diff --git a/eval/plot.py b/eval/plot.py
index 9fdccded166afbb51b57a4bc4f37ebda819773a2..601d8e86bfda57e69a0f88fa305a6a7fa674e23a 100644
--- a/eval/plot.py
+++ b/eval/plot.py
@@ -36,8 +36,14 @@ def plot(means, stdevs, mins, maxs, title, label, loc):
plt.legend(loc=loc)
-def plot_results(path, data_machine="machine0", data_node=0):
+def plot_results(path, centralized, data_machine="machine0", data_node=0):
folders = os.listdir(path)
+ if centralized.lower() in ['true', '1', 't', 'y', 'yes']:
+ centralized = True
+ print("Centralized")
+ else:
+ centralized = False
+
folders.sort()
print("Reading folders from: ", path)
print("Folders: ", folders)
@@ -82,7 +88,10 @@ def plot_results(path, data_machine="machine0", data_node=0):
)
# Plot Testing loss
plt.figure(2)
- means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in main_data])
+ if centralized:
+ means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in main_data])
+ else:
+ means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in results])
plot(means, stdevs, mins, maxs, "Testing Loss", folder, "upper right")
df = pd.DataFrame(
{
@@ -98,7 +107,10 @@ def plot_results(path, data_machine="machine0", data_node=0):
)
# Plot Testing Accuracy
plt.figure(3)
- means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in main_data])
+ if centralized:
+ means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in main_data])
+ else:
+ means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in results])
plot(means, stdevs, mins, maxs, "Testing Accuracy", folder, "lower right")
df = pd.DataFrame(
{
@@ -241,6 +253,9 @@ def plot_parameters(path):
if __name__ == "__main__":
- assert len(sys.argv) == 2
- plot_results(sys.argv[1])
+ assert len(sys.argv) == 3
+ # The args are:
+ # 1: the folder with the data
+ # 2: True/False: If True then the evaluation on the test set was centralized
+ plot_results(sys.argv[1], sys.argv[2])
# plot_parameters(sys.argv[1])
diff --git a/eval/plotting_from_csv.py b/eval/plotting_from_csv.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8d4320c507d1b7532f3fef1fba5f2d02c8d926a
--- /dev/null
+++ b/eval/plotting_from_csv.py
@@ -0,0 +1,183 @@
+import distutils
+import json
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+
+def plot(x_axis, means, stdevs, pos, nb_plots, title, label, loc, xlabel):
+ cmap = plt.get_cmap("gist_rainbow")
+ plt.title(title)
+ plt.xlabel(xlabel)
+ y_axis = list(means)
+ err = list(stdevs)
+ print("label:", label)
+ print("color: ", cmap(1 / nb_plots * pos))
+ plt.errorbar(
+ list(x_axis), y_axis, yerr=err, label=label, color=cmap(1 / nb_plots * pos)
+ )
+ plt.legend(loc=loc)
+
+
+def plot_results(path, epochs, global_epochs="True"):
+ if global_epochs.lower() in ['true', '1', 't', 'y', 'yes']:
+ global_epochs = True
+ else:
+ global_epochs = False
+ epochs = int(epochs)
+ # rounds = int(rounds)
+ folders = os.listdir(path)
+ folders.sort()
+ print("Reading folders from: ", path)
+ print("Folders: ", folders)
+ bytes_means, bytes_stdevs = {}, {}
+ meta_means, meta_stdevs = {}, {}
+ data_means, data_stdevs = {}, {}
+
+ files = os.listdir(path)
+ files = [f for f in files if f.endswith(".csv")]
+ train_loss = sorted([f for f in files if f.startswith("train_loss")])
+ test_acc = sorted([f for f in files if f.startswith("test_acc")])
+ test_loss = sorted([f for f in files if f.startswith("test_loss")])
+ min_losses = []
+ for i, f in enumerate(train_loss):
+ filepath = os.path.join(path, f)
+ with open(filepath, "r") as inf:
+ results_csv = pd.read_csv(inf)
+ # Plot Training loss
+ plt.figure(1)
+ if global_epochs:
+ rounds = results_csv["rounds"].iloc[0]
+ print("Rounds: ", rounds)
+ results_cr = results_csv[results_csv.rounds <= epochs*rounds]
