Fairness Indicators on TF-Hub Text Embeddings

In this tutorial, you will learn how to use Fairness Indicators to evaluate embeddings from TF Hub. This notebook uses the Civil Comments dataset.

Setup

Install the required libraries.

!pip install -q -U pip==20.2

!pip install fairness-indicators \
  "absl-py==0.12.0" \
  "pyarrow==2.0.0" \
  "apache-beam==2.40.0" \
  "avro-python3==1.9.1"

Import other required libraries.

import os
import tempfile
import apache_beam as beam
from datetime import datetime
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_model_analysis as tfma
from tensorflow_model_analysis.addons.fairness.view import widget_view
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from fairness_indicators import example_model
from fairness_indicators.tutorial_utils import util

2022-12-14 10:44:16.681887: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 10:44:16.682004: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 10:44:16.682013: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

Dataset

In this notebook, you work with the Civil Comments dataset which contains approximately 2 million public comments made public by the Civil Comments platform in 2017 for ongoing research. This effort was sponsored by Jigsaw, who have hosted competitions on Kaggle to help classify toxic comments as well as minimize unintended model bias.

Each individual text comment in the dataset has a toxicity label, with the label being 1 if the comment is toxic and 0 if the comment is non-toxic. Within the data, a subset of comments are labeled with a variety of identity attributes, including categories for gender, sexual orientation, religion, and race or ethnicity.

Prepare the data

TensorFlow parses features from data using tf.io.FixedLenFeature and tf.io.VarLenFeature. Map out the input feature, output feature, and all other slicing features of interest.

BASE_DIR = tempfile.gettempdir()

# The input and output features of the classifier
TEXT_FEATURE = 'comment_text'
LABEL = 'toxicity'

FEATURE_MAP = {
    # input and output features
    LABEL: tf.io.FixedLenFeature([], tf.float32),
    TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),

    # slicing features
    '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)
}

IDENTITY_TERMS = ['gender', 'sexual_orientation', 'race', 'religion', 'disability']

By default, the notebook downloads a preprocessed version of this dataset, but you may use the original dataset and re-run the processing steps if desired.

In the original dataset, each comment is labeled with the percentage of raters who believed that a comment corresponds to a particular identity. For example, a comment might be labeled with the following: { male: 0.3, female: 1.0, transgender: 0.0, heterosexual: 0.8, homosexual_gay_or_lesbian: 1.0 }.

The processing step groups identity by category (gender, sexual_orientation, etc.) and removes identities with a score less than 0.5. So the example above would be converted to the following: of raters who believed that a comment corresponds to a particular identity. For example, the comment above would be labeled with the following: { gender: [female], sexual_orientation: [heterosexual, homosexual_gay_or_lesbian] }

Download the dataset.

download_original_data = False

if download_original_data:
  train_tf_file = tf.keras.utils.get_file('train_tf.tfrecord',
                                          'https://storage.googleapis.com/civil_comments_dataset/train_tf.tfrecord')
  validate_tf_file = tf.keras.utils.get_file('validate_tf.tfrecord',
                                             'https://storage.googleapis.com/civil_comments_dataset/validate_tf.tfrecord')

  # The identity terms list will be grouped together by their categories
  # (see 'IDENTITY_COLUMNS') on threshold 0.5. Only the identity term column,
  # text column and label column will be kept after processing.
  train_tf_file = util.convert_comments_data(train_tf_file)
  validate_tf_file = util.convert_comments_data(validate_tf_file)

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

Downloading data from https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord
488153424/488153424 [==============================] - 13s 0us/step
Downloading data from https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord
324941336/324941336 [==============================] - 6s 0us/step

Create a TensorFlow Model Analysis Pipeline

The Fairness Indicators library operates on TensorFlow Model Analysis (TFMA) models. TFMA models wrap TensorFlow models with additional functionality to evaluate and visualize their results. The actual evaluation occurs inside of an Apache Beam pipeline.

