TEES.Evaluators.BioNLP11GeniaTools

1 import sys, os, shutil 2 import subprocess 3 import tempfile 4 thisPath = os.path.dirname(os.path.abspath(__file__)) 5 sys.path.append(os.path.join(thisPath,"..")) 6 import Utils.Settings as Settings 7 import Utils.Download as Download 8 9 # TODO: Move somewhere else 10 #sys.path.append(os.path.abspath(os.path.join(thisPath, "../GeniaChallenge/evaluation"))) 11 #from EvaluateSharedTask import evaluate as evaluateGE09 12 evaluateGE09 = None 13 14 #perlDir = os.path.expanduser("~/data/BioNLP11SharedTask/evaluators/BioNLP-ST_2011_genia_tools") 15

16 -def install(destDir=None, downloadDir=None, redownload=False):

17 print >> sys.stderr, "Installing BioNLP'11 evaluators" 18 settings = {} 19 if downloadDir == None: 20 downloadDir = Settings.DATAPATH 21 if destDir == None: 22 destDir = Settings.DATAPATH 23 for corpus in ["GE", "BB", "BI", "CO"]: 24 print >> sys.stderr, "Installing BioNLP'11", corpus, "evaluator" 25 settings[corpus + "_EVALUATOR"] = Download.getTopDir(destDir + "/tools/evaluators/", Download.downloadAndExtract(Settings.URL[corpus + "_EVALUATOR"], destDir + "/tools/evaluators/", downloadDir + "/tools/download/")) 26 print >> sys.stderr, "Installing BioNLP'11", corpus, "evaluator gold data" 27 Download.downloadAndExtract(Settings.URL[corpus + "_DEVEL"], destDir + "/tools/evaluators/gold/" + corpus + "-devel", downloadDir + "/corpora/BioNLP11-original/corpus/", os.path.basename(Settings.URL[corpus + "_DEVEL"])[:-len(".tar.gz")]) 28 return settings

29 30 #def resultsToCSV(results, filename=None): 31 # import Utils.TableUtils as TableUtils 32 # rows = [] 33 # for k1 in sorted(results.keys()): 34 # for k2 in sorted(results[k1].keys()): 35 # rows.append({}) 36 # rows[-1]["eval"] = k1 37 # rows[-1]["event_class"] = k2 38 # for k3 in sorted(results[k1][k2].keys()): 39 # rows[-1][k3] = results[k1][k2][k3] 40 # if filename != None: 41 # fieldnames = ["eval", "event_class", "gold", "gold_match", "answer", "answer_match", "recall", "precision", "fscore"] 42 # TableUtils.writeCSV(rows, filename, fieldnames) 43 # return rows 44

45 -def parseResults(lines):

46 lines = lines[3:] 47 results = {} 48 for line in lines: 49 if line[0] == "-": 50 continue 51 splits = line.strip().split() 52 # define row name 53 name = splits[0] 54 name = name.replace("=","") 55 name = name.replace("[","") 56 name = name.replace("]","") 57 results[name] = {} 58 # add columns 59 results[name]["gold"] = int(splits[1]) 60 results[name]["gold_match"] = int(splits[3][:-1]) 61 results[name]["answer"] = int(splits[4]) 62 results[name]["answer_match"] = int(splits[6][:-1]) 63 results[name]["recall"] = float(splits[7]) 64 results[name]["precision"] = float(splits[8]) 65 results[name]["fscore"] = float(splits[9]) 66 return results

67

68 -def printLines(lines):

69 for line in lines: 70 print >> sys.stderr, line[:-1]

71

72 -def getFolds(path, folds, seed=0):

73 import Core.Split as Split 74 files = os.listdir(path) 75 docNumbers = set() 76 for file in files: 77 numPart = file.split(".",1)[0] 78 if numPart.isdigit(): 79 docNumbers.add(int(numPart)) 80 docNumbers = list(docNumbers) 81 folds = Split.getFolds(len(docNumbers), folds, seed) 82 foldByDocNumber = {} 83 for i in range(len(docNumbers)): 84 foldByDocNumber[docNumbers[i]] = folds[i] 85 return foldByDocNumber

