File size: 19,343 Bytes
eb90a8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 | """
Program for verification of downloaded classification datsets.
"""
import sys
import os
from os import path
import argparse
import csv
class DsProblemStat:
"""
Statistics on problems usage in dataset
"""
def __init__(self):
self.similar_left = set()
self.similar_right = set()
self.n_similar_left = 0
self.n_similar_right = 0
self.left = set()
self.n_left = 0
self.right = set()
self.n_right = 0
def update_similar(self, s1, s2):
"""
Update statistics for similarity sample
Parameters:
s1, s2 -- solutions used in similarity sample
"""
self.similar_left.add(s1)
self.similar_right.add(s2)
self.n_similar_right += 1
self.n_similar_left += 1
def update_left(self, s):
"""
Update statistics for dissimilarity sample
"""
self.left.add(s)
self.n_left += 1
def update_right(self, s):
"""
Update statistics for dissimilarity sample
"""
self.right.add(s)
self.n_right += 1
#-------End of class DsProblemStat ------------------------
class SamplePartStat:
"""
Statistics of the part of set of samples:
similar, left or right
"""
def __init__(self):
"""
Initialize statistics of the part of sample:
similar, left or right
"""
self.min_fract_sol_used = 1
self.max_fract_sol_used = 0
self.min_uniqueness = 1
self.max_uniqueness = 0
self.sols_used = 0
self.n_total = 0
def update(self, n_unique, n_used, n_total):
"""
Update statistics of the part of sample
with data on solutions of one problem
Parameters:
- n_unique -- number of unique problem solutions used in samples
- n_used -- number of total problem solutions used in samples
- n_total -- number of all problem solutions existed
"""
#Fraction of all problem solutions used in samples
_fract_used = float(n_unique) / float(n_total)
self.min_fract_sol_used = min(self.min_fract_sol_used, _fract_used)
self.max_fract_sol_used = max(self.max_fract_sol_used, _fract_used)
#Ratio of number of problem solutions are used in sample to
#number of cases where the problem is used in samples
#Indicator how unique is usage of solution in the samples
_uniqueness = float(n_unique) / float(n_used)
self.min_uniqueness = min(self.min_uniqueness, _uniqueness)
self.max_uniqueness = max(self.max_uniqueness, _uniqueness)
#Number of cases where problem is used in the samples
self.sols_used += n_unique
self.n_total += n_total
def report(self, title):
"""
Report statistics
"""
print(title)
print(f"Total {self.sols_used} from all {self.n_total} " +
"of problem solutions are used")
print("Fraction of used problem solutions varies from " +
f"{self.min_fract_sol_used:5.3f} to {self.max_fract_sol_used:5.3f} ")
print("Uniqueness of used problem solutions varies from " +
f"{self.min_uniqueness:7.5f} to {self.max_uniqueness:7.5f}")
#-------End of class SamplePartStat ------------------------
class DsStat:
"""
Class defining statistics of dataset: test validation or training
"""
def __init__(self):
self.simil_left_stat = SamplePartStat()
self.simil_right_stat = SamplePartStat()
self.left_stat = SamplePartStat()
self.right_stat = SamplePartStat()
self.total_sols = 0
self.min_sols = 999999999
self.min_problem = None
self.max_sols = 0
self.max_problem = None
self.n_problems = 0
def update(self, problem, problem_stat, n_total):
"""
Update statistics of problem solutions usage
Parameters:
- problem -- problem name
- problem_stat -- Statistics of problem solutions
in the form of DsProblemStat object
- n_total -- Total number of solutions of the problem
"""
self.simil_left_stat.update(len(problem_stat.similar_left),
problem_stat.n_similar_left,
n_total)
self.simil_right_stat.update(len(problem_stat.similar_right),
problem_stat.n_similar_right,
n_total)
self.left_stat.update(len(problem_stat.left),
problem_stat.n_left,
n_total)
self.right_stat.update(len(problem_stat.right),
problem_stat.n_right,
