cat2cat.cat2cat_ml
==================

.. py:module:: cat2cat.cat2cat_ml


Functions
---------

.. autoapisummary::

   cat2cat.cat2cat_ml.cat2cat_ml_run


Module Contents
---------------

.. py:function:: cat2cat_ml_run(mappings: cat2cat.dataclass.cat2cat_mappings, ml: cat2cat.dataclass.cat2cat_ml, **kwargs: Any) -> cat2cat_ml_run_results

   Automatic mapping in a panel dataset - cat2cat procedure

   :param mappings: dataclass with mappings related arguments.
                    Please check out the `cat2cat.dataclass.cat2cat_mappings` for more information.
   :type mappings: cat2cat_mappings
   :param ml: dataclass with ml related arguments.
              Please check out the `cat2cat.dataclass.cat2cat_ml` for more information.
   :type ml: Optional[cat2cat_ml]
   :param \*\*kwargs: additional arguments passed to the `cat2cat_ml_run` function.
                      min_match (float): minimum share of categories from the base period that have to be matched in the mapping table. Between 0 and 1. Default 0.8.
                      test_prop (float): share of the data used for testing. Between 0 and 1. Default 0.2.
                      split_seed (int): random seed for the train_test_split function. Default 42.

   :returns: cat2cat_ml_run_class

   .. note:: Please check out the `cat2cat.cat2cat.cat2cat` for more information.

   >>> from cat2cat import cat2cat
   >>> from cat2cat.cat2cat_ml import cat2cat_ml_run
   >>> from cat2cat.dataclass import cat2cat_data, cat2cat_mappings, cat2cat_ml
   >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
   >>> from sklearn.tree import DecisionTreeClassifier
   >>> from cat2cat.datasets import load_trans, load_occup
   >>> trans = load_trans()
   >>> occup = load_occup()
   >>> o_old = occup.loc[occup.year == 2008, :].copy()
   >>> o_new = occup.loc[occup.year == 2010, :].copy()
   >>> mappings = cat2cat_mappings(trans = trans, direction = "backward")
   >>> ml = cat2cat_ml(
   ...    occup.loc[occup.year >= 2010, :].copy(),
   ...    "code",
   ...    ["salary", "age", "edu", "sex"],
   ...    [DecisionTreeClassifier(random_state=1234), LinearDiscriminantAnalysis()]
   ... )
   >>> cat2cat_ml_run(mappings = mappings, ml = ml)
   ...