86

87 -def removeDocuments(path, folds, foldToRemove):

88 files = os.listdir(path) 89 for file in files: 90 numPart = file.split(".",1)[0] 91 if numPart.isdigit(): 92 numPart = int(numPart) 93 assert folds.has_key(numPart) 94 if folds[numPart] == foldToRemove: 95 os.remove(os.path.join(path, file))

96

97 -def evaluateVariance(sourceDir, task, folds):

98 results = [] 99 for i in range(folds): 100 results.append( evaluate(sourceDir, task, folds, i) ) 101 print >> sys.stderr, "##### Variance estimation results #####" 102 for r in results: 103 print >> sys.stderr, r["approximate"]["ALL-TOTAL"]

104

105 -def hasGoldDocuments(sourceDir, goldDir):

106 goldDocIds = set() 107 for filename in os.listdir(goldDir): 108 if filename[-4:] == ".txt": 109 goldDocIds.add(filename.split(".", 1)[0]) 110 for filename in os.listdir(sourceDir): 111 if filename.find(".a2") != -1: 112 if filename.split(".", 1)[0] in goldDocIds: 113 return True 114 return False

115 116 #def getSourceDir(input): 117 # if input.endswith(".tar.gz"): 118 # import tarfile 119 # tempDir = tempfile.mkdtemp() 120 # f = tarfile.open(input) 121 # f.extractall(tempDir) 122 # f.close() 123 # return tempDir, True 124 # else: 125 # return input, False 126

127 -def getFScore(results, task):

128 if task in ["GE", "GE09"]: 129 path = ["approximate", "ALL-TOTAL", "fscore"] 130 elif task in ["EPI", "ID", "REN"]: 131 path = ["TOTAL", "fscore"] 132 elif task in ["BB", "BI"]: 133 path = ["fscore"] 134 elif task == "CO": 135 path = ["MENTION LINKING", "fscore"] 136 else: 137 assert False 138 139 current = results 140 for step in path: 141 if step in current: 142 current = current[step] 143 else: 144 return -1 145 return current

146

147 -def evaluate(source, task, goldDir=None, debug=False):

148 print >> sys.stderr, "BioNLP'11 task", task, "devel evaluation" 149 # Determine task 150 subTask = "1" 151 if "." in task: 152 task, subTask = task.split(".") 153 # Do the evaluation 154 if task in ["GE", "GE09"]: 155 results = evaluateGE(source, task, subTask, goldDir=goldDir, debug=debug) 156 elif task in ["EPI", "ID"]: 157 results = evaluateEPIorID(task, source, goldDir) 158 elif task == "REN": 159 results = evaluateREN(source, goldDir) 160 elif task in ["BB", "BI"]: 161 results = evaluateBX(task, source, goldDir) 162 elif task == "CO": 163 results = evaluateCO(source, goldDir) 164 else: 165 results = None 166 print >> sys.stderr, "No BioNLP'11 evaluator for task", task 167 # Return results 168 if results == None: 169 return None 170 return (getFScore(results, task), results)

171

172 -def checkEvaluator(corpus, sourceDir, goldDir = None):

