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위키 토크 댓글 독성 예측

이 예에서는 Wiki 토론 페이지에 게시 된 토론 댓글에 유해한 콘텐츠가 포함되어 있는지 (즉, "무례하고, 무례하거나, 불합리한"콘텐츠가 포함되어 있는지) 예측하는 작업을 고려합니다. 우리는 Conversation AI 프로젝트에서 발표 한 공개 데이터 세트를 사용합니다. 여기에는 크라우드 워커가 주석이 달린 English Wikipedia의 10 만 개 이상의 댓글이 포함되어 있습니다 (라벨링 방법론에 대한 문서 참조).

이 데이터 세트의 과제 중 하나는 댓글의 극히 일부가 성이나 종교와 같은 민감한 주제를 다루고 있다는 것입니다. 따라서이 데이터 세트에서 신경망 모델을 학습하면 더 작은 민감한 주제에 대해 서로 다른 성능이 발생합니다. 이는 해당 주제에 대한 무해한 진술이 더 높은 비율에서 '독성'으로 잘못 표시되어 말이 부당하게 검열 될 수 있음을 의미 할 수 있습니다.

훈련 중에 제약을 부과함으로써 우리는 서로 다른 주제 그룹에서보다 공평하게 수행되는 더 공정한 모델을 훈련 할 수 있습니다.

TFCO 라이브러리를 사용하여 교육 중에 공정성 목표를 최적화합니다.

먼저 관련 라이브러리를 설치하고 가져 오겠습니다. 런타임의 오래된 패키지로 인해 첫 번째 셀을 실행 한 후 colab을 한 번 다시 시작해야 할 수 있습니다. 그렇게 한 후에는 수입에 더 이상 문제가 없어야합니다.


!pip install -q git+https://github.com/google-research/tensorflow_constrained_optimization
!pip install -q git+https://github.com/tensorflow/fairness-indicators
ERROR: apache-beam 2.22.0 has requirement oauth2client<4,>=2.0.1, but you'll have oauth2client 4.1.3 which is incompatible.
ERROR: tfx-bsl 0.22.0 has requirement absl-py<0.9,>=0.7, but you'll have absl-py 0.9.0 which is incompatible.
ERROR: tensorflow-model-analysis 0.22.1 has requirement absl-py<0.9,>=0.7, but you'll have absl-py 0.9.0 which is incompatible.
ERROR: tensorflow-transform 0.22.0 has requirement absl-py<0.9,>=0.7, but you'll have absl-py 0.9.0 which is incompatible.
ERROR: tensorflow-data-validation 0.22.0 has requirement absl-py<0.9,>=0.7, but you'll have absl-py 0.9.0 which is incompatible.
ERROR: tensorflow-data-validation 0.22.0 has requirement pandas<1,>=0.24, but you'll have pandas 1.0.4 which is incompatible.

아래 셀을 실행하는시기에 따라 Colab에서 TensorFlow 2.X로 전환하는 기본 버전의 TensorFlow에 대한 경고가 곧 표시 될 수 있습니다. 이 노트북은 TensorFlow 1.X 및 2.X와 호환되도록 설계되었으므로이 경고를 무시해도됩니다.


import io
import os
import shutil
import sys
import tempfile
import time
import urllib
import zipfile

import apache_beam as beam
from IPython.display import display
from IPython.display import HTML
import numpy as np
import pandas as pd

import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.preprocessing import text
import tensorflow_constrained_optimization as tfco
import tensorflow_model_analysis as tfma
import fairness_indicators as fi
from tensorflow_model_analysis.addons.fairness.view import widget_view
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_evaluate_graph
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_extractor
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_predict as agnostic_predict

TFCO는 eager 및 그래프 실행과 호환되지만이 노트북은 eager 실행이 기본적으로 활성화되어 있다고 가정합니다. 중단되지 않도록하기 위해 아래 셀에서 즉시 실행이 활성화됩니다.


if tf.__version__ < "2.0.0":
  tf.enable_eager_execution()
  print("Eager execution enabled.")
else:
  print("Eager execution enabled by default.")

