main.py

import pandas as pd
import numpy as np
from data_loader import DataLoader
from data_formatter import DataFormatter
from event_splitter import EventSplitter
from model_rf import Amber_RF
#from model import Amber
from kfold_cv import KFoldCrossValidation
from evaluator import evaluate_model_performance
from config import config

# Define your DataFrame and parameter
mypath = 'Data/Train.csv'
df = pd.read_csv(mypath)
target_column = 'label'  # Name of the target column

# Step 1: Load Data
data_loader = DataLoader(dataframe=df, time_steps=config.N_TIME_STEPS, step=config.step, target_column=target_column)
segments, labels = data_loader.load_data()

# Step 2: Format Data
data_formatter = DataFormatter(config=config)
X_train_reshaped, X_test_reshaped, y_train, y_test = data_formatter.format_data_by_event(segments, labels)

# Reshape y_test correctly
y_test_reshaped = np.asarray(y_test, dtype=np.float32)

# Initialize model with concat
#ts_model = Amber(row_hidden=config.row_hidden, col_hidden=config.row_hidden, num_classes=config.N_CLASSES)

# Initialize model with residual fusion layer
#ts_model = Amber_RF(row_hidden=config.row_hidden, col_hidden=config.row_hidden, num_classes=config.N_CLASSES)

# Initialize model
ts_model = PANN(row_hidden=config.row_hidden, col_hidden=config.row_hidden, num_classes=config.N_CLASSES)


# Create an instance of KFoldCrossValidation
kfold_cv = KFoldCrossValidation(ts_model, [X_train_reshaped['Feature_1'], X_train_reshaped['Feature_2']], y_train)

# Run the cross-validation
kfold_cv.run()

# Evaluate the model performance
evaluation_results = evaluate_model_performance(ts_model, [X_test_reshaped['Feature_1'], X_test_reshaped['Feature_2']], y_test_reshaped)


# Access individual metrics
print("Accuracy:", evaluation_results["accuracy"])
print("F1 Score:", evaluation_results["f1"])
print("Cohen's Kappa:", evaluation_results["cohen_kappa"])
print("MCC:", evaluation_results["mcc"])
print()
print("Confusion Matrix:\n", evaluation_results["confusion_matrix"])
print()