173 # Check evaluator 174 if not hasattr(Settings, "BIONLP_EVALUATOR_DIR"): 175 print >> sys.stderr, corpus, "BIONLP_EVALUATOR_DIR setting not defined" 176 evaluatorDir = None 177 else: 178 evaluatorDir = os.path.join(Settings.BIONLP_EVALUATOR_DIR, Settings.EVALUATOR[corpus]) 179 # Check source data 180 tempdir = None 181 if sourceDir.endswith(".tar.gz"): 182 tempdir = tempfile.mkdtemp() 183 Download.extractPackage(sourceDir, os.path.join(tempdir, "source")) 184 sourceDir = os.path.join(tempdir, "source") 185 elif corpus == "GE09": # a2 files have to be renamed 186 tempdir = tempfile.mkdtemp() 187 shutil.copytree(sourceDir, os.path.join(tempdir, "source")) 188 sourceDir = os.path.join(tempdir, "source") 189 # Check gold data 190 if goldDir == None: 191 if not hasattr(Settings, "BIONLP_EVALUATOR_GOLD_DIR"): 192 print >> sys.stderr, corpus, "BIONLP_EVALUATOR_GOLD_DIR setting not defined" 193 return evaluatorDir, None 194 goldDir = os.path.join(Settings.BIONLP_EVALUATOR_GOLD_DIR, Settings.EVALUATOR[corpus + "-gold"]) 195 if not os.path.exists(goldDir): 196 print >> sys.stderr, corpus, "Evaluator gold data directory", goldDir, "does not exist" 197 goldDir = None 198 if goldDir != None and goldDir.endswith(".tar.gz"): 199 if tempdir == None: 200 tempdir = tempfile.mkdtemp() 201 goldDir = Download.getTopDir(os.path.join(tempdir, "gold"), Download.extractPackage(goldDir, os.path.join(tempdir, "gold"))) 202 print >> sys.stderr, "Uncompressed evaluation gold to", goldDir 203 if goldDir != None and not hasGoldDocuments(sourceDir, goldDir): 204 print >> sys.stderr, "Evaluation input has no gold documents" 205 goldDir = None 206 # Use absolute paths 207 sourceDir = os.path.abspath(sourceDir) 208 if evaluatorDir != None: 209 evaluatorDir = os.path.abspath(evaluatorDir) 210 if goldDir != None: 211 goldDir = os.path.abspath(goldDir) 212 if tempdir != None: 213 tempdir = os.path.abspath(tempdir) 214 return evaluatorDir, sourceDir, goldDir, tempdir

215

216 -def evaluateGE(sourceDir, mainTask="GE", task=1, goldDir=None, folds=-1, foldToRemove=-1, evaluations=["strict", "approximate", "decomposition"], verbose=True, silent=False, debug=False):

