def setup_class(
cls,
normalize_features=True,
statistics_level="basic",
n_node_features=0,
timeout=10,
):
"""Initializes the class by adding descriptions for all features.
Args:
normalize_features (bool): normalise features by number of nodes and number of edges
statistics_level (str): 'basic', 'advanced' - for features that provide distributions
we must compute statistics.
n_node_features (int): dimension of node features for feature constructors
timeout (int): number of seconds before the calculation for a feature is cancelled
Returns:
(DataFrame): dataframe with feature information
"""
cls.normalize_features = normalize_features
cls.statistics_level = statistics_level
cls.n_node_features = n_node_features
inst = cls(get_trivial_graph(n_node_features=n_node_features))
features = inst.get_features(all_features=True)
feature_info = pd.DataFrame()
for feature in features:
feat_info = inst.get_feature_info(feature)
feature_info[feature] = pd.Series(feat_info)
# we set the timeout only for real computation for setup speed up
cls.timeout = timeout
return feature_info
def test_trivial_graph():
"""test if the features are computable on trivial graph"""
from hcga.utils import get_trivial_graph
graph = get_trivial_graph()
for feature_class in test_feature_classes:
feature_inst = feature_class(graph)
feature_inst.get_features()
def setup_class(
cls,
normalize_features=True,
statistics_level="basic",
n_node_features=0,
timeout=10,
):
"""Initializes the class by adding descriptions for all features."""
cls.normalize_features = normalize_features
cls.statistics_level = statistics_level
cls.n_node_features = n_node_features
cls.timeout = timeout
inst = cls(get_trivial_graph(n_node_features=n_node_features))
features = inst.get_features(all_features=True)
feature_info = pd.DataFrame()
for feature in features:
feat_info = inst.get_feature_info(feature)
feature_info[feature] = pd.Series(feat_info)
return feature_info