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plot_performance_data.py

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+import numpy as np
+import glob
+import os
+from natsort import natsorted
+from functions.fisher_exact import fisher_excat_upper, fisher_excat_lower
+import matplotlib.pyplot as plt
+
+p_threshold: float = 1.0 / 100.0
+file_selection: int = 0
+number_of_pattern: int = 60000
+path: str = "performance_data"
+data_source: str = "test_accuracy"
+filename_list = natsorted(glob.glob(os.path.join(path, "performances_*.npz")))
+assert file_selection < len(filename_list)
+
+filename = filename_list[file_selection]
+
+data = np.load(filename)
+
+understand_parameter: bool = False
+percentage: bool = True
+if data_source.upper() == str("test_accuracy").upper():
+    to_print = data["test_accuracy"]
+    understand_parameter = True
+    percentage = True
+elif data_source.upper() == str("train_accuracy").upper():
+    to_print = data["train_accuracy"]
+    understand_parameter = True
+    percentage = True
+elif data_source.upper() == str("train_losses").upper():
+    to_print = data["train_losses"]
+    understand_parameter = True
+    percentage = False
+elif data_source.upper() == str("test_losses").upper():
+    to_print = data["test_losses"]
+    understand_parameter = True
+    percentage = False
+assert understand_parameter
+
+if percentage:
+    correct_count = np.round(to_print * number_of_pattern / 100.0).astype(np.int64)
+    upper = np.zeros((correct_count.shape[0]))
+    lower = np.zeros((correct_count.shape[0]))
+
+    for id in range(0, correct_count.shape[0]):
+        upper[id] = fisher_excat_upper(
+            correct_pattern_count=correct_count[id],
+            number_of_pattern=number_of_pattern,
+            p_threshold=p_threshold,
+        )
+
+        lower[id] = fisher_excat_lower(
+            correct_pattern_count=correct_count[id],
+            number_of_pattern=number_of_pattern,
+            p_threshold=p_threshold,
+        )
+
+    x = np.arange(1, to_print.shape[0] + 1)
+    y = 100.0 - to_print
+    plt.plot(x, y + upper, "k--")
+    plt.plot(x, y - lower, "k--")
+    plt.plot(x, y, "r")
+    plt.ylim([0, (100.0 - to_print.min()) * 1.1])
+    plt.xlim([1, to_print.shape[0]])
+    plt.xlabel("Epochs")
+    plt.ylabel("Error [%]")
+    plt.title(data_source.replace("_", " "))
+else:
+    x = np.arange(1, to_print.shape[0] + 1)
+    plt.plot(x, to_print)
+    plt.ylim([0, to_print.max() * 1.1])
+    plt.xlim([1, to_print.shape[0]])
+    plt.xlabel("Epochs")
+    plt.ylabel("Loss")
+    plt.title(data_source.replace("_", " "))
+
+plt.show()