217 task = str(task) 218 assert mainTask in ["GE", "GE09"], mainTask 219 assert task in ["1","2","3"], task 220 if not silent: 221 print >> sys.stderr, mainTask, "task", task, "evaluation of", sourceDir, "against", goldDir 222 if mainTask == "GE": 223 evaluatorDir, sourceDir, goldDir, tempDir = checkEvaluator("GE", sourceDir, goldDir) 224 taskSuffix = ".a2" 225 else: 226 evaluatorDir, sourceDir, goldDir, tempDir = checkEvaluator("GE09", sourceDir, goldDir) 227 # Rename files in the copied source directory 228 taskSuffix = ".a2.t1" 229 for filename in os.listdir(sourceDir): 230 if filename.endswith(".a2"): 231 if task == 1: 232 taskSuffix = ".a2.t1" 233 elif task == 2: 234 taskSuffix = ".a2.t12" 235 else: 236 taskSuffix = ".a2.t123" 237 shutil.move(os.path.join(sourceDir, filename), os.path.join(sourceDir, filename.rsplit(".", 1)[0] + taskSuffix)) 238 if goldDir == None: 239 return None 240 241 origDir = os.getcwd() 242 os.chdir(evaluatorDir) 243 if tempDir == None: 244 tempDir = tempfile.mkdtemp() 245 if folds != -1: 246 folds = getFolds(sourceDir, folds) 247 sourceSubsetDir = tempDir + "/source-subset" 248 if os.path.exists(sourceSubsetDir): 249 shutil.rmtree(sourceSubsetDir) 250 shutil.copytree(sourceDir, sourceSubsetDir) 251 removeDocuments(sourceSubsetDir, folds, foldToRemove) 252 else: 253 sourceSubsetDir = sourceDir 254 255 results = {} 256 257 # Prepare gold data 258 if mainTask == "GE09": 259 preparedGoldDir = os.path.join(tempDir, "prepared-gold") 260 commands = "perl prepare-gold.pl " + goldDir + " " + preparedGoldDir 261 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 262 if verbose and not silent: 263 printLines(p.stderr.readlines()) 264 printLines(p.stdout.readlines()) 265 else: # Not reading the lines causes some error in the perl script! 266 p.stderr.readlines() 267 p.stdout.readlines() 268 goldDir = preparedGoldDir 269 270 # Prepare evaluated data 271 outDir = tempDir + "/output" 272 if mainTask == "GE": 273 commands = "perl a2-normalize.pl -g " + goldDir 274 commands += " -o " + outDir 275 commands += " " + sourceSubsetDir + "/*" + taskSuffix #".a2" 276 else: 277 commands = "perl prepare-eval.pl -g " + goldDir 278 commands += " " + sourceSubsetDir + " " + outDir 279 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 280 if verbose and not silent: 281 printLines(p.stderr.readlines()) 282 printLines(p.stdout.readlines()) 283 else: # Not reading the lines causes some error in the perl script! 284 p.stderr.readlines() 285 p.stdout.readlines() 286 287 if "strict" in evaluations: 288 #commands = "export PATH=$PATH:./ ; " 289 commands = "perl a2-evaluate.pl" 290 if mainTask == "GE": commands += " -t " + str(task) 291 if debug: commands += " -v -d" 292 commands += " -g " + goldDir + " " + outDir + "/*" + taskSuffix #".a2" 293 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 294 stderrLines = p.stderr.readlines() 295 stdoutLines = p.stdout.readlines() 296 if not silent: 297 printLines(stderrLines) 298 print >> sys.stderr, "##### strict evaluation mode #####" 299 printLines(stdoutLines) 300 results["strict"] = parseResults(stdoutLines) 301 302 if "approximate" in evaluations: 303 if not silent: 304 print >> sys.stderr, "##### approximate span and recursive mode #####" 305 #commands = "export PATH=$PATH:./ ; " 306 commands = "perl a2-evaluate.pl" 307 if mainTask == "GE": commands += " -t " + str(task) 308 if debug: commands += " -v -d" 309 commands += " -g " + goldDir + " -sp " + outDir + "/*" + taskSuffix #".a2" 310 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 311 stderrLines = p.stderr.readlines() 312 stdoutLines = p.stdout.readlines() 313 if not silent: 314 printLines(stderrLines) 315 printLines(stdoutLines) 316 results["approximate"] = parseResults(stdoutLines) 317 318 if "decomposition" in evaluations: 319 if not silent: 320 print >> sys.stderr, "##### event decomposition in the approximate span mode #####" 321 #commands = "export PATH=$PATH:./ ; " 322 commands = "perl a2-evaluate.pl" 323 if mainTask == "GE": commands += " -t " + str(task) 324 if debug: commands += " -v -d" 325 commands += " -g " + goldDir + " -sp " + outDir + "/*" + taskSuffix #".a2" 326 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 327 stderrLines = p.stderr.readlines() 328 stdoutLines = p.stdout.readlines() 329 if not silent: 330 printLines(stderrLines) 331 printLines(stdoutLines) 332 results["decomposition"] = parseResults(stdoutLines) 333 334 if not debug: 335 shutil.rmtree(tempDir) 336 else: 337 print >> sys.stderr, "Temporary directory left at", tempDir 338 339 # return to current dir 340 os.chdir(origDir) 341 return results

342

343 -def printLinesBX(lines):

344 # queue = [] 345 # category = None 346 for line in lines: 347 print >> sys.stderr, line[:-1]

348 349 # for line in lines: 350 # if ":" in line: 351 # if len(queue) > 0: 352 # print >> sys.stderr, str(category) + ":", ", ".join(queue) 353 # queue = [] 354 # category = line.split(":")[0].strip() 355 # elif line.startswith(" "): 356 # queue.append(line.strip()) 357 # else: 358 # print >> sys.stderr, line[:-1] 359 # if len(queue) > 0: 360 # print >> sys.stderr, str(category) + ":", ", ".join(queue) 361 # queue = [] 362

363 -def evaluateBX(corpusName, sourceDir, goldDir=None, silent=False):

