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, 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, **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, 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, 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