Fix the bug in fairness indicators due to deprecation of tf estimator…

… and feature column utils.

PiperOrigin-RevId: 627540407

RELEASE.md

@@ -3,7 +3,7 @@
 # Current Version (Still in Development)
 
 ## Major Features and Improvements
-
+Update example model to use Keras models instead of estimators.
 ## Bug Fixes and Other Changes
 
 *  Deprecated python 3.8 support

fairness_indicators/example_model.py

@@ -14,132 +14,92 @@
 # ==============================================================================
 """Demo script to train and evaluate a model.
 
-This scripts contains boilerplate code to train a DNNClassifier
+This scripts contains boilerplate code to train a Keras Text Classifier
 and evaluate it using Tensorflow Model Analysis. Evaluation
 results can be visualized using tools like TensorBoard.
-
-Usage:
-
-1. Train model:
-  demo_script.train_model(...)
-
-2. Evaluate:
-  demo_script.evaluate_model(...)
 """
 
-import os
-import tempfile
+from tensorflow import keras
 import tensorflow.compat.v1 as tf
-from tensorflow.compat.v1 import estimator as tf_estimator
-import tensorflow_hub as hub
 import tensorflow_model_analysis as tfma
 from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators  # pylint: disable=unused-import
 
 
-def train_model(model_dir,
-                train_tf_file,
-                label,
-                text_feature,
-                feature_map,
-                module_spec='https://tfhub.dev/google/nnlm-en-dim128/1'):
-  """Train model using DNN Classifier.
-
-  Args:
-    model_dir: Directory path to save trained model.
-    train_tf_file: File containing training TFRecordDataset.
-    label: Groundtruth label.
-    text_feature: Text feature to be evaluated.
-    feature_map: Dict of feature names to their data type.
-    module_spec: A module spec defining the module to instantiate or a path
-      where to load a module spec.
-
-  Returns:
-    Trained DNNClassifier.
-  """
-
-  def train_input_fn():
-    """Train Input function."""
-
-    def parse_function(serialized):
-      parsed_example = tf.io.parse_single_example(
-          serialized=serialized, features=feature_map)
-      # Adds a weight column to deal with unbalanced classes.
-      parsed_example['weight'] = tf.add(parsed_example[label], 0.1)
-      return (parsed_example, parsed_example[label])
-
-    train_dataset = tf.data.TFRecordDataset(
-        filenames=[train_tf_file]).map(parse_function).batch(512)
-    return train_dataset
+TEXT_FEATURE = 'comment_text'
+LABEL = 'toxicity'
+SLICE = 'slice'
+FEATURE_MAP = {
+    LABEL: tf.io.FixedLenFeature([], tf.float32),
+    TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),
+    SLICE: tf.io.VarLenFeature(tf.string),
+}
 
-  text_embedding_column = hub.text_embedding_column(
-      key=text_feature, module_spec=module_spec)
 
-  classifier = tf_estimator.DNNClassifier(
-      hidden_units=[500, 100],
-      weight_column='weight',
-      feature_columns=[text_embedding_column],
-      n_classes=2,
-      optimizer=tf.train.AdagradOptimizer(learning_rate=0.003),
-      model_dir=model_dir)
+class ExampleParser(keras.layers.Layer):
+  """A Keras layer that parses the tf.Example."""
 
-  classifier.train(input_fn=train_input_fn, steps=1000)
-  return classifier
+  def __init__(self, input_feature_key):
+    self._input_feature_key = input_feature_key
+    super().__init__()
 
-
-def evaluate_model(classifier, validate_tf_file, tfma_eval_result_path,
-                   selected_slice, label, feature_map):
+  def call(self, serialized_examples):
+    def get_feature(serialized_example):
+      parsed_example = tf.io.parse_single_example(
+          serialized_example, features=FEATURE_MAP
+      )
+      return parsed_example[self._input_feature_key]
+
+    return tf.map_fn(get_feature, serialized_examples)
+
+
+class ExampleModel(keras.Model):
+  """A Example Keras NLP model."""
+
+  def __init__(self, input_feature_key):
+    super().__init__()
+    self.parser = ExampleParser(input_feature_key)
+    self.text_vectorization = keras.layers.TextVectorization(
+        max_tokens=32,
+        output_mode='int',
+        output_sequence_length=32,
+    )
+    self.text_vectorization.adapt(
+        ['nontoxic', 'toxic comment', 'test comment', 'abc', 'abcdef', 'random']
+    )
+    self.dense1 = keras.layers.Dense(32, activation='relu')
+    self.dense2 = keras.layers.Dense(1)
+
+  def call(self, inputs, training=True, mask=None):
+    parsed_example = self.parser(inputs)
+    text_vector = self.text_vectorization(parsed_example)
+    output1 = self.dense1(tf.cast(text_vector, tf.float32))
+    output2 = self.dense2(output1)
+    return output2
+
+
+def evaluate_model(
+    classifier_model_path,
+    validate_tf_file_path,
+    tfma_eval_result_path,
+    eval_config,
+):
   """Evaluate Model using Tensorflow Model Analysis.
 
