def test_add_fairness_metrics_ips_reweighting_model(self):
config = tf.estimator.RunConfig(model_dir=self.model_dir,
save_checkpoints_steps=2)
feature_columns, _, _, label_column_name = self.load_dataset.get_feature_columns(include_sensitive_columns=True) # pylint: disable=line-too-long
estimator = ips_reweighting_model.get_estimator(
feature_columns=feature_columns,
label_column_name=label_column_name,
config=config,
model_dir=self.model_dir,
hidden_units=self.hidden_units,
batch_size=self.batch_size)
self.assertIsInstance(estimator, tf.estimator.Estimator)
# Adds additional fairness metrics to estimator
eval_metrics_fn = self.fairness_metrics.create_fairness_metrics_fn()
estimator = tf.estimator.add_metrics(estimator, eval_metrics_fn)
train_input_fn, test_input_fn = self._get_train_test_input_fn()
estimator.train(input_fn=train_input_fn, steps=self.train_steps)
eval_results = estimator.evaluate(input_fn=test_input_fn,
steps=self.test_steps)
self.assertNotEmpty(eval_results)
# # Checks if auc metric is computed for all subgroups
for subgroup in self.subgroups:
self.assertIn('auc subgroup {}'.format(subgroup), eval_results)
def test_create_estimator_ips_without_label(self):
config = tf.estimator.RunConfig(model_dir=self.model_dir,
save_checkpoints_steps=2)
feature_columns, _, _, label_column_name = self.load_dataset.get_feature_columns(include_sensitive_columns=False) # pylint: disable=line-too-long
estimator = ips_reweighting_model.get_estimator(
feature_columns=feature_columns,
label_column_name=label_column_name,
config=config,
model_dir=self.model_dir,
reweighting_type='IPS_without_label',
hidden_units=self.hidden_units,
batch_size=self.batch_size,
learning_rate=0.01,
optimizer='Adagrad',
activation=tf.nn.relu)
self.assertIsInstance(estimator, tf.estimator.Estimator)
def test_global_steps_ips_reweighting_model(self):
config = tf.estimator.RunConfig(model_dir=self.model_dir,
save_checkpoints_steps=2)
feature_columns, _, _, label_column_name = self.load_dataset.get_feature_columns(include_sensitive_columns=True) # pylint: disable=line-too-long
estimator = ips_reweighting_model.get_estimator(
feature_columns=feature_columns,
label_column_name=label_column_name,
config=config,
model_dir=self.model_dir,
hidden_units=self.hidden_units,
batch_size=self.batch_size)
self.assertIsInstance(estimator, tf.estimator.Estimator)
train_input_fn, test_input_fn = self._get_train_test_input_fn()
estimator.train(input_fn=train_input_fn, steps=self.train_steps)
eval_results = estimator.evaluate(input_fn=test_input_fn,
steps=self.test_steps)
# Checks if global step has reached specified number of train_steps
self.assertIn('global_step', eval_results)
self.assertEqual(eval_results['global_step'], self.train_steps)
def test_eval_results_ips_reweighting_model(self):
config = tf.estimator.RunConfig(model_dir=self.model_dir,
save_checkpoints_steps=2)
feature_columns, _, _, label_column_name = self.load_dataset.get_feature_columns(include_sensitive_columns=True) # pylint: disable=line-too-long
estimator = ips_reweighting_model.get_estimator(
feature_columns=feature_columns,
label_column_name=label_column_name,
config=config,
model_dir=self.model_dir,
hidden_units=self.hidden_units,
batch_size=self.batch_size)
self.assertIsInstance(estimator, tf.estimator.Estimator)
train_input_fn, test_input_fn = self._get_train_test_input_fn()
estimator.train(input_fn=train_input_fn, steps=self.train_steps)
eval_results = estimator.evaluate(input_fn=test_input_fn,
steps=self.test_steps)
self.assertNotEmpty(eval_results)
# # Checks if all tp,tn,fp,fn keys are present in eval_results dictionary
self.assertIn('auc', eval_results)
self.assertIn('fp', eval_results)
self.assertIn('fn', eval_results)
self.assertIn('tp', eval_results)
self.assertIn('tn', eval_results)
def get_estimator(model_dir, model_name, feature_columns, protected_groups,
label_column_name):
"""Instantiates and returns a model estimator.
Args:
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into an estimator
to continue training a previously saved model.
model_name: (string) name of the estimator to instantiate.
feature_columns: list of feature_columns.
protected_groups: list of protected_groups. For example, ["sex","race"].
label_column_name: (string) name of the target variable.
Returns:
An instance of `tf.estimator.Estimator'.
Raises:
ValueError: if estimator for model_name is not implemented.
ValueError: if activation function is not implemented.
"""
# Defines activation function to be used for the model. Append activation
# functions that we want to use here.
if FLAGS.activation == "relu":
activation_fn = tf.nn.relu
elif FLAGS.activation == "linear":
activation_fn = tf.nn.linear
else:
raise ValueError("Activation {} is not supported.".format(
FLAGS.activation))
kwargs = {
"feature_columns":
feature_columns,
"label_column_name":
label_column_name,
"config":
tf.estimator.RunConfig(
model_dir=model_dir,
save_checkpoints_steps=FLAGS.min_eval_frequency),
"model_dir":
model_dir,
"batch_size":
FLAGS.batch_size,
"activation":
activation_fn,
"optimizer":
FLAGS.optimizer
}
# Instantiates estimators to be used. Append new estimators that we want to use here.
if model_name == "baseline":
estimator = baseline_model.get_estimator(
hidden_units=FLAGS.primary_hidden_units,
learning_rate=FLAGS.primary_learning_rate,
**kwargs)
elif model_name == "inverse_propensity_weighting":
estimator = ips_reweighting_model.get_estimator(
reweighting_type=FLAGS.reweighting_type,
hidden_units=FLAGS.primary_hidden_units,
learning_rate=FLAGS.primary_learning_rate,
**kwargs)
elif model_name == "robust_learning":
estimator = robust_learning_model.get_estimator(
adversary_loss_type=FLAGS.adversary_loss_type,
adversary_include_label=FLAGS.adversary_include_label,
upweight_positive_instance_only=FLAGS.
upweight_positive_instance_only,
pretrain_steps=FLAGS.pretrain_steps,
primary_hidden_units=FLAGS.primary_hidden_units,
adversary_hidden_units=FLAGS.adversary_hidden_units,
primary_learning_rate=FLAGS.primary_learning_rate,
adversary_learning_rate=FLAGS.adversary_learning_rate,
**kwargs)
elif model_name == "adversarial_subgroup_reweighting":
estimator = adversarial_subgroup_reweighting_model.get_estimator(
protected_column_names=protected_groups,
pretrain_steps=FLAGS.pretrain_steps,
primary_hidden_units=FLAGS.primary_hidden_units,
adversary_hidden_units=FLAGS.adversary_hidden_units,
primary_learning_rate=FLAGS.primary_learning_rate,
adversary_learning_rate=FLAGS.adversary_learning_rate,
**kwargs)
else:
raise ValueError("Model {} is not implemented.".format(model_name))
return estimator
