From fed6b3c77616cd271e46851a1ea3d60a67f6d347 Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Wed, 14 May 2025 17:43:01 -0300
Subject: [PATCH 1/9] feat: Add to teste do a-topsis a partir da leitura de um
 arquivo csv

---
 scripts/atopsis_test_from_file.py | 51 +++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 scripts/atopsis_test_from_file.py

diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_test_from_file.py
new file mode 100644
index 0000000..e5cfd84
--- /dev/null
+++ b/scripts/atopsis_test_from_file.py
@@ -0,0 +1,51 @@
+import os
+import pandas as pd
+import numpy as np
+import sys
+sys.path.append("../src")
+from decision_making import ATOPSIS
+
+def load_dataset(file_folder_path):
+    try:
+        dataset = pd.read_csv(file_folder_path, sep=",")
+        return dataset
+    except Exception as e:
+        print(f"Erro ao carregar os dados! Error:{e}\n")
+        return None
+    
+
+if __name__=="__main__":
+    file_folder_path = "../dataset/agg_metablockse_jbhi.csv"
+    dataset = load_dataset(file_folder_path)
+    ## Filtrar pelo modelo desejado
+    # dataset = dataset[dataset['visual-feature-extractor']=="resnet-50"]
+
+    ## Filtrar os dados pela métrica desejada
+    dataset = dataset[dataset['missing-data-percenteage']==0]
+
+    ## Para os dados referentes aos parâmetros desejos
+    # dataset = dataset[dataset['metric']!=('auc' and 'f1_score')]
+
+
+    print(dataset)
+    # Os algoritmos a serem testados
+    alg_names = dataset['method'].unique()
+
+    # Ordenar as saídas dos dados por parâmetro
+    avg_mat = np.array(dataset['AVG']).reshape(len(alg_names),-1)
+    std_mat = np.array(dataset['STD']).reshape(len(alg_names),-1)
+    
+    # Pesos das decisões
+    weights = [0.7, 0.3]
+        
+    try:
+        atop = ATOPSIS(avg_mat, std_mat, avg_cost_ben="benefit", std_cost_ben="cost", weights=weights)
+        atop.get_ranking(True)
+        atop.plot_ranking(alg_names=alg_names)
+    except Exception as e:
+        print(f"An error occurred during A-TOPSIS processing: {e}")
+
+    print("-" * 50)
+    print("")
+
+

From f48bde3e2abe52cb0b89eda97cd8e9ca7e17987b Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Wed, 14 May 2025 18:27:26 -0300
Subject: [PATCH 2/9] feat: add dos arquivos usados de teste no gitignore

---
 scripts/atopsis_test_from_file.py | 46 ++++++++++++++++++-------------
 1 file changed, 27 insertions(+), 19 deletions(-)

diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_test_from_file.py
index e5cfd84..9672d5a 100644
--- a/scripts/atopsis_test_from_file.py
+++ b/scripts/atopsis_test_from_file.py
@@ -14,30 +14,40 @@ def load_dataset(file_folder_path):
         return None
     
 
-if __name__=="__main__":
-    file_folder_path = "../dataset/agg_metablockse_jbhi.csv"
+if __name__ == "__main__":
+    file_folder_path = "../dataset/agg_metablockse_jbhi_pad-25.csv"
     dataset = load_dataset(file_folder_path)
-    ## Filtrar pelo modelo desejado
-    # dataset = dataset[dataset['visual-feature-extractor']=="resnet-50"]
+    
+    if dataset is None:
+        sys.exit("Dataset could not be loaded. Exiting...")
 
-    ## Filtrar os dados pela métrica desejada
-    dataset = dataset[dataset['missing-data-percenteage']==0]
+    # Group the dataset by the 'comb_method'
+    grouped_data = dataset.groupby('comb_method')
 
-    ## Para os dados referentes aos parâmetros desejos
-    # dataset = dataset[dataset['metric']!=('auc' and 'f1_score')]
+    # Initialize an empty list to store the ordered groups
+    ordered_dataset = []
 
+    # Iterate through each group (i.e., each unique comb_method)
+    for comb_method, group in grouped_data:
+        ordered_dataset.append(group)  # Append each group to the list
 
