* Updated docs and bumped to version 1.0.1

docs/api/api.rst

@@ -11,7 +11,6 @@ Docs for scooch functions and classes are found here.
    configurable
    config
    param
-   configurable_param
    config_list
    config_collection
    configurable_factory

docs/benefits_of_scooch.rst

@@ -57,7 +57,6 @@ Often in ML pipelines you will want to reuse functionality (and hence configurat
     from features import FeatureTransform
     from batcher import Batcher
     from scooch import Configurable
-    from scooch import ConfigurableParam
 
     class Task(Configurable):
         """
@@ -70,17 +69,17 @@ Often in ML pipelines you will want to reuse functionality (and hence configurat
         Batches data.
         """
 
-        _feature = ConfigurableParam(FeatureTransform, doc="Transforms features for neural net input")
-        _batcher = ConfigurableParam(Batcher, doc="Batches feature Data")
+        _feature = Param(FeatureTransform, doc="Transforms features for neural net input")
+        _batcher = Param(Batcher, doc="Batches feature Data")
         ...
 
     class InferenceTask(Task):
         """
         Applies inference.
         """
 
-        _feature = ConfigurableParam(FeatureTransform, doc="Transforms features for neural net input")
-        _model = ConfigurableParam(Model, doc="A trained model for analyzing features")
+        _feature = Param(FeatureTransform, doc="Transforms features for neural net input")
+        _model = Param(Model, doc="A trained model for analyzing features")
         ...
 
 Both classes reuse the same :code:`FeatureTransform` interface, and in the Scooch :code:`config.yaml` file, we can reuse the same configuration:

docs/conf.py

@@ -35,11 +35,11 @@
 # -- Project information -----------------------------------------------------
 
 project = 'Scooch'
-copyright = '2021 Pandora Media, LLC.'
+copyright = '2023 Pandora Media, LLC.'
 author = 'Matt C. McCallum'
 
 # The full version, including alpha/beta/rc tags
-release = '0.0.9'
+release = '1.0.1'
 
 
 # -- General configuration ---------------------------------------------------

docs/getting_started/existing_codebase.rst

@@ -87,19 +87,19 @@ For example, to add all keras loss functions to our Scooch hierarchy we could do
         pass
 
     clsmembers = inspect.getmembers(sys.modules[tf.keras.losses.__name__], inspect.isclass)
-    configurable_tf_losses = [configurize(mem[0], base_class) for mem in clsmembers if mem[0] != 'Loss']
+    configurable_tf_losses = [configurize(mem[0], Loss) for mem in clsmembers if mem[0] != 'Loss']
 
-With the above code, classes can now be defined with a :code:`ConfigurableParam` of type :code:`Loss`, which will now be able to use all keras loss functions in your :code:`config.yaml` file:
+With the above code, classes can now be defined with a :code:`Param` of type :code:`Loss`, which will now be able to use all keras loss functions in your :code:`config.yaml` file:
 
 .. code-block:: python
 
     ...
 
-    from scooch import ConfigurableParam
+    from scooch import Param
 
     class Experiment(Configurable):
 
-        ConfigurableParam(Loss, doc="A Loss function to train a model with.")
+        Param(Loss, doc="A Loss function to train a model with.")
         ...
 
 Configurizing Code that is not Object Oriented

docs/getting_started/new_codebase.rst

@@ -175,7 +175,7 @@ For data augmentations we'll want to parameterize that augmentation itself. Let'
             noise_data = np.random.normal(scale=power_linear, size=sample.shape)
             return sample + noise_data
 
-We can now employ this new :code:`Configurable` inside the :code:`Batcher` class by adding a :code:`ConfigurableParam` in the class definition of :code:`Batcher`, e.g., 
+We can now employ this new :code:`Configurable` inside the :code:`Batcher` class by adding a :code:`Param` with a type of another :code:`Configurable` class, (i.e., the :code:`NoiseAugmenter` class), in the class definition of :code:`Batcher`, e.g., 
 
 .. code-block:: python
 
@@ -192,7 +192,7 @@ We can now employ this new :code:`Configurable` inside the :code:`Batcher` class
 
         _batch_size = Param(int, default=128, doc="The number of samples in each mini-batch")
         _audio_samples_per_smaple = Param(int, default=1024, doc="The number of audio samples to extract each feature from")
-        _augmenter = ConfigurableParam(NoiseAugmenter, doc="An augmentation transformation to be applied to each sample")
+        _augmenter = Param(NoiseAugmenter, doc="An augmentation transformation to be applied to each sample")
 
     ...
 
@@ -270,7 +270,7 @@ Scooch configures not only classes, but class hierarchies. As this codebase deve
             # Produce a random dB value for the noise
             ...
 
-We now adjust the :code:`ConfigurableParam` in the :code:`Batcher` class to refer to any class that derives from :code:`Augmenter`:
+We now adjust the :code:`Configurable` \ :code:`Param` in the :code:`Batcher` class to refer to any class that derives from :code:`Augmenter`:
 
 .. code-block:: python
 
@@ -287,7 +287,7 @@ We now adjust the :code:`ConfigurableParam` in the :code:`Batcher` class to refe
 
         _batch_size = Param(int, default=128, doc="The number of samples in each mini-batch")
         _audio_samples_per_smaple = Param(int, default=1024, doc="The number of audio samples to extract each feature from")
-        _augmenter = ConfigurableParam(Augmenter, doc="An augmentation transformation to be applied to each sample")
+        _augmenter = Param(Augmenter, doc="An augmentation transformation to be applied to each sample")
 
     ...
 