The steps you follow to create a TFMA pipeline are:

1. Build a TensorFlow model
2. Build a TFMA model on top of the TensorFlow model
3. Run the model analysis in an orchestrator. The example model in this notebook uses Apache Beam as the orchestrator.

def embedding_fairness_result(embedding, identity_term='gender'):

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

  print("Training classifier for " + embedding)
  classifier = example_model.train_model(model_dir,
                                         train_tf_file,
                                         LABEL,
                                         TEXT_FEATURE,
                                         FEATURE_MAP,
                                         embedding)

  # Create a unique path to store the results for this embedding.
  embedding_name = embedding.split('/')[-2]
  eval_result_path = os.path.join(BASE_DIR, 'eval_result', embedding_name)

  example_model.evaluate_model(classifier,
                               validate_tf_file,
                               eval_result_path,
                               identity_term,
                               LABEL,
                               FEATURE_MAP)
  return tfma.load_eval_result(output_path=eval_result_path)

Run TFMA & Fairness Indicators

Fairness Indicators Metrics

Some of the metrics available with Fairness Indicators are:

• Negative Rate, False Negative Rate (FNR), and True Negative Rate (TNR)
• Positive Rate, False Positive Rate (FPR), and True Positive Rate (TPR)
• Accuracy
• Precision and Recall
• Precision-Recall AUC
• ROC AUC

Text Embeddings

TF-Hub provides several text embeddings. These embeddings will serve as the feature column for the different models. This tutorial uses the following embeddings:

• random-nnlm-en-dim128: random text embeddings, this serves as a convenient baseline.
• nnlm-en-dim128: a text embedding based on A Neural Probabilistic Language Model.
• universal-sentence-encoder: a text embedding based on Universal Sentence Encoder.

Fairness Indicator Results

Compute fairness indicators with the embedding_fairness_result pipeline, and then render the results in the Fairness Indicator UI widget with widget_view.render_fairness_indicator for all the above embeddings.

Note: You may need to run the widget_view.render_fairness_indicator cells twice for the visualization to be displayed.

Random NNLM

eval_result_random_nnlm = embedding_fairness_result('https://tfhub.dev/google/random-nnlm-en-dim128/1')

Training classifier for https://tfhub.dev/google/random-nnlm-en-dim128/1
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/train/20221214-104438', '_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': '/tmpfs/tmp/train/20221214-104438', '_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/python3.8/site-packages/tensorflow/python/training/training_util.py:396: 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/python3.8/site-packages/tensorflow/python/training/training_util.py:396: 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.
2022-12-14 10:44:43.467914: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 26 into an existing graph with producer version 1286. 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
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/site-packages/tensorflow_estimator/python/estimator/canned/head.py:399: 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/python3.8/site-packages/tensorflow_estimator/python/estimator/canned/head.py:399: 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/python3.8/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/python3.8/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/python3.8/site-packages/tensorflow/python/training/adagrad.py:138: 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/python3.8/site-packages/tensorflow/python/training/adagrad.py:138: 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.
2022-12-14 10:44:46.936838: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT64
    }
  }
}
 is neither a subtype nor a supertype of the combined inputs preceding it:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT32
    }
  }
}

