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"""
Evaluation script for trained model with comprehensive analysis
"""
import argparse
import sys
import os
import numpy as np
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
# Add parent directory to path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src import (
load_config,
compute_metrics_factory,
plot_confusion_matrix,
print_classification_report
)
from src.data_loader import prepare_datasets_for_training
def analyze_errors(
test_dataset,
predictions: np.ndarray,
labels: np.ndarray,
id2label: dict,
tokenizer,
top_n: int = 10
) -> pd.DataFrame:
"""
Analyze misclassified examples.
Args:
test_dataset: Test dataset
predictions: Predicted labels
labels: True labels
id2label: Label mapping
tokenizer: Tokenizer to decode text
top_n: Number of examples to show per error type
Returns:
DataFrame with error analysis
"""
errors = []
for i, (pred, true_label) in enumerate(zip(predictions, labels)):
if pred != true_label:
# Decode the comment (approximate, as original text is removed)
# Note: This is a limitation - we'd need to keep original text
errors.append({
'index': i,
'true_label': id2label[true_label],
'predicted_label': id2label[pred],
'error_type': f"{id2label[true_label]} -> {id2label[pred]}"
})
error_df = pd.DataFrame(errors)
if len(error_df) > 0:
print(f"\nError Analysis:")
print(f"Total errors: {len(error_df)}")
print(f"\nError type distribution:")
print(error_df['error_type'].value_counts())
return error_df
def evaluate_model(
model_path: str,
config_path: str = "config.yaml",
save_plots: bool = True
):
"""
Evaluate trained model on test set with comprehensive analysis.
Args:
model_path: Path to the trained model
config_path: Path to configuration file
save_plots: Whether to save visualization plots
"""
print("=" * 60)
print("Model Evaluation")
print("=" * 60)
# Load config
config = load_config(config_path)
# Create output directory
output_dir = config['training'].get('output_dir', './results')
os.makedirs(output_dir, exist_ok=True)
# Load datasets
print("\n[1/5] Loading datasets...")
tokenized_datasets, label2id, id2label, _ = prepare_datasets_for_training(config_path)
test_dataset = tokenized_datasets['test']
print(f"✓ Test samples: {len(test_dataset)}")
# Load model and tokenizer
print("\n[2/5] Loading trained model...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
print(f"✓ Model loaded from {model_path}")
# Create trainer for evaluation
print("\n[3/5] Running evaluation...")
compute_metrics_fn = compute_metrics_factory(id2label)
trainer = Trainer(
model=model,
tokenizer=tokenizer,
compute_metrics=compute_metrics_fn
)
# Get predictions
predictions_output = trainer.predict(test_dataset)
predictions = np.argmax(predictions_output.predictions, axis=1)
labels = predictions_output.label_ids
# Print metrics
print("\n[4/5] Computing detailed metrics...")
print("\n" + "=" * 60)
print("Test Set Results")
print("=" * 60)
metrics = predictions_output.metrics
# Overall metrics
print("\nOverall Metrics:")
overall_metrics = ['accuracy', 'f1_weighted', 'f1_macro', 'precision_weighted', 'recall_weighted']
for metric in overall_metrics:
key = f'test_{metric}'
if key in metrics:
print(f" {metric.replace('_', ' ').title()}: {metrics[key]:.4f}")
# Per-class metrics
print("\nPer-Class Metrics:")
label_names = [id2label[i] for i in range(len(id2label))]
for label_name in label_names:
precision_key = f'test_precision_{label_name}'
recall_key = f'test_recall_{label_name}'
f1_key = f'test_f1_{label_name}'
if precision_key in metrics:
print(f"\n {label_name.upper()}:")
print(f" Precision: {metrics[precision_key]:.4f}")
print(f" Recall: {metrics[recall_key]:.4f}")
print(f" F1-Score: {metrics[f1_key]:.4f}")
print(f" Support: {metrics.get(f'test_support_{label_name}', 'N/A')}")
# Detailed classification report
print("\n" + "=" * 60)
print_classification_report(labels, predictions, label_names)
# Plot confusion matrix
print("\n[5/5] Generating visualizations...")
if save_plots:
plot_confusion_matrix(
labels,
predictions,
label_names,
save_path=os.path.join(output_dir, "confusion_matrix.png"),
normalize=False
)
# Also save normalized version
plot_confusion_matrix(
labels,
predictions,
label_names,
save_path=os.path.join(output_dir, "confusion_matrix_normalized.png"),
normalize=True
)
# Error analysis
error_df = analyze_errors(test_dataset, predictions, labels, id2label, tokenizer)
if len(error_df) > 0 and save_plots:
error_path = os.path.join(output_dir, "error_analysis.csv")
error_df.to_csv(error_path, index=False)
print(f"✓ Error analysis saved to {error_path}")
print("\n" + "=" * 60)
print("Evaluation Complete! 🎉")
print("=" * 60)
print(f"\nResults saved to: {output_dir}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Evaluate trained model")
parser.add_argument(
"--model-path",
type=str,
default="./results/final_model",
help="Path to the trained model"
)
parser.add_argument(
"--config",
type=str,
default="config.yaml",
help="Path to configuration file"
)
parser.add_argument(
"--no-plots",
action="store_true",
help="Skip generating visualization plots"
)
args = parser.parse_args()
evaluate_model(args.model_path, args.config, save_plots=not args.no_plots)
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