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개요
이 활동에서는 사용합니다 공정성 지표를 탐색하는 야생 데이터 세트에서 레이블이 얼굴에 FaceSSD 예측을 . 공정성 지표의 상단에 내장 도구입니다 TensorFlow 모델 분석 제품 파이프 라인의 공정성 메트릭의 정기적 인 평가를 할 수 있습니다.
데이터세트 정보
이 연습에서는 FaceSSD API로 생성된 약 200,000개의 서로 다른 이미지 예측 및 groundtruths인 FaceSSD 예측 데이터 세트로 작업합니다.
도구 정보
TensorFlow 모델 분석 TensorFlow 및 비 TensorFlow 기계 학습 모델을 모두 평가하기위한 라이브러리입니다. 이를 통해 사용자는 분산 방식으로 많은 양의 데이터에 대한 모델을 평가하고 다양한 데이터 조각에 대해 그래프 내 및 기타 메트릭을 계산하고 노트북에서 시각화할 수 있습니다.
TensorFlow 데이터 유효성 검사는 당신이 당신의 데이터를 분석하는 데 사용할 수있는 하나의 도구이다. 이를 사용하여 결측값 및 데이터 불균형과 같이 공정성 불일치로 이어질 수 있는 데이터의 잠재적 문제를 찾을 수 있습니다.
와 공정성 지표 , 사용자는 다음을 할 수 있습니다
- 정의된 사용자 그룹에 걸쳐 분할된 모델 성능 평가
- 여러 임계값에서 신뢰 구간 및 평가를 통해 결과에 대해 확신을 갖습니다.
가져오기
다음 코드를 실행하여 fairness_indicators 라이브러리를 설치합니다. 이 패키지에는 이 연습에서 사용할 도구가 포함되어 있습니다. 런타임 재시작을 요청할 수 있지만 필수는 아닙니다.
pip install -q -U pip==20.2pip install fairness-indicators
import os
import tempfile
import apache_beam as beam
import numpy as np
import pandas as pd
from datetime import datetime
import tensorflow_hub as hub
import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_data_validation as tfdv
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from tensorflow_model_analysis.addons.fairness.view import widget_view
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_predict as agnostic_predict
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_evaluate_graph
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_extractor
from witwidget.notebook.visualization import WitConfigBuilder
from witwidget.notebook.visualization import WitWidget
데이터 다운로드 및 이해
야생에서 얼굴 레이블은 또한 한 쌍의 정합라고도 얼굴 인증을위한 공개 기준 데이터 집합이다. LFW에는 웹에서 수집한 13,000개 이상의 얼굴 이미지가 포함되어 있습니다.
주어진 이미지에 얼굴이 있는지 여부를 예측하기 위해 이 데이터 세트에서 FaceSSD 예측을 실행했습니다. 이 Colab에서는 성별에 따라 데이터를 분할하여 다른 성별 그룹에 대한 모델 성능 간에 유의미한 차이가 있는지 관찰합니다.
이미지에 두 개 이상의 얼굴이 있는 경우 성별은 "MISSING"으로 표시됩니다.
편의를 위해 Google Cloud Platform에서 데이터세트를 호스팅했습니다. 다음 코드를 실행하여 GCP에서 데이터를 다운로드합니다. 데이터를 다운로드하고 분석하는 데 약 1분이 소요됩니다.
