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Module for making dataset for similarity detection of
of source codes by sequence or bag of tokens technique
Has two operation modes:
- Validiation on solutions of the same problems
- Valdiation on solutions of the problems different from
the ones seen at training
However, test datset is always constructed from the problems
that were used neither for training nor validation.
Loads tokenized samples of source codes
corresponding to solution of programming problems.
Each file there contains all samples of
one class of samples - tokenised source code files
The module has an absract base class for more specialized
constructors of datasets
"""
import sys
import os
from abc import ABC, abstractmethod
import numpy as np
import math
import random
import csv
from DataLoader import SeqOfTokensLoader
from DsUtilities import DataRand
class SimilarityDSMaker(SeqOfTokensLoader):
"""
Abstract class for making dataset for
source code similarity analysis by bag or sequence of Tokens technique
Loads files of tokenized source code files from the given directory and
Prepares datasets for Bag or Sequence Tokens code similarity detector
Test datset is always constructed for problems that are not used
either for training or validation
"""
def __init__(self, dir_name, min_n_solutions = 1,
problem_list = None, max_n_problems = None,
short_code_th = 4, long_code_th = None,
test = 0, labels01 = True):
"""
Initialize Dataset maker
Parameters:
- dir_name -- directory with files of tokenized source code files
- min_n_solutions -- min number of solutions of problem
required to load that problem
- problem_list -- list of problem to process
If it is not defined the problem are
selected from all tokenized problems
using specified criteria
- max_n_problems -- maximum number of problems to load
If it is None
all the specified problems are loaded
- short_code_th -- minimum length of code to load
- long_code_th -- max length of solution code to load
if it is None sys.maxsize is used
- test -- amount of problems reserved for test dataset
if test = 0 no problems test dataset is not created
if test < 1 it defines fraction of all problems
if test > 1 it defines number of all problems
- labels01 -- label types flag:
True: 0/1 labels
Flase: -1/+1 labels
"""
super(SimilarityDSMaker, self).__init__(
dir_name, min_n_solutions = min_n_solutions,
problem_list = problem_list, max_n_problems = max_n_problems,
short_code_th = short_code_th, long_code_th = long_code_th)
self.labels01 = labels01
#Size of the array used for selection of problem solutions
#with probabilities proportional to the number
#of each problem solutions
self.problems_solutions, self.solution_names = \
self.getPartitionedSampes()
self.n_problems = len(self.problems_solutions)
self.n_test_problems = int(float(self.n_problems) * test) \
if test < 1 else int(test)
if test and self.n_test_problems == 1:
sys.exit(f"Too few {test} problems for test dataset")
#Reserve problems for test dataset
self.n_tran_ds_problems = self.n_problems - self.n_test_problems
if self.n_test_problems > 0:
self.test_problems = self.problems[-self.n_test_problems :]
self.writeProblemList(self.test_problems,
"test_problems.txt")
self.train_ds_problems = \
self.problems[: -self.n_test_problems]
self.test_problem_solutions = \
self.problems_solutions[-self.n_test_problems :]
self.train_ds_probl_solutions = \
self.problems_solutions[: -self.n_test_problems]
print(f"Dataset of {self.n_problems} problems is split "+
f"into {self.n_test_problems} and " +
f"{self.n_tran_ds_problems} for test and training with validation")
else:
self.test_problems = None
self.train_ds_problems = self.problems
self.test_problem_solutions = None
self.train_ds_probl_solutions = self.problems_solutions
print("No test dataset was defined")
self._SEL_SIZE = 128 * 1024
self.writeProblemList(self.train_ds_problems,
"train_valid_problems.txt")
self.writeProblemList(self.problems, "all_problems.txt")
def trainValidDsSameProblems(self, val_train_split,
val_size, train_size,
similar_part):
"""
Make datasets for training source code similarity analyser
Both training and validation parts of dataset are made.
