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Recommended changes to Conviction documentation to allow copy-paste ru (
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#92)

NOTE: Needs confirmation that my changes match intent of documentation
in terms of the proper selection of details that are provided in the
example and surrounding context. Several things have changed since this
documentation was first drafted and may need revised. Will take any
direction on changing of wording and/or direct dictation.

Changes to examples to fix syntax issues and allow copy-paste runs.
Tested in python 3.9 and 3.13 in conda environments.
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@nuvious
nuvious authored Nov 8, 2024
1 parent 6708375 commit 3416a50
Showing 1 changed file with 57 additions and 25 deletions.
82 changes: 57 additions & 25 deletions source/user_guide/basic_capabilities/conviction.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@ it can reveal information such as why a case was anomalous. For example, if a NB
Setup
^^^^^
The user guide assumes you have created and setup a :class:`~Trainee` as demonstrated in :doc:`basic workflow <../basic_capabilities/basic_workflow>`.
The created :class:`~Trainee` will be referenced as ``trainee`` in the sections below.
The created :class:`~Trainee` will be referenced as ``trainee`` in the sections below. This guide also assumes you have installed the `pmlb` python library for the dataset used.

:ref:`familiarity_conviction`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Expand All @@ -54,11 +54,14 @@ through :py:meth:`Trainee.get_cases`
trainee.react_into_features(
familiarity_conviction_addition=True,
familiarity_conviction_remove=True
familiarity_conviction_removal=True
)
familiarity_conviction_addition = trainee.get_cases(
session=trainee.active_session,
features=['familiarity_conviction_addition', 'familiarity_conviction_removal']
features=[
'familiarity_conviction_addition',
'familiarity_conviction_removal'
]
)
:ref:`similarity_conviction`
Expand All @@ -83,24 +86,16 @@ specific cases in :py:meth:`Trainee.react`
.. code-block:: python
details = {
'global_case_feature_residual_convictions_robust': True,
'local_case_feature_residual_convictions_robust': True
'feature_residuals_robust': True
}
# React to get the details of each case
results = trainee.react(
test_case[context_features],
context_features=context_features,
action_features=action_features,
details=Details
details=details
)
# Extract the global and local case feature residual convictions
global_case_feature_residual_convictions = pd.DataFrame(
results['details']['global_case_feature_residual_convictions_robust'])[df.columns.tolist()]
local_case_feature_residual_convictions = pd.DataFrame(
results['details']['local_case_feature_residual_convictions_robust'])[df.columns.tolist()]
Complete Code
^^^^^^^^^^^^^
The code from all of the steps in this guide is combined below:
Expand All @@ -126,30 +121,31 @@ The code from all of the steps in this guide is combined below:
action_features = ['target']
context_features = features.get_names(without=action_features)
trainee = Trainee(features=features)
trainee = Trainee(features=features)
trainee.train(df)
trainee.analyze(context_features=context_features, action_features=action_features)
trainee.react_into_features(
familiarity_conviction_addition=True,
familiarity_conviction_remove=True,
similarity_conviction = True
familiarity_conviction_removal=True,
similarity_conviction=True
)
convictions = trainee.get_cases(
familiarity_conviction_addition = trainee.get_cases(
session=trainee.active_session,
features=[
'familiarity_conviction_addition',
'familiarity_conviction_removal',
'similarity_conviction'
'familiarity_conviction_removal'
]
)
print(familiarity_conviction_addition)
details = {
'global_case_feature_residual_convictions_robust': True,
'local_case_feature_residual_convictions_robust': True
'feature_residuals_robust': True,
'similarity_conviction': True
}
results = trainee.react(
Expand All @@ -158,10 +154,46 @@ The code from all of the steps in this guide is combined below:
action_features=action_features,
details=details
)
local_case_feature_residual_convictions = pd.DataFrame(
results['details']['local_case_feature_residual_convictions_robust'])
print(results)
Below is an example of expected output from this sample code:

.. code-block:: bash
$ python conviction_example.py
familiarity_conviction_addition familiarity_conviction_removal
0 0.424315 0.481610
1 24.344436 24.373889
2 0.495148 0.555847
3 0.463858 0.523487
4 0.288355 0.248439
... ... ...
1994 6.460913 6.248667
1995 46.903956 46.594968
1996 2.195260 2.305391
1997 24.788612 24.992936
1998 0.740464 0.812168
[1999 rows x 2 columns]
target
0 1
{'action_features': ['target'],
'feature_residuals_robust': [{'age': 8.888516681825308,
'capital-gain': 416.7392605164004,
'capital-loss': 59.906358535804515,
'education': 0.4523004291045252,
'education-num': 0.4655826176126248,
'fnlwgt': 65946.6678484109,
'hours-per-week': 6.298493661647657,
'marital-status': 0.512476275479471,
'native-country': 0.07145970131801563,
'occupation': 0.8772108612524578,
'race': 0.16017621174491645,
'relationship': 0.7104566198137716,
'sex': 0.3580994265834227,
'target': 0.09681983534852417,
'workclass': 0.18761097169888336}],
'similarity_conviction': [0.9699384581322016]}
API References
--------------
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