# Path: hyperengine/model/data_set.py
# class IterableDataProvider(DataProvider):
#   def __init__(self):
#     super(IterableDataProvider, self).__init__()
#     self._size = 0
#     self._step = 0
#     self._epochs_completed = 0
#     self._index_in_epoch = 0
#     self._just_completed = False
# 
#   @property
#   def size(self):
#     """
#     Data size (number of rows)
#     """
#     return self._size
# 
#   @property
#   def step(self):
#     """
#     The number of batches processed
#     """
#     return self._step
# 
#   @property
#   def index(self):
#     """
#     Total index of input rows (over all epochs)
#     """
#     return self._epochs_completed * self._size + self._index_in_epoch
# 
#   @property
#   def index_in_epoch(self):
#     """
#     The index of input rows in a current epoch
#     """
#     return self._index_in_epoch
# 
#   @property
#   def epochs_completed(self):
#     """
#     A number of completed epochs
#     """
#     return self._epochs_completed
# 
#   @property
#   def just_completed(self):
#     """
#     Whether the previous epoch was just completed
#     """
#     return self._just_completed
# 
#   def reset_counters(self):
#     """
#     Resets all counters.
#     """
#     self._step = 0
#     self._epochs_completed = 0
#     self._index_in_epoch = 0
#     self._just_completed = False
# 
#   def next_batch(self, batch_size):
#     """
#     Returns the next `batch_size` examples from this data set.
#     """
#     raise NotImplementedError
# 
#   def _inc_index(self):
#     index = self._index_in_epoch + 1
#     if index >= self._size:
#       self._index_in_epoch = 0
#       self._epochs_completed += 1
#       self._just_completed = True
#     else:
#       self._index_in_epoch = index
#       self._just_completed = False


import inspect
import numpy as np
from ..base import *
from .data_set import IterableDataProvider
      return

    if self._test_set is None:
      warn('Test set is not provided. Skip test evaluation')
      return

    eval_ = self._evaluate(self._test_set)
    test_accuracy = eval_.get('accuracy')
    if test_accuracy is None:
      warn('Test accuracy evaluation is not available')
      return

    info('Final test_accuracy=%.4f' % test_accuracy)
    return eval_

  def _log_iteration(self, name, loss, accuracy, mark_best):
    message = 'Epoch %2d, iteration %7d' % (self._train_set.epochs_completed, self._train_set.index)
    if accuracy is not None:
      marker = ' *' if mark_best and (accuracy > self._max_val_accuracy) else ''
      if loss is None:
        info('%s: %s=%.4f%s' % (message, name, accuracy, marker))
      else:
        info('%s: loss=%.6f, %s=%.4f%s' % (message, loss, name, accuracy, marker))
    else:
      if loss is not None:
        info('%s: loss=%.6f' % (message, loss))
      else:
        info('%s: -- no loss or accuracy defined --' % message)

  def _evaluate(self, data_set):

# Path: hyperengine/model/data_set.py
# class IterableDataProvider(DataProvider):
#   def __init__(self):
#     super(IterableDataProvider, self).__init__()
#     self._size = 0
#     self._step = 0
#     self._epochs_completed = 0
#     self._index_in_epoch = 0
#     self._just_completed = False
# 
#   @property
#   def size(self):
#     """
#     Data size (number of rows)
#     """
#     return self._size
# 
#   @property
#   def step(self):
#     """
#     The number of batches processed
#     """
#     return self._step
# 
#   @property
#   def index(self):
#     """
#     Total index of input rows (over all epochs)
#     """
#     return self._epochs_completed * self._size + self._index_in_epoch
# 
#   @property
#   def index_in_epoch(self):
#     """
#     The index of input rows in a current epoch
#     """
#     return self._index_in_epoch
# 
#   @property
#   def epochs_completed(self):
#     """
#     A number of completed epochs
#     """
#     return self._epochs_completed
# 
#   @property
#   def just_completed(self):
#     """
#     Whether the previous epoch was just completed
#     """
#     return self._just_completed
# 
#   def reset_counters(self):
#     """
#     Resets all counters.
#     """
#     self._step = 0
#     self._epochs_completed = 0
#     self._index_in_epoch = 0
#     self._just_completed = False
# 
#   def next_batch(self, batch_size):
#     """
#     Returns the next `batch_size` examples from this data set.
#     """
#     raise NotImplementedError
# 
#   def _inc_index(self):
#     index = self._index_in_epoch + 1
#     if index >= self._size:
#       self._index_in_epoch = 0
#       self._epochs_completed += 1
#       self._just_completed = True
#     else:
#       self._index_in_epoch = index
#       self._just_completed = False


# Path: hyperengine/model/base_solver.py
import inspect
import numpy as np
from ..base import *
from .data_set import IterableDataProvider

      return

    if self._test_set is None:
      warn('Test set is not provided. Skip test evaluation')
      return

    eval_ = self._evaluate(self._test_set)
    test_accuracy = eval_.get('accuracy')
    if test_accuracy is None:
      warn('Test accuracy evaluation is not available')
      return

    info('Final test_accuracy=%.4f' % test_accuracy)
    return eval_

  def _log_iteration(self, name, loss, accuracy, mark_best):
    message = 'Epoch %2d, iteration %7d' % (self._train_set.epochs_completed, self._train_set.index)
    if accuracy is not None:
      marker = ' *' if mark_best and (accuracy > self._max_val_accuracy) else ''
      if loss is None:
        info('%s: %s=%.4f%s' % (message, name, accuracy, marker))
      else:
        info('%s: loss=%.6f, %s=%.4f%s' % (message, loss, name, accuracy, marker))
    else:
      if loss is not None:
        info('%s: loss=%.6f' % (message, loss))
      else:
        info('%s: -- no loss or accuracy defined --' % message)

  def _evaluate(self, data_set):

    assert isinstance(data_set, IterableDataProvider), 'Currently can evaluate only IterableDataProviders'