print("TensorFlow " + tf.__version__)
print("TFMA " + tfma.__version__)
print("FI " + fi.version.__version__)
Eager execution enabled by default.
TensorFlow 2.2.0
TFMA 0.22.1
FI 0.1.1

하이퍼 파라미터

먼저 데이터 전처리 및 모델 학습에 필요한 몇 가지 하이퍼 매개 변수를 설정합니다.

hparams = {
    "batch_size": 128,
    "cnn_filter_sizes": [128, 128, 128],
    "cnn_kernel_sizes": [5, 5, 5],
    "cnn_pooling_sizes": [5, 5, 40],
    "constraint_learning_rate": 0.01,
    "embedding_dim": 100,
    "embedding_trainable": False,
    "learning_rate": 0.005,
    "max_num_words": 10000,
    "max_sequence_length": 250
}

데이터 세트로드 및 전처리

다음으로 데이터 세트를 다운로드하고 전처리합니다. 학습, 테스트 및 유효성 검사 세트는 별도의 CSV 파일로 제공됩니다.

toxicity_data_url = ("https://raw.githubusercontent.com/conversationai/"
                     "unintended-ml-bias-analysis/master/data/")

data_train = pd.read_csv(toxicity_data_url + "wiki_train.csv")
data_test = pd.read_csv(toxicity_data_url + "wiki_test.csv")
data_vali = pd.read_csv(toxicity_data_url + "wiki_dev.csv")

data_train.head()

comment 열에는 토론 댓글이 포함되고 is_toxic 열은 댓글이 독성으로 주석 처리되었는지 여부를 나타냅니다.

다음에서 우리는 :

  1. 라벨 분리
  2. 텍스트 주석 토큰 화
  3. 민감한 주제 용어가 포함 된 댓글 식별

먼저 기차, 테스트 및 검증 세트에서 레이블을 분리합니다. 레이블은 모두 이진 (0 또는 1)입니다.

labels_train = data_train["is_toxic"].values.reshape(-1, 1) * 1.0
labels_test = data_test["is_toxic"].values.reshape(-1, 1) * 1.0
labels_vali = data_vali["is_toxic"].values.reshape(-1, 1) * 1.0

다음으로 Keras 제공하는 Tokenizer 사용하여 텍스트 주석을 토큰 화합니다. 우리는 훈련 세트 주석만을 사용하여 토큰 어휘를 구축하고이를 사용하여 모든 주석을 동일한 길이의 (패딩 된) 토큰 시퀀스로 변환합니다.

tokenizer = text.Tokenizer(num_words=hparams["max_num_words"])
tokenizer.fit_on_texts(data_train["comment"])

def prep_text(texts, tokenizer, max_sequence_length):
    # Turns text into into padded sequences.
    text_sequences = tokenizer.texts_to_sequences(texts)
    return sequence.pad_sequences(text_sequences, maxlen=max_sequence_length)

text_train = prep_text(data_train["comment"], tokenizer, hparams["max_sequence_length"])
text_test = prep_text(data_test["comment"], tokenizer, hparams["max_sequence_length"])
text_vali = prep_text(data_vali["comment"], tokenizer, hparams["max_sequence_length"])

마지막으로 특정 민감한 주제 그룹과 관련된 댓글을 식별합니다. 우리는의 부분 집합 고려 신원 용어 성적, 성 정체성, 종교, 인종 : 데이터 세트 및 그룹을 네 가지 주제 그룹으로 함께 제공합니다.

terms = {
    'sexuality': ['gay', 'lesbian', 'bisexual', 'homosexual', 'straight', 'heterosexual'], 
    'gender identity': ['trans', 'transgender', 'cis', 'nonbinary'],
    'religion': ['christian', 'muslim', 'jewish', 'buddhist', 'catholic', 'protestant', 'sikh', 'taoist'],
    'race': ['african', 'african american', 'black', 'white', 'european', 'hispanic', 'latino', 'latina', 
             'latinx', 'mexican', 'canadian', 'american', 'asian', 'indian', 'middle eastern', 'chinese', 
             'japanese']}

group_names = list(terms.keys())
num_groups = len(group_names)

