Improve docstring of base tuner and assessor

docs/en_US/Makefile

@@ -16,4 +16,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/en_US/sdk_reference.rst

@@ -41,10 +41,13 @@ Assessor
 ..  autoclass:: nni.assessor.Assessor
     :members:
 
-..  autoclass:: nni.curvefitting_assessor.curvefitting_assessor.CurvefittingAssessor
+..  autoclass:: nni.assessor.AssessResult
     :members:
 
-..  autoclass:: nni.medianstop_assessor.medianstop_assessor.MedianstopAssessor
+..  autoclass:: nni.curvefitting_assessor.CurvefittingAssessor
+    :members:
+
+..  autoclass:: nni.medianstop_assessor.MedianstopAssessor
     :members:
 
 
@@ -57,4 +60,4 @@ Advisor
     :members:
 
 ..  autoclass:: nni.bohb_advisor.bohb_advisor.BOHB
-    :members:
+    :members:

src/sdk/pynni/nni/assessor.py

@@ -18,45 +18,113 @@
 # OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 # ==================================================================================================
 
+"""
+Assessor analyzes trial's intermediate results (e.g., periodically evaluated accuracy on test dataset)
+to tell whether this trial can be early stopped or not.
+
+See :class:`Assessor`' specification and ``docs/en_US/assessors.rst`` for details.
+"""
 
-import logging
 from enum import Enum
+import logging
 
 from .recoverable import Recoverable
 
+__all__ = ['AssessResult', 'Assessor']
+
 _logger = logging.getLogger(__name__)
 
+
 class AssessResult(Enum):
+    """
+    Enum class for :meth:`Assessor.assess_trial` return value.
+    """
+
     Good = True
+    """The trial works well."""
+
     Bad = False
+    """The trial works poorly and should be early stopped."""
+
 
 class Assessor(Recoverable):
+    """
+    Assessor analyzes trial's intermediate results (e.g., periodically evaluated accuracy on test dataset)
+    to tell whether this trial can be early stopped or not.
+
+    This is the abstract base class for all assessors.
+    Early stopping algorithms should derive this class and override :meth:`assess_trial` method,
+    which receives intermediate results from trials and give an assessing result.
+
+    If :meth:`assess_trial` returns :obj:`AssessResult.Bad` for a trial,
+    it hints NNI framework that the trial is likely to result in a poor final accuracy,
+    and therefore should be killed to save resource.
+
+    If an accessor want's to get notified when a trial ends, it can also override :meth:`trial_end`.
+
+    To write a new assessor, you can reference :class:`~nni.medianstop_assessor.MedianstopAssessor`'s code as an example.
+
+    See Also
+    --------
+    Builtin assessors:
+    :class:`~nni.medianstop_assessor.MedianstopAssessor`
+    :class:`~nni.curvefitting_assessor.CurvefittingAssessor`
+    """
 
     def assess_trial(self, trial_job_id, trial_history):
-        """Determines whether a trial should be killed. Must override.
-        trial_job_id: identifier of the trial (str).
-        trial_history: a list of intermediate result objects.
-        Returns AssessResult.Good or AssessResult.Bad.
+        """
+        Abstract method for determining whether a trial should be killed. Must override.
+
+        The NNI framework has little guarantee on ``trial_history``.
+        This method is not guaranteed to be invoked for each time ``trial_history`` get updated.
+        It is also possible that a trial's history keeps updateing after receiving a bad result.
+        And if the trial failed and retried, ``trial_history`` may be inconsistent with its previous value.
+
+        The only guarantee is that ``trial_history`` is always growing.
+        It will not be empty and will always be longer than previous value.
+
+        This is an example of how :meth:`assess_trial` get invoked sequentially:
+
+        ::
+
+            trial_job_id | trial_history   | return value
+            ------------ | --------------- | ------------
+            Trial_A      | [1.0, 2.0]      | Good
+            Trial_B      | [1.5, 1.3]      | Bad
+            Trial_B      | [1.5, 1.3, 1.9] | Good
+            Trial_A      | [0.9, 1.8, 2.3] | Good
+
+        Parameters
+        ----------
+        trial_job_id: str
+            Unique identifier of the trial.
+        trial_history: list
+            Intermediate results of this trial. The element type is decided by trial code.
+
+        Returns
+        -------
+        AssessResult
+            :obj:`AssessResult.Good` or :obj:`AssessResult.Bad`.
         """
         raise NotImplementedError('Assessor: assess_trial not implemented')
 
     def trial_end(self, trial_job_id, success):
-        """Invoked when a trial is completed or terminated. Do nothing by default.
-        trial_job_id: identifier of the trial (str).
-        success: True if the trial successfully completed; False if failed or terminated.
         """
+        Abstract method invoked when a trial is completed or terminated. Do nothing by default.
 