-    print(dataset)
-    # Os algoritmos a serem testados
-    alg_names = dataset['method'].unique()
+    # Concatenate all the groups into a single ordered DataFrame
+    ordered_dataset = pd.concat(ordered_dataset)
 
-    # Ordenar as saídas dos dados por parâmetro
-    avg_mat = np.array(dataset['AVG']).reshape(len(alg_names),-1)
-    std_mat = np.array(dataset['STD']).reshape(len(alg_names),-1)
-    
-    # Pesos das decisões
-    weights = [0.7, 0.3]
+    print(f"Dataset reordenado:\n{ordered_dataset}\n")
+    # Get the unique 'alg_names' for the ordered dataset
+    alg_names = ordered_dataset['comb_method'].unique()
+
+    # Prepare the matrices for A-TOPSIS
+    avg_mat = np.array(ordered_dataset['AVG']).reshape(len(alg_names), -1)
+    std_mat = np.array(ordered_dataset['STD']).reshape(len(alg_names), -1)
         
+    print("Average matrix:")
+    print(avg_mat)
+        
+    # Weights for decision-making
+    weights = [0.7, 0.3]
+       
     try:
         atop = ATOPSIS(avg_mat, std_mat, avg_cost_ben="benefit", std_cost_ben="cost", weights=weights)
         atop.get_ranking(True)
@@ -47,5 +57,3 @@ def load_dataset(file_folder_path):
 
     print("-" * 50)
     print("")
-
-

From 4b43540fe2b758a5efbdd46bca047dff9359ad6d Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Thu, 15 May 2025 14:36:13 -0300
Subject: [PATCH 3/9] =?UTF-8?q?feat:=20Add=20descri=C3=A7=C3=A3o=20dos=20d?=
 =?UTF-8?q?ados=20dos=20eixos=20'x'=20e=20'y',=20al=C3=A9m=20da=20leitura?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 scripts/atopsis_test_from_file.py             | 34 ++++++--
 ...ted-cross-attention-models-atopsis-test.py | 87 +++++++++++++++++++
 2 files changed, 114 insertions(+), 7 deletions(-)
 create mode 100644 scripts/gated-cross-attention-models-atopsis-test.py

diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_test_from_file.py
index 9672d5a..25877d1 100644
--- a/scripts/atopsis_test_from_file.py
+++ b/scripts/atopsis_test_from_file.py
@@ -34,7 +34,10 @@ def load_dataset(file_folder_path):
     # Concatenate all the groups into a single ordered DataFrame
     ordered_dataset = pd.concat(ordered_dataset)
 
+    # ordered_dataset = ordered_dataset[ordered_dataset["metric"]]
     print(f"Dataset reordenado:\n{ordered_dataset}\n")
+
+    # Filtrar os dados do 
     # Get the unique 'alg_names' for the ordered dataset
     alg_names = ordered_dataset['comb_method'].unique()
 
@@ -42,18 +45,35 @@ def load_dataset(file_folder_path):
     avg_mat = np.array(ordered_dataset['AVG']).reshape(len(alg_names), -1)
     std_mat = np.array(ordered_dataset['STD']).reshape(len(alg_names), -1)
         
-    print("Average matrix:")
-    print(avg_mat)
-        
     # Weights for decision-making
     weights = [0.7, 0.3]
        
     try:
-        atop = ATOPSIS(avg_mat, std_mat, avg_cost_ben="benefit", std_cost_ben="cost", weights=weights)
+        atop = ATOPSIS(avg_mat, std_mat, avg_cost_ben="benefit", std_cost_ben="cost", weights=weights, normalize=False)
         atop.get_ranking(True)
-        atop.plot_ranking(alg_names=alg_names)
+        # atop.plot_ranking(alg_names=alg_names)
     except Exception as e:
         print(f"An error occurred during A-TOPSIS processing: {e}")
 
