 TensorFlow.org에서 보기 |  Google Colab에서 실행 |  GitHub에서 보기 |  노트북 다운로드 |  TF 허브 모델 보기 |
개요
공정성 지표의 상단에 내장 도구입니다 TensorFlow 모델 분석 (TFMA) 제품 파이프 라인의 공정성 메트릭의 정기적 인 평가를 할 수 있습니다. TFMA는 TensorFlow 및 비 TensorFlow 기계 학습 모델을 모두 평가하기 위한 라이브러리입니다. 이를 통해 분산 방식으로 대량의 데이터에 대한 모델을 평가하고, 다양한 데이터 조각에 대해 그래프 내 및 기타 메트릭을 계산하고, 노트북에서 시각화할 수 있습니다.
공정성 지표와 함께 패키지 TensorFlow 데이터 유효성 검사 (TFDV) 와 어떤-IF 도구 . 공정성 지표를 사용하면 다음을 수행할 수 있습니다.
- 정의된 사용자 그룹에 걸쳐 분할된 모델 성능 평가
- 여러 임계값에서 신뢰 구간 및 평가를 통해 결과에 대한 신뢰 확보
- 데이터세트 분포 평가
- 근본 원인과 개선 기회를 탐색하기 위해 개별 조각에 대해 자세히 알아보십시오.
이 노트북에서, 당신은 당신이 사용 훈련 모델의 공정성 문제를 해결하기 위해 공정성 지표를 사용 시민이 데이터 집합을 댓글 . 이 시계 비디오 실제 시나리오 이에 대한 자세한 내용과 문맥하는 것은있는 기반도 공정성 지표를 만들기위한 주요 동기 중 하나입니다.
데이터세트
으로이 노트북에서는 작동 시민이 데이터 집합을 댓글 약 2 백만 공공 코멘트에 의해 공개, 남북 플랫폼을 댓글 지속적인 연구를 위해 2017 년. 이러한 노력은 후원했다 직소 아니라 의도하지 않은 모델 바이어스를 최소화로 독성 의견 분류 도움말을 Kaggle에 대회를 개최했다.
데이터세트의 각 개별 텍스트 주석에는 독성 레이블이 있으며 주석이 유독한 경우 레이블이 1이고 주석이 독성이 없는 경우 0입니다. 데이터 내에서 댓글의 하위 집합은 성별, 성적 취향, 종교, 인종 또는 민족에 대한 범주를 포함하여 다양한 정체성 속성으로 레이블이 지정됩니다.
설정
설치 fairness-indicators 와 witwidget .
pip install -q -U pip==20.2pip install -q fairness-indicatorspip install -q witwidget
설치 후 Colab 런타임을 다시 시작해야 합니다. 선택 런타임>는 Colab 메뉴에서 다시 시작 런타임.
먼저 런타임을 다시 시작하지 않고 이 자습서의 나머지 부분을 진행하지 마십시오.
다른 모든 필수 라이브러리를 가져옵니다.
import os
import tempfile
import apache_beam as beam
import numpy as np
import pandas as pd
from datetime import datetime
import pprint
from google.protobuf import text_format
import tensorflow_hub as hub
import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_data_validation as tfdv
from tfx_bsl.tfxio import tensor_adapter
from tfx_bsl.tfxio import tf_example_record
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from tensorflow_model_analysis.addons.fairness.view import widget_view
from fairness_indicators.tutorial_utils import util
from witwidget.notebook.visualization import WitConfigBuilder
from witwidget.notebook.visualization import WitWidget
from tensorflow_metadata.proto.v0 import schema_pb2
데이터 다운로드 및 분석
기본적으로 이 노트북은 이 데이터 세트의 사전 처리된 버전을 다운로드하지만 원하는 경우 원래 데이터 세트를 사용하고 처리 단계를 다시 실행할 수 있습니다. 원본 데이터세트에서 각 댓글은 특정 ID에 해당하는 댓글이 있다고 믿는 평가자의 비율로 레이블이 지정됩니다. 예를 들어, 댓글에는 { 남성: 0.3, 여성: 1.0, 트랜스젠더: 0.0, 이성애자: 0.8, homosexual_gay_or_lesbian: 1.0 } 레이블이 지정될 수 있습니다. 0.5 미만의 점수를 가진 ID. 따라서 위의 예는 다음과 같이 변환됩니다. 댓글이 특정 ID에 해당한다고 믿는 평가자. 예를 들어, 댓글에는 { 성별: [여성], 성적 지향: [이성애자, 동성애자_게이_또는_레즈비언] }라는 레이블이 지정됩니다.
download_original_data = False
if download_original_data:
train_tf_file = tf.keras.utils.get_file('train_tf.tfrecord',
'https://storage.googleapis.com/civil_comments_dataset/train_tf.tfrecord')
validate_tf_file = tf.keras.utils.get_file('validate_tf.tfrecord',
'https://storage.googleapis.com/civil_comments_dataset/validate_tf.tfrecord')
# The identity terms list will be grouped together by their categories
# (see 'IDENTITY_COLUMNS') on threshould 0.5. Only the identity term column,
# text column and label column will be kept after processing.
train_tf_file = util.convert_comments_data(train_tf_file)
validate_tf_file = util.convert_comments_data(validate_tf_file)
else:
train_tf_file = tf.keras.utils.get_file('train_tf_processed.tfrecord',
'https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord')
validate_tf_file = tf.keras.utils.get_file('validate_tf_processed.tfrecord',
'https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord')
TFDV를 사용하여 데이터를 분석하고 공정성 불일치로 이어질 수 있는 누락된 값 및 데이터 불균형과 같은 잠재적인 문제를 찾습니다.
stats = tfdv.generate_statistics_from_tfrecord(data_location=train_tf_file)
tfdv.visualize_statistics(stats)
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/stats_util.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)` WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/stats_util.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)`
TFDV는 편향된 모델 결과로 이어질 수 있는 데이터에 상당한 불균형이 있음을 보여줍니다.
독성 라벨(모델에서 예측한 값)이 불균형합니다. 훈련 세트에 있는 예제 중 8%만 유독합니다. 이는 분류기가 모든 주석이 무독성이라고 예측함으로써 92%의 정확도를 얻을 수 있음을 의미합니다.
