--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/split_data.py	Fri Nov 21 15:58:13 2025 +0000
@@ -0,0 +1,179 @@
+import argparse
+import logging
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+logger = logging.getLogger("ImageLearner")
+
+
+def split_data_0_2(
+    df: pd.DataFrame,
+    split_column: str,
+    validation_size: float = 0.1,
+    random_state: int = 42,
+    label_column: Optional[str] = None,
+) -> pd.DataFrame:
+    """Given a DataFrame whose split_column only contains {0,2}, re-assign a portion of the 0s to become 1s (validation)."""
+    out = df.copy()
+    out[split_column] = pd.to_numeric(out[split_column], errors="coerce").astype(int)
+
+    idx_train = out.index[out[split_column] == 0].tolist()
+
+    if not idx_train:
+        logger.info("No rows with split=0; nothing to do.")
+        return out
+    stratify_arr = None
+    if label_column and label_column in out.columns:
+        label_counts = out.loc[idx_train, label_column].value_counts()
+        if label_counts.size > 1:
+            # Force stratify even with fewer samples - adjust validation_size if needed
+            min_samples_per_class = label_counts.min()
+            if min_samples_per_class * validation_size < 1:
+                # Adjust validation_size to ensure at least 1 sample per class, but do not exceed original validation_size
+                adjusted_validation_size = min(
+                    validation_size, 1.0 / min_samples_per_class
+                )
+                if adjusted_validation_size != validation_size:
+                    validation_size = adjusted_validation_size
+                    logger.info(
+                        f"Adjusted validation_size to {validation_size:.3f} to ensure at least one sample per class in validation"
+                    )
+            stratify_arr = out.loc[idx_train, label_column]
+            logger.info("Using stratified split for validation set")
+        else:
+            logger.warning("Only one label class found; cannot stratify")
+    if validation_size <= 0:
+        logger.info("validation_size <= 0; keeping all as train.")
+        return out
+    if validation_size >= 1:
+        logger.info("validation_size >= 1; moving all train → validation.")
+        out.loc[idx_train, split_column] = 1
+        return out
+    # Always try stratified split first
+    try:
+        train_idx, val_idx = train_test_split(
+            idx_train,
+            test_size=validation_size,
+            random_state=random_state,
+            stratify=stratify_arr,
+        )
+        logger.info("Successfully applied stratified split")
+    except ValueError as e:
+        logger.warning(f"Stratified split failed ({e}); falling back to random split.")
+        train_idx, val_idx = train_test_split(
+            idx_train,
+            test_size=validation_size,
+            random_state=random_state,
+            stratify=None,
+        )
+    out.loc[train_idx, split_column] = 0
+    out.loc[val_idx, split_column] = 1
+    out[split_column] = out[split_column].astype(int)
+    return out
+
+
+def create_stratified_random_split(
+    df: pd.DataFrame,
+    split_column: str,
+    split_probabilities: list = [0.7, 0.1, 0.2],
+    random_state: int = 42,
+    label_column: Optional[str] = None,
+) -> pd.DataFrame:
+    """Create a stratified random split when no split column exists."""
+    out = df.copy()
+
+    # initialize split column
+    out[split_column] = 0
+
+    if not label_column or label_column not in out.columns:
+        logger.warning(
+            "No label column found; using random split without stratification"
+        )
+        # fall back to simple random assignment
+        indices = out.index.tolist()
+        np.random.seed(random_state)
+        np.random.shuffle(indices)
+
+        n_total = len(indices)
+        n_train = int(n_total * split_probabilities[0])
+        n_val = int(n_total * split_probabilities[1])
+
+        out.loc[indices[:n_train], split_column] = 0
+        out.loc[indices[n_train:n_train + n_val], split_column] = 1
+        out.loc[indices[n_train + n_val:], split_column] = 2
+
+        return out.astype({split_column: int})
+
+    # check if stratification is possible
+    label_counts = out[label_column].value_counts()
+    min_samples_per_class = label_counts.min()
+
+    # ensure we have enough samples for stratification:
+    # Each class must have at least as many samples as the number of splits,
+    # so that each split can receive at least one sample per class.
+    min_samples_required = len(split_probabilities)
+    if min_samples_per_class < min_samples_required:
+        logger.warning(
+            f"Insufficient samples per class for stratification (min: {min_samples_per_class}, required: {min_samples_required}); using random split"
+        )
+        # fall back to simple random assignment
+        indices = out.index.tolist()
+        np.random.seed(random_state)
+        np.random.shuffle(indices)
+
+        n_total = len(indices)
+        n_train = int(n_total * split_probabilities[0])
+        n_val = int(n_total * split_probabilities[1])
+
+        out.loc[indices[:n_train], split_column] = 0
+        out.loc[indices[n_train:n_train + n_val], split_column] = 1
+        out.loc[indices[n_train + n_val:], split_column] = 2
+
+        return out.astype({split_column: int})
+
+    logger.info("Using stratified random split for train/validation/test sets")
+
+    # first split: separate test set
+    train_val_idx, test_idx = train_test_split(
+        out.index.tolist(),
+        test_size=split_probabilities[2],
+        random_state=random_state,
+        stratify=out[label_column],
+    )
+
+    # second split: separate training and validation from remaining data
+    val_size_adjusted = split_probabilities[1] / (
+        split_probabilities[0] + split_probabilities[1]
+    )
+    train_idx, val_idx = train_test_split(
+        train_val_idx,
+        test_size=val_size_adjusted,
+        random_state=random_state,
+        stratify=out.loc[train_val_idx, label_column] if label_column and label_column in out.columns else None,
+    )
+
+    # assign split values
+    out.loc[train_idx, split_column] = 0
+    out.loc[val_idx, split_column] = 1
+    out.loc[test_idx, split_column] = 2
+
+    logger.info("Successfully applied stratified random split")
+    logger.info(
+        f"Split counts: Train={len(train_idx)}, Val={len(val_idx)}, Test={len(test_idx)}"
+    )
+    return out.astype({split_column: int})
+
+
+class SplitProbAction(argparse.Action):
+    def __call__(self, parser, namespace, values, option_string=None):
+        train, val, test = values
+        total = train + val + test
+        if abs(total - 1.0) > 1e-6:
+            parser.error(
+                f"--split-probabilities must sum to 1.0; "
+                f"got {train:.3f} + {val:.3f} + {test:.3f} = {total:.3f}"
+            )
+        setattr(namespace, self.dest, values)