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The following commit(s) were added to refs/heads/main by this push:
new 9f8ba3d fix(ml): handle pylint warning (#113)
9f8ba3d is described below
commit 9f8ba3d5195149659ab35ed0293c379a197608d5
Author: SoJ <[email protected]>
AuthorDate: Sat Nov 16 14:34:15 2024 +0800
fix(ml): handle pylint warning (#113)
---
.../examples/correct_and_smooth_example.py | 25 +++++-----------
.../src/hugegraph_ml/examples/gatne_example.py | 2 +-
hugegraph-ml/src/hugegraph_ml/models/agnn.py | 8 +++---
hugegraph-ml/src/hugegraph_ml/models/appnp.py | 2 +-
hugegraph-ml/src/hugegraph_ml/models/arma.py | 2 ++
hugegraph-ml/src/hugegraph_ml/models/bgnn.py | 33 ++--------------------
hugegraph-ml/src/hugegraph_ml/models/bgrl.py | 14 ++++-----
hugegraph-ml/src/hugegraph_ml/models/care_gnn.py | 2 ++
.../src/hugegraph_ml/models/cluster_gcn.py | 6 ++--
.../src/hugegraph_ml/models/correct_and_smooth.py | 7 ++---
hugegraph-ml/src/hugegraph_ml/models/dagnn.py | 6 ++--
hugegraph-ml/src/hugegraph_ml/models/deepergcn.py | 17 ++---------
hugegraph-ml/src/hugegraph_ml/models/gatne.py | 4 ++-
hugegraph-ml/src/hugegraph_ml/models/pgnn.py | 4 ++-
hugegraph-ml/src/hugegraph_ml/models/seal.py | 2 ++
.../hugegraph_ml/tasks/fraud_detector_caregnn.py | 7 +++--
.../src/tests/test_examples/test_examples.py | 4 +--
17 files changed, 52 insertions(+), 93 deletions(-)
diff --git
a/hugegraph-ml/src/hugegraph_ml/examples/correct_and_smooth_example.py
b/hugegraph-ml/src/hugegraph_ml/examples/correct_and_smooth_example.py
index 6faae7f..aa09808 100644
--- a/hugegraph-ml/src/hugegraph_ml/examples/correct_and_smooth_example.py
+++ b/hugegraph-ml/src/hugegraph_ml/examples/correct_and_smooth_example.py
@@ -24,28 +24,17 @@ import argparse
def cs_example(n_epochs=200):
hg2d = HugeGraph2DGL()
graph = hg2d.convert_graph(vertex_label="CORA_vertex",
edge_label="CORA_edge")
- if args.model == "mlp":
- model = MLP(
- in_dim=graph.ndata["feat"].shape[1],
- hid_dim=64,
- out_dim=graph.ndata["label"].unique().shape[0],
- num_layers=3,
- dropout=0.4,
- )
- elif args.model == "linear":
- model = MLPLinear(
- in_dim=graph.ndata["feat"].shape[1],
- out_dim=graph.ndata["label"].unique().shape[0],
- )
- else:
- raise NotImplementedError(f"Model {args.model} is not supported.")