정체성 용어와 관련된 분야에서는 108만 건의 훈련 사례 중 660만건(0.61%)만이 동성애를 다루고 있고, 양성애와 관련된 사례는 더욱 드물다. 이는 훈련 데이터 부족으로 인해 이러한 슬라이스의 성능이 저하될 수 있음을 나타냅니다.
데이터 준비
데이터를 구문 분석할 기능 맵을 정의합니다. 각각의 예는 라벨, 주석 텍스트를해야합니다 및 ID 특징 sexual orientation , gender , religion , race , 그리고 disability 텍스트와 연결됩니다.
BASE_DIR = tempfile.gettempdir()
TEXT_FEATURE = 'comment_text'
LABEL = 'toxicity'
FEATURE_MAP = {
# Label:
LABEL: tf.io.FixedLenFeature([], tf.float32),
# Text:
TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),
# Identities:
'sexual_orientation':tf.io.VarLenFeature(tf.string),
'gender':tf.io.VarLenFeature(tf.string),
'religion':tf.io.VarLenFeature(tf.string),
'race':tf.io.VarLenFeature(tf.string),
'disability':tf.io.VarLenFeature(tf.string),
}
다음으로, 모델에 데이터를 공급하기 위한 입력 함수를 설정합니다. 각 예제에 가중치 열을 추가하고 TFDV에 의해 식별된 클래스 불균형을 설명하기 위해 유독한 예제의 가중치를 높입니다. 평가 단계에서는 ID 기능만 사용합니다. 교육 중에는 설명만 모델에 제공되기 때문입니다.
def train_input_fn():
def parse_function(serialized):
parsed_example = tf.io.parse_single_example(
serialized=serialized, features=FEATURE_MAP)
# Adds a weight column to deal with unbalanced classes.
parsed_example['weight'] = tf.add(parsed_example[LABEL], 0.1)
return (parsed_example,
parsed_example[LABEL])
train_dataset = tf.data.TFRecordDataset(
filenames=[train_tf_file]).map(parse_function).batch(512)
return train_dataset
모델 훈련
데이터에 대한 딥 러닝 모델을 만들고 훈련합니다.
model_dir = os.path.join(BASE_DIR, 'train', datetime.now().strftime(
"%Y%m%d-%H%M%S"))
embedded_text_feature_column = hub.text_embedding_column(
key=TEXT_FEATURE,
module_spec='https://tfhub.dev/google/nnlm-en-dim128/1')
classifier = tf.estimator.DNNClassifier(
hidden_units=[500, 100],
weight_column='weight',
feature_columns=[embedded_text_feature_column],
optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.003),
loss_reduction=tf.losses.Reduction.SUM,
n_classes=2,
model_dir=model_dir)
classifier.train(input_fn=train_input_fn, steps=1000)
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20210923-205025', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20210923-205025', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2021-09-23 20:50:26.540914: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 808. Shape inference will have run different parts of the graph with different producer versions.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/head/base_head.py:512: NumericColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/head/base_head.py:512: NumericColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/feature_column/feature_column.py:2192: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/feature_column/feature_column.py:2192: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adagrad.py:84: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adagrad.py:84: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20210923-205025/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20210923-205025/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 59.34932, step = 0
INFO:tensorflow:loss = 59.34932, step = 0
INFO:tensorflow:global_step/sec: 108.435
INFO:tensorflow:global_step/sec: 108.435
INFO:tensorflow:loss = 56.416668, step = 100 (0.924 sec)
INFO:tensorflow:loss = 56.416668, step = 100 (0.924 sec)
INFO:tensorflow:global_step/sec: 116.367
INFO:tensorflow:global_step/sec: 116.367
INFO:tensorflow:loss = 47.250374, step = 200 (0.859 sec)
INFO:tensorflow:loss = 47.250374, step = 200 (0.859 sec)
INFO:tensorflow:global_step/sec: 116.333
INFO:tensorflow:global_step/sec: 116.333
INFO:tensorflow:loss = 55.81682, step = 300 (0.860 sec)
INFO:tensorflow:loss = 55.81682, step = 300 (0.860 sec)
INFO:tensorflow:global_step/sec: 116.844
INFO:tensorflow:global_step/sec: 116.844
INFO:tensorflow:loss = 55.814293, step = 400 (0.856 sec)
INFO:tensorflow:loss = 55.814293, step = 400 (0.856 sec)
INFO:tensorflow:global_step/sec: 114.434
INFO:tensorflow:global_step/sec: 114.434
INFO:tensorflow:loss = 41.805046, step = 500 (0.874 sec)
INFO:tensorflow:loss = 41.805046, step = 500 (0.874 sec)
INFO:tensorflow:global_step/sec: 115.693
INFO:tensorflow:global_step/sec: 115.693
INFO:tensorflow:loss = 45.53726, step = 600 (0.864 sec)
INFO:tensorflow:loss = 45.53726, step = 600 (0.864 sec)
INFO:tensorflow:global_step/sec: 115.772
INFO:tensorflow:global_step/sec: 115.772
INFO:tensorflow:loss = 51.17028, step = 700 (0.864 sec)
INFO:tensorflow:loss = 51.17028, step = 700 (0.864 sec)
INFO:tensorflow:global_step/sec: 116.131
INFO:tensorflow:global_step/sec: 116.131
INFO:tensorflow:loss = 47.696205, step = 800 (0.861 sec)
INFO:tensorflow:loss = 47.696205, step = 800 (0.861 sec)
INFO:tensorflow:global_step/sec: 115.609
INFO:tensorflow:global_step/sec: 115.609
INFO:tensorflow:loss = 47.800926, step = 900 (0.865 sec)
INFO:tensorflow:loss = 47.800926, step = 900 (0.865 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20210923-205025/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20210923-205025/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Loss for final step: 50.67367.
INFO:tensorflow:Loss for final step: 50.67367.
<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifierV2 at 0x7f113351ebd0>
모델 분석
훈련된 모델을 얻은 후 이를 분석하여 TFMA 및 공정성 지표를 사용하여 공정성 메트릭을 계산합니다. A와 모델을 수출하여 시작 SavedModel .