그런 다음 기차, 테스트 및 유효성 검사 세트에 대해 별도의 그룹 멤버십 매트릭스를 생성합니다. 여기서 행은 주석에 해당하고 열은 4 개의 민감한 그룹에 해당하며 각 항목은 주석에 주제 그룹의 용어가 포함되어 있는지 여부를 나타내는 부울입니다.

def get_groups(text):
    # Returns a boolean NumPy array of shape (n, k), where n is the number of comments, 
    # and k is the number of groups. Each entry (i, j) indicates if the i-th comment 
    # contains a term from the j-th group.
    groups = np.zeros((text.shape[0], num_groups))
    for ii in range(num_groups):
        groups[:, ii] = text.str.contains('|'.join(terms[group_names[ii]]), case=False)
    return groups

groups_train = get_groups(data_train["comment"])
groups_test = get_groups(data_test["comment"])
groups_vali = get_groups(data_vali["comment"])

아래에 표시된 것처럼 4 개의 주제 그룹 모두 전체 데이터 세트의 작은 부분에 불과하며 다양한 비율의 독성 댓글이 있습니다.

print("Overall label proportion = %.1f%%" % (labels_train.mean() * 100))

group_stats = []
for ii in range(num_groups):
    group_proportion = groups_train[:, ii].mean()
    group_pos_proportion = labels_train[groups_train[:, ii] == 1].mean()
    group_stats.append([group_names[ii],
                        "%.2f%%" % (group_proportion * 100), 
                        "%.1f%%" % (group_pos_proportion * 100)])
group_stats = pd.DataFrame(group_stats, 
                           columns=["Topic group", "Group proportion", "Label proportion"])
group_stats
Overall label proportion = 9.7%

데이터 세트의 1.3 %만이성에 관련된 댓글을 포함하고 있습니다. 이 중 37 %의 댓글이 독성으로 주석 처리되었습니다. 이것은 독성으로 주석이 달린 댓글의 전체 비율보다 훨씬 큽니다. 이는 이러한 정체성 용어를 사용한 소수의 댓글이 경멸적인 맥락에서 그렇게했기 때문일 수 있습니다. 위에서 언급했듯이 이로 인해 모델이 해당 용어를 포함 할 때 댓글을 유해한 것으로 잘못 분류 할 수 있습니다. 이것이 문제이므로 모델의 성능을 평가할 때 오 탐률 을 확인해야합니다.

CNN 독성 예측 모델 구축

데이터 세트를 준비 했으므로 이제 독성 예측을위한 Keras 모델을 구축합니다. 우리가 사용하는 모델은 Conversation AI 프로젝트에서 편향성 분석을 위해 사용하는 것과 동일한 아키텍처를 사용하는 컨볼 루션 신경망 (CNN)입니다. 모델 계층을 구성하기 위해 제공되는 코드를 적용 합니다 .

모델은 임베딩 레이어를 사용하여 텍스트 토큰을 고정 길이 벡터로 변환합니다. 이 레이어는 입력 텍스트 시퀀스를 벡터 시퀀스로 변환하고이를 여러 레이어의 컨볼 루션 및 풀링 작업을 통과 한 다음 최종 완전 연결 레이어를 통과합니다.

사전 훈련 된 GloVe 단어 벡터 임베딩을 사용하며 아래에서 다운로드합니다. 완료하는 데 몇 분 정도 걸릴 수 있습니다.

zip_file_url = "http://nlp.stanford.edu/data/glove.6B.zip"
zip_file = urllib.request.urlopen(zip_file_url)
archive = zipfile.ZipFile(io.BytesIO(zip_file.read()))

다운로드 한 GloVe 임베딩을 사용하여 임베딩 매트릭스를 생성합니다. 여기에서 행에는 Tokenizer 의 어휘에있는 토큰에 대한 단어 임베딩이 포함됩니다.

embeddings_index = {}
glove_file = "glove.6B.100d.txt"

with archive.open(glove_file) as f:
    for line in f:
        values = line.split()
        word = values[0].decode("utf-8") 
        coefs = np.asarray(values[1:], dtype="float32")
        embeddings_index[word] = coefs

embedding_matrix = np.zeros((len(tokenizer.word_index) + 1, hparams["embedding_dim"]))
num_words_in_embedding = 0
for word, i in tokenizer.word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        num_words_in_embedding += 1
        embedding_matrix[i] = embedding_vector

