old_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Enum for model prediction keys."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow_estimator.python.estimator import model_fn
  
  
  class MetricKeys(object):
    """Metric key strings."""
    LOSS = model_fn.LOSS_METRIC_KEY
    LOSS_MEAN = model_fn.AVERAGE_LOSS_METRIC_KEY
    LOSS_REGULARIZATION = 'regularization_loss'
  
    ACCURACY = 'accuracy'
    PRECISION = 'precision'
    RECALL = 'recall'
    # This is the best the model could do by always predicting one class.
    # Should be < ACCURACY in a trained model.
    ACCURACY_BASELINE = 'accuracy_baseline'
    AUC = 'auc'
    AUC_PR = 'auc_precision_recall'
    LABEL_MEAN = 'label/mean'
    PREDICTION_MEAN = 'prediction/mean'
  
    # The following require a threshold applied, should be float in range (0, 1).
    ACCURACY_AT_THRESHOLD = 'accuracy/positive_threshold_%g'
    PRECISION_AT_THRESHOLD = 'precision/positive_threshold_%g'
    RECALL_AT_THRESHOLD = 'recall/positive_threshold_%g'
  
    # The following require a constraint on a competing metric to be applied,
    # float in range (0, 1).
    PRECISION_AT_RECALL = 'precision_at_recall_%g'
    RECALL_AT_PRECISION = 'recall_at_precision_%g'
    SENSITIVITY_AT_SPECFICITY = 'sensitivity_at_specficity_%g'
    SPECFICITY_AT_SENSITIVITY = 'specficity_at_sensitivity_%g'
  
    # The following require a class id applied.
    PROBABILITY_MEAN_AT_CLASS = 'probability_mean/class%d'
    AUC_AT_CLASS = 'auc/class%d'
    AUC_PR_AT_CLASS = 'auc_precision_recall/class%d'
  
    # The following require a class name applied.
    PROBABILITY_MEAN_AT_NAME = 'probability_mean/%s'
    AUC_AT_NAME = 'auc/%s'
    AUC_PR_AT_NAME = 'auc_precision_recall/%s'
  
new_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Enum for model prediction keys."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow_estimator.python.estimator import model_fn
  
  
  class MetricKeys(object):
    """Metric key strings."""
    LOSS = model_fn.LOSS_METRIC_KEY
    LOSS_MEAN = model_fn.AVERAGE_LOSS_METRIC_KEY
    LOSS_REGULARIZATION = 'regularization_loss'
  
    ACCURACY = 'accuracy'
    PRECISION = 'precision'
    RECALL = 'recall'
    # This is the best the model could do by always predicting one class.
    # Should be < ACCURACY in a trained model.
    ACCURACY_BASELINE = 'accuracy_baseline'
    AUC = 'auc'
    AUC_PR = 'auc_precision_recall'
    LABEL_MEAN = 'label/mean'
    PREDICTION_MEAN = 'prediction/mean'
  
    # The following require a threshold applied, should be float in range (0, 1).
    ACCURACY_AT_THRESHOLD = 'accuracy/positive_threshold_%g'
    PRECISION_AT_THRESHOLD = 'precision/positive_threshold_%g'
    RECALL_AT_THRESHOLD = 'recall/positive_threshold_%g'
  
    # The following require a constraint on a competing metric to be applied,
    # float in range (0, 1).
    PRECISION_AT_RECALL = 'precision_at_recall_%g'
    RECALL_AT_PRECISION = 'recall_at_precision_%g'
    SENSITIVITY_AT_SPECIFICITY = 'sensitivity_at_specificity_%g'
    SPECIFICITY_AT_SENSITIVITY = 'specificity_at_sensitivity_%g'
  
    # The following require a class id applied.
    PROBABILITY_MEAN_AT_CLASS = 'probability_mean/class%d'
    AUC_AT_CLASS = 'auc/class%d'
    AUC_PR_AT_CLASS = 'auc_precision_recall/class%d'
  
    # The following require a class name applied.
    PROBABILITY_MEAN_AT_NAME = 'probability_mean/%s'
    AUC_AT_NAME = 'auc/%s'
    AUC_PR_AT_NAME = 'auc_precision_recall/%s'
  
subject: Fix typo