-    print("-" * 50)
-    print("")
+    # 2) Extrai scores do atributo final_ranking
+    scores = atop.final_ranking
+
+    # 3) Monta o DataFrame de resultado
+    result = pd.DataFrame({
+        "attention_mecanism": alg_names,
+        "atopsis_score":       scores
+    })
+
+    # 4) Calcula o rank (maior score = melhor posição)
+    result["rank"] = (
+        result["atopsis_score"]
+            .rank(ascending=False, method="min")
+            .astype(int)
+    )
+
+    print("\n=== Ranking Final ===")
+    print(result.sort_values("rank"))
+
+    # 6) Plota com os próprios nomes
+    atop.plot_ranking(save_path="../images/a_topsis.png", alg_names=alg_names, show=True, font_size=22, title="A-TOPSIS for PAD-UFES-25 dataset", y_axis_title="Scores", x_axis_title="Methods")
+
diff --git a/scripts/gated-cross-attention-models-atopsis-test.py b/scripts/gated-cross-attention-models-atopsis-test.py
new file mode 100644
index 0000000..4d2702c
--- /dev/null
+++ b/scripts/gated-cross-attention-models-atopsis-test.py
@@ -0,0 +1,87 @@
+import sys
+import pandas as pd
+import numpy as np
+
+# 1) Ajuste do path para importar o ATOPSIS
+sys.path.append("../../src")
+from decision_making import ATOPSIS
+
+
+def main(file_path):
+    # 2) Carrega o CSV e limpa nomes de coluna
+    df = pd.read_csv(file_path, sep=",")
+    df.columns = df.columns.str.strip()
+    print("Column names:", df.columns.tolist())
+
+    # 3) Métricas consideradas
+    metrics = ["accuracy", "balanced_accuracy", "auc"]
+
+
+    # wanted_model_name = "densenet169"
+
+    # Obter as informações referentes ao modelo desejado
+    # df=df[df['model_name'] == wanted_model_name]
+    # df = df[df['attention_mecanism']==["no-metadata", "att-intramodal+residual+cross-attention-metadados"]]
+    print(df)
+    # 4) Extrai média e desvio padrão
+    for m in metrics:
+        df[[f"{m}_mean", f"{m}_std"]] = (
+            df[m]
+            .str.extract(r"([\d\.]+)\s*±\s*([\d\.]+)")
+            .astype(float)
+        )
+
+    # 5) Agrupa por attention_mecanism e computa média de cada grupo
+    grouped = df.groupby("attention_mecanism").agg(
+        **{f"{m}_mean": (f"{m}_mean", "mean") for m in metrics},
+        **{f"{m}_std":  (f"{m}_std",  "mean") for m in metrics},
+    ).reset_index()
+
+    # 6) Prepara as matrizes em memória
+    avg_mat = grouped[[f"{m}_mean" for m in metrics]].values.tolist()
+    std_mat = grouped[[f"{m}_std"  for m in metrics]].values.tolist()
+    alg_names = grouped["attention_mecanism"].tolist()
+
+    # 7) Executa A‑TOPSIS (listas de listas)
+    #    avg_cost_ben="benefit"
+    #    std_cost_ben="cost"
+    weights_presetted = [0.75, 0.25]
+
+    # Executa A‑TOPSIS em memória (listas de listas)
+    atop = ATOPSIS(
+        avg_mat,
+        std_mat,
+        avg_cost_ben="benefit",
+        std_cost_ben="cost",
+        weights=weights_presetted,
+        normalize=True
+    )
+    # 1) Gera o ranking (printa se verbose=True)
+    atop.get_ranking(verbose=True)
+
+    # 2) Extrai scores do atributo final_ranking
+    scores = atop.final_ranking
+
+    # 3) Monta o DataFrame de resultado
+    result = pd.DataFrame({
+        "attention_mecanism": alg_names,
+        "atopsis_score":       scores
+    })
+
+    # 4) Calcula o rank (maior score = melhor posição)
+    result["rank"] = (
+        result["atopsis_score"]
+            .rank(ascending=False, method="min")
+            .astype(int)
+    )
+
+    print("\n=== Ranking Final ===")
+    print(result.sort_values("rank"))
+
+    # 6) Plota com os próprios nomes
+    atop.plot_ranking(save_path="../../data/images/a_topsis.png", alg_names=alg_names, show=True, font_size=22, title="A-TOPSIS for PAD-UFES-20 dataset", y_axis_title="Scores", x_axis_title="Methods")
+
+if __name__=="__main__":
+    file_path = "../../data/metrics.csv"
+    # Função principal
+    main(file_path=file_path)
\ No newline at end of file

From fa9d0dfbc6bdd4f20ece7720d9216666a6c39914 Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Thu, 15 May 2025 16:40:31 -0300
Subject: [PATCH 4/9] feat: Fix tamanho da fonte

