(labels, epistemic)): json_output.append( PredictionResponse( label=label, # The __getitem__ method allows to get from batch results to utterance results model_output=model_output[idx], postprocessed_output=postprocessed_output[idx], preprocessing_steps=[ preprocessing_step[idx] for preprocessing_step in preprocessing_steps ], postprocessing_steps=[ postprocessing_step[idx] for postprocessing_step in postprocessing_steps ], entropy=entropy(postprocessed_output[idx].probs[0]), epistemic=epis, ) ) return json_output def get_postprocessed_output( self, input_batch: Dataset, model_output ) -> Tuple[ PostProcessingIO, PostProcessingIO, List[PreprocessingStep], List[PostprocessingStep] ]: """Get postprocessed output from model output. Args: input_batch: Model input model_output: Output of the model. Returns: Raw and postprocessed output. """ if isinstance(model_output, PipelineOutputProtocol): # User is following our contract, we can work with it. # Constructing PostProcessingIO object so indexing works. if isinstance(model_output, PipelineOutputProtocolV2): preprocessing_steps = [ PreprocessingStep(class_name=step["class_name"], text=step["text"]) for step in model_output.preprocessing_steps ] postprocessing_steps = [ PostprocessingStep( class_name=step["class_name"], output=PostProcessingIO( texts=input_batch[self.config.columns.text_input], probs=cast(PostProcessingIO, step["output"]).probs, preds=cast(PostProcessingIO, step["output"]).preds, logits=cast(PostProcessingIO, step["output"]).logits, ), ) for step in model_output.postprocessing_steps ] else: preprocessing_steps, postprocessing_steps = [], [] return ( PostProcessingIO( texts=input_batch[self.config.columns.text_input], probs=model_output.model_output.probs, preds=model_output.model_output.preds, logits=model_output.model_output.logits, ), PostProcessingIO( texts=input_batch[self.config.columns.text_input], probs=model_output.postprocessor_output.probs, preds=model_output.postprocessor_output.preds, logits=model_output.postprocessor_output.logits, ), preprocessing_steps, postprocessing_steps, ) rejection_class_idx = self.get_dataset_split_manager().rejection_class_idx rejection_class_threshold = self.get_threshold() probs = self.extract_probs_from_output(model_output) logits = ( np.log(probs + EPSILON) if np.allclose(probs.sum(-1), 1.0) else np.log(probs / (1 - probs) + EPSILON) ) model_out_formatted = PostProcessingIO( texts=input_batch[self.config.columns.text_input], logits=logits, probs=probs, preds=np.argmax(probs, axis=-1), ) postprocessed_steps = self.run_postprocessing( model_out_formatted, threshold=rejection_class_threshold, rejection_class_idx=rejection_class_idx, ) postprocessed_output = ( postprocessed_steps[-1].output if postprocessed_steps else model_out_formatted ) # Preprocessing steps are not supported 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30,996	ServiceNow/azimuth	refs/heads/main	/azimuth/task_manager.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, Callable, Dict, List, Optional, Tuple import structlog from distributed import Client, SpecCluster from azimuth.config import AzimuthConfig from azimuth.modules.base_classes import DaskModule, ExpirableMixin from azimuth.modules.task_mapping import model_contract_methods, modules from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod, SupportedTask from azimuth.utils.cluster import default_cluster log = structlog.get_logger() class TaskManagerLockedException(Exception): pass class TaskManager: """The Task Manager responsibility is to start tasks and scale the Cluster as needed. Args: config: The application configuration. cluster: Dask cluster to use, we will spawn a default one if not provided. """ def __init__(self, config: AzimuthConfig, cluster: Optional[SpecCluster] = None): self.config = config self.cluster = cluster or default_cluster(large=config.large_dask_cluster) self.client = Client(cluster) self.tasks: Dict[str, type] = {} self.current_tasks: Dict[str, DaskModule] = {} self._is_locked = False self._register_all_tasks({"methods": model_contract_methods, "modules": modules}) def lock(self): if self._is_locked: raise ValueError("TaskManager already locked!") self._is_locked = True def unlock(self): if not self._is_locked: raise ValueError("TaskManager was not locked!") self._is_locked = False @property def is_locked(self): return self._is_locked def close(self): """ Close down the task manager and Client. """ for mod in self.current_tasks.values(): if mod.future is not None: try: mod.future.cancel() except Exception: pass self.client.close() def register_task(self, name, cls): # Add the task `cls` to the list of available tasks. self.tasks[name] = cls def _register_all_tasks(self, root_routes: Dict[str, Any]): """Register all tasks from azimuth.modules so that they can be used.""" for route, tasks in root_routes.items(): for name, task in tasks.items(): page_name = f"{route}/{name}" log.debug(f"Registering new task in {route}.", name=page_name, clss=task) self.register_task(name, task) def get_all_tasks_status(self, task: Optional[str] = None) -> Dict[str, str]: """Get all the tasks related to `task` argument. Args: task: Returns all tasks named as such. If None, return all tasks. Returns: Dict[name, status] for all matching tasks. """ if task is not None: # Get specific tasks return {str(k): v.status() for k, v in self.current_tasks.items() if task in k} else: # Get all tasks return {str(k): v.status() for k, v in self.current_tasks.items()} def get_task( self, task_name: SupportedTask, dataset_split_name: DatasetSplitName, mod_options: Optional[ModuleOptions] = None, last_update: float = -1, dependencies: Optional[List[DaskModule]] = None, ) -> Tuple[str, Optional[DaskModule]]: """Get the task `name` run on indices. It will spawn the task if not there. Args: task_name: Name of the task. dataset_split_name: Which dataset split to use. mod_options: Options for the module. last_update: Last known update of the dataset_split. dependencies: Which Modules should complete before this one. Returns: Key and task. Raises: TaskManagerLockedException if the task manager is locked """ if self._is_locked: raise TaskManagerLockedException("Can't get/start tasks when TaskManager is locked!") task_cls = self.tasks.get(task_name) if mod_options: mod_options = mod_options.copy(deep=True) else: mod_options = ModuleOptions() if task_cls is not None: # We found the task, we can instantiate it. if isinstance(task_name, SupportedMethod): mod_options = mod_options.copy(update={"model_contract_method_name": task_name}) task: DaskModule = task_cls( dataset_split_name=dataset_split_name, config=self.config.copy(deep=True), mod_options=mod_options, ) # Check if this task already exist. key = task.task_id task = self.current_tasks.setdefault(key, task) is_expired = isinstance(task, ExpirableMixin) and task.is_expired(last_update) if task.should_be_started() or is_expired: if dependencies is not None: dependencies = [d for d in dependencies if not d.done()] task.start_task_on_dataset_split(self.client, dependencies=dependencies) return key, task else: log.error("Task not found!", name=task_name) return "", None def get_custom_task( self, task_name: SupportedTask, custom_query: Dict[str, Any], mod_options: Optional[ModuleOptions] = None, ) -> Tuple[str, Optional[DaskModule]]: """Get the task `name` run on a custom query. It will spawn the task if not there. Args: task_name: Name of the task. custom_query: Query fed to the Module. mod_options: Options for the module. Returns: Key and task. Raises: TaskManagerLockedException if the task manager is locked """ if self._is_locked: raise TaskManagerLockedException("Can't get/start tasks when TaskManager is locked!") task_cls: Optional[Callable] = self.tasks.get(task_name) mod_options = mod_options or ModuleOptions() mod_options.model_contract_method_name = ( task_name if isinstance(task_name, SupportedMethod) else None ) if task_cls is not None: # We found the task, we can instantiate it. task: DaskModule = task_cls( dataset_split_name=DatasetSplitName.eval, # Placeholder config=self.config.copy(deep=True), mod_options=mod_options, ) # Check if this task already exist. key = task.custom_query_task_id(custom_query) task = self.current_tasks.setdefault(key, task) # Always start task task.start_task(self.client, custom_query) return key, task else: log.warning("Task not found!", name=task_name) return "", None def status(self): """Utils method to get the status of everything.""" cluster = (self.cluster.workers,) return { 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30,997	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_perturbation_testing/test_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. import re from azimuth.modules.perturbation_testing.perturbation_testing import PerturbationTestingModule from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions from azimuth.types.perturbation_testing import ( PerturbationTestClass, PerturbationTestFailureReason, PerturbationTestFamily, PerturbationTestName, PerturbationTestType, PerturbationType, PerturbedUtteranceResult, ) from azimuth.types.tag import SmartTag from azimuth.utils.ml.perturbation_functions import ( get_utterances_diff, remove_or_add_contractions, remove_or_add_final_punctuation, typo, ) from azimuth.utils.ml.perturbation_test import PerturbationTest from tests.utils import get_table_key def test_perturbation_testing(simple_text_config): mod = PerturbationTestingModule( DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=0, indices=[0, 1]), ) res = mod.compute_on_dataset_split() assert len(res) == 2 # filter by punctuation perturbation tests. punctuation_test_results = [ list( filter(lambda test_res: test_res.family == PerturbationTestFamily.punctuation, utt_res) ) for utt_res in res ] assert len(punctuation_test_results[0]) > 0 # Check that different punctuation tests create different confidences. assert all( not any(utt_res_punctuation[0].confidence == v.confidence for v in utt_res_punctuation[1:]) for utt_res_punctuation in punctuation_test_results ) # filter tests that failed because of the prediction confidence threshold. failed_threshold_test_results = [ list( filter( lambda test_res: test_res.failure_reason == PerturbationTestFailureReason.PredConfThreshold, utt_res, ) ) for utt_res in res ] assert len(failed_threshold_test_results) > 0 assert all( all(v.confidence_delta > 0.005 for v in utt_res_failed) for utt_res_failed in failed_threshold_test_results ) num_suf_neutrals = len(mod.config.behavioral_testing.neutral_token.suffix_list) num_pre_neutrals = len(mod.config.behavioral_testing.neutral_token.prefix_list) # Assert the number of generated test are correct # 7 is for 4 punctuation tests + 3 typo tests. # >= is because contraction tests don't always apply. assert len(res[0]) >= num_suf_neutrals + num_pre_neutrals + 7 def test_typo_deterministic(simple_text_config): mod = PerturbationTestingModule( DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=0), ) typo_test_case = PerturbationTest( name=PerturbationTestName.typos, family=PerturbationTestFamily.fuzzy_matching, test_fn=typo, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=mod.config.behavioral_testing.fuzzy_matching.threshold, expect_flip=False, ) batch_to_try = { "utterance": ["Let's see if it is deterministic!"], "label": [1], DatasetColumn.row_idx: [0], } first_try = mod.get_test_results( typo_test_case, batch=batch_to_try, ) # we have 3 typo tests assert len(first_try[0]) == 3 second_try = mod.get_test_results( typo_test_case, batch=batch_to_try, ) assert all( [ first.perturbed_utterance[0] == sec.perturbed_utterance[0] for first, sec in zip(first_try[0], second_try[0]) ] ) def test_typo_hyphens(simple_text_config): original = "what's the total I've spent on shoes this month?" perturbed_utterance_details = typo(original, simple_text_config) for perturbed_utterance_detail in perturbed_utterance_details: assert ( len(perturbed_utterance_detail.perturbations) == simple_text_config.behavioral_testing.typo.nb_typos_per_utterance and "'" not in perturbed_utterance_detail.perturbations ) def test_typo_aug_fixes(simple_text_config): # Many opportunities for nac to alter: quote, apostrophe, spacing around punctuation original = "Allô ! Je l’adore. Est-ce que c'est $42?" re_punctuation = re.compile(r"(\w+)") punctuation_original = re_punctuation.split(original)[::2] perturbed_utterance_details = typo(original, simple_text_config) perturbed_utterance = perturbed_utterance_details[0].perturbed_utterance punctuation_perturbed = re_punctuation.split(perturbed_utterance)[::2] assert punctuation_original == punctuation_perturbed, ( f"Augmenter has introduced spacing or punctuation errors.\noriginal: " f"{original}\nperturbed: {perturbed_utterance}" ) perturbed_utterance_details_b = remove_or_add_final_punctuation(original, ".") perturbed_utterance_b = perturbed_utterance_details_b[0].perturbed_utterance assert perturbed_utterance_b[-2] != " ", "Penultimate space incorrectly kept." def test_if_failed(simple_text_config): FAILED = True tests = [ # Same pred, test not failed ((1, 0.9), (1, 0.9), 0.01, False, (not FAILED, PerturbationTestFailureReason.NA, 0.00)), # Confidence diff is not big enough. ((1, 0.9), (1, 0.89), 0.01, False, (not FAILED, PerturbationTestFailureReason.NA, 0.01)), # Difference pred ((1, 0.9), (0, 0.9), 0.01, False, (FAILED, PerturbationTestFailureReason.PredClass, None)), # Change in confidence is too large ( (1, 0.9), (1, 0.6888), 0.01, False, (FAILED, PerturbationTestFailureReason.PredConfThreshold, 0.2112), ), # Same pred, expected to flip ((1, 0.9), (1, 0.9), 0.01, True, (FAILED, PerturbationTestFailureReason.PredClass, None)), ] for test, original, threshold, expect_flip, should_fail in tests: test_case = PerturbationTest( name=PerturbationTestName.ending_period, family=PerturbationTestFamily.punctuation, test_fn=lambda x: x, test_class=PerturbationTestClass.robustness, test_type=PerturbationTestType.invariant, conf_delta_threshold=threshold, expect_flip=expect_flip, ) assert test_case.is_failed(test, original) == should_fail def test_get_modifications(): utterance = "hehe, I should write this test." new_sents = ["hehe, 1 should wrile this test.", "hehe,I should writ this test"] modifs = [get_utterances_diff(utterance, new_sen) for new_sen in new_sents] assert set(modifs[0]) == {"1", "wrile"} assert set(modifs[1]) == {"hehe,I", "writ", "test"} def test_contractions(simple_text_config): utterance = "I shouldn't write this test, but I am doing it anyway." results = remove_or_add_contractions(utterance, simple_text_config) assert ( results[0].perturbed_utterance == "I should not write this test, but I am doing it anyway." ) assert "should" in results[0].perturbations and "not" in results[0].perturbations assert results[1].perturbed_utterance == "I shouldn't write this test, but I'm doing it anyway." assert "I'm" in results[1].perturbations def test_save(simple_text_config): mod = PerturbationTestingModule( DatasetSplitName.eval, config=simple_text_config, mod_options=ModuleOptions(pipeline_index=0), ) dm = mod.get_dataset_split_manager() tests = [PerturbationTestName.ending_period, PerturbationTestName.typos] test_families = [PerturbationTestFamily.punctuation, PerturbationTestFamily.fuzzy_matching] res = [ [ PerturbedUtteranceResult( name=t, perturbed_utterance="hello", prediction=idx % 2, confidence=0.5, confidence_delta=0.1, perturbations=[], perturbation_type=PerturbationType.Insertion, failure_reason=PerturbationTestFailureReason.PredClass, failed=((test_id + idx) % 2) == 0, description="", family=tf, ) for test_id, (t, tf) in enumerate(zip(tests, test_families)) ] for idx in range(dm.num_rows) ] mod.save_result(res, dm) simple_table_key = get_table_key(simple_text_config) assert any(dm.get_dataset_split(simple_table_key)[SmartTag.failed_fuzzy_matching]) assert any(dm.get_dataset_split(simple_table_key)[SmartTag.failed_punctuation])	{"/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", 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["/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"]}
