Update the FAQ docs.

docs/source/FAQ/index.rst

@@ -6,5 +6,4 @@ FAQ
     :maxdepth: 2
 
     prohibited_data_patterns
-    v3_migration
-    data_manager_migration
+    predictor_evaluation_migration

docs/source/FAQ/predictor_evaluation_migration.rst

@@ -0,0 +1,51 @@
+==================================
+Migrating to Predictor Evaluations
+==================================
+
+Summary
+=======
+
+In version 4.0, :py:class:`Predictor Evaluation Workflows <citrine.resources.predictor_evaluation_workflow.PredictorEvaluationWorkflowCollection>` and :py:class:`Predictor Evaluation Executions <citrine.resources.predictor_evaluation_execution.PredictorEvaluationExecutionCollection>` (collectively, PEWs) will be merged into a single entity called :py:class:`Predictor Evaluations  <citrine.resources.predictor_evaluation.PredictorEvaluationCollection>`. The new entity will retain the functionality of its predecessors, while simplyfing interactions with it. And it will support the continuing evolution of the platform.
+
+Basic Usage
+===========
+
+The most common pattern for interacting with PEWs is executing the default evaluators and waiting for the result:
+
+.. code:: python
+
+    pew = project.predictor_evaluation_workflows.create_default(predictor_id=predictor.uid)
+    execution = next(pew.executions.list(), None)
+    execution = wait_while_executing(collection=project.predictor_evaluation_executions, execution=execution)
+
+With Predictor Evaluations, it's more straight-forward:
+
+.. code:: python
+
+    evaluation = project.predictor_evaluations.trigger_default(predictor_id=predictor.uid)
+    evaluation = wait_while_executing(collection=project.predictor_evaluations, execution=evaluation)
+
+The evaluators used are available with :py:meth:`~citrine.informatics.executions.predictor_evaluation.PredictorEvaluation.evaluators`.
+
+Working With Evaluators
+=======================
+
+You can still construct evaluators (such as :class:`~~citrine.informatics.predictor_evaluator.CrossValidationEvaluator`) the same way as you always have, and run them against your predictor:
+
+.. code:: python
+
+    evaluation = project.predictor_evaluations.trigger(predictor_id=predictor.uid, evaluators=evaluators)
+
+If you don't wish to construct evaluators by hand, you can retrieve the default one(s):
+
+.. code:: python
+
+    evaluators = project.predictor_evaluations.default(predictor_id=predictor.uid)
+
+You can evaluate your predictor even if it hasn't been registered to the platform yet:
+
+.. code:: python
+
+    evaluators = project.predictor_evaluations.default_from_config(predictor)
+
+Once evaluation is complete, the results will be available by calling :py:meth:`~citrine.informatics.executions.predictor_evaluation.PredictorEvaluation.results` with the name of the desired evaluator (which are all available through :py:meth:`~citrine.informatics.executions.predictor_evaluation.PredictorEvaluation.evaluator_names`).

src/citrine/__version__.py

@@ -1 +1 @@
-__version__ = "3.25.2"
+__version__ = "3.26.0"