    while inferring type of node 'dnn/zero_fraction/cond/output/_18'
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 /tmpfs/tmp/train/20221214-104438/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/train/20221214-104438/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 64.31444, step = 0
INFO:tensorflow:loss = 64.31444, step = 0
INFO:tensorflow:global_step/sec: 101.22
INFO:tensorflow:global_step/sec: 101.22
INFO:tensorflow:loss = 68.741905, step = 100 (0.990 sec)
INFO:tensorflow:loss = 68.741905, step = 100 (0.990 sec)
INFO:tensorflow:global_step/sec: 114.616
INFO:tensorflow:global_step/sec: 114.616
INFO:tensorflow:loss = 58.825935, step = 200 (0.872 sec)
INFO:tensorflow:loss = 58.825935, step = 200 (0.872 sec)
INFO:tensorflow:global_step/sec: 113.792
INFO:tensorflow:global_step/sec: 113.792
INFO:tensorflow:loss = 59.20277, step = 300 (0.879 sec)
INFO:tensorflow:loss = 59.20277, step = 300 (0.879 sec)
INFO:tensorflow:global_step/sec: 123.083
INFO:tensorflow:global_step/sec: 123.083
INFO:tensorflow:loss = 60.871086, step = 400 (0.812 sec)
INFO:tensorflow:loss = 60.871086, step = 400 (0.812 sec)
INFO:tensorflow:global_step/sec: 121.093
INFO:tensorflow:global_step/sec: 121.093
INFO:tensorflow:loss = 56.997547, step = 500 (0.826 sec)
INFO:tensorflow:loss = 56.997547, step = 500 (0.826 sec)
INFO:tensorflow:global_step/sec: 124.644
INFO:tensorflow:global_step/sec: 124.644
INFO:tensorflow:loss = 57.30348, step = 600 (0.802 sec)
INFO:tensorflow:loss = 57.30348, step = 600 (0.802 sec)
INFO:tensorflow:global_step/sec: 116.456
INFO:tensorflow:global_step/sec: 116.456
INFO:tensorflow:loss = 61.71228, step = 700 (0.859 sec)
INFO:tensorflow:loss = 61.71228, step = 700 (0.859 sec)
INFO:tensorflow:global_step/sec: 121.95
INFO:tensorflow:global_step/sec: 121.95
INFO:tensorflow:loss = 58.572346, step = 800 (0.820 sec)
INFO:tensorflow:loss = 58.572346, step = 800 (0.820 sec)
INFO:tensorflow:global_step/sec: 122.007
INFO:tensorflow:global_step/sec: 122.007
INFO:tensorflow:loss = 55.92981, step = 900 (0.820 sec)
INFO:tensorflow:loss = 55.92981, step = 900 (0.820 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 /tmpfs/tmp/train/20221214-104438/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmpfs/tmp/train/20221214-104438/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: 59.498924.
INFO:tensorflow:Loss for final step: 59.498924.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/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 API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/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 API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
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
2022-12-14 10:44:58.995941: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 26 into an existing graph with producer version 1286. 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/python3.8/site-packages/tensorflow_estimator/python/estimator/canned/head.py:635: 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/python3.8/site-packages/tensorflow_estimator/python/estimator/canned/head.py:635: 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.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:84: 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 API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:84: 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 API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
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 /tmpfs/tmp/train/20221214-104438/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/train/20221214-104438/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfma_eval_model/temp-1671014698/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfma_eval_model/temp-1671014698/assets
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfma_eval_model/temp-1671014698/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfma_eval_model/temp-1671014698/saved_model.pb
WARNING:absl:Tensorflow version (2.11.0) 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:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:163: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.saved_model.load` instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.8/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:163: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.saved_model.load` instead.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfma_eval_model/1671014698/variables/variables
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfma_eval_model/1671014698/variables/variables
2022-12-14 10:45:03.205303: W tensorflow/c/c_api.cc:291] Operation '{name:'dnn/metrics/auc_precision_recall/false_positives/Assign' id:1200 op device:{requested: '', assigned: ''} def:{ { {node dnn/metrics/auc_precision_recall/false_positives/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dnn/metrics/auc_precision_recall/false_positives, dnn/metrics/auc_precision_recall/false_positives/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
2022-12-14 10:45:03.551105: W tensorflow/c/c_api.cc:291] Operation '{name:'dnn/metrics/auc_precision_recall/false_positives/Assign' id:1200 op device:{requested: '', assigned: ''} def:{ { {node dnn/metrics/auc_precision_recall/false_positives/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dnn/metrics/auc_precision_recall/false_positives, dnn/metrics/auc_precision_recall/false_positives/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
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/python3.8/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:110: 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/python3.8/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:110: 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)`

widget_view.render_fairness_indicator(eval_result=eval_result_random_nnlm)

FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…

NNLM

eval_result_nnlm = embedding_fairness_result('https://tfhub.dev/google/nnlm-en-dim128/1')