30,998	ServiceNow/azimuth	refs/heads/main	/tests/test_documentation.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 dataclasses import importlib import inspect import os import re from itertools import compress from pydantic.main import BaseModel import azimuth from azimuth.modules.base_classes import Module ROOT_MODULE = os.path.dirname(azimuth.__file__) modules = ["azimuth"] # We do not force these functions to be compliant. accepted_name = [ "JSONResponseIgnoreNan", "compute_on_dataset_split", "save_result", "create_histogram_mean_std", # False positive in the exception: arguments order is wrong. ] # We do not force these modules to be compliant. accepted_module = [ # TODO investigate: this module used to not cause errors in CI, and it still doesn't locally. "azimuth.routers.utterances", "azimuth.utils.ml.third_parties.contractions", "azimuth.utils.ml.third_parties.transformations_types", ] # Functions or classes with less than 'MIN_CODE_SIZE' lines can be ignored MIN_CODE_SIZE = 10 def accepted_classtype(cls): return issubclass(cls, BaseModel) or dataclasses.is_dataclass(cls) def handle_class_init(name, member, exceptions): init_args = [ arg for arg in list(inspect.signature(member.__init__).parameters.keys()) if arg not in ["self", "args", "kwargs"] ] try: assert_args_presence(init_args, member.__doc__, member, name, exceptions) except Exception as e: raise ValueError(f"An exception was raised when validating {name}: {e}") def handle_class(name, member, exceptions): if is_accepted(name, member) or accepted_classtype(member): return if member.__doc__ is None and not member_too_small(member) and not issubclass(member, Module): exceptions.append( ( "{} class doesn't have any documentation".format(name), member.__module__, inspect.getmodule(member).__file__, ) ) if not issubclass(member, Module): handle_class_init(name, member, exceptions) for n, met in inspect.getmembers(member): if inspect.isfunction(met) and not n.startswith("_"): handle_method(n, met, exceptions) def handle_function(name, member, exceptions): if is_accepted(name, member) or member_too_small(member): # We don't need to check this one. return doc = member.__doc__ if doc is None: exceptions.append( ( "{} function doesn't have any documentation".format(str(member)), member.__module__, inspect.getmodule(member).__file__, ) ) return args = list(filter(lambda k: k != "self", inspect.signature(member).parameters.keys())) try: assert_function_style(name, member, doc, args, exceptions) assert_args_presence(args, doc, member, name, exceptions) assert_doc_style(name, member, doc, args, exceptions) except Exception as e: raise ValueError(f"An exception was raised when validating {name}: {e}") def assert_doc_style(name, member, doc, args, exceptions): lines = doc.split("\n") first_line = lines[0] if first_line != "" and first_line.strip()[-1] != ".": exceptions.append(("{} first line should end with a '.'".format(member), member.__module__)) def assert_function_style(name, member, doc, args, exceptions): code = inspect.getsource(member) has_return = re.findall(r"\sreturn \S+", code, re.MULTILINE) if has_return and "Returns:" not in doc: innerfunction = [ inspect.getsource(x) for x in member.__code__.co_consts if inspect.iscode(x) ] return_in_sub = [ ret for code_inner in innerfunction for ret in re.findall(r"\sreturn \S+", code_inner, re.MULTILINE) ] if len(return_in_sub) < len(has_return): exceptions.append(("{} needs a 'Returns:' section".format(member), member.__module__)) has_raise = re.findall(r"^\sraise \S+", code, re.MULTILINE) if ( has_raise and "Raises:" not in doc and not any(["NotImplementedError" in row for row in has_raise]) ): innerfunction = [ inspect.getsource(x) for x in member.__code__.co_consts if inspect.iscode(x) ] raise_in_sub = [ ret for code_inner in innerfunction for ret in re.findall(r"\sraise \S+", code_inner, re.MULTILINE) ] if len(raise_in_sub) < len(has_raise): exceptions.append(("{} needs a 'Raises:' section".format(member), member.__module__)) if len(args) > 0 and "Args:" not in doc: exceptions.append(("{} needs a 'Args' section".format(member), member.__module__)) assert_blank_before(name, member, doc, ["Args:", "Raises:", "Returns:"], exceptions) def assert_blank_before(name, member, doc, keywords, exceptions): doc_lines = [x.strip() for x in doc.split("\n")] for keyword in keywords: if keyword in doc_lines: index = doc_lines.index(keyword) if doc_lines[index - 1] != "": exceptions.append( ( "{} '{}' should have a blank line above.".format(member, keyword), member.__module__, ) ) def is_accepted(name, member): if "azimuth" not in str(member.__module__): return True return name in accepted_name or member.__module__ in accepted_module def member_too_small(member): code = inspect.getsource(member).split("\n") return len(code) < MIN_CODE_SIZE def assert_args_presence(args, doc, member, name, exceptions): if len(args) == 0 or doc is None: return args_not_in_doc = [arg not in doc for arg in args] if any(args_not_in_doc): exceptions.append( ( "{} {} arguments are not present in documentation ".format( name, list(compress(args, args_not_in_doc)) ), member.__module__, ) ) return words = doc.replace("", "").replace(":", " ").split() # Check arguments styling styles = [re.search(r"^\s({}):.$".format(arg), doc, re.MULTILINE) is None for arg in args] if any(styles): exceptions.append( ( "{} {} are not styled properly; should be 'argument: documentation'".format( name, list(compress(args, styles)) ), member.__module__, ) ) # Check arguments order if "Args" in words: words = words[words.index("Args") :] indexes = [words.index(arg) for arg in args] if indexes != sorted(indexes): exceptions.append( ( "{} arguments order is different from the documentation".format(name), member.__module__, ) ) def handle_method(name, member, exceptions): if name in accepted_name or member.__module__ in accepted_module: return handle_function(name, member, exceptions) def handle_module(mod, exceptions, history): history.append(mod) for name, mem in inspect.getmembers(mod): if inspect.isclass(mem): handle_class(name, mem, exceptions) elif inspect.isfunction(mem): handle_function(name, mem, exceptions) elif inspect.ismodule(mem) and (hasattr(mem, "__file__") and ROOT_MODULE in mem.__file__): # Only test azimuth' modules if mem not in history: handle_module(mem, exceptions, history) def test_doc(): exceptions = [] history = [] for module in modules: mod = importlib.import_module(module) handle_module(mod, exceptions, history) exceptions = set(v[0] for v in exceptions) assert not exceptions if __name__ == "__main__": test_doc()	{"/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", 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30,999	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/object_loader.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 importlib import inspect import os import subprocess import sys import structlog from azimuth.config import AzimuthValidationError, CustomObject log = structlog.get_logger(__file__) def install(remote: str): """ Install a Python package. Args: remote: Can be a path to a folder with a setup.py or a package name in pip or a public git repository remote. Raises: FileNotFoundError if we can't find the remote locally or on Pypi. """ if os.path.exists(remote): if os.path.isfile(os.path.join(remote, "setup.py")): # Install locally log.info(subprocess.check_call(["pip", "install", remote])) elif os.path.isfile(os.path.join(remote, "requirements.txt")): # Install deps locally log.info( subprocess.check_call( [ "pip", "install", "-t", remote, "-r", os.path.join(remote, "requirements.txt"), ] ) ) # In any case, add the repo the system path. sys.path.insert(0, remote) else: # We assume that this is a package try: subprocess.check_call([sys.executable, "-m", "pip", "install", remote]) except subprocess.CalledProcessError: # Was probably a misspecified folder raise AzimuthValidationError(f"Can't find remote {repr(remote)} locally or on Pypi.") importlib.invalidate_caches() def load_custom_object( value: CustomObject, reject=None, allow_install_package=True, force_kwargs=False, kwargs ): """ Load an object dynamically and recursively. Args: value: Custom objects to instantiate. reject: List of `kwargs` to remove from the func call. allow_install_package: If True, will install missing packages. force_kwargs: If True will send all supplied kwargs to the class. This can break the supplied class if they don't accept extra parameters in kwargs. kwargs: Args to add to the function. Returns: Object """ if value.remote and allow_install_package: install(value.remote) return load_class(kwargs, reject, value, force_kwargs=force_kwargs) def get_class_init_varnames(cls): # We check if func.__init__ is overloaded, otherwise func.__init__ has no attribute __code__. return cls.__init__.__code__.co_varnames if cls.__init__ != object.__init__ else [] def load_args(v, kwargs): if isinstance(v, CustomObject): return load_custom_object(v, kwargs) if isinstance(v, list): return [load_args(vi, kwargs) for vi in v] else: return v def load_class(kwargs, reject, value, force_kwargs=False): """ Will recursively load an object. Args: kwargs: additional kwargs for the function. reject: list of arguments you do not want to include. value: A Dict with key `class` where the classname is. force_kwargs: If True will send all supplied kwargs to the class. This can break the supplied class if they don't accept extra parameters in kwargs. Returns: object, the loaded object. """ try: func = load_obj(value.class_name) except (ModuleNotFoundError, AttributeError) as e: raise AzimuthValidationError(f"Invalid class_name {repr(value.class_name)}: {e}") varnames = get_class_init_varnames(func) if inspect.isclass(func) else func.__code__.co_varnames not_present = {k: v for k, v in kwargs.items() if k not in varnames} # Manage kwargs (we override the value if needed here!!) kwargs_ins = {value.kwargs, kwargs} # Argument supplied by us can't modify *kwargs from the method. kwargs_ins = {k: v for k, v in kwargs_ins.items() if k not in not_present or force_kwargs} if reject is not None: kwargs_ins = {k: v for k, v in kwargs.items() if k not in reject} kwargs_ins = {k: load_args(v, kwargs) for k, v in kwargs_ins.items()} try: obj = func(value.args, **kwargs_ins) except Exception as e: raise AzimuthValidationError(e) return obj def load_obj(cls): """Get the function object and load the required modules.""" mod, cls = cls.rsplit(".", 1) return getattr(importlib.import_module(mod), cls)	{"/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": 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31,000	ServiceNow/azimuth	refs/heads/main	/azimuth/types/tag.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 Any, Dict, List from azimuth.types import ModuleResponse Tag = str class DataAction(Tag, Enum): relabel = "relabel" augment_with_similar = "augment_with_similar" define_new_class = "define_new_class" merge_classes = "merge_classes" remove = "remove" investigate = "investigate" # Always last for sorting purposes no_action = "NO_ACTION" class SmartTag(Tag, Enum): # Syntax multi_sent = "multiple_sentences" long = "long_utterance" short = "short_utterance" no_subj = "missing_subj" no_obj = "missing_obj" no_verb = "missing_verb" # Similar conflicting_neighbors_train = "conflicting_neighbors_train" conflicting_neighbors_eval = "conflicting_neighbors_eval" no_close_train = "no_close_train" no_close_eval = "no_close_eval" # Perturbation testing failed_punctuation = "failed_punctuation" failed_fuzzy_matching = "failed_fuzzy_matching" # Bayesian model averaging high_epistemic_uncertainty = "high_epistemic_uncertainty" # Prediction correct_top_3 = "correct_top_3" correct_low_conf = "correct_low_conf" # Pipeline Comparison incorrect_for_all_pipelines = "incorrect_for_all_pipelines" pipeline_disagreement = "pipeline_disagreement" # Always last for sorting purposes no_smart_tag = "NO_SMART_TAGS" class SmartTagFamily(str, Enum): extreme_length = "extreme_length" partial_syntax = "partial_syntax" dissimilar = "dissimilar" almost_correct = "almost_correct" behavioral_testing = "behavioral_testing" pipeline_comparison = "pipeline_comparison" uncertain = "uncertain" DATASET_SMART_TAG_FAMILIES = [ SmartTagFamily.extreme_length, SmartTagFamily.partial_syntax, SmartTagFamily.dissimilar, ] PIPELINE_SMART_TAG_FAMILIES = [ SmartTagFamily.almost_correct, SmartTagFamily.behavioral_testing, SmartTagFamily.pipeline_comparison, SmartTagFamily.uncertain, ] ALL_DATA_ACTION_FILTERS: List[Tag] = list(DataAction) ALL_DATA_ACTIONS: List[Tag] = [a for a in ALL_DATA_ACTION_FILTERS if a != DataAction.no_action] ALL_SMART_TAG_FILTERS = list(SmartTag) ALL_SMART_TAGS: List[Tag] = [a for a in ALL_SMART_TAG_FILTERS if a != SmartTag.no_smart_tag] ALL_TAGS = ALL_SMART_TAGS + ALL_DATA_ACTIONS SMART_TAGS_FAMILY_MAPPING: Dict[SmartTagFamily, List[Tag]] = { SmartTagFamily.extreme_length: [ SmartTag.multi_sent, SmartTag.short, SmartTag.long, ], SmartTagFamily.partial_syntax: [ SmartTag.no_verb, SmartTag.no_subj, SmartTag.no_obj, ], SmartTagFamily.dissimilar: [ SmartTag.conflicting_neighbors_train, SmartTag.conflicting_neighbors_eval, SmartTag.no_close_train, SmartTag.no_close_eval, ], SmartTagFamily.almost_correct: [ SmartTag.correct_top_3, SmartTag.correct_low_conf, ], SmartTagFamily.behavioral_testing: [ SmartTag.failed_punctuation, SmartTag.failed_fuzzy_matching, ], SmartTagFamily.pipeline_comparison: [ SmartTag.pipeline_disagreement, SmartTag.incorrect_for_all_pipelines, ], SmartTagFamily.uncertain: [ SmartTag.high_epistemic_uncertainty, ], } ALL_PREDICTION_TAGS: List[Tag] = [ tag for family in PIPELINE_SMART_TAG_FAMILIES for tag in SMART_TAGS_FAMILY_MAPPING[family] ] ALL_STANDARD_TAGS: List[Tag] = list(set(ALL_TAGS) - set(ALL_PREDICTION_TAGS)) class TaggingResponse(ModuleResponse): tags: Dict[Tag, bool] adds: Dict[str, Any]	{"/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": 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31,001	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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. import os from os.path import join as pjoin from typing import List from fastapi import APIRouter, Depends, Query from starlette.responses import FileResponse from azimuth.app import get_config, get_task_manager from azimuth.config import AzimuthConfig from azimuth.modules.perturbation_testing import PerturbationTestingModule from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod from azimuth.types.perturbation_testing import ( PRETTY_PERTURBATION_TYPES, PerturbationTestFailureReason, PerturbationType, PerturbedUtteranceDetailedResult, ) from azimuth.types.task import SaliencyResponse from azimuth.utils.conversion import orjson_dumps from azimuth.utils.routers import get_custom_task_result, require_pipeline_index router = APIRouter() @router.get( "/perturbed_utterances", summary="Get perturbed utterances based on custom utterances.", description="Get perturbed utterances based on custom utterances.", ) def get_perturbed_utterances( utterances: List[str] = Query([], title="Utterances"), config: AzimuthConfig = Depends(get_config), ) -> FileResponse: perturb_testing = PerturbationTestingModule( dataset_split_name=DatasetSplitName.eval, config=config, # Mandatory mod_option, but the module doesn't compute any new prediction in this case. mod_options=ModuleOptions(pipeline_index=0), ) mocked_test_result = [] for perturbation_test in perturb_testing.perturbation_tests: all_perturbed_utterance_details = perturb_testing.generate_perturbations( utterances, perturbation_test=perturbation_test ) perturbation_test_results = [ PerturbedUtteranceDetailedResult( name=perturbation_test.name, description=perturbation_test.description.format( type=PRETTY_PERTURBATION_TYPES[ PerturbationType(perturbed_utterance_detail.perturbation_type) ] ), family=perturbation_test.family, perturbed_utterance=perturbed_utterance_detail.perturbed_utterance, perturbations=perturbed_utterance_detail.perturbations, perturbation_type=perturbed_utterance_detail.perturbation_type, confidence=0.0, confidence_delta=None, failed=False, failure_reason=PerturbationTestFailureReason.NA, prediction="NA", original_prediction="NA", original_confidence=0.0, label="NA", original_utterance=original_utt, ).dict() for original_utt, perturbed_utterance_details in zip( utterances, all_perturbed_utterance_details ) for perturbed_utterance_detail in perturbed_utterance_details ] mocked_test_result += perturbation_test_results pt = pjoin( config.get_project_path(), "azimuth_generate_perturbation_tests.json", ) with open(pt, "w") as f: f.write(orjson_dumps(mocked_test_result).decode()) return FileResponse(pt, filename=os.path.basename(pt)) @router.get( "/saliency", summary="Get saliency for custom utterances.", description="Get saliency for custom utterances.", response_model=List[SaliencyResponse], ) def get_saliency( utterances: List[str] = Query([], title="Utterances"), pipeline_index: int = Depends(require_pipeline_index), task_manager: TaskManager = Depends(get_task_manager), ) -> List[SaliencyResponse]: task_result: List[SaliencyResponse] = get_custom_task_result( SupportedMethod.Saliency, task_manager=task_manager, custom_query={task_manager.config.columns.text_input: utterances}, mod_options=ModuleOptions(pipeline_index=pipeline_index), ) return task_result	{"/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", 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"/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"]}
31,002	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_config.py	import os.path from fastapi import FastAPI from starlette.status import ( HTTP_200_OK, HTTP_400_BAD_REQUEST, HTTP_403_FORBIDDEN, HTTP_500_INTERNAL_SERVER_ERROR, ) from starlette.testclient import TestClient from azimuth.config import SupportedLanguage, config_defaults_per_language from azimuth.types import SupportedModelContract from tests.utils import get_enum_validation_error_msg, is_sorted def test_get_default_config(app: FastAPI): client = TestClient(app) res = client.get("/config/default").json() assert res == { "name": "New project", "dataset": {"class_name": "", "args": [], "kwargs": {}, "remote": None}, "model_contract": "hf_text_classification", "columns": { "text_input": "utterance", "raw_text_input": "utterance_raw", "label": "label", "failed_parsing_reason": "failed_parsing_reason", "persistent_id": "row_idx", }, "rejection_class": "REJECTION_CLASS", "artifact_path": os.path.abspath("cache"), "batch_size": 32, "use_cuda": "auto", "large_dask_cluster": False, "read_only_config": False, "language": "en", "syntax": { "short_utterance_max_word": 3, "long_utterance_min_word": 12, "spacy_model": "en_core_web_sm", "subj_tags": ["nsubj", "nsubjpass"], "obj_tags": ["dobj", "pobj", "obj"], }, "dataset_warnings": { "min_num_per_class": 20, "max_delta_class_imbalance": 0.5, "max_delta_representation": 0.05, "max_delta_mean_words": 3.0, "max_delta_std_words": 3.0, }, "similarity": { "faiss_encoder": "all-MiniLM-L12-v2", "conflicting_neighbors_threshold": 0.9, "no_close_threshold": 0.5, }, "pipelines": [ { "name": "Pipeline_0", "model": {"class_name": "", "args": [], "kwargs": {}, "remote": None}, "postprocessors": [ { "class_name": "azimuth.utils.ml.postprocessing.Thresholding", "args": [], "kwargs": {"threshold": 0.5}, "remote": None, "threshold": 0.5, } ], } ], "uncertainty": {"iterations": 1, "high_epistemic_threshold": 0.1}, "saliency_layer": "auto", "behavioral_testing": { "neutral_token": { "threshold": 1.0, "suffix_list": ["pls", "please", "thank you", "appreciated"], "prefix_list": ["pls", "please", "hello", "greetings"], }, "punctuation": {"threshold": 1.0}, "fuzzy_matching": {"threshold": 1.0}, "typo": {"threshold": 1.0, "nb_typos_per_utterance": 1}, "seed": 300, }, "metrics": { "Accuracy": { "class_name": "datasets.load_metric", "args": [], "kwargs": {"path": "accuracy"}, "remote": None, "additional_kwargs": {}, }, "Precision": { "class_name": "datasets.load_metric", "args": [], "kwargs": {"path": "precision"}, "remote": None, "additional_kwargs": {"average": "weighted"}, }, "Recall": { "class_name": "datasets.load_metric", "args": [], "kwargs": {"path": "recall"}, "remote": None, "additional_kwargs": {"average": "weighted"}, }, "F1": { "class_name": "datasets.load_metric", "args": [], "kwargs": {"path": "f1"}, "remote": None, "additional_kwargs": {"average": "weighted"}, }, }, } def test_get_default_config_french(app: FastAPI): client = TestClient(app) res = client.get("/config/default?language=fr").json() defaults = config_defaults_per_language[SupportedLanguage.fr] assert res["language"] == "fr" assert res["behavioral_testing"]["neutral_token"]["prefix_list"] == defaults.prefix_list assert res["behavioral_testing"]["neutral_token"]["suffix_list"] == defaults.suffix_list assert res["syntax"]["spacy_model"] == defaults.spacy_model assert res["syntax"]["subj_tags"] == defaults.subj_tags assert res["syntax"]["obj_tags"] == defaults.obj_tags assert res["similarity"]["faiss_encoder"] == defaults.faiss_encoder def test_get_config_history(app: FastAPI): client = TestClient(app) resp = client.get("/config/history") assert resp.status_code == HTTP_200_OK, resp.text history = resp.json() assert len(history) >= 1 assert is_sorted([item["created_on"] for item in history]) # The hash has 128 bits and is represented as a string of hex characters (4 bits each). assert all(len(item["hash"]) == 128 / 4 for item in history) def test_get_config(app: FastAPI): client = TestClient(app) res = client.get("/config").json() assert res == { "artifact_path": "/tmp/azimuth_test_cache", "batch_size": 16, "behavioral_testing": { "fuzzy_matching": {"threshold": 1.0}, "neutral_token": { "prefix_list": ["pls", "hello"], "suffix_list": ["pls", "thanks"], "threshold": 1.0, }, "punctuation": {"threshold": 1.0}, "seed": 300, "typo": {"nb_typos_per_utterance": 1, "threshold": 0.005}, }, "columns": { "failed_parsing_reason": "failed_parsing_reason", "label": "label", "persistent_id": "row_idx", "raw_text_input": "utterance_raw", "text_input": "utterance", }, "dataset": { "args": [], "class_name": "tests.test_loading_resources.load_sst2_dataset", "kwargs": {}, "remote": None, }, "dataset_warnings": { "max_delta_mean_words": 3.0, "max_delta_representation": 0.05, "max_delta_std_words": 3.0, "min_num_per_class": 20, "max_delta_class_imbalance": 0.5, }, "language": "en", "large_dask_cluster": False, "metrics": { "Accuracy": { "additional_kwargs": {}, "args": [], "class_name": "datasets.load_metric", "kwargs": {"path": "accuracy"}, "remote": None, }, "F1": { "additional_kwargs": {"average": "weighted"}, "args": [], "class_name": "datasets.load_metric", "kwargs": {"path": "f1"}, "remote": None, }, "Precision": { "additional_kwargs": {"average": "weighted"}, "args": [], "class_name": "datasets.load_metric", "kwargs": {"path": "precision"}, "remote": None, }, "Recall": { "additional_kwargs": {"average": "weighted"}, "args": [], "class_name": "datasets.load_metric", "kwargs": {"path": "recall"}, "remote": None, }, }, "model_contract": "custom_text_classification", "name": "sentiment-analysis", "pipelines": [ { "model": { "args": [], "class_name": "tests.test_loading_resources.config_structured_output", "kwargs": {"num_classes": 2, "threshold": 0.4}, "remote": None, }, "name": "Pipeline_0", "postprocessors": None, } ], "rejection_class": None, "saliency_layer": "auto", "similarity": { "faiss_encoder": "all-MiniLM-L12-v2", "conflicting_neighbors_threshold": 0.9, "no_close_threshold": 0.5, }, "syntax": { "long_utterance_min_word": 12, "short_utterance_max_word": 3, "spacy_model": "en_core_web_sm", "subj_tags": ["nsubj", "nsubjpass"], "obj_tags": ["dobj", "pobj", "obj"], }, "uncertainty": {"high_epistemic_threshold": 0.1, "iterations": 1}, "use_cuda": False, "read_only_config": False, } def test_update_config(app: FastAPI, wait_for_startup_after): client = TestClient(app) initial_config = client.get("/config").json() initial_config_count = len(client.get("/config/history").json()) resp = client.patch("/config", json={"artifact_path": "something/else"}) assert resp.status_code == HTTP_403_FORBIDDEN, resp.text relative_artifact_path = os.path.relpath(initial_config["artifact_path"]) assert relative_artifact_path != initial_config["artifact_path"] resp = client.patch("/config", json={"artifact_path": relative_artifact_path}) assert resp.status_code == HTTP_200_OK, resp.text assert resp.json() == initial_config new_config_count = len(client.get("/config/history").json()) assert new_config_count == initial_config_count resp = client.patch( "/config", json={"model_contract": "file_based_text_classification", "pipelines": None}, ) assert resp.json()["model_contract"] == "file_based_text_classification" get_config = client.get("/config").json() assert get_config["model_contract"] == "file_based_text_classification" assert not get_config["pipelines"] new_config_count = len(client.get("/config/history").json()) assert new_config_count == initial_config_count + 1 # Config Validation Error resp = client.patch("/config", json={"model_contract": "potato"}) assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text assert resp.json()["detail"] == ( f"AzimuthConfig['model_contract']: {get_enum_validation_error_msg(SupportedModelContract)}" ) get_config = client.get("/config").json() assert get_config["model_contract"] == "file_based_text_classification" # Validation Module Error # TODO assert error detail # Should be 400, but during tests, AzimuthValidationError gets wrapped in a MultipleExceptions resp = client.patch( "/config", json={"pipelines": [{"model": {"class_name": "", "remote": "lol"}}]} ) assert resp.status_code == HTTP_500_INTERNAL_SERVER_ERROR, resp.text # TODO assert resp.json()["detail"] == "Can't find remote 'lol' locally or on Pypi." resp = client.patch("/config", json={"pipelines": [{"model": {"class_name": "potato.hair"}}]}) assert resp.status_code == HTTP_500_INTERNAL_SERVER_ERROR, resp.text resp = client.patch( "/config", json={"pipelines": [{"model": {"class_name": "tests.test_loading_resources.hair"}}]}, ) assert resp.status_code == HTTP_500_INTERNAL_SERVER_ERROR, resp.text resp = client.patch( "/config", json={ "pipelines": [ {"model": {"class_name": "tests.test_loading_resources.load_intent_data"}} ] }, ) assert resp.status_code == HTTP_500_INTERNAL_SERVER_ERROR, resp.text get_config = client.get("/config").json() assert not get_config["pipelines"] # Empty update resp = client.patch("/config", json={}) assert resp.status_code == HTTP_200_OK, resp.text assert get_config == client.get("/config").json() loaded_configs = client.get("/config/history").json() assert len(loaded_configs) == new_config_count, "No config should have been saved since." assert loaded_configs[-1]["config"]["model_contract"] == "file_based_text_classification" assert not loaded_configs[-1]["config"]["pipelines"] # Revert config change _ = client.patch("/config", json=initial_config) loaded_configs = client.get("/config/history").json() assert loaded_configs[-1]["config"] == loaded_configs[initial_config_count - 1]["config"] assert loaded_configs[-1]["hash"] == loaded_configs[initial_config_count - 1]["hash"]	{"/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"], 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31,003	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/perturbation_testing/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 collections import defaultdict from typing import Dict, List, Optional, Tuple, cast import numpy as np from datasets import Dataset from azimuth.config import PerturbationTestingConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.modules.base_classes import DatasetResultModule 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 DatasetColumn, DatasetSplitName, ModuleOptions, SupportedMethod from azimuth.types.perturbation_testing import ( PRETTY_PERTURBATION_TYPES, PerturbationTestClass, PerturbationTestFamily, PerturbationTestName, PerturbationTestType, PerturbationType, PerturbedUtteranceDetails, PerturbedUtteranceResult, ) from azimuth.types.tag import SmartTag, Tag from azimuth.types.task import PredictionResponse from azimuth.utils.conversion import flatten from azimuth.utils.ml.perturbation_functions import ( add_all_neutral_tokens, remove_or_add_contractions, remove_or_add_final_period, remove_or_add_final_question_mark, remove_or_add_inside_comma, remove_or_add_inside_period, typo, ) from azimuth.utils.ml.perturbation_test import PerturbationTest from azimuth.utils.ml.seeding import RandomContext from azimuth.utils.validation import assert_not_none class PerturbationTestingModule(DatasetResultModule[PerturbationTestingConfig]): """ This module generates perturbed utterances based on a list of perturbation tests and provides detailed results on the prediction results for the new perturbed utterances. Notes: To add a new test: 1. In `perturbation_functions.py`, make a new function with the same signature as the `typo` one. 2. Add your test in self.perturbation_test with your function. """ required_mod_options = {"pipeline_index"} # This module doesn't call self.get_model() but requires the model (predict_task.compute(batch)) # TODO Find a more robust way to determine when modules require models. worker = Worker.model def __init__( self, dataset_split_name: DatasetSplitName, config: PerturbationTestingConfig, mod_options: Optional[ModuleOptions] = None, ): super().