Wiki Talk 댓글 독성 예측

TensorFlow.org에서 보기 Google Colab에서 실행 GitHub에서 보기 노트북 다운로드

이 예에서 우리는 Wiki 토론 페이지에 게시된 토론 댓글에 유해한 내용이 포함되어 있는지 여부를 예측하는 작업을 고려합니다(예: "무례하거나 무례하거나 불합리한" 내용 포함). 우리는 공공 사용 데이터 세트 에 의해 발표 회화 인공 지능 (참조 군중 근로자 주석되는 영어 위키 백과에서 이상 100,000 주석이 포함 프로젝트를 종이 라벨 방법론을).

이 데이터 세트의 문제 중 하나는 매우 적은 비율의 댓글이 섹슈얼리티나 종교와 같은 민감한 주제를 다룬다는 것입니다. 따라서 이 데이터 세트에서 신경망 모델을 훈련하면 더 작고 민감한 주제에 대해 서로 다른 성능이 발생합니다. 이는 해당 주제에 대한 무해한 진술이 더 높은 비율로 '독성'으로 잘못 표시되어 발언이 부당하게 검열될 수 있음을 의미할 수 있습니다.

훈련 기간 동안 제약 조건을 부과함으로써, 우리는 공정한 모델을 훈련 할 수 수행하는 더 공평하게 서로 다른 주제 그룹간에.

TFCO 라이브러리를 사용하여 교육 중 공정성 목표를 최적화합니다.

설치

먼저 관련 라이브러리를 설치하고 가져오도록 하겠습니다. 런타임의 오래된 패키지로 인해 첫 번째 셀을 실행한 후 colab을 한 번 다시 시작해야 할 수 있습니다. 그렇게 한 후에는 수입에 더 이상 문제가 없어야 합니다.

핍 설치

아래 셀을 실행하는 시기에 따라 Colab에서 TensorFlow의 기본 버전이 곧 TensorFlow 2.X로 전환된다는 경고를 받을 수 있습니다. 이 노트북은 TensorFlow 1.X 및 2.X와 호환되도록 설계되었으므로 이 경고는 무시해도 됩니다.

모듈 가져오기

TFCO는 Eager 및 그래프 실행과 호환되지만 이 노트북은 기본적으로 Eager 실행이 활성화되어 있다고 가정합니다. 아무 것도 중단되지 않도록 아래 셀에서 즉시 실행이 활성화됩니다.

Eager 실행 및 인쇄 버전 활성화

Eager execution enabled by default.
TensorFlow 2.3.2
TFMA 0.26.0
FI 0.27.0.dev

하이퍼 매개변수

먼저 데이터 전처리와 모델 훈련에 필요한 하이퍼파라미터를 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
}

데이터세트 로드 및 사전 처리

다음으로 데이터 세트를 다운로드하고 전처리합니다. 훈련, 테스트 및 검증 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 열은 토론 주석을 포함하고 is_toxic 열 주석이 유독 주석이 있는지 여부를 나타냅니다.

다음에서 우리는:

  1. 라벨을 분리
  2. 텍스트 주석 토큰화
  3. 민감한 주제 용어가 포함된 댓글 식별

먼저 훈련, 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, 우리는 사용하여 텍스트 주석 토큰 화 Tokenizer 에 의해 제공 Keras . 훈련 세트 주석만 사용하여 토큰 어휘를 만들고 모든 주석을 동일한 길이의 (패딩된) 토큰 시퀀스로 변환하는 데 사용합니다.

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 행, 테스트 및 유효성 검사 세트에 대해 별도의 그룹 멤버십 매트릭스를 생성합니다. 여기서 행은 주석에 해당하고 열은 4개의 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"])

아래에서 볼 수 있듯이 네 가지 주제 그룹은 모두 전체 데이터 세트의 작은 부분에 불과하며 다양한 비율의 유독성 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%

데이터세트의 1.3%만이 섹슈얼리티와 관련된 댓글을 포함하고 있음을 알 수 있습니다. 그 중 37%의 댓글이 유독하다는 주석이 달렸습니다. 이는 유독성 주석이 달린 전체 댓글 비율보다 훨씬 더 많습니다. 이는 해당 정체성 용어를 사용한 소수의 댓글이 경멸적인 맥락에서 그렇게 했기 때문일 수 있습니다. 위에서 언급했듯이, 이로 인해 우리 모델이 댓글에 해당 용어가 포함될 때 댓글을 과도하게 유해한 것으로 잘못 분류할 수 있습니다. 이것이 문제이기 때문에, 우리는 우리가 모델의 성능을 평가 할 때 위양성률 볼 수 있는지 확인합니다.

CNN 독성 예측 모델 구축

데이터 집합을 준비하는 데, 우리는 이제 구축 Keras 예측 독성 모델을. 우리가 사용하는 모델은 편향성 제거 분석을 위해 Conversation AI 프로젝트에서 사용하는 것과 동일한 아키텍처를 가진 CNN(Convolutional Neural Network)입니다. 우리는 적응 코드 모델 층을 구성하는 그들에 의해 제공합니다.