364 assert corpusName in ["BI", "BB"], corpusName 365 evaluatorDir, sourceDir, goldDir, tempDir = checkEvaluator(corpusName, sourceDir, goldDir) 366 if goldDir == None: 367 return None 368 369 if corpusName == "BI": 370 commands = "java -jar " + evaluatorDir + "/BioNLP-ST_2011_bacteria_interactions_evaluation_software.jar " + goldDir + " " + sourceDir 371 elif corpusName == "BB": 372 commands = "java -jar " + evaluatorDir + "/BioNLP-ST_2011_Bacteria_Biotopes_evaluation_software.jar " + goldDir + " " + sourceDir 373 else: 374 assert False, corpusName 375 376 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 377 stderrLines = p.stderr.readlines() 378 stdoutLines = p.stdout.readlines() 379 if not silent: 380 printLinesBX(stderrLines) 381 printLinesBX(stdoutLines) 382 383 results = {} 384 if corpusName == "BI": 385 category = None 386 for line in stdoutLines: 387 if ":" in line: 388 category = line.split(":")[0].strip() 389 if category == "Global scores" and line.startswith(" "): 390 key, value = line.strip().split("=") 391 key = key.strip() 392 value = value.strip() 393 assert key not in results 394 if key == "f-score": 395 key = "fscore" 396 if value == "NaN": 397 results[key] = 0.0 398 else: 399 results[key] = float(value) 400 elif corpusName == "BB": 401 for line in stdoutLines: 402 key, value = line.strip().split("=") 403 key = key.strip() 404 value = value.strip() 405 assert key not in results 406 if key == "F-score": 407 key = "fscore" 408 if value == "NaN": 409 results[key] = 0.0 410 else: 411 results[key] = float(value) 412 if tempDir != None: 413 shutil.rmtree(tempDir) 414 return results

415

416 -def evaluateEPIorID(corpus, sourceDir, goldDir=None, silent=False):

417 assert corpus in ["EPI", "ID"], corpus 418 evaluatorDir, sourceDir, goldDir, tempDir = checkEvaluator(corpus, sourceDir, goldDir) 419 if goldDir == None: 420 return None 421 commands = "cd " + evaluatorDir 422 commands += " ; " + "python evaluation.py -s -p -r " + goldDir + " " + sourceDir + "/*.a2" 423 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 424 stderrLines = p.stderr.readlines() 425 stdoutLines = p.stdout.readlines() 426 if not silent: 427 for line in stderrLines: 428 print >> sys.stderr, line, 429 for line in stdoutLines: 430 print >> sys.stderr, line, 431 print >> sys.stderr 432 for line in stderrLines + stdoutLines: 433 if "No such file or directory" in line: 434 return None 435 if tempDir != None: 436 shutil.rmtree(tempDir) 437 return parseResults(stdoutLines)

438

439 -def evaluateREN(sourceDir, goldDir=None, silent=False):

440 evaluatorDir, sourceDir, goldDir, tempDir = checkEvaluator("REN", sourceDir, goldDir) 441 if goldDir == None: 442 return None 443 commands = "cd " + evaluatorDir 444 commands += " ; " + "java -jar eval_rename.jar " + goldDir + " " + sourceDir 445 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 446 stderrLines = p.stderr.readlines() 447 stdoutLines = p.stdout.readlines() 448 if not silent: 449 for line in stderrLines: 450 print >> sys.stderr, line, 451 for line in stdoutLines: 452 print >> sys.stderr, line, 453 print >> sys.stderr 454 results = {} 455 for line in stdoutLines: 456 category, value = line.strip().split(":") 457 value = value.strip() 458 if value == "NaN": 459 value = 0.0 460 elif "." in value: 461 value = float(value) 462 else: 463 value = int(value) 464 results[category.strip()] = value 465 if tempDir != None: 466 shutil.rmtree(tempDir) 467 return results

468

469 -def evaluateCO(sourceDir, goldDir=None, silent=False):