__init__(dataset_split_name, config, mod_options) self.perturbation_testing_config = assert_not_none(self.config.behavioral_testing) self.perturbation_tests = [ PerturbationTest( name=PerturbationTestName.neutral_token, description="{type} a neutral token to the utterance.", test_fn=add_all_neutral_tokens, family=PerturbationTestFamily.fuzzy_matching, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.neutral_token.threshold, expect_flip=False, ), PerturbationTest( name=PerturbationTestName.question_mark, description="{type} question mark at the end of the utterance.", test_fn=remove_or_add_final_question_mark, family=PerturbationTestFamily.punctuation, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.punctuation.threshold, expect_flip=False, ), PerturbationTest( name=PerturbationTestName.ending_period, description="{type} period at the end of the utterance.", test_fn=remove_or_add_final_period, family=PerturbationTestFamily.punctuation, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.punctuation.threshold, expect_flip=False, ), PerturbationTest( name=PerturbationTestName.inner_comma, description="{type} comma inside the utterance.", test_fn=remove_or_add_inside_comma, family=PerturbationTestFamily.punctuation, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.punctuation.threshold, expect_flip=False, ), PerturbationTest( name=PerturbationTestName.inner_period, description="{type} period inside the utterance.", test_fn=remove_or_add_inside_period, family=PerturbationTestFamily.punctuation, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.punctuation.threshold, expect_flip=False, ), PerturbationTest( name=PerturbationTestName.typos, description="{type} characters in the utterance to create typos.", test_fn=typo, family=PerturbationTestFamily.fuzzy_matching, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.typo.threshold, expect_flip=False, ), PerturbationTest( name=PerturbationTestName.contractions, description="{type} expressions in the utterance.", family=PerturbationTestFamily.fuzzy_matching, test_fn=remove_or_add_contractions, test_type=PerturbationTestType.invariant, test_class=PerturbationTestClass.robustness, conf_delta_threshold=self.perturbation_testing_config.fuzzy_matching.threshold, expect_flip=False, ), ] def get_preds(self, batch: Dataset) -> List[Tuple[int, float]]: """Calls the prediction task to get new predictions for the perturbed utterances. Args: batch: batch of utterances to run inference on. Returns: list of predictions Tuple[result, confidence]. """ # INFO: creating this object cost nothing. predict_task = model_contract_task_mapping( dataset_split_name=self.dataset_split_name, config=self.config, mod_options=ModuleOptions( model_contract_method_name=SupportedMethod.Predictions, pipeline_index=self.mod_options.pipeline_index, ), ) result: List[PredictionResponse] = predict_task.compute(batch) top_probs = [float(np.max(record.postprocessed_output.probs)) for record in result] preds = [int(record.postprocessed_output.preds) for record in result] return list(zip(preds, top_probs)) def get_test_results( self, perturbation_test: PerturbationTest, batch: Dataset ) -> Dict[int, Optional[List[PerturbedUtteranceResult]]]: """ Calculates the confidence on the newly perturbed utterances, checks whether the test fails and populates the resulting list. Args: perturbation_test: Perturbation test to create. batch: The original utterances. Returns: results as Dict[int, List[PerturbedUtteranceResult]]. For each perturbed_utterance, we have a list of test. A list may be empty if a test case is not available (ex: contractions) """ results: Dict[int, Optional[List[PerturbedUtteranceResult]]] = {} original_preds = self.get_preds(batch=batch) original_labels = batch[self.config.columns.label] row_indices = batch[DatasetColumn.row_idx] original_utterances = cast(List[str], batch[self.config.columns.text_input]) # Create perturbed utterances. all_perturbed_utterance_details = self.generate_perturbations( original_utterances, perturbation_test ) formatted_indices: List[int] = sum( [ [row_indices[i]] * len(all_perturbed_utterance_details[i]) for i in range(len(row_indices)) ], [], ) batch_all_perturbations = Dataset.from_dict( { self.config.columns.text_input: flatten( [ [test.perturbed_utterance for test in utterance_tests] for utterance_tests in all_perturbed_utterance_details ] ), self.config.columns.label: flatten( [ [original_labels[idx] for _ in range(len(utterance_tests))] for idx, utterance_tests in enumerate(all_perturbed_utterance_details) ] ), DatasetColumn.row_idx: formatted_indices, } ) perturbed_predictions = ( self.get_preds(batch_all_perturbations) if batch_all_perturbations[DatasetColumn.row_idx] else [] ) perturbed_predictions_per_indices = defaultdict(list) for row_idx, pred in zip(formatted_indices, perturbed_predictions): perturbed_predictions_per_indices[row_idx].append(pred) for idx, (original_pred, perturbed_utterance_details, original_idx) in enumerate( zip(original_preds, all_perturbed_utterance_details, row_indices) ): if perturbed_utterance_details: perturbed_utterance_preds = perturbed_predictions_per_indices[original_idx] # Check if the test has failed or not for each perturbed utterance. test_passed = [ perturbation_test.is_failed(perturbed_utterance_pred, original_pred) for perturbed_utterance_pred in perturbed_utterance_preds ] # Create PerturbedUtteranceResult based on the result. perturbed_utterance_results = [ PerturbedUtteranceResult( name=perturbation_test.name, description=perturbation_test.description.format( type=PRETTY_PERTURBATION_TYPES[ PerturbationType(perturbed_utterance_detail.perturbation_type) ] ), family=perturbation_test.family, perturbed_utterance=perturbed_utterance_detail.perturbed_utterance, perturbations=perturbed_utterance_detail.perturbations, perturbation_type=perturbed_utterance_detail.perturbation_type, confidence=new_conf, confidence_delta=delta, failed=failed, failure_reason=reason, prediction=new_pred, ) for perturbed_utterance_detail, (new_pred, new_conf), ( failed, reason, delta, ) in zip(perturbed_utterance_details, perturbed_utterance_preds, test_passed) ] results[idx] = perturbed_utterance_results else: results[idx] = None return results def generate_perturbations( self, utterances: List[str], perturbation_test: PerturbationTest ) -> List[List[PerturbedUtteranceDetails]]: """Generate new utterances for a perturbation test for a set of utterances. Args: utterances: Original utterances. perturbation_test: Test to run. Returns: Perturbed utterances for each utterance. """ with RandomContext(self.perturbation_testing_config.seed): all_perturbed_utterance_details = [ perturbation_test.test_fn(ut, self.config) for ut in utterances ] return all_perturbed_utterance_details def compute(self, batch: Dataset) -> List[List[PerturbedUtteranceResult]]: # type: ignore """Calculate all perturbation tests for the given batch. Args: batch: The original batch. Returns: List of results for all perturbation tests per provided batch. """ # Get the results on each test on the batch. results_per_test = [self.get_test_results(test, batch) for test in self.perturbation_tests] records = [] for idx in range(len(batch[DatasetColumn.row_idx])): record = [] for perturbed_utterance_results in results_per_test: perturbed_utterance_result = perturbed_utterance_results[idx] if perturbed_utterance_result: # We merge the results of perturbation tests for a perturbed_utterance record.extend(perturbed_utterance_result) # We append the results of the perturbation tests for all utterances in the batch records.append(record) return records def _save_result( # type: ignore self, res: List[List[PerturbedUtteranceResult]], dm: DatasetSplitManager ): """Compute tags related to Perturbation Tests""" tags = {} for idx, test_results in enumerate(res): tag: Dict[Tag, bool] = { SmartTag.failed_fuzzy_matching: any( t.failed for t in test_results if t.family == PerturbationTestFamily.fuzzy_matching ), SmartTag.failed_punctuation: any( t.failed 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31,004	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_class_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 fastapi import FastAPI from starlette.status import HTTP_200_OK from starlette.testclient import TestClient def test_class_overlap_plot_route(app: FastAPI) -> None: client = TestClient(app) resp = client.get("dataset_splits/train/class_overlap/plot") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert data["defaultOverlapThreshold"] is not None assert list(data["plot"].keys()) == ["data", "layout"] def test_class_overlap_route(app: FastAPI) -> None: client = TestClient(app) resp_no_pipeline = client.get("dataset_splits/train/class_overlap") # No pipeline assert resp_no_pipeline.status_code == HTTP_200_OK, resp_no_pipeline.text data_no_pipeline = resp_no_pipeline.json() assert data_no_pipeline == { "classPairs": [ { "sourceClass": "negative", "targetClass": "positive", "overlapScoreTrain": 0.22608695652173913, "pipelineConfusionEval": None, "utteranceCountSourceTrain": 23, "utteranceCountSourceEval": 22, "utteranceCountWithOverlapTrain": 16, }, { "sourceClass": "positive", "targetClass": "negative", "overlapScoreTrain": 0.4421052631578947, "pipelineConfusionEval": None, "utteranceCountSourceTrain": 19, "utteranceCountSourceEval": 20, "utteranceCountWithOverlapTrain": 16, }, ] } resp_pipeline_0 = client.get("dataset_splits/train/class_overlap?pipeline_index=0") assert resp_pipeline_0.status_code == HTTP_200_OK, resp_pipeline_0.text data_pipeline_0 = resp_pipeline_0.json() assert data_pipeline_0 == { "classPairs": [ { "sourceClass": "negative", "targetClass": "positive", "overlapScoreTrain": 0.22608695652173913, "pipelineConfusionEval": 3, "utteranceCountSourceTrain": 23, "utteranceCountSourceEval": 22, "utteranceCountWithOverlapTrain": 16, }, { "sourceClass": "positive", "targetClass": "negative", "overlapScoreTrain": 0.4421052631578947, "pipelineConfusionEval": 1, "utteranceCountSourceTrain": 19, "utteranceCountSourceEval": 20, "utteranceCountWithOverlapTrain": 16, }, ] }	{"/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", 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31,005	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_perturbation_testing/test_perturbation_testing_summary.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.modules.perturbation_testing import ( PerturbationTestingMergedModule, PerturbationTestingModule, PerturbationTestingSummaryModule, ) from azimuth.types import DatasetSplitName, ModuleOptions from tests.utils import get_tiny_text_config_one_ds_name def test_perturbation_testing_summary(tiny_text_config): mod = PerturbationTestingMergedModule( dataset_split_name=DatasetSplitName.all, config=tiny_text_config, mod_options=ModuleOptions(pipeline_index=0), ) [res] = mod.compute_on_dataset_split() assert 0 <= res.eval_failure_rate <= 1.0 assert 0 <= res.train_failure_rate <= 1.0 mod_sum = PerturbationTestingSummaryModule( dataset_split_name=DatasetSplitName.all, config=tiny_text_config, mod_options=ModuleOptions(pipeline_index=0), ) res = mod_sum.compute_on_dataset_split()[0].all_tests_summary # Check that all perturbation tests are included in the table (except contractions which is not # always triggered) perturb_testing = PerturbationTestingModule( dataset_split_name=DatasetSplitName.eval, config=tiny_text_config, mod_options=ModuleOptions(pipeline_index=0), ) for test in perturb_testing.perturbation_tests: if test.name != "Contractions": assert any(test.name in r.name for r in res) def test_perturbation_testing_summary_one_ds(tiny_text_config_one_ds): mod = PerturbationTestingMergedModule( dataset_split_name=DatasetSplitName.all, config=tiny_text_config_one_ds, mod_options=ModuleOptions(pipeline_index=0), ) [res] = mod.compute_on_dataset_split() ds_name, other_ds_name = get_tiny_text_config_one_ds_name(tiny_text_config_one_ds) assert ( getattr(res, f"{ds_name}_failure_rate") >= 0.0 and getattr(res, f"{other_ds_name}_failure_rate") == 0.0 ) mod_sum = PerturbationTestingSummaryModule( dataset_split_name=DatasetSplitName.all, config=tiny_text_config_one_ds, mod_options=ModuleOptions(pipeline_index=0), ) res = mod_sum.compute_on_dataset_split()[0] assert all(getattr(t, f"{other_ds_name}_count") == 0 for t in res.all_tests_summary)	{"/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", 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31,006	ServiceNow/azimuth	refs/heads/main	/tests/test_task_manager.