The validation part is made from solutions of the same
problems that were used for making the training part
of the dataset.
However, the test dataset is made from solutions of
the problems different from ones used here for training
and validation
The function provides the following:
- No same code samples (problem solutions) are used
for creation both validation and training datasets
- Each problem is represented in both created datasets
with the same fraction of its code samples
- Number of samples of similar solutions of each problem
is proportional to the number of all pairs of solutions
of that problem.
- All pairs of similar solutions are constructed from
different samples, i.e. no pair has same solution.
No self similar solution is constructed.
!!!Possibly this condition should be parameterized.
- Pairs of dissimilar solutions are constructed such as
each problem is selected with the probability
proportional to the number of its solutions.
Similar samples are constructed as pairs of solutions
of the same problem
Dissimilar samples are constructed as pairs of solution
of different problems
Samples of each problem solutions are splitted individually.
The obtained subsets of are used for constructing training
and validation datasets.
Parameters:
- val_train_split -- Fraction of training samples that is used
for constructing validation dataset.
It speciified as float
Samples of each problem solutions are splitted
individually according to this parameters
- val_size -- Size of validation dataset to create
- train_size -- Size of training dataset to create
- similar_part -- Fraction of samples of the created dataset
representing similar source code samples
Returns:
- validation and training datasets:
Each of them has the following items:
- constructed dataset
* either as single numpy array
* or list of 2 them
* or TF dataset
- labels as numpy array
- list of similarity samples in form of 4-tuples
<problem1, solution1, problem2, solution2>
"""
print("Constructing training and validation datasets " +
"from different solutions of the same problems")
_val_problem_solutions = \
[_solutions[: int(float(len(_solutions)) *
val_train_split)]
for _solutions in self.train_ds_probl_solutions]
_train_problem_solutions = \
[_solutions[int(float(len(_solutions)) *
val_train_split) :]
for _solutions in self.train_ds_probl_solutions]
DataRand.setSeed("SIMIL_TRAIN_DS_SEED")
self.train_ds = self._makeDs(0, _train_problem_solutions,
train_size, similar_part)
DataRand.setSeed("SIMIL_VALID_DS_SEED")
self.val_ds = self._makeDs(0, _val_problem_solutions,
val_size, similar_part)
self.reportDatasetStatistics(0, self.n_tran_ds_problems,
self.val_ds[2],
0, self.n_tran_ds_problems,
self.train_ds[2])
return self.val_ds, self.train_ds
def trainValidDsDifferentProblems(self, val_train_split,
val_size, train_size,
similar_part):
"""
Make datasets for training source code similarity analyser
Both training and validation parts of dataset are made.
The validation part is made from solutions of different
problems that were used for making the training part
of the dataset.
Validation uses the problems of the beginning of the list
Training uses problems that are between validation and test ones
The function provides the following:
- No same code samples (problems) are used
for creation both validation and training datasets
- Validation and training datasets are created from
solutions of different problems
- Number of samples of similar solutions of each problem
is proportional to the number of all pairs of solutions
of that problem.
- All pairs of similar solutions are constructed from
different samples, i.e. no pair has same solution.
No self similar solution is constructed.
!!!Possibly this condition should be parameterized.
- Pairs of dissimilar solutions are constructed such as
each problem is selected with the probability
proportional to the number of its solutions.
Similar samples are constructed as pairs of solutions
of the same problem
Dissimilar samples are constructed as pairs of solution
of different problems
All problem solutions are splitted for ones used
for training and ones used for validation. The solutions
of the resulted subsets of problems are used for
constructing training and validation datasets.
Parameters:
- val_train_split -- Fraction of original probles used
for constructing validation dataset.
