""" Module for constructing and analysing confusion matrix of source code classification Prints confusion report and writes down statistics of classification accuracy """ import sys import os from abc import ABC, abstractmethod import numpy as np import csv import sklearn.metrics as metrics from ConfusionAnalysis import ConfusionAnalysis class ClassConfusAnalysis(ConfusionAnalysis): """ Class for constructing and analysing confusion matrix """ def __init__(self, probabilities, labels, solutions, problems, extreme = 0.1): """ Construct confusion matrix Parameters: - probabilities - classification probabilities as numpy array - labels - list of samples labels - solutions - list of file names of problem solutions, i.e sample names for this case - problems - list of problems i.e names of classes for this case - extreme - fraction of cases to be reported as extreme ones """ super(ClassConfusAnalysis, self).__init__(probabilities, np.asarray(labels, dtype = np.int32), solutions, problems, extreme) self.class_stat_fn = "class_statistics.lst" self.class_stat_csv = "class_statistics.csv" self.confused_classes_fn = "confused_classes.lst" self.confused_classes_csv = "confused_classes.csv" #Number of best and worst correct predictions of #worst classified classes to report self.n_right_pred_report = 32 #Max number of confusions to report self.max_n_confus_report = 64 #Max number of worst and best classes to report mistakes self.max_n_class_report = 16 #Max number of misclassifications to report self.max_n_misclass_report = 32 #Confidence in classification self.confidence = np.max(probabilities, axis=1) #Number of samples in each class as numpy array self._n_class_samples = self.conf_mat.sum(axis=1) #Number of predictions of each class as numpy array self._n_class_predict = self.conf_mat.sum(axis=0) #Number of correct predictions of each class self.n_corr_class_samples = np.diag(self.conf_mat) #Confusion matrix normalized with number of class samples self.norm_conf_mat = \ self.conf_mat / (self._n_class_samples[:, np.newaxis]) #Accuracy of class predictions as python list self.class_accuracy = \ np.diag(self.norm_conf_mat).tolist() def getPredictions(self): """ Get classification predictions Returns: classification predictions """ return np.argmax(self.probabilities, axis=1) def reportClasses(self, classes, f): """ Report classification of set of classes Parameters: - classes -- classes to report - f -- file to write report to """ self.reportClassProblems(self._n_class_samples, self.n_corr_class_samples, classes, f) self.csvClassProblems(self._n_class_samples, self.n_corr_class_samples, classes) def csvClassProblems(self, tot_samples, corr_samples, problems): """ Writes absolute and relative numbers of correct and incorrect classifications of problems to csv file Parameters: - tot_samples -- vector of total numbers of samples corresponding to each problem - corr_samples -- vector of numbers of correctly classified samples - problems -- list of indices of problems to report """ with open(f"{self.report_dir}/{self.class_stat_csv}", 'w', newline='') as _csv_f: _w = csv.writer(_csv_f) _w.writerow(("Label", "Accuracy", "N files", "N errors", "Problem")) for _c in problems: _w.writerow((_c, 100.0 * float(corr_samples[_c]) / float(tot_samples[_c]), tot_samples[_c], tot_samples[_c] - corr_samples[_c], self.problems[_c])) def reportClassProblems(self, tot_samples, corr_samples, problems, f): """ Writes absolute and relative numbers of correct and incorrect classifications of problems Parameters: - tot_samples -- vector of total numbers of samples corresponding to each problem - corr_samples -- vector of numbers of correctly classified samples - problems -- list of indices of problems to report - f -- file to write to """ f.write("Label Accur Size N errs Problem name\n") for _c in sorted(problems, key = lambda _p: float(corr_samples[_p]) / float(tot_samples[_p])): f.write("{:4d} {:5.1f}% {:5d} {:5d} {:s}\n". format(_c, 100.0 * float(corr_samples[_c]) / float(tot_samples[_c]), tot_samples[_c], tot_samples[_c] - corr_samples[_c], self.problems[_c])) def getRightClassifications(self, classes): """ Get list of correct classifications of given classes Parameters: - classes -- indices of classes to report Returns list of lists of pairs, each of which is Sublists correspond to given classes Each sublist is sorted according to confidence """ _classifications = [[] for i in range(len(classes))] for _i in range(self._n_samples): if (self.predictions[_i] == self.labels[_i] and self.labels[_i] in classes): _idx = classes.index(self.labels[_i]) _classifications[_idx].append((self.solutions[_i], self.confidence[_i])) for _cls in _classifications: if len(_cls) < 2: continue _cls.sort(key = lambda _c: _c[1], reverse = True) return _classifications def reportRightClassifications(self, classes, f): """ Report correct classifications of given classes Parameters: - classes -- indices of classes to report - f -- file to write to """ _classifications = self.getRightClassifications(classes) for _i, _c in enumerate(sorted(classes, key = lambda _cl: self.class_accuracy[_cl])): if not _classifications[_i]: continue f.write(f"#{_i} Problem {self.problems[_c]} number {_c} " + f"is classified with {100.0 *self.class_accuracy[_c]:.2f}% " + "accuracy\n") f.write(f"There are {len(_classifications[_i])} correct classifications\n") if len(_classifications[_i]) < 2 * self.n_right_pred_report + 3: self.writeClassifications(_classifications[_i], "Solution predictions", f) else: self.writeClassifications( _classifications[_i][: self.n_right_pred_report], "Strongest predictions", f) self.writeClassifications( _classifications[_i][-self.n_right_pred_report :], "Weakest predictions", f) def writeClassifications(self, predictions, title, f): """ Write down information on classification predictions Parameters: - predictions - list of predictions each of which is a pair - title - text to put in the first line - f - file to write to """ _line = title _punct = ': ' for _pred in predictions: _line += _punct _punct = ', ' if len(_line) > 80: f.write(_line + "\n") _line = "" _line += f"{_pred[0]}/{100 * _pred[1]:.2f}%" if _line: f.write(_line + "\n") def getClassMistakes(self, classes): """ Get list of misclassifications for given classes Parameters: - classes -- indices of classes to report Returns list of lists of miscalssifications: pairs Sublists gives misclassifications of classes """ #Number of classification mistakes _mistakes = [[] for i in range(len(classes))] for _i in range(self._n_samples): if (self.predictions[_i] != self.labels[_i] and self.labels[_i] in classes): _bad_idx = classes.index(self.labels[_i]) _mistakes[_bad_idx].append((_i, self.predictions[_i])) return _mistakes def reportMisclassifications(self, classes, mistakes, f): """ Report misclassifications of given classes Parameters: - classes -- indices of classes to report - mistakes -- list of lists of miscalssifications: pairs Sublists gives misclassifications of classes - f -- file to write to """ for _i, _c in enumerate(sorted(classes, key = lambda _cl: self.class_accuracy[_cl])): if not mistakes[_i]: continue f.write(f"#{_i} Problem {self.problems[_c]} number {_c} " + f"is classified with {100.0 *self.class_accuracy[_c]:.2f}% " + "accuracy\n") _line = f"There are {len(mistakes[_i])} misclassified solutions" _punct = ': ' for _err in sorted(mistakes[_i][: self.max_n_misclass_report], key = lambda _e: self.confidence[_e[0]], reverse = True): _conf = "{:5.2f}".format(100.0 * self.confidence[_err[0]]) _line += _punct _punct = ', ' if len(_line) > 80: f.write(_line + "\n") _line = "" _line += (self.solutions[_err[0]] + " => " + self.problems[_err[1]] + ' (' + _conf + '%)') if _line: f.write(_line + "\n") def reportConfusedClasses(self): """ Report worst classification confusions, i.e. confused pairs of confused problems """ _confusions = self.worstConfusions() _confusions = [_c for _c in _confusions if _c[0] != _c[1]] with open(f"{self.report_dir}/{self.confused_classes_fn}", 'w') as _lst, \ open(f"{self.report_dir}/{self.confused_classes_csv}", 'w', newline='') as _csv: _lst.write("Most confused pairs of problems\n") _lst.write("-------------------------------\n") _lst.write( " Orig Pred N N %% Original Predicted\n") _lst.write( "Label Label Samples Errors Errors Problem Problem \n") _w = csv.writer(_csv) _w.writerow(("Original_Label", "Predicted_Label", "N_samples", "%%_of_errors", "Original_Problem", "Predicted Problem")) for _c in sorted(_confusions, key = lambda _conf: self.norm_conf_mat[_conf[0], _conf[1]], reverse = True): _lorig, _lpred = _c _lst.write("{:5d} {:5d} {:6d} {:6d} {:6.2f} {:12s} {:12s}\n". format(_lorig, _lpred, self._n_class_samples[_lorig], self.conf_mat[_lorig, _lpred], self.norm_conf_mat[_lorig, _lpred] * 100, self.problems[_lorig], self.problems[_lpred])) _w.writerow((_lorig, _lpred, self._n_class_samples[_lorig], self.norm_conf_mat[_lorig, _lpred] * 100, self.problems[_lorig], self.problems[_lpred])) def worstConfusions(self): """ Compute worst confusions of classification Returns list of pairs , each of which is a classified problem and the problem predicted """ _n_confusions_report = \ max(min(int(self._n_problems * self._n_problems * self._n_extreme_cases), self.max_n_confus_report), self._n_problems) _flat_conf = self.norm_conf_mat.flatten() _part_index = \ self._n_problems * self._n_problems _part_index -= (_n_confusions_report + self._n_problems) _worst_idx = \ np.argpartition(_flat_conf, _part_index)[_part_index :] _worst_idx = \ np.unravel_index(_worst_idx, self.norm_conf_mat.shape) _worst_idx = zip(_worst_idx[0].tolist(), _worst_idx[1].tolist()) return _worst_idx def printClassAccuracy(self, f): """ Report accuracy of classifications and their misclassified samples Parameters: -f -- file to write report to """ f.write("\nClassification statistics\n") f.write("-------------------------\n") self.reportClasses(range(self._n_problems), f) _class_acc = \ sorted(zip(range(self._n_problems), self.class_accuracy), key = lambda _s : _s[1]) _n_class_report = min(self._n_problems // 2, self.max_n_class_report) _classes = _class_acc[: _n_class_report] _worst_classes = list(zip(*_classes))[0] _classes = _class_acc[-_n_class_report :] _best_classes = list(zip(*_classes))[0] _mistakes = self.getClassMistakes(_worst_classes + _best_classes) f.write("\nWorst classified classes\n") f.write("--------------------------\n") self.reportClasses(_worst_classes, f) f.write("\nMisclassifications of the worst classified classes:\n") f.write("-----------------------------------------------------\n") self.reportMisclassifications(_worst_classes, _mistakes[: len(_worst_classes)], f) f.write("\nCorrect classifications of the worst classified classes:\n") f.write("-----------------------------------------------------\n") self.reportRightClassifications(_worst_classes, f) f.write("\nBest classified classes\n") f.write("-------------------------\n") self.reportClasses(_best_classes, f) f.write("\nMisclassifications of the best classified classes:\n") f.write("---------------------------------------------------\n") self.reportMisclassifications(_best_classes, _mistakes[len(_worst_classes) :], f) def writeReport(self): """ Write report on classification and misclassification """ with open(f"{self.report_dir}/{self._conf_mat_fn}", 'w') as _f: _f.write("Unnormalized confusion matrix\n") _f.write("row and column indices are labels\n") _f.write("---------------------------------\n") self.writeLargeMatrixInt(self.conf_mat, _f) _f.write("Normalized confusion matrix\n") _f.write("row and column indices are labels\n") _f.write("---------------------------------\n") self.writeLargeMatrixPct(self.norm_conf_mat, _f, 100.0) with open(f"{self.report_dir}/{self.class_stat_fn}", 'w') as _f: self.printClassAccuracy(_f) self.reportConfusedClasses() print("\nClassification report") print("-----------------------") print(metrics.classification_report(self.labels, self.predictions, target_names = self.problems)) #---------------- End of class ClassConfusAnalysis ----------------------