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 azimuth.config import SyntaxConfig from azimuth.modules.base_classes import AggregationModule, FilterableModule, Module from azimuth.task_manager import TaskManagerLockedException from azimuth.types import DatasetSplitName, ModuleOptions, SupportedMethod, SupportedModule def test_get_all_task(tiny_text_task_manager): # We can find a task key, mod = tiny_text_task_manager.get_task( SupportedMethod.Inputs, dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(pipeline_index=0, indices=[0]), ) assert mod is not None # The task can be awaited mod.result() # The task history is logged tasks = tiny_text_task_manager.get_all_tasks_status("Inputs") assert len(tasks) == 1 # We can make a new task! key, mod = tiny_text_task_manager.get_task( SupportedModule.SyntaxTagging, dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(indices=[0]), ) assert mod is not None # The length stays the same assert len(tiny_text_task_manager.get_all_tasks_status("Inputs")) == 1 assert len(tiny_text_task_manager.get_all_tasks_status("SyntaxTagging")) == 1 assert len(tiny_text_task_manager.get_all_tasks_status(task=None)) == 2 # We can get info on the cluster info = tiny_text_task_manager.status() assert "cluster" in info # If we request something that does not exists, it is None key, mod = tiny_text_task_manager.get_task("allo", dataset_split_name=DatasetSplitName.eval) assert mod is None def get_module_data(simple_text_config): # Dummy module with same config and dataset split. mod = Module(DatasetSplitName.eval, simple_text_config) return ( len(mod.artifact_manager.dataset_split_managers_mapping) > 0, len(mod.artifact_manager.models_mapping) > 0, ) def test_expired_task(tiny_text_task_manager, tiny_text_config): class ExpirableModule(FilterableModule[SyntaxConfig]): def compute(self, batch): return ["ExpirableModule"] class NotExpirableModule(AggregationModule[SyntaxConfig]): def compute(self, batch): return ["NotExpirableModule"] current_update = time.time() tiny_text_task_manager.register_task("ExpirableModule", ExpirableModule) tiny_text_task_manager.register_task("NotExpirableModule", NotExpirableModule) _, not_expirable_task = tiny_text_task_manager.get_task( "NotExpirableModule", dataset_split_name=DatasetSplitName.eval, last_update=current_update, ) # Get an expirable task _, expirable_task = tiny_text_task_manager.get_task( "ExpirableModule", dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(pipeline_index=0), last_update=current_update, ) assert not not_expirable_task.done() and not expirable_task.done() _ = expirable_task.wait(), not_expirable_task.wait() # If we don't change the time, the task are cached _, not_expirable_task = tiny_text_task_manager.get_task( "NotExpirableModule", dataset_split_name=DatasetSplitName.eval, last_update=current_update, ) # Get an expirable task _, expirable_task = tiny_text_task_manager.get_task( "ExpirableModule", dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(pipeline_index=0), last_update=current_update, ) assert not_expirable_task.done() and expirable_task.done() # If we update the dataset_split, the expirable task will be recomputed current_update = time.time() _, not_expirable_task = tiny_text_task_manager.get_task( "NotExpirableModule", dataset_split_name=DatasetSplitName.eval, last_update=current_update, ) # Get an expirable task _, expirable_task = tiny_text_task_manager.get_task( "ExpirableModule", dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(pipeline_index=0), last_update=current_update, ) assert not_expirable_task.done() and not expirable_task.done() def test_lock(tiny_text_task_manager): assert not tiny_text_task_manager.is_locked # Can't unlock a TaskManager that is Unlock with pytest.raises(ValueError): tiny_text_task_manager.unlock() tiny_text_task_manager.lock() # Can't lock a TaskManager that is lock with pytest.raises(ValueError): tiny_text_task_manager.lock() # Cant schedule task: with pytest.raises(TaskManagerLockedException): tiny_text_task_manager.get_task( SupportedMethod.Predictions, dataset_split_name=DatasetSplitName.eval, ) tiny_text_task_manager.unlock() _ = tiny_text_task_manager.get_task( SupportedMethod.Predictions, dataset_split_name=DatasetSplitName.eval, mod_options=ModuleOptions(pipeline_index=0, indices=[0, 1]), ) def test_custom_query(tiny_text_task_manager): _, pred_task = tiny_text_task_manager.get_custom_task( SupportedMethod.Predictions, custom_query={ tiny_text_task_manager.config.columns.text_input: ["hello, this is fred"], tiny_text_task_manager.config.columns.label: [-1], }, mod_options=ModuleOptions(pipeline_index=0), ) assert not pred_task.done() result = pred_task.result() assert len(result) == 1 # Wait for callbacks pred_task.wait() # Check that we have the same module _, pred_task2 = tiny_text_task_manager.get_custom_task( SupportedMethod.Predictions, custom_query={ tiny_text_task_manager.config.columns.text_input: ["hello, this is fred"], tiny_text_task_manager.config.columns.label: [-1], }, mod_options=ModuleOptions(pipeline_index=0), ) assert pred_task2 is pred_task	{"/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", 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31,007	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/base_classes/aggregation_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. from abc import ABC from typing import List from datasets import Dataset from azimuth.modules.base_classes import ConfigScope, ExpirableMixin, Module from azimuth.types import DatasetSplitName, ModuleResponse from azimuth.utils.dataset_operations import filter_dataset_split class AggregationModule(Module[ConfigScope], ABC): """Same as Module, but caching is done over a set of indices.""" def get_caching_indices(self) -> List[int]: return [-1] # Aggregation Module cache on one index. def compute(self, batch: Dataset) -> List[ModuleResponse]: raise NotImplementedError("AggregationModule should not implement compute!") class ComparisonModule(AggregationModule[ConfigScope], ABC): """Aggregation Module that works on dataset split all.""" allowed_splits = {DatasetSplitName.all} class FilterableModule(AggregationModule[ConfigScope], ExpirableMixin, ABC): """Filterable Module are affected by filters in mod options.""" required_mod_options = {"pipeline_index"} optional_mod_options = {"filters", "without_postprocessing"} def get_dataset_split(self, name: DatasetSplitName = None) -> Dataset: """Get the specified dataset_split, filtered according to mod_options. Args: name: Which dataset_split to get. Returns: The loaded dataset_split. 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31,008	ServiceNow/azimuth	refs/heads/main	/azimuth/types/general/modules.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 SupportedTask(str, Enum): pass class SupportedModelContract(SupportedTask): hf_text_classification = "hf_text_classification" file_based_text_classification = "file_based_text_classification" custom_text_classification = "custom_text_classification" class SupportedMethod(SupportedTask): Inputs = "Inputs" Predictions = "Predictions" PostProcess = "PostProcess" Saliency = "Saliency" class SupportedModule(SupportedTask): ClassOverlap = "ClassOverlap" ConfidenceBinIndex = "ConfidenceBinIndex" ConfidenceHistogram = "ConfidenceHistogram" ConfusionMatrix = "ConfusionMatrix" DatasetWarnings = "DatasetWarnings" FAISS = "FAISS" Metrics = "Metrics" MetricsPerFilter = "MetricsPerFilter" NeighborsTagging = "NeighborsTagging" Outcome = "Outcome" OutcomeCountPerFilter = "OutcomeCountPerFilter" OutcomeCountPerThreshold = "OutcomeCountPerThreshold" PerturbationTesting = "PerturbationTesting" PerturbationTestingMerged = "PerturbationTestingMerged" PerturbationTestingSummary = "PerturbationTestingSummary" PredictionComparison = "PredictionComparison" SyntaxTagging = "SyntaxTagging" TokensToWords = "TokensToWords" TopWords = "TopWords" Validation = "Validation"	{"/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", 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31,009	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_model_performance/test_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 fastapi import FastAPI from starlette.status import HTTP_200_OK, HTTP_400_BAD_REQUEST from starlette.testclient import TestClient def test_get_outcome_count_per_threshold(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/outcome_count/per_threshold?pipeline_index=0") assert resp.status_code == HTTP_400_BAD_REQUEST, resp.text assert resp.json()["detail"] == "Postprocessing is not editable" def test_get_outcome_count_per_filter(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_splits/eval/outcome_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 def test_outcome_count_empty_filters(app: FastAPI): client = TestClient(app) resp = client.get( "/dataset_splits/eval/outcome_count/per_filter?pipeline_index=0&utterance=yukongold" ) assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert data["utteranceCount"] == 0 # This used to fail when we were filtering on an empty dataset resp = client.get( "/dataset_splits/eval/outcome_count/per_filter" "?pipeline_index=0&utterance=yukongold&data_action=relabel" ) assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert data["utteranceCount"] == 0	{"/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", 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31,010	ServiceNow/azimuth	refs/heads/main	/azimuth_shr/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. from pathlib import Path import numpy as np import structlog import torch from datasets import ClassLabel, Dataset, DatasetDict, Features, load_dataset from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, Pipeline, TextClassificationPipeline, ) from azimuth.config import AzimuthConfig log = structlog.get_logger(__file__) AZ_SHR_DIR = Path(__file__).parent.resolve() _CACHED_MODELS_DIR = str(AZ_SHR_DIR.joinpath("cached_models")) 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: log.debug(f"Loading text classification model from {checkpoint_path} ...") model = AutoModelForSequenceClassification.from_pretrained( checkpoint_path, cache_dir=_CACHED_MODELS_DIR ) tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, cache_dir=_CACHED_MODELS_DIR) device = 0 if _should_use_cuda(azimuth_config) 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() -> DatasetDict: log.debug("Loading dataset sst2...") datasets = load_dataset("glue", "sst2") datasets["validation"] = datasets["validation"].rename_column("sentence", "utterance") datasets["train"] = datasets["train"].rename_column("sentence", "utterance") # Cutting training set from 60k to 1k. return DatasetDict( {"train": datasets["train"].select(np.arange(1000)), "validation": datasets["validation"]} ) def load_CLINC150_data(full_path, python_loader, train=True, eval=True) -> Dataset: """This dataset contains train, validation and test in one file.""" log.debug("Loading dataset for CLINC150 intent classification") dst = load_dataset(python_loader, data_files={"full": full_path}) if not train: dst.pop("train") if not eval: dst.pop("validation") dst.pop("test") return dst def align_labels(ds_dict: DatasetDict, azimuth_config: AzimuthConfig) -> DatasetDict: features: Features = next(iter(ds_dict.values())).features if not isinstance(features[azimuth_config.columns.label], ClassLabel): # Get all classes from both set and apply the same mapping to every dataset. classes = sorted( list( set(label for ds in ds_dict.values() for label in ds[azimuth_config.columns.label]) ) ) ds_dict = ds_dict.class_encode_column(azimuth_config.columns.label) ds_dict = ds_dict.align_labels_with_mapping( {class_name: i for i, class_name in enumerate(classes)}, azimuth_config.columns.label ) return ds_dict def load_csv(azimuth_config, train_path=None, validation_path=None) -> DatasetDict: data_files = dict() if train_path: data_files["train"] = train_path if validation_path: data_files["validation"] = validation_path ds_dict = load_dataset(path="csv", data_files=data_files) ds_dict = align_labels(ds_dict, azimuth_config) # Remove any empty utterances (which may exist in csv input) for ds_name, ds in ds_dict.items(): ds_dict[ds_name] = ds_dict[ds_name].filter( lambda x: x[azimuth_config.columns.text_input] is not None ) 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": 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31,011	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/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 typing import Dict, List from fastapi import APIRouter, Depends from azimuth.app import get_dataset_split_manager_mapping, get_task_manager from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, SupportedModule from azimuth.types.dataset_warnings import DatasetWarningGroup, DatasetWarningsResponse from azimuth.utils.routers import get_last_update, get_standard_task_result router = APIRouter() @router.get( "", summary="Get dataset warnings.", description="Get the warnings related to the dataset, such as differences between the training " "and the evaluation set.", response_model=List[DatasetWarningGroup], ) def get_dataset_warnings( task_manager: TaskManager = Depends(get_task_manager), dataset_split_managers: Dict[DatasetSplitName, DatasetSplitManager] = Depends( get_dataset_split_manager_mapping ), ) -> List[DatasetWarningGroup]: task_result: DatasetWarningsResponse = get_standard_task_result( SupportedModule.DatasetWarnings, dataset_split_name=DatasetSplitName.all, task_manager=task_manager, last_update=get_last_update(list(dataset_split_managers.values())), )[0] return task_result.warning_groups	{"/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": 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"/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"]}
31,012	ServiceNow/azimuth	refs/heads/main	/tests/test_routers/test_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 fastapi import FastAPI from starlette.status import HTTP_200_OK, HTTP_404_NOT_FOUND from starlette.testclient import TestClient from azimuth.routers.utterances import UtterancesSortableColumn from azimuth.types import DatasetSplitName from azimuth.types.outcomes import OutcomeName from azimuth.types.tag import ALL_DATA_ACTION_FILTERS, ALL_SMART_TAG_FILTERS from tests.utils import get_enum_validation_error_msg def test_openapi(app: FastAPI): # Check that we can generate the openapi.json client = TestClient(app) resp = client.get("/openapi.json") assert resp.status_code == HTTP_200_OK, resp.text def test_get_status(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/status") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() assert data["startupTasksReady"] is True assert all( status in ["not_started", "finished"] for status in data["startupTasksStatus"].values() ) def test_get_dataset_info(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/dataset_info") assert resp.status_code == HTTP_200_OK, resp.text data = resp.json() statuses = data.pop("startupTasks") assert all(status in ["not_started", "finished"] for status in statuses.values()) assert data == { "availableDatasetSplits": {"eval": True, "train": True}, "utteranceCountPerDatasetSplit": {"eval": 42, "train": 42}, "modelContract": "custom_text_classification", "perturbationTestingAvailable": True, "postprocessingEditable": [False], "predictionAvailable": True, "projectName": "sentiment-analysis", "dataActions": ALL_DATA_ACTION_FILTERS, "similarityAvailable": True, "smartTags": ALL_SMART_TAG_FILTERS, } def test_perturbation_summary(app: FastAPI) -> None: client = TestClient(app) resp = client.get("/perturbation_testing_summary?pipeline_index=0").json() assert len(resp) == 2 def test_custom_metrics_definition(app: FastAPI): client = TestClient(app) resp = client.get("/custom_metrics_info").json() assert not {"Precision", "Recall"}.difference(resp.keys()) assert all(v["description"] != "" for v in resp.values()) def test_validation_error(app: FastAPI): client = TestClient(app) resp = client.get("/dataset_splits/c/utterances?outcome=a&outcome=b&sort=d&pipeline_index=0") assert resp.status_code == HTTP_404_NOT_FOUND, resp.text assert resp.json()["detail"] == ( f"query parameter outcome=a: {get_enum_validation_error_msg(OutcomeName)}\n" f"query parameter outcome=b: {get_enum_validation_error_msg(OutcomeName)}\n" f"path parameter dataset_split_name=c: {get_enum_validation_error_msg(DatasetSplitName)}\n" f"query parameter sort=d: {get_enum_validation_error_msg(UtterancesSortableColumn)}" )	{"/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", 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"/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", 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"/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"], 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"/azimuth/types/task.py"]}