Training classifier for https://tfhub.dev/google/nnlm-en-dim128/1
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/train/20221214-104653', '_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': '/tmpfs/tmp/train/20221214-104653', '_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:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-12-14 10:46:57.594318: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 26 into an existing graph with producer version 1286. 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
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 /tmpfs/tmp/train/20221214-104653/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/train/20221214-104653/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 58.12857, step = 0
INFO:tensorflow:loss = 58.12857, step = 0
INFO:tensorflow:global_step/sec: 89.1201
INFO:tensorflow:global_step/sec: 89.1201
INFO:tensorflow:loss = 55.783634, step = 100 (1.124 sec)
INFO:tensorflow:loss = 55.783634, step = 100 (1.124 sec)
INFO:tensorflow:global_step/sec: 123.184
INFO:tensorflow:global_step/sec: 123.184
INFO:tensorflow:loss = 47.349377, step = 200 (0.812 sec)
INFO:tensorflow:loss = 47.349377, step = 200 (0.812 sec)
INFO:tensorflow:global_step/sec: 114.945
INFO:tensorflow:global_step/sec: 114.945
INFO:tensorflow:loss = 55.80595, step = 300 (0.870 sec)
INFO:tensorflow:loss = 55.80595, step = 300 (0.870 sec)
INFO:tensorflow:global_step/sec: 117.469
INFO:tensorflow:global_step/sec: 117.469
INFO:tensorflow:loss = 55.90926, step = 400 (0.851 sec)
INFO:tensorflow:loss = 55.90926, step = 400 (0.851 sec)
INFO:tensorflow:global_step/sec: 118.485
INFO:tensorflow:global_step/sec: 118.485
INFO:tensorflow:loss = 41.258957, step = 500 (0.844 sec)
INFO:tensorflow:loss = 41.258957, step = 500 (0.844 sec)
INFO:tensorflow:global_step/sec: 120.328
INFO:tensorflow:global_step/sec: 120.328
INFO:tensorflow:loss = 45.53152, step = 600 (0.831 sec)
INFO:tensorflow:loss = 45.53152, step = 600 (0.831 sec)
INFO:tensorflow:global_step/sec: 120.514
INFO:tensorflow:global_step/sec: 120.514
INFO:tensorflow:loss = 51.562172, step = 700 (0.830 sec)
INFO:tensorflow:loss = 51.562172, step = 700 (0.830 sec)
INFO:tensorflow:global_step/sec: 122.375
INFO:tensorflow:global_step/sec: 122.375
INFO:tensorflow:loss = 47.565388, step = 800 (0.817 sec)
INFO:tensorflow:loss = 47.565388, step = 800 (0.817 sec)
INFO:tensorflow:global_step/sec: 113.79
INFO:tensorflow:global_step/sec: 113.79
INFO:tensorflow:loss = 48.00003, step = 900 (0.879 sec)
INFO:tensorflow:loss = 48.00003, step = 900 (0.879 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 /tmpfs/tmp/train/20221214-104653/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmpfs/tmp/train/20221214-104653/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: 50.69049.
INFO:tensorflow:Loss for final step: 50.69049.
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
2022-12-14 10:47:09.823907: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 26 into an existing graph with producer version 1286. 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: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 /tmpfs/tmp/train/20221214-104653/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/train/20221214-104653/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfma_eval_model/temp-1671014829/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfma_eval_model/temp-1671014829/assets
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfma_eval_model/temp-1671014829/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfma_eval_model/temp-1671014829/saved_model.pb
WARNING:absl:Tensorflow version (2.11.0) found. Note that TFMA support for TF 2.0 is currently in beta
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfma_eval_model/1671014829/variables/variables
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfma_eval_model/1671014829/variables/variables
2022-12-14 10:47:13.861038: W tensorflow/c/c_api.cc:291] Operation '{name:'dnn/metrics/auc_precision_recall/false_positives/Assign' id:1194 op device:{requested: '', assigned: ''} def:{ { {node dnn/metrics/auc_precision_recall/false_positives/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dnn/metrics/auc_precision_recall/false_positives, dnn/metrics/auc_precision_recall/false_positives/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
2022-12-14 10:47:14.423856: W tensorflow/c/c_api.cc:291] Operation '{name:'dnn/metrics/auc_precision_recall/false_positives/Assign' id:1194 op device:{requested: '', assigned: ''} def:{ { {node dnn/metrics/auc_precision_recall/false_positives/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dnn/metrics/auc_precision_recall/false_positives, dnn/metrics/auc_precision_recall/false_positives/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.

widget_view.render_fairness_indicator(eval_result=eval_result_nnlm)

FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…

Universal Sentence Encoder

eval_result_use = embedding_fairness_result('https://tfhub.dev/google/universal-sentence-encoder/2')