---
 scripts/atopsis_test_from_file.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_test_from_file.py
index 25877d1..d99548a 100644
--- a/scripts/atopsis_test_from_file.py
+++ b/scripts/atopsis_test_from_file.py
@@ -67,7 +67,7 @@ def load_dataset(file_folder_path):
     # 4) Calcula o rank (maior score = melhor posição)
     result["rank"] = (
         result["atopsis_score"]
-            .rank(ascending=False, method="min")
+            .rank(ascending=True, method="min")
             .astype(int)
     )
 
@@ -75,5 +75,5 @@ def load_dataset(file_folder_path):
     print(result.sort_values("rank"))
 
     # 6) Plota com os próprios nomes
-    atop.plot_ranking(save_path="../images/a_topsis.png", alg_names=alg_names, show=True, font_size=22, title="A-TOPSIS for PAD-UFES-25 dataset", y_axis_title="Scores", x_axis_title="Methods")
+    atop.plot_ranking(save_path="../images/a_topsis.png", alg_names=alg_names, show=True, font_size=22, title="A-TOPSIS for PAD-UFES-20-Extended dataset", y_axis_title="Scores", x_axis_title="Methods", ascending=True)
 

From 4ce05181a06c4852ed2ddc41623debd4175de7bf Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Tue, 20 May 2025 17:09:51 -0300
Subject: [PATCH 5/9] =?UTF-8?q?feat:=20Altera=C3=A7=C3=A3o=20da=20cor=20do?=
 =?UTF-8?q?s=20gr=C3=A1ficos?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 scripts/atopsis_test_from_file.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_test_from_file.py
index d99548a..91bbe72 100644
--- a/scripts/atopsis_test_from_file.py
+++ b/scripts/atopsis_test_from_file.py
@@ -46,7 +46,7 @@ def load_dataset(file_folder_path):
     std_mat = np.array(ordered_dataset['STD']).reshape(len(alg_names), -1)
         
     # Weights for decision-making
-    weights = [0.7, 0.3]
+    weights = [0.5, 0.5]
        
     try:
         atop = ATOPSIS(avg_mat, std_mat, avg_cost_ben="benefit", std_cost_ben="cost", weights=weights, normalize=False)
@@ -75,5 +75,5 @@ def load_dataset(file_folder_path):
     print(result.sort_values("rank"))
 
     # 6) Plota com os próprios nomes
-    atop.plot_ranking(save_path="../images/a_topsis.png", alg_names=alg_names, show=True, font_size=22, title="A-TOPSIS for PAD-UFES-20-Extended dataset", y_axis_title="Scores", x_axis_title="Methods", ascending=True)
+    atop.plot_ranking(save_path="../images/a_topsis.png", alg_names=alg_names, show=True, font_size=25, title="A-TOPSIS for PAD-UFES-20-Extended dataset", y_axis_title="Scores", x_axis_title="Methods", ascending=True)
 

From 794b5d85aba587e07bee2aaaa934c8b123c96713 Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Thu, 22 May 2025 10:36:08 -0300
Subject: [PATCH 6/9] =?UTF-8?q?feat:=20Add=20'fig=5Fsize'=20para=20salvar?=
 =?UTF-8?q?=20a=20imagem=20no=20formato=20pr=C3=A9-definido?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 scripts/atopsis_test_from_file.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_test_from_file.py
index 91bbe72..d759739 100644
--- a/scripts/atopsis_test_from_file.py
+++ b/scripts/atopsis_test_from_file.py
@@ -75,5 +75,5 @@ def load_dataset(file_folder_path):
     print(result.sort_values("rank"))
 
     # 6) Plota com os próprios nomes
-    atop.plot_ranking(save_path="../images/a_topsis.png", alg_names=alg_names, show=True, font_size=25, title="A-TOPSIS for PAD-UFES-20-Extended dataset", y_axis_title="Scores", x_axis_title="Methods", ascending=True)
+    atop.plot_ranking(save_path="../images/a_topsis_PAD_20_extended.png", alg_names=alg_names, show=True, font_size=25, title="", y_axis_title="Scores", x_axis_title="", ascending=True, fig_size=(21, 14))
 

