first version of the plot

plot_maker.py

@@ -0,0 +1,38 @@
+import os
+import matplotlib.pyplot as plt
+
+datasets = {
+    "cleaned": "games_march2025_cleaned",
+    "cleaned_2k": "games_march2025_cleaned_2k",
+    "cleaned_10k": "games_march2025_cleaned_10k"
+}
+# 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", "")
+            with open(os.path.join(folder, filename), "r") as f:
+                for line in f:
+                    if line.strip().startswith("weighted avg"):
+                        parts = line.split()
+                        f1_score = float(parts[3])  # precision recall f1-score support
+                        results[dataset_name][model_name] = f1_score
+
+# Plot
+models = sorted(results["cleaned"].keys())  # alphabetisch sortieren für gleiche Reihenfolge
+x = range(len(models))
+
+plt.figure(figsize=(12,6))
+plt.bar([i - 0.25 for i in x], [results["cleaned"][m] for m in models], width=0.25, label="cleaned")
+plt.bar(x, [results["cleaned_2k"][m] for m in models], width=0.25, label="cleaned_2k")
+plt.bar([i + 0.25 for i in x], [results["cleaned_10k"][m] for m in models], width=0.25, label="cleaned_10k")
+
+plt.xticks(x, models, rotation=45)
+plt.ylabel("Weighted F1-Score")
+plt.title("Model Performance across Datasets")
+plt.legend()
+plt.tight_layout()
+plt.show()