:py:mod:`python.dgbpy.mlio`
===========================

.. py:module:: python.dgbpy.mlio


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


Functions
~~~~~~~~~

.. autoapisummary::

   python.dgbpy.mlio.getInfo
   python.dgbpy.mlio.datasetCount
   python.dgbpy.mlio.getDatasetNms
   python.dgbpy.mlio.getChunks
   python.dgbpy.mlio.hasScaler
   python.dgbpy.mlio.getDatasetsByGroup
   python.dgbpy.mlio.getSomeDatasets
   python.dgbpy.mlio.getTrainingData
   python.dgbpy.mlio.getTrainingDataByInfo
   python.dgbpy.mlio.getClasses
   python.dgbpy.mlio.normalize_class_vector
   python.dgbpy.mlio.unnormalize_class_vector
   python.dgbpy.mlio.saveModel
   python.dgbpy.mlio.getModel
   python.dgbpy.mlio.getApplyInfoFromFile
   python.dgbpy.mlio.getApplyInfo
   python.dgbpy.mlio.modelNameIsFree
   python.dgbpy.mlio.modelNameExists
   python.dgbpy.mlio.dbInfoForModel
   python.dgbpy.mlio.getModelType
   python.dgbpy.mlio.getSaveLoc



Attributes
~~~~~~~~~~

.. autoapisummary::

   python.dgbpy.mlio.nladbdirid
   python.dgbpy.mlio.mlinpgrp
   python.dgbpy.mlio.mltrlgrp
   python.dgbpy.mlio.dgbtrl
   python.dgbpy.mlio.dblistall


.. py:data:: nladbdirid
   :annotation: = 100060

   

.. py:data:: mlinpgrp
   :annotation: = Deep Learning Example Data

   

.. py:data:: mltrlgrp
   :annotation: = Deep Learning Model

   

.. py:data:: dgbtrl
   :annotation: = dGB

   

.. py:function:: getInfo(filenm, quick=False)

   Gets information from an example file

   Parameters:
     * filenm (str): ffile name/path in hdf5 format
     * quick (bool): when set to True, info is gottenn quickly leaving out some info(e.g. datasets), 
                     defaults to False and loads all informaton

   Returns:
     * dict: information from data file or model file                    


.. py:function:: datasetCount(dsets)

   Gets count of dataset

   Parameters:
     * dsets (dict): dataset

   Returns:
     * dict: counts for target attribute(s) or well(s) for  project


.. py:function:: getDatasetNms(dsets, validation_split=None, valid_inputs=None)

   Gets train and validation indices of dataset

   Parameters:
     * dsets (dict): dataset
     * validation_split (float): size of validation data (between 0-1)
     * valid_inputs (iter): 

   Returns:
     * dict: train and validation indices


.. py:function:: getChunks(dsets, nbchunks)

   Splits dataset object into smaller chunks

   Parameters:
     * dsets (dict): dataset
     * nbchunks (int): number of data chunks to be created

   Returns:
     * dict: chunks from dataset stored as dictionaries


.. py:function:: hasScaler(infos, inputsel=None)

   Checks if example file has scaleror not from info

   Parameters:
     * infos (dict): information about example file
     * inputsel (bool or NoneType):

   Returns:
     bool: True if dataset info has scaler key, False if other


.. py:function:: getDatasetsByGroup(dslist, groupnm)


.. py:function:: getSomeDatasets(dslist, decim=None)


.. py:function:: getTrainingData(filenm, decim=False)

   Gets training data from file name

   Parameters:
     * filenm (str): path to file in hdf5 format
     * decim (bool): 

   Returns:
     * dict: train, validation datasets as arrays, and info on example file


.. py:function:: getTrainingDataByInfo(info, dsetsel=None)

   Gets training data from file info

   Parameters:
     * info (dict): information about example file
     * dsetsel ():

   Returns:
     * dict: train, validation datasets as arrays, and info on example file


.. py:function:: getClasses(info, y_vectors)


.. py:function:: normalize_class_vector(arr, classes)


.. py:function:: unnormalize_class_vector(arr, classes)


.. py:function:: saveModel(model, inpfnm, platform, infos, outfnm)

   Saves trained model for any platform workflow

   Parameters:
     * model (obj): trained model on any platform
     * inpfnm (str): example file name in hdf5 format
     * platform (str): machine learning platform (options; keras, Scikit-learn, torch)
     * infos (dict): example file info
     * outfnm (str): name of model to be saved


.. py:function:: getModel(modelfnm, fortrain=False, pars=None)

   Get model and model information

   Parameters:
     * modelfnm (str): model file path/name in hdf5 format
     * fortrain (bool): specifies if the model might be further trained
     * pars (dict): parameters to be used when restoring the model if needed

   Returs:
     * tuple: (trained model and model/project info)


.. py:function:: getApplyInfoFromFile(modelfnm, outsubsel=None)

   Gets model apply info from file name

   Parameters:
     * modelfnm (str): model file path/name in hdf5 format
     * outsubsel ():

   Returns:
     * dict: apply information


.. py:function:: getApplyInfo(infos, outsubsel=None)

   Gets model apply info from example file info

   Parameters:
     * infos (dict): example file info
     * outsubsel ():

   Returns:
     * dict: apply information


.. py:data:: dblistall
   

   

.. py:function:: modelNameIsFree(modnm, type, args, reload=True)


.. py:function:: modelNameExists(modnm, type, args, reload=True)


.. py:function:: dbInfoForModel(modnm, args, reload=True)


.. py:function:: getModelType(infos)

   Gets model type

   Parameters:
     * infos (dict): example file info

   Returns:
     * str: Type ofmodel/workflow


.. py:function:: getSaveLoc(outnm, ftype, args)