Source code for Orange.evaluation.performance_curves

import numpy as np

[docs] class Curves: # names of scores are standard acronyms, pylint: disable=invalid-name """ Computation of performance curves (ca, f1, precision, recall and the rest of the zoo) from test results. The class works with binary classes. Attribute `probs` contains ordered probabilities and all curves represent performance statistics if an instance is classified as positive if it equals or exceeds the threshold in `probs`, that is, `sensitivity[i]` is the sensitivity of the classifier that classifies an instances as positive if the probability of being positive is at least `probs[i]`. Class can be constructed by giving `probs` and `ytrue`, or from test results (see :obj:`Curves.from_results`). The latter removes instances with missing class values or predicted probabilities. The class treats all results as obtained from a single run instead of computing separate curves and fancy averaging. Arguments: probs (np.ndarray): vector of predicted probabilities ytrue (np.ndarray): corresponding true classes Attributes: probs (np.ndarray): ordered vector of predicted probabilities ytrue (np.ndarray): corresponding true classes tot (int): total number of data instances p (int): number of real positive instances n (int): number of real negative instances tp (np.ndarray): number of true positives (property computed from `tn`) fp (np.ndarray): number of false positives (property computed from `tn`) tn (np.ndarray): number of true negatives (property computed from `tn`) fn (np.ndarray): number of false negatives (precomputed, not a property) """ def __init__(self, ytrue, probs): sortind = np.argsort(probs) self.probs = np.hstack((probs[sortind], [1])) self.ytrue = ytrue[sortind] self.fn = np.hstack(([0], np.cumsum(self.ytrue))) self.tot = len(probs) self.p = self.fn[-1] self.n = self.tot - self.p
[docs] @classmethod def from_results(cls, results, target_class=None, model_index=None): """ Construct an instance of `Curves` from test results. Args: results (:obj:`Orange.evaluation.testing.Results`): test results target_class (int): target class index; if the class is binary, this defaults to `1`, otherwise it must be given model_index (int): model index; if there is only one model, this argument can be omitted Returns: curves (:obj:`Curves`) """ if model_index is None: if results.probabilities.shape[0] != 1: raise ValueError("Argument 'model_index' is required when " "there are multiple models") model_index = 0 if target_class is None: if results.probabilities.shape[2] != 2: raise ValueError("Argument 'target_class' is required when the " "class is not binary") target_class = 1 actual = results.actual probs = results.probabilities[model_index, :, target_class] nans = np.isnan(actual) + np.isnan(probs) if nans.any(): actual = actual[~nans] probs = probs[~nans] return cls(actual == target_class, probs)
@property def tn(self): return np.arange(self.tot + 1) - self.fn @property def tp(self): return self.p - self.fn @property def fp(self): return self.n -
[docs] def ca(self): """Classification accuracy curve""" return ( + / self.tot
[docs] def f1(self): """F1 curve""" return 2 * / (2 * + self.fp + self.fn)
[docs] def sensitivity(self): """Sensitivity curve""" return / self.p
[docs] def specificity(self): """Specificity curve""" return / self.n
[docs] def precision(self): """ Precision curve The last element represents precision at threshold 1. Unless such a probability appears in the data, the precision at this point is undefined. To avoid this, we copy the previous value to the last. """ tp_fp = np.arange(self.tot, -1, -1) tp_fp[-1] = 1 # avoid division by zero prec = / tp_fp prec[-1] = prec[-2] return prec
[docs] def recall(self): """Recall curve""" return self.sensitivity()
[docs] def ppv(self): """PPV curve; see the comment at :obj:`precision`""" return self.precision()
[docs] def npv(self): """ NPV curve The first value is undefined (no negative instances). To avoid this, we copy the second value into the first. """ tn_fn = np.arange(self.tot + 1) tn_fn[0] = 1 # avoid division by zero npv = / tn_fn npv[0] = npv[1] return npv
[docs] def fpr(self): """FPR curve""" return self.fp / self.n
[docs] def tpr(self): """TPR curve""" return self.sensitivity()