+ model = MLP(
+ in_dim=graph.ndata["feat"].shape[1],
+ hid_dim=64,
+ out_dim=graph.ndata["label"].unique().shape[0],
+ num_layers=3,
+ dropout=0.4,
+ )
node_clf_task = NodeClassify(graph, model)
node_clf_task.train(lr=0.005, weight_decay=0.0005, n_epochs=n_epochs,
patience=200)
print(node_clf_task.evaluate())
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="Base predictor(C&S)")
- parser.add_argument("--model", type=str, default="mlp", choices=["mlp",
"linear"])
- args = parser.parse_args()
cs_example()
diff --git a/hugegraph-ml/src/hugegraph_ml/examples/gatne_example.py
b/hugegraph-ml/src/hugegraph_ml/examples/gatne_example.py
index 0c9c0c5..6a994f0 100644
--- a/hugegraph-ml/src/hugegraph_ml/examples/gatne_example.py
+++ b/hugegraph-ml/src/hugegraph_ml/examples/gatne_example.py
@@ -39,7 +39,7 @@ def gatne_example(n_epochs=200):
20,
)
gatne_task = HeteroSampleEmbedGATNE(graph, model)
- embs = gatne_task.train_and_embed(lr=0.005, n_epochs=n_epochs)
+ gatne_task.train_and_embed(lr=0.005, n_epochs=n_epochs)
if __name__ == "__main__":
diff --git a/hugegraph-ml/src/hugegraph_ml/models/agnn.py
b/hugegraph-ml/src/hugegraph_ml/models/agnn.py
index b3c3767..e693ddf 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/agnn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/agnn.py
@@ -25,10 +25,10 @@ Author's code:
DGL code:
https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/agnnconv.py
"""
-import dgl
-import torch
+
+
from dgl.nn.pytorch.conv import AGNNConv
-import torch.nn as nn
+from torch import nn
import torch.nn.functional as F
@@ -40,7 +40,7 @@ class AGNN(nn.Module):
self.attention_layers = nn.ModuleList()
# 2-layer AGNN
- for i in range(self.num_layers):
+ for _ in range(self.num_layers):
self.attention_layers.append(AGNNConv())
self.output_layer = nn.Linear(hid_dim, out_dim, bias=False)
diff --git a/hugegraph-ml/src/hugegraph_ml/models/appnp.py
b/hugegraph-ml/src/hugegraph_ml/models/appnp.py
index 04cb19d..8c65daa 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/appnp.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/appnp.py
@@ -25,7 +25,7 @@ Author's code: https://github.com/klicperajo/ppnp
DGL code: https://github.com/dmlc/dgl/tree/master/examples/pytorch/appnp
"""
-import torch.nn as nn
+from torch import nn
from dgl.nn.pytorch.conv import APPNPConv
diff --git a/hugegraph-ml/src/hugegraph_ml/models/arma.py
b/hugegraph-ml/src/hugegraph_ml/models/arma.py
index 7b07c68..76ca096 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/arma.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/arma.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.right (c) 2024 by jinsong, All Rights Reserved.
+# pylint: disable=E1101
+
"""
auto-regressive moving average (ARMA)
diff --git a/hugegraph-ml/src/hugegraph_ml/models/bgnn.py
b/hugegraph-ml/src/hugegraph_ml/models/bgnn.py
index 3db380d..b137043 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/bgnn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/bgnn.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E1102,E0401,E0711,E0606,E0602
+
"""
Boost-GNN (BGNN)
@@ -28,7 +30,7 @@ DGL code:
https://github.com/dmlc/dgl/tree/master/examples/pytorch/bgnn
import itertools
import time
from collections import defaultdict as ddict
-
+import dgl
import numpy as np
import pandas as pd
import torch
@@ -650,35 +652,6 @@ class GNNModelDGL(torch.nn.Module):
return logits
-
-def read_input(input_folder):
- X = pd.read_csv(f"{input_folder}/X.csv")
- y = pd.read_csv(f"{input_folder}/y.csv")
-
- categorical_columns = []
- if os.path.exists(f"{input_folder}/cat_features.txt"):
- with open(f"{input_folder}/cat_features.txt") as f:
- for line in f:
- if line.strip():
- categorical_columns.append(line.strip())
-
- cat_features = None
- if categorical_columns:
- columns = X.columns
- cat_features = np.where(columns.isin(categorical_columns))[0]
-
- for col in list(columns[cat_features]):
- X[col] = X[col].astype(str)
-
- gs, _ = load_graphs(f"{input_folder}/graph.dgl")
- graph = gs[0]
-
- with open(f"{input_folder}/masks.json") as f:
- masks = json.load(f)
-
- return graph, X, y, cat_features, masks
-
-
def normalize_features(X, train_mask, val_mask, test_mask):
min_max_scaler = preprocessing.MinMaxScaler()
A = X.to_numpy(copy=True)
diff --git a/hugegraph-ml/src/hugegraph_ml/models/bgrl.py
b/hugegraph-ml/src/hugegraph_ml/models/bgrl.py
index 6d991ee..ed87a43 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/bgrl.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/bgrl.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.right (c) 2024 by jinsong, All Rights Reserved.