It speciified as float
Set of all problem is splitted according
to this parameters
- val_size -- Size of validation dataset to create
- train_size -- Size of training dataset to create
- similar_part -- Fraction of samples of the created dataset
representing similar source code samples
Returns:
- validation and training datasets:
Each of them has the following items:
- constructed dataset
* either as single numpy array
* or list of 2 them
* or TF dataset
- labels as numpy array
- list of similarity samples in form of 4-tuples
<problem1, solution1, problem2, solution2>
"""
print("Constructing training and validation datasets " +
"from solutions of different problems")
_n_val_probls = int(float(self.n_tran_ds_problems) *
val_train_split)
print(f"Validation and training use {_n_val_probls} " +
f"and {self.n_tran_ds_problems - _n_val_probls} problems respectively")
if _n_val_probls < 2 or self.n_tran_ds_problems - _n_val_probls < 2:
sys.exit(f"Too few problems for training " +
f"{self.n_tran_ds_problems - _n_val_probls} " +
f"or validation {_n_val_probls}")
_val_problem_solutions = \
self.train_ds_probl_solutions[: _n_val_probls]
_train_problem_solutions = \
self.train_ds_probl_solutions[_n_val_probls :]
self.writeProblemList(self.train_ds_problems[: _n_val_probls],
"validation_problems.txt")
self.writeProblemList(self.train_ds_problems[_n_val_probls :],
"training_problems.txt")
DataRand.setSeed("SIMIL_VALID_DS_SEED")
self.val_ds = self._makeDs(0, _val_problem_solutions,
val_size, similar_part)
DataRand.setSeed("SIMIL_TRAIN_DS_SEED")
self.train_ds = self._makeDs(_n_val_probls,
_train_problem_solutions, train_size, similar_part)
self.reportDatasetStatistics(0, _n_val_probls, self.val_ds[2],
_n_val_probls, self.n_tran_ds_problems - _n_val_probls,
self.train_ds[2])
self.writeSamplesCsv(self.val_ds[2], "val_samples.csv")
self.writeSamplesCsv(self.train_ds[2], "train_samples.csv")
return self.val_ds, self.train_ds
def testDataset(self, size, similar_part):
"""
Make test dataset
Test datset is always constructed from problems different
from ones used for training and validation
It uses the last problems in the list
More details are in comments to self.trainValidDsDifferentProblems
Parameters:
- size -- Size of dataset to create
- similar_part -- Fraction of samples of the created dataset
representing similar source code samples
Returns:
- Test datasets having the following items:
- constructed dataset
* either as single numpy array
* or list of 2 them
* or TF dataset
- labels as numpy array
- list of similarity samples in form of 4-tuples
<problem1, solution1, problem2, solution2>
"""
print("Constructing test dataset")
if not self.test_problem_solutions:
sys.exit("Test datset cannot be created as it was not defined.")
DataRand.setSeed("SIMIL_TEST_DS_SEED")
_start_problem = self.n_problems - self.n_test_problems
self.test_ds = self._makeDs(_start_problem,
self.test_problem_solutions, size, similar_part)
with open(f"{self.report_dir}/TestDatasetStatistics.lst", 'w') as _f:
_f.write("PROBLEM DISTRIBUTION IN TEST DATASET\n")
self.writeProblemDistribution(_start_problem,
self.n_test_problems, self.test_ds[2], _f)
self.writeSamplesCsv(self.test_ds[2], "test_samples.csv")
return self.test_ds
def _makeDs(self, start_problem, problems_solutions,
size, similar_part):
"""
Make a dataset for source code similarity analyser
Parameters:
- start_problem -- index of fist problem to use for samples
- problems_solutions -- source code problems solutions used
for constructing the dataset
- size -- Size of dataset to create