data_location = tf.keras.utils.get_file('lfw_dataset.tf', 'https://storage.googleapis.com/facessd_dataset/lfw_dataset.tfrecord')
stats = tfdv.generate_statistics_from_tfrecord(data_location=data_location)
tfdv.visualize_statistics(stats)
Downloading data from https://storage.googleapis.com/facessd_dataset/lfw_dataset.tfrecord 200835072/200828483 [==============================] - 1s 0us/step 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: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/python3.6/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/python3.6/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)`
상수 정의
BASE_DIR = tempfile.gettempdir()
tfma_eval_result_path = os.path.join(BASE_DIR, 'tfma_eval_result')
compute_confidence_intervals = True
slice_key = 'object/groundtruth/Gender'
label_key = 'object/groundtruth/face'
prediction_key = 'object/prediction/face'
feature_map = {
slice_key:
tf.io.FixedLenFeature([], tf.string, default_value=['none']),
label_key:
tf.io.FixedLenFeature([], tf.float32, default_value=[0.0]),
prediction_key:
tf.io.FixedLenFeature([], tf.float32, default_value=[0.0]),
}
TFMA용 모델 불가지론적 구성
model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
label_keys=[label_key],
prediction_keys=[prediction_key],
feature_spec=feature_map)
model_agnostic_extractors = [
model_agnostic_extractor.ModelAgnosticExtractor(
model_agnostic_config=model_agnostic_config, desired_batch_size=3),
tfma.extractors.slice_key_extractor.SliceKeyExtractor(
[tfma.slicer.SingleSliceSpec(),
tfma.slicer.SingleSliceSpec(columns=[slice_key])])
]
공정성 콜백 및 공정성 메트릭 계산
# Helper class for counting examples in beam PCollection
class CountExamples(beam.CombineFn):
def __init__(self, message):
self.message = message
def create_accumulator(self):
return 0
def add_input(self, current_sum, element):
return current_sum + 1
def merge_accumulators(self, accumulators):
return sum(accumulators)
def extract_output(self, final_sum):
if final_sum:
print("%s: %d"%(self.message, final_sum))
metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators(
thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
labels_key=label_key,
target_prediction_keys=[prediction_key]),
tfma.post_export_metrics.auc(
curve='PR',
labels_key=label_key,
target_prediction_keys=[prediction_key]),
]
eval_shared_model = tfma.types.EvalSharedModel(
add_metrics_callbacks=metrics_callbacks,
construct_fn=model_agnostic_evaluate_graph.make_construct_fn(
add_metrics_callbacks=metrics_callbacks,
config=model_agnostic_config))
with beam.Pipeline() as pipeline:
# Read data.
data = (
pipeline
| 'ReadData' >> beam.io.ReadFromTFRecord(data_location))
# Count all examples.
data_count = (
data | 'Count number of examples' >> beam.CombineGlobally(
CountExamples('Before filtering "Gender:MISSING"')))
# If there are more than one face in image, the gender feature is 'MISSING'
# and we are filtering that image out.
def filter_missing_gender(element):
example = tf.train.Example.FromString(element)
if example.features.feature[slice_key].bytes_list.value[0] != b'MISSING':
yield element
filtered_data = (
data
| 'Filter Missing Gender' >> beam.ParDo(filter_missing_gender))
# Count after filtering "Gender:MISSING".
filtered_data_count = (
filtered_data | 'Count number of examples after filtering'
>> beam.CombineGlobally(
CountExamples('After filtering "Gender:MISSING"')))
# Because LFW data set has always faces by default, we are adding
# labels as 1.0 for all images.
def add_face_groundtruth(element):
example = tf.train.Example.FromString(element)
example.features.feature[label_key].float_list.value[:] = [1.0]
yield example.SerializeToString()
final_data = (
filtered_data
| 'Add Face Groundtruth' >> beam.ParDo(add_face_groundtruth))
# Run TFMA.
_ = (
final_data
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
compute_confidence_intervals=compute_confidence_intervals,
output_path=tfma_eval_result_path,
extractors=model_agnostic_extractors))
eval_result = tfma.load_eval_result(output_path=tfma_eval_result_path)
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: 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<class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/post_export_metrics/post_export_metrics.py:178: auc (from tensorflow.python.ops.metrics_impl) is deprecated and will be removed in a future version. Instructions for updating: The value of AUC returned by this may race with the update so this is deprecated. Please use tf.keras.metrics.AUC instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/post_export_metrics/post_export_metrics.py:178: auc (from tensorflow.python.ops.metrics_impl) is deprecated and will be removed in a future version. Instructions for updating: The value of AUC returned by this may race with the update so this is deprecated. Please use tf.keras.metrics.AUC instead. Before filtering "Gender:MISSING": 13836 After filtering "Gender:MISSING": 11544 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:
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.
이 사용 사례와 관련된 메트릭은 재현율이라고도 하는 참 긍정 비율입니다. 왼쪽에 있는 선택기를 사용하여 true_positive_rate에 대한 그래프를 선택합니다. 이 측정 값은 표시 값과 일치 모델 카드를 .
일부 사진의 경우 사진 속 인물이 너무 어려서 정확하게 주석을 달 수 없는 경우 성별이 남성 또는 여성 대신 젊음으로 표시됩니다.
widget_view.render_fairness_indicator(eval_result=eval_result,
slicing_column=slice_key)
FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…