Crawto-Quality
A Collection of Tools for improving code quality
Crawto-Doc
A command-line-tool to add numpy style docstrings to your python projects.
From reading just your code, its a docstring with a description, parameters, and and attributes & examples where applicable.
While it cannot totally complete your documentation, Crawto-Doc can fill in as
much information you give it. Using mypy will fill in some missing pieces.
Why
Because documentation is a oft boring or an after-thought.
From
class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
@_deprecate_positional_args
def __init__(self, *, strategy="warn", random_state=None, constant=None):
self.strategy = strategy
self.random_state = random_state
self.constant = constant
To
class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
"""<#TODO Description>
Parameters
----------
strategy : <#TODO type definition>, default=warn
<#TODO Description>
random_state : <#TODO type definition>, default=None
<#TODO Description>
constant : <#TODO type definition>, default=None
<#TODO Description>
Attributes
----------
sparse_output_ : <#TODO type definition>
<#TODO Attribute Description>
n_outputs_ : <#TODO type definition>
<#TODO Attribute Description>
n_features_in_ : <#TODO type definition>
<#TODO Attribute Description>
outputs_2d_ : <#TODO type definition>
<#TODO Attribute Description>
Examples
--------
>>> from crawto-quality import crawto_doc
>>> example = DummyClassifier(strategy='warn', random_state=None, constant=None)
>>> example.fit(X=<#TODO Example Value>, y=<#TODO Example Value>, sample_weight=None)
<#TODO Method Return Value>
>>> example.predict(X=<#TODO Example Value>)
<#TODO Method Return Value>
>>> example.predict_proba(X=<#TODO Example Value>)
<#TODO Method Return Value>
>>> example.predict_log_proba(X=<#TODO Example Value>)
<#TODO Method Return Value>
>>> example.score(X=<#TODO Example Value>, y=<#TODO Example Value>, sample_weight=None)
<#TODO Method Return Value>
"""