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Module for loading samples of tokenized source codes
for constructing dataset for code classifiers and
similarity checkers
The module has an absract base class for more specialized
constructors of datasets
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
Number of classes (lables) is defined automatically
"""
import sys
import os
import math
from abc import ABC, abstractmethod
import numpy as np
import random
import json
from DsUtilities import *
class WrongToken(Exception):
"""
Exception on incorrect token
"""
def __init__(self):
pass
class SeqOfTokensLoader(ABC):
"""
Abstract base class of classes for making datasets
to train bag and sequence of tokens classifiers
Provides basic functions for child classes
Child classes should reimplement method makeSample
transforming sequence of tokens into sample to classify
"""
def __init__(self, dir_name, min_n_solutions = 1,
problem_list = None, max_n_problems = None,
short_code_th = 4, long_code_th = None,
report_dir = "./dataset_statistics"):
"""
Initialize SeqOfTokensLoader with files to process
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 -- number of problems to select from the dataset
The problems are selected from
randomly shuffled list of all loaded problems
If max_n_problems is None all the specified
problems are selected
- 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
- report_dir -- directory for writing reports on
loaded dataset
"""
super(SeqOfTokensLoader, self).__init__()
self.short_code_th = short_code_th
if long_code_th:
self.long_code_th = long_code_th
else:
self.long_code_th = sys.maxsize
self.dir_name = dir_name
self._min_n_solutions = min_n_solutions
self._max_n_problems = max_n_problems
self.report_dir = report_dir
if not os.path.exists(self.report_dir):
os.makedirs(self.report_dir)
self.token_set, self.n_token_types = \
getTokensInfo(self.dir_name)
print(f"Loading dataset tokenized with {self.token_set} " +
f"token set having {self.n_token_types} tokens")
#Compute list of problems to load
if problem_list:
self.problem_list = []
_all_problems = getProblemSet(self.dir_name, 1, None)
_all_problems = frozenset(tuple(zip(*_all_problems))[0])
for _p in problem_list:
if _p in _all_problems:
self.problem_list.append(_p)
else:
print(f"there is no problem {_p} is dataset {self.dir_name}")
else:
self.problem_list = getProblemSet(self.dir_name,
self._min_n_solutions, None)
self.problem_list, _ = zip(*self.problem_list)
self.problem_list = list(self.problem_list)
#Shuffle problems randomly
DataRand.randUnordered(self.problem_list, "ALL_PROBLEM_SEED")
if self._max_n_problems:
if self._max_n_problems > len(self.problem_list):
sys.exit(f"Cannot select required {self._max_n_problems} " +
f"because only {len(self.problem_list)} were loaded")
#Select random subset of of problem and guarantee reproducebility
self.problem_list = self.problem_list[: self._max_n_problems]
#Computed maximum lengh of the code
self.code_max_length = 0
#Problem names of constructed dataset in order of their labels
self.problems = []
#Dictionary of problems:
#key is problem name,
#value is a pair <label, number of solutions)
self.problem_dict = {}
#Flag of errors in data samples
self.bad_data = False
#Dictionaries of problems with too long and too short solutions
#Key: problem name
#Value: list of pairs, where each pair is
# <solution name, its length>
self._long_solutions = {}
self._short_solutions = {}
def _registerSolution(self, reg_dict, name, data):
"""
Register problem solution
Parameters:
- reg_dict -- dictionary where to register
Key: problem name. Value: data
- name -- problem name
- data -- solution data
"""
if name in reg_dict:
reg_dict[name].append(data)
else:
reg_dict[name] = [data]
def _writeSolutionsLengths(self, reg_dict, fout):
"""
Print registered problem solutions and their lengths
Parameters:
- reg_dict -- Dictionary with registered problems
Key: problem name
Value: list of pairs, where each pair is
<solution name, its length>
- fout -- file to write
"""
for _i, _item in enumerate(reg_dict.items()):
_p, _data = _item
_line = "{:3d} {:16s}".format(_i + 1, _p)
_sep = ":"
for _s, _l in _data:
_line += f"{_sep} {_s}/{_l}"
_sep = ","
if len(_line) > 80:
fout.write(_line + '\n')
_line = " " * 5
_sep = ""
if len(_line) > 5: fout.write(_line + '\n')
def loadAllSamples(self):
"""
Loads tokenized samples of all solutions of programming problems.
