Fix part of oppia#4374: Add docstrings to core.domain.classifier_doma…

…in (oppia#4458)

* Fix part of oppia#4374

* Made Review Changes

* Review changes

core/domain/classifier_domain.py

@@ -42,7 +42,7 @@ class ClassifierTrainingJob(object):
             belongs.
         exp_id: str. The id of the exploration that contains the state
             for which the classifier will be generated.
-        exp_version: str. The version of the exploration when
+        exp_version: int. The version of the exploration when
             the training job was generated.
         next_scheduled_check_time: datetime.datetime. The next scheduled time to
             check the job.
@@ -84,17 +84,17 @@ def __init__(self, job_id, algorithm_id, interaction_id, exp_id,
             belongs.
         exp_id: str. The id of the exploration id that contains the state
             for which classifier will be generated.
-        exp_version: str. The version of the exploration when
+        exp_version: int. The version of the exploration when
             the training job was generated.
         next_scheduled_check_time: datetime.datetime. The next scheduled time to
             check the job.
         state_name: str. The name of the state for which the classifier will be
             generated.
         status: str. The status of the training job request. This can be either
             NEW (default), PENDING (when a job has been picked up) or COMPLETE.
-        training_data: list. The training data that is used for training the
-            classifier. This is populated lazily when the job request is picked
-            up by the VM. The list contains dicts where each dict
+        training_data: list(dict). The training data that is used for training
+            the classifier. This is populated lazily when the job request is
+            picked up by the VM. The list contains dicts where each dict
             represents a single training data group, for example:
             training_data = [
                 {
@@ -125,46 +125,123 @@ def __init__(self, job_id, algorithm_id, interaction_id, exp_id,
 
     @property
     def job_id(self):
+        """Returns the job_id of the classifier training job.
+
+        Returns:
+            str. The unique id of the classifier training job.
+        """
         return self._job_id
 
     @property
     def algorithm_id(self):
+        """Returns the algorithm_id of the algorithm used for generating
+        the classifier.
+
+        Returns:
+            str. The id of the algorithm used for generating the classifier.
+        """
         return self._algorithm_id
 
     @property
     def interaction_id(self):
+        """Returns the interaction_id to which the algorithm belongs.
+
+        Returns:
+            str. The id of the interaction to which the algorithm belongs.
+        """
         return self._interaction_id
 
     @property
     def exp_id(self):
+        """Returns the exploration id for which the classifier will be
+        generated.
+
+        Returns:
+            str. The id of the exploration that contains the state
+            for which classifier will be generated.
+        """
         return self._exp_id
 
     @property
     def exp_version(self):
+        """Returns the exploration version.
+
+        Returns:
+            int. The version of the exploration when the training job was
+            generated.
+        """
         return self._exp_version
 
     @property
     def next_scheduled_check_time(self):
+        """Returns the next scheduled time to check the job.
+
+        Returns:
+            datetime.datetime. The next scheduled time to check the job.
+        """
         return self._next_scheduled_check_time
 
     @property
     def state_name(self):
+        """Returns the state_name for which the classifier will be generated.
+
+        Returns:
+            str. The name of the state for which the classifier will be
+            generated.
+        """
         return self._state_name
 
     @property
     def status(self):
+        """Returns the status of the training job request.
+
+        Returns:
+            str. The status of the training job request. This can be either
+            NEW (default), PENDING (when a job has been picked up) or
+            COMPLETE.
+        """
         return self._status
 
     @property
     def training_data(self):
+        """Returns the training data used for training the classifier.
+
+        Returns:
+            list(dict). The training data that is used for training the
+            classifier. This is populated lazily when the job request is
+            picked up by the VM. The list contains dicts where each dict
+            represents a single training data group, for example:
+            training_data = [
+                {
+                    'answer_group_index': 1,
+                    'answers': ['a1', 'a2']
+                },
+                {
+                    'answer_group_index': 2,
+                    'answers': ['a2', 'a3']
+                }
+            ]
+        """
         return self._training_data
 
     @property
     def classifier_data(self):
+        """Returns the classifier data.
+
+        Returns:
+            dict. The actual classifier model used for
+            classification purpose.
+        """
         return self._classifier_data
 
     @property
     def data_schema_version(self):
+        """Returns the schema version of the data used by the classifier.
+
+        Returns:
+            int. Schema version of the data used by the
+            classifier. This depends on the algorithm ID.
+        """
         return self._data_schema_version
 
     def update_status(self, status):
@@ -348,18 +425,40 @@ def __init__(self, exp_id, exp_version, state_name, job_id):
 
     @property
     def exp_id(self):
+        """Returns the exploration id.
+
+        Returns:
+            str. The id of the exploration.
+        """
         return self._exp_id
 
     @property
     def exp_version(self):
+        """Returns the exploration version.
+
+        Returns:
+            int. The exploration version at the time the
+            corresponding classifier's training job was created.
+        """
         return self._exp_version
 
     @property
     def state_name(self):
+        """Returns the state_name to which the classifier belongs.
+
+        Returns:
+            str. The name of the state to which the classifier belongs.
+        """
         return self._state_name
 
     @property
     def job_id(self):
+        """Returns the job_id of the training job.
+
+        Returns:
+            str. The unique ID of the training job in the
+            job-exploration mapping.
+        """
         return self._job_id
 
     def to_dict(self):