:py:mod:`anomalib.data.utils.split`
===================================

.. py:module:: anomalib.data.utils.split

.. autoapi-nested-parse::

   Dataset Split Utils.

   This module contains function in regards to splitting normal images in training set,
   and creating validation sets from test sets.

   These function are useful
       - when the test set does not contain any normal images.
       - when the dataset doesn't have a validation set.



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


Functions
~~~~~~~~~

.. autoapisummary::

   anomalib.data.utils.split.split_normal_images_in_train_set
   anomalib.data.utils.split.create_validation_set_from_test_set



.. py:function:: split_normal_images_in_train_set(samples: pandas.core.frame.DataFrame, split_ratio: float = 0.1, seed: Optional[int] = None, normal_label: str = 'good') -> pandas.core.frame.DataFrame

   Split normal images in train set.

       This function splits the normal images in training set and assigns the
       values to the test set. This is particularly useful especially when the
       test set does not contain any normal images.

       This is important because when the test set doesn't have any normal images,
       AUC computation fails due to having single class.

   :param samples: Dataframe containing dataset info such as filenames, splits etc.
   :type samples: DataFrame
   :param split_ratio: Train-Test normal image split ratio. Defaults to 0.1.
   :type split_ratio: float, optional
   :param seed: Random seed to ensure reproducibility. Defaults to 0.
   :type seed: int, optional
   :param normal_label: Name of the normal label. For MVTec AD, for instance, this is normal_label.
   :type normal_label: str

   :returns: Output dataframe where the part of the training set is assigned to test set.
   :rtype: DataFrame


.. py:function:: create_validation_set_from_test_set(samples: pandas.core.frame.DataFrame, seed: Optional[int] = None, normal_label: str = 'good') -> pandas.core.frame.DataFrame

   Craete Validation Set from Test Set.

   This function creates a validation set from test set by splitting both
   normal and abnormal samples to two.

   :param samples: Dataframe containing dataset info such as filenames, splits etc.
   :type samples: DataFrame
   :param seed: Random seed to ensure reproducibility. Defaults to 0.
   :type seed: int, optional
   :param normal_label: Name of the normal label. For MVTec AD, for instance, this is normal_label.
   :type normal_label: str