def __init__(self, variable_visitor, file_to_trace, ignores=[], **kwargs):
self.variable_visitor = variable_visitor
self.file_to_trace = file_to_trace
self.ignores = ignores
self.prev_line_no_map = {}
self.lines_seen = set()
O.__init__(self, **kwargs)
def __init__(self, graph, permitted="conferences", ignores=set()): O.__init__(self, graph=graph) self.vectorizer = None self.doc_2_vec = None self.documents = None self.permitted = permitted self.ignores = ignores
def __init__(self):
O.__init__(self)
self.paper_nodes = None # Paper Nodes
self.author_nodes = None # Author Nodes
self.author_edges = None # Directed Edges between author and paper
self.cite_edges = None # Directed Edges between reference paper and base paper
self.collaborator_edges = None # Weighted Undirected edges between authors
def __init__(self): O.__init__(self) self.paper_nodes = None # Paper Nodes self.author_nodes = None # Author Nodes self.author_edges = None # Directed Edges between author and paper self.cite_edges = None # Directed Edges between reference paper and base paper self.collaborator_edges = None # Weighted Undirected edges between authors
def __init__(self, predicted=None, actual=None):
O.__init__(self)
if predicted is not None and actual is not None:
self.accuracy = sk_metrics.accuracy_score(actual, predicted)
self.precision = sk_metrics.precision_score(actual, predicted, average='weighted')
self.recall = sk_metrics.recall_score(actual, predicted, average='weighted')
self.f_score = sk_metrics.f1_score(actual, predicted, average='weighted')
def __init__(self, **kwargs): O.__init__(self, **kwargs) self.name = "dict" self.is_valid = True self.is_dict = True self.key_type = None self.val_type = None
def __init__(self, **kwargs):
self.file_source = None
self.method_name = None
self.start_pos = None
self.end_pos = None
self._ast = None
self.is_return = False
O.__init__(self, **kwargs)
def __init__(self,
functions,
distance_function=execution_distance,
**kwargs):
self.functions = functions
self.distance_function = distance_function
self.union_find = uf.UnionFind(functions)
O.__init__(self, **kwargs)
def __init__(self):
O.__init__(self)
self.root = None
self.features = []
self.groups = []
self.leaves = []
self.con = []
self.cost = []
self.featureNum = 0
def __init__(self, id, parent = None, node_type = 'o'):
O.__init__(self)
self.id = id
self.parent = parent
self.node_type = node_type
self.children = []
if node_type == 'g':
self.g_u = 1
self.g_d = 0
def __init__(self):
"""
Points to root of the tree and number of children under it
:return:
"""
O.__init__(self)
self.n = 0
self.left = None
self.right = None
def __init__(self): """ Points to root of the tree and number of children under it :return: """ O.__init__(self) self.n = 0 self.left = None self.right = None
def __init__(self, **kwargs):
self.sim_score = None
self.n_mismatched = 0
self.size_diff = None
self.row_diff = None
self.col_diff = None
self.n_val1_empty = 0
self.n_val2_empty = 0
self.n_both_empty = 0
O.__init__(self, **kwargs)
def __init__(self, model, settings): """ Initialize an algorithm :param model: :param settings: :return: """ O.__init__(self) self.model = model self.settings = settings
def __init__(self, **kwargs): self.title = None self.keywords = None self.abstract = None self.category = None self.decision = "reject" self.raw_decision = "reject" self.conference = None self.year = None self.authors = None O.__init__(self, **kwargs)
def __init__(self, **kwargs):
self.title = None
self.keywords = None
self.abstract = None
self.category = None
self.decision = "reject"
self.raw_decision = "reject"
self.conference = None
self.year = None
self.authors = None
O.__init__(self, **kwargs)
def __init__(self, name, scope, var_type, positions, **kwargs):
self.name = name
self.scope = scope
self.var_type = var_type
self.positions = positions
self.type = None
self._store_positions = set()
self._updated_positions = set()
self._prev_value = None
self._is_type_set = False
O.__init__(self, **kwargs)
def __init__(self, predicted=None, actual=None):
O.__init__(self)
if predicted is not None and actual is not None:
self.accuracy = sk_metrics.accuracy_score(actual, predicted)
self.precision = sk_metrics.precision_score(actual,
predicted,
average='weighted')
self.recall = sk_metrics.recall_score(actual,
predicted,
average='weighted')
self.f_score = sk_metrics.f1_score(actual,
predicted,
average='weighted')
def __init__(self, outputs_json=None, **kwargs):
O.__init__(self, **kwargs)
self.returns = []
self.errors = []
self.durations = []
if outputs_json is not None:
for output_json in outputs_json:
self.returns.append(output_json["return"] if "return" in
output_json else None)
self.errors.append(output_json["errorMessage"]
if "errorMessage" in output_json else None)
self.durations.append(output_json["duration"] if "duration" in
output_json else None)
def __init__(self, **kwargs):
self.file_source = None
self.name = None
self.return_type = None
self.start_pos = None