n_total)
#Number of all problem solutions existed
self.total_sols += n_total
#Minimum number of problem solutions
if n_total < self.min_sols:
self.min_sols = n_total
self.min_problem = problem
#Maximum number of problem solutions
if n_total > self.max_sols:
self.max_sols = n_total
self.max_problem = problem
self.n_problems += 1
def update_solutions(self, n_total):
"""
Update statistics of dataset problem solutions
with data on solutions of one problem
- n_total -- number of all problem solutions existed
"""
#Number of all problem solutions existed
self.total_sols += n_total
#Minimum number of problem solutions
self.min_sols = min(self.min_sols, n_total)
#Maximum number of propblem solutions
self.max_sols = max(self.max_sols, n_total)
self.n_problems += 1
def report(self, title):
"""
Printing report of dataset statistics
Parameters:
- title -- title of the report
"""
print(title)
print(f"Dataset uses {self.n_problems} problems " +
f"having total {self.total_sols} solutions")
print("Average number of problem solutions " +
f"is {float(self.total_sols) / float(self.n_problems)}")
print(f"Problem {self.min_problem} has lowest " +
f"number {self.min_sols} of solutions")
print(f"Problem {self.max_problem} has highest " +
f"number {self.max_sols} of solutions")
self.simil_left_stat.report("Statistics of left side of similarity samples:")
self.simil_right_stat.report("Statistics of right side of similarity samples:")
self.left_stat.report("Statistics of left side of dissimilarity samples:")
self.right_stat.report("Statistics of right side of dissimilarity samples:")
print("\n")
#-------End of class DsStat ------------------------
def readDsProblems(fname):
"""
Read problems of the dataset
Parameters:
-fname -- file name
Returns:
- initialized dictionary
* key - problem name
* value - dictionary of number of problem used
in similar and dissimilar samples as right and left one
- number of errors found
"""
if not os.path.exists(fname):
sys.exit(f"File {fname} of problems of datset is not found")
_problem_dict = {}
_n_errors = 0
with open(fname) as _f:
for _p in _f:
_p = _p.rstrip('\n')
if _p in _problem_dict:
print(f"Error: multiple occurrences of problem {_p} in file{fname}")
_n_errors +=1
else:
_problem_dict[_p] = DsProblemStat()
return _problem_dict, _n_errors
def readDsSamples(cvs_file, problems):
"""
Read samples of dataset
Parameters;
- cvs_file -- csv file defining dataset
- problems -- dictionary of problems of the dataset
key: problem name
value: Statistics of problem usage
in the form of DsProblemStat
Fills in problems(dictionary of problems of the dataset)
Returns:
- number of samples in the loaded datasets
- number of similar samples
- number of dissimilar samples
- number of errors found
"""
_n_errors = 0
if not os.path.exists(cvs_file):
sys.exit(f"File {cvs_file} with datset is not found")
_n_samples = 0
_n_similar = 0
_n_disssimilar = 0
with open(cvs_file, newline='') as _:
_reader = csv.reader(_)
next(_reader)
for _p1, _s1, _p2, _s2 in _reader:
_n_samples += 1
if _p1 not in problems:
print(f"Error: problem {_p1} occurred in file {cvs_file} " +
"is not among problems of that dataset")
_n_errors +=1
if _p2 != _p1 and _p2 not in problems:
print(f"Error: problem {_p2} occurred in file {cvs_file} " +
"is not among problems of that dataset")
_n_errors +=1
if _p1 == _p2:
_n_similar += 1
problems[_p1].update_similar(_s1, _s2)
else:
_n_disssimilar += 1
problems[_p1].update_left(_s1)
problems[_p2].update_right(_s2)
return _n_samples, _n_similar, _n_disssimilar, _n_errors
def checkNoCommonProblems(ds1, ds2, fname1, fname2):
"""
Check that no common problems in two datasets
Parameters:
- ds1 -- 1-st dataset as dictionary of its problems
dictionary of problems of the dataset
key: problem name
value: Statistics of problem usage as DsProblemStat
