wip: add function to create stratified train-test split from STFT data

code/src/ml/model_selection.py

@@ -0,0 +1,50 @@
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split as sklearn_split
+
+
+def create_train_test_split(
+    ready_data: pd.DataFrame,
+    test_size: float = 0.2,
+    random_state: int = 42,
+    stratify: np.ndarray = None,
+) -> tuple:
+    """
+    Create a stratified train-test split from STFT data.
+
+    Parameters:
+    -----------
+    data : pd.DataFrame
+        The input DataFrame containing STFT data
+    test_size : float
+        Proportion of data to use for testing (default: 0.2)
+    random_state : int
+        Random seed for reproducibility (default: 42)
+    stratify : np.ndarray, optional
+        Labels to use for stratified sampling
+
+    Returns:
+    --------
+    tuple
+        (X_train, X_test, y_train, y_test) - Split datasets
+    """
+    y_data = [i for i in range(len(ready_data))]
+
+    for i in range(len(y_data)):
+        y_data[i] = [y_data[i]] * ready_data[i].shape[0]
+        y_data[i] = np.array(y_data[i])
+
+    # Extract features and labels
+    X = (
+        ready_data.drop("label_column", axis=1)
+        if "label_column" in ready_data.columns
+        else ready_data
+    )
+    y = ready_data["label_column"] if "label_column" in ready_data.columns else stratify
+
+    # Create split
+    X_train, X_test, y_train, y_test = sklearn_split(
+        X, y, test_size=test_size, random_state=random_state, stratify=stratify
+    )
+
+    return X_train, X_test, y_train, y_test