Transforms each sample into bag or sequence of tokens
Actual transformation is defined with child class
Makes dataset of them to be used for machine learning
Prints sizes of shortest solutions of each problem.
Returns the following items:
- list of loaded samples, made with makeSample function
Currently they are either:
* bags of tokens or
* sequences of tokens of problem solutions
- list of indices idicating, where sub-arrays of solutions
of each problem start and end in the above list
- list of names of samples i.e. names of problem solutions
"""
#list of samples (either bags or sequences of tokens)
_samples = []
#Names of samples i.e. names of problem solutions
_sample_names = []
#List of indices where solutions of problem #i starts/ends
_problem_solutions = []
print(f"\nDataset has solutions for the following {len(self.problem_list)} problems:\n")
print("Problem Label N Aver Min Max Shortest Longest")
print("file samples size size size code code")
_i = 0 #Label counter
for _problem in self.problem_list:
_problem_solutions.append(len(_samples))
try:
_n_samples, _n_all_tokens, _min_n_tokens, \
_max_n_tokens, _short_solution, _long_solution = \
self.loadSolutions(_problem, _samples, _sample_names)
except OSError:
print(f"Failed to read file of problem {_problem} in directory {self.dir_name}")
print("Probably the file does not exist")
self.bad_data = True
continue
if not _n_samples:
print(f"Problem {_problem} has 0 samples loaded")
self.bad_data = True
continue
self.code_max_length = max(self.code_max_length, _max_n_tokens)
self.problems.append(_problem)
self.problem_dict[_problem] = (_i, _n_samples)
print("{:10s} {:2d} {:5d} {:6.1f} {:6d} {:6d} {:16s} {:16s}".
format(_problem, _i, _n_samples,
_n_all_tokens / _n_samples,
_min_n_tokens, _max_n_tokens,
_short_solution, _long_solution))
_i += 1
_problem_solutions.append(len(_samples))
if self.bad_data == True:
print("Warning: There are errors in data samples")
print("-----------------------------------------------")
if len(_samples) < 2 *_i or _i < 2:
sys.exit(f"Error: Loaded only {len(_samples)} " +
f"solutions of {len(self.problems)} problems")
print(f"Successfully loaded {len(_samples)} code solutions " +
f"for {_i} problems")
print(f"Longest code has {self.code_max_length} tokens\n")
self.reportWrongLengthCode()
self.n_labels = len(self.problems)
return _samples, _problem_solutions, _sample_names
def reportWrongLengthCode(self):
"""
Report too long and too short source code files
"""
if self._short_solutions:
print(f"There are {len(self._short_solutions)} " +
"too short source code files")
with open(f"{self.report_dir}/TooShortCode.lst", 'w') as _f:
self._writeSolutionsLengths(self._short_solutions, _f)
if self._long_solutions:
print(f"There are {len(self._long_solutions)} " +
"too long source code files")
with open(f"{self.report_dir}/TooLongCode.lst", 'w') as _f:
self._writeSolutionsLengths(self._long_solutions, _f)
def getPartitionedSampes(self):
"""
Load tokenized samples of all solutions of programming problems,
transform each sample into bag or sequence of tokens,
Partition whole dataset into sublists of samples corresponding to
programming problems
Returns:
- list of sub-lists of samples
Each sub-list contains samples of one problem
All sublists are part of the whole list of samples
- list of sub-lists of sample names i.e. names of
problem solutions
Each sub-list contains sample names of one problem
All sublists are part of the whole list of sample names
"""
_samples, _problem_sol_indices, _sample_names = \
self.loadAllSamples()
_named_samples = list(zip(_sample_names, _samples))
_named_probl_sols = \
[_named_samples[_problem_sol_indices[_i - 1] :
_problem_sol_indices[_i]]
for _i in range(1, len(_problem_sol_indices))]
#Randomize order of solutions of each problem individually
DataRand.setSeed("PROBLEM_SOLUTIONS_SEED")
for _l in _named_probl_sols:
_l.sort(key = lambda _s: _s[0])
random.shuffle(_l)
_sample_names, _samples = zip(*_named_samples)
_sample_names = list(_sample_names)
_samples = list(_samples)
_problems_solutions = \
[_samples[_problem_sol_indices[_i - 1] :
_problem_sol_indices[_i]]
