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modifica per usare un file di config

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Dmitri 2025-11-20 00:11:05 +01:00
parent 1179f93485
commit 9964843459
Signed by: kanopo
GPG Key ID: 759ADD40E3132AC7
3 changed files with 559 additions and 487 deletions
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74
fetus-event-detection-classification/experiments.json Normal file
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@ -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"
}
]

83
fetus-event-detection-classification/src/experiments.py Normal file
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@ -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

177
fetus-event-detection-classification/src/main.py
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@ -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"
# TEST_NAME = "1_k1_fetus"
# TEST_NAME = "2_k1_mother"
# TEST_NAME = "3_k1_mother_fetus"
# TEST_NAME = "4_k1_fetus_mother"
# TEST_NAME = "5_k5_all"
# TEST_NAME = "6_k5_fetus"
# TEST_NAME = "7_k5_mother"
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
device = get_device()
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
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:
if os.path.exists("dataset.csv") and os.path.exists("mother.csv") and os.path.exists("fetus.csv"):
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(