이제 Keras 레이어를 지정할 준비가되었습니다. 우리는 새 모델을 생성하는 함수를 작성합니다. 새 모델을 훈련하고자 할 때마다 호출 할 것입니다.

def create_model():
    model = keras.Sequential()

    # Embedding layer.
    embedding_layer = layers.Embedding(
        embedding_matrix.shape[0],
        embedding_matrix.shape[1],
        weights=[embedding_matrix],
        input_length=hparams["max_sequence_length"],
        trainable=hparams['embedding_trainable'])
    model.add(embedding_layer)

    # Convolution layers.
    for filter_size, kernel_size, pool_size in zip(
        hparams['cnn_filter_sizes'], hparams['cnn_kernel_sizes'],
        hparams['cnn_pooling_sizes']):

        conv_layer = layers.Conv1D(
            filter_size, kernel_size, activation='relu', padding='same')
        model.add(conv_layer)

        pooled_layer = layers.MaxPooling1D(pool_size, padding='same')
        model.add(pooled_layer)

    # Add a flatten layer, a fully-connected layer and an output layer.
    model.add(layers.Flatten())
    model.add(layers.Dense(128, activation='relu'))
    model.add(layers.Dense(1))
    
    return model

또한 무작위 시드를 설정하는 방법을 정의합니다. 이는 재현 가능한 결과를 보장하기 위해 수행됩니다.

def set_seeds():
  np.random.seed(121212)
  tf.compat.v1.set_random_seed(212121)

공정성 지표

또한 공정성 지표를 그리는 함수를 작성합니다.

def create_examples(labels, predictions, groups, group_names):
  # Returns tf.examples with given labels, predictions, and group information.  
  examples = []
  sigmoid = lambda x: 1/(1 + np.exp(-x)) 
  for ii in range(labels.shape[0]):
    example = tf.train.Example()
    example.features.feature['toxicity'].float_list.value.append(
        labels[ii])
    example.features.feature['prediction'].float_list.value.append(
        sigmoid(predictions[ii]))  # predictions need to be in [0, 1].
    for jj in range(groups.shape[1]):
      example.features.feature[group_names[jj]].bytes_list.value.append(
          b'Yes' if groups[ii, jj] else b'No')
    examples.append(example)
  return examples
def evaluate_results(labels, predictions, groups, group_names):
  # Evaluates fairness indicators for given labels, predictions and group
  # membership info.
  examples = create_examples(labels, predictions, groups, group_names)

  # Create feature map for labels, predictions and each group.
  feature_map = {
      'prediction': tf.io.FixedLenFeature([], tf.float32),
      'toxicity': tf.io.FixedLenFeature([], tf.float32),
  }
  for group in group_names:
    feature_map[group] = tf.io.FixedLenFeature([], tf.string)

  # Serialize the examples.
  serialized_examples = [e.SerializeToString() for e in examples]

  BASE_DIR = tempfile.gettempdir()
  OUTPUT_DIR = os.path.join(BASE_DIR, 'output')

  with beam.Pipeline() as pipeline:
    model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
              label_keys=['toxicity'],
              prediction_keys=['prediction'],
              feature_spec=feature_map)
    
    slices = [tfma.slicer.SingleSliceSpec()]
    for group in group_names:
      slices.append(
          tfma.slicer.SingleSliceSpec(columns=[group]))

    extractors = [
            model_agnostic_extractor.ModelAgnosticExtractor(
                model_agnostic_config=model_agnostic_config),
            tfma.extractors.slice_key_extractor.SliceKeyExtractor(slices)
        ]

    metrics_callbacks = [
      tfma.post_export_metrics.fairness_indicators(
          thresholds=[0.5],
          target_prediction_keys=['prediction'],
          labels_key='toxicity'),
      tfma.post_export_metrics.example_count()]