self.end_pos = None
self.args = None
self.statement_blocks = [] # [<Statements>]
self._statement_groups = None # [[<Statements>], [<Statements>]]
self._ast = None
self._scope = None
self._prerequisite_statements = []
O.__init__(self, **kwargs)
def __init__(self, documents):
O.__init__(self)
document_map = OrderedDict()
agency_map = OrderedDict()
for document in documents:
document_map[document.id] = document
for agency in document.agencies:
a_documents = agency_map.get(agency, [])
a_documents.append(document.id)
agency_map[agency] = a_documents
self.agency_map = agency_map
self.document_map = document_map
self.vectorizer = None
def __init__(self, name, problem): """ Base class algorithm :param name: Name of the algorithm :param problem: Instance of the problem :return: """ O.__init__(self) self.name = name self.problem = problem self.stat = Stat(problem, self) self.select = None self.evolve = None self.recombine = None self._reference = None self.is_pareto = True self.gen = 0
def __init__(self, **kwargs):
Function._id += 1
self.id = Function._id
self.name = None
self.body = None
self.dataset = None
self.package = None
self.className = None
self.source = None
self.lines_touched = None
self.span = None
self.input_key = None
self.return_attribute = None
self.outputs = None
# Meta-info
self.useful = None
self.source = None
self.is_cloned = False
self.base_name = None
O.__init__(self, **kwargs)
def __init__(self, predicted, actual, positive, negative, raw_decisions):
O.__init__(self)
self.tp, self.fp, self.fn, self.tn = 0, 0, 0, 0
self.pre_reject, self.pre_reject_missed = 0, 0
for i, (p, a) in enumerate(zip(predicted, actual)):
if p == positive and a == positive:
self.tp += 1
elif p == positive and a == negative:
self.fp += 1
elif p == negative and a == positive:
self.fn += 1
else:
self.tn += 1
if raw_decisions[i] == PRE_REJECT and p == positive:
self.pre_reject_missed += 1
elif raw_decisions[i] == PRE_REJECT:
self.pre_reject += 1
self.accuracy = (self.tp + self.tn) / len(predicted)
self.precision = self.tp / (self.tp + self.fp + Metrics.EPS)
self.recall = self.tp / (self.tp + self.fn + Metrics.EPS)
self.specificity = self.tn / (self.tn + self.fp + Metrics.EPS)
self.f_score = 2 * self.precision * self.recall / (self.precision + self.recall + Metrics.EPS)
def __init__(self, predicted, actual, positive, negative, raw_decisions):
O.__init__(self)
self.tp, self.fp, self.fn, self.tn = 0, 0, 0, 0
self.pre_reject, self.pre_reject_missed = 0, 0
for i, (p, a) in enumerate(zip(predicted, actual)):
if p == positive and a == positive:
self.tp += 1
elif p == positive and a == negative:
self.fp += 1
elif p == negative and a == positive:
self.fn += 1
else:
self.tn += 1
if raw_decisions[i] == PRE_REJECT and p == positive:
self.pre_reject_missed += 1
elif raw_decisions[i] == PRE_REJECT:
self.pre_reject += 1
self.accuracy = (self.tp + self.tn) / len(predicted)
self.precision = self.tp / (self.tp + self.fp + Metrics.EPS)
self.recall = self.tp / (self.tp + self.fn + Metrics.EPS)
self.specificity = self.tn / (self.tn + self.fp + Metrics.EPS)
self.f_score = 2 * self.precision * self.recall / (
self.precision + self.recall + Metrics.EPS)
def __init__(self, **kwargs): O.__init__(self, **kwargs) self.name = "tuple" self.is_valid = True self.is_tuple = True self.types = []
def __init__(self, **kwargs): self.name = "list" self.is_list = True self.is_valid = True self.type = None O.__init__(self, **kwargs)
def __init__(self, name, parent, **kwargs):
self.name = name
self.parent = parent
self.children = {}
self._danglings = {}
O.__init__(self, **kwargs)
def __init__(self, functions, **kwargs):
self.functions = functions
self.union_find = uf.UnionFind(functions)
O.__init__(self, **kwargs)
def __init__(self, dataset, **kwargs):
O.__init__(self, **kwargs)
self.dataset = dataset
def __init__(self, source, target, weight=None): O.__init__(self, source=source, target=target) self.weight = 1 if weight is None else weight
def __init__(self, _id, tokens, label): O.__init__(self, id=_id, tokens=tokens, label=label)
def __init__(self, functions, **kwargs):
self.functions = functions
# noinspection PyUnresolvedReferences
self.X = np.arange(len(self.functions)).reshape(-1, 1)
O.__init__(self, **kwargs)
def __init__(self, raw=None): O.__init__(self) self.raw = raw self.vector = None self.topics_count = None self.topics_score = None
def __init__(self, **kwargs): self.name = None self.is_primitive_type = None self.module = None # Set only for non primitives self.is_valid = True O.__init__(self, **kwargs)
def __init__(self, id, literals, literals_pos):
O.__init__(self)
self.id = id
self.literals = literals
self.li_pos = literals_pos
def __init__(self):
O.__init__(self)
def __init__(self, **kwargs):
O.__init__(self, **kwargs)
self.id = Document.id
Document.id += 1
def __init__(self, decisions, objectives=None): O.__init__(self) Point.id += 1 self.id = Point.id self.decisions = decisions self.objectives = objectives