이 모델은 임베딩 레이어를 사용하여 텍스트 토큰을 고정 길이 벡터로 변환합니다. 이 레이어는 입력 텍스트 시퀀스를 벡터 시퀀스로 변환하고 여러 레이어의 컨볼루션 및 풀링 작업을 거친 후 최종 완전 연결 레이어를 통과합니다.

아래에서 다운로드하는 사전 훈련된 GloV 단어 벡터 임베딩을 사용합니다. 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()))

우리는 행이있는 토큰 단어 묻어 포함 매립 매트릭스 만들 다운로드 한 장갑 묻어을 사용하여 Tokenizer 의 어휘를.

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

우리는 지금 지정 할 준비가 Keras 레이어를. 우리는 새로운 모델을 훈련시키고 싶을 때마다 호출할 새로운 모델을 생성하는 함수를 작성합니다.

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 임의의 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 표시하는 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)

제약 조건이 없는 모델 학습

첫 번째 모델 우리 열차의 경우, 우리는 어떤 제약없이 간단한 크로스 엔트로피 손실을 최적화 ..

# 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 [==============================] - 51s 69ms/step - loss: 0.1590
Epoch 2/2
748/748 [==============================] - 48s 65ms/step - loss: 0.1217
<tensorflow.python.keras.callbacks.History at 0x7f55603a1d30>

제약이 없는 모델을 훈련시킨 후 테스트 세트에서 모델에 대한 다양한 평가 메트릭을 플로팅합니다.

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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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_plots_and_validations_writer.py:113: 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_plots_and_validations_writer.py:113: 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 것처럼 우리는 위양성 비율에 집중하고 있습니다. 현재 버전(0.1.2)에서 공정성 지표는 기본적으로 위음성 비율을 선택합니다. 아래 줄을 실행한 후 false_negative_rate를 선택 취소하고 false_positive_rate를 선택하여 관심 있는 메트릭을 확인합니다.

plot_fairness_indicators(eval_result_unconstrained, "Unconstrained")

전체 위양성률은 2% 미만이지만 성 관련 댓글의 위양성률은 훨씬 높습니다. 이는 섹슈얼리티 그룹의 규모가 매우 작고 유독성 주석이 달린 댓글의 비율이 불균형적으로 높기 때문입니다. 따라서 제약 조건 없이 모델을 훈련하면 성 관련 용어가 독성의 강력한 지표라고 모델이 믿게 됩니다.

위양성 비율에 대한 제약 조건으로 훈련

서로 다른 그룹 간의 잘못된 긍정 비율의 큰 차이를 피하기 위해 다음으로 각 그룹에 대한 긍정 오류 비율을 원하는 제한 내로 제한하여 모델을 학습합니다. 이 경우, 우리는 낮은 것으로 당 그룹에 거짓 긍정적 인 요금을 모델 피사체의 오류 비율을 최적화 2 %에 해당.

그룹당 제약 조건이 있는 미니배치에 대한 훈련은 이 데이터 세트에 대해 어려울 수 있습니다. 그러나 제약하려는 그룹은 모두 크기가 작고 개별 미니배치에는 각 그룹의 예제가 거의 없기 때문입니다. 따라서 훈련 중에 계산하는 기울기는 노이즈가 발생하고 모델이 매우 느리게 수렴됩니다.

이 문제를 완화하려면 두 개의 미니배치 스트림을 사용하는 것이 좋습니다. 첫 번째 스트림은 이전과 같이 전체 훈련 세트에서 형성되고 두 번째 스트림은 민감한 그룹 예제에서만 형성됩니다. 첫 번째 스트림의 미니 배치를 사용하여 목표를 계산하고 두 번째 스트림의 미니 배치를 사용하여 그룹당 제약 조건을 계산합니다. 두 번째 스트림의 배치에는 각 그룹의 더 많은 수의 예제가 포함될 가능성이 높기 때문에 업데이트가 덜 시끄럽습니다.

두 스트림의 미니 배치를 보유하기 위해 별도의 기능, 레이블 및 그룹 텐서를 만듭니다.

# 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 목표로 하고 그룹별 가양성 비율에 대한 제약 조건을 사용하여 제한된 최적화 문제를 설정합니다.

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())

우리는 모델을 훈련할 준비가 되었습니다. 두 개의 미니배치 스트림에 대해 별도의 카운터를 유지 관리합니다. 우리가 그라데이션 업데이트를 수행 할 때마다, 우리는 텐서가 첫 번째 스트림에서 minibatch의 내용을 복사해야합니다 features_tensorlabels_tensor 및 텐서 두 번째 스트림에서 minibatch 내용이 features_tensor_sen , labels_tensor_sengroups_tensor_sen .

# 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 = 180s, Loss = 0.068, Violation = -0.020

제한된 모델을 훈련시킨 후 테스트 세트에서 모델에 대한 다양한 평가 메트릭을 플로팅합니다.

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:

지난 시간과 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, 제약이 없는 모델과 비교하여 제약이 있는 모델은 섹슈얼리티 관련 댓글에 대한 오탐률이 현저히 낮고 전체 정확도가 약간만 떨어집니다.