- similar_part -- Fraction of samples of the created dataset
representing similar source code samples
Returns:
- constructed dataset
* either as single numpy array
* or list of 2 them
* or TF dataset
- labels as numpy array
- list of similarity samples in form of 4-tuples
<problem1, solution1, problem2, solution2>
"""
_annotations = [] #Similarity samples to construct
_n_similar = int(float(size) * similar_part)
#Add samples with similar solutions
self._addSimilarSamples(_annotations, start_problem,
problems_solutions,_n_similar)
_n_similar_solutions = len(_annotations)
self._addDisSimilarSamples(_annotations, start_problem,
problems_solutions,
size - _n_similar_solutions)
_n_samples = len(_annotations)
_n_dissimilar_solutions = _n_samples - _n_similar_solutions
random.shuffle(_annotations)
_labels = self._makeLabels(_annotations)
_samples = self.makeSimDataset(_annotations, _labels)
print(f"Similarity dataset of {_n_samples} samples is ready")
print(f"Dataset has {_n_similar_solutions} samples with similar solutions" +
f" and {_n_dissimilar_solutions} samples with dissimilar solutions")
return (_samples, _labels, _annotations)
def _addSimilarSamples(self, ds, start_problem, problems_solutions,
size):
"""
Add samples with similar solutions of the problems
to the given dataset
Parameters:
- ds -- dataset for adding samples to
- start_problem -- index of fist problem to use for samples
- problems_solutions -- source code solutions for constructing
samples of similar solutions
- size -- Size of dataset to create
"""
#Number of all pairs of similar solutions that can be constructed
#from the given data
_n_pairs = \
float(sum(map(lambda _array: len(_array) * (len(_array) -1),
problems_solutions)))
for _p, _solutions in enumerate(problems_solutions):
if len(_solutions) <= 1:
continue #Cannot make any pair from 1 or 0 items
#Number of pairs of similar solutions that can be constructed
#from solutions of this problem
_n_samples = \
int((float(size) *
float(len(_solutions) * (len(_solutions) -1)) /
_n_pairs))
for _i in range(_n_samples):
while True:
_s1_idx = random.randint(0, len(_solutions) - 1)
_s2_idx = random.randint(0, len(_solutions) - 1)
if _s1_idx != _s2_idx: break
_sample_annot = (start_problem + _p, _s1_idx,
start_problem + _p, _s2_idx)
ds.append(_sample_annot)
def _addDisSimilarSamples(self, ds, start_problem,
problems_solutions, size):
"""
Expand the given dataset with pairs of dissimilar solutions
Parameters:
- ds -- dataset for adding samples to
- start_problem -- index of fist problem to use for samples
- problems_solutions -- source code solutions for constructing
samples of similar solutions
- size -- Size of dataset to create
"""
#Total number of problem solution
_n_solutions = float(sum(map(len, problems_solutions)))
#Probabilities of random selection of each problem solution
_probl_probabilities = [float(len(_s)) / _n_solutions
for _s in problems_solutions]
#Utility array for selection of problem solutions with
#probabilities proportional to the number of solutions
#of each problem
_selection = np.zeros(self._SEL_SIZE, dtype = int)
_curr_probl_idx = 0
for _i, _p in enumerate(_probl_probabilities):
_next_probl_idx = int(float(_curr_probl_idx) +
_p * float(self._SEL_SIZE))
_selection[_curr_probl_idx :_next_probl_idx] = _i
_curr_probl_idx = _next_probl_idx
_selection[_curr_probl_idx :] = _i
#Constructing pairs of solutions of different problems
for _ in range(size):
#Select pair of different problems
#with probabilities proportional to the number
#of their solutions
while True:
_rand_idx = random.randint(0, self._SEL_SIZE - 1)
_p1_idx = _selection[_rand_idx]
