Ajuda a proteger a Grande Barreira de Corais com TensorFlow em Kaggle Junte Desafio

Colab de exemplo de plug-in do TensorBoard de indicadores de imparcialidade

Veja no TensorFlow.org Executar no Google Colab Ver no GitHub Baixar caderno

Visão geral

Nesta atividade, você usará Fairness Indicadores para TensorBoard . Com o plug-in, você pode visualizar avaliações de imparcialidade para suas corridas e comparar facilmente o desempenho entre os grupos.

Importando

Execute o código a seguir para instalar as bibliotecas necessárias.

pip install -q -U pip==20.2

pip install fairness_indicators 'absl-py<0.9,>=0.7'
pip install google-api-python-client==1.8.3
pip install tensorboard-plugin-fairness-indicators
pip install tensorflow-serving-api==2.7.0

Reinicie o tempo de execução. Depois que o tempo de execução for reiniciado, continue com as células seguintes sem executar a célula anterior novamente.

# %tf.disable_v2_behavior() # Uncomment this line if running in Google Colab.
import datetime
import os
import tempfile
from tensorboard_plugin_fairness_indicators import summary_v2
import tensorflow.compat.v1 as tf

# example_model.py is provided in fairness_indicators package to train and
# evaluate an example model. 
from fairness_indicators import example_model

tf.compat.v1.enable_eager_execution()

Dados e Constantes

# To know about dataset, check Fairness Indicators Example Colab at:
# https://github.com/tensorflow/fairness-indicators/blob/master/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb

train_tf_file = tf.keras.utils.get_file('train.tf', 'https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord')
validate_tf_file = tf.keras.utils.get_file('validate.tf', 'https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord')

BASE_DIR = tempfile.gettempdir()
TEXT_FEATURE = 'comment_text'
LABEL = 'toxicity'
FEATURE_MAP = {
    # Label:
    LABEL: tf.io.FixedLenFeature([], tf.float32),
    # Text:
    TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),

    # Identities:
    'sexual_orientation': tf.io.VarLenFeature(tf.string),
    'gender': tf.io.VarLenFeature(tf.string),
    'religion': tf.io.VarLenFeature(tf.string),
    'race': tf.io.VarLenFeature(tf.string),
    'disability': tf.io.VarLenFeature(tf.string),
}
Downloading data from https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord
488161280/488153424 [==============================] - 11s 0us/step
488169472/488153424 [==============================] - 11s 0us/step
Downloading data from https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord
324943872/324941336 [==============================] - 9s 0us/step
324952064/324941336 [==============================] - 9s 0us/step

Treine o modelo

model_dir = os.path.join(BASE_DIR, 'train',
                         datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))