   Args:
-    classifier: Trained classifier model to be evaluted.
-    validate_tf_file: File containing validation TFRecordDataset.
-    tfma_eval_result_path: Directory path where eval results will be written.
-    selected_slice: Feature for slicing the data.
-    label: Groundtruth label.
-    feature_map: Dict of feature names to their data type.
+    classifier_model_path: Trained classifier model to be evaluted.
+    validate_tf_file_path: File containing validation TFRecordDataset.
+    tfma_eval_result_path: Path to export tfma-related eval path.
+    eval_config: tfma eval_config.
   """
 
-  def eval_input_receiver_fn():
-    """Eval Input Receiver function."""
-    serialized_tf_example = tf.compat.v1.placeholder(
-        dtype=tf.string, shape=[None], name='input_example_placeholder')
-
-    receiver_tensors = {'examples': serialized_tf_example}
-
-    features = tf.io.parse_example(serialized_tf_example, feature_map)
-    features['weight'] = tf.ones_like(features[label])
-
-    return tfma.export.EvalInputReceiver(
-        features=features,
-        receiver_tensors=receiver_tensors,
-        labels=features[label])
-
-  tfma_export_dir = tfma.export.export_eval_savedmodel(
-      estimator=classifier,
-      export_dir_base=os.path.join(tempfile.gettempdir(), 'tfma_eval_model'),
-      eval_input_receiver_fn=eval_input_receiver_fn)
-
-  # Define slices that you want the evaluation to run on.
-  slice_spec = [
-      tfma.slicer.SingleSliceSpec(),  # Overall slice
-      tfma.slicer.SingleSliceSpec(columns=[selected_slice]),
-  ]
-
-  # Add the fairness metrics.
-  # pytype: disable=module-attr
-  add_metrics_callbacks = [
-      tfma.post_export_metrics.fairness_indicators(
-          thresholds=[0.1, 0.3, 0.5, 0.7, 0.9], labels_key=label)
-  ]
-  # pytype: enable=module-attr
-
   eval_shared_model = tfma.default_eval_shared_model(
-      eval_saved_model_path=tfma_export_dir,
-      add_metrics_callbacks=add_metrics_callbacks)
+      eval_saved_model_path=classifier_model_path, eval_config=eval_config
+  )
 
   # Run the fairness evaluation.
   tfma.run_model_analysis(
       eval_shared_model=eval_shared_model,
-      data_location=validate_tf_file,
+      data_location=validate_tf_file_path,
       output_path=tfma_eval_result_path,
-      slice_spec=slice_spec)
+      eval_config=eval_config,
+  )

fairness_indicators/example_model_test.py

@@ -21,22 +21,17 @@
 import datetime
 import os
 import tempfile
+
 from fairness_indicators import example_model
+import numpy as np
 import six
+from tensorflow import keras
 import tensorflow.compat.v1 as tf
 import tensorflow_model_analysis as tfma
-from tensorflow_model_analysis.slicer import slicer_lib as slicer
 
-tf.compat.v1.enable_eager_execution()
+from google.protobuf import text_format
 
-TEXT_FEATURE = 'comment_text'
-LABEL = 'toxicity'
-SLICE = 'slice'
-FEATURE_MAP = {
-    LABEL: tf.io.FixedLenFeature([], tf.float32),
-    TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),
-    SLICE: tf.io.VarLenFeature(tf.string),
-}
+tf.compat.v1.enable_eager_execution()
 
 
 class ExampleModelTest(tf.test.TestCase):
@@ -51,13 +46,13 @@ def setUp(self):
 
   def _create_example(self, comment_text, label, slice_value):
     example = tf.train.Example()
-    example.features.feature[TEXT_FEATURE].bytes_list.value[:] = [
+    example.features.feature[example_model.TEXT_FEATURE].bytes_list.value[:] = [
         six.ensure_binary(comment_text, 'utf8')
     ]
-    example.features.feature[SLICE].bytes_list.value[:] = [
+    example.features.feature[example_model.SLICE].bytes_list.value[:] = [
         six.ensure_binary(slice_value, 'utf8')
     ]
-    example.features.feature[LABEL].float_list.value[:] = [label]
+    example.features.feature[example_model.LABEL].float_list.value[:] = [label]
     return example
 
