|
public static SaveOutputs ( AST formula_nodes, |
||
| formula_nodes | AST | |
| dag | ||
| 리턴 | string>.System.Collections.Generic.Dictionary | |
// Get dictionary of inputs and the error they produce
public Dictionary <AST.Address, Tuple <string, double> > TopOfKErrors(AST.Address[] terminal_formula_nodes, CellDict inputs, int k, CellDict correct_outputs, Excel.Application app, Excel.Workbook wb, string classification_file, DAG dag)
{
var eg = new ErrorGenerator();
var c = Classification.Deserialize(classification_file);
var max_error_produced_dictionary = new Dictionary <AST.Address, Tuple <string, double> >();
foreach (KeyValuePair <AST.Address, string> pair in inputs)
{
AST.Address addr = pair.Key;
string orig_value = pair.Value;
//Load in the classification's dictionaries
double max_error_produced = 0.0;
string max_error_string = "";
// get k strings, in parallel
string[] errorstrings = eg.GenerateErrorStrings(orig_value, c, k);
for (int i = 0; i < k; i++)
{
CellDict cd = new CellDict();
cd.Add(addr, errorstrings[i]);
//inject the typo
Utility.InjectValues(app, wb, cd);
// save function outputs
CellDict incorrect_outputs = Utility.SaveOutputs(terminal_formula_nodes, dag);
//remove the typo that was introduced
cd.Clear();
cd.Add(addr, orig_value);
Utility.InjectValues(app, wb, cd);
double total_error = Utility.CalculateTotalError(correct_outputs, incorrect_outputs);
//keep track of the largest observed max error
if (total_error > max_error_produced)
{
max_error_produced = total_error;
max_error_string = errorstrings[i];
}
}
//Add entry for this TreeNode in our dictionary with its max_error_produced
max_error_produced_dictionary.Add(addr, new Tuple <string, double>(max_error_string, max_error_produced));
}
return(max_error_produced_dictionary);
}
// 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);
}
// returns the number of cells inspected
public int Run(int nboots, // number of bootstraps
string xlfile, // name of the workbook
double significance, // significance threshold for test
CutoffKind ck, // kind of threshold function to use
Excel.Application app, // reference to Excel app
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,
Excel.Workbook wb,
AST.Address[] terminal_formula_cells,
AST.Range[] terminal_input_vectors,
CellDict original_inputs,
CellDict correct_outputs,
long max_duration_in_ms,
String logfile, //filename for the output log
ProgBar pb
)
{
//set wbname and path
_wb_name = xlfile;
_wb_path = wb.Path;
_analysis_type = analysisType;
_significance = significance;
_all_outputs = all_outputs;
_weighted = weighted;
//Now we want to inject the errors from _errors
Utility.InjectValues(app, wb, _errors);
// save function outputs
CellDict incorrect_outputs = Utility.SaveOutputs(terminal_formula_cells, dag);
//Time the removal of errors
Stopwatch sw = new Stopwatch();
sw.Start();
// remove errors until none remain; MODIFIES WORKBOOK
_user = SimulateUser(nboots, significance, ck, dag, original_inputs, _errors, correct_outputs, wb, app, analysisType, weighted, all_outputs, max_duration_in_ms, sw, logfile, pb);
sw.Stop();
TimeSpan elapsed = sw.Elapsed;
_analysis_time = elapsed.TotalSeconds;
// save partially-corrected outputs
var partially_corrected_outputs = Utility.SaveOutputs(terminal_formula_cells, dag);
// compute total relative error
_error = Utility.CalculateNormalizedError(correct_outputs, partially_corrected_outputs, _user.max_errors);
_total_relative_error = Utility.TotalRelativeError(_error);
// compute starting total relative error (normalized by max_errors)
ErrorDict starting_error = Utility.CalculateNormalizedError(correct_outputs, incorrect_outputs, _user.max_errors);
_initial_total_relative_error = Utility.TotalRelativeError(starting_error);
// effort
_max_effort = dag.allCells().Length;
_effort = (_user.true_positives.Count + _user.false_positives.Count);
_expended_effort = (double)_effort / (double)_max_effort;
// compute average precision
// AveP = (\sum_{k=1}^n (P(k) * rel(k))) / |total positives|
// where P(k) is the precision at threshold k,
// rel(k) = \{ 1 if item at k is a true positive, 0 otherwise
_average_precision = _user.PrecRel_at_k.Sum() / (double)_errors.Count;
// restore original values
Utility.InjectValues(app, wb, original_inputs);
_tree_construct_time = dag.AnalysisMilliseconds / 1000.0;
// flag that we're done; safe to print output results
_simulation_run = true;
// return the number of cells inspected
return(_effort);
}
