modifica per usare un file di config

fetus-event-detection-classification/experiments.json

@@ -0,0 +1,74 @@
+[
+    {
+        "name": "all_dataset_k1",
+        "k_fold": 1,
+        "dataset_type": "all",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "fetus_dataset_k1",
+        "k_fold": 1,
+        "dataset_type": "fetus",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "mother_dataset_k1",
+        "k_fold": 1,
+        "dataset_type": "mother",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "fetus_mother_dataset_k1",
+        "k_fold": 1,
+        "dataset_type": "fetus-mother",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "mother_fetus_dataset_k1",
+        "k_fold": 1,
+        "dataset_type": "mother-fetus",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "all_dataset_k5",
+        "k_fold": 5,
+        "dataset_type": "all",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "fetus_dataset_k5",
+        "k_fold": 5,
+        "dataset_type": "fetus",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    },
+    {
+        "name": "mother_dataset_k5",
+        "k_fold": 5,
+        "dataset_type": "mother",
+        "oversampling": false,
+        "undersampling": false,
+        "epochs": 50,
+        "path": "~/Documents/womb-wise-data"
+    }
+]

fetus-event-detection-classification/src/experiments.py

@@ -0,0 +1,83 @@
+import json
+import os
+
+
+def create_experiments(experiments_path, base_path):
+    if os.path.exists(experiments_path):
+        with open(experiments_path, "r") as f:
+            return json.load(f)
+
+    experiments = [
+        {
+            "name": "all_dataset_k1",
+            "k_fold": 1,
+            "dataset_type": "all",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "fetus_dataset_k1",
+            "k_fold": 1,
+            "dataset_type": "fetus",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "mother_dataset_k1",
+            "k_fold": 1,
+            "dataset_type": "mother",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "fetus_mother_dataset_k1",
+            "k_fold": 1,
+            "dataset_type": "fetus-mother",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "mother_fetus_dataset_k1",
+            "k_fold": 1,
+            "dataset_type": "mother-fetus",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "all_dataset_k5",
+            "k_fold": 5,
+            "dataset_type": "all",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "fetus_dataset_k5",
+            "k_fold": 5,
+            "dataset_type": "fetus",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+        {
+            "name": "mother_dataset_k5",
+            "k_fold": 5,
+            "dataset_type": "mother",
+            "oversampling": False,
+            "undersampling": False,
+            "epochs": 50,
+        },
+    ]
+
+    for experiment in experiments:
+        experiment["path"] = base_path
+
+    with open(experiments_path, "w") as f:
+        json.dump(experiments, f, indent=4)
+
+    return experiments

fetus-event-detection-classification/src/main.py

@@ -9,18 +9,17 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import Dataset, DataLoader
 import seaborn as sns
-import argparse
 from imblearn.over_sampling import RandomOverSampler
 from imblearn.under_sampling import RandomUnderSampler
 from collections import Counter
 from sklearn.model_selection import StratifiedKFold
 from sklearn.model_selection import train_test_split
-from datetime import datetime
 
 from load_dataset import get_dataset
 from model import SimpleLSTM
 from training import training_loop
 from validation import validation
+from experiments import create_experiments
 
 warnings.simplefilter(action="ignore", category=FutureWarning)
 
@@ -53,8 +52,6 @@ def setup_model_training(
         optimizer, mode="min", factor=0.1, patience=25
     )
 
-    # criterion = nn.CrossEntropyLoss()
-    # criterion = nn.BCELoss()
     criterion = nn.BCEWithLogitsLoss()
 
     return (model, optimizer, scheduler, criterion)
@@ -161,112 +158,31 @@ class FetusDataset(Dataset):
         x = self.data[idx]["data"]
         y = self.data[idx]["label"]
 
-        # Conversione del tipo di dato
         x = x.astype(np.float32)
         y = np.eye(self.classes)[y]
 
-        # Conversione in tensori
         x = torch.tensor(x, dtype=torch.float32)
         y = torch.tensor(y, dtype=torch.int32)
 
-        # Gestione di valori NaN o infiniti
         x = torch.nan_to_num(
             x
-        )  # Sostituisce NaN con 0 e valori infiniti con numeri molto grandi o piccoli
+        )
 
-        # Normalizzazione solo durante il training
         if self.train:
             mean = x.mean()
             std = x.std()
 
