def __get_code(solutions: pd.DataFrame, index: int, task_index: int,
canon_tree: ast.AST, anon_tree: ast.AST) -> Code:
tests_results = __get_column_value(
solutions, index, consts.CODE_TRACKER_COLUMN.TESTS_RESULTS)
rate = get_rate(tests_results, task_index)
log.info(f'Task index is :{task_index}, rate is: {rate}')
return Code(anon_tree, canon_tree, rate)
def get_two_vertices(sg: SolutionGraph) -> List[Vertex]:
source_0 = 'print(\'Hi\')'
source_1 = 'x = 6\nif x > 5:\n x = 5\nprint(x)'
sources = [source_0, source_1]
rates = [TEST_RESULT.CORRECT_CODE.value] * len(sources)
return [
Vertex(sg, code=Code.from_source(s, rates[i]))
for i, s in enumerate(sources)
]
def create_user_trees(
self,
source_code: str,
profile: Profile,
rate: Optional[float] = None) -> Tuple[AnonTree, ast.AST]:
code_info = CodeInfo(User(profile))
code = Code.from_source(source_code, rate=rate, task=self._graph.task)
anon_tree = AnonTree(code.anon_tree, code.rate,
self.get_file_path(f'{TREE_TYPE.ANON.value}'),
code_info)
anon_tree.find_medians()
return anon_tree, code.canon_tree
def __remove_loops(code_info_chain: List[Tuple[Code, CodeInfo]],
user: User) -> List[Tuple[Code, CodeInfo]]:
# Add empty code
# We want to remove loops, but we can have the situation: Tree1 -> Tree2 -> Tree3 -> Empty Tree -> Tree5
# In the case we will get after loops removing: Tree1 -> Tree2 -> Tree3 -> Empty Tree -> Tree5,
# but it is incorrect, because we expect Empty Tree -> Tree5
# If we add empty code, we will have: Empty Tree -> Tree1 -> Tree2 -> Tree3 -> Empty Tree -> Tree5
# and we will get after loops removing: Empty Tree -> Tree5
code_info_chain = [(Code.from_source(
'', TEST_RESULT.CORRECT_CODE.value), CodeInfo(user))] + code_info_chain
current_tree_index = 0
while current_tree_index < len(code_info_chain):
current_anon_tree = code_info_chain[current_tree_index][0].anon_tree
same_tree_indexes = []
for next_tree_index in range(current_tree_index + 1,
len(code_info_chain)):
next_anon_tree = code_info_chain[next_tree_index][0].anon_tree
if are_asts_equal(current_anon_tree, next_anon_tree):
same_tree_indexes.append(next_tree_index)
if len(same_tree_indexes) > 0:
del code_info_chain[current_tree_index:max(same_tree_indexes)]
current_tree_index += 1
return code_info_chain
def get_code_chain_from_sources(start_index: int = 0, end_index: int = len(sources)) -> List[Tuple[Code, CodeInfo]]:
IdCounter.reset_all()
return [(Code.from_source(source, TEST_RESULT.CORRECT_CODE.value), code_info)
for source in sources[start_index: end_index]]
def get_code_info_chain(sources: List[str]) -> List[Tuple[Code, CodeInfo]]:
user = User()
return [(Code.from_source(s,
TEST_RESULT.CORRECT_CODE.value), CodeInfo(user))
for s in sources]
def __get_code_by_source(source: str, is_goal: bool = False) -> Code:
anon_tree, canon_tree = get_trees(source, {TREE_TYPE.ANON, TREE_TYPE.CANON})
rate = 0 if not is_goal else TEST_RESULT.FULL_SOLUTION.value
return Code(anon_tree=anon_tree, canon_tree=canon_tree, rate=rate)
def create_code_from_source(source: str,
rate: float = TEST_RESULT.CORRECT_CODE.value
) -> Code:
anon_tree, canon_tree = get_trees(source,
{TREE_TYPE.ANON, TREE_TYPE.CANON})
return Code(anon_tree, canon_tree, rate)