Reformat

datasets/flwr_datasets/partitioner/__init__.py

@@ -35,7 +35,7 @@
     "LinearPartitioner",
     "NaturalIdPartitioner",
     "Partitioner",
-    "SemanticPartitioner"
+    "SemanticPartitioner",
     "ShardPartitioner",
     "SizePartitioner",
     "SquarePartitioner",

datasets/flwr_datasets/partitioner/semantic_partitioner.py

@@ -44,31 +44,31 @@ class SemanticPartitioner(Partitioner):
 
     References：
     What Do We Mean by Generalization in Federated Learning? (accepted by ICLR 2022)
-    https://arxiv.org/abs/2110.14216 
+    https://arxiv.org/abs/2110.14216
 
     (Cited from section 4.1 in the paper)
-    
-    Semantic partitioner's goal is to reverse-engineer the federated dataset-generating 
-    process so that each client possesses semantically similar data. For example, for 
-    the EMNIST dataset, we expect every client (writer) to (i) write in a consistent style 
-    for each digit  (intra-client intra-label similarity) and (ii) use a consistent writing 
-    style across all digits  (intra-client inter-label similarity). A simple approach might 
-    be to cluster similar examples together  and sample client data from clusters. However, 
-    if one directly clusters the entire dataset,  the resulting clusters may end up largely 
-    correlated to labels. To disentangle the effect of label  heterogeneity and semantic 
-    heterogeneity, we propose the following algorithm to enforce  intra-client intra-label 
+
+    Semantic partitioner's goal is to reverse-engineer the federated dataset-generating
+    process so that each client possesses semantically similar data. For example, for
+    the EMNIST dataset, we expect every client (writer) to (i) write in a consistent style
+    for each digit  (intra-client intra-label similarity) and (ii) use a consistent writing
+    style across all digits  (intra-client inter-label similarity). A simple approach might
+    be to cluster similar examples together  and sample client data from clusters. However,
+    if one directly clusters the entire dataset,  the resulting clusters may end up largely
+    correlated to labels. To disentangle the effect of label  heterogeneity and semantic
+    heterogeneity, we propose the following algorithm to enforce  intra-client intra-label
     similarity and intra-client inter-label similarity in two separate stages.
-    
-    • Stage 1: For each label, we embed examples using a pretrained neural network 
-    (extracting semantic features), and fit a Gaussian Mixture Model to cluster pretrained 
-    embeddings into groups. Note that this results in multiple groups per label. 
+
+    • Stage 1: For each label, we embed examples using a pretrained neural network
+    (extracting semantic features), and fit a Gaussian Mixture Model to cluster pretrained
+    embeddings into groups. Note that this results in multiple groups per label.
     This stage enforces intra-client intra-label consistency.
-    
-    • Stage 2: To package the clusters from different labels into clients, we aim to compute 
-    an optimal multi-partite matching with cost-matrix defined by KL-divergence between 
-    the Gaussian clusters. To reduce complexity, we heuristically solve the optimal multi-partite 
-    matching by progressively solving the optimal bipartite matching at each time for 
-    randomly-chosen label pairs. 
+
+    • Stage 2: To package the clusters from different labels into clients, we aim to compute
+    an optimal multi-partite matching with cost-matrix defined by KL-divergence between
+    the Gaussian clusters. To reduce complexity, we heuristically solve the optimal multi-partite
+    matching by progressively solving the optimal bipartite matching at each time for
+    randomly-chosen label pairs.
     This stage enforces intra-client inter-label consistency.
 
     Parameters
@@ -338,9 +338,7 @@ def _determine_partition_id_to_indices_if_needed(self) -> None:
         self._partition_id_to_indices_determined = True
 
     def _preprocess_dataset_images(self):
-        images = np.array(
-            self.dataset[self._data_column_name], dtype=np.float32
-        )
+        images = np.array(self.dataset[self._data_column_name], dtype=np.float32)
         if len(images.shape) == 3:  # 1D
             images = np.reshape(
                 images, (images.shape[0], 1, images.shape[1], images.shape[2])
@@ -401,7 +399,7 @@ def _check_data_validation_if_needed(self):
                 )
             elif len(data.shape) == 3:
                 x, y, z = data.shape
-                if not ((x < y and x < z) or (z < x and z < y)) :
+                if not ((x < y and x < z) or (z < x and z < y)):
                     raise ValueError(
                         "The 3D image shape should be [C, H, W] or [H, W, C]. "
                         f"Now: {data.shape}. "