-            # Normalizzazione condizionale (solo se std > 0)
             if std > 0:
                 x = (x - mean) / std
 
         return x, y
 
 
-def createArgParser():
-    parser = argparse.ArgumentParser(description="Womb Wise")
-    parser.add_argument(
-        "-rd",
-        "--reload-dataset",
-        action="store_true",
-        help="Reload the dataset",
-    )
-
-    # path to the dataset
-    parser.add_argument(
-        "-p",
-        "--path",
-        action="store",
-        help="Path to the dataset",
-        default="~/Documents/womb-wise-data",
-    )
-
-    # epoch
-    parser.add_argument(
-        "-e",
-        "--epochs",
-        action="store",
-        help="Number of epochs",
-        default=10,
-    )
-
-    parser.add_argument(
-        "-k",
-        "--kfold",
-        action="store",
-        help="Number of folds for kfold cross validation",
-        default=1,
-    )
-
-    parser.add_argument(
-        "-o",
-        "--oversampling",
-        action="store_true",
-        help="Apply oversampling",
-    )
-
-    parser.add_argument(
-        "-u",
-        "--undersampling",
-        action="store_true",
-        help="Apply undersampling",
-    )
-
-    parser.add_argument(
-        "-d",
-        "--dataset",
-        action="store",
-        default="all",
-        choices=["all", "fetus", "mother", "fetus-mother", "mother-fetus"],
-        help="Choose the dataset: all, fetus, mother or train with mother and test with fetus or viceversa",
-    )
-
-    args = parser.parse_args()
-
-    print(
-        f"""
-    ARGS:
-    \n
-    reload-dataset: {args.reload_dataset}
-    path: {args.path}
-    epochs: {args.epochs}
-    kfold: {args.kfold}
-    oversampling: {args.oversampling}
-    undersampling: {args.undersampling}
-    dataset: {args.dataset}
-    """
-    )
-    return args
-
-
 if __name__ == "__main__":
+    BASE_PATH = "~/Documents/womb-wise-data"
     CLASSES = ["baseline", "opcl", "yawn"]
     FEATURE_SIZE = 10
     SERIES_LENGTH = 60
-    # SINGLE_FRAME_LENGTH = FEATURE_SIZE * SERIES_LENGTH
     BATCH_SIZE = 4
     WEIGHT_DECAY = 1e-5
     LEARNING_RATE = 1e-3
@@ -275,15 +191,27 @@ if __name__ == "__main__":
     DROP_OUT = 0.0
     NUM_LAYERS = 2
     EPS = 1e-7
+    EARLY_STOPPING = True
 
-    TEST_NAME = "0_k1_all"
+    seed = 42
-    # TEST_NAME = "1_k1_fetus"
+    np.random.seed(seed)
-    # TEST_NAME = "2_k1_mother"
+    torch.manual_seed(seed)
-    # TEST_NAME = "3_k1_mother_fetus"
+    torch.cuda.manual_seed(seed)
-    # TEST_NAME = "4_k1_fetus_mother"
+    torch.cuda.manual_seed_all(seed)
-    # TEST_NAME = "5_k5_all"
+
-    # TEST_NAME = "6_k5_fetus"
+    device = get_device()
-    # TEST_NAME = "7_k5_mother"
+
+    experiments = create_experiments("experiments.json", BASE_PATH)
+    for experiment in experiments:
+        TEST_NAME = experiment["name"]
+        K_FOLD = experiment["k_fold"]
+        OVER_SAMPLING = experiment["oversampling"]
+        UNDER_SAMPLING = experiment["undersampling"]
+        DATASET_TYPE = experiment["dataset_type"]
+        EPOCHS = experiment["epochs"]
+        PATH = experiment["path"]
+
+        print(f"Running experiment: {TEST_NAME}")
 
         if not os.path.exists("output/" + TEST_NAME):
             os.makedirs("output/" + TEST_NAME)
@@ -297,26 +225,7 @@ if __name__ == "__main__":
         if not os.path.exists("output/" + TEST_NAME + "/metrics"):
             os.makedirs("output/" + TEST_NAME + "/metrics")
 
-    # fix the seed
+        if os.path.exists("dataset.csv") and os.path.exists("mother.csv") and os.path.exists("fetus.csv"):
-    seed = 42
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-    device = get_device()
-    args = createArgParser()
-
-    PATH = args.path
-    EPOCHS = int(args.epochs)
-    K_FOLD = int(args.kfold)
-    OVER_SAMPLING = args.oversampling
-    UNDER_SAMPLING = args.undersampling
-    EARLY_STOPPING = True
-    DATASET_TYPE = args.dataset
-
-
-    if os.path.exists("dataset.csv") and args.reload_dataset is False:
             dataset = pd.read_csv("dataset.csv")
             mother = pd.read_csv("mother.csv")
             fetus = pd.read_csv("fetus.csv")
@@ -359,8 +268,6 @@ if __name__ == "__main__":
             dataset = pd.concat([mother, fetus])
             dataset.to_csv("dataset.csv")
 
