project-codenet / model-experiments /token-based-similarity-classification /src /PostProcessor /ConfusionAnalysis.py
| """ | |
| Module with base class for analysis of confusion information | |
| """ | |
| import os | |
| from abc import ABC, abstractmethod | |
| import numpy as np | |
| import sklearn.metrics as metrics | |
| class ConfusionAnalysis(ABC): | |
| """ | |
| Base class for analysis of confusion information | |
| It performs intialization common for child classes | |
| and provides utility functions for them | |
| """ | |
| def writeLargeMatrixInt(cls, m, f): | |
| """ | |
| Write large matrix to file | |
| Parameters: | |
| - m -- matrix to write as numpy array of floats | |
| - f -- file to write the matrix | |
| """ | |
| _n = m.shape[0] | |
| _s = " " | |
| for _i in range(_n): | |
| _s += "{:5d} ".format(_i) | |
| f.write(_s + "\n") | |
| for _i in range(_n): | |
| _s = "{:3d} ".format(_i) | |
| for _j in range(_n): | |
| _s += "{:5d} ".format(m[_i, _j]) | |
| f.write(_s + "\n") | |
| def writeLargeMatrixPct(cls, m, f, scale): | |
| """ | |
| Write large matrix to file | |
| Parameters: | |
| - m -- matrix to write as numpy array of floats | |
| - f -- file to write the matrix | |
| - scale -- multiplier to scale elements of matrix | |
| """ | |
| _n = m.shape[0] | |
| _s = " " | |
| for _i in range(_n): | |
| _s += "{:4d} ".format(_i) | |
| f.write(_s + "\n") | |
| for _i in range(_n): | |
| _s = "{:3d} ".format(_i) | |
| for _j in range(_n): | |
| _s += "{:4.1f} ".format(scale * m[_i, _j]) | |
| f.write(_s + "\n") | |
| def __init__(self, probabilities, labels, solutions, | |
| problems, extreme): | |
| """ | |
| Initialize base class | |
| for analysis and reporting confusion information | |
| Parameters: | |
| - probabilities - classification probabilities as numpy array | |
| - labels - labels of samples as numpy array | |
| - solutions - list of sub-lists of file names of problem solutions | |
| Each sub-list contains sample names of one problem | |
| All sublists are part of the whole list of sample names | |
| - problems - list of names of problems | |
| - extreme - specification of number of cases to be reported | |
| as extreme ones; | |
| * if extreme < 1.0 it is a fraction of number of problems | |
| * if extreme >= 1 it is the number of problems | |
| """ | |
| super(ConfusionAnalysis, self).__init__() | |
| self.probabilities = probabilities | |
| self.labels = labels | |
| self._n_samples = self.labels.shape[0] | |
| self.problems = problems | |
| self._n_problems = len(self.problems) | |
| self.extreme = extreme | |
| #Number of problems to reports as the best or worst | |
| self._n_extreme_cases = int(extreme) if extreme >= 1 else \ | |
| max(2, int(extreme * float(self._n_problems))) | |
| self.solutions = solutions | |
| #Directory for writing confusion reports to | |
| self.report_dir = "confusion_report" | |
| if not os.path.exists(self.report_dir): | |
| os.makedirs(self.report_dir) | |
| #Name of file to write large confusion matrices | |
| self._conf_mat_fn = "confusion_matrix.lst" | |
| self.predictions = self.getPredictions() | |
| self.conf_mat = metrics.confusion_matrix( | |
| self.labels, self.predictions) | |
| def getPredictions(self): | |
| """ | |
| Abstract method for getting classification predictions | |
| Returns: classification predictions | |
| """ | |
| raise NotImplementedError() | |
| #---------------- End of class ConfusionAnalysis ---------------------- | |