This release is a pre-release and may not be stable for production use.
graphspot
Graph anomaly detection for people who have to score things they have never seen before.
- Inductive by contract. Every detector's
decision_functionscores nodes absent at fit time. NeverNotImplementedError. - Installs without torch. The core is numpy/scipy/sklearn/pandas/xgboost. Deep detectors
live behind
pip install graphspot[deep]. - Honest baselines. Every evaluation can include a no-graph tabular baseline, and graphspot warns loudly when the graph model fails to beat it.
Status: pre-release, under active development. The v0 detector set is
NeighborAggregation (transform), XGBGraph, RFGraph, FlatBaseline, with
FlatUnsupervised, OddBall, Fraudar and BWGNN (behind [deep]) on the way.
import graphspot
from graphspot.detectors import XGBGraph, FlatBaseline
from graphspot.datasets import load_yelpchi
g = load_yelpchi()
det = XGBGraph(random_state=0).fit(g, y=g.node_labels)
flat = FlatBaseline(random_state=0).fit(g, y=g.node_labels)
print(graphspot.evaluate(
g.node_labels,
det.decision_scores_,
baseline_scores=flat.decision_scores_,
))
det.explain(k=5) # e.g. [("2hop_mean(f21)", 0.14), ("1hop_max(f3)", 0.11), ...]
Works directly on transaction dataframes:
g = graphspot.Graph.from_pandas(
tx, source="buyer_id", target="seller_id",
edge_features=["amount"], time="ts",
node_features=accounts.set_index("account_id"),
)
License: BSD-3-Clause.
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