Add model ratio plotting

eval/plot_model.py

+import json
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+from matplotlib import pyplot as plt
+from pyexpat import model
+
+
+def plot(x, y, label, *args):
+    plt.plot(x, y, *args, label=label)
+    plt.legend()
+
+
+def reject_outliers(data, m=2.0):
+    d = np.abs(data - np.median(data))
+    mdev = np.median(d)
+    s = d / (mdev if mdev else 1.0)
+    return data[s < m]
+
+
+def plot_model(path, title):
+    model_path = os.path.join(path, "plots")
+    Path(model_path).mkdir(parents=True, exist_ok=True)
+    files = [f for f in os.listdir(path) if f.endswith("json")]
+    for file in files:
+        filepath = os.path.join(path, file)
+        with open(filepath, "r") as inf:
+            model_vec = json.load(inf)
+            del model_vec["order"]
+            del model_vec["shapes"]
+            model_vec = np.array(model_vec[list(model_vec.keys())[0]])
+        num_elements = model_vec.shape[0]
+        x_axis = np.arange(1, num_elements + 1)
+        plt.clf()
+        plt.title(title)
+        plot(x_axis, model_vec, "unsorted", ".")
+        model_vec = np.sort(model_vec)
+        plot(x_axis, model_vec, "sorted")
+        plt.savefig(os.path.join(model_path, file[0:-5]))
+
+
+def plot_ratio(path_change, path_val, title):
+    model_path = os.path.join(path_change, "plots_ratio")
+    Path(model_path).mkdir(parents=True, exist_ok=True)
+    files_change = [f for f in os.listdir(path_change) if f.endswith("json")]
+    files_val = [f for f in os.listdir(path_val) if f.endswith("json")]
+    for i, file in enumerate(files_change):
+        print("Processed ", file)
+        filepath_change = os.path.join(path_change, file)
+        filepath_val = os.path.join(path_val, files_val[i])
+        with open(filepath_change, "r") as inf:
+            model_change = json.load(inf)
+            del model_change["order"]
+            del model_change["shapes"]
+            model_change = np.array(model_change[list(model_change.keys())[0]])
+        with open(filepath_val, "r") as inf:
+            model_val = json.load(inf)
+            del model_val["order"]
+            del model_val["shapes"]
+            model_val = np.array(model_val[list(model_val.keys())[0]])
+        num_elements = model_val.shape[0]
+        x_axis = np.arange(1, num_elements + 1)
+        plt.clf()
+        plt.title(title)
+        model_vec = np.divide(
+            model_change,
+            model_val,
+            out=np.zeros_like(model_change),
+            where=model_val != 0.0,
+        )
+        model_vec = reject_outliers(model_vec)
+        num_elements = model_vec.shape[0]
+        x_axis = np.arange(1, num_elements + 1)
+        plot(x_axis, model_vec, "unsorted", ".")
+        model_vec = np.sort(model_vec)
+        plot(x_axis, model_vec, "sorted")
+        plt.savefig(os.path.join(model_path, file[0:-5]))
+
+
+if __name__ == "__main__":
+    assert len(sys.argv) == 3
+    plot_model(
+        os.path.join(sys.argv[1], "model_change", sys.argv[2]), "Change in Weights"
+    )
+    plot_model(os.path.join(sys.argv[1], "model_val", sys.argv[2]), "Model Parameters")
+    plot_ratio(
+        os.path.join(sys.argv[1], "model_change", sys.argv[2]),
+        os.path.join(sys.argv[1], "model_val", sys.argv[2]),
+        "Ratio",
+    )

src/decentralizepy/training/ChangeAccumulator.py

@@ -105,7 +105,7 @@ class ChangeAccumulator(Training):
             shapes[k] = list(v1.shape)
         output_dict["shapes"] = shapes
 
-        output_dict[self.communication_round] = v.tolist()
+        output_dict["tensor"] = v.tolist()
 
         with open(
             os.path.join(