    # Create a model agnostic aggregator.
    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))

    # Run Model Agnostic Eval.
    _ = (
        pipeline
        | beam.Create(serialized_examples)
        | 'ExtractEvaluateAndWriteResults' >>
          tfma.ExtractEvaluateAndWriteResults(
              eval_shared_model=eval_shared_model,
              output_path=OUTPUT_DIR,
              extractors=extractors,
              compute_confidence_intervals=True
          )
    )

  fairness_ind_result = tfma.load_eval_result(output_path=OUTPUT_DIR)

  # Also evaluate accuracy of the model.
  accuracy = np.mean(labels == (predictions > 0.0))

  return fairness_ind_result, accuracy
def plot_fairness_indicators(eval_result, title):
  fairness_ind_result, accuracy = eval_result
  display(HTML("<center><h2>" + title + 
               " (Accuracy = %.2f%%)" % (accuracy * 100) + "</h2></center>"))
  widget_view.render_fairness_indicator(fairness_ind_result)
def plot_multi_fairness_indicators(multi_eval_results):
 
  multi_results = {}
  multi_accuracy = {}
  for title, (fairness_ind_result, accuracy) in multi_eval_results.items():
    multi_results[title] = fairness_ind_result
    multi_accuracy[title] = accuracy
  
  title_str = "<center><h2>"
  for title in multi_eval_results.keys():
      title_str+=title + " (Accuracy = %.2f%%)" % (multi_accuracy[title] * 100) + "; "
  title_str=title_str[:-2]
  title_str+="</h2></center>"
  # fairness_ind_result, accuracy = eval_result
  display(HTML(title_str))
  widget_view.render_fairness_indicator(multi_eval_results=multi_results)

제약없는 모델 학습

우리가 훈련하는 첫 번째 모델의 경우 제약 없이 간단한 교차 엔트로피 손실 최적화합니다.

# Set random seed for reproducible results.
set_seeds()
# Optimizer and loss.
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams["learning_rate"])
loss = lambda y_true, y_pred: tf.keras.losses.binary_crossentropy(
    y_true, y_pred, from_logits=True)

# Create, compile and fit model.
model_unconstrained = create_model()
model_unconstrained.compile(optimizer=optimizer, loss=loss)

model_unconstrained.fit(
    x=text_train, y=labels_train, batch_size=hparams["batch_size"], epochs=2)
Epoch 1/2
748/748 [==============================] - 5s 6ms/step - loss: 0.1583
Epoch 2/2
748/748 [==============================] - 5s 6ms/step - loss: 0.1207

<tensorflow.python.keras.callbacks.History at 0x7fbae84a8d30>

제약이없는 모델을 학습 한 후 테스트 세트에 모델에 대한 다양한 평가 메트릭을 표시합니다.

scores_unconstrained_test = model_unconstrained.predict(text_test)
eval_result_unconstrained = evaluate_results(
    labels_test, scores_unconstrained_test, groups_test, group_names)
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.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
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Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

INFO:tensorflow:ExampleCount post export metric: could not find any of the standard keys in predictions_dict (keys were: dict_keys(['prediction']))

INFO:tensorflow:ExampleCount post export metric: could not find any of the standard keys in predictions_dict (keys were: dict_keys(['prediction']))

INFO:tensorflow:Using the first key from predictions_dict: prediction

INFO:tensorflow:Using the first key from predictions_dict: prediction
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Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/writers/metrics_and_plots_serialization.py:122: 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_model_analysis/writers/metrics_and_plots_serialization.py:122: 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)`

위에서 설명한 바와 같이 우리는 오 탐률에 집중하고 있습니다. 현재 버전 (0.1.2)에서 공정성 지표는 기본적으로 거짓 음성 비율을 선택합니다. 아래 줄을 실행 한 후 계속해서 false_negative_rate를 선택 취소하고 false_positive_rate를 선택하여 관심있는 메트릭을 확인하십시오.

plot_fairness_indicators(eval_result_unconstrained, "Unconstrained")

전체적인 위양성 비율은 2 % 미만이지만 성 관련 댓글에 대한 위양성 비율은 상당히 높습니다. 이는 섹슈얼리티 그룹의 규모가 매우 작고 독성으로 주석이 달린 댓글의 비율이 지나치게 높기 때문입니다. 따라서 제약없이 모델을 훈련하면 성 관련 용어가 독성의 강력한 지표라고 모델이 믿게됩니다.