31,013	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/exclude_fields_from_cache.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 Collection, Dict, Mapping, TypeVar from pydantic import BaseModel Model = TypeVar("Model", bound=BaseModel) def exclude_fields_from_cache(model: Model): """Exclude pydantic fields with exclude_from_cache=True. Args: model: Model, inheriting from pydantic.BaseModel. Returns: The format expected by pydantic.BaseModel.dict(exclude=...), json() and _iter() """ exclude = dict() for key, field in model.__fields__.items(): if field.field_info.extra.get("exclude_from_cache"): exclude[key] = ... # TODO update pydantic to >=1.9 and change `...` to `True` else: val = getattr(model, key) if isinstance(val, BaseModel): set_if(exclude, key, exclude_fields_from_cache(val)) elif isinstance(val, Collection): sub: Dict = dict() for sub_key, sub_val in val.items() if isinstance(val, Mapping) else enumerate(val): if isinstance(sub_val, BaseModel): set_if(sub, sub_key, exclude_fields_from_cache(sub_val)) set_if(exclude, key, sub) return exclude def set_if(d: Dict, key, val): if val: d[key] = val	{"/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", 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"/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", 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31,014	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/dataset_analysis/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 itertools import os from collections import defaultdict from typing import Dict, List, Optional, Tuple import numpy as np import torch from datasets import Dataset from filelock import FileLock from sentence_transformers import SentenceTransformer from tqdm import tqdm from azimuth.config import SimilarityConfig, SimilarityOptions from azimuth.dataset_split_manager import FEATURE_FAISS, DatasetSplitManager from azimuth.modules.base_classes import DatasetResultModule, IndexableModule from azimuth.modules.base_classes.dask_module import Worker from azimuth.modules.task_execution import get_task_result from azimuth.types import Array, DatasetColumn, DatasetSplitName, ModuleOptions from azimuth.types.similarity_analysis import FAISSResponse from azimuth.types.tag import SmartTag, TaggingResponse from azimuth.utils.validation import assert_not_none NUM_NEIGHBORS = 20 class FAISSModule(IndexableModule[SimilarityConfig]): """Compute the FAISS features for a dataset split.""" worker = Worker.encoder def __init__( self, dataset_split_name: DatasetSplitName, config: SimilarityConfig, mod_options: Optional[ModuleOptions] = None, ): self.encoder = None super().__init__(dataset_split_name, config, mod_options) def get_encoder_name_or_path(self): model_name_or_path = self.config.similarity.faiss_encoder if os.environ.get("TRANSFORMERS_OFFLINE"): home = os.environ.get("SENTENCE_TRANSFORMERS_HOME") model_name_or_path = os.path.join(home, f"sentence-transformers_{model_name_or_path}") return model_name_or_path def get_encoder(self): if self.worker != Worker.encoder: raise RuntimeError("This module cannot load the encoder. Modify self.worker.") if self.encoder is None: with FileLock(os.path.join(self.cache_dir, "st.lock")): self.encoder = SentenceTransformer(self.get_encoder_name_or_path()) return self.encoder def compute_on_dataset_split(self) -> List[FAISSResponse]: # type: ignore ds = self.get_dataset_split() encoder = self.get_encoder() encoded = encoder.encode( ds[self.config.columns.text_input], batch_size=self.config.batch_size, show_progress_bar=True, normalize_embeddings=True, ) self.encoder = None torch.cuda.empty_cache() self.get_dataset_split_manager().add_faiss_index(encoded.astype(np.float32)) return [FAISSResponse(features=f) for f in encoded] def compute(self, batch: Dataset): raise NotImplementedError class NeighborsTaggingModule(DatasetResultModule[SimilarityConfig]): """Compute neighbors for each utterance in a dataset split.""" def compute_on_dataset_split(self) -> List[TaggingResponse]: # type: ignore # Get the features from FAISS for all available splits to make sure they are all computed # and that we can compute neighbors for all splits. features_dict = { split: self._get_features_from_faiss(split) for split in self.available_dataset_splits } dm = self.get_dataset_split_manager() indices = self.get_indices() features = features_dict[self.dataset_split_name] neighbors = self.get_neighbors([features[i] for i in indices]) similarity_config: SimilarityOptions = assert_not_none(self.config.similarity) conflicting_neighbors_tags = defaultdict(list) no_close_tags = defaultdict(list) for split_name, tagname_conflicting_neighbors, tagname_no_close in zip( [DatasetSplitName.train, DatasetSplitName.eval], [SmartTag.conflicting_neighbors_train, SmartTag.conflicting_neighbors_eval], [SmartTag.no_close_train, SmartTag.no_close_eval], ): if split_name not in self.available_dataset_splits: continue conflicting_neighbors_tags[split_name] = self.get_conflicting_neighbors_tag( labels=dm.get_dataset_split().select(indices)[self.config.columns.label], neighbors=neighbors[split_name], dm=self.get_dataset_split_manager(split_name), threshold=similarity_config.conflicting_neighbors_threshold, tag_name=tagname_conflicting_neighbors, ) no_close_tags[split_name] = self.get_no_close_tag( neighbors=neighbors[split_name], threshold=similarity_config.no_close_threshold, tag_name=tagname_no_close, ) results = [] for ( train_neighbors, eval_neighbors, conflicting_neighbors_train, conflicting_neighbors_eval, no_close_train, no_close_eval, ) in itertools.zip_longest( neighbors[DatasetSplitName.train], neighbors[DatasetSplitName.eval], conflicting_neighbors_tags[DatasetSplitName.train], conflicting_neighbors_tags[DatasetSplitName.eval], no_close_tags[DatasetSplitName.train], no_close_tags[DatasetSplitName.eval], ): results.append( TaggingResponse( tags={ SmartTag.conflicting_neighbors_train: False if conflicting_neighbors_train is None else conflicting_neighbors_train[SmartTag.conflicting_neighbors_train], SmartTag.conflicting_neighbors_eval: False if conflicting_neighbors_eval is None else conflicting_neighbors_eval[SmartTag.conflicting_neighbors_eval], SmartTag.no_close_train: False if no_close_train is None else no_close_train[SmartTag.no_close_train], SmartTag.no_close_eval: False if no_close_eval is None else no_close_eval[SmartTag.no_close_eval], }, adds={ DatasetColumn.neighbors_train: train_neighbors or [], DatasetColumn.neighbors_eval: eval_neighbors or [], }, ) ) return results def _get_features_from_faiss(self, ds_split_name: DatasetSplitName) -> List[Array]: """Get Sentence embedding features from FAISS module. Args: ds_split_name: Name of the dataset_split to get Returns: Features for all indices. """ mod = FAISSModule( dataset_split_name=ds_split_name, config=self.config, ) result = get_task_result(mod, List[FAISSResponse]) return [r.features for r in result] def _save_result(self, res: List[TaggingResponse], dm: DatasetSplitManager): # type: ignore """Save tags for nearest neighbors. Args: res: Results from `compute_on_dataset_split` dm: the dataset_split manager used to get `res`. """ # We don't need the key as this is not a "prediction tag" dm.add_tags(dict(zip(itertools.count(), (r.tags for r in res)))) for col_name in res[0].adds.keys(): dm.add_column(key=col_name, features=[r.adds[col_name] for r in res]) @staticmethod def get_conflicting_neighbors_tag( labels: List[int], neighbors: List[List[Tuple[int, float]]], dm: DatasetSplitManager, threshold: float, tag_name: SmartTag, ) -> List[Dict[SmartTag, bool]]: """Compute the `conflicting_neighbors_train` and `conflicting_neighbors_eval` tags. Given a list of examples, determine whether a sufficient number of their neighbors have the same label. If this is not the case, tag them with the relevant smart tag. Args: labels: List of labels per index. neighbors: Neighbors of each index. dm: DatasetSplitManager of the neighbors. threshold: Using this threshold to compare the ratio of items in the neighborhood that belong to the same class. If below this threshold, the tag will be set. tag_name: Name of the tag. """ tags = [] for row_label, row_neighbors in zip(labels, neighbors): neighbor_indices = [neighbor_idx for neighbor_idx, neighbor_similarity in row_neighbors] ngbr_labls = dm.get_dataset_split().select(neighbor_indices)[dm.config.columns.label] # Compute the ratio of neighbours with different label. different_labels_ratio = sum(label != row_label for label in ngbr_labls) / NUM_NEIGHBORS # Set the tag according to the threshold. tags.append({tag_name: different_labels_ratio >= threshold}) return tags @staticmethod def get_no_close_tag( neighbors: List[List[Tuple[int, float]]], threshold: float, tag_name: SmartTag, ) -> List[Dict[SmartTag, bool]]: """Compute the `no_close_train` and `no_close_eval` tags. Given a list of examples, determine whether the nearest neighbor (class irrelevant) of each is farther/less than a certain similarity threshold. If so, tag it with the appropriate smart tag. Do this relative to neighbors in both the train and eval sets. Args: neighbors: Neighbors of each index. threshold: Threshold used to determine whether an example has neighbors nearby. If the nearest neighbor's similarity is below this threshold, the tag will be set. tag_name: Name of the tag. """ tags = [{tag_name: row_neighbors[0][1] < threshold} for row_neighbors in neighbors] return tags def get_neighbors( self, features: List[Array] ) -> Dict[DatasetSplitName, List[List[Tuple[int, float]]]]: """Get neighbors in each dataset_split for all indices. Given a list of examples, get the NUM_NEIGHBORS closest indices. Args: features: Queries to find neighbors to. """ neighbors = defaultdict(list) for ds_name in self.available_dataset_splits: dm_ = self.get_dataset_split_manager(ds_name) ds_tag = dm_.dataset_split_with_index() own_ds_name = ds_name == self.dataset_split_name delta = 1 if own_ds_name else 0 for idx, query in tqdm( enumerate(features), desc=f"Finding neighbors in {ds_name}", total=len(features) ): # Get N + delta neighbours because it will get itself as neighbor if we are on the # same set. # TODO: Batch this with `get_nearest_examples_batch` ? scores, items = ds_tag.get_nearest_examples( FEATURE_FAISS, query.astype(np.float32), k=NUM_NEIGHBORS + delta ) neighbors[ds_name].append( [ (i, s) for i, s in zip(items[DatasetColumn.row_idx], scores) if not own_ds_name or i != idx ] ) return neighbors	{"/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"], 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31,015	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/ml/model_performance.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, TypeVar from azimuth.types.model_performance import UtteranceCountPerFilterValue from azimuth.types.outcomes import OutcomeName T = TypeVar("T", bound=UtteranceCountPerFilterValue) def sorted_by_utterance_count( metrics: List[T], ) -> List[T]: return sorted(metrics, key=lambda r: r.utterance_count, reverse=True) def sorted_by_utterance_count_with_last(metrics: List[T], index_to_put_first: int) -> List[T]: """Sort list of metrics by utterance_count, then place a specific item at the beginning. Args: metrics: List of metrics to sort. index_to_put_first: Filter to put first in the list, usually REJECTION_CLASS/NO_SMART_TAGS/NO_ACTION. Returns: List of metrics sorted, but with `index_to_put_first` as first. """ first = metrics.pop(index_to_put_first) return [first, *sorted_by_utterance_count(metrics)] def compute_outcome(prediction: int, label: int, rejection_class_idx) -> OutcomeName: """Compute prediction outcome based on the prediction and label. Note: It was moved out of the OutcomesModule class for circular import issues. Args: prediction: Class index of the prediction label: Class index of the label rejection_class_idx: Class index of the rejection class Returns: Outcome value """ rejected = prediction == rejection_class_idx if prediction == label: return OutcomeName.CorrectAndRejected if rejected else OutcomeName.CorrectAndPredicted else: return OutcomeName.IncorrectAndRejected if rejected else OutcomeName.IncorrectAndPredicted	{"/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", 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31,016	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/ml/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 typing import List, Tuple, cast import numpy as np from azimuth.types.model_performance import ConfidenceBinDetails from azimuth.types.outcomes import OutcomeName def compute_ece_from_bins(bins: List[ConfidenceBinDetails]) -> Tuple[float, np.ndarray, np.ndarray]: """Compute the expected calibration error from a ConfidenceBinDetails. Args: bins: List of bins. Returns: ECE, the accuracy per bin and the mid confidence per bin. """ acc_pred = np.array( [ ( r.outcome_count[OutcomeName.CorrectAndPredicted] + r.outcome_count[OutcomeName.CorrectAndRejected] ) / max(1, sum(r.outcome_count.values())) for r in bins ] ) average_confidence = np.array([r.mean_bin_confidence for r in bins]) support = np.array([sum(r.outcome_count.values()) for r in bins]) bin_confidence = np.array([r.bin_confidence for r in bins]) # From baal code n = support.sum() return ( cast(float, ((support / n) * np.abs(acc_pred - average_confidence)).sum()), acc_pred, bin_confidence, )	{"/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"], 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31,017	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/perturbation_testing/__init__.py	from azimuth.modules.perturbation_testing.perturbation_testing import ( PerturbationTestingModule, ) from azimuth.modules.perturbation_testing.perturbation_testing_summary import ( PerturbationTestingMergedModule, PerturbationTestingSummaryModule, )	{"/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", 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31,018	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/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 pytest from azimuth.modules.base_classes import ArtifactManager @pytest.fixture(autouse=True) def cleanup_class(): yield # Code that will run after your test, for example: ArtifactManager.clear_instance()	{"/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", 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"/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", 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31,019	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/model_contract_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. from typing import Mapping, Optional, Type from azimuth.config import ModelContractConfig from azimuth.modules.base_classes import ModelContractModule from azimuth.modules.model_contracts import ( CustomTextClassificationModule, FileBasedTextClassificationModule, HFTextClassificationModule, ) from azimuth.types import DatasetSplitName, ModuleOptions, SupportedModelContract # Black formats this in a way where the line is too long. # fmt: off ALL_MODEL_CONTRACTS: Mapping[str, Type[ModelContractModule]] = { SupportedModelContract.file_based_text_classification: FileBasedTextClassificationModule, SupportedModelContract.hf_text_classification: HFTextClassificationModule, SupportedModelContract.custom_text_classification: CustomTextClassificationModule, } # fmt: on def model_contract_task_mapping( dataset_split_name: DatasetSplitName, config: ModelContractConfig, mod_options: Optional[ModuleOptions] = None, ): """ Get the right model contract according to the config. Args: dataset_split_name: Which dataset_split to compute it on. config: Application config. mod_options: Options for the Module. Returns: The right model contract module for the specified method. Raises: ValueError if the method is unknown. """ model_contract = ALL_MODEL_CONTRACTS.get(config.model_contract) if not model_contract: raise ValueError( f"Cannot find module for {config.model_contract}. 