   def _create_data(self):
@@ -85,34 +80,86 @@ def _write_tf_records(self, examples):
     return data_location
 
   def test_example_model(self):
-    train_tf_file = self._write_tf_records(self._create_data())
-    classifier = example_model.train_model(self._model_dir, train_tf_file,
-                                           LABEL, TEXT_FEATURE, FEATURE_MAP)
-
-    validate_tf_file = self._write_tf_records(self._create_data())
+    data = self._create_data()
+    classifier = example_model.ExampleModel(example_model.TEXT_FEATURE)
+    classifier.compile(optimizer=keras.optimizers.Adam(), loss='mse')
+    print([e.SerializeToString() for e in data])
+    classifier.predict(tf.constant([e.SerializeToString() for e in data]))
+    classifier.fit(
+        tf.constant([e.SerializeToString() for e in data]),
+        np.array([
+            e.features.feature[example_model.LABEL].float_list.value[:][0]
+            for e in data
+        ]),
+    )
+    classifier.save(self._model_dir, save_format='tf')
+
+    eval_config = text_format.Parse(
+        """
+        model_specs {
+          signature_name: "serving_default"
+          prediction_key: "predictions" # placeholder
+          label_key: "toxicity" # placeholder
+        }
+        slicing_specs {}
+        slicing_specs {
+          feature_keys: ["slice"]
+        }
+        metrics_specs {
+          metrics {
+            class_name: "ExampleCount"
+          }
+          metrics {
+            class_name: "FairnessIndicators"
+          }
+        }
+  """,
+        tfma.EvalConfig(),
+    )
+
+    validate_tf_file_path = self._write_tf_records(data)
     tfma_eval_result_path = os.path.join(self._model_dir, 'tfma_eval_result')
-    example_model.evaluate_model(classifier, validate_tf_file,
-                                 tfma_eval_result_path, SLICE, LABEL,
-                                 FEATURE_MAP)
+    example_model.evaluate_model(
+        self._model_dir,
+        validate_tf_file_path,
+        tfma_eval_result_path,
+        eval_config,
+    )
 
-    expected_slice_keys = [
-        'Overall', 'slice:slice3', 'slice:slice1', 'slice:slice2'
-    ]
     evaluation_results = tfma.load_eval_result(tfma_eval_result_path)
 
-    self.assertLen(evaluation_results.slicing_metrics, 4)
-
-    # Verify if false_positive_rate metrics are computed for all values of
-    # slice.
-    for (slice_key, metric_value) in evaluation_results.slicing_metrics:
-      slice_key = slicer.stringify_slice_key(slice_key)
-      self.assertIn(slice_key, expected_slice_keys)
-      self.assertGreaterEqual(
-          1.0, metric_value['']['']
-          ['post_export_metrics/false_positive_rate@0.50']['doubleValue'])
-      self.assertLessEqual(
-          0.0, metric_value['']['']
-          ['post_export_metrics/false_positive_rate@0.50']['doubleValue'])
+    expected_slice_keys = [
+        (),
+        (('slice', 'slice1'),),
+        (('slice', 'slice2'),),
+        (('slice', 'slice3'),),
+    ]
+    slice_keys = [
+        slice_key for slice_key, _ in evaluation_results.slicing_metrics
+    ]
+    self.assertEqual(set(expected_slice_keys), set(slice_keys))
+    # Verify part of the metrics of fairness indicators
+    metric_values = dict(evaluation_results.slicing_metrics)[(
+        ('slice', 'slice1'),
+    )]['']['']
+    self.assertEqual(metric_values['example_count'], {'doubleValue': 5.0})
+
+    self.assertEqual(
+        metric_values['fairness_indicators_metrics/false_positive_rate@0.1'],
+        {'doubleValue': 0.0},
+    )
+    self.assertEqual(
+        metric_values['fairness_indicators_metrics/false_negative_rate@0.1'],
+        {'doubleValue': 1.0},
+    )
+    self.assertEqual(
+        metric_values['fairness_indicators_metrics/true_positive_rate@0.1'],
+        {'doubleValue': 0.0},
+    )
+    self.assertEqual(
+        metric_values['fairness_indicators_metrics/true_negative_rate@0.1'],
+        {'doubleValue': 1.0},
+    )
 
 
 if __name__ == '__main__':