Merge pull request #942 from CitrineInformatics/docs/update_descriptor_method_ref

Docs/update descriptor method ref

Author: jspeerless

docs/source/formulations_example.rst

@@ -564,8 +564,8 @@ We also use ``blend time`` as an input.
 
 Where did the descriptor keys ``margarita~blend time`` and ``margarita~tastiness`` come from?
 They came from concatenating the headers in the variables in the table, and the bounds and units came from the attribute templates.
-It's a lot to keep track of, which is why there is the :func:`~citrine.resources.descriptors.DescriptorMethods.descriptors_from_data_source` method.
-Calling ``project.descriptors.descriptors_from_data_source(data_source)`` returns a list of all of the descriptors emitted by the data source.
+It's a lot to keep track of, which is why there is the :func:`~citrine.resources.descriptors.DescriptorMethods.from_data_source` method.
+Calling ``project.descriptors.from_data_source(data_source)`` returns a list of all of the descriptors emitted by the data source.
 Make sure that these are the descriptors you are using as inputs to your predictor.
 
 

docs/source/workflows/predictors.rst

@@ -38,7 +38,7 @@ Both binary and multi-class classification are supported automatically based on
 Models are trained using data provided by a :class:`~citrine.informatics.data_sources.DataSource` specified when creating a predictor.
 The inputs and outputs are descriptors, which must correspond precisely to descriptors that exist in the training data or are produced by other predictors in the graphical model.
 There are two important helper methods in this regard.
-:func:`~citrine.resources.descriptors.DescriptorMethods.descriptors_from_data_source` can provide all of the descriptors that are present in the training data.
+:func:`~citrine.resources.descriptors.DescriptorMethods.from_data_source` can provide all of the descriptors that are present in the training data.
 :func:`~citrine.resources.descriptors.DescriptorMethods.from_predictor_responses` can tell you what the outputs of a predictor will be, which is especially useful for featurizers.
 
 The following example demonstrates how to use the Citrine Python client to create an :class:`~citrine.informatics.predictors.auto_ml_predictor.AutoMLPredictor`, register the predictor to a project and wait for validation:

src/citrine/__version__.py

@@ -1 +1 @@
-__version__ = "3.2.10"
+__version__ = "3.2.11"