오 탐지율에 대한 제약을두고 훈련

서로 다른 그룹에서 잘못된 긍정 비율의 큰 차이를 방지하기 위해 다음으로 각 그룹의 잘못된 긍정 비율을 원하는 한도 내로 제한하여 모델을 학습시킵니다. 이 경우 그룹 별 오 탐률이 2 % 이하인 모델의 오류율을 최적화합니다.

그룹 별 제약 조건이있는 미니 배치에 대한 교육은이 데이터 세트에 대해 어려울 수 있지만 제약하려는 그룹의 크기가 모두 작기 때문에 개별 미니 배치에 각 그룹의 예제가 거의 포함되지 않을 가능성이 높습니다. 따라서 우리가 훈련하는 동안 계산하는 기울기는 잡음이 많고 모델이 매우 느리게 수렴됩니다.

이 문제를 완화하려면 두 개의 미니 배치 스트림을 사용하는 것이 좋습니다. 첫 번째 스트림은 전체 학습 세트에서 이전과 같이 형성되고 두 번째 스트림은 민감한 그룹 예제에서만 형성됩니다. 첫 번째 스트림의 미니 배치를 사용하여 목표를 계산하고 두 번째 스트림의 미니 배치를 사용하여 그룹 별 제약 조건을 계산합니다. 두 번째 스트림의 배치에는 각 그룹의 더 많은 수의 예제가 포함될 가능성이 높기 때문에 업데이트시 소음이 적을 것으로 예상됩니다.

두 스트림의 미니 배치를 유지하기 위해 별도의 기능, 레이블 및 그룹 텐서를 생성합니다.

# Set random seed.
set_seeds()

# Features tensors.
batch_shape = (hparams["batch_size"], hparams['max_sequence_length'])
features_tensor = tf.Variable(np.zeros(batch_shape, dtype='int32'), name='x')
features_tensor_sen = tf.Variable(np.zeros(batch_shape, dtype='int32'), name='x_sen')

# Labels tensors.
batch_shape = (hparams["batch_size"], 1)
labels_tensor = tf.Variable(np.zeros(batch_shape, dtype='float32'), name='labels')
labels_tensor_sen = tf.Variable(np.zeros(batch_shape, dtype='float32'), name='labels_sen')

# Groups tensors.
batch_shape = (hparams["batch_size"], num_groups)
groups_tensor_sen = tf.Variable(np.zeros(batch_shape, dtype='float32'), name='groups_sen')

새 모델을 인스턴스화하고 두 스트림에서 미니 배치에 대한 예측을 계산합니다.

# Create model, and separate prediction functions for the two streams. 
# For the predictions, we use a nullary function returning a Tensor to support eager mode.
model_constrained = create_model()

def predictions():
  return model_constrained(features_tensor)

def predictions_sen():
  return model_constrained(features_tensor_sen)

그런 다음 오류율을 목표로하고 그룹 별 오 탐율에 대한 제약 조건이있는 제약 최적화 문제를 설정했습니다.

epsilon = 0.02  # Desired false-positive rate threshold.

# Set up separate contexts for the two minibatch streams.
context = tfco.rate_context(predictions, lambda:labels_tensor)
context_sen = tfco.rate_context(predictions_sen, lambda:labels_tensor_sen)

# Compute the objective using the first stream.
objective = tfco.error_rate(context)

# Compute the constraint using the second stream.
# Subset the examples belonging to the "sexuality" group from the second stream 
# and add a constraint on the group's false positive rate.
context_sen_subset = context_sen.subset(lambda: groups_tensor_sen[:, 0] > 0)
constraint = [tfco.false_positive_rate(context_sen_subset) <= epsilon]

# Create a rate minimization problem.
problem = tfco.RateMinimizationProblem(objective, constraint)

# Set up a constrained optimizer.
optimizer = tfco.ProxyLagrangianOptimizerV2(
    optimizer=tf.keras.optimizers.Adam(learning_rate=hparams["learning_rate"]),
    num_constraints=problem.num_constraints)

