🔁 Adding GitHub actions for PyTest and Coverage

.github/workflows/pytest.yaml

@@ -0,0 +1,28 @@
+name: Run Unit Test via Pytest  
+
+on: [push]  
+
+jobs:  
+  build:  
+    runs-on: ubuntu-latest  
+    strategy:  
+      matrix:  
+        python-version: ["3.8"]  
+
+    steps:  
+      - uses: actions/checkout@v3  
+      - name: Set up Python ${{ matrix.python-version }}  
+        uses: actions/setup-python@v4  
+        with:  
+          python-version: ${{ matrix.python-version }}  
+      - name: Install dependencies  
+        run: |  
+          python -m pip install --upgrade pip  
+          pip install -e .[dev]
+          pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+      - name: Test with pytest  
+        run: |  
+          coverage run -m pytest  -v -s -p no:warnings
+      - name: Generate Coverage Report  
+        run: |  
+          coverage report -m

benchmarking/gcn/seastar/train.py

@@ -6,22 +6,22 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from stgraph.graph.static.StaticGraph import StaticGraph
-from stgraph.dataset.CoraDataLoader import CoraDataLoader
-from utils import to_default_device, accuracy
+from stgraph.dataset import CoraDataLoader
+from utils import to_default_device, accuracy, generate_test_mask, generate_train_mask
 from model import GCN
 
-def main(args):
 
+def main(args):
     cora = CoraDataLoader(verbose=True)
 
     # To account for the initial CUDA Context object for pynvml
-    tmp = StaticGraph([(0,0)], [1], 1)
-    
+    tmp = StaticGraph([(0, 0)], [1], 1)
+
     features = torch.FloatTensor(cora.get_all_features())
     labels = torch.LongTensor(cora.get_all_targets())
-    
-    train_mask = cora.get_train_mask()
-    test_mask = cora.get_test_mask()
+
+    train_mask = generate_train_mask(len(features), 0.6)
+    test_mask = generate_test_mask(len(features), 0.6)
 
     train_mask = torch.BoolTensor(train_mask)
     test_mask = torch.BoolTensor(test_mask)
@@ -47,7 +47,9 @@ def main(args):
 
     # A simple sanity check
     print("Measuerd Graph Size (pynvml): ", graph_mem, " B", flush=True)
-    print("Measuerd Graph Size (pynvml): ", (graph_mem)/(1024**2), " MB", flush=True)
+    print(
+        "Measuerd Graph Size (pynvml): ", (graph_mem) / (1024**2), " MB", flush=True
+    )
 
     # normalization
     degs = torch.from_numpy(g.weighted_in_degrees()).type(torch.int32)
@@ -58,23 +60,18 @@ def main(args):
 
     num_feats = features.shape[1]
     n_classes = int(max(labels) - min(labels) + 1)
-    print("Num Classes: ",n_classes)
-
-    model = GCN(g,
-                num_feats,
-                args.num_hidden,
-                n_classes,
-                args.num_layers,
-                F.relu)
-
+    print("Num Classes: ", n_classes)
+
+    model = GCN(g, num_feats, args.num_hidden, n_classes, args.num_layers, F.relu)
+
     if cuda:
         model.cuda()
     loss_fcn = torch.nn.CrossEntropyLoss()
 
     # use optimizer
-    optimizer = torch.optim.Adam(model.parameters(),
-                                 lr=args.lr,
-                                 weight_decay=args.weight_decay)
+    optimizer = torch.optim.Adam(
+        model.parameters(), lr=args.lr, weight_decay=args.weight_decay
+    )
 
     # initialize graph
     dur = []
@@ -106,40 +103,45 @@ def main(args):
             dur.append(run_time_this_epoch)
 
         train_acc = accuracy(logits[train_mask], labels[train_mask])
-        print('Epoch {:05d} | Time(s) {:.4f} | train_acc {:.6f} | Used_Memory {:.6f} mb '.format(
-            epoch, run_time_this_epoch, train_acc, (now_mem * 1.0 / (1024**2))
-        ))
+        print(
+            "Epoch {:05d} | Time(s) {:.4f} | train_acc {:.6f} | Used_Memory {:.6f} mb ".format(
+                epoch, run_time_this_epoch, train_acc, (now_mem * 1.0 / (1024**2))
+            )
+        )
 
-    Used_memory /= (1024**3)
-    print('^^^{:6f}^^^{:6f}'.format(Used_memory, np.mean(dur)))
+    Used_memory /= 1024**3
+    print("^^^{:6f}^^^{:6f}".format(Used_memory, np.mean(dur)))
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='GCN')
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="GCN")
 
