tfclass_predict.predictor
=========================

.. py:module:: tfclass_predict.predictor


Classes
-------

.. autoapisummary::

   tfclass_predict.predictor.ClassAUC
   tfclass_predict.predictor.Predictor


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

.. py:class:: ClassAUC(name='ClassAUC', **kwargs)

   Bases: :py:obj:`tensorflow.metrics.AUC`


   Metric used in training steps - needs to be kept for model usage.


.. py:class:: Predictor(bed_data, tokenizer, model_path, genome_file)

   Predictor class takes care about the prediction / model execution.


   .. py:attribute:: tokenizer


   .. py:attribute:: bed_data


   .. py:attribute:: SequenceProcessor


   .. py:attribute:: model


   .. py:method:: _init_model(model_path)

      Load the TFClass model. Initializes the  TFBert model.
      :param model_path: Path to TFClass model.
      :return: Initialized TFClass model.



   .. py:method:: predict_bed_data(subseq_length, stride_length, batch_size)

      Processes genomic sequences from the bed_data DataFrame, extracts subsequences, converts them into tokenized k-mers,
      and uses the TFClass model to make predictions on these sequences. The predictions are aggregated and associated
      with their corresponding sequence indices.

      Workflow:
      1. Initializes lists to store aggregated predictions and their corresponding sequence indices.
      2. Iterates over each row in the bed_data DataFrame.
      3. For each row:
         - Extracts the genomic sequence based on 'seqnames', 'start', and 'end' with a desired length of 150.
         - Skips sequences that are empty or shorter than the desired length.
         - Generates subsequences from the full sequence.
         - Converts each subsequence into k-mers and then tokenizes them.
         - Accumulates tokenized sequences until the batch size is reached.
         - Uses a machine learning model to make predictions on the batch of tokenized sequences.
         - Stores the predictions and their corresponding indices in the aggregated lists.
      4. Processes any remaining sequences that did not form a complete batch.


      :param subseq_length: Length in which a read should be split into subsequences (i.e. K-mer size).
      :param stride_length: Defines the number of basepairs a window will move in the next step. (=1 sliding window, =subseq_length k_mer splits)
      :param batch_size: Number of intervals that should be processed in one batch.
      :return: