def _get_estimator(self): config = tf.estimator.RunConfig(model_dir=self.model_dir, save_checkpoints_steps=1) feature_columns, _, _, label_column_name = self.load_dataset.get_feature_columns(include_sensitive_columns=True) # pylint: disable=line-too-long estimator = robust_learning_model.get_estimator( feature_columns=feature_columns, label_column_name=label_column_name, config=config, model_dir=self.model_dir, primary_hidden_units=self.primary_hidden_units, batch_size=self.batch_size, pretrain_steps=self.pretrain_steps, primary_learning_rate=0.01, adversary_learning_rate=0.01, optimizer='Adagrad', activation=tf.nn.relu, adversary_loss_type='ce_loss', adversary_include_label=True, upweight_positive_instance_only=False) return estimator
def test_global_steps_robust_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 = robust_learning_model.get_estimator( feature_columns=feature_columns, label_column_name=label_column_name, config=config, model_dir=self.model_dir, primary_hidden_units=self.primary_hidden_units, batch_size=self.batch_size, pretrain_steps=self.pretrain_steps) 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 # # As a artifact of the way train_ops is defined in _RobustEstimator. # # Training stops two steps after the specified number of train_steps. self.assertIn('global_step', eval_results) self.assertEqual(eval_results['global_step'], self.train_steps + 2)
def test_eval_results_robust_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 = robust_learning_model.get_estimator( feature_columns=feature_columns, label_column_name=label_column_name, config=config, model_dir=self.model_dir, primary_hidden_units=self.primary_hidden_units, batch_size=self.batch_size, pretrain_steps=self.pretrain_steps) 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