     # COMMENT IF SNOOP IS TO BE ENABLED
     snoop.install(enabled=False)
 
-    parser.add_argument("--dropout", type=float, default=0.5,
-            help="dropout probability")
-    parser.add_argument("--dataset", type=str,
-            help="Datset to train your model")
-    parser.add_argument("--gpu", type=int, default=0,
-            help="gpu")
-    parser.add_argument("--lr", type=float, default=1e-2,
-            help="learning rate")
-    parser.add_argument("--num_epochs", type=int, default=200,
-            help="number of training epochs")
-    parser.add_argument("--num_hidden", type=int, default=16,
-            help="number of hidden gcn units")
-    parser.add_argument("--num_layers", type=int, default=1,
-            help="number of hidden gcn layers")
-    parser.add_argument("--weight-decay", type=float, default=5e-4,
-            help="Weight for L2 loss")
-    parser.add_argument("--self-loop", action='store_true',
-            help="graph self-loop (default=False)")
+    parser.add_argument(
+        "--dropout", type=float, default=0.5, help="dropout probability"
+    )
+    parser.add_argument("--dataset", type=str, help="Datset to train your model")
+    parser.add_argument("--gpu", type=int, default=0, help="gpu")
+    parser.add_argument("--lr", type=float, default=1e-2, help="learning rate")
+    parser.add_argument(
+        "--num_epochs", type=int, default=200, help="number of training epochs"
+    )
+    parser.add_argument(
+        "--num_hidden", type=int, default=16, help="number of hidden gcn units"
+    )
+    parser.add_argument(
+        "--num_layers", type=int, default=1, help="number of hidden gcn layers"
+    )
+    parser.add_argument(
+        "--weight-decay", type=float, default=5e-4, help="Weight for L2 loss"
+    )
+    parser.add_argument(
+        "--self-loop", action="store_true", help="graph self-loop (default=False)"
+    )
     parser.set_defaults(self_loop=False)
     args = parser.parse_args()
     print(args)
 
-    main(args)
+    main(args)

benchmarking/gcn/seastar/utils.py

@@ -1,21 +1,32 @@
 import torch
 
+
 def accuracy(logits, labels):
     _, indices = torch.max(logits, dim=1)
     correct = torch.sum(indices == labels)
     return correct.item() * 1.0 / len(labels)
 
+
 # GPU | CPU
 def get_default_device():
-
     if torch.cuda.is_available():
-        return torch.device('cuda:0')
+        return torch.device("cuda:0")
     else:
-        return torch.device('cpu')
+        return torch.device("cpu")
+
 
 def to_default_device(data):
-
-    if isinstance(data,(list,tuple)):
-        return [to_default_device(x,get_default_device()) for x in data]
-
-    return data.to(get_default_device(),non_blocking = True)
+    if isinstance(data, (list, tuple)):
+        return [to_default_device(x, get_default_device()) for x in data]
+
+    return data.to(get_default_device(), non_blocking=True)
+
+
+def generate_train_mask(size: int, train_test_split: int) -> list:
+    cutoff = size * train_test_split
+    return [1 if i < cutoff else 0 for i in range(size)]
+
+
+def generate_test_mask(size: int, train_test_split: int) -> list:
+    cutoff = size * train_test_split
+    return [0 if i < cutoff else 1 for i in range(size)]

pyproject.toml

@@ -44,7 +44,8 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
     "black",
-    "pytest",
+    "pytest >= 7.4.3",
+    "pytest-cov >= 4.1.0",
     "tqdm >= 4.64.1",
     "build >= 0.10.0",
     "gdown >= 4.6.6",

stgraph/dataset/dynamic/england_covid_dataloader.py

@@ -53,6 +53,7 @@ class EnglandCovidDataLoader(STGraphDynamicDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(
@@ -65,6 +66,16 @@ def __init__(
         """COVID-19 cases in England's NUTS3 regions."""
         super().__init__()
 
+        if not isinstance(lags, int):
+            raise TypeError("lags must be of type int")
+        if lags < 0:
+            raise ValueError("lags must be a positive integer")
+
+        if cutoff_time is not None and not isinstance(cutoff_time, int):
+            raise TypeError("cutoff_time must be of type int")
+        if cutoff_time is not None and cutoff_time < 0:
+            raise ValueError("cutoff_time must be a positive integer")
+
         self.name = "England_COVID"
         self._url = "https://raw.githubusercontent.com/benedekrozemberczki/pytorch_geometric_temporal/master/dataset/england_covid.json"
         self._verbose = verbose

stgraph/dataset/static/cora_dataloader.py

@@ -61,6 +61,7 @@ class CoraDataLoader(STGraphStaticDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

stgraph/dataset/stgraph_dataset.py

@@ -67,6 +67,7 @@ def __init__(self: STGraphDataset) -> None:
 