From a01d1583da9ef411d226f06d4bfb0a35817ad5ee Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Mon, 26 May 2025 17:19:39 -0300
Subject: [PATCH 7/9] =?UTF-8?q?feat:=20Add=20modifica=C3=A7=C3=B5es=20nos?=
 =?UTF-8?q?=20scripts=20para=20rodar=20o=20teste=20com=20o=20A-TOPSIS=20de?=
 =?UTF-8?q?=20forma=20autom=C3=A1tica?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                                    |  8 +-
 README.md                                     |  6 ++
 ...test_from_file.py => atopsis_from_file.py} |  6 +-
 ...ted-cross-attention-models-atopsis-test.py | 11 +--
 src/decision_making/a_topsis.py               |  4 +-
 src/decision_making/topsis.py                 | 87 +++++++++++++------
 6 files changed, 83 insertions(+), 39 deletions(-)
 rename scripts/{atopsis_test_from_file.py => atopsis_from_file.py} (86%)

diff --git a/.gitignore b/.gitignore
index cb18546..fc175df 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,4 +150,10 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
\ No newline at end of file
+#.idea/
+dataset/agg_metablockse_jbhi.csv
+dataset/arquivo_de_teste_pad-25.csv
+dataset/agg_metablockse_jbhi_pad-25.csv
+a_topsis.png
+images/*
+dataset/agg_residual-block-pad-ufes-20.csv
diff --git a/README.md b/README.md
index ffb9777..f16e1f1 100644
--- a/README.md
+++ b/README.md
@@ -33,6 +33,12 @@ inside the folder :test_tube:
 All methods are documented using the docstring format. However, to understand how the algorithms work, you must
 refer to their paper linked in the [references section](#references).
 
+# Running an automatic A-TOPSIS test
+
+Now, you can export your benchmarck's results from `deep`
+
+Add your aggregated results inside the folder 'dataset', change the configuration of the a-topsis in the script 'scripts/atopsis_from_file.py' and then run the command: `python3 scripts/atopsis_from_file.py`
+
 
 ## References
 All implementations follow the standard approach described in the paper. However, for TODIM algorithm, we included the
diff --git a/scripts/atopsis_test_from_file.py b/scripts/atopsis_from_file.py
similarity index 86%
rename from scripts/atopsis_test_from_file.py
rename to scripts/atopsis_from_file.py
index d759739..edf8731 100644
--- a/scripts/atopsis_test_from_file.py
+++ b/scripts/atopsis_from_file.py
@@ -2,7 +2,7 @@
 import pandas as pd
 import numpy as np
 import sys
-sys.path.append("../src")
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
 from decision_making import ATOPSIS
 
 def load_dataset(file_folder_path):
@@ -15,7 +15,7 @@ def load_dataset(file_folder_path):
     
 
 if __name__ == "__main__":
-    file_folder_path = "../dataset/agg_metablockse_jbhi_pad-25.csv"
+    file_folder_path = "./dataset/agg_metablockse_jbhi_pad-25.csv"
     dataset = load_dataset(file_folder_path)
     
     if dataset is None:
@@ -75,5 +75,5 @@ def load_dataset(file_folder_path):
     print(result.sort_values("rank"))
 
     # 6) Plota com os próprios nomes
-    atop.plot_ranking(save_path="../images/a_topsis_PAD_20_extended.png", alg_names=alg_names, show=True, font_size=25, title="", y_axis_title="Scores", x_axis_title="", ascending=True, fig_size=(21, 14))
+    atop.plot_ranking(save_path="./images/a_topsis_PAD_20_extended.png", alg_names=alg_names, show=True, font_size=25, title="", y_axis_title="Scores", x_axis_title="", ascending=True, fig_size=(21, 14))
 
diff --git a/scripts/gated-cross-attention-models-atopsis-test.py b/scripts/gated-cross-attention-models-atopsis-test.py
index 4d2702c..eeab82b 100644
--- a/scripts/gated-cross-attention-models-atopsis-test.py
+++ b/scripts/gated-cross-attention-models-atopsis-test.py
@@ -1,9 +1,10 @@
+import os
 import sys
 import pandas as pd
 import numpy as np
 
 # 1) Ajuste do path para importar o ATOPSIS
-sys.path.append("../../src")
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
 from decision_making import ATOPSIS
 
