fixed more backend issues

examples/playground/run_backend.py

@@ -1,22 +1,30 @@
 import pygrank as pg
 import torch
+from timeit import default_timer as time
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+_, graph, community = next(pg.load_datasets_one_community(["youtube"], graph_api=pg, min_group_size=50))
+print(f"Nodes {len(graph)}, edges {graph.number_of_edges()}")
+
+ppr = pg.HeatKernel(
+    normalization="symmetric",
+    assume_immutability=True
+)
+signal = pg.to_signal(graph, {node: 1.0 for node in community})
+preprocessor = ppr.preprocessor
+#ppr = pg.ParameterTuner(preprocessor=preprocessor)
+"""
+with pg.Backend("numpy"):
+    preprocessor(graph)
+    torch.cuda.synchronize()  # correct timing
+    tic = time()
+    scores = ppr(signal)
+    print("numpy", ppr.convergence, "actual time", time()-tic)"""
 
 with pg.Backend("torch_sparse", device=device):
-    _, graph, community = next(pg.load_datasets_one_community(["amazon"]))
-    ppr = pg.PageRank(
-        alpha=0.9,
-        normalization="symmetric",
-        assume_immutability=True,
-        convergence=pg.ConvergenceManager(max_iters=38, error_type="iters"),
-    )
-    ppr.preprocessor(graph)
-    signal = pg.to_signal(graph, {node: 1.0 for node in community})
+    preprocessor(graph)
     torch.cuda.synchronize()  # correct timing
+    tic = time()
     scores = ppr(signal)
-    print(ppr.convergence)
-    print(scores["B00005MHUG"])  # 0.00508212111890316
-    print(scores["B00006RGI2"])  # 0.70645672082901
-    print(scores["0006497993"])  # 0.19633759558200836
+    print("torch_sparse", ppr.convergence, "actual time", time()-tic)

pygrank/algorithms/postprocess/postprocess.py

@@ -375,7 +375,7 @@ class Sweep(Postprocessor):
     Applies a sweep procedure that divides personalized node ranks by corresponding non-personalized ones.
     """
 
-    def __init__(self, ranker: NodeRanking = None, uniform_ranker: NodeRanking = None):
+    def __init__(self, ranker: NodeRanking = None, uniform_ranker: NodeRanking = None, assume_immutability: bool = True):
         """
         Initializes the sweep procedure.
 
@@ -404,7 +404,7 @@ def __init__(self, ranker: NodeRanking = None, uniform_ranker: NodeRanking = Non
         super().__init__(ranker)
         self.uniform_ranker = ranker if uniform_ranker is None else uniform_ranker
         self.centrality = MethodHasher(
-            lambda graph: self.uniform_ranker.rank(graph), assume_immutability=True
+            lambda graph: self.uniform_ranker.rank(graph), assume_immutability=assume_immutability
         )
 
     def _transform(self, ranks: GraphSignal, **kwargs):

pygrank/core/backend/pytorch.py

@@ -41,7 +41,7 @@ def backend_init(mode="dense", device=None):
             return
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         warnings.warn(
-            f"[pygrank.backend.pytorch] Automatically detected device to run on {device}: {torch.cuda.get_device_name(device)}"
+            f"[pygrank.backend.pytorch] Automatically detected device to run on {device}: {torch.get_device(device)}"
         )
     if device is not None and isinstance(device, str):
         device = torch.device(device)

pygrank/core/backend/torch_sparse.py

@@ -61,7 +61,7 @@ def backend_init(device="auto"):
             return
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         warnings.warn(
-            f"[pygrank.backend.torch_sparse] Automatically detected device to run on {device}: {torch.cuda.get_device_name(device)}"
+            f"[pygrank.backend.torch_sparse] Automatically detected device to run on {device}: {torch.device(device)}"
         )
     if device is not None and isinstance(device, str):
         device = torch.device(device)