Remove continuous_eval_predicate_fn from Experiment class.

Change: 145594656

2 files changed, 33 insertions, 31 deletions

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 70b12631d4..8669616d87 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -74,8 +74,7 @@ class Experiment(object):
                continuous_eval_throttle_secs=60,
                min_eval_frequency=1,
                delay_workers_by_global_step=False,
-               export_strategies=None,
-               continuous_eval_predicate_fn=None):
+               export_strategies=None):
     """Constructor for `Experiment`.
 
     Creates an Experiment instance. None of the functions passed to this
@@ -111,12 +110,6 @@ class Experiment(object):
       delay_workers_by_global_step: if `True` delays training workers
         based on global step instead of time.
       export_strategies: A list of `ExportStrategy`s, or a single one, or None.
-      continuous_eval_predicate_fn: A predicate function determining whether to
-        continue eval after each iteration. `predicate_fn` takes the evaluation
-        results as arguments. At the beginning of evaluation, the passed eval
-        results will be None so it's expected that the predicate function
-        handles that gracefully. When `predicate_fn` is not specified,
-        continuous eval will run in an infinite loop.
 
     Raises:
       ValueError: if `estimator` does not implement `Evaluable` and `Trainable`,
@@ -142,8 +135,6 @@ class Experiment(object):
     self._train_monitors = train_monitors[:] if train_monitors else []
     self._eval_hooks = eval_hooks[:] if eval_hooks else []
     self._set_export_strategies(export_strategies)
-    # Mutable fields, using the setters.
-    self.continuous_eval_predicate_fn = continuous_eval_predicate_fn
 
   @property
   def estimator(self):
@@ -161,17 +152,6 @@ class Experiment(object):
   def eval_steps(self):
     return self._eval_steps
 
-  @property
-  def continuous_eval_predicate_fn(self):
-    return self._continuous_eval_predicate_fn
-
-  @continuous_eval_predicate_fn.setter
-  def continuous_eval_predicate_fn(self, value):
-    if value is not None and not callable(value):
-      raise ValueError(
-          "`continuous_eval_predicate_fn` must be a callable, or None.")
-    self._continuous_eval_predicate_fn = value
-
   def _set_export_strategies(self, value):
     if value is None:
       self._export_strategies = []
@@ -288,12 +268,15 @@ class Experiment(object):
       self._min_eval_frequency = self._local_eval_frequency
       return self.train_and_evaluate()
 
+  # TODO(xiejw): Allow continuous_eval_predicate_fn to be passed via constructor
+  # once stopping all jobs is implemented.
   def _continuous_eval(self,
                        input_fn,
                        name,
                        delay_secs,
                        throttle_delay_secs,
-                       evaluate_checkpoint_only_once=True):
+                       evaluate_checkpoint_only_once=True,
+                       continuous_eval_predicate_fn=None):
     """Run continuous eval.
 
     Runs infinite eval on the evaluation data set. This function starts
@@ -311,7 +294,22 @@ class Experiment(object):
         self._continuous_eval_throttle_secs.
       evaluate_checkpoint_only_once: Whether to skip evaluation of checkpoints
         that have already been evaluated. Default is `True`.
+      continuous_eval_predicate_fn: A predicate function determining whether to
+        continue eval after each iteration. `predicate_fn` takes the evaluation
+        results as arguments. At the beginning of evaluation, the passed eval
+        results will be None so it's expected that the predicate function
+        handles that gracefully. When `predicate_fn` is not specified,
+        continuous eval will run in an infinite loop.
+
+    Raises:
+      ValueError: if `continuous_eval_predicate_fn` is neither None nor
+        callable.
     """
+    if (continuous_eval_predicate_fn is not None and
+        not callable(continuous_eval_predicate_fn)):
+      raise ValueError(
+          "`continuous_eval_predicate_fn` must be a callable, or None.")
+
     if delay_secs is None:
       delay_secs = self._eval_delay_secs
     if throttle_delay_secs is None:
@@ -324,8 +322,8 @@ class Experiment(object):
     previous_path = None
     eval_result = None
     last_warning_time = 0
-    while (not self.continuous_eval_predicate_fn or
-           self.continuous_eval_predicate_fn(eval_result)):
+    while (not continuous_eval_predicate_fn or
+           continuous_eval_predicate_fn(eval_result)):
       start = time.time()
 
       error_msg = None
@@ -370,22 +368,26 @@ class Experiment(object):
   def continuous_eval(self,
                       delay_secs=None,
                       throttle_delay_secs=None,
-                      evaluate_checkpoint_only_once=True):
+                      evaluate_checkpoint_only_once=True,
+                      continuous_eval_predicate_fn=None):
     self._continuous_eval(
         self._eval_input_fn,
         name="continuous",
         delay_secs=delay_secs,
         throttle_delay_secs=throttle_delay_secs,
-        evaluate_checkpoint_only_once=evaluate_checkpoint_only_once)
+        evaluate_checkpoint_only_once=evaluate_checkpoint_only_once,
+        continuous_eval_predicate_fn=continuous_eval_predicate_fn)
 
   def continuous_eval_on_train_data(self,
                                     delay_secs=None,
-                                    throttle_delay_secs=None):
+                                    throttle_delay_secs=None,
+                                    continuous_eval_predicate_fn=None):
     self._continuous_eval(
         self._train_input_fn,
         name="continuous_on_train_data",
         delay_secs=delay_secs,
-        throttle_delay_secs=throttle_delay_secs)
+        throttle_delay_secs=throttle_delay_secs,
+        continuous_eval_predicate_fn=continuous_eval_predicate_fn)
 
   def train_and_evaluate(self):
     """Interleaves training and evaluation.
diff --git a/tensorflow/contrib/learn/python/learn/experiment_test.py b/tensorflow/contrib/learn/python/learn/experiment_test.py
index cf937443f0..398fc6e176 100644
--- a/tensorflow/contrib/learn/python/learn/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/experiment_test.py
@@ -342,9 +342,9 @@ class ExperimentTest(test.TestCase):
         eval_metrics='eval_metrics',
         eval_hooks=[noop_hook],
         eval_delay_secs=0,
-        continuous_eval_throttle_secs=0,
-        continuous_eval_predicate_fn=_predicate_fn)
-    ex.continuous_eval(evaluate_checkpoint_only_once=False)
+        continuous_eval_throttle_secs=0)
+    ex.continuous_eval(evaluate_checkpoint_only_once=False,
+                       continuous_eval_predicate_fn=_predicate_fn)
     self.assertEqual(0, est.fit_count)
     self.assertEqual(3, est.eval_count)
     self.assertEqual([noop_hook], est.eval_hooks)