Wiki Talk Komentar Prediksi Toksisitas

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Jalankan di Google Colab

Lihat di GitHub

Unduh buku catatan

Dalam contoh ini, kami mempertimbangkan tugas untuk memprediksi apakah komentar diskusi yang diposting di halaman pembicaraan Wiki berisi konten beracun (yaitu berisi konten yang "kasar, tidak sopan, atau tidak masuk akal"). Kami menggunakan public dataset yang dirilis oleh Percakapan AI proyek, yang berisi lebih dari 100k komentar dari Wikipedia bahasa Inggris yang dijelaskan oleh pekerja kerumunan (lihat kertas untuk metodologi pelabelan).

Salah satu tantangan dengan kumpulan data ini adalah bahwa sebagian kecil komentar mencakup topik sensitif seperti seksualitas atau agama. Dengan demikian, melatih model jaringan saraf pada kumpulan data ini menghasilkan kinerja yang berbeda pada topik sensitif yang lebih kecil. Ini dapat berarti bahwa pernyataan tidak berbahaya tentang topik tersebut mungkin salah ditandai sebagai 'beracun' pada tingkat yang lebih tinggi, menyebabkan ucapan disensor secara tidak adil

Dengan memberlakukan kendala selama pelatihan, kita bisa melatih model yang lebih adil yang melakukan lebih merata di seluruh kelompok topik yang berbeda.

Kami akan menggunakan perpustakaan TFCO untuk mengoptimalkan tujuan keadilan kami selama pelatihan.

Instalasi

Mari kita menginstal dan mengimpor perpustakaan yang relevan terlebih dahulu. Perhatikan bahwa Anda mungkin harus memulai ulang colab satu kali setelah menjalankan sel pertama karena paket usang dalam waktu proses. Setelah melakukan itu, seharusnya tidak ada masalah lebih lanjut dengan impor.

pemasangan pip

pip install -q -U pip==20.2

pip install git+https://github.com/google-research/tensorflow_constrained_optimization
pip install git+https://github.com/tensorflow/fairness-indicators

Perhatikan bahwa bergantung pada waktu Anda menjalankan sel di bawah ini, Anda mungkin menerima peringatan tentang versi default TensorFlow di Colab yang akan segera beralih ke TensorFlow 2.X. Anda dapat mengabaikan peringatan tersebut dengan aman karena notebook ini dirancang agar kompatibel dengan TensorFlow 1.X dan 2.X.

Impor Modul

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

Meskipun TFCO kompatibel dengan eksekusi bersemangat dan grafik, notebook ini mengasumsikan bahwa eksekusi bersemangat diaktifkan secara default. Untuk memastikan tidak ada yang rusak, eksekusi bersemangat akan diaktifkan di sel di bawah ini.

Aktifkan Eksekusi Bersemangat dan Versi Cetak

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 2.3.2
TFMA 0.26.0
FI 0.27.0.dev

Parameter hiper

Pertama, kami menetapkan beberapa parameter hiper yang diperlukan untuk prapemrosesan data dan pelatihan model.

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
}

Muat dan pra-proses dataset

Selanjutnya, kami mengunduh dataset dan memprosesnya terlebih dahulu. Rangkaian pelatihan, pengujian, dan validasi disediakan sebagai file CSV terpisah.

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()

The comment kolom berisi komentar diskusi dan is_toxic kolom menunjukkan apakah atau tidak komentar yang dijelaskan sebagai beracun.

Berikut ini, kami:

1. Pisahkan labelnya
2. Tokenisasi komentar teks
3. Identifikasi komentar yang mengandung istilah topik sensitif

Pertama, kami memisahkan label dari rangkaian kereta, pengujian, dan validasi. Label semuanya biner (0 atau 1).

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

Berikutnya, kita tokenize komentar tekstual menggunakan Tokenizer disediakan oleh Keras . Kami menggunakan komentar set pelatihan saja untuk membangun kosakata token, dan menggunakannya untuk mengubah semua komentar menjadi urutan token (berisi) dengan panjang yang sama.

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"])

Terakhir, kami mengidentifikasi komentar yang terkait dengan grup topik sensitif tertentu. Kami menganggap subset dari istilah identitas disediakan dengan dataset dan kelompok mereka menjadi empat kelompok topik yang luas: seksualitas, identitas gender, agama, dan ras.

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)

Kami kemudian membuat matriks keanggotaan grup terpisah untuk rangkaian kereta, pengujian dan validasi, di mana baris sesuai dengan komentar, kolom sesuai dengan empat grup sensitif, dan setiap entri adalah boolean yang menunjukkan apakah komentar berisi istilah dari grup topik.

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"])

Seperti yang ditunjukkan di bawah ini, keempat kelompok topik hanya merupakan sebagian kecil dari keseluruhan dataset, dan memiliki proporsi komentar beracun yang bervariasi.

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%

Kami melihat bahwa hanya 1,3% dari kumpulan data yang berisi komentar terkait seksualitas. Di antara mereka, 37% dari komentar telah dijelaskan sebagai beracun. Perhatikan bahwa ini secara signifikan lebih besar dari keseluruhan proporsi komentar yang dianotasi sebagai beracun. Ini bisa jadi karena beberapa komentar yang menggunakan istilah identitas tersebut melakukannya dalam konteks yang merendahkan. Seperti yang disebutkan di atas, hal ini dapat menyebabkan model kami secara tidak proporsional salah mengklasifikasikan komentar sebagai beracun jika menyertakan istilah tersebut. Karena ini adalah kekhawatiran ini, kami akan pastikan untuk melihat Tingkat Positif Palsu ketika kita mengevaluasi kinerja model.