 
@@ -41,11 +42,11 @@ def main(file_path):
     avg_mat = grouped[[f"{m}_mean" for m in metrics]].values.tolist()
     std_mat = grouped[[f"{m}_std"  for m in metrics]].values.tolist()
     alg_names = grouped["attention_mecanism"].tolist()
-
+    # alg_names = grouped["model_name"].tolist()
     # 7) Executa A‑TOPSIS (listas de listas)
     #    avg_cost_ben="benefit"
     #    std_cost_ben="cost"
-    weights_presetted = [0.75, 0.25]
+    weights_presetted = [0.7, 0.3]
 
     # Executa A‑TOPSIS em memória (listas de listas)
     atop = ATOPSIS(
@@ -79,9 +80,9 @@ def main(file_path):
     print(result.sort_values("rank"))
 
     # 6) Plota com os próprios nomes
-    atop.plot_ranking(save_path="../../data/images/a_topsis.png", alg_names=alg_names, show=True, font_size=22, title="A-TOPSIS for PAD-UFES-20 dataset", y_axis_title="Scores", x_axis_title="Methods")
+    atop.plot_ranking(save_path="./images/a_topsis_PAD_UFES_20.png", alg_names=alg_names, show=True, font_size=16, title="", y_axis_title="Scores", x_axis_title="Methods", ascending=True)
 
 if __name__=="__main__":
-    file_path = "../../data/metrics.csv"
+    file_path = "dataset/agg_residual-block-pad-ufes-20.csv"
     # Função principal
     main(file_path=file_path)
\ No newline at end of file
diff --git a/src/decision_making/a_topsis.py b/src/decision_making/a_topsis.py
index 1a4871b..04f51e3 100644
--- a/src/decision_making/a_topsis.py
+++ b/src/decision_making/a_topsis.py
@@ -104,7 +104,7 @@ def get_ranking(self, verbose=True):
         self.final_topsis.get_closeness_coefficient(verbose)
         self.final_ranking = self.final_topsis.clos_coefficient
 
-    def plot_ranking(self, alg_names=None, save_path=None, show=True):
+    def plot_ranking(self, alg_names=None, save_path=None, show=True, font_size=16, title="A-TOPSIS test", y_axis_title="Scores", x_axis_title="Methods", ascending=False, fig_size=(6,4)):
         """
         This method plots the ranking, according to the final_ranking, in a bar plot.
 
@@ -125,7 +125,7 @@ def plot_ranking(self, alg_names=None, save_path=None, show=True):
         """
         if alg_names is None:
             alg_names = self.final_topsis.alternatives
-        self.final_topsis.plot_ranking(alg_names, save_path, show)
+        self.final_topsis.plot_ranking(alg_names, save_path, show, font_size, title, y_axis_title, x_axis_title, ascending=ascending, fig_size=fig_size)
 
 
 
diff --git a/src/decision_making/topsis.py b/src/decision_making/topsis.py
index 366171a..49173aa 100644
--- a/src/decision_making/topsis.py
+++ b/src/decision_making/topsis.py
@@ -214,43 +214,74 @@ def get_closeness_coefficient(self, verbose=False, compute_distance_ideal=True):
         if verbose:
             print (self.clos_coefficient)
 
-    def plot_ranking(self, alt_names=None, save_path=None, show=True):
+    def plot_ranking(self, alt_names=None, save_path=None, show=True, font_size=16,
+                 title="A-TOPSIS Test", y_axis_title="Scores", x_axis_title="Methods",
+                 ascending=True, fig_size=(6, 4)):
         """
-        This method plots the ranking, according to the closeness coefficient, in a bar plot.
+        Plots a bar chart representing the ranking based on the closeness coefficient.
 