+ means = results_cr["mean"].to_numpy()
+ stdevs = results_cr["std"].to_numpy()
+ x_axis = results_cr["rounds"].to_numpy() / rounds # list(np.arange(0, len(means), 1))
+ x_label = "global epochs"
+ else:
+ results_cr = results_csv[results_csv.rounds <= epochs]
+ means = results_cr["mean"].to_numpy()
+ stdevs = results_cr["std"].to_numpy()
+ x_axis = results_cr["rounds"].to_numpy()
+ x_label = "communication rounds"
+ min_losses.append(np.min(means))
+
+ plot(
+ x_axis,
+ means,
+ stdevs,
+ i,
+ len(train_loss),
+ "Training Loss",
+ f[len("train_loss") + 1 : -len(":2022-03-24T17:54.csv")],
+ "upper right",
+ x_label,
+ )
+
+ min_tlosses = []
+ for i, f in enumerate(test_loss):
+ filepath = os.path.join(path, f)
+ with open(filepath, "r") as inf:
+ results_csv = pd.read_csv(inf)
+ if global_epochs:
+ rounds = results_csv["rounds"].iloc[0]
+ print("Rounds: ", rounds)
+ results_cr = results_csv[results_csv.rounds <= epochs*rounds]
+ means = results_cr["mean"].to_numpy()
+ stdevs = results_cr["std"].to_numpy()
+ x_axis = results_cr["rounds"].to_numpy() / rounds # list(np.arange(0, len(means), 1))
+ x_label = "global epochs"
+ else:
+ results_cr = results_csv[results_csv.rounds <= epochs]
+ means = results_cr["mean"].to_numpy()
+ stdevs = results_cr["std"].to_numpy()
+ x_axis = results_cr["rounds"].to_numpy()
+ x_label = "communication rounds"
+ print("x axis:", x_axis)
+ min_tlosses.append(np.min(means))
+ # Plot Testing loss
+ plt.figure(2)
+ plot(
+ x_axis,
+ means,
+ stdevs,
+ i,
+ len(test_loss),
+ "Testing Loss",
+ f[len("test_loss") + 1 : -len(":2022-03-24T17:54.csv")],
+ "upper right",
+ x_label,
+ )
+
+ max_taccs = []
+ for i, f in enumerate(test_acc):
+ filepath = os.path.join(path, f)
+ with open(filepath, "r") as inf:
+ results_csv = pd.read_csv(inf)
+ if global_epochs:
+ rounds = results_csv["rounds"].iloc[0]
+ print("Rounds: ", rounds)
+ results_cr = results_csv[results_csv.rounds <= epochs*rounds]
+ means = results_cr["mean"].to_numpy()
+ stdevs = results_cr["std"].to_numpy()
+ x_axis = results_cr["rounds"].to_numpy() / rounds # list(np.arange(0, len(means), 1))
+ x_label = "global epochs"
+ else:
+ results_cr = results_csv[results_csv.rounds <= epochs]
+ means = results_cr["mean"].to_numpy()
+ stdevs = results_cr["std"].to_numpy()
+ x_axis = results_cr["rounds"].to_numpy()
+ x_label = "communication rounds"
+ max_taccs.append(np.max(means))
+ # Plot Testing Accuracy
+ plt.figure(3)
+ plot(
+ x_axis,
+ means,
+ stdevs,
+ i,
+ len(test_acc),
+ "Testing Accuracy",
+ f[len("test_acc") + 1 : -len(":2022-03-24T17:54.csv")],
+ "lower right",
+ x_label,
+ )
+
+ names_loss = [
+ f[len("train_loss") + 1 : -len(":2022-03-24T17:54.csv")] for f in train_loss
+ ]
+ names_acc = [
+ f[len("test_acc") + 1 : -len(":2022-03-24T17:54.csv")] for f in test_acc
+ ]
+ print(names_loss)
+ print(names_acc)
+ pf = pd.DataFrame(
+ {
+ "test_accuracy": max_taccs,
+ "test_losses": min_tlosses,
+ "train_losses": min_losses,
+ },
+ names_loss,
+ )
+ pf = pf.sort_values(["test_accuracy"], 0, ascending=False)
+ pf.to_csv(os.path.join(path, "best_results.csv"))
+
+ plt.figure(1)
+ plt.savefig(os.path.join(path, "ge_train_loss.png"), dpi=300)
+ plt.figure(2)
+ plt.savefig(os.path.join(path, "ge_test_loss.png"), dpi=300)
+ plt.figure(3)
+ plt.savefig(os.path.join(path, "ge_test_acc.png"), dpi=300)
+
+
+if __name__ == "__main__":
+ assert len(sys.argv) == 4
+ # The args are:
+ # 1: the folder with the csv files,
+ # 2: the number of epochs / comm rounds to plot for,
+ # 3: True/False with True meaning plot global epochs and False plot communication rounds
+ print(sys.argv[1], sys.argv[2], sys.argv[3])
+ plot_results(sys.argv[1], sys.argv[2], sys.argv[3])