for _i in range(1, len(_problem_sol_indices))]
_solution_names = \
[_sample_names[_problem_sol_indices[_i - 1] :
_problem_sol_indices[_i]]
for _i in range(1, len(_problem_sol_indices))]
return _problems_solutions, _solution_names
def getPartitionedSampesOld(self):
"""
Load tokenized samples of all solutions of programming problems,
transform each sample into bag or sequence of tokens,
Partition whole dataset into sublists of samples corresponding to
programming problems
Returns:
- list of samples
- list of sub-lists of samples
Each sub-list contains samples of one problem
All sublists are part of the whole list of samples
- list of names of samples i.e. names of problem solutions
"""
_samples, _problem_sol_indices, _sample_names = \
self.loadAllSamples()
_named_samples = list(zip(_sample_names, _samples))
_named_probl_sols = \
[_named_samples[_problem_sol_indices[_i - 1] :
_problem_sol_indices[_i]]
for _i in range(1, len(_problem_sol_indices))]
#Randomize order of solutions of each problem individually
DataRand.setSeed("PROBLEM_SOLUTIONS_SEED")
for _l in _named_probl_sols:
_l.sort(key = lambda _s: _s[0])
random.shuffle(_l)
_problems_solutions = \
[_samples[_problem_sol_indices[_i - 1] :
_problem_sol_indices[_i]]
for _i in range(1, len(_problem_sol_indices))]
return _samples, _problems_solutions, _sample_names
def getShuffledLabeledSamples(self):
"""
Load tokenized samples of all solutions of programming problems,
transform each sample into bag or sequence of tokens,
create samples labels corresponding to the programming problems,
make shuffled dataset of them to be used for machine learning
Returns:
- shuffled list of samples
- shulled list of labels
- shuffled list of names of samples,
i.e. names of problem solutions
Same shuffling is produced by using same random seed
"""
_samples, _problem_sol_indices, _sample_names = \
self.loadAllSamples()
#Randomize order of solutions of each problem individually
_named_samples = list(zip(_sample_names, _samples))
_named_probl_sols = \
[_named_samples[_problem_sol_indices[_i - 1] :
_problem_sol_indices[_i]]
for _i in range(1, len(_problem_sol_indices))]
DataRand.setSeed("PROBLEM_SOLUTIONS_SEED")
for _l in _named_probl_sols:
_l.sort(key = lambda _s: _s[0])
random.shuffle(_l)
_sample_names, _samples = zip(*_named_samples)
_sample_names = list(_sample_names)
_samples = list(_samples)
#Create class labels
_labels = []
for _i in range(len(_problem_sol_indices) - 1):
_labels.extend([_i] * (_problem_sol_indices[_i + 1] -
_problem_sol_indices[_i]))
#Randomize all samples, their labels and sample names
DataRand.randPreordered(_samples, "ALL_SOLUTIONS_SEED")
DataRand.randPreordered(_labels, "ALL_SOLUTIONS_SEED")
DataRand.randPreordered(_sample_names, "ALL_SOLUTIONS_SEED")
return _samples, _labels, _sample_names
def getShuffledLabeledSamplesOld(self):
"""
Load tokenized samples of all solutions of programming problems,
transform each sample into bag or sequence of tokens,
create samples labels corresponding to the programming problems,
make shuffled dataset of them to be used for machine learning
Returns:
- shuffled list of samples
- shulled list of labels
- shuffled list of names of samples,
i.e. names of problem solutions
Same shuffling is produced by using same random seed
"""
_samples, _problems_solutions, _sample_names = \
self.getPartitionedSampes()
_labels = [_j for _j, _solutions
in enumerate(_problems_solutions)
for _ in range(len(_solutions))]
#Randomize all samples, their labels and sample names
DataRand.randPreordered(_samples, "ALL_SOLUTIONS_SEED")
DataRand.randPreordered(_labels, "ALL_SOLUTIONS_SEED")
DataRand.randPreordered(_sample_names, "ALL_SOLUTIONS_SEED")
return _samples, _labels, _sample_names
def loadSolutions(self, problem, samples, sample_names):
"""
Load tokenized code samples of one class
i.e. source code files solving one programming problem
The loaded samples are appended to given sample list
The names of samples i.e. names of problem solutions
are appended to given list of sample names
Parameters:
- problem -- name of problem to load tokenized solutions
- samples -- list to add samples