-
-
         mother = mother.drop(columns=["top_bottom_distance"])
         fetus = fetus.drop(columns=["top_bottom_distance"])
         dataset = dataset.drop(columns=["top_bottom_distance"])
@@ -388,7 +295,8 @@ if __name__ == "__main__":
 
             if group.shape[0] < SERIES_LENGTH:
                 group = np.vstack(
-                [group, np.zeros((SERIES_LENGTH - group.shape[0], FEATURE_SIZE))]
+                    [group, np.zeros(
+                        (SERIES_LENGTH - group.shape[0], FEATURE_SIZE))]
                 )
 
             elif group.shape[0] > SERIES_LENGTH:
@@ -420,7 +328,8 @@ if __name__ == "__main__":
 
             if group.shape[0] < SERIES_LENGTH:
                 group = np.vstack(
-                [group, np.zeros((SERIES_LENGTH - group.shape[0], FEATURE_SIZE))]
+                    [group, np.zeros(
+                        (SERIES_LENGTH - group.shape[0], FEATURE_SIZE))]
                 )
 
             elif group.shape[0] > SERIES_LENGTH:
@@ -453,7 +362,8 @@ if __name__ == "__main__":
 
             if group.shape[0] < SERIES_LENGTH:
                 group = np.vstack(
-                [group, np.zeros((SERIES_LENGTH - group.shape[0], FEATURE_SIZE))]
+                    [group, np.zeros(
+                        (SERIES_LENGTH - group.shape[0], FEATURE_SIZE))]
                 )
 
             elif group.shape[0] > SERIES_LENGTH:
@@ -470,7 +380,6 @@ if __name__ == "__main__":
             )
 
         if K_FOLD == 1:
-
             x_all = [d["data"] for d in data]
             y_all = [d["label"] for d in data]
 
@@ -565,9 +474,9 @@ if __name__ == "__main__":
                 device,
             )
 
-        # save classification report to a file
             df = pd.DataFrame(classification_rep).transpose()
-        df.to_csv("output/" + TEST_NAME + "/metrics/classification_report.csv")
+            df.to_csv("output/" + TEST_NAME +
+                      "/metrics/classification_report.csv")
 
             torch.save(
                 trained_model.state_dict(),
@@ -588,11 +497,13 @@ if __name__ == "__main__":
             plt.xlabel("Predicted")
             plt.ylabel("Actual")
 
-        plt.savefig("output/" + TEST_NAME + "/confusion_matrix/confusion_matrix.png")
+            plt.savefig("output/" + TEST_NAME +
+                        "/confusion_matrix/confusion_matrix.png")
 
             plt.figure(figsize=(19.20, 10.80))
             plt.title("Confusion Matrix Percentage")
-        conf_matrix_percent = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100
+            conf_matrix_percent = conf_matrix.astype(
+                'float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100
             sns.heatmap(
                 conf_matrix_percent,
                 annot=True,
@@ -605,7 +516,8 @@ if __name__ == "__main__":
             plt.xlabel("Predicted")
             plt.ylabel("Actual")
 
-        plt.savefig("output/" + TEST_NAME + "/confusion_matrix/confusion_matrix_percentage.png")
+            plt.savefig("output/" + TEST_NAME +
+                        "/confusion_matrix/confusion_matrix_percentage.png")
 
         else:
             x_all = [d["data"] for d in data]
@@ -659,7 +571,8 @@ if __name__ == "__main__":
                 eps=EPS,
             )
 
-        kf = StratifiedKFold(n_splits=K_FOLD, shuffle=True, random_state=seed)
+            kf = StratifiedKFold(
+                n_splits=K_FOLD, shuffle=True, random_state=seed)
             model_index = 0
 
             x = None
@@ -720,7 +633,8 @@ if __name__ == "__main__":
                     device=device,
                     epochs=EPOCHS,
                     early_stopping=EARLY_STOPPING,
-                log_dir="output/" + TEST_NAME + "/metrics/" + f"{model_index}",
+                    log_dir="output/" + TEST_NAME +
+                    "/metrics/" + f"{model_index}",
                 )
 
                 loss, conf_matrix, classification_rep = validation(
@@ -730,7 +644,6 @@ if __name__ == "__main__":
                     device,
                 )
 
-            # save classification report to a file
                 df = pd.DataFrame(classification_rep).transpose()
                 df.to_csv(
                     "output/"
@@ -742,7 +655,8 @@ if __name__ == "__main__":
 
                 torch.save(
                     trained_model.state_dict(),
-                "output/" + TEST_NAME + "/weights/model_" + str(model_index) + ".pth",
+                    "output/" + TEST_NAME + "/weights/model_" +
+                    str(model_index) + ".pth",
                 )
                 plt.figure(figsize=(19.20, 10.80))
                 plt.title("Confusion Matrix")
@@ -764,7 +678,8 @@ if __name__ == "__main__":
                     + str(model_index)
                     + ".png"
                 )
-            conf_matrix_percent = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100
+                conf_matrix_percent = conf_matrix.astype(
+                    'float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100
                 plt.figure(figsize=(19.20, 10.80))
                 plt.title("Confusion Matrix Percentage")
                 sns.heatmap(