********
Examples
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.. toctree::
   :maxdepth: 5

Subpackages
===========

.. toctree::

   sample.core.data
   sample.core.evaluation
   sample.core.model
   sample.core.preprocessing
   sample.core.stats


core.easy\_experiment module
============================
.. py:function:: core.easy_experiment.easy_experiment(source, target_test, target_retrain=None, domain_adaptive=False, models='all', binary_evaluation=True, evaluation_metrics='all', thread_num=4, remove_time=True, plot=False)
    :noindex:

    :func:`~core.easy_experiment.easy_experiment` function allows the user to quickly estimate the occupancy state
    of a room leveraging the specified models and evaluation metrics. Refer to the sample code
    for :func:`~core.easy_experiment.easy_set_experiment` for more details.

.. py:function:: core.easy_experiment.easy_set_experiment(source_set, target_test_set=None, split_percentage=0.8, target_retrain=None, domain_adaptive=False, models='all', binary_evaluation=True, evaluation_metrics='all', thread_num=4, remove_time=True, plot=False)
    :noindex:

    :func:`~core.easy_experiment.easy_set_experiment` function allows the user to quickly perform occupancy estimation
    on multiple data sets using the specified models and evaluation metrics. We recommend the
    use of :func:`~core.easy_experiment.easy_set_experiment` instead of :func:`~core.easy_experiment.easy_experiment`
    to get the formatted return value for :class:`~core.evaluation.superclass.Result`, which is essential for plotting.

    .. note::

        Always pass a ``list`` as the argument of :func:`~core.data.load_sample` function when
        using :func:`~core.easy_experiment.easy_set_experiment` to obtain a dictionary that maps data set names
        to :class:`~core.data.dataset.Dataset` objects.

    .. code-block:: python
        :linenos:

        import core
        import pprint

        # Load and separate the train and test data set
        data = core.data.load_sample(["umons-all", "aifb-all"])

        # Perform occupancy estimation
        score, predict_result = core.easy_set_experiment(data, models=["RandomForest"])

        pprint(score)
        pprint(predict_result)

        # Make the scores ready to plot
        result = core.evaluation.Result()
        result.set_result(score)

        # If you somehow want to use "easy_experiment", here is the code
        train, test = data["umons-all"].split(0.8)

        # Perform occupancy estimation
        score, predict_result = core.easy_experiment(train, test, models=["RandomForest"])

        pprint(score)
        pprint(predict_result)