# List of variables to optimize include the model weights, 
# and the trainable variables from the rate minimization problem and 
# the constrained optimizer.
var_list = (model_constrained.trainable_weights + problem.trainable_variables +
            optimizer.trainable_variables())

모델을 훈련 할 준비가되었습니다. 우리는 두 개의 미니 배치 스트림에 대해 별도의 카운터를 유지합니다. 그래디언트 업데이트를 수행 할 때마다 첫 번째 스트림에서 텐서 features_tensorlabels_tensor 배치 콘텐츠를 복사하고 두 번째 스트림의 미니 배치 콘텐츠를 features_tensor_sen , labels_tensor_sengroups_tensor_sen 텐서로 labels_tensor_sen groups_tensor_sen 합니다.

# Indices of sensitive group members.
protected_group_indices = np.nonzero(groups_train.sum(axis=1))[0]

num_examples = text_train.shape[0]
num_examples_sen = protected_group_indices.shape[0]
batch_size = hparams["batch_size"]

# Number of steps needed for one epoch over the training sample.
num_steps = int(num_examples / batch_size)

start_time = time.time()

# Loop over minibatches.
for batch_index in range(num_steps):
    # Indices for current minibatch in the first stream.
    batch_indices = np.arange(
        batch_index * batch_size, (batch_index + 1) * batch_size)
    batch_indices = [ind % num_examples for ind in batch_indices]

    # Indices for current minibatch in the second stream.
    batch_indices_sen = np.arange(
        batch_index * batch_size, (batch_index + 1) * batch_size)
    batch_indices_sen = [protected_group_indices[ind % num_examples_sen]
                         for ind in batch_indices_sen]

    # Assign features, labels, groups from the minibatches to the respective tensors.
    features_tensor.assign(text_train[batch_indices, :])
    labels_tensor.assign(labels_train[batch_indices])

    features_tensor_sen.assign(text_train[batch_indices_sen, :])
    labels_tensor_sen.assign(labels_train[batch_indices_sen])
    groups_tensor_sen.assign(groups_train[batch_indices_sen, :])

    # Gradient update.
    optimizer.minimize(problem, var_list=var_list)
    
    # Record and print batch training stats every 10 steps.
    if (batch_index + 1) % 10 == 0 or batch_index in (0, num_steps - 1):
      hinge_loss = problem.objective()
      max_violation = max(problem.constraints())

      elapsed_time = time.time() - start_time
      sys.stdout.write(
          "\rStep %d / %d: Elapsed time = %ds, Loss = %.3f, Violation = %.3f" % 
          (batch_index + 1, num_steps, elapsed_time, hinge_loss, max_violation))
    
Step 747 / 747: Elapsed time = 42s, Loss = 0.061, Violation = -0.020

제한된 모델을 학습 한 후 테스트 세트에 모델에 대한 다양한 평가 메트릭을 표시합니다.

scores_constrained_test = model_constrained.predict(text_test)
eval_result_constrained = evaluate_results(
    labels_test, scores_constrained_test, groups_test, group_names)
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INFO:tensorflow:ExampleCount post export metric: could not find any of the standard keys in predictions_dict (keys were: dict_keys(['prediction']))

INFO:tensorflow:ExampleCount post export metric: could not find any of the standard keys in predictions_dict (keys were: dict_keys(['prediction']))

INFO:tensorflow:Using the first key from predictions_dict: prediction

INFO:tensorflow:Using the first key from predictions_dict: prediction
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: 

지난번과 마찬가지로 false_positive_rate를 선택하는 것을 잊지 마십시오.

plot_fairness_indicators(eval_result_constrained, "Constrained")
multi_results = {
    'constrained':eval_result_constrained,
    'unconstrained':eval_result_unconstrained,
}
plot_multi_fairness_indicators(multi_eval_results=multi_results)

공정성 지표에서 볼 수 있듯이 제약이없는 모델과 비교하여 제약이있는 모델은 성 관련 댓글에 대한 오 탐률이 현저히 낮으며 전체 정확도가 약간만 떨어집니다.