- ds2 -- 2-nd dataset as dictionary of its problems
similar to ds1
- fname1 -- file name of 1-st dataset
- fname2 -- file name of 2-nd dataset
Returns number of errors found
"""
_n_errors = 0
_common_problems = frozenset(ds1.keys()).intersection(ds2.keys())
if _common_problems:
_n_errors = len(_common_problems)
print(f"Error datasets {fname1} and {fname2} " +
"have following {_n_errors} common problems:")
print(_common_problems)
return _n_errors
def checkDsSolVsDir(problem, solutions, ds_fn, ds_dir, fn_extension):
"""
Check that all solutiosn of a given problem are in dataset
Parameters:
- problem -- problem to check
- solutions -- set of solutions to check
- ds_fn -- file name of dataset whose problem
and solutions are checked
- ds_dir -- directory with source dataset
- fn_extension -- extensions of source code files of problem solutions
Returns number of errors
"""
_n_errors = 0
for _s in solutions:
if not os.path.exists(f"{ds_dir}/{problem}/{_s}.{fn_extension}"):
print(f"Solution {_s}.{fn_extension} occurring in file {ds_fn} " +
f"is not in problem directory {ds_dir}/{problem}")
_n_errors +=1
return _n_errors
def checkDsVsDir(ds, ds_dir, ds_fn, fn_extension):
"""
Check loaded dataset with respect to source dataset directory
Parameters:
- ds -- dictionary defining dataset to be checked
key: problem name
value: Statistics of problem usage as DsProblemStat
- ds_dir -- directory with source dataset
- ds_fn -- file name of dataset whose problem
and solutions are checked
- fn_extension -- extensions of source code files of problem solutions
Returns:
- number of errors found
"""
_n_errors = 0
for _p, _samples in ds.items():
if not os.path.exists(f"{ds_dir}/{_p}"):
print(f"Problem {_p} is not in dataset directory {ds_dir}")
_n_errors +=1
_n_errors += checkDsSolVsDir(
_p, _samples.similar_right, ds_fn, ds_dir, fn_extension)
_n_errors += checkDsSolVsDir(
_p, _samples.similar_left, ds_fn, ds_dir, fn_extension)
_n_errors += checkDsSolVsDir(
_p, _samples.right, ds_fn, ds_dir, fn_extension)
_n_errors += checkDsSolVsDir(
_p, _samples.left, ds_fn, ds_dir, fn_extension)
return _n_errors
def checkDirVsDatasets(source, train, val, test):
"""
Check that all problems from source dataset directory are in one
of datasets
Parameters:
- source -- directory with source dataset
- train -- dictionary defining training dataset to be checked
key: problem name
value: Statistics of problem usage as DsProblemStat
- val -- dictionary defining validation dataset to be checked
similar to train
- test -- dictionary defining test dataset to be checked
similar to train
Returns:
- number of problems in source dataset directory
- number of solutions in source dataset directory
- number of errors found
"""
_test_stat = DsStat()
_val_stat = DsStat()
_train_stat = DsStat()
_problems = os.listdir(source)
_n_total_solutions = 0
_n_errors = 0
if not _problems:
sys.exit(f"Directory {source} of source code dataset is empty")
for _p in _problems:
_solutions = os.listdir(f"{source}/{_p}")
_n_solutions = len(_solutions)
if not _solutions:
print(f"Error: Directory {source}/{_p} of source code dataset is empty")
_n_errors += 1
continue
_n_total_solutions += _n_solutions
if _p in test:
_test_stat.update(_p, test[_p], _n_solutions)
elif _p in train:
_train_stat.update(_p, train[_p], _n_solutions)
elif _p in val:
_val_stat.update(_p, val[_p], _n_solutions)
else:
print(f"Error: problem {_p} is not present in any datasets")
_n_errors +=1
print("\n")
_train_stat.report("Statistics of training dataset")
_val_stat.report("Statistics of validation dataset")
_test_stat.report("Statistics of test dataset")
return len(_problems), _n_total_solutions, _n_errors
#------------- End of functions -----------------------------
def main(args):
"""
Main function of program for verifying dataset
Parameters:
- args -- Parsed command line arguments