저장된 모델 내보내기
def eval_input_receiver_fn():
serialized_tf_example = tf.compat.v1.placeholder(
dtype=tf.string, shape=[None], name='input_example_placeholder')
# This *must* be a dictionary containing a single key 'examples', which
# points to the input placeholder.
receiver_tensors = {'examples': serialized_tf_example}
features = tf.io.parse_example(serialized_tf_example, FEATURE_MAP)
features['weight'] = tf.ones_like(features[LABEL])
return tfma.export.EvalInputReceiver(
features=features,
receiver_tensors=receiver_tensors,
labels=features[LABEL])
tfma_export_dir = tfma.export.export_eval_savedmodel(
estimator=classifier,
export_dir_base=os.path.join(BASE_DIR, 'tfma_eval_model'),
eval_input_receiver_fn=eval_input_receiver_fn)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/encoding.py:141: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/encoding.py:141: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Saver not created because there are no variables in the graph to restore 2021-09-23 20:50:39.359797: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 808. Shape inference will have run different parts of the graph with different producer versions. INFO:tensorflow:Saver not created because there are no variables in the graph to restore INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Signatures INCLUDED in export for Classify: None INFO:tensorflow:Signatures INCLUDED in export for Classify: None INFO:tensorflow:Signatures INCLUDED in export for Regress: None INFO:tensorflow:Signatures INCLUDED in export for Regress: None INFO:tensorflow:Signatures INCLUDED in export for Predict: None INFO:tensorflow:Signatures INCLUDED in export for Predict: None INFO:tensorflow:Signatures INCLUDED in export for Train: None INFO:tensorflow:Signatures INCLUDED in export for Train: None INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval'] INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval'] WARNING:tensorflow:Export includes no default signature! WARNING:tensorflow:Export includes no default signature! INFO:tensorflow:Restoring parameters from /tmp/train/20210923-205025/model.ckpt-1000 INFO:tensorflow:Restoring parameters from /tmp/train/20210923-205025/model.ckpt-1000 INFO:tensorflow:Assets added to graph. INFO:tensorflow:Assets added to graph. INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1632430239/assets INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1632430239/assets INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1632430239/saved_model.pb INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1632430239/saved_model.pb
공정성 지표 계산
오른쪽 패널의 드롭다운을 사용하여 메트릭을 계산할 ID와 신뢰 구간으로 실행할지 여부를 선택합니다.
공정성 지표 계산 옵션
tfma_eval_result_path = os.path.join(BASE_DIR, 'tfma_eval_result')
slice_selection = 'sexual_orientation'
print(f'Slice selection: {slice_selection}')
compute_confidence_intervals = False
print(f'Compute confidence intervals: {compute_confidence_intervals}')
# Define slices that you want the evaluation to run on.
eval_config_pbtxt = """
model_specs {
label_key: "%s"
}
metrics_specs {
metrics {
class_name: "FairnessIndicators"
config: '{ "thresholds": [0.1, 0.3, 0.5, 0.7, 0.9] }'
}
}
slicing_specs {} # overall slice
slicing_specs {
feature_keys: ["%s"]
}
options {
compute_confidence_intervals { value: %s }
disabled_outputs { values: "analysis" }
}
""" % (LABEL, slice_selection, compute_confidence_intervals)
eval_config = text_format.Parse(eval_config_pbtxt, tfma.EvalConfig())
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=tfma_export_dir)
schema = text_format.Parse(
"""
tensor_representation_group {
key: ""
value {
tensor_representation {
key: "comment_text"
value {
dense_tensor {
column_name: "comment_text"
shape {}
}
}
}
}
}
feature {
name: "comment_text"
type: BYTES
}
feature {
name: "toxicity"
type: FLOAT
}
feature {
name: "sexual_orientation"
type: BYTES
}
feature {
name: "gender"
type: BYTES
}
feature {
name: "religion"
type: BYTES
}
feature {
name: "race"
type: BYTES
}
feature {
name: "disability"
type: BYTES
}
""", schema_pb2.Schema())
tfxio = tf_example_record.TFExampleRecord(
file_pattern=validate_tf_file,
schema=schema,
raw_record_column_name=tfma.ARROW_INPUT_COLUMN)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfxio.ArrowSchema(),
tensor_representations=tfxio.TensorRepresentations())
with beam.Pipeline() as pipeline:
(pipeline
| 'ReadFromTFRecordToArrow' >> tfxio.BeamSource()
| 'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
output_path=tfma_eval_result_path,
tensor_adapter_config=tensor_adapter_config))
eval_result = tfma.load_eval_result(output_path=tfma_eval_result_path)
Slice selection: sexual_orientation Compute confidence intervals: False WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:169: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:169: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0. INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1632430239/variables/variables INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1632430239/variables/variables WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:189: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:189: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info. WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in target path matching: WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in target path matching: WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in target path matching:
What-if 도구를 사용하여 데이터 시각화
이 섹션에서는 What-If 도구의 대화형 시각적 인터페이스를 사용하여 미시적 수준에서 데이터를 탐색하고 조작합니다.
오른쪽 패널에 있는 산점도의 각 점은 도구에 로드된 하위 집합의 예 중 하나를 나타냅니다. 포인트 중 하나를 클릭하면 왼쪽 패널에서 이 특정 예에 대한 세부 정보를 볼 수 있습니다. 주석 텍스트, 정답 독성 및 해당 ID가 표시됩니다. 이 왼쪽 패널의 맨 아래에는 방금 훈련한 모델의 추론 결과가 표시됩니다.
예제의 텍스트를 수정하고 변경 사항이 변화에 대한 인식 독성 예측을 발생하는 방법 다음보기로 실행 추론 버튼을 클릭합니다.
DEFAULT_MAX_EXAMPLES = 1000
# Load 100000 examples in memory. When first rendered,
# What-If Tool should only display 1000 of these due to browser constraints.
def wit_dataset(file, num_examples=100000):
dataset = tf.data.TFRecordDataset(
filenames=[file]).take(num_examples)
return [tf.train.Example.FromString(d.numpy()) for d in dataset]
wit_data = wit_dataset(train_tf_file)
config_builder = WitConfigBuilder(wit_data[:DEFAULT_MAX_EXAMPLES]).set_estimator_and_feature_spec(
classifier, FEATURE_MAP).set_label_vocab(['non-toxicity', LABEL]).set_target_feature(LABEL)
wit = WitWidget(config_builder)
렌더링 공정성 지표
내보낸 평가 결과로 공정성 지표 위젯을 렌더링합니다.
