public static BothNumbers ( string value1, string value2 ) : bool | ||
value1 | string | |
value2 | string | |
return | bool |
// remove errors until none remain private UserResults SimulateUser(int nboots, double significance, CutoffKind ck, DAG dag, CellDict original_inputs, CellDict errord, CellDict correct_outputs, Excel.Workbook wb, Excel.Application app, AnalysisType analysis_type, bool weighted, bool all_outputs, long max_duration_in_ms, Stopwatch sw, String logfile, ProgBar pb ) { // init user results data structure var o = new UserResults(); HashSet <AST.Address> known_good = new HashSet <AST.Address>(); // initialize procedure var errors_remain = true; var max_errors = new ErrorDict(); var incorrect_outputs = Utility.SaveOutputs(dag.terminalFormulaNodes(all_outputs), dag); var errors_found = 0; var number_of_true_errors = errord.Count; Utility.UpdatePerFunctionMaxError(correct_outputs, incorrect_outputs, max_errors); // the corrected state of the spreadsheet CellDict partially_corrected_outputs = correct_outputs.ToDictionary(p => p.Key, p => p.Value); // remove errors loop var cells_inspected = 0; List <KeyValuePair <AST.Address, int> > filtered_high_scores = null; bool correction_made = true; while (errors_remain) { Console.Write("."); AST.Address flagged_cell = null; // choose the appropriate test if (analysis_type == AnalysisType.CheckCell5 || analysis_type == AnalysisType.CheckCell10 ) { flagged_cell = SimulationStep.CheckCell_Step(o, significance, ck, nboots, dag, app, weighted, all_outputs, correction_made, known_good, ref filtered_high_scores, max_duration_in_ms, sw, pb); } else if (analysis_type == AnalysisType.NormalPerRange) { flagged_cell = SimulationStep.NormalPerRange_Step(dag, wb, known_good, max_duration_in_ms, sw); } else if (analysis_type == AnalysisType.NormalAllInputs) { flagged_cell = SimulationStep.NormalAllOutputs_Step(dag, app, wb, known_good, max_duration_in_ms, sw); } // stop if the test no longer returns anything or if // the test is simply done inspecting based on a fixed threshold if (flagged_cell == null || (ck.isCountBased && ck.Threshold == cells_inspected)) { errors_remain = false; } else // a cell was flagged { //cells_inspected should only be incremented when a cell is actually flagged. If nothing is flagged, //then nothing is inspected, so cells_inspected doesn't increase. cells_inspected += 1; // check to see if the flagged value is actually an error if (errord.ContainsKey(flagged_cell)) { correction_made = true; errors_found += 1; // P(k) * rel(k) o.PrecRel_at_k.Add(errors_found / (double)cells_inspected); o.true_positives.Add(flagged_cell); // correct flagged cell flagged_cell.GetCOMObject(app).Value2 = original_inputs[flagged_cell]; Utility.UpdatePerFunctionMaxError(correct_outputs, partially_corrected_outputs, max_errors); // compute total error after applying this correction var current_total_error = Utility.CalculateTotalError(correct_outputs, partially_corrected_outputs); o.current_total_error.Add(current_total_error); // save outputs partially_corrected_outputs = Utility.SaveOutputs(dag.terminalFormulaNodes(all_outputs), dag); } else { correction_made = false; // numerator is 0 here because rel(k) = 0 when no error was found o.PrecRel_at_k.Add(0.0); o.false_positives.Add(flagged_cell); } // mark it as known good -- at this point the cell has been // 'inspected' regardless of whether it was an error // It was either corrected or marked as OK known_good.Add(flagged_cell); // compute output error magnitudes var output_error_magnitude = Utility.MeanErrorMagnitude(partially_corrected_outputs, correct_outputs); // compute input error magnitude double num_input_error_magnitude; double str_input_error_magnitude; if (errord.ContainsKey(flagged_cell)) { if (Utility.BothNumbers(errord[flagged_cell], original_inputs[flagged_cell])) { num_input_error_magnitude = Utility.NumericalMagnitudeChange(Double.Parse(errord[flagged_cell]), Double.Parse(original_inputs[flagged_cell])); str_input_error_magnitude = 0; } else { num_input_error_magnitude = 0; str_input_error_magnitude = Utility.StringMagnitudeChange(errord[flagged_cell], original_inputs[flagged_cell]); } } else { num_input_error_magnitude = 0; str_input_error_magnitude = 0; } // write error log var logentry = new LogEntry(analysis_type, wb.Name, flagged_cell, original_inputs[flagged_cell], errord.ContainsKey(flagged_cell) ? errord[flagged_cell] : original_inputs[flagged_cell], output_error_magnitude, num_input_error_magnitude, str_input_error_magnitude, true, correction_made, significance, ck.Threshold); logentry.WriteLog(logfile); _error_log.Add(logentry); } } // find all of the false negatives o.false_negatives = Utility.GetFalseNegatives(o.true_positives, o.false_positives, errord); o.max_errors = max_errors; var last_out_err_mag = Utility.MeanErrorMagnitude(partially_corrected_outputs, correct_outputs); // write out all false negative information foreach (AST.Address fn in o.false_negatives) { double num_input_error_magnitude; double str_input_error_magnitude; if (Utility.BothNumbers(errord[fn], original_inputs[fn])) { num_input_error_magnitude = Utility.NumericalMagnitudeChange(Double.Parse(errord[fn]), Double.Parse(original_inputs[fn])); str_input_error_magnitude = 0; } else { num_input_error_magnitude = 0; str_input_error_magnitude = Utility.StringMagnitudeChange(errord[fn], original_inputs[fn]); } // write error log _error_log.Add(new LogEntry(analysis_type, wb.Name, fn, original_inputs[fn], errord[fn], last_out_err_mag, num_input_error_magnitude, str_input_error_magnitude, false, true, significance, ck.Threshold)); } return(o); }
// For running a simulation from the batch runner // returns the number of cells inspected public int RunFromBatch(int nboots, // number of bootstraps string xlfile, // name of the workbook double significance, // significance threshold for test Excel.Application app, // reference to Excel app CutoffKind ck, Classification c, // data from which to generate errors Random r, // a random number generator AnalysisType analysisType, // the type of analysis to run bool weighted, // should we weigh things? bool all_outputs, // if !all_outputs, we only consider terminal outputs DAG dag, // the computation tree of the spreadsheet Excel.Workbook wb, // the workbook being analyzed CellDict errors, // the errors that will be introduced in the spreadsheet AST.Range[] terminal_input_vectors, // the inputs AST.Address[] terminal_formula_cells, // the outputs CellDict original_inputs, // original values of the inputs CellDict correct_outputs, // the correct outputs long max_duration_in_ms, String logfile //filename for the output log ) { if (terminal_input_vectors.Length == 0) { throw new NoRangeInputs(); } if (original_inputs.Count() == 0) { throw new NoFormulas(); } _errors = errors; // find the error with the largest magnitude // this is mostly useful for the single-perturbation experiments var num_errs = _errors.Where(pair => Utility.BothNumbers(pair.Value, original_inputs[pair.Key])); var str_errs = _errors.Where(pair => !Utility.BothNumbers(pair.Value, original_inputs[pair.Key])); _num_max_err_diff_mag = num_errs.Count() != 0 ? num_errs.Select( (KeyValuePair <AST.Address, string> pair) => Utility.NumericalMagnitudeChange(Double.Parse(pair.Value), Double.Parse(original_inputs[pair.Key])) ).Max() : 0; _str_max_err_diff_mag = str_errs.Count() != 0 ? str_errs.Select( (KeyValuePair <AST.Address, string> pair) => Utility.StringMagnitudeChange(pair.Value, original_inputs[pair.Key]) ).Max() : 0; // find the output with the largest magnitude var num_outs = correct_outputs.Where(pair => Utility.IsNumber(pair.Value)); var str_outs = correct_outputs.Where(pair => !Utility.IsNumber(pair.Value)); _num_max_output_diff_mag = num_outs.Count() != 0 ? num_outs.Select( (KeyValuePair <AST.Address, string> pair) => Utility.NumericalMagnitudeChange(Double.Parse(pair.Value), Double.Parse(correct_outputs[pair.Key])) ).Max() : 0; _str_max_output_diff_mag = str_outs.Count() != 0 ? str_outs.Select( (KeyValuePair <AST.Address, string> pair) => Utility.StringMagnitudeChange(pair.Value, correct_outputs[pair.Key]) ).Max() : 0; return(Run(nboots, xlfile, significance, ck, app, c, r, analysisType, weighted, all_outputs, dag, wb, terminal_formula_cells, terminal_input_vectors, original_inputs, correct_outputs, max_duration_in_ms, logfile, null)); }