+# pylint: disable=E1102
+
"""
Bootstrapped Graph Latents (BGRL)
@@ -26,16 +28,14 @@ DGL code:
https://github.com/dmlc/dgl/tree/master/examples/pytorch/bgrl
"""
import copy
-
-import dgl
-
import torch
-from dgl.nn.pytorch.conv import GraphConv, SAGEConv
from torch import nn
-from torch.nn import BatchNorm1d, Parameter
-from torch.nn.init import ones_, zeros_
-from dgl.transforms import Compose, DropEdge, FeatMask
+from torch.nn import BatchNorm1d
from torch.nn.functional import cosine_similarity
+import dgl
+from dgl.nn.pytorch.conv import GraphConv
+from dgl.transforms import Compose, DropEdge, FeatMask
+import numpy as np
class MLP_Predictor(nn.Module):
r"""MLP used for predictor. The MLP has one hidden layer.
diff --git a/hugegraph-ml/src/hugegraph_ml/models/care_gnn.py
b/hugegraph-ml/src/hugegraph_ml/models/care_gnn.py
index 7046569..84e850f 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/care_gnn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/care_gnn.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E1101
+
"""
CAmouflage-REsistant GNN (CARE-GNN)
diff --git a/hugegraph-ml/src/hugegraph_ml/models/cluster_gcn.py
b/hugegraph-ml/src/hugegraph_ml/models/cluster_gcn.py
index ce4fb94..09fd04a 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/cluster_gcn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/cluster_gcn.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E1101,E0401
+
"""
Cluster-GCN
@@ -25,13 +27,9 @@ Author's code:
https://github.com/google-research/google-research/tree/master/cl
DGL code: https://github.com/dmlc/dgl/tree/master/examples/pytorch/cluster_gcn
"""
-import dgl
import dgl.nn as dglnn
-import torch
import torch.nn as nn
import torch.nn.functional as F
-import torchmetrics.functional as MF
-
class SAGE(nn.Module):
def __init__(self, in_feats, n_hidden, n_classes):
diff --git a/hugegraph-ml/src/hugegraph_ml/models/correct_and_smooth.py
b/hugegraph-ml/src/hugegraph_ml/models/correct_and_smooth.py
index 34f8ec7..70b8f9e 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/correct_and_smooth.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/correct_and_smooth.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E1101,E1102
+
"""
Correct and Smooth (C&S)
@@ -27,7 +29,7 @@ DGL code:
https://github.com/dmlc/dgl/tree/master/examples/pytorch/correct_and_s
import dgl.function as fn
import torch
-import torch.nn as nn
+from torch import nn
import torch.nn.functional as F
@@ -98,8 +100,6 @@ class LabelPropagation(nn.Module):
Description
-----------
- Introduced in `Learning from Labeled and Unlabeled Data with Label
Propagation
<https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.3864&rep=rep1&type=pdf>`_
-
.. math::
\mathbf{Y}^{\prime} = \alpha \cdot \mathbf{D}^{-1/2} \mathbf{A}
\mathbf{D}^{-1/2} \mathbf{Y} + (1 - \alpha) \mathbf{Y},
@@ -166,7 +166,6 @@ class CorrectAndSmooth(nn.Module):
Description
-----------
- Introduced in `Combining Label Propagation and Simple Models Out-performs
Graph Neural Networks <https://arxiv.org/abs/2010.13993>`_
Parameters
----------
diff --git a/hugegraph-ml/src/hugegraph_ml/models/dagnn.py
b/hugegraph-ml/src/hugegraph_ml/models/dagnn.py
index 2e9d260..8afd8db 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/dagnn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/dagnn.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E1101
+
"""
Deep Adaptive Graph Neural Network (DAGNN)
@@ -26,12 +28,10 @@ DGL code:
https://github.com/dmlc/dgl/tree/master/examples/pytorch/dagnn
"""
import dgl.function as fn
-
-import numpy as np
import torch
from torch import nn
from torch.nn import functional as F, Parameter
-import random
+
class DAGNNConv(nn.Module):
diff --git a/hugegraph-ml/src/hugegraph_ml/models/deepergcn.py
b/hugegraph-ml/src/hugegraph_ml/models/deepergcn.py
index 03fc292..0781ded 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/deepergcn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/deepergcn.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+
+
"""
DeeperGCN
@@ -30,17 +32,15 @@ import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.functional import edge_softmax
from dgl.nn.pytorch.glob import AvgPooling
-from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
import torch
+# pylint: disable=E1101,E0401
class DeeperGCN(nn.Module):
r"""
Description
-----------
- Introduced in "DeeperGCN: All You Need to Train Deeper GCNs
<https://arxiv.org/abs/2006.07739>"
-
Parameters
----------
node_feat_dim: int
@@ -137,8 +137,6 @@ class GENConv(nn.Module):
Description
-----------
- Generalized Message Aggregator was introduced in "DeeperGCN: All You Need
to Train Deeper GCNs <https://arxiv.org/abs/2006.07739>"
-
Parameters
----------
in_dim: int
@@ -244,13 +242,6 @@ class GENConv(nn.Module):
class MLP(nn.Sequential):
- r"""
-
- Description
- -----------
- From equation (5) in "DeeperGCN: All You Need to Train Deeper GCNs
<https://arxiv.org/abs/2006.07739>"
- """
-
def __init__(self, channels, act="relu", dropout=0.0, bias=True):
layers = []
@@ -269,8 +260,6 @@ class MessageNorm(nn.Module):
Description
-----------
- Message normalization was introduced in "DeeperGCN: All You Need to Train
Deeper GCNs <https://arxiv.org/abs/2006.07739>"
-
Parameters
----------
learn_scale: bool
diff --git a/hugegraph-ml/src/hugegraph_ml/models/gatne.py
b/hugegraph-ml/src/hugegraph_ml/models/gatne.py
index 794d1cc..b0fe329 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/gatne.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/gatne.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.right (c) 2024 by jinsong, All Rights Reserved.