아래에는 선택한 메트릭에 대한 각 데이터 조각의 성능을 표시하는 막대 차트가 표시됩니다. 시각화 상단의 드롭다운 메뉴를 사용하여 기준 비교 조각과 표시된 임계값을 조정할 수 있습니다.
공정성 표시기 위젯은 위에서 렌더링된 What-If 도구와 통합됩니다. 막대 차트에서 데이터의 한 조각을 선택하면 What-If 도구가 업데이트되어 선택한 조각의 예를 보여줍니다. 위의 어떤-IF 도구의 데이터를 다시로드는 독성 색상으로 수정하려고하면. 이렇게 하면 예제의 독성 균형을 조각별로 시각적으로 이해할 수 있습니다.
event_handlers={'slice-selected':
wit.create_selection_callback(wit_data, DEFAULT_MAX_EXAMPLES)}
widget_view.render_fairness_indicator(eval_result=eval_result,
slicing_column=slice_selection,
event_handlers=event_handlers
)
FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'prediction/…
이 특정 데이터 세트 및 작업을 사용하면 특정 ID에 대해 체계적으로 더 높은 위양성 및 위음성 비율이 부정적인 결과를 초래할 수 있습니다. 예를 들어, 콘텐츠 중재 시스템에서 특정 그룹에 대한 전체 오탐지율보다 높으면 해당 목소리가 무음으로 이어질 수 있습니다. 따라서 모델을 개발 및 개선할 때 이러한 유형의 기준을 정기적으로 평가하고 공정성 지표, TFDV 및 WIT와 같은 도구를 활용하여 잠재적인 문제를 밝히는 것이 중요합니다. 공정성 문제를 식별한 후에는 새로운 데이터 소스, 데이터 균형 조정 또는 기타 기술을 실험하여 실적이 저조한 그룹의 성능을 개선할 수 있습니다.
참조 여기 공정성 지표를 사용하는 방법에 대한 자세한 내용 및 지침.
공정성 평가 결과 활용
eval_result 객체에서 위의 렌더링 render_fairness_indicator() , 당신이 당신의 프로그램에 TFMA 결과를 읽을 활용할 수있는 고유의 API가 있습니다.
평가된 조각 및 측정항목 가져오기
사용 get_slice_names() 와 get_metric_names() 각각 평가 슬라이스와 통계를 얻을 수 있습니다.
pp = pprint.PrettyPrinter()
print("Slices:")
pp.pprint(eval_result.get_slice_names())
print("\nMetrics:")
pp.pprint(eval_result.get_metric_names())
Slices:
[(),
(('sexual_orientation', 'homosexual_gay_or_lesbian'),),
(('sexual_orientation', 'heterosexual'),),
(('sexual_orientation', 'bisexual'),),
(('sexual_orientation', 'other_sexual_orientation'),)]
Metrics:
['fairness_indicators_metrics/negative_rate@0.1',
'fairness_indicators_metrics/positive_rate@0.7',
'fairness_indicators_metrics/false_discovery_rate@0.9',
'fairness_indicators_metrics/false_negative_rate@0.3',
'fairness_indicators_metrics/false_omission_rate@0.1',
'accuracy',
'fairness_indicators_metrics/false_discovery_rate@0.7',
'fairness_indicators_metrics/false_negative_rate@0.7',
'label/mean',
'fairness_indicators_metrics/true_positive_rate@0.5',
'fairness_indicators_metrics/false_positive_rate@0.1',
'recall',
'fairness_indicators_metrics/false_omission_rate@0.7',
'fairness_indicators_metrics/false_positive_rate@0.7',
'auc_precision_recall',
'fairness_indicators_metrics/negative_rate@0.7',
'fairness_indicators_metrics/negative_rate@0.3',
'fairness_indicators_metrics/false_discovery_rate@0.3',
'fairness_indicators_metrics/true_negative_rate@0.9',
'fairness_indicators_metrics/false_omission_rate@0.3',
'fairness_indicators_metrics/false_negative_rate@0.1',
'fairness_indicators_metrics/true_negative_rate@0.3',
'fairness_indicators_metrics/true_positive_rate@0.7',
'fairness_indicators_metrics/false_positive_rate@0.3',
'fairness_indicators_metrics/true_positive_rate@0.1',
'fairness_indicators_metrics/true_positive_rate@0.9',
'fairness_indicators_metrics/false_negative_rate@0.9',
'fairness_indicators_metrics/positive_rate@0.5',
'fairness_indicators_metrics/positive_rate@0.9',
'fairness_indicators_metrics/negative_rate@0.9',
'fairness_indicators_metrics/true_negative_rate@0.1',
'fairness_indicators_metrics/false_omission_rate@0.5',
'post_export_metrics/example_count',
'fairness_indicators_metrics/false_omission_rate@0.9',
'fairness_indicators_metrics/negative_rate@0.5',
'fairness_indicators_metrics/false_positive_rate@0.5',
'fairness_indicators_metrics/positive_rate@0.3',
'prediction/mean',
'accuracy_baseline',
'fairness_indicators_metrics/true_negative_rate@0.5',
'fairness_indicators_metrics/false_discovery_rate@0.5',
'fairness_indicators_metrics/false_discovery_rate@0.1',
'precision',
'fairness_indicators_metrics/false_positive_rate@0.9',
'fairness_indicators_metrics/true_positive_rate@0.3',
'auc',
'average_loss',
'fairness_indicators_metrics/positive_rate@0.1',
'fairness_indicators_metrics/false_negative_rate@0.5',
'fairness_indicators_metrics/true_negative_rate@0.7']
사용 get_metrics_for_slice() 에 대한 사전 매핑 메트릭 이름과 같은 특정 슬라이스에 대한 통계를 얻기 위해 메트릭 값 .
