:mod:`autofunc.get_precision_recall`
====================================

.. py:module:: autofunc.get_precision_recall

.. autoapi-nested-parse::

   Find the accuracy of a prediction based on the known results in a testing set and the predicted results from
   a training set. The accuracy is represented in the F1 score.



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


Functions
~~~~~~~~~

.. autoapisummary::

   autofunc.get_precision_recall.precision_recall


.. function:: precision_recall(thresh_results, test_records)

   Imports the results of the frequency-finding and thresholding algorithm, which are the results predicted to be in
   future components. The functions and flows for the components in the testing set are predicted based on these
   results, which are compared with the actual results and used to calculate the accuracy of how well the prediction
   performed.

   +--------+-----+---------------------------------+
   |              |            Predicted?           |
   +--------+-----+----------------+----------------+
   |              | Yes            | No             |
   +--------+-----+----------------+----------------+
   |        | Yes | True Positive  | False Negative |
   |Actual? +-----+----------------+----------------+
   |        | No  | False Positive | True Negative  |
   +--------+-----+----------------+----------------+

   TP = True Positive, FP = False Positive, FN = False Negative, TN = True Negative

   Precision is the ratio of correct predictions to all predictions made by the classifier (TP/(TP + FP)).
   This number is the ratio of predictions that were identified as being in the product that are actually in the product.

   Recall is the ratio of correct predictions to all actual results made by the classifier (TP/(TP + FN)).
   This number is the ratio of the actual results that were correctly predicted.

   Recall is representative of the confidence that no positives have been missed and precision is
   representative of the confidence in the True Positives.

   The F1 score is the harmonic mean of precision and recall

   (2 * precision * recall) / (precision + recall)

   :param thresh_results:
   :type thresh_results: dict
   :param The results of the "find_top_thresh" function. These are the predicted function and flow combinations for each:
   :param component:
   :param test_records:
   :type test_records: list
   :param The function and flow combinations for each component in the testing set:
   :param organized in a list.:

   :returns: * *learned_dict*
             * *Returns a dictionary of what was learned from the results of the data mining automation*
             * *matched*
             * *A dictionary of the functions and flows that were True Positives*
             * *overmatched*
             * *A dictionary of the functions and flows that were False Positives*
             * *unmatched*
             * *A dictionary of the functions and flows that were False Negatives*
             * *recall*
             * *A single number for the recall score for this combination of testing and training sets*
             * *precision*
             * *A single number for the precision score for this combination of testing and training sets*
             * *f1*
             * *A single number for the F1 score for this combination of testing and training sets*