AutoGL Feature Engineering¶

We provide a series of node and graph feature engineers for you to compose within a feature engineering pipeline. An automatic feature engineering algorithm is also provided.

Quick Start¶

# 1. Choose a dataset.
from autogl.datasets import build_dataset_from_name
data = build_dataset_from_name('cora')

# 2. Compose a feature engineering pipeline
from autogl.module.feature import BaseFeature,AutoFeatureEngineer
from autogl.module.feature.generators import GeEigen
from autogl.module.feature.selectors import SeGBDT
from autogl.module.feature.graph import SgNetLSD
# you may compose feature engineering bases through BaseFeature.compose
fe = BaseFeature.compose([
GeEigen(size=32) ,
SeGBDT(fixlen=100),
SgNetLSD()
])
# or just through '&' operator
fe = fe & AutoFeatureEngineer(fixlen=200,max_epoch=3)

# 3. Fit and transform the data
fe.fit(data)
data1=fe.transform(data,inplace=False)

List of FE base names¶

Now three kinds of feature engineering bases are supported,namely generators, selectors , graph.You can import bases from according module as is mentioned in the Quick Start part. Or you may want to just list names of bases in configurations or as arguments of the autogl solver.

generators

Base	Description
`graphlet`	concatenate local graphlet numbers as features.
`eigen`	concatenate Eigen features.
`pagerank`	concatenate Pagerank scores.
`PYGLocalDegreeProfile`	concatenate Local Degree Profile features.
`PYGNormalizeFeatures`	Normalize all node features
`PYGOneHotDegree`	concatenate degree one-hot encoding.
`onehot`	concatenate node id one-hot encoding.

selectors

Base	Description
`SeFilterConstant`	delete all constant and one-hot encoding node features.
`gbdt`	select top-k important node features ranked by Gradient Descent Decision Tree.

graph

netlsd is a graph feature generation method. please refer to the according document.

A set of graph feature extractors implemented in NetworkX are wrapped, please refer to NetworkX for details. (NxLargeCliqueSize, NxAverageClusteringApproximate, NxDegreeAssortativityCoefficient, NxDegreePearsonCorrelationCoefficient, NxHasBridge ,``NxGraphCliqueNumber``, NxGraphNumberOfCliques, NxTransitivity, NxAverageClustering, NxIsConnected, NxNumberConnectedComponents, NxIsDistanceRegular, NxLocalEfficiency, NxGlobalEfficiency, NxIsEulerian)

The taxonomy of base types is based on the way of transforming features. generators concatenate the original features with ones newly generated or just overwrite the original ones. Instead of generating new features , selectors try to select useful features and keep learned selecting methods in the base itself. The former two types of bases can be exploited in node or edge level (modification upon each node or edge feature) ,while graph focuses on feature engineering in graph level (modification upon each graph feature). For your convenience in further development,you may want to design a new item by inheriting one of them. Of course, you can directly inherit the BaseFeature as well.

Create Your Own FE¶

You can create your own feature engineering object by simply inheriting one of feature engineering base types ,namely generators, selectors , graph, and overloading methods _fit and _transform.

# for example : create a node one-hot feature.
from autogl.module.feature.generators.base import BaseGenerator
import numpy as np
class GeOnehot(BaseGenerator):
    def __init__(self):
        super(GeOnehot,self).__init__(data_t='np',multigraph=True,subgraph=False)
        # data type in mid is 'numpy',
        # and it can be used for multigraph,
        # but not suitable for subgraph feature extraction.

    def _fit(self):
        pass # nothing to train or memorize

    def _transform(self, data):
        fe=np.eye(data.x.shape[0])
        data.x=np.concatenate([data.x,fe],axis=1)
        return data