Reformat

eval/plot.py

@@ -4,8 +4,8 @@ import sys
 
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 import torch
+from matplotlib import pyplot as plt
 
 
 def get_stats(l):
@@ -238,4 +238,4 @@ def plot_parameters(path):
 if __name__ == "__main__":
     assert len(sys.argv) == 2
     plot_results(sys.argv[1])
-    # plot_parameters(sys.argv[1])
+    # plot_parameters(sys.argv[1])
\ No newline at end of file

eval/plot_percentile.py

@@ -4,8 +4,8 @@ import sys
 
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 import torch
+from matplotlib import pyplot as plt
 
 
 def get_stats(l):
@@ -70,7 +70,7 @@ def plot_results(path):
             for sp in shared_params:
                 filepath = os.path.join(mf_path, sp)
                 with open(filepath, "r") as spf:
-                    all_shared_params.append(np.array(json.load(spf), dtype = np.int32))
+                    all_shared_params.append(np.array(json.load(spf), dtype=np.int32))
 
         # Plot Training loss
         plt.figure(1)
@@ -78,19 +78,19 @@ def plot_results(path):
         mean = np.mean(all_shared_params, axis=0)
         std = np.std(all_shared_params, axis=0)
         with open(
-                os.path.join(path, "shared_params_avg_"+folder+".json"), "w"
+            os.path.join(path, "shared_params_avg_" + folder + ".json"), "w"
         ) as mf:
             json.dump(mean.tolist(), mf)
 
         with open(
-                os.path.join(path, "shared_params_std_"+folder+".json"), "w"
+            os.path.join(path, "shared_params_std_" + folder + ".json"), "w"
         ) as sf:
             json.dump(std.tolist(), sf)
 
         # copy jupyter notebook code
         percentile = np.percentile(mean, np.arange(0, 100, 1))
         plt.plot(np.arange(0, 100, 1), percentile, label=folder)
-        plt.title('Shared parameters Percentiles')
+        plt.title("Shared parameters Percentiles")
         # plt.ylabel("Absolute frequency value")
         plt.xlabel("Percentiles")
         plt.xticks(np.arange(0, 110, 10))
@@ -101,14 +101,15 @@ def plot_results(path):
         print(sort)
         length = sort.shape[0]
         length = int(length / 20)
-        bins = [torch.sum(sort[length * i: length * (i + 1)]).item() for i in range(20)]
+        bins = [
+            torch.sum(sort[length * i : length * (i + 1)]).item() for i in range(20)
+        ]
         total = np.sum(bins)
-        perc = bins / total #np.divide(bins, total)
+        perc = bins / total  # np.divide(bins, total)
         print(perc)
-        plt.bar(np.arange(0, 97.5, 5), perc, width=5, align='edge',
-                label=folder)
+        plt.bar(np.arange(0, 97.5, 5), perc, width=5, align="edge", label=folder)
 
-        plt.title('Shared parameters Percentiles')
+        plt.title("Shared parameters Percentiles")
         # plt.ylabel("Absolute frequency value")
         plt.xlabel("Percentiles")
         plt.legend(loc="lower right")
@@ -122,4 +123,4 @@ def plot_results(path):
 
 if __name__ == "__main__":
     assert len(sys.argv) == 2
-    plot_results(sys.argv[1])
+    plot_results(sys.argv[1])
\ No newline at end of file

src/decentralizepy/datasets/Shakespeare.py

@@ -8,7 +8,6 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
-from torch.autograd import Variable
 from torch.utils.data import DataLoader
 
 from decentralizepy.datasets.Data import Data

src/decentralizepy/node/Node.py

@@ -422,7 +422,10 @@ class Node:
         if self.model.shared_parameters_counter is not None:
             logging.info("Saving the shared parameter counts")
             with open(
-                    os.path.join(self.log_dir, "{}_shared_parameters.json".format(self.rank)), "w"
+                os.path.join(
+                    self.log_dir, "{}_shared_parameters.json".format(self.rank)
+                ),
+                "w",
             ) as of:
                 json.dump(self.model.shared_parameters_counter.numpy().tolist(), of)
         self.communication.disconnect_neighbors()

src/decentralizepy/sharing/PartialModel.py

@@ -126,7 +126,9 @@ class PartialModel(Sharing):
             )
             Path(self.folder_path).mkdir(parents=True, exist_ok=True)
 
-        self.model.shared_parameters_counter = torch.zeros(self.change_transformer(self.init_model).shape[0], dtype = torch.int32)
+        self.model.shared_parameters_counter = torch.zeros(
+            self.change_transformer(self.init_model).shape[0], dtype=torch.int32
+        )
 
     def extract_top_gradients(self):
         """