Bangun model prediksi toksisitas CNN

Setelah disiapkan dataset, kita sekarang membangun Keras model untuk toksisitas prediksi. Model yang kami gunakan adalah convolutional neural network (CNN) dengan arsitektur yang sama yang digunakan oleh proyek Conversation AI untuk analisis debiasing mereka. Kami beradaptasi kode yang disediakan oleh mereka untuk membangun lapisan Model.

Model menggunakan lapisan penyematan untuk mengonversi token teks menjadi vektor dengan panjang tetap. Lapisan ini mengubah urutan teks input menjadi urutan vektor, dan melewatinya melalui beberapa lapisan operasi konvolusi dan penggabungan, diikuti oleh lapisan akhir yang terhubung sepenuhnya.

Kami menggunakan penyematan vektor kata GloVe yang telah dilatih sebelumnya, yang kami unduh di bawah. Ini mungkin membutuhkan waktu beberapa menit untuk menyelesaikannya.

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()))

Kami menggunakan embeddings sarung download untuk membuat matriks embedding, di mana baris berisi embeddings kata untuk token di Tokenizer kosakata 's.

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

Kita sekarang siap untuk menentukan Keras lapisan. Kami menulis fungsi untuk membuat model baru, yang akan kami panggil kapan pun kami ingin melatih model baru.

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

Kami juga mendefinisikan metode untuk mengatur benih acak. Hal ini dilakukan untuk memastikan hasil yang dapat direproduksi.

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

Indikator keadilan

Kami juga menulis fungsi untuk merencanakan indikator keadilan.

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)

Melatih model tanpa kendala

Untuk pertama model yang kita kereta, kami mengoptimalkan kerugian lintas-entropi sederhana tanpa kendala ..

# 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>

Setelah melatih model yang tidak dibatasi, kami memplot berbagai metrik evaluasi untuk model pada set pengujian.

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.
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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']))
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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)`

Seperti dijelaskan di atas, kami berkonsentrasi pada tingkat positif palsu. Dalam versi mereka saat ini (0.1.2), Indikator Kewajaran memilih tingkat negatif palsu secara default. Setelah menjalankan baris di bawah, lanjutkan dan batalkan pilihan false_negative_rate dan pilih false_positive_rate untuk melihat metrik yang kami minati.

plot_fairness_indicators(eval_result_unconstrained, "Unconstrained")

Sementara tingkat positif palsu keseluruhan kurang dari 2%, tingkat positif palsu pada komentar terkait seksualitas secara signifikan lebih tinggi. Ini karena kelompok seksualitas berukuran sangat kecil, dan memiliki fraksi komentar yang jauh lebih tinggi yang dianotasi sebagai beracun. Oleh karena itu, melatih model tanpa kendala menghasilkan model yang percaya bahwa istilah yang berhubungan dengan seksualitas adalah indikator toksisitas yang kuat.

Berlatih dengan batasan pada tarif positif palsu

Untuk menghindari perbedaan besar dalam tingkat positif palsu di seluruh kelompok yang berbeda, kami selanjutnya melatih model dengan membatasi tingkat positif palsu untuk setiap kelompok berada dalam batas yang diinginkan. Dalam hal ini, kami akan mengoptimalkan tingkat kesalahan subjek model untuk per-kelompok tingkat positif palsu menjadi lebih kecil atau sama dengan 2%.

Pelatihan minibatch dengan batasan per-grup dapat menjadi tantangan untuk kumpulan data ini, namun, karena grup yang ingin kita buat batasan semuanya berukuran kecil, dan kemungkinan minibatch individu berisi sangat sedikit contoh dari setiap grup. Oleh karena itu, gradien yang kami hitung selama pelatihan akan menjadi bising, dan menghasilkan model yang konvergen sangat lambat.

Untuk mengurangi masalah ini, sebaiknya gunakan dua aliran minibatch, dengan aliran pertama terbentuk seperti sebelumnya dari seluruh rangkaian pelatihan, dan aliran kedua terbentuk semata-mata dari contoh grup sensitif. Kami akan menghitung tujuan menggunakan minibatch dari aliran pertama dan kendala per-grup menggunakan minibatch dari aliran kedua. Karena kumpulan dari aliran kedua cenderung berisi lebih banyak contoh dari setiap grup, kami berharap pembaruan kami tidak terlalu berisik.

Kami membuat fitur, label, dan tensor grup terpisah untuk menampung minibatch dari dua aliran.

# 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')

Kami membuat instance model baru, dan menghitung prediksi untuk minibatch dari dua aliran.

# 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)

Kami kemudian membuat masalah optimasi terbatas dengan tingkat kesalahan sebagai tujuan dan dengan kendala pada tingkat positif palsu per kelompok.

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())

Kami siap melatih modelnya. Kami mempertahankan penghitung terpisah untuk dua aliran minibatch. Setiap kali kita melakukan update gradien, kita harus menyalin isi minibatch dari aliran pertama ke tensor features_tensor dan labels_tensor , dan isi minibatch dari aliran kedua ke tensor features_tensor_sen , labels_tensor_sen dan groups_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

Setelah melatih model yang dibatasi, kami memplot berbagai metrik evaluasi untuk model pada set pengujian.

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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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: <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'>
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:

Seperti terakhir kali, ingatlah untuk memilih false_positive_rate.

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)

Seperti yang dapat kita lihat dari Indikator Kewajaran, dibandingkan dengan model yang tidak dibatasi, model yang dibatasi menghasilkan tingkat positif palsu yang jauh lebih rendah untuk komentar terkait seksualitas, dan melakukannya dengan hanya sedikit penurunan dalam akurasi keseluruhan.