as object returned by ArgumentParser
"""
if not os.path.exists(args.ds):
sys.exit(f"Directory {args.source} of source code dataset is not found")
n_total_errors = 0
train_problems, _n_errors = readDsProblems(args.train_problems)
n_total_errors += _n_errors
val_problems, _n_errors = readDsProblems(args.val_problems)
n_total_errors += _n_errors
test_problems, _n_errors = readDsProblems(args.test_problems)
n_total_errors += _n_errors
n_trains, n_train_similars, n_train_dissimilars, _n_errors = \
readDsSamples(args.train_samples, train_problems)
n_total_errors += _n_errors
n_vals, n_val_similars, n_val_dissimilars, _n_errors = \
readDsSamples(args.val_samples, val_problems)
n_total_errors += _n_errors
n_tests, n_test_similars, n_test_dissimilars, _n_errors = \
readDsSamples(args.test_samples, test_problems)
n_total_errors += _n_errors
n_total_errors += checkNoCommonProblems(train_problems, val_problems,
args.train_problems,
args.val_problems)
n_total_errors += checkNoCommonProblems(train_problems, test_problems,
args.train_problems,
args.test_problems)
n_total_errors += checkNoCommonProblems(test_problems, val_problems,
args.test_problems,
args.val_problems)
n_total_errors += checkDsVsDir(train_problems, args.ds,
args.train_samples, args.sol_ext)
n_total_errors += checkDsVsDir(val_problems, args.ds,
args.val_samples, args.sol_ext)
n_total_errors += checkDsVsDir(test_problems, args.ds,
args.test_samples, args.sol_ext)
print(f"Training dataset has {n_trains} samples: " +
f"{n_train_similars} similar and {n_train_dissimilars} " +
f"dissimilar ones built on {len(train_problems)} problems")
print(f"Validation dataset has {n_vals} samples: " +
f"{n_val_similars} similar and {n_val_dissimilars} " +
f"dissimilar ones built on {len(val_problems)} problems")
print(f"Test dataset has {n_trains} samples: " +
f"{n_test_similars} similar and {n_test_dissimilars} " +
f"dissimilar ones built on {len(test_problems)} problems")
n_source_problems, n_source_solutions, _n_errors = checkDirVsDatasets(
args.ds, train_problems, val_problems, test_problems)
print(f"Source dataset directory has {n_source_problems} problems " +
f"with {n_source_solutions} total solutions")
if n_total_errors:
print(f"There were found {n_total_errors} errors in datasets")
else:
print("No errors were found in test and training datasets")
#------------- End of function main -----------------------------
################################################################################
# Command line arguments are described below
################################################################################
if __name__ == '__main__':
print("\nVERIFYING CONSISTENCY OF TESTING, VALIDATION AND TRAINING SIMILARITY DATASETS")
#Command-line arguments
parser = argparse.ArgumentParser(
description = "Verication of testing, validation and training datsets " +
"of similarity analysis")
parser.add_argument("ds", type=str,
help="Directory with source dataset")
parser.add_argument("--test_samples", type=str,
help="file with samples of test dataset")
parser.add_argument("--val_samples", type=str, required=True,
help="file with samples of validation dataset")
parser.add_argument("--train_samples", type=str, required=True,
help="file with samples of training datset")
parser.add_argument("--test_problems", type=str, required=True,
help="file with problems of test dataset")
parser.add_argument("--val_problems", type=str, required=True,
help="file with problems of validation dataset")
parser.add_argument("--train_problems", type=str, required=True,
help="file with problems of training datset")
parser.add_argument("--sol_ext", type=str, default="cpp",
help="extentions of files with solutions")
args = parser.parse_args()
print("Parameter settings used:")
for k,v in sorted(vars(args).items()):
print("{}: {}".format(k,v))
main(args)
|