baseline_slice = ()
heterosexual_slice = (('sexual_orientation', 'heterosexual'),)
print("Baseline metric values:")
pp.pprint(eval_result.get_metrics_for_slice(baseline_slice))
print("\nHeterosexual metric values:")
pp.pprint(eval_result.get_metrics_for_slice(heterosexual_slice))
Baseline metric values:
{'accuracy': {'doubleValue': 0.7174859642982483},
'accuracy_baseline': {'doubleValue': 0.9198060631752014},
'auc': {'doubleValue': 0.796409547328949},
'auc_precision_recall': {'doubleValue': 0.3000231087207794},
'average_loss': {'doubleValue': 0.5615971088409424},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.9139404145348933},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.8796606156634021},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.816806708107944},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.7090802784427505},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.4814937210839392},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.006079867348348763},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.08696628437197734},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.2705713693519414},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.5445108470360647},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.891598728755009},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 0.006604499315158452},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.017811407791031682},
'fairness_indicators_metrics/false_omission_rate@0.5': {'doubleValue': 0.03187681488249431},
'fairness_indicators_metrics/false_omission_rate@0.7': {'doubleValue': 0.04993640137936933},
'fairness_indicators_metrics/false_omission_rate@0.9': {'doubleValue': 0.07271999842219298},
'fairness_indicators_metrics/false_positive_rate@0.1': {'doubleValue': 0.9202700382800194},
'fairness_indicators_metrics/false_positive_rate@0.3': {'doubleValue': 0.5818879187535954},
'fairness_indicators_metrics/false_positive_rate@0.5': {'doubleValue': 0.28355525303665063},
'fairness_indicators_metrics/false_positive_rate@0.7': {'doubleValue': 0.09679333307231039},
'fairness_indicators_metrics/false_positive_rate@0.9': {'doubleValue': 0.00877639469079322},
'fairness_indicators_metrics/negative_rate@0.1': {'doubleValue': 0.07382367199944595},
'fairness_indicators_metrics/negative_rate@0.3': {'doubleValue': 0.39155620195304386},
'fairness_indicators_metrics/negative_rate@0.5': {'doubleValue': 0.6806884133250225},
'fairness_indicators_metrics/negative_rate@0.7': {'doubleValue': 0.8744414433132488},
'fairness_indicators_metrics/negative_rate@0.9': {'doubleValue': 0.9832342960038783},
'fairness_indicators_metrics/positive_rate@0.1': {'doubleValue': 0.926176328000554},
'fairness_indicators_metrics/positive_rate@0.3': {'doubleValue': 0.6084437980469561},
'fairness_indicators_metrics/positive_rate@0.5': {'doubleValue': 0.3193115866749775},
'fairness_indicators_metrics/positive_rate@0.7': {'doubleValue': 0.12555855668675117},
'fairness_indicators_metrics/positive_rate@0.9': {'doubleValue': 0.016765703996121616},
'fairness_indicators_metrics/true_negative_rate@0.1': {'doubleValue': 0.0797299617199806},
'fairness_indicators_metrics/true_negative_rate@0.3': {'doubleValue': 0.41811208124640464},
'fairness_indicators_metrics/true_negative_rate@0.5': {'doubleValue': 0.7164447469633494},
'fairness_indicators_metrics/true_negative_rate@0.7': {'doubleValue': 0.9032066669276896},
'fairness_indicators_metrics/true_negative_rate@0.9': {'doubleValue': 0.9912236053092068},
'fairness_indicators_metrics/true_positive_rate@0.1': {'doubleValue': 0.9939201326516512},
'fairness_indicators_metrics/true_positive_rate@0.3': {'doubleValue': 0.9130337156280227},
'fairness_indicators_metrics/true_positive_rate@0.5': {'doubleValue': 0.7294286306480586},
'fairness_indicators_metrics/true_positive_rate@0.7': {'doubleValue': 0.45548915296393533},
'fairness_indicators_metrics/true_positive_rate@0.9': {'doubleValue': 0.10840127124499102},
'label/mean': {'doubleValue': 0.08019392192363739},
'post_export_metrics/example_count': {'doubleValue': 721950.0},
'precision': {'doubleValue': 0.18319329619407654},
'prediction/mean': {'doubleValue': 0.3998037576675415},
'recall': {'doubleValue': 0.7294286489486694} }
Heterosexual metric values:
{'accuracy': {'doubleValue': 0.5203251838684082},
'accuracy_baseline': {'doubleValue': 0.7601625919342041},
'auc': {'doubleValue': 0.6672822833061218},
'auc_precision_recall': {'doubleValue': 0.4065391719341278},
'average_loss': {'doubleValue': 0.8273133039474487},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.7541666666666667},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.7272727272727273},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.7062937062937062},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.655367231638418},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.4473684210526316},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.0847457627118644},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.288135593220339},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.4830508474576271},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.8220338983050848},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.10416666666666667},
'fairness_indicators_metrics/false_omission_rate@0.5': {'doubleValue': 0.1650485436893204},
'fairness_indicators_metrics/false_omission_rate@0.7': {'doubleValue': 0.18095238095238095},
'fairness_indicators_metrics/false_omission_rate@0.9': {'doubleValue': 0.21365638766519823},
'fairness_indicators_metrics/false_positive_rate@0.1': {'doubleValue': 0.9679144385026738},
'fairness_indicators_metrics/false_positive_rate@0.3': {'doubleValue': 0.7700534759358288},
'fairness_indicators_metrics/false_positive_rate@0.5': {'doubleValue': 0.5401069518716578},
'fairness_indicators_metrics/false_positive_rate@0.7': {'doubleValue': 0.31016042780748665},
'fairness_indicators_metrics/false_positive_rate@0.9': {'doubleValue': 0.045454545454545456},
'fairness_indicators_metrics/negative_rate@0.1': {'doubleValue': 0.024390243902439025},
'fairness_indicators_metrics/negative_rate@0.3': {'doubleValue': 0.1951219512195122},
'fairness_indicators_metrics/negative_rate@0.5': {'doubleValue': 0.4186991869918699},
'fairness_indicators_metrics/negative_rate@0.7': {'doubleValue': 0.6402439024390244},
'fairness_indicators_metrics/negative_rate@0.9': {'doubleValue': 0.9227642276422764},
'fairness_indicators_metrics/positive_rate@0.1': {'doubleValue': 0.975609756097561},
'fairness_indicators_metrics/positive_rate@0.3': {'doubleValue': 0.8048780487804879},
'fairness_indicators_metrics/positive_rate@0.5': {'doubleValue': 0.5813008130081301},
'fairness_indicators_metrics/positive_rate@0.7': {'doubleValue': 0.3597560975609756},
'fairness_indicators_metrics/positive_rate@0.9': {'doubleValue': 0.07723577235772358},
'fairness_indicators_metrics/true_negative_rate@0.1': {'doubleValue': 0.03208556149732621},
'fairness_indicators_metrics/true_negative_rate@0.3': {'doubleValue': 0.22994652406417113},
'fairness_indicators_metrics/true_negative_rate@0.5': {'doubleValue': 0.45989304812834225},
'fairness_indicators_metrics/true_negative_rate@0.7': {'doubleValue': 0.6898395721925134},
'fairness_indicators_metrics/true_negative_rate@0.9': {'doubleValue': 0.9545454545454546},
'fairness_indicators_metrics/true_positive_rate@0.1': {'doubleValue': 1.0},
'fairness_indicators_metrics/true_positive_rate@0.3': {'doubleValue': 0.9152542372881356},
'fairness_indicators_metrics/true_positive_rate@0.5': {'doubleValue': 0.711864406779661},
'fairness_indicators_metrics/true_positive_rate@0.7': {'doubleValue': 0.5169491525423728},
'fairness_indicators_metrics/true_positive_rate@0.9': {'doubleValue': 0.17796610169491525},
'label/mean': {'doubleValue': 0.2398373931646347},
'post_export_metrics/example_count': {'doubleValue': 492.0},
'precision': {'doubleValue': 0.2937062978744507},
'prediction/mean': {'doubleValue': 0.5578703880310059},
'recall': {'doubleValue': 0.7118644118309021} }
사용 get_metrics_for_all_slices() 실행에서 얻을 해당 메트릭 각 슬라이스 사전 사전 매핑 모든 조각에 대한 통계를 얻기 위해 get_metrics_for_slice() 그 위에.