_rand_idx = random.randint(0, self._SEL_SIZE - 1)
_p2_idx = _selection[_rand_idx]
#_p1_idx = random.choice(_selection)
#_p2_idx = random.choice(_selection)
if _p1_idx != _p2_idx: break
_solutions1 = problems_solutions[_p1_idx]
_solutions2 = problems_solutions[_p2_idx]
#Selection of solution of each problem selected
_s1_idx = random.randint(0, len(_solutions1) - 1)
_s2_idx = random.randint(0, len(_solutions2) - 1)
_sample_annot = (start_problem + _p1_idx, _s1_idx,
start_problem + _p2_idx, _s2_idx)
ds.append(_sample_annot)
def _makeLabels(self, annotations):
"""
Make lables from set of similarity samples
Parameters:
- annotations -- list of similarity samples in form of 4-tuples
<problem1, solution1, problem2, solution2>
Returns:
- numpy array of lables
"""
_dissimilar = 0 if self.labels01 else -1
return np.asarray(tuple(map(
lambda _a: 1 if _a[0] == _a[2] else _dissimilar,
annotations)), dtype=np.int32)
def reportDatasetStatistics(self, val_start, n_val_problems, val_ds,
train_start, n_train_problems, train_ds):
"""
Write down report on statistics of generated dataset
Parameters:
- val_start -- index of start problem for validation
- n_val_problems -- number of problems used for validation
- val_ds -- samples of validation dataset
- train_start -- index of start problem for training
- n_train_problems -- number of problems used for training
- train_ds -- samples of training dataset
"""
with open(f"{self.report_dir}/TrainDatasetStatistics.lst", 'w') as _f:
_f.write("PROBLEM DISTRIBUTION IN VALIDATION DATASET\n")
self.writeProblemDistribution(val_start, n_val_problems,
val_ds, _f)
_f.write("\n\n")
_f.write("PROBLEM DISTRIBUTION IN TRAINING DATASET\n")
self.writeProblemDistribution(train_start, n_train_problems,
train_ds, _f)
def writeProblemList(self, problems, fn):
"""
Write problem list to file
to report test, train, validation problems
Parameters:
- problems -- list of problems
- fn -- name of file
"""
with open(f"{self.report_dir}/{fn}", 'w') as _f:
for _p in problems:
_f.write(_p + "\n")
def writeSamplesTxt(self, samples, fn):
"""
Write dataset samples in plain text format
to report test, train, validation datset
Parameters:
- samples -- similarity samples in the form of 4-tuples
<problem1, solution1, problem2, solution2>
- fn -- name file to write down report
"""
with open(f"{self.report_dir}/{fn}", 'w') as _f:
_f.write("{:8s} {:12s} {:8s} {:12s}\n".
format("problem1", "solution1", "problem2", "solution2"))
_f.write("------------------------------------------------\n")
for _sample in samples:
_p1, _sol1, _p2, _sol2 = _sample
_f.write("{:8s} {:12s} {:8s} {:12s}\n".
format(self.problems[_p1],
self.solution_names[_p1][_sol1],
self.problems[_p2],
self.solution_names[_p2][_sol2]))
def writeSamplesCsv(self, samples, fn):
"""
Write dataset samples in csv format
to report test, train, validation datset
Parameters:
- samples -- similarity samples in the form of 4-tuples
<problem1, solution1, problem2, solution2>
- fn -- file to write down csv file
"""
with open(f"{self.report_dir}/{fn}", 'w', newline='') as _f:
_writer = csv.writer(_f, lineterminator=os.linesep)
_writer.writerow(["problem1", "solution1",
"problem2", "solution2"])
for _sample in samples:
_p1, _sol1, _p2, _sol2 = _sample
_writer.writerow([self.problems[_p1],
self.solution_names[_p1][_sol1],
self.problems[_p2],
self.solution_names[_p2][_sol2]])
def writeProblemDistribution(self, start_problem,
n_problems, samples, f):
"""
Compute and write problem distribution in samples
The distribution is computed only for problems used
for constructing samples