Training classifier for https://tfhub.dev/google/universal-sentence-encoder/2
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/train/20221214-104905', '_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': '/tmpfs/tmp/train/20221214-104905', '_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:Calling model_fn.
INFO:tensorflow:Calling model_fn.
2022-12-14 10:49:14.901633: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 26 into an existing graph with producer version 1286. 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
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
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 /tmpfs/tmp/train/20221214-104905/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/train/20221214-104905/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 59.12257, step = 0
INFO:tensorflow:loss = 59.12257, step = 0
INFO:tensorflow:global_step/sec: 11.6574
INFO:tensorflow:global_step/sec: 11.6574
INFO:tensorflow:loss = 49.35492, step = 100 (8.580 sec)
INFO:tensorflow:loss = 49.35492, step = 100 (8.580 sec)
INFO:tensorflow:global_step/sec: 12.1753
INFO:tensorflow:global_step/sec: 12.1753
INFO:tensorflow:loss = 46.402603, step = 200 (8.213 sec)
INFO:tensorflow:loss = 46.402603, step = 200 (8.213 sec)
INFO:tensorflow:global_step/sec: 12.2897
INFO:tensorflow:global_step/sec: 12.2897
INFO:tensorflow:loss = 48.4608, step = 300 (8.137 sec)
INFO:tensorflow:loss = 48.4608, step = 300 (8.137 sec)
INFO:tensorflow:global_step/sec: 12.3272
INFO:tensorflow:global_step/sec: 12.3272
INFO:tensorflow:loss = 44.718792, step = 400 (8.112 sec)
INFO:tensorflow:loss = 44.718792, step = 400 (8.112 sec)
INFO:tensorflow:global_step/sec: 12.3388
INFO:tensorflow:global_step/sec: 12.3388
INFO:tensorflow:loss = 35.265553, step = 500 (8.104 sec)
INFO:tensorflow:loss = 35.265553, step = 500 (8.104 sec)
INFO:tensorflow:global_step/sec: 12.2607
INFO:tensorflow:global_step/sec: 12.2607
INFO:tensorflow:loss = 42.24031, step = 600 (8.156 sec)
INFO:tensorflow:loss = 42.24031, step = 600 (8.156 sec)
INFO:tensorflow:global_step/sec: 12.2489
INFO:tensorflow:global_step/sec: 12.2489
INFO:tensorflow:loss = 40.55222, step = 700 (8.164 sec)
INFO:tensorflow:loss = 40.55222, step = 700 (8.164 sec)
INFO:tensorflow:global_step/sec: 12.2135
INFO:tensorflow:global_step/sec: 12.2135
INFO:tensorflow:loss = 37.232937, step = 800 (8.187 sec)
INFO:tensorflow:loss = 37.232937, step = 800 (8.187 sec)
INFO:tensorflow:global_step/sec: 12.2573
INFO:tensorflow:global_step/sec: 12.2573
INFO:tensorflow:loss = 32.866703, step = 900 (8.159 sec)
INFO:tensorflow:loss = 32.866703, step = 900 (8.159 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 /tmpfs/tmp/train/20221214-104905/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmpfs/tmp/train/20221214-104905/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: 47.08837.
INFO:tensorflow:Loss for final step: 47.08837.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
2022-12-14 10:51:00.021379: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 26 into an existing graph with producer version 1286. 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
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
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 /tmpfs/tmp/train/20221214-104905/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/train/20221214-104905/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfma_eval_model/temp-1671015059/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfma_eval_model/temp-1671015059/saved_model.pb
WARNING:absl:Tensorflow version (2.11.0) found. Note that TFMA support for TF 2.0 is currently in beta
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfma_eval_model/1671015059/variables/variables
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfma_eval_model/1671015059/variables/variables
2022-12-14 10:51:13.272497: W tensorflow/c/c_api.cc:291] Operation '{name:'dnn/metrics/auc_precision_recall/false_positives/Assign' id:9154 op device:{requested: '', assigned: ''} def:{ { {node dnn/metrics/auc_precision_recall/false_positives/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dnn/metrics/auc_precision_recall/false_positives, dnn/metrics/auc_precision_recall/false_positives/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
2022-12-14 10:51:14.020215: W tensorflow/c/c_api.cc:291] Operation '{name:'dnn/metrics/auc_precision_recall/false_positives/Assign' id:9154 op device:{requested: '', assigned: ''} def:{ { {node dnn/metrics/auc_precision_recall/false_positives/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dnn/metrics/auc_precision_recall/false_positives, dnn/metrics/auc_precision_recall/false_positives/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.

widget_view.render_fairness_indicator(eval_result=eval_result_use)

FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…

Comparing Embeddings

You can also use Fairness Indicators to compare embeddings directly. For example, compare the models generated from the NNLM and USE embeddings.

widget_view.render_fairness_indicator(multi_eval_results={'nnlm': eval_result_nnlm, 'use': eval_result_use})

FairnessIndicatorViewer(evalName='nnlm', evalNameCompare='use', slicingMetrics=[{'sliceValue': 'Overall', 'sli…