-        Parameters:
-        -----------
-        alt_names: (list), optional
-        This is a list of names for each alternative within the decision matrix. If you're using a DataFrame, you have
-        already defined when you set the alt_col_name. So, you may let it as None. However, if you're using a matrix
-        list or a numpy array, you may pass the alternatives name here. If you let it as None, there will be a default
-        alternatives name in the plot (ex: A1, A2, etc). Default is None
-
-        save_path: (str), optional
-        It's the full path (including the figure name and extension) to save the plot. If you let it None, the plot
-        won't be saved. Default is None.
-
-        show: (boolean), optional
-        Set is as True if you want to show the plot on the screen. Default is False.
+        Parameters
+        ----------
+        alt_names : list, optional
+            Names for each alternative. If None and using a DataFrame, the names from `self.alternatives` are used.
+            Otherwise, defaults to ["A1", "A2", ...].
+
+        save_path : str, optional
+            Full path (including filename and extension) to save the plot. If None, the plot is not saved.
+
+        show : bool, optional
+            If True, displays the plot.
+
+        font_size : int, optional
+            Base font size for labels and ticks.
+
+        title : str, optional
+            Title of the plot.
+
+        y_axis_title : str, optional
+            Label for the Y-axis.
+
+        x_axis_title : str, optional
+            Label for the X-axis.
+
+        ascending : bool, optional
+            If True, sorts the alternatives by score in ascending order. Default is False (descending).
         """
 
         sns.set_style("whitegrid")
-        if self.alternatives is not None:
-            alt_names = self.alternatives
-        if alt_names is not None:
-            a = sns.barplot(alt_names, self.clos_coefficient, palette="BuGn_d")
-        else:
-            temp = [f"A{n}" for n in range(1, len(self.clos_coefficient)+1, 1)]
-            a = sns.barplot(temp, self.clos_coefficient, palette="BuGn_d")
-        a.set_ylabel("Closeness Coefficient")
-        a.set_xlabel('Alternatives')
-        fig = a.get_figure()
 
+        # Determine alternative names
+        if alt_names is None:
+            if hasattr(self, 'alternatives') and self.alternatives is not None:
+                alt_names = self.alternatives
+            else:
+                alt_names = [f"A{i+1}" for i in range(len(self.clos_coefficient))]
+
+        # Sort based on closeness coefficient
+        sorted_data = sorted(zip(alt_names, self.clos_coefficient), key=lambda x: x[1], reverse=not ascending)
+        sorted_names, sorted_scores = zip(*sorted_data)
+
+        # Criar figura com tamanho específico
+        fig, ax = plt.subplots(figsize=fig_size)
+
+        # Plot
+        sns.barplot(x=list(sorted_names), y=list(sorted_scores), palette="Blues", ax=ax)
+        ax.set_title(title, fontsize=font_size)
+        ax.set_ylabel(y_axis_title, fontsize=font_size)
+        ax.set_xlabel(x_axis_title, fontsize=font_size)
+        ax.tick_params(labelsize=font_size)
+        # plt.xticks(rotation=30, ha='right')
+
+        
         if show:
             plt.show()
+            # Save or show
+            if save_path:
+                fig.savefig(save_path, dpi=400, bbox_inches='tight')
+        else:
+            plt.close(fig)
 
-        if save_path is not None:
-            fig.savefig(save_path)
+        return fig
 
 ########################################################################################################################
 # Static methods

From 57f0629b015da2db845b7e12f325b92e7e3f3cfe Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Mon, 26 May 2025 17:27:01 -0300
Subject: [PATCH 8/9] =?UTF-8?q?feat:=20Ignorar=20todos=20os=20dados=20de?=
 =?UTF-8?q?=20agrega=C3=A7=C3=A3o=20dos=20experimentos?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index fc175df..b7e4df3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -156,4 +156,4 @@ dataset/arquivo_de_teste_pad-25.csv
 dataset/agg_metablockse_jbhi_pad-25.csv
 a_topsis.png
 images/*
-dataset/agg_residual-block-pad-ufes-20.csv
+dataset/*

From 4a1c2bdd340f9fd8c4b13d786e9706cd290537b6 Mon Sep 17 00:00:00 2001
From: wyctorfogos <wyctor.fogos@gmail.com>
Date: Mon, 26 May 2025 17:38:36 -0300
Subject: [PATCH 9/9] =?UTF-8?q?docs:=20Atualiza=C3=A7=C3=A3o=20da=20docume?=
 =?UTF-8?q?nta=C3=A7=C3=A3o?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f16e1f1..1259083 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ refer to their paper linked in the [references section](#references).
 
 # Running an automatic A-TOPSIS test
 
-Now, you can export your benchmarck's results from `deep`
+Now, you can export your benchmarck's results from `deep-hub-pipelines`
 
 Add your aggregated results inside the folder 'dataset', change the configuration of the a-topsis in the script 'scripts/atopsis_from_file.py' and then run the command: `python3 scripts/atopsis_from_file.py`