-    def load_checkpoint(self):
-        """Load the checkpoint of assessr.
-        path: checkpoint directory for assessor
+        Parameters
+        ----------
+        trial_job_id: str
+            Unique identifier of the trial.
+        success: bool
+            True if the trial successfully completed; False if failed or terminated.
         """
+
+    def load_checkpoint(self):
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Load checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
 
     def save_checkpoint(self):
-        """Save the checkpoint of assessor.
-        path: checkpoint directory for assessor
-        """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Save checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
 

src/sdk/pynni/nni/curvefitting_assessor/__init__.py

@@ -0,0 +1 @@
+from .curvefitting_assessor import CurvefittingAssessor

src/sdk/pynni/nni/medianstop_assessor/__init__.py

@@ -0,0 +1 @@
+from .medianstop_assessor import MedianstopAssessor

src/sdk/pynni/nni/tuner.py

@@ -17,31 +17,119 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
 # OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 # ==================================================================================================
+
+"""
+Tuner is an AutoML algorithm, which generates a new configuration for the next try.
+A new trial will run with this configuration.
+
+See :class:`Tuner`' specification and ``docs/en_US/tuners.rst`` for details.
+"""
+
 import logging
 
 import nni
 
 from .recoverable import Recoverable
 
+__all__ = ['Tuner']
+
 _logger = logging.getLogger(__name__)
 
 
 class Tuner(Recoverable):
+    """
+    Tuner is an AutoML algorithm, which generates a new configuration for the next try.
+    A new trial will run with this configuration.
+
+    This is the abstract base class for all tuners.
+    Tuning algorithms should derive this class and override :meth:`update_search_space`, :meth:`receive_trial_result`,
+    as well as :meth:`generate_parameters` or :meth:`generate_multiple_parameters`.
+
+    After initializing, NNI will first call :meth:`update_search_space` to tell tuner the feasible region,
+    and then call :meth:`generate_parameters` one or more times to request for hyper-parameter configurations.
+
+    The framework will train several models with given configuration.
+    When one of them is finished, the final accuracy will be reported to :meth:`receive_trial_result`.
+    And then another configuration will be reqeusted and trained, util the whole experiment finish.
+
+    If a tuner want's to know when a trial ends, it can also override :meth:`trial_end`.
+
+    The type/format of search space and hyper-parameters are not limited,
+    as long as they are JSON-serializable and in sync with trial code.
+    For HPO tuners, however, there is a widely shared common interface,
+    which supports ``choice``, ``randint``, ``uniform``, and so on.
+    See ``docs/en_US/Tutorial/SearchSpaceSpec.md`` for details of this interface.
+
+    [WIP] For advanced tuners which take advantage of trials' intermediate results,
+    an ``Advisor`` interface is under development.
+
+    See Also
+    --------
+    Builtin tuners:
+    :class:`~nni.hyperopt_tuner.hyperopt_tuner.HyperoptTuner`
+    :class:`~nni.evolution_tuner.evolution_tuner.EvolutionTuner`
+    :class:`~nni.smac_tuner.smac_tuner.SMACTuner`
+    :class:`~nni.gridsearch_tuner.gridsearch_tuner.GridSearchTuner`
+    :class:`~nni.networkmorphism_tuner.networkmorphism_tuner.NetworkMorphismTuner`
+    :class:`~nni.metis_tuner.mets_tuner.MetisTuner`
+    """
 
     def generate_parameters(self, parameter_id, **kwargs):
-        """Returns a set of trial (hyper-)parameters, as a serializable object.
-        User code must override either this function or 'generate_multiple_parameters()'.
+        """
+        Abstract method which provides one set of hyper-parameters.
+
+        This method will get called when the framework is about to launch a new trial,
+        if user does not override :meth:`generate_multiple_parameters`.
+
+        The return value will be received by trials via :func:`nni.get_next_parameter`.
+        It should fit in the search space, though the framework will not verify this.
+
+        User code must override either this method or :meth:`generate_multiple_parameters`.
+
+        Parameters
+        ----------
         parameter_id: int
+            Unique identifier for requested hyper-parameters. This will later be used in :meth:`receive_trial_result`.
+        **kwargs:
+            Unstable parameters which should be ignored by normal users.
+
+        Returns
+        -------
+        any
+            The hyper-parameters, a dict in most cases, but could be any JSON-serializable type when needed.
+
+        Raises
+        ------
+        nni.NoMoreTrialError
+            If the search space is fully explored, tuner can raise this exception.
+            [FIXME] Currently some tuners also raise this exception when they are waiting more trial results.
         """
         raise NotImplementedError('Tuner: generate_parameters not implemented')
 