Parameters:
- start_problem -- index of fist problem used for samples
- n_problems -- number of problems used for samples
- samples -- similarity samples in the form of 4-tuples
<problem1, solution1, problem2, solution2>
- f -- file to write down reports
"""
_same_problems = [0] * n_problems
_left_problems = [0] * n_problems
_right_problems = [0] * n_problems
_pair_cnt = np.zeros((n_problems, n_problems), dtype=float)
for _p1, _, _p2, _ in samples:
_pair_cnt[_p1 - start_problem, _p2 - start_problem] += 1
if _p1 == _p2:
_same_problems[_p1 - start_problem] += 1
else:
_left_problems[_p1 - start_problem] += 1
_right_problems[_p2 - start_problem] += 1
_max_pair = _pair_cnt.max(axis=1)
_min_pair = _pair_cnt.min(axis=1)
_mean_pair = _pair_cnt.mean(axis=1)
_median_pair = np.median(_pair_cnt, axis=1)
_std_pair = _pair_cnt.std(axis=1)
_min_problem = np.argmin(_pair_cnt, axis=1)
_max_problem = np.argmax(_pair_cnt, axis=1)
_n_fully_omitted = 0
_n_same_omitted = 0
_n_left_omitted = 0
_n_right_omitted = 0
_n_different_omitted = 0
_n_samples = len(samples)
f.write(
"Probl Same Same% Left Left% Right Right% Min N Max N Median Mean N Std N Problem Pare Often\n" +
"Index samples sample samples pairs pairs pairs pairs pairs name problem Problem\n")
f.write(
"---------------------------------------------------------------------------------------------------------\n")
for _i in range(n_problems):
if (_same_problems[_i] + _left_problems[_i] +
_right_problems[_i]) == 0:
_n_fully_omitted += 1
continue
f.write("{:4d} {:6d} {:5.2f} {:6d} {:5.2f} {:6d} {:5.2f} {:6f} {:6f} {:6f} {:8.1f} {:6.2f} {:7s} {:7s} {:7s} \n".
format(_i + start_problem, _same_problems[_i],
(100.0 * float(_same_problems[_i])) / float(_n_samples),
_left_problems[_i],
100.0 * float(_left_problems[_i]) / float(_n_samples),
_right_problems[_i],
100.0 * float(_right_problems[_i]) / float(_n_samples),
_min_pair[_i], _max_pair[_i], _median_pair[_i],
_mean_pair[_i], _std_pair[_i],
self.problems[_i + start_problem],
self.problems[_min_problem[_i] + start_problem] \
if _min_pair[_i] > 0 else " - ",
self.problems[_max_problem[_i] + start_problem] \
if _max_pair[_i] > 0 else " - "))
if _same_problems[_i] == 0: _n_same_omitted += 1
if _left_problems[_i] == 0: _n_left_omitted += 1
if _right_problems[_i] == 0: _n_right_omitted += 1
if _left_problems[_i] + _right_problems[_i] == 0:
_n_different_omitted += 1
f.write("-------------------------------------------\n")
f.write(f"{_n_fully_omitted} problems are not present in ANY samples\n")
f.write(f"Among the other {n_problems - _n_fully_omitted} problems:")
f.write(f" - {_n_same_omitted} problems are not present in SAME samples\n")
f.write(f" - {_n_different_omitted} problems are not present in DIFFERENT samples\n")
f.write(f" - {_n_left_omitted} problems are not present in left side of DIFFERENT samples\n")
f.write(f" - {_n_right_omitted} problems are not present in right side of DIFFERENT samples\n")
@abstractmethod
def makeSimDataset(samples, labels):
"""
Make similarity dataset in the form of numpy arrays of
tensorflow datasets
Parameters:
- samples -- list of dataset samples
Each sample is represented as 4-tuple
<problem 1, solution 1, problem 2, solution 2>,
where problems and solutions are their indices
- labels -- labels as numpy array
used only in implementations constructing
TF datasets
Returns:
- constructed dataset
* either as single numpy array
* or tuple of them
* or TF dataset
"""
raise NotImplementedError()
#---------------- End of class SimilarityDSMaker ----------------------------
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