:py:mod:`kwcoco.metrics.functional`
===================================

.. py:module:: kwcoco.metrics.functional


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


Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.metrics.functional.fast_confusion_matrix
   kwcoco.metrics.functional._truncated_roc
   kwcoco.metrics.functional._pr_curves
   kwcoco.metrics.functional._average_precision



.. py:function:: fast_confusion_matrix(y_true, y_pred, n_labels, sample_weight=None)

   faster version of sklearn confusion matrix that avoids the
   expensive checks and label rectification

   :Parameters: * **y_true** (*ndarray[int]*) -- ground truth class label for each sample
                * **y_pred** (*ndarray[int]*) -- predicted class label for each sample
                * **n_labels** (*int*) -- number of labels
                * **sample_weight** (*ndarray[int|float]*) -- weight of each sample

   :returns:     matrix where rows represent real and cols represent pred and the
                 value at each cell is the total amount of weight
   :rtype: ndarray[int64|float64, dim=2]

   .. rubric:: Example

   >>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 0,  0, 1])
   >>> y_pred = np.array([0, 0, 0, 0, 0, 0, 0, 1,  1, 1])
   >>> fast_confusion_matrix(y_true, y_pred, 2)
   array([[4, 2],
          [3, 1]])
   >>> fast_confusion_matrix(y_true, y_pred, 2).ravel()
   array([4, 2, 3, 1])


.. py:function:: _truncated_roc(y_df, bg_idx=-1, fp_cutoff=None)

   Computes truncated ROC info


.. py:function:: _pr_curves(y)

   Compute a PR curve from a method

   :Parameters: **y** (*pd.DataFrame | DataFrameArray*) -- output of detection_confusions

   :returns: Tuple[float, ndarray, ndarray]

   .. rubric:: Example

   >>> # xdoctest: +REQUIRES(module:sklearn)
   >>> import pandas as pd
   >>> y1 = pd.DataFrame.from_records([
   >>>     {'pred': 0, 'score': 10.00, 'true': -1, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  1.65, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  8.64, 'true': -1, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  3.97, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  1.68, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  5.06, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  0.25, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  1.75, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  8.52, 'true':  0, 'weight': 1.00},
   >>>     {'pred': 0, 'score':  5.20, 'true':  0, 'weight': 1.00},
   >>> ])
   >>> import kwcoco as nh
   >>> import kwarray
   >>> y2 = kwarray.DataFrameArray(y1)
   >>> _pr_curves(y2)
   >>> _pr_curves(y1)


.. py:function:: _average_precision(tpr, ppv)

   Compute average precision of a binary PR curve. This is simply the area
   under the curve.

   :Parameters: * **tpr** (*ndarray*) -- true positive rate - aka recall
                * **ppv** (*ndarray*) -- positive predictive value - aka precision