classifier = example_model.train_model(model_dir,
                                       train_tf_file,
                                       LABEL,
                                       TEXT_FEATURE,
                                       FEATURE_MAP)
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-180912', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-180912', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:397: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:397: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-01-07 18:09:22.433489: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:400: NumericColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:400: NumericColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/feature_column/feature_column.py:2188: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/feature_column/feature_column.py:2188: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:139: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:139: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20220107-180912/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20220107-180912/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 58.65023, step = 0
INFO:tensorflow:loss = 58.65023, step = 0
INFO:tensorflow:global_step/sec: 76.6785
INFO:tensorflow:global_step/sec: 76.6785
INFO:tensorflow:loss = 55.854782, step = 100 (1.306 sec)
INFO:tensorflow:loss = 55.854782, step = 100 (1.306 sec)
INFO:tensorflow:global_step/sec: 82.6
INFO:tensorflow:global_step/sec: 82.6
INFO:tensorflow:loss = 47.47064, step = 200 (1.210 sec)
INFO:tensorflow:loss = 47.47064, step = 200 (1.210 sec)
INFO:tensorflow:global_step/sec: 85.8208
INFO:tensorflow:global_step/sec: 85.8208
INFO:tensorflow:loss = 55.59231, step = 300 (1.166 sec)
INFO:tensorflow:loss = 55.59231, step = 300 (1.166 sec)
INFO:tensorflow:global_step/sec: 86.1252
INFO:tensorflow:global_step/sec: 86.1252
INFO:tensorflow:loss = 56.18415, step = 400 (1.161 sec)
INFO:tensorflow:loss = 56.18415, step = 400 (1.161 sec)
INFO:tensorflow:global_step/sec: 86.5735
INFO:tensorflow:global_step/sec: 86.5735
INFO:tensorflow:loss = 42.37696, step = 500 (1.155 sec)
INFO:tensorflow:loss = 42.37696, step = 500 (1.155 sec)
INFO:tensorflow:global_step/sec: 86.6948
INFO:tensorflow:global_step/sec: 86.6948
INFO:tensorflow:loss = 45.75257, step = 600 (1.153 sec)
INFO:tensorflow:loss = 45.75257, step = 600 (1.153 sec)
INFO:tensorflow:global_step/sec: 87.1878
INFO:tensorflow:global_step/sec: 87.1878
INFO:tensorflow:loss = 50.73873, step = 700 (1.147 sec)
INFO:tensorflow:loss = 50.73873, step = 700 (1.147 sec)
INFO:tensorflow:global_step/sec: 85.2284
INFO:tensorflow:global_step/sec: 85.2284
INFO:tensorflow:loss = 47.609695, step = 800 (1.173 sec)
INFO:tensorflow:loss = 47.609695, step = 800 (1.173 sec)
INFO:tensorflow:global_step/sec: 85.6373
INFO:tensorflow:global_step/sec: 85.6373
INFO:tensorflow:loss = 48.22233, step = 900 (1.168 sec)
INFO:tensorflow:loss = 48.22233, step = 900 (1.168 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-180912/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-180912/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Loss for final step: 51.06088.
INFO:tensorflow:Loss for final step: 51.06088.

Execute a análise do modelo TensorFlow com indicadores de imparcialidade

Esta etapa pode levar de 2 a 5 minutos.

tfma_eval_result_path = os.path.join(BASE_DIR, 'tfma_eval_result')

example_model.evaluate_model(classifier,
                             validate_tf_file,
                             tfma_eval_result_path,
                             'gender',
                             LABEL,
                             FEATURE_MAP)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/encoding.py:132: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/encoding.py:132: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-01-07 18:09:39.350323: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:640: 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/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:640: 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:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/train/20220107-180912/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmp/train/20220107-180912/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641578979/assets
INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641578979/assets
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641578979/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641578979/saved_model.pb
WARNING:absl:Tensorflow version (2.8.0-rc0) found. Note that TFMA support for TF 2.0 is currently in beta
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:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:164: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:164: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641578979/variables/variables
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641578979/variables/variables
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:184: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:184: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
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/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: 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/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: 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)`

Visualize indicadores de imparcialidade no TensorBoard

Abaixo, você visualizará os indicadores de imparcialidade no Tensorboard e comparará o desempenho de cada fatia dos dados nas métricas selecionadas. Você pode ajustar a fatia de comparação da linha de base, bem como os limites exibidos usando os menus suspensos na parte superior da visualização. Você também pode selecionar diferentes execuções de avaliação usando o menu suspenso no canto superior esquerdo.

Escreva o resumo dos indicadores de imparcialidade

Escreva o arquivo de resumo contendo todas as informações necessárias para visualizar os indicadores de imparcialidade no TensorBoard.

import tensorflow.compat.v2 as tf2

writer = tf2.summary.create_file_writer(
    os.path.join(model_dir, 'fairness_indicators'))
with writer.as_default():
  summary_v2.FairnessIndicators(tfma_eval_result_path, step=1)
writer.close()

Iniciar TensorBoard

Navegue até a guia "Indicadores de imparcialidade" para visualizar os indicadores de imparcialidade.

%load_ext tensorboard
%tensorboard --logdir=$model_dir