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概要
このアクティビティでは、使用しますフェアネス指標を探索するためにワイルドデータセット中の標識された顔にFaceSSD予測を。公正インジケータの上に構築されたツールのスイートですTensorFlowモデル解析製品パイプラインにおける公平性メトリックの定期的な評価を可能にします。
データセットについて
この演習では、FaceSSD予測データセット、FaceSSDAPIによって生成された約200kの異なる画像予測とグラウンドトゥルースを使用します。
ツールについて
TensorFlowモデル分析はTensorFlowと非TensorFlow機械学習モデルの両方を評価するためのライブラリです。これにより、ユーザーは大量のデータでモデルを分散して評価し、さまざまなデータスライスでグラフ内およびその他のメトリックを計算し、ノートブックで視覚化できます。
TensorFlowデータ検証は、あなたのデータを分析するために使用できる単一のツールです。これを使用して、欠測値やデータの不均衡など、公平性の不一致につながる可能性のあるデータの潜在的な問題を見つけることができます。
公平性の指標、ユーザーはことができるようになります。
- 定義されたユーザーグループ間でスライスされたモデルのパフォーマンスを評価します
- 信頼区間と複数のしきい値での評価で結果に自信を持ってください
インポート
次のコードを実行して、fairness_indicatorsライブラリをインストールします。このパッケージには、この演習で使用するツールが含まれています。再起動ランタイムが要求される場合がありますが、必須ではありません。
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
データをダウンロードして理解する
野生で標識された顔はまた、ペアマッチングとしても知られている顔認証用の公開ベンチマークデータセットです。 LFWには、Webから収集された13,000を超える顔の画像が含まれています。
このデータセットに対してFaceSSD予測を実行して、特定の画像に顔が存在するかどうかを予測しました。このコラボでは、性別ごとにデータをスライスして、性別グループごとにモデルのパフォーマンスに有意差があるかどうかを観察します。
画像に複数の顔がある場合、性別は「MISSING」とラベル付けされます。
便宜上、Google CloudPlatformでデータセットをホストしています。次のコードを実行してGCPからデータをダウンロードします。データのダウンロードと分析には、約1分かかります。
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)`
定数の定義
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]),
}
TFMAのモデルにとらわれない構成
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])])
]
公平性のコールバックと公平性の指標の計算
# 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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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:
公平性指標をレンダリングする
エクスポートされた評価結果を使用して、公平性インジケーターウィジェットをレンダリングします。
以下に、選択したメトリックのデータの各スライスのパフォーマンスを示す棒グラフを示します。ビジュアライゼーションの上部にあるドロップダウンメニューを使用して、ベースライン比較スライスと表示されるしきい値を調整できます。
このユースケースに関連する指標は、真陽性率であり、リコールとも呼ばれます。左側のセレクターを使用して、true_positive_rateのグラフを選択します。これらのメトリック値がに表示される値と一致するモデルのカードを。
一部の写真では、写真に写っている人物が若すぎて正確に注釈を付けることができない場合、性別は男性または女性ではなく若いとラベル付けされます。
widget_view.render_fairness_indicator(eval_result=eval_result,
slicing_column=slice_key)
FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…