pp.pprint(eval_result.get_metrics_for_all_slices())
{(): {'accuracy': {'doubleValue': 0.7174859642982483},
'accuracy_baseline': {'doubleValue': 0.9198060631752014},
'auc': {'doubleValue': 0.796409547328949},
'auc_precision_recall': {'doubleValue': 0.3000231087207794},
'average_loss': {'doubleValue': 0.5615971088409424},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.9139404145348933},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.8796606156634021},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.816806708107944},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.7090802784427505},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.4814937210839392},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.006079867348348763},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.08696628437197734},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.2705713693519414},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.5445108470360647},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.891598728755009},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 0.006604499315158452},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.017811407791031682},
'fairness_indicators_metrics/false_omission_rate@0.5': {'doubleValue': 0.03187681488249431},
'fairness_indicators_metrics/false_omission_rate@0.7': {'doubleValue': 0.04993640137936933},
'fairness_indicators_metrics/false_omission_rate@0.9': {'doubleValue': 0.07271999842219298},
'fairness_indicators_metrics/false_positive_rate@0.1': {'doubleValue': 0.9202700382800194},
'fairness_indicators_metrics/false_positive_rate@0.3': {'doubleValue': 0.5818879187535954},
'fairness_indicators_metrics/false_positive_rate@0.5': {'doubleValue': 0.28355525303665063},
'fairness_indicators_metrics/false_positive_rate@0.7': {'doubleValue': 0.09679333307231039},
'fairness_indicators_metrics/false_positive_rate@0.9': {'doubleValue': 0.00877639469079322},
'fairness_indicators_metrics/negative_rate@0.1': {'doubleValue': 0.07382367199944595},
'fairness_indicators_metrics/negative_rate@0.3': {'doubleValue': 0.39155620195304386},
'fairness_indicators_metrics/negative_rate@0.5': {'doubleValue': 0.6806884133250225},
'fairness_indicators_metrics/negative_rate@0.7': {'doubleValue': 0.8744414433132488},
'fairness_indicators_metrics/negative_rate@0.9': {'doubleValue': 0.9832342960038783},
'fairness_indicators_metrics/positive_rate@0.1': {'doubleValue': 0.926176328000554},
'fairness_indicators_metrics/positive_rate@0.3': {'doubleValue': 0.6084437980469561},
'fairness_indicators_metrics/positive_rate@0.5': {'doubleValue': 0.3193115866749775},
'fairness_indicators_metrics/positive_rate@0.7': {'doubleValue': 0.12555855668675117},
'fairness_indicators_metrics/positive_rate@0.9': {'doubleValue': 0.016765703996121616},
'fairness_indicators_metrics/true_negative_rate@0.1': {'doubleValue': 0.0797299617199806},
'fairness_indicators_metrics/true_negative_rate@0.3': {'doubleValue': 0.41811208124640464},
'fairness_indicators_metrics/true_negative_rate@0.5': {'doubleValue': 0.7164447469633494},
'fairness_indicators_metrics/true_negative_rate@0.7': {'doubleValue': 0.9032066669276896},
'fairness_indicators_metrics/true_negative_rate@0.9': {'doubleValue': 0.9912236053092068},
'fairness_indicators_metrics/true_positive_rate@0.1': {'doubleValue': 0.9939201326516512},
'fairness_indicators_metrics/true_positive_rate@0.3': {'doubleValue': 0.9130337156280227},
'fairness_indicators_metrics/true_positive_rate@0.5': {'doubleValue': 0.7294286306480586},
'fairness_indicators_metrics/true_positive_rate@0.7': {'doubleValue': 0.45548915296393533},
'fairness_indicators_metrics/true_positive_rate@0.9': {'doubleValue': 0.10840127124499102},
'label/mean': {'doubleValue': 0.08019392192363739},
'post_export_metrics/example_count': {'doubleValue': 721950.0},
'precision': {'doubleValue': 0.18319329619407654},
'prediction/mean': {'doubleValue': 0.3998037576675415},
'recall': {'doubleValue': 0.7294286489486694} },
(('sexual_orientation', 'bisexual'),): {'accuracy': {'doubleValue': 0.5258620977401733},
'accuracy_baseline': {'doubleValue': 0.8017241358757019},
'auc': {'doubleValue': 0.6252922415733337},
'auc_precision_recall': {'doubleValue': 0.3546649217605591},
'average_loss': {'doubleValue': 0.7461641430854797},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.7870370370370371},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.7816091954022989},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.7666666666666667},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.7037037037037037},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.17391304347826086},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.391304347826087},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.6521739130434783},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.9130434782608695},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.13793103448275862},
'fairness_indicators_metrics/false_omission_rate@0.5': {'doubleValue': 0.16071428571428573},
'fairness_indicators_metrics/false_omission_rate@0.7': {'doubleValue': 0.16853932584269662},
'fairness_indicators_metrics/false_omission_rate@0.9': {'doubleValue': 0.18421052631578946},
'fairness_indicators_metrics/false_positive_rate@0.1': {'doubleValue': 0.9139784946236559},
'fairness_indicators_metrics/false_positive_rate@0.3': {'doubleValue': 0.7311827956989247},
'fairness_indicators_metrics/false_positive_rate@0.5': {'doubleValue': 0.4946236559139785},
'fairness_indicators_metrics/false_positive_rate@0.7': {'doubleValue': 0.20430107526881722},
