changes

compare_graph_maker_3.py

@@ -0,0 +1,59 @@
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+
+datasets = {
+    #"cleaned": "games_march2025_cleaned",
+    #"cleaned_2k": "games_march2025_cleaned_2k",
+    #"cleaned_10k": "games_march2025_cleaned_10k"
+    "cleaned_2k": "games_march2025_cleaned_2k_i3k",
+}
+# def results
+results = {}
+
+for dataset_name, folder in datasets.items():
+    results[dataset_name] = {}
+    for filename in os.listdir(folder):
+        if filename.endswith(".txt"):
+            model_name = filename.replace(".txt", "")
+            print("model " + model_name)
+            results[dataset_name][model_name] = {}
+            with open(os.path.join(folder, filename), "r") as f:
+                for line in f:
+                    if line.strip().startswith("micro avg"):
+                        print("micro")
+                        results[dataset_name][model_name][0] = float(line.split()[4]) # micro f1
+                    if line.strip().startswith("macro avg"):
+                        print("macro")
+                        results[dataset_name][model_name][1] = float(line.split()[4]) # macro f1
+                    if line.strip().startswith("weighted avg"):
+                        print("weight")
+                        results[dataset_name][model_name][2] = float(line.split()[4]) # weighted avg f1
+
+# Plot
+#models = sorted(results["cleaned_2k"].keys())  # alphabetisch sortieren für gleiche Reihenfolge
+models = dict(sorted(results["cleaned_2k"].items(), key=lambda i: i[1][2], reverse=True)) # nach values sortieren
+print(models)
+x = range(len(models))
+
+fig = plt.figure()
+#ax = fig.add_subplot(projection='3d')
+
+plt.bar([i - 0.25 for i in x], [results["cleaned_2k"][m][0] for m in models], width=0.25, label="Micro")
+plt.bar(x,                     [results["cleaned_2k"][m][1] for m in models], width=0.25, label="Macro")
+plt.bar([i + 0.25 for i in x], [results["cleaned_2k"][m][2] for m in models], width=0.25, label="Weighted")
+
+plt.xticks(x, models, rotation=90)
+plt.ylabel("F1 Score")
+#ax.set_zlabel("F1 Value")
+plt.ylim(0,1)
+plt.title("Model Performance - 2k Dataset")
+plt.legend()
+plt.tight_layout()
+plt.savefig('compare_graph_latest_3.png')
+plt.show()
+
+# On the y-axis let's only label the discrete values that we have data for.
+#ax.set_yticks(yticks)
+
+plt.show()