text: # Copyright 2019 The FastEstimator 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.
  # ==============================================================================
  from typing import Iterable, Union
  
  import numpy as np
  
  from fastestimator.trace.meta.per_ds import per_ds
  from fastestimator.trace.trace import Trace
  from fastestimator.util.data import Data
  from fastestimator.util.traceability_util import traceable
  from fastestimator.util.util import to_number
  
  
  @per_ds
  @traceable()
  class Accuracy(Trace):
      """A trace which computes the accuracy for a given set of predictions.
  
      Consider using MCC instead: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941312/
  
      Args:
          true_key: Name of the key that corresponds to ground truth in the batch dictionary.
          pred_key: Name of the key that corresponds to predicted score in the batch dictionary.
          mode: What mode(s) to execute this Trace in. For example, "train", "eval", "test", or "infer". To execute
              regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
              like "!infer" or "!train".
          ds_id: What dataset id(s) to execute this Trace in. To execute regardless of ds_id, pass None. To execute in all
              ds_ids except for a particular one, you can pass an argument like "!ds1".
          from_logits: Whether y_pred is from logits. If True, a sigmoid will be applied to the prediction.
          output_name: What to call the output from this trace (for example in the logger output).
          per_ds: Whether to automatically compute this metric individually for every ds_id it runs on, in addition to
              computing an aggregate across all ds_ids on which it runs. This is automatically False if `output_name`
              contains a "|" character.
      """
      def __init__(self,
                   true_key: str,
                   pred_key: str,
                   mode: Union[None, str, Iterable[str]] = ("eval", "test"),
                   ds_id: Union[None, str, Iterable[str]] = None,
                   from_logits: bool = False,
                   output_name: str = "accuracy",
                   per_ds: bool = True) -> None:
          super().__init__(inputs=(true_key, pred_key), mode=mode, outputs=output_name, ds_id=ds_id)
          self.from_logits = from_logits
          self.total = 0
          self.correct = 0
          self.per_ds = per_ds
  
      @property
      def true_key(self) -> str:
          return self.inputs[0]
  
      @property
      def pred_key(self) -> str:
          return self.inputs[1]
  
      def on_epoch_begin(self, data: Data) -> None:
          self.total = 0
          self.correct = 0
  
      def on_batch_end(self, data: Data) -> None:
          y_true, y_pred = to_number(data[self.true_key]), to_number(data[self.pred_key])
          if y_true.shape[-1] > 1 and y_true.ndim > 1:
              y_true = np.argmax(y_true, axis=-1)
          if y_pred.shape[-1] > 1:
              y_pred = np.argmax(y_pred, axis=-1)
          else:  # binaray classification (pred shape is [batch, 1])
              if self.from_logits:
                  y_pred = 1 / (1 + np.exp(-y_pred))
              y_pred = np.round(y_pred)
          assert y_pred.size == y_true.size
          self.correct += np.sum(y_pred.ravel() == y_true.ravel())
          self.total += len(y_pred.ravel())
  
      def on_epoch_end(self, data: Data) -> None:
          data.write_with_log(self.outputs[0], self.correct / self.total)
  