| public static NumericalMagnitudeChange ( double error, double correct ) : double | ||
| error | double | |
| correct | double | |
| Результат | double | |
// this method only works for functions with numerical inputs
public string GenerateSubtleErrorString(double input, Classification c)
{
string errstr;
double errmag = 100;
do
{
// generate an error
errstr = GenerateErrorString(Convert.ToString(input), c);
double errval;
if (Double.TryParse(errstr, out errval))
{
// it's a numerical error
// get the magnitude of the error
errmag = Utility.NumericalMagnitudeChange(errval, input);
}
} while (errmag >= 0);
return(errstr);
}
// 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));
}