- sample_names -- names of samples,
i.e. names of problem solutions
Returns tuple of the following items:
- number of samples (solutions) loaded
- total number of tokens in all code samples
- min number of tokens of code samples
- max number of tokens of code samples
- name of shortest solution
- name of longest solution
"""
_full_fn = self.dir_name + '/' + problem + ".tkn"
_short_solution = None
_long_solution = None
with open(_full_fn) as _f:
_n_solutions = 0
_n_all_tokens = 0
_min_n_tokens = sys.maxsize
_max_n_tokens = 0
for _line in _f:
try:
_tokens, _solution = self._seqOfTokens(_line)
_n_tokens = len(_tokens)
except WrongToken:
print("Failed parsing the tokenization of solution " +
f"{_solution} of problem {problem}")
print("Tokenized code is: \n", repr(_line))
self.bad_data = True
continue
if _n_tokens < self.short_code_th:
self._registerSolution(self._short_solutions,
problem, (_solution, _n_tokens))
continue
if _n_tokens > self.long_code_th:
self._registerSolution(self._long_solutions,
problem, (_solution, _n_tokens))
continue
_n_solutions += 1
_n_all_tokens += _n_tokens
_min_n_tokens = min(_min_n_tokens, _n_tokens)
if(_min_n_tokens == _n_tokens):
_short_solution = _solution
_max_n_tokens = max(_max_n_tokens, _n_tokens)
if(_max_n_tokens == _n_tokens):
_long_solution = _solution
samples.append(self.makeSample(_tokens))
sample_names.append(_solution)
return _n_solutions, _n_all_tokens, _min_n_tokens, \
_max_n_tokens, _short_solution, _long_solution
#!!!This is used in clustering experiments.
#!!!However it should be updated to reflect changes in API of self.loadSolutions
def loadProblem(self, fn):
"""
Load tokenized code samples of one class
i.e. source code files solving one programming problem
Parameters:
- fn -- name of problem
Returns tuple of the following items:
- list of samples (bags or sequence of tokens) of
source code solutions
- number of samples (solutions) loaded
- total number of tokens in all code samples
- min number of tokens of code samples
- max number of tokens of code samples
"""
_solutions = []
_n_solutions, _n_all_tokens, _min_n_tokens, \
_max_n_tokens, _short_solution = \
self.loadSolutions(fn + ".tkn", _solutions)
return _solutions, _n_solutions, _n_all_tokens, \
_min_n_tokens, _max_n_tokens
def _seqOfTokens(self, line):
"""
Load sequence of tokens for solution source code
Transforms string representation into int
Parameters:
- line -- description of solution code of programming task
as string in the following form;
<name of solution>:<sequence of tokens separtated with ','>
Returns:
- sequence of source code tokens
- name of solution
"""
_solution, _tokens = line.split(":")
_tokens = _tokens.split(',')
try:
_tokens = list(map(int, _tokens))
except ValueError:
print(f"Not all tokens are valid numbers")
self.bad_data = True
raise WrongToken()
if any(_tok >= self.n_token_types for _tok in _tokens):
print(
f"Token of source code {_solution} exceeds " +
f"maximum {self.n_token_types}")
raise WrongToken(line)
self.bad_data = True
return _tokens, _solution
@abstractmethod
def makeSample(self, tokens):
"""
Abstract method for computing data sample
from list of tokens
Parameters:
- tokens list of tokens as int values
Returns:
- data sample as numpy object
"""
raise NotImplementedError()
def problemFromLabel(self, label):
"""
Get problem name corresponding to a given label
"""
return self.problems[label]
def labelFromProblem(self, problem):
"""
Get label number for a given problem
"""
return self.problem_dict[problem][0]
def makeBagOfTokens(self, tokens):
"""
Compute bag of tokens from list of tokens
where <bag of tokens> is a vector of tokens frequences
Parameters:
- tokens list of tokens as int values
Returns:
- bag of tokens as numpy object
"""
_code_signature = np.zeros(self.n_token_types,
dtype = np.float32)
for _tok in tokens:
_code_signature[_tok] += 1
_v = math.sqrt(np.dot(_code_signature, _code_signature))
np.divide(_code_signature, _v, out = _code_signature)
return _code_signature
#---------------- End of class SeqOfTokensLoader -----------------------------
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