     def generate_multiple_parameters(self, parameter_id_list, **kwargs):
-        """Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
-        Call 'generate_parameters()' by 'count' times by default.
-        User code must override either this function or 'generate_parameters()'.
-        If there's no more trial, user should raise nni.NoMoreTrialError exception in generate_parameters().
-        If so, this function will only return sets of trial (hyper-)parameters that have already been collected.
+        """
+        Callback method which provides multiple sets of hyper-parameters.
+
+        This method will get called when the framework is about to launch one or more new trials.
+
+        If user does not override this method, it will invoke :meth:`generate_parameters` on each parameter ID.
+
+        See :meth:`generate_parameters` for details.
+
+        User code must override either this method or :meth:`generate_parameters`.
+
+        Parameters
+        ----------
         parameter_id_list: list of int
+            Unique identifiers for each set of requested hyper-parameters.
+            These will later be used in :meth:`receive_trial_result`.
+        **kwargs:
+            Unstable parameters which should be ignored by normal users.
+
+        Returns
+        -------
+        list
+            List of hyper-parameters. An empty list indicates there are no more trials.
         """
         result = []
         for parameter_id in parameter_id_list:
@@ -54,56 +142,74 @@ def generate_multiple_parameters(self, parameter_id_list, **kwargs):
         return result
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
-        """Invoked when a trial reports its final result. Must override.
-        By default this only reports results of algorithm-generated hyper-parameters.
-        Use `accept_customized_trials()` to receive results from user-added parameters.
+        """
+        Abstract method invoked when a trial reports its final result. Must override.
+
+        This method only reports results of algorithm-generated hyper-parameters.
+        Currently customized trials added from web UI will not report result to this method.
+
+        Parameters
+        ----------
         parameter_id: int
-        parameters: object created by 'generate_parameters()'
-        value: object reported by trial
-        customized: bool, true if the trial is created from web UI, false if generated by algorithm
-        trial_job_id: str, only available in multiphase mode.
+            Unique identifier of used hyper-parameters, same with :meth:`generate_parameters`.
+        parameters
+            Hyper-parameters generated by :meth:`generate_parameters`.
+        value
+            Result from trial (the return value of :func:`nni.report_final_result`).
+        **kwargs:
+            Unstable parameters which should be ignored by normal users.
         """
         raise NotImplementedError('Tuner: receive_trial_result not implemented')
 
-    def accept_customized_trials(self, accept=True):
-        """Enable or disable receiving results of user-added hyper-parameters.
-        By default `receive_trial_result()` will only receive results of algorithm-generated hyper-parameters.
-        If tuners want to receive those of customized parameters as well, they can call this function in `__init__()`.
-        """
+    def _accept_customized_trials(self, accept=True):
+        # FIXME: because Tuner is designed as interface, this API should not be here
+
+        # Enable or disable receiving results of user-added hyper-parameters.
+        # By default `receive_trial_result()` will only receive results of algorithm-generated hyper-parameters.
+        # If tuners want to receive those of customized parameters as well, they can call this function in `__init__()`.
+
         # pylint: disable=attribute-defined-outside-init
-        # FIXME: because tuner is designed as interface, this API should not be here
         self._accept_customized = accept
 
     def trial_end(self, parameter_id, success, **kwargs):
-        """Invoked when a trial is completed or terminated. Do nothing by default.
-        parameter_id: int
-        success: True if the trial successfully completed; False if failed or terminated
+        """
+        Abstract method invoked when a trial is completed or terminated. Do nothing by default.
+
+        Parameters
+        ----------
+        trial_job_id: str
+            Unique identifier of the trial.
+        success: bool
+            True if the trial successfully completed; False if failed or terminated.
         """
 
     def update_search_space(self, search_space):
-        """Update the search space of tuner. Must override.
-        search_space: JSON object
+        """
+        Abstract method for updating the search space. Must override.
+
+        Tuners are advised to support updating search space at run-time.
+        If a tuner can only set search space once before generating first hyper-parameters,
+        it should explicitly document this behaviour.
+
+        Parameters
+        ----------
+        search_space
+            JSON object defined by experiment owner.
         """
         raise NotImplementedError('Tuner: update_search_space not implemented')
 
     def load_checkpoint(self):
-        """Load the checkpoint of tuner.
-        path: checkpoint directory for tuner
-        """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Load checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)
 
     def save_checkpoint(self):
-        """Save the checkpoint of tuner.
-        path: checkpoint directory for tuner
-        """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Save checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)
 
     def import_data(self, data):
-        """Import additional data for tuning
-        data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
-        """
+        # Import additional data for tuning
+        # data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        pass
 
     def _on_exit(self):
         pass

src/sdk/pynni/tests/test_tuner.py

@@ -34,7 +34,7 @@ def __init__(self):
         self.param = 0
         self.trial_results = []
         self.search_space = None
-        self.accept_customized_trials()
+        self._accept_customized_trials()
 
     def generate_parameters(self, parameter_id, **kwargs):
         # report Tuner's internal states to generated parameters,