470 evaluatorDir, sourceDir, goldDir, tempDir = checkEvaluator("CO", sourceDir, goldDir) 471 if goldDir == None: 472 return None 473 # Run the evaluation program, which writes the result into a file 474 if tempDir == None: 475 tempDir = tempfile.mkdtemp() 476 resultDir = os.path.join(tempDir, "result") 477 os.makedirs(resultDir) 478 commands = "cd " + evaluatorDir 479 commands += " ; " + "java -jar CRScorer.jar " + goldDir + " " + sourceDir + " " + resultDir 480 p = subprocess.Popen(commands, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 481 stderrLines = p.stderr.readlines() 482 stdoutLines = p.stdout.readlines() 483 if not silent: 484 for i in range(len(stdoutLines)): 485 # Skip mentions of processing a document if no error happened in that document 486 if (not stdoutLines[i].strip().endswith("...")) or (i < len(stdoutLines) - 1 and not stdoutLines[i+1].strip().endswith("...")): 487 print >> sys.stderr, stdoutLines[i], 488 for line in stderrLines: 489 print >> sys.stderr, line, 490 print >> sys.stderr 491 # Read the evaluation result from the file it has been written to 492 f = open(os.path.join(resultDir, "eval.results"), "rt") 493 resultLines = f.readlines() 494 f.close() 495 results = {"MENTION DETECTION":{}, "MENTION LINKING":{}} 496 currentBlock = None 497 for line in resultLines: 498 line = line.replace("\t", " ") 499 print >> sys.stderr, line.rstrip() 500 if line[0] == "*": 501 continue 502 if "EVALUATION OF MENTION DETECTION" in line: 503 currentBlock = results["MENTION DETECTION"] 504 elif "EVALUATION OF MENTION LINKING" in line: 505 currentBlock = results["MENTION LINKING"] 506 elif ":" in line: 507 name, value = line.split(":") 508 name = name.strip() 509 value = int(value) 510 currentBlock[name] = value 511 elif line[0] == "P": 512 splits = line.split() 513 assert splits[0] == "P" and splits[1] == "=" and splits[3] == "R" and splits[4] == "=" and splits[6] == "F" and splits[7] == "=", line 514 currentBlock["precision"] = float(splits[2]) 515 currentBlock["recall"] = float(splits[5]) 516 currentBlock["fscore"] = float(splits[8]) 517 # Remove temporary directory 518 if tempDir != None: 519 shutil.rmtree(tempDir) 520 return results

521 522 if __name__=="__main__": 523 # Import Psyco if available 524 try: 525 import psyco 526 psyco.full() 527 print >> sys.stderr, "Found Psyco, using" 528 except ImportError: 529 print >> sys.stderr, "Psyco not installed" 530 531 from optparse import OptionParser 532 optparser = OptionParser(description="Evaluate BioNLP Shared Task predictions") 533 optparser.add_option("-i", "--input", default=None, dest="input", help="input directory with predicted shared task files", metavar="FILE") 534 optparser.add_option("-g", "--gold", default=None, dest="gold", help="optional gold directory (default is the task development set)", metavar="FILE") 535 optparser.add_option("-t", "--task", default="GE.2", dest="task", help="") 536 optparser.add_option("-v", "--variance", default=0, type="int", dest="variance", help="variance folds") 537 optparser.add_option("-d", "--debug", default=False, action="store_true", dest="debug", help="debug") 538 optparser.add_option("--install", default=None, dest="install", help="Install directory (or DEFAULT)") 539 (options, args) = optparser.parse_args() 540 #assert(options.task in [1,2,3]) 541 542 if options.install == None: 543 assert(options.input != None) 544 evalResult = evaluate(options.input, options.task, options.gold, debug=options.debug) 545 if options.debug: 546 print >> sys.stderr, "evaluate output:", evalResult 547 else: 548 downloadDir = None 549 destDir = None 550 if options.install != "DEFAULT": 551 if "," in options.install: 552 destDir, downloadDir = options.install.split(",") 553 else: 554 destDir = options.install 555 settings = install(destDir, downloadDir) 556 for key in sorted(settings.keys()): 557 print key + "=\"" + str(settings[key]) + "\"" 558 559 # if options.corpus == "GE": 560 # if options.variance == 0: 561 # evaluate(options.input, options.task, debug = options.debug) 562 # else: 563 # evaluateVariance(options.input, options.task, options.variance) 564 # elif options.corpus in ["EPI", "ID"]: 565 # print evaluateEPIorID(options.input, options.corpus) 566 # elif options.corpus == "REN": 567 # print evaluateREN(options.input) 568 # else: 569 # print evaluateBX(options.input, options.corpus) 570

Source Code for Module TEES.Evaluators.BioNLP11GeniaTools