@@ -360,4 +360,4 @@ The structure of configuration for a :code:`Configurable` can become quite compl
 
     scooch construct -c ./default_config.yaml -m batcher -f Batcher
 
-This will prompt for the type of each :code:`ConfigurableParam` in the class hierarchy, and construct a configuration for the :code:`Batcher` class in the :code:`batcher` module and place it in the file :code:`./default_config.yaml`.
+This will prompt for the type of each :code:`Param` that is of type :code:`Configurable` in the class hierarchy, and construct a configuration for the :code:`Batcher` class in the :code:`batcher` module and place it in the file :code:`./default_config.yaml`.

docs/index.rst

@@ -77,7 +77,6 @@ Each class in this configuration corresponds directly to a Scooch :code:`Configu
 
     from scooch import Configurable
     from scooch import Param
-    from scooch import ConfigurableParam
     from scooch import ConfigList
 
     class SpectrogramFeature(Configurable):
@@ -109,12 +108,12 @@ Each class in this configuration corresponds directly to a Scooch :code:`Configu
     class Batcher(Configurable):
         
         _batch_size = Param(int, default=256, doc="Number of samples per batch")
-        _feature = ConfigurableParam(SpectrogramFeature, doc="The feature to produce samples of")
-        _augmenters = ConfigurableParam(ConfigList(Augmenter), doc="A list of data augmenters to sample from")
+        _feature = Param(SpectrogramFeature, doc="The feature to produce samples of")
+        _augmenters = Param(ConfigList(Augmenter), doc="A list of data augmenters to sample from")
         
         ...
 
-In the above snippet, we can see abstraction, polymorphism, inheritance, and encapsulation employed within the classes, and their Scooch parameters. Once configured, within each of the classes above the :code:`Param`\ s and :code:`ConfigurableParam`\ s will become accessible as attributes of the encapsulating :code:`Configurable` class instance. Furthermore, the Scooch :code:`Param` / :code:`ConfigurableParam` documentation will be added to the :code:`Configurable` class's doc string for accessibilty in any auto-generated documentation.
+In the above snippet, we can see abstraction, polymorphism, inheritance, and encapsulation employed within the classes, and their Scooch parameters. Once configured, within each of the classes above the :code:`Param`\ s will become accessible as attributes of the encapsulating :code:`Configurable` class instance. Furthermore, the Scooch :code:`Param` documentation will be added to the :code:`Configurable` class's doc string for accessibilty in any auto-generated documentation.
 
 With the class definitions and the :code:`config.yaml` file provided above, configuring the :code:`Batcher` class and running the code in a script could be as simple as:
 
@@ -135,6 +134,12 @@ With the class definitions and the :code:`config.yaml` file provided above, conf
     # Use the class to produce a batch with the configured parameters
     samples = a_batcher.get_batch()
 
+As your codebases grow having a tool to explore the available functionality of that codebase will become more important. Fortunately, if your codebase is SCOOCH configurable, the SCOOCH CLI can help. For example to view the details of all of the :code:`Augmenter` types in the `batcher` module described above, you can simply type the following on the command line:
+
+.. code-block:: bash
+
+    scooch options -m batcher -f Augmenter
+
 Index
 ==================
 

examples/batcher_example/batcher/batcher.py

@@ -16,7 +16,6 @@
 import random
 import numpy as np
 from scooch import Configurable
-from scooch import ConfigurableParam
 from scooch import Param
 from .augmenters import Augmenter
 
@@ -27,7 +26,7 @@ class Batcher(Configurable):
 
     _batch_size = Param(int, default=128, doc="The number of samples in each mini-batch")
     _audio_samples_per_smaple = Param(int, default=1024, doc="The number of audio samples to extract each feature from")
-    _augmenter = ConfigurableParam(Augmenter, doc="An augmentation transformation to be applied to each sample")
+    _augmenter = Param(Augmenter, doc="An augmentation transformation to be applied to each sample")
 
     def set_data(self, data):
         # Save a reference to a data array, to sample / batch from

scooch/configurable_meta.py

@@ -75,7 +75,7 @@ def __new__(cls, name, bases, attrs):
             if attr_name.isnumeric():
                 raise ValueError(f"The Configurable class, {cls.__name__}, has a numeric parameter named {attr_name}, which is disallowed")
 
-        # Create Param / ConfigurableParam attributes that don't already exist from the scooch Configurable dictionaries
+        # Create Param attributes that don't already exist from the scooch Configurable dictionaries
         for attr_name in attrs['__PARAMS__']:
             if '_'+attr_name not in attrs and attr_name not in attrs:
                 if attr_name in attrs['__CONFIGURABLES__']:

setup.py

@@ -105,15 +105,15 @@ def run(self):
         'make_reqs': MakeReqsCommand
     },
     name='scooch',
-    version='1.0.0',
+    version='1.0.1',
     description='A python module for configuring hierarchical class structures in yaml with defaults',
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="http://www.mattcmccallum.com/scooch/docs",
     author="Matt C. McCallum",
     author_email="scooch@mattcmccallum.com",
     classifiers=[
-        'Development Status :: 3 - Alpha',
+        'Development Status :: 5 - Production/Stable',
         'Intended Audience :: Developers',
         'Intended Audience :: Education',
         'Intended Audience :: Science/Research',