docs: tutorial: model: Mention file paths of files we're editing

Fixes: #464

Signed-off-by: John Andersen <[email protected]>

Author: pdxjohnny

CHANGELOG.md

@@ -5,6 +5,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+### Fixed
+- New model tutorial mentions file paths that should be edited.
 
 ## [0.3.5] - 2020-03-10
 ### Added

dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py

@@ -57,11 +57,11 @@ class MiscModelConfig:
     features: Features = field("Features to train on. For SLR only 1 allowed")
     directory: pathlib.Path = field(
         "Directory where state should be saved",
-        default=pathlib.Path("~", ".cache", "dffml", "miscmodel"),
+        default=pathlib.Path("~", ".cache", "dffml", "misc"),
     )
 
 
-@entrypoint("miscmodel")
+@entrypoint("misc")
 class MiscModel(SimpleModel):
     # The configuration class needs to be set as the CONFIG property
     CONFIG: Type[MiscModelConfig] = MiscModelConfig

docs/tutorials/model.rst

@@ -39,6 +39,8 @@ If you're planing on importing any third party packages, anything on
 `PyPi <https://pypi.org>`_, you'll want to add it to the ``setup.py`` file
 first.
 
+**setup.py**
+
 .. code-block:: python
 
     common.KWARGS["install_requires"] += ["scikit-learn>=0.21.2"]
@@ -75,11 +77,15 @@ We're going to need a few modules from the standard library, let's import them.
 - ``typing`` is for Python's static type hinting. It lets use give hints to our
   editor or IDE so they can help us check our code before we run it.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 1-3
 
 We'll also need a few things from DFFML.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 5-16
 
@@ -100,6 +106,8 @@ Config
 Anything that a user might want to tweak about a models behavior should go in
 the ``Config`` class for the model.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 54-61
 
@@ -129,12 +137,16 @@ model from the DFFML command line and other interfaces.
 
 - We must set the ``CONFIG`` attribute to the respective ``Config`` class.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 64-67
 
 - We must make sure ``setup.py``'s ``"entry_points"`` field correctly references
   the location of our model.
 
+**setup.py**
+
 .. literalinclude:: /../dffml/skel/model/setup.py
     :lines: 12-14
 
@@ -156,6 +168,8 @@ Models should save their state to disk after training. Classes derived from
 ``SimpleModel`` can put anything they want saved into ``self.storage``, which
 is saved and loaded from a JSON on disk.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 76-91
 
@@ -165,6 +179,8 @@ Accuracy
 We saved the accuracy as the 2nd index into the ``"regression_line"`` key in the
 ``self.storage`` dictionary. When we assess the accuracy we reload from there.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 93-101
 
@@ -179,6 +195,8 @@ We call :py:meth:`record.predicted <dffml.record.Record.predicted>`
 passing it the name of the feature we predicted, the predicted value, and the
 confidence in our prediction.
 
+**dffml_model_myslr/misc.py**
+
 .. literalinclude:: /../dffml/skel/model/REPLACE_IMPORT_PACKAGE_NAME/misc.py
     :lines: 103-122
 
@@ -193,7 +211,10 @@ Rename the imports
 We need to make sure our model is imported via it's new name, ``MySRL`` instead
 of ``Misc``.
 
+**tests/test_model.py**
+
 .. code-block:: python
+
     from dffml_model_myslr.misc import MySLRModel, MySLRModelConfig
 
 Test data
@@ -202,6 +223,8 @@ Test data
 We usually try to randomly generate training and test data, but for this example
 we're just going to hard code in some data.
 
+**tests/test_model.py**
+
 .. literalinclude:: /../dffml/skel/model/tests/test_model.py
     :lines: 7-34
 
@@ -211,26 +234,50 @@ Rename the test class
 Change the test class's name, and make sure ``cls.model`` is instantiating a
 ``MySLR`` model instead of the ``Misc`` model.
 
-We create a temporary directory for our tests to use 
+We create a temporary directory for our tests to use, and clean it up when
+they're done. The tests are prefixed with numbers to indicate what order they
+should be run in, ensuring that accuracy and predict test always have a trained
+model to work with.
+
+**tests/test_model.py**
 
 .. literalinclude:: /../dffml/skel/model/tests/test_model.py
     :lines: 37-60
 
 Testing Train
 ~~~~~~~~~~~~~
 
+Similarly to the quickstart, all we need to to is pass the model and training
+data to the :py:func:`train <dffml.train>` function.
+
+**tests/test_model.py**
+
 .. literalinclude:: /../dffml/skel/model/tests/test_model.py
     :lines: 62-64
 
 Testing Accuracy
 ~~~~~~~~~~~~~~~~
 
+Once again, all we need to to is pass the model and test data to the
+:py:func:`accuracy <dffml.accuracy>` function. Then we check if it's in an
+acceptable range. This test is helpful to make sure you never make any horribly
+wrong changes to your model, since it will check that the accuracy is within an
+acceptable range.
+
+**tests/test_model.py**
+
 .. literalinclude:: /../dffml/skel/model/tests/test_model.py
     :lines: 66-70
 
 Testing Prediction
 ~~~~~~~~~~~~~~~~~~
 
+Finally, we use the test data and model with the
+:py:func:`predict <dffml.predict>` function. Then we check if each predicted Y
+value is within 10% of what it should be.
+
+**tests/test_model.py**
+
 .. literalinclude:: /../dffml/skel/model/tests/test_model.py
     :lines: 72-83