precision ,recall, confusion metrix code

from sklearn.metrics import classification_report, precision_score, recall_score, confusion_matrix, average_precision_score, precision_recall_curve
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Convert predictions and true labels to numpy arrays
y_pred_probs = resnet_model.predict(validation_data)
y_pred = np.argmax(y_pred_probs, axis=1)
y_true = np.concatenate([y for x, y in validation_data], axis=0)

# Ensure the target_names match the two classes: '1L' and '2L'
target_names = ['1L', '2L']

# Calculate metrics
precision = precision_score(y_true, y_pred, average=None)
recall = recall_score(y_true, y_pred, average=None)
f1_scores = 2 * (precision * recall) / (precision + recall)

# AP50 and AR50
precision_points, recall_points, _ = precision_recall_curve(y_true, y_pred_probs[:, 1])
ap50 = average_precision_score(y_true, y_pred_probs[:, 1])
ar50 = np.mean(recall_points)

# IoU Calculation
def calculate_iou(y_true, y_pred):
    intersection = np.sum((y_true == y_pred) & (y_true == 1))
    union = np.sum((y_true | y_pred))
    return intersection / union if union > 0 else 0

iou = calculate_iou(y_true, y_pred)

# Custom Table in Classification Report Style
metrics_report = pd.DataFrame({
    "precision": list(precision) + [ap50, ar50, None],
    "recall": list(recall) + [None, None, None],
    "f1-score": list(f1_scores) + [None, None, None],
    "support": [sum(y_true == 0), sum(y_true == 1), None, None, None]
}, index=["1L", "2L", "AP50", "AR50", "IoU"])

# Add overall accuracy, macro avg, and weighted avg
accuracy = (y_pred == y_true).mean()
metrics_report.loc["accuracy"] = [None, None, None, accuracy]
metrics_report.loc["macro avg"] = [precision.mean(), recall.mean(), f1_scores.mean(), len(y_true)]
metrics_report.loc["weighted avg"] = [precision_score(y_true, y_pred, average='weighted'),
                                      recall_score(y_true, y_pred, average='weighted'),
                                      None, len(y_true)]

# Display the table
print("\nCustom Metrics Report:")
print(metrics_report)

# Plot confusion matrix
conf_matrix = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=target_names, yticklabels=target_names)
plt.title("Confusion Matrix")
plt.xlabel("Predicted")
plt.ylabel("True")
plt.show()