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How to use pull_node method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

testtree.py

Source:testtree.py

...66        links = OrderedSet()67        for node in self.hierarchy:68            links.update(node.links)69        return links70    def pull_node(self):71        # type: () -> "_N"72        node = copy.copy(self)73        node.parent_suite = None74        return node75    def __str__(self):76        return "<%s %s>" % (self.__class__.__name__, self.path)77_N = TypeVar("_N", bound=BaseTreeNode)78TreeNodeHierarchy = Union[Tuple[str, ...], List, BaseTreeNode, str]79def normalize_node_hierarchy(hierarchy):80    # type: (TreeNodeHierarchy) -> Tuple[str, ...]81    if type(hierarchy) is tuple:82        return hierarchy83    elif type(hierarchy) is list:84        return tuple(hierarchy)85    elif isinstance(hierarchy, BaseTreeNode):86        return tuple(p.name for p in hierarchy.hierarchy)87    else:  # assume str88        return tuple(hierarchy.split("."))89class BaseTest(BaseTreeNode):90    pass91T = TypeVar("T", bound=BaseTest)92class BaseSuite(BaseTreeNode):93    def __init__(self, name, description):94        BaseTreeNode.__init__(self, name, description)95        # NB: use OrderedDict instead of list to enable fast test lookup in suites96        # containing a large number of tests97        self._tests = OrderedDict()98        self._suites = []99    def add_test(self, test):100        """101        Add test to the suite.102        """103        test.parent_suite = self104        self._tests[test.name] = test105    def get_tests(self):106        """107        Get suite's tests.108        """109        return list(self._tests.values())110    def get_test_by_name(self, name):111        return self._tests[name]112    def add_suite(self, suite):113        """114        Add a sub-suite to the suite.115        """116        suite.parent_suite = self117        self._suites.append(suite)118    def get_suites(self):119        return self._suites120    def is_empty(self):121        if len(self.get_tests()) != 0:122            return False123        for sub_suite in self.get_suites():124            if not sub_suite.is_empty():125                return False126        return True127    def pull_node(self):128        # type: () -> "BaseSuite"129        node = BaseTreeNode.pull_node(self)130        node._tests = OrderedDict()131        node._suites = []132        return node133    def filter(self, test_filter):134        suite = self.pull_node()135        for test in self.get_tests():136            if test_filter(test):137                suite.add_test(test.pull_node())138        for sub_suite in filter_suites(self.get_suites(), test_filter):139            suite.add_suite(sub_suite)140        return suite141def filter_suites(suites, test_filter):142    return list(143        filter(144            lambda s: not s.is_empty(), (s.filter(test_filter) for s in suites)145        )146    )147S = TypeVar("S", bound=BaseSuite)148def flatten_suites(suites):149    # type: (Sequence[S]) -> Generator[S]150    for suite in suites:151        yield suite...

