Modification du script de création des graphs pour utiliser le dossier avec les résultats générés

testModel/show_results.py

@@ -2,48 +2,70 @@ import sys
 import os
 import matplotlib.pyplot as plt
 import numpy as np
+import json
+import matplotlib.ticker as ticker
+from matplotlib.ticker import MaxNLocator
+
 
-# Ajouter le répertoire parent au chemin de recherche
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
 
-from parsers.jsonParser import parseJsonFile
+from variables.articles import LENGTH_CATEGORIES
+from variables.models import MODELS
+
+def get_result_filename(model):
+    return model.replace(" ", "_").replace("-", "_").replace(".", "_").replace("/", "-")
+
+RESULTS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "./results")) 
+GRAPHS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "./results/graphs"))
+
+length_categories = LENGTH_CATEGORIES
+length_categories.append("ALL")
+
+models = [model for model in MODELS.keys()]
+
+metrics = ["TPR", "TNR", "Precision", "F1"]
+
+for model in models:
+    print(get_result_filename(model))
+
+models_results = {}
+
+for model in models:
+    with open(f"{RESULTS_DIR}/{get_result_filename(model)}.json", "r", encoding="utf-8") as file:
+        data = json.load(file)
+
+        for length_category in length_categories:
+            values = []
+
+            for metric in metrics:
+                values.append(data["results"][length_category][metric])
+
+            data["results"][length_category] = values
+
+        models_results[model] = data["results"]
 
-RESULTS_DIR_NAME = "./results"
-RESULTS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), RESULTS_DIR_NAME))
+print(models_results)
 
-try:
-    results = parseJsonFile(f"{RESULTS_DIR}/zero_shot/v2/condensed_results.json")
-    print(results)
-except Exception as e:
-    print(f"Error: {e}")
+x = np.arange(len(models))
+width = 0.2
 
-data = results
-models = [entry["model"] for entry in data]
-categories = [entry["Name"] for entry in data[0]["data"]]
+for length_category in length_categories:
 
-fig, axes = plt.subplots(5, 1, figsize=(18, 50), sharey=True)
+    fig, ax = plt.subplots(figsize=(14, 8))
 
-colors = ["skyblue", "orange", "green"]
-metrics = ["TPR", "TNR", "Precision"]
-bar_width = 0.2
-x_positions = np.arange(len(models))
+    for i in range(len(metrics)):
+        ax.bar(x + i * width, np.array([models_results[model][length_category][i] for model in models]), width, label=metrics[i])
 
-for i, category in enumerate(categories):
-    ax = axes[i]
-    
-    for j, metric in enumerate(metrics):
-        values = []
-        for model_data in data:
-            category_data = next(item for item in model_data["data"] if item["Name"] == category)
-            values.append(category_data[metric])
+    ax.set_xticks(x + width * (len(metrics) / 2 - 0.5))
+    ax.set_xticklabels(models, rotation=20, ha="right")
 
-        ax.bar(x_positions + j * bar_width, values, width=bar_width, label=metric, color=colors[j])
+    ax.yaxis.set_major_locator(MaxNLocator(nbins=10))
+    ax.yaxis.set_minor_locator(ticker.AutoMinorLocator(2))
 
-    ax.set_title(category)
-    ax.set_xticks(x_positions + bar_width)
-    ax.set_xticklabels(models, rotation=15)
-    ax.set_ylabel("Valeur")
+    ax.set_ylabel("Score")
+    ax.set_title("Comparaison des modèles selon les métriques")
     ax.legend()
 
-# plt.tight_layout()
-plt.savefig(f"{RESULTS_DIR}/zero_shot/v2/condensed_results.png")
\ No newline at end of file
+    plt.tight_layout()
+    plt.savefig(f"{GRAPHS_DIR}/{length_category}.png")
+    plt.close(fig)
\ No newline at end of file