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31,020	ServiceNow/azimuth	refs/heads/main	/tests/test_modules/test_model_performance/test_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. import numpy as np from azimuth.modules.model_performance.confidence_binning import ( CONFIDENCE_BINS_COUNT, ConfidenceBinIndexModule, ConfidenceHistogramModule, ) from azimuth.types import DatasetColumn, DatasetFilters, DatasetSplitName, ModuleOptions from azimuth.types.outcomes import OutcomeName from tests.utils import save_outcomes, save_predictions UNKNOWN_TARGET = [3] BIN_GAP = 0.06 def test_confidence_histogram(tiny_text_config): save_predictions(tiny_text_config) save_outcomes(tiny_text_config) mod = ConfidenceHistogramModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0), ) out = mod.compute_on_dataset_split()[0].bins ds = mod.get_dataset_split() assert len(out) == CONFIDENCE_BINS_COUNT # We have CONFIDENCE_BINS_COUNT bins. # We should have the right number of samples. assert sum(count for v in out for count in v.outcome_count.values()) == len(ds) # The mean confidence should be close to the bin confidence. assert all( np.allclose(v.bin_confidence, v.mean_bin_confidence, atol=BIN_GAP) for v in out if sum(v.outcome_count.values()) > 0 ) mod_without_postprocessing = ConfidenceHistogramModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0, without_postprocessing=True), ) out_without_postprocessing = mod_without_postprocessing.compute_on_dataset_split()[0].bins # Confidences should be lower with postprocessing mean_with_post = np.mean( [v.mean_bin_confidence for v in out if sum(v.outcome_count.values()) > 0] ) mean_without_post = np.mean( [ v.mean_bin_confidence for v in out_without_postprocessing if sum(v.outcome_count.values()) > 0 ] ) assert mean_without_post > mean_with_post # REJECTION_CLASS should be predicted more with postprocessing count_rej_class_with_post = sum( [ v.outcome_count[OutcomeName.CorrectAndRejected] + v.outcome_count[OutcomeName.IncorrectAndRejected] for v in out ] ) count_rej_class_without_post = sum( [ v.outcome_count[OutcomeName.CorrectAndRejected] + v.outcome_count[OutcomeName.IncorrectAndRejected] for v in out_without_postprocessing ] ) assert count_rej_class_with_post > count_rej_class_without_post def test_confidence_histogram_100_percent_confidence(file_text_config_top1): save_predictions(file_text_config_top1) save_outcomes(file_text_config_top1) mod = ConfidenceHistogramModule( DatasetSplitName.eval, file_text_config_top1, mod_options=ModuleOptions(pipeline_index=0), ) bins = mod.compute_on_dataset_split()[0].bins ds = mod.get_dataset_split() assert len(bins) == CONFIDENCE_BINS_COUNT # We should have at least one sample with 100% confidence for the test to be relevant. assert 1 in np.max(ds[DatasetColumn.postprocessed_confidences], axis=1) # We should have the right number of samples. assert sum(count for b in bins for count in b.outcome_count.values()) == len(ds) def test_confidence_histogram_empty(simple_text_config, apply_mocked_startup_task): mod = ConfidenceHistogramModule( DatasetSplitName.eval, simple_text_config, mod_options=ModuleOptions(filters=DatasetFilters(label=UNKNOWN_TARGET), pipeline_index=0), ) out = mod.compute_on_dataset_split()[0].bins assert len(out) == CONFIDENCE_BINS_COUNT # We have CONFIDENCE_BINS_COUNT bins. # When filters cause an empty dataset_split, we should return empty bins assert sum(count for v in out for count in v.outcome_count.values()) == 0 def test_confidence_bin_idx(tiny_text_config): save_predictions(tiny_text_config) save_outcomes(tiny_text_config) mod = ConfidenceBinIndexModule( DatasetSplitName.eval, tiny_text_config, mod_options=ModuleOptions(pipeline_index=0), ) out = mod.compute_on_dataset_split() ds = mod.get_dataset_split() assert len(out) == len(ds) # We have bin_idx for all utterances # Bin index matches confidence for bin_id, confidences in zip(out, ds[DatasetColumn.postprocessed_confidences]): bin_index = np.floor(np.max(confidences) * CONFIDENCE_BINS_COUNT) assert bin_index == bin_id	{"/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", 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31,021	ServiceNow/azimuth	refs/heads/main	/azimuth_shr/models/file_based.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 import numpy as np import pandas as pd from datasets import Dataset """ These comes from Azimuth codebase. You can access them from user-defined modules. """ from azimuth.types import DatasetColumn, DatasetSplitName from azimuth.utils.object_loader import load_custom_object MAX_PREDS = 5 class FileBasedModel: def __init__(self, test_path, azimuth_config): self.azimuth_config = azimuth_config train_ds: Dataset = load_custom_object( azimuth_config.dataset, azimuth_config=azimuth_config )[DatasetSplitName.train] self.train_labels = train_ds.features[azimuth_config.columns.label].names self.data = { DatasetSplitName.train: train_ds.to_pandas(), DatasetSplitName.eval: pd.read_csv(test_path, delimiter=",", keep_default_na=False), } self.data = {split: self._make_probs(df) for split, df in self.data.items()} def parse_label(self, cls_name): return self.train_labels.index(cls_name) def _make_probs(self, df: pd.DataFrame) -> pd.DataFrame: """Add column `probs` to df. Reads Pred{i}, Pred{i}Score from the dataframe and create a probability distribution by matching Pred to class indices (from train set's). If some mass is missing, we fill the remaining classes with what's missing. Args: df: current dataframe Returns: Dataframe with `probs` columns """ def fn(item): probabilities = np.zeros(len(self.train_labels)) pairs = [ (self.parse_label(item[f"Pred{i}"]), item[f"Pred{i}Score"]) for i in range(MAX_PREDS) if f"Pred{i}" in item ] if pairs: idx, probs = zip(*pairs) if probs == ("",): probs = 1.0 probabilities[np.array(idx)] = np.array(probs) else: # If we have no pred, we get the label probabilities[item[self.azimuth_config.columns.label]] = 1.0 if not np.isclose(0.0, diff := 1.0 - probabilities.sum()): probabilities[probabilities == 0.0] = diff / (probabilities == 0.0).sum() return probabilities df["probs"] = df.apply(fn, axis=1) return df def __call__(self, utterances: Dict[str, Any], dataset_split): row_idx = utterances[DatasetColumn.row_idx] probs = np.stack(self.data[dataset_split].loc[row_idx, "probs"].to_numpy()) return probs	{"/tests/test_config.py": ["/azimuth/config.py", "/azimuth/utils/project.py"], "/azimuth/types/general/module_arguments.py": 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"/azimuth/types/task.py"]}
31,022	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/base_classes/artifact_manager.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 Callable, Dict import structlog from datasets import DatasetDict from azimuth.config import AzimuthConfig from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.types import DatasetSplitName from azimuth.types.tag import ALL_PREDICTION_TAGS, ALL_STANDARD_TAGS from azimuth.utils.conversion import md5_hash from azimuth.utils.object_loader import load_custom_object from azimuth.utils.project import load_dataset_from_config from azimuth.utils.validation import assert_not_none Hash = int log = structlog.get_logger() class Singleton: """ A non-thread-safe helper class to ease implementing singletons. This should be used as a decorator -- not a metaclass -- to the class that should be a singleton. To get the singleton instance, use the `instance` method. Trying to use `__call__` will result in a `TypeError` being raised. Args: decorated: Decorated class """ def __init__(self, decorated): self._decorated = decorated def instance(self): """ Returns the singleton instance. Upon its first call, it creates a new instance of the decorated class and calls its `__init__` method. On all subsequent calls, the already created instance is returned. Returns: Instance of the decorated class """ try: return self._instance except AttributeError: self._instance = self._decorated() return self._instance def __call__(self): raise TypeError("Singletons must be accessed through `instance()`.") def clear_instance(self): """For test purposes only""" if hasattr(self, "_instance"): delattr(self, "_instance") @Singleton class ArtifactManager: """This class is a singleton which holds different artifacts. Artifacts include dataset_split_managers, datasets and models for each config, so they don't need to be reloaded many times for a same module. Inspired from https://stackoverflow.com/questions/31875/is-there-a-simple-elegant-way-to-define-singletons. """ def __init__(self): # The keys of the dict are a hash of the config. self.dataset_dict_mapping: Dict[Hash, DatasetDict] = {} self.dataset_split_managers_mapping: Dict[ Hash, Dict[DatasetSplitName, DatasetSplitManager] ] = defaultdict(dict) self.models_mapping: Dict[Hash, Dict[int, Callable]] = defaultdict(dict) self.metrics = {} log.debug(f"Creating new Artifact Manager {id(self)}.") def get_dataset_split_manager( self, config: AzimuthConfig, name: DatasetSplitName ) -> DatasetSplitManager: """Get the DatasetSplitManager object associated to a config and a name. Args: config: config associated to the DatasetSplitManager. name: Which DatasetSplitManager to get. Returns: DatasetSplitManager instance. Raises: If there is no dataset defined in the config or if the dataset split is not defined. """ if config.dataset is None: raise ValueError("No dataset has been specified in config.") dataset_dict = self.get_dataset_dict(config) if name not in dataset_dict: raise ValueError( f"No '{name}' dataset in the supplied dataset(s). " f"Found {tuple(dataset_dict.keys())}." ) project_hash: Hash = config.get_project_hash() if name not in self.dataset_split_managers_mapping[project_hash]: self.dataset_split_managers_mapping[project_hash][name] = DatasetSplitManager( name=name, config=config, initial_tags=ALL_STANDARD_TAGS, initial_prediction_tags=ALL_PREDICTION_TAGS, dataset_split=dataset_dict[name], ) log.debug(f"New {name} DM in Artifact Manager {id(self)}") return self.dataset_split_managers_mapping[project_hash][name] def get_dataset_dict(self, config) -> DatasetDict: """Save and get user-defined DatasetDict. Will load the DatasetDict if not cached already. Args: config: config associated to the Dataset. Returns: DatasetDict associated with the config. """ project_hash: Hash = config.get_project_hash() if project_hash not in self.dataset_dict_mapping: self.dataset_dict_mapping[project_hash] = load_dataset_from_config(config) return self.dataset_dict_mapping[project_hash] def get_model(self, config: AzimuthConfig, pipeline_idx: int): """Load the model according to the config and the pipeline_idx. Args: config: config associated to the Model. pipeline_idx: pipeline index for which to get the model. Returns: Loaded model. """ model_contract_hash: Hash = config.get_model_contract_hash() if pipeline_idx not in self.models_mapping[model_contract_hash]: log.debug(f"Loading pipeline {pipeline_idx}.") pipelines = assert_not_none(config.pipelines) self.models_mapping[model_contract_hash][pipeline_idx] = load_custom_object( assert_not_none(pipelines[pipeline_idx].model), azimuth_config=config ) return self.models_mapping[model_contract_hash][pipeline_idx] def get_metric(self, config, name: str, kwargs): metric_hash: Hash = md5_hash({"name": name, kwargs}) if metric_hash not in self.metrics: self.metrics[metric_hash] = load_custom_object(config.metrics[name], **kwargs) return self.metrics[metric_hash] @classmethod def instance(cls): # Implemented in decorator raise NotImplementedError	{"/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", 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31,023	ServiceNow/azimuth	refs/heads/main	/azimuth/modules/dataset_analysis/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 dataclasses import asdict from typing import List import numpy as np from spectral_metric.estimator import CumulativeGradientEstimator from azimuth.config import SimilarityConfig from azimuth.modules.base_classes import AggregationModule from azimuth.modules.dataset_analysis.similarity_analysis import FAISSModule from azimuth.modules.task_execution import get_task_result from azimuth.types import Array, DatasetSplitName from azimuth.types.class_overlap import ClassOverlapResponse from azimuth.types.similarity_analysis import FAISSResponse def take(generator, length): lst = [] for _, v in zip(range(length), generator): lst.append(v) return lst def get_volume(dt) -> float: """Get the volume of the minimum box around a set of points. A Parzen-window is a non-parametric KDE method. We define a box around the origin (ie the point we want to analyse) bounded by all neighbors. We then compute the volume of this box. Args: dt: Features we would like to get the volume of. dt[0] is the origin. Returns: float, the volume of the box. """ # Compute the volume of the parzen-window dst = (np.abs(dt[1:] - dt[0]).max(0) * 2).prod() # Minimum is 1e-4 to avoid division per 0 res: float = max(1e-4, dst) return res class ClassOverlapModule(AggregationModule[SimilarityConfig]): allowed_splits = {DatasetSplitName.train} def compute_on_dataset_split(self): # We use the train set to get the features. train_dm = self.get_dataset_split_manager(DatasetSplitName.train) train_features = np.array(self._get_features(DatasetSplitName.train)) labels = np.array(train_dm.get_dataset_split()[self.config.columns.label]) estimator = CumulativeGradientEstimator(M_sample=1, k_nearest=5) estimator.fit(data=train_features, target=labels) # Convert SimilarityArrays to dicts to be loaded by Pydantic. similarity_arrays = { class_idx: {index: asdict(sim_array) for index, sim_array in class_dict.items()} for class_idx, class_dict in estimator.similarity_arrays.items() } return [ ClassOverlapResponse( evals=estimator.evals, evecs=estimator.evecs, difference=estimator.difference, s_matrix=estimator.S, similarity_arrays=similarity_arrays, ) ] def _get_features(self, ds_split_name: DatasetSplitName) -> List[Array]: """ Get Sentence embedding features Args: ds_split_name: Name of the dataset_split to get Returns: Features for all indices. 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"/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"]}