+# pylint: disable=E1101
+
"""
General Attributed Multiplex HeTerogeneous Network Embedding (GATNE)
@@ -33,7 +35,7 @@ from collections import defaultdict
import numpy as np
import torch
-import torch.nn as nn
+from torch import nn
import torch.nn.functional as F
from numpy import random
from torch.nn.parameter import Parameter
diff --git a/hugegraph-ml/src/hugegraph_ml/models/pgnn.py
b/hugegraph-ml/src/hugegraph_ml/models/pgnn.py
index de0f740..1c6dcb5 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/pgnn.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/pgnn.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E1101,E0401
+
"""
Position-aware Graph Neural Networks (P-GNN)
@@ -27,7 +29,7 @@ DGL code:
https://github.com/dmlc/dgl/tree/master/examples/pytorch/P-GNN
import dgl.function as fn
import torch
-import torch.nn as nn
+from torch import nn
import torch.nn.functional as F
import multiprocessing as mp
import random
diff --git a/hugegraph-ml/src/hugegraph_ml/models/seal.py
b/hugegraph-ml/src/hugegraph_ml/models/seal.py
index 09060ed..3c167c5 100644
--- a/hugegraph-ml/src/hugegraph_ml/models/seal.py
+++ b/hugegraph-ml/src/hugegraph_ml/models/seal.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E0401
+
"""
SEAL
diff --git a/hugegraph-ml/src/hugegraph_ml/tasks/fraud_detector_caregnn.py
b/hugegraph-ml/src/hugegraph_ml/tasks/fraud_detector_caregnn.py
index fe14d6d..632cfe3 100644
--- a/hugegraph-ml/src/hugegraph_ml/tasks/fraud_detector_caregnn.py
+++ b/hugegraph-ml/src/hugegraph_ml/tasks/fraud_detector_caregnn.py
@@ -15,12 +15,13 @@
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=E0401
+
import torch
-from dgl import DGLGraph
from torch import nn
-from sklearn.metrics import recall_score, roc_auc_score
from torch.nn.functional import softmax
-
+from dgl import DGLGraph
+from sklearn.metrics import recall_score, roc_auc_score
class DetectorCaregnn:
def __init__(self, graph: DGLGraph, model: nn.Module):
diff --git a/hugegraph-ml/src/tests/test_examples/test_examples.py
b/hugegraph-ml/src/tests/test_examples/test_examples.py
index 861b240..4adca94 100644
--- a/hugegraph-ml/src/tests/test_examples/test_examples.py
+++ b/hugegraph-ml/src/tests/test_examples/test_examples.py
@@ -96,13 +96,13 @@ class TestHugegraph2DGL(unittest.TestCase):
def test_bgnn_example(self):
try:
- bgnn_example(n_epochs=self.test_n_epochs)
+ bgnn_example()
except ValueError:
self.fail("model bgnn example failed")
def test_bgrl_example(self):
try:
- bgrl_example(n_epochs=self.test_n_epochs)
+ bgrl_example(n_epochs_embed=self.test_n_epochs,
n_epochs_clf=self.test_n_epochs)
except ValueError:
self.fail("model bgrl example failed")