q3_tools.py

Source:q3_tools.py

1import numpy as np2import os, sys3# to import social_network4path2add = os.path.normpath(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'social_network')))5sys.path.append(path2add)6import sn_tools7def get_edges_cascade(pull_node, list_edges_activated):8    list_new_nodes = [pull_node]9    list_cascade_edges = []10    while(len(list_new_nodes) != 0):11        tamp = []12        for node_head in list_new_nodes:13            for edge in list_edges_activated:14                if node_head == edge[0]:15                    list_cascade_edges.append(edge)16                    tamp.append(edge[1])17        list_new_nodes = tamp18    return list_cascade_edges19    # Find nodes who activate an A node.20def credit_nodes(list_A_node_activated, list_edges_activated, pulled_super_arm):21    list_credit = [0]*len(pulled_super_arm)22    for i in range(len(pulled_super_arm)):23        pull_node = pulled_super_arm[i]24        if pull_node in list_A_node_activated:25# If it is an initial node, we add 1 to his credit because it is possible that it has no edges.26            list_credit[i] += 127        tamp = get_edges_cascade(pull_node, list_edges_activated)28        if len(tamp) != 0:29            list_cascade_node = [num[1] for num in tamp] # get the inherited nodes from the cascade30            for node in list_cascade_node:31                if node in list_A_node_activated: # add a credit if it activates an A node.32                    list_credit[i] +=133    return list_credit34def calculate_reward(pulled_super_arm, env, list_nodes_info, budget):35    list_rewards_super_arm = [0] * len(pulled_super_arm)36    # Simulate an episode.37    [episode , list_edges_activated]= sn_tools.simulate_episode(init_prob_matrix = env.p, n_steps_max = 100, budget = env.budget, perfect_nodes =  pulled_super_arm)38    # We count only nodes activated regardless of their message. If track = True then we assign rewards at a specific node by tracing the root. If track = False we give the same rewards to all the nodes.39    credits_of_each_node, score_by_seeds_history, nbr_activates = sn_tools.credits_assignment(dataset = [episode], dataset_edges = [list_edges_activated], list_nodes = list_nodes_info, track = env.bool_track, budget = budget)40    # print(credits_of_each_node, '\n')41    if env.bool_track == False:42        i = 043        for node in pulled_super_arm:44            list_rewards_super_arm[i] = (credits_of_each_node[node])/(len(list_nodes_info)) #practilly is the % of nodes activated in the  network45            i +=146    else:47        i = 048        for node in pulled_super_arm:49            list_rewards_super_arm[i] = (credits_of_each_node[node]/len(list_nodes_info))50            i +=151    return list_rewards_super_arm52def get_list_features(list_of_nodes, dim):53    list_features = [[0]*dim]*len(list_of_nodes)54    i = 055    for nodes in list_of_nodes:56        list_gender = (nodes.features['gender'])57        list_age = (nodes.features['age'])58        list_interests = (nodes.features['interests'])59        list_location = (nodes.features['location'])60        list_features[i]= list_gender+list_age + list_interests + list_location61        i +=1...

code_traverser.py

Source:code_traverser.py

1import astroid2from .classifier import Classifier3from .classifier import Classification4def lazify(ast: astroid.Module) -> set:5    """6    Read the input program and prepare VIEWs, but no PULLs.7    Modifies the input variable in-place and returns a list of names that can be PULLed.8    """9    classifier = Classifier()10    state = {}11    lineno = 012    while lineno < len(ast.body):13        line = ast.body[lineno]14        classification: Classification = classifier.classify(line, state)15        if classification:16            del ast.body[lineno]17            state[classification.name] = classification18            lazy_node = gen_lazy_node(classification.backend)19            ast.last_p2d2_node += 120            ast.body.insert(ast.last_p2d2_node, lazy_node)21        lineno += 122    return set(state)  # only the keys23def eagerfy(ast: astroid.Module, eagerfiable: set) -> None:24    """25    inserts PULL nodes in ast26    """27    names_gen = ast.nodes_of_class(astroid.Name)28    names_in_ast = {name.name for name in names_gen}29    to_eagerfy = names_in_ast & eagerfiable30    for view in to_eagerfy:31        pull_node = gen_eager_node(view)32        ast.last_p2d2_node += 133        ast.body.insert(ast.last_p2d2_node, pull_node)34def get_line_name(line: astroid.Expr) -> str:35    """36    Handles 2 cases:37    1. Expr is an Assignment -> return target38    2. Expr is not an Assignment -> return39    """40    return str41def gen_lazy_node(backend_code: str) -> astroid.Expr:42    """43    returns a Call node (wrapped in an Expr) corresponding to this line of code:44    cur.execute("SQL..")45    """46    return astroid.parse(f"cur.execute({backend_code})").body[0]47def gen_eager_node(table_name: str) -> astroid.Expr:48    """49    returns a Call node (wrapped in an Expr) corresponding to this line of code:50    pandas.read_sql_table(table_name= table_name, con=conn)51    """52    return astroid.parse(53        f"pandas.read_sql_table(table_name= {table_name}, con=conn)"...

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