'fairness_indicators_metrics/false_positive_rate@0.9': {'doubleValue': 0.0},
'fairness_indicators_metrics/negative_rate@0.1': {'doubleValue': 0.06896551724137931},
'fairness_indicators_metrics/negative_rate@0.3': {'doubleValue': 0.25},
'fairness_indicators_metrics/negative_rate@0.5': {'doubleValue': 0.4827586206896552},
'fairness_indicators_metrics/negative_rate@0.7': {'doubleValue': 0.7672413793103449},
'fairness_indicators_metrics/negative_rate@0.9': {'doubleValue': 0.9827586206896551},
'fairness_indicators_metrics/positive_rate@0.1': {'doubleValue': 0.9310344827586207},
'fairness_indicators_metrics/positive_rate@0.3': {'doubleValue': 0.75},
'fairness_indicators_metrics/positive_rate@0.5': {'doubleValue': 0.5172413793103449},
'fairness_indicators_metrics/positive_rate@0.7': {'doubleValue': 0.23275862068965517},
'fairness_indicators_metrics/positive_rate@0.9': {'doubleValue': 0.017241379310344827},
'fairness_indicators_metrics/true_negative_rate@0.1': {'doubleValue': 0.08602150537634409},
'fairness_indicators_metrics/true_negative_rate@0.3': {'doubleValue': 0.26881720430107525},
'fairness_indicators_metrics/true_negative_rate@0.5': {'doubleValue': 0.5053763440860215},
'fairness_indicators_metrics/true_negative_rate@0.7': {'doubleValue': 0.7956989247311828},
'fairness_indicators_metrics/true_negative_rate@0.9': {'doubleValue': 1.0},
'fairness_indicators_metrics/true_positive_rate@0.1': {'doubleValue': 1.0},
'fairness_indicators_metrics/true_positive_rate@0.3': {'doubleValue': 0.8260869565217391},
'fairness_indicators_metrics/true_positive_rate@0.5': {'doubleValue': 0.6086956521739131},
'fairness_indicators_metrics/true_positive_rate@0.7': {'doubleValue': 0.34782608695652173},
'fairness_indicators_metrics/true_positive_rate@0.9': {'doubleValue': 0.08695652173913043},
'label/mean': {'doubleValue': 0.1982758641242981},
'post_export_metrics/example_count': {'doubleValue': 116.0},
'precision': {'doubleValue': 0.23333333432674408},
'prediction/mean': {'doubleValue': 0.4908219575881958},
'recall': {'doubleValue': 0.6086956262588501} },
(('sexual_orientation', 'heterosexual'),): {'accuracy': {'doubleValue': 0.5203251838684082},
'accuracy_baseline': {'doubleValue': 0.7601625919342041},
'auc': {'doubleValue': 0.6672822833061218},
'auc_precision_recall': {'doubleValue': 0.4065391719341278},
'average_loss': {'doubleValue': 0.8273133039474487},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.7541666666666667},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.7272727272727273},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.7062937062937062},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.655367231638418},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.4473684210526316},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.0847457627118644},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.288135593220339},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.4830508474576271},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.8220338983050848},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.10416666666666667},
'fairness_indicators_metrics/false_omission_rate@0.5': {'doubleValue': 0.1650485436893204},
'fairness_indicators_metrics/false_omission_rate@0.7': {'doubleValue': 0.18095238095238095},
'fairness_indicators_metrics/false_omission_rate@0.9': {'doubleValue': 0.21365638766519823},
'fairness_indicators_metrics/false_positive_rate@0.1': {'doubleValue': 0.9679144385026738},
'fairness_indicators_metrics/false_positive_rate@0.3': {'doubleValue': 0.7700534759358288},
'fairness_indicators_metrics/false_positive_rate@0.5': {'doubleValue': 0.5401069518716578},
'fairness_indicators_metrics/false_positive_rate@0.7': {'doubleValue': 0.31016042780748665},
'fairness_indicators_metrics/false_positive_rate@0.9': {'doubleValue': 0.045454545454545456},
'fairness_indicators_metrics/negative_rate@0.1': {'doubleValue': 0.024390243902439025},
'fairness_indicators_metrics/negative_rate@0.3': {'doubleValue': 0.1951219512195122},
'fairness_indicators_metrics/negative_rate@0.5': {'doubleValue': 0.4186991869918699},
'fairness_indicators_metrics/negative_rate@0.7': {'doubleValue': 0.6402439024390244},
'fairness_indicators_metrics/negative_rate@0.9': {'doubleValue': 0.9227642276422764},
'fairness_indicators_metrics/positive_rate@0.1': {'doubleValue': 0.975609756097561},
'fairness_indicators_metrics/positive_rate@0.3': {'doubleValue': 0.8048780487804879},
'fairness_indicators_metrics/positive_rate@0.5': {'doubleValue': 0.5813008130081301},
'fairness_indicators_metrics/positive_rate@0.7': {'doubleValue': 0.3597560975609756},
'fairness_indicators_metrics/positive_rate@0.9': {'doubleValue': 0.07723577235772358},
'fairness_indicators_metrics/true_negative_rate@0.1': {'doubleValue': 0.03208556149732621},
'fairness_indicators_metrics/true_negative_rate@0.3': {'doubleValue': 0.22994652406417113},
'fairness_indicators_metrics/true_negative_rate@0.5': {'doubleValue': 0.45989304812834225},
'fairness_indicators_metrics/true_negative_rate@0.7': {'doubleValue': 0.6898395721925134},
'fairness_indicators_metrics/true_negative_rate@0.9': {'doubleValue': 0.9545454545454546},
'fairness_indicators_metrics/true_positive_rate@0.1': {'doubleValue': 1.0},
'fairness_indicators_metrics/true_positive_rate@0.3': {'doubleValue': 0.9152542372881356},
'fairness_indicators_metrics/true_positive_rate@0.5': {'doubleValue': 0.711864406779661},
'fairness_indicators_metrics/true_positive_rate@0.7': {'doubleValue': 0.5169491525423728},