         _load_dataset()
         Loads the dataset from cache
+
         """
         self.name = ""
         self.gdata = {}
@@ -106,6 +107,7 @@ def _has_dataset_cache(self: STGraphDataset) -> bool:
                 # The dataset is cached, continue cached operations
             else:
                 # The dataset is not cached, continue load and save operations
+
         """
         user_home_dir = os.path.expanduser("~")
         stgraph_dir = user_home_dir + "/.stgraph"
@@ -128,6 +130,7 @@ def _get_cache_file_path(self: STGraphDataset) -> str:
         -------
         str
             The absolute path of the cached dataset file
+
         """
         user_home_dir = os.path.expanduser("~")
         stgraph_dir = user_home_dir + "/.stgraph"

stgraph/dataset/temporal/hungarycp_dataloader.py

@@ -58,6 +58,7 @@ class HungaryCPDataLoader(STGraphTemporalDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

stgraph/dataset/temporal/metrla_dataloader.py

@@ -67,6 +67,7 @@ class METRLADataLoader(STGraphTemporalDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

stgraph/dataset/temporal/montevideobus_dataloader.py

@@ -66,6 +66,7 @@ class MontevideoBusDataLoader(STGraphTemporalDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

stgraph/dataset/temporal/pedalme_dataloader.py

@@ -58,6 +58,7 @@ class PedalMeDataLoader(STGraphTemporalDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

stgraph/dataset/temporal/wikimath_dataloader.py

@@ -65,6 +65,7 @@ class WikiMathDataLoader(STGraphTemporalDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

stgraph/dataset/temporal/windmilloutput_dataloader.py

@@ -82,6 +82,7 @@ class WindmillOutputDataLoader(STGraphTemporalDataset):
         The name of the dataset.
     gdata : dict
         Graph meta data.
+
     """
 
     def __init__(

tests/dataset/dynamic/test_EnglandCovidDataLoader.py

@@ -1,5 +1,4 @@
-import numpy as np
-import urllib.request
+import pytest
 
 from stgraph.dataset import EnglandCovidDataLoader
 
@@ -51,14 +50,26 @@ def EnglandCovidDataCheck(eng_covid: EnglandCovidDataLoader):
 def test_EnglandCovidDataLoader():
     eng_covid = EnglandCovidDataLoader(verbose=True)
     eng_covid_1 = EnglandCovidDataLoader(cutoff_time=30)
-    eng_covid_2 = EnglandCovidDataLoader(
-        url="https://raw.githubusercontent.com/benedekrozemberczki/pytorch_geometric_temporal/master/dataset/england_covid.json"
-    )
     eng_covid_3 = EnglandCovidDataLoader(lags=12)
-    # eng_covid_4 = EnglandCovidDataLoader(redownload=True)
+    eng_covid_4 = EnglandCovidDataLoader(redownload=True)
 
     EnglandCovidDataCheck(eng_covid)
     EnglandCovidDataCheck(eng_covid_1)
-    # EnglandCovidDataCheck(eng_covid_2)
     EnglandCovidDataCheck(eng_covid_3)
-    # EnglandCovidDataCheck(eng_covid_4)
+    EnglandCovidDataCheck(eng_covid_4)
+
+    with pytest.raises(TypeError) as exec:
+        EnglandCovidDataLoader(lags="lags")
+    assert str(exec.value) == "lags must be of type int"
+
+    with pytest.raises(ValueError) as exec:
+        EnglandCovidDataLoader(lags=-1)
+    assert str(exec.value) == "lags must be a positive integer"
+
+    with pytest.raises(TypeError) as exec:
+        EnglandCovidDataLoader(cutoff_time="time")
+    assert str(exec.value) == "cutoff_time must be of type int"
+
+    with pytest.raises(ValueError) as exec:
+        EnglandCovidDataLoader(cutoff_time=-1)
+    assert str(exec.value) == "cutoff_time must be a positive integer"

tests/dataset/static/test_CoraDataLoader.py

@@ -19,11 +19,8 @@ def CoraDataCheck(cora: CoraDataLoader):
 
 
 def test_CoraDataLoader():
-    cora = CoraDataLoader()
-
-    cora_1 = CoraDataLoader(
-        url="https://raw.githubusercontent.com/bfGraph/STGraph-Datasets/main/cora.json",
-    )
+    cora = CoraDataLoader(verbose=True)
+    cora_1 = CoraDataLoader(redownload=True)
 
     CoraDataCheck(cora)
     CoraDataCheck(cora_1)