31,024	ServiceNow/azimuth	refs/heads/main	/azimuth/utils/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, TypeVar T = TypeVar("T") def assert_not_none(val: Optional[T]) -> T: """ This function makes sure that the variable is not None and has a fixed type for mypy purposes. Args: val: any value which is Optional. Returns: val [T]: The same value with a defined type. Raises: Assertion error if val is None. """ if val is None: raise AssertionError(f"value of {val} is None, expected not None") return val def assert_is_list(val: Any) -> List: """ This function makes sure that the variable is a list as a fixed type for mypy purposes. Args: val: any value which is a List. Returns: val [T]: The same value with a defined type. Raises: Assertion error if val is not a List. """ if not isinstance(val, List): raise AssertionError(f"value of {val} is not a List, expected a List") return val	{"/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": 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31,025	ServiceNow/azimuth	refs/heads/main	/azimuth/types/model_performance.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 typing from typing import Dict, Generic, List, Optional, Tuple, TypeVar from pydantic import Field from pydantic.generics import GenericModel from azimuth.types import AliasModel, Array, ModuleResponse, PlotSpecification from azimuth.types.outcomes import OutcomeName from azimuth.types.tag import DATASET_SMART_TAG_FAMILIES, PIPELINE_SMART_TAG_FAMILIES T = TypeVar("T") class MetricsResponseCommonFields(ModuleResponse): outcome_count: Dict[OutcomeName, int] = Field(..., title="Outcome Count") utterance_count: int = Field(..., title="Total number of utterances") custom_metrics: Dict[str, float] = Field(..., title="Custom Metrics") ece: float = Field(..., title="ECE") class MetricsAPIResponse(MetricsResponseCommonFields): ece_plot: Optional[PlotSpecification] = Field(..., title="ECE Plot", nullable=True) class MetricsModuleResponse(MetricsResponseCommonFields): ece_plot_args: Optional[Tuple] class UtteranceCountPerFilterValue(AliasModel): utterance_count: int = Field(..., title="Total number of utterances for the filter") filter_value: str = Field(..., title="Filter value") if typing.TYPE_CHECKING: class ValuePerDatasetSmartTag(GenericModel, Generic[T]): pass class ValuePerPipelineSmartTag(GenericModel, Generic[T]): pass else: ValuePerDatasetSmartTag = AliasModel.with_fields( "ValuePerDatasetSmartTag", GenericModel, Generic[T], {k.value: (List[T], Field(...)) for k in DATASET_SMART_TAG_FAMILIES} ) ValuePerPipelineSmartTag = AliasModel.with_fields( "ValuePerPipelineSmartTag", GenericModel, Generic[T], {k.value: (List[T], Field(...)) for k in PIPELINE_SMART_TAG_FAMILIES} ) class ValuePerDatasetFilter(ValuePerDatasetSmartTag[T], GenericModel, Generic[T]): label: List[T] = Field(..., title="Label") data_action: List[T] = Field(..., title="Data action tag") class ValuePerPipelineFilter(ValuePerPipelineSmartTag[T], GenericModel, Generic[T]): prediction: List[T] = Field(..., title="Prediction") outcome: List[T] = Field(..., title="Outcome") class UtteranceCountPerFilter(ValuePerDatasetFilter[UtteranceCountPerFilterValue]): pass class UtteranceCountPerFilterResponse(AliasModel): count_per_filter: UtteranceCountPerFilter = Field(..., title="Rubrics per filter") utterance_count: int = Field(..., title="Total number of utterances") class OutcomeCountPerFilterValue(UtteranceCountPerFilterValue): outcome_count: Dict[OutcomeName, int] = Field(..., title="Prediction count per outcome") class OutcomeCountPerFilter( ValuePerDatasetFilter[OutcomeCountPerFilterValue], ValuePerPipelineFilter[OutcomeCountPerFilterValue], ): pass class OutcomeCountPerFilterResponse(ModuleResponse): count_per_filter: OutcomeCountPerFilter = Field(..., title="Outcome count per filter") utterance_count: int = Field(..., title="Total number of utterances") class MetricsPerFilterValue(MetricsResponseCommonFields, UtteranceCountPerFilterValue): pass class MetricsPerFilter( ValuePerDatasetSmartTag[MetricsPerFilterValue], ValuePerPipelineSmartTag[MetricsPerFilterValue], ): label: List[MetricsPerFilterValue] = Field(..., title="Label") prediction: List[MetricsPerFilterValue] = Field(..., title="Prediction") class MetricsPerFilterModuleResponse(ModuleResponse): metrics_per_filter: MetricsPerFilter = Field(..., title="Metrics per filter") utterance_count: int = Field(..., title="Total number of utterances") class MetricsPerFilterAPIResponse(MetricsPerFilterModuleResponse): metrics_overall: List[MetricsPerFilterValue] = Field(..., title="Metrics overall") class ConfusionMatrixResponse(ModuleResponse): confusion_matrix: Array[float] = Field(..., title="Confusion Matrix") class_names: List[str] = Field(..., title="Ordered class names for the confusion matrix") normalize: bool = Field(..., title="State of the normalization of the Confusion Matrix") reorder_classes: bool = Field(..., title="State of the order of the Confusion Matrix") rejection_class: str = Field(..., title="Rejection class name") class ConfidenceBinDetails(AliasModel): bin_index: int = Field(..., title="Bin index") bin_confidence: float = Field(..., title="Bin confidence") mean_bin_confidence: float = Field(..., title="Bin mean confidence") outcome_count: Dict[OutcomeName, int] = Field(..., title="Outcome count") class ConfidenceHistogramResponse(ModuleResponse): bins: List[ConfidenceBinDetails] = Field(..., title="Details for all bins") confidence_threshold: Optional[float] = Field( ..., title="Confidence threshold in the selected pipeline", nullable=True ) class OutcomeCountPerThresholdValue(AliasModel): threshold: float outcome_count: Dict[OutcomeName, int] = Field(..., title="Prediction count per outcome") class OutcomeCountPerThresholdResponse(ModuleResponse): outcome_count_per_threshold: List[OutcomeCountPerThresholdValue] = Field( ..., title="Outcome count for all thresholds" ) confidence_threshold: Optional[float] = Field( ..., title="Confidence threshold in the selected pipeline", nullable=True ) utterance_count: int = Field(..., title="Total number of utterances")	{"/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", 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31,026	ServiceNow/azimuth	refs/heads/main	/azimuth/types/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. from typing import List, Optional, Union from pydantic import Field from azimuth.types import AliasModel from azimuth.types.model_performance import ValuePerDatasetSmartTag, ValuePerPipelineSmartTag from azimuth.types.outcomes import OutcomeName from azimuth.types.tag import DataAction from azimuth.utils.ml.postprocessing import PostprocessingStepAPIResponse from azimuth.utils.ml.preprocessing import PreprocessingStepAPIResponse class ModelPrediction(AliasModel): model_predictions: List[str] = Field(..., tittle="Model Predictions") postprocessed_prediction: str = Field(..., title="Postprocessed prediction") model_confidences: List[float] = Field(..., tittle="Model Confidences") postprocessed_confidences: List[float] = Field(..., tittle="Postprocessed Confidences") model_outcome: OutcomeName = Field(..., title="Model Outcome") postprocessed_outcome: OutcomeName = Field(..., title="Postprocessed Outcome") preprocessing_steps: List[PreprocessingStepAPIResponse] = Field( ..., title="Preprocessing Steps" ) postprocessing_steps: List[PostprocessingStepAPIResponse] = Field( ..., title="Postprocessing Steps" ) class ModelSaliency(AliasModel): tokens: List[str] = Field(..., title="Tokens") saliencies: List[float] = Field(..., title="Saliency") class UtterancePersistentId(AliasModel): # Union[int, str] in this order so FastAPI tries to cast to int() first, then defaults to str(). persistent_id: Union[int, str] = Field(..., title="Persistent id") class UtterancePatch(UtterancePersistentId): data_action: DataAction = Field(..., title="Data action tag") class BaseUtterance(UtterancePersistentId): index: int = Field(..., title="Index", description="Row index created by Azimuth") utterance: str = Field(..., title="Utterance") label: str = Field(..., title="Label") class Utterance( ValuePerDatasetSmartTag[str], ValuePerPipelineSmartTag[str], UtterancePatch, BaseUtterance ): model_prediction: Optional[ModelPrediction] = Field( ..., title="Model prediction", nullable=True ) model_saliency: Optional[ModelSaliency] = Field(..., title="Model saliency", nullable=True) class GetUtterancesResponse(AliasModel): utterances: List[Utterance] = Field(..., title="Utterances") utterance_count: int = Field(..., title="Utterance count") confidence_threshold: Optional[float] = Field( ..., title="Confidence threshold in the selected pipeline (if any)", nullable=True ) class Config: schema_extra = { "example": { "utterances": [ { "index": 1, "modelPrediction": { "modelPredictions": ["dog", "cat", "other"], "postprocessedPrediction": 18, "modelConfidences": [0.6, 0.3, 0.1], "postprocessedConfidences": [0.6, 0.3, 0.1], "model_outcome": "IncorrectAndPredicted", "postprocessed_outcome": "IncorrectAndPredicted", }, "data_action": None, "smartTags": ["missing_verb"], "label": 0, "utterance": "this movie good.", "modelSaliency": { "tokens": ["[CLS]", "this", "movie", "good", ".", "[SEP]"], "saliencies": [ 0.04012051224708557, 0.13875797390937805, 0.46019017696380615, 0.1719111204147339, 0.08197823911905289, 0.10704188048839569, ], }, }, { "index": 2, "modelPrediction": { "modelPredictions": ["cat", "dog", "other"], "postprocessedPrediction": 0, "modelConfidences": [0.5, 0.3, 0.2], "postprocessedConfidences": [0.5, 0.3, 0.2], "model_outcome": "CorrectAndPredicted", "postprocessed_outcome": "CorrectAndPredicted", }, "data_action": None, "smartTags": ["multiple_sentences"], "label": 0, "utterance": "this movie is bad. i hate it.", "modelSaliency": { "tokens": [ "[CLS]", "this", "movie", "is", "bad", ".", "i", "hat", "##e", "it", ".", "[SEP]", ], "saliencies": [ 0.02849223092198372, 0.07048150897026062, 0.31859099864959717, 0.07001485675573349, 0.16567286849021912, 0.049969661980867386, 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31,027	ServiceNow/azimuth	refs/heads/main	/azimuth/routers/model_performance/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 typing import List from fastapi import APIRouter, Depends, Query from azimuth.app import get_dataset_split_manager, get_task_manager from azimuth.dataset_split_manager import DatasetSplitManager from azimuth.modules.model_performance.metrics import MetricsModule from azimuth.task_manager import TaskManager from azimuth.types import DatasetSplitName, ModuleOptions, NamedDatasetFilters, SupportedModule from azimuth.types.model_performance import ( MetricsAPIResponse, MetricsModuleResponse, MetricsPerFilterAPIResponse, MetricsPerFilterModuleResponse, MetricsPerFilterValue, ) from azimuth.utils.routers import ( build_named_dataset_filters, get_standard_task_result, require_pipeline_index, ) router = APIRouter() @router.get( "", summary="Get metrics.", description="Get metrics (ECE, outcome count, precision and so on).", response_model=MetricsAPIResponse, ) def get_metrics( dataset_split_name: DatasetSplitName, named_filters: NamedDatasetFilters = Depends(build_named_dataset_filters), task_manager: TaskManager = Depends(get_task_manager), dataset_split_manager: DatasetSplitManager = Depends(get_dataset_split_manager), pipeline_index: int = Depends(require_pipeline_index), without_postprocessing: bool = Query(False, title="Without Postprocessing"), ) -> MetricsAPIResponse: mod_options = ModuleOptions( filters=named_filters.to_dataset_filters(dataset_split_manager.get_class_names()), pipeline_index=pipeline_index, without_postprocessing=without_postprocessing, ) module_response: List[MetricsModuleResponse] = get_standard_task_result( SupportedModule.Metrics, dataset_split_name, task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, ) api_response = MetricsModule.module_to_api_response(module_response) return api_response[0] @router.get( "/per_filter", summary="Get metrics for each filter.", description="Get metrics for each filter based on the current filtering.", response_model=MetricsPerFilterAPIResponse, ) def get_metrics_per_filter( 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), ) -> MetricsPerFilterAPIResponse: mod_options = ModuleOptions(pipeline_index=pipeline_index) metrics_per_filter_result: MetricsPerFilterModuleResponse = get_standard_task_result( SupportedModule.MetricsPerFilter, dataset_split_name, task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, )[0] metrics_result: MetricsModuleResponse = get_standard_task_result( SupportedModule.Metrics, dataset_split_name, task_manager, mod_options=mod_options, last_update=dataset_split_manager.last_update, )[0] api_result = MetricsPerFilterAPIResponse( **metrics_per_filter_result.dict(), metrics_overall=[ MetricsPerFilterValue( outcome_count=metrics_result.outcome_count, utterance_count=metrics_result.utterance_count, custom_metrics=metrics_result.custom_metrics, ece=metrics_result.ece, filter_value="overall", ) ] ) return api_result	{"/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", 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