3. Hierarchical SVM.py

"""
Goat vocalizations
Università degli studi di Milano

@author: Giulia Cuttone
"""

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns


'---------------------------------------------------------------------------------------------------'

def final_classification(y, predictions, clf_pos, clf_neg):
    
    """
    Final classification based on Level 1 predictions.
    
    Uses the Level 1 predictions ('Positive' or 'Negative')
    to determine which Level 2 classifier should be used for the final classification.
    If the Level 1 prediction is 'Positive', the function uses the `clf_pos` classifier.
    If the prediction is 'Negative', it uses the `clf_neg` classifier.
    If the label is neither 'Positive' nor 'Negative', the function returns 'Unknown'.
    ----------------------------------------------------------------------------------
    Paramenters:
        y (list): Labels from Level 1 classifier predictions ('Positive' or 'Negative').
        predictions (list): List to store the final predictions.
        clf_pos (classifier): Level 2 classifier for positive classes.
        clf_neg (classifier): Level 2 classifier for negative classes.
    --------------------------------------------------------------------
    Returns:
        predictions (list): A list of final predictions for each observation. 
                            Returns 'Unknown' for invalid labels.
    """
    
    for i, x_test in enumerate(X_test):
        if y[i] == 'Positive':
            predictions.append(clf_pos.best_estimator_.predict([x_test])[0])
        else:
            predictions.append(clf_neg.best_estimator_.predict([x_test])[0])
    return predictions


"Plot function:"

def plot_confusion_matrix(cm, size, class_names, title='Confusion Matrix'):
    
    """
    Displays a confusion matrix as a heatmap.
    -----------------------------------------
    Parameters:
        cm (ndarray): Confusion matrix (2D array).
        size ((float, float)): Size of the confusion matrix.
        class_names (list or array): List of class labels to be displayed on the x and y axes.
        title (str): Title of the plot (default: 'Confusion Matrix').
    ------------------------------------------------------------------------------------------
    Returns:
        None: Displays the confusion matrix as a heatmap.
    """
    
    plt.figure(figsize=size)
    sns.heatmap(cm, annot=True, fmt='d', cmap='Greens', xticklabels=class_names, yticklabels=class_names)
    plt.title(title)
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()


'---------------------------------------------------------------------------------------------------'

"Main code:"

# Load dataset
file_path = './Vocapra_postprocessing.csv'
df = pd.read_csv(file_path)

# Features (X) and labels (y) extraction
X = df.drop(columns=['Class', 'Emotional_state']).values   # Features
y1 = df['Emotional_state'].values                          # Emotional states
y2 = df['Class'].values                                    # Classes

# Split data into training and test set
X_train, X_test, y1_train, y1_test, y2_train, y2_test = train_test_split(X, y1, y2, test_size=0.2, random_state=42)


# Set up parameter grid for SVM:
param_grid = [
    {'kernel': ['linear'], 'C': np.logspace(-3, 3, 7)},
    {'kernel': ['rbf'], 'C': np.logspace(-3, 3, 7), 'gamma': np.logspace(-3, 3, 7)} ]


'---------------------------------------------------------------------------------------------------'

'Level 1:  Positive vs Negative states'

# Support Vector Classifier (SVM)
svm_1 = SVC()
grid_search = GridSearchCV(svm_1, param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train, y1_train)
y1_pred_svm = grid_search.best_estimator_.predict(X_test)


# Model evaluation:
print("\nLevel 1 classification report:\n")

print("Support Vector Machine Classifier:")
print("Best parameters:", grid_search.best_params_, "\n")       # {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
print(classification_report(y1_test, y1_pred_svm))              # Accuracy: 95%

# Confusion matrix:
cm_1 = confusion_matrix(y1_test, y1_pred_svm, labels=grid_search.classes_)
plot_confusion_matrix(cm_1, (4, 3), grid_search.classes_, 'Level 1 Confusion Matrix')


'---------------------------------------------------------------------------------------------------'

'Level 2: Final Classification (Based on Level 1 prediction, train separate classifiers for Level 2)'
 
# Split the data based on predicted labels
X_train_pos = X_train[y1_train == 'Positive']
y2_train_pos = y2_train[y1_train == 'Positive']

X_train_neg = X_train[y1_train == 'Negative']
y2_train_neg = y2_train[y1_train == 'Negative']


# Initialize the Support Vector Classifiers (SVM) for Level 2 (Positive & Negative)
svm2_pos = SVC()
svm2_neg = SVC()

# Train Level 2 classifiers with Randomized Search
random_search_pos = RandomizedSearchCV(svm2_pos, param_grid, n_iter=20, cv=5, random_state=42)
random_search_pos.fit(X_train_pos, y2_train_pos)

random_search_neg = RandomizedSearchCV(svm2_neg, param_grid, n_iter=20, cv=5, random_state=42)
random_search_neg.fit(X_train_neg, y2_train_neg)


# Final predictions (hierarchical)
final_predictions_svm = []
final_classification(y1_pred_svm, final_predictions_svm, random_search_pos, random_search_neg)


# Models evaluation:
print("\nLevel 2 classification report:\n")

print("Support Vector Machine Classifier:")
print("Best parameters (Positive):", random_search_pos.best_params_)          # {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
print("Best parameters (Negative):", random_search_neg.best_params_, "\n")    # {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}
print(classification_report(y2_test, final_predictions_svm))                  # Accuracy: 85%

# Confusion matrix:
cm_2 = confusion_matrix(y2_test, final_predictions_svm, labels=np.unique(y2_test))
plot_confusion_matrix(cm_2, (10, 7), np.unique(y2_test), 'Level 2 Confusion Matrix')