removed test script not needed anymore for testing pre-trained model

test_model.py

@@ -1,57 +0,0 @@
-"""Simple script to test the pre-trained splicing model.
-
-This script uses the approach from the original notebooks:
-- figures/generate_csv_for_supplementary.ipynb
-- 2022_03_11_figures/position_specific_activations.ipynb
-
-Requires: Python 3.10 + TensorFlow 2.10 (see README for setup)
-"""
-
-import sys
-
-# Add figures directory to path so we can import quad_model
-sys.path.insert(0, 'figures')
-
-# Import from quad_model - this auto-registers all custom layers
-# via @tf.keras.utils.register_keras_serializable() decorators
-from quad_model import *
-from tensorflow.keras.models import load_model
-from joblib import load as jload
-import numpy as np
-
-print("Loading model...")
-model = load_model('output/custom_adjacency_regularizer_20210731_124_step3.h5')
-print("Model loaded successfully!")
-
-print("\nLoading test data...")
-xTe = jload('data/xTe_ES7_HeLa_ABC.pkl.gz')
-yTe = jload('data/yTe_ES7_HeLa_ABC.pkl.gz')
-
-num_samples = len(xTe[0]) if isinstance(xTe, list) else len(xTe)
-print(f"Number of test samples: {num_samples}")
-
-print("\nRunning predictions...")
-predictions = model.predict(xTe, verbose=0)
-
-print(f"\nResults:")
-print(f"Predictions shape: {predictions.shape}")
-print(f"\nFirst 10 predictions vs actual PSI values:")
-print("-" * 50)
-print(f"{'Predicted PSI':<15} {'Actual PSI':<15} {'Diff':<10}")
-print("-" * 50)
-for i in range(min(10, len(predictions))):
-    pred = predictions[i, 0]
-    actual = yTe[i]
-    diff = pred - actual
-    print(f"{pred:<15.4f} {actual:<15.4f} {diff:<10.4f}")
-
-# Calculate overall metrics
-from sklearn.metrics import mean_squared_error, r2_score
-mse = mean_squared_error(yTe, predictions)
-r2 = r2_score(yTe, predictions)
-correlation = np.corrcoef(yTe.flatten(), predictions.flatten())[0, 1]
-
-print(f"\nOverall Metrics:")
-print(f"  MSE: {mse:.6f}")
-print(f"  R2 Score: {r2:.4f}")
-print(f"  Correlation: {correlation:.4f}")