'fairness_indicators_metrics/true_positive_rate@0.9': {'doubleValue': 0.17796610169491525},
'label/mean': {'doubleValue': 0.2398373931646347},
'post_export_metrics/example_count': {'doubleValue': 492.0},
'precision': {'doubleValue': 0.2937062978744507},
'prediction/mean': {'doubleValue': 0.5578703880310059},
'recall': {'doubleValue': 0.7118644118309021} },
(('sexual_orientation', 'homosexual_gay_or_lesbian'),): {'accuracy': {'doubleValue': 0.5851936340332031},
'accuracy_baseline': {'doubleValue': 0.7182232141494751},
'auc': {'doubleValue': 0.7057511806488037},
'auc_precision_recall': {'doubleValue': 0.469566285610199},
'average_loss': {'doubleValue': 0.7369641661643982},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.7107050831576481},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.6717557251908397},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.6172690763052209},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.5427319211102994},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.4092664092664093},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.0016168148746968471},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.06143896523848019},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.22958771220695232},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.4939369442198868},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.8763136620856912},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 0.01652892561983471},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.08909730363423213},
'fairness_indicators_metrics/false_omission_rate@0.5': {'doubleValue': 0.14947368421052631},
'fairness_indicators_metrics/false_omission_rate@0.7': {'doubleValue': 0.20225091029460443},
'fairness_indicators_metrics/false_omission_rate@0.9': {'doubleValue': 0.2624061970467199},
'fairness_indicators_metrics/false_positive_rate@0.1': {'doubleValue': 0.9622581668252458},
'fairness_indicators_metrics/false_positive_rate@0.3': {'doubleValue': 0.7535680304471931},
'fairness_indicators_metrics/false_positive_rate@0.5': {'doubleValue': 0.4874722486520774},
'fairness_indicators_metrics/false_positive_rate@0.7': {'doubleValue': 0.2356485886457342},
'fairness_indicators_metrics/false_positive_rate@0.9': {'doubleValue': 0.03361877576910879},
'fairness_indicators_metrics/negative_rate@0.1': {'doubleValue': 0.0275626423690205},
'fairness_indicators_metrics/negative_rate@0.3': {'doubleValue': 0.19430523917995443},
'fairness_indicators_metrics/negative_rate@0.5': {'doubleValue': 0.4328018223234624},
'fairness_indicators_metrics/negative_rate@0.7': {'doubleValue': 0.6881548974943053},
'fairness_indicators_metrics/negative_rate@0.9': {'doubleValue': 0.941002277904328},
'fairness_indicators_metrics/positive_rate@0.1': {'doubleValue': 0.9724373576309795},
'fairness_indicators_metrics/positive_rate@0.3': {'doubleValue': 0.8056947608200455},
'fairness_indicators_metrics/positive_rate@0.5': {'doubleValue': 0.5671981776765376},
'fairness_indicators_metrics/positive_rate@0.7': {'doubleValue': 0.31184510250569475},
'fairness_indicators_metrics/positive_rate@0.9': {'doubleValue': 0.05899772209567198},
'fairness_indicators_metrics/true_negative_rate@0.1': {'doubleValue': 0.0377418331747542},
'fairness_indicators_metrics/true_negative_rate@0.3': {'doubleValue': 0.24643196955280686},
'fairness_indicators_metrics/true_negative_rate@0.5': {'doubleValue': 0.5125277513479226},
'fairness_indicators_metrics/true_negative_rate@0.7': {'doubleValue': 0.7643514113542658},
'fairness_indicators_metrics/true_negative_rate@0.9': {'doubleValue': 0.9663812242308912},
'fairness_indicators_metrics/true_positive_rate@0.1': {'doubleValue': 0.9983831851253031},
'fairness_indicators_metrics/true_positive_rate@0.3': {'doubleValue': 0.9385610347615198},
'fairness_indicators_metrics/true_positive_rate@0.5': {'doubleValue': 0.7704122877930477},
'fairness_indicators_metrics/true_positive_rate@0.7': {'doubleValue': 0.5060630557801131},
'fairness_indicators_metrics/true_positive_rate@0.9': {'doubleValue': 0.12368633791430882},
'label/mean': {'doubleValue': 0.2817767560482025},
'post_export_metrics/example_count': {'doubleValue': 4390.0},
'precision': {'doubleValue': 0.3827309310436249},
'prediction/mean': {'doubleValue': 0.5428739786148071},
'recall': {'doubleValue': 0.770412266254425} },
(('sexual_orientation', 'other_sexual_orientation'),): {'accuracy': {'doubleValue': 0.6000000238418579},
'accuracy_baseline': {'doubleValue': 0.800000011920929},
'auc': {'doubleValue': 1.0},
'auc_precision_recall': {'doubleValue': 1.0},
'average_loss': {'doubleValue': 0.7521011829376221},
'fairness_indicators_metrics/false_discovery_rate@0.1': {'doubleValue': 0.8},
'fairness_indicators_metrics/false_discovery_rate@0.3': {'doubleValue': 0.75},
'fairness_indicators_metrics/false_discovery_rate@0.5': {'doubleValue': 0.6666666666666666},
'fairness_indicators_metrics/false_discovery_rate@0.7': {'doubleValue': 0.5},
'fairness_indicators_metrics/false_discovery_rate@0.9': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.1': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.3': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.5': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.7': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_negative_rate@0.9': {'doubleValue': 0.0},
'fairness_indicators_metrics/false_omission_rate@0.1': {'doubleValue': 'NaN'},
'fairness_indicators_metrics/false_omission_rate@0.3': {'doubleValue': 0.0},
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