Basic version to compare embeddings and the last level opf prediction

predictions/plotting.py

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+import json
+
+from matplotlib import pyplot as plt
+
+
+def load_results():
+    with open("predictions/test_results/knn.json", 'r') as f:
+        results = json.load(f)
+    return results
+
+def plot_all():
+    results = load_results()
+    print(f"average elapsed time to detect a sign: {get_general_elapsed_time(results)}")
+    plot_general_accuracy(results)
+    for label in results.keys():
+        plot_accuracy_per_label(results, label)
+
+
+def general_accuracy(results):
+    label_accuracy = get_label_accuracy(results)
+    accuracy = []
+    amount = []
+    response = []
+    for label in label_accuracy.keys():
+        for index, value in enumerate(label_accuracy[label]):
+            if index >= len(accuracy):
+                accuracy.append(0)
+                amount.append(0)
+            accuracy[index] += label_accuracy[label][index]
+            amount[index] += 1
+    for a, b in zip(accuracy, amount):
+        if b < 5:
+            break
+        response.append(a / b)
+    return response
+def plot_general_accuracy(results):
+    accuracy = general_accuracy(results)
+    plt.plot(accuracy)
+    plt.title = "General accuracy"
+    plt.ylabel('accuracy')
+    plt.xlabel('buffer')
+    plt.show()
+
+
+def plot_accuracy_per_label(results, label):
+    accuracy = get_label_accuracy(results)
+    plt.plot(accuracy[label], label=label)
+    plt.titel = f"Accuracy per label {label}"
+    plt.ylabel('accuracy')
+    plt.xlabel('prediction')
+    plt.legend()
+    plt.show()
+
+def get_label_accuracy(results):
+    accuracy = {}
+    amount = {}
+    response = {}
+    for label, predictions in results.items():
+        if label not in accuracy:
+            accuracy[label] = []
+            amount[label] = []
+        for prediction in predictions:
+            for index, value in enumerate(prediction["predictions"]):
+                if index >= len(accuracy[label]):
+                    accuracy[label].append(0)
+                    amount[label].append(0)
+                accuracy[label][index] += 1 if value["correct"] else 0
+                amount[label][index] += 1
+    for label in accuracy:
+        response[label] = []
+        for index, value in enumerate(accuracy[label]):
+            if amount[label][index] < 2:
+                break
+            response[label].append(accuracy[label][index] / amount[label][index])
+    return response
+
+def get_general_elapsed_time(results):
+    label_time = get_label_elapsed_time(results)
+    return sum([label_time[label] for label in results]) / len(results)
+
+def get_label_elapsed_time(results):
+    return {label: sum([result["elapsed_time"] for result in results[label]]) / len(results[label]) for label in results}
+
+
+if __name__ == '__main__':
+    plot_all()