import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

# Separate features and labels
X = balanced_data.drop(columns=['Label'])
y = balanced_data['Label']

# Normalize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Perform LDA
lda = LDA_fs(m=1)  # Instanciate LDA object with 1 axis
X_lda = lda.fit_transform(X_scaled, y) # Fit the model and project the data

# Plot the LDA projection
plt.figure(figsize=(10, 6))
plt.hist(X_lda[y == 'Good'], bins=20, alpha=0.7, label='Good', color='green')
plt.hist(X_lda[y == 'Bad'], bins=20, alpha=0.7, label='Bad', color='red')
plt.title("LDA Projection of Good and Bad Parts")
plt.xlabel("LDA Component")
plt.ylabel("Frequency")
plt.legend()
plt.show()

# Examine feature contributions to the LDA component
feature_importance = pd.DataFrame({'Feature': X.columns, 'LDA Coefficient': lda.W[0]})
feature_importance = feature_importance.sort_values(by='LDA Coefficient', ascending=False)

# Display feature importance
print("Feature Contributions to LDA Component:")
print(feature_importance)