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annotate split_data.py @ 21:d5c582cf74bc draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam.git commit eed8c1e1d99a8a0c8f3a6bfdd8af48a5bfa19444
author goeckslab
date Tue, 20 Jan 2026 01:25:35 +0000
parents 64872c48a21f
children
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bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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1 import argparse
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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2 import logging
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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3 from typing import Optional
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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4
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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5 import numpy as np
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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6 import pandas as pd
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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7 from sklearn.model_selection import train_test_split
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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8
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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9 logger = logging.getLogger("ImageLearner")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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10
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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11
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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12 def split_data_0_2(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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13 df: pd.DataFrame,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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14 split_column: str,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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15 validation_size: float = 0.1,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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16 random_state: int = 42,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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17 label_column: Optional[str] = None,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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18 ) -> pd.DataFrame:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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19 """Given a DataFrame whose split_column only contains {0,2}, re-assign a portion of the 0s to become 1s (validation)."""
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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20 out = df.copy()
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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21 out[split_column] = pd.to_numeric(out[split_column], errors="coerce").astype(int)
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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22
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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23 idx_train = out.index[out[split_column] == 0].tolist()
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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24
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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25 if not idx_train:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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26 logger.info("No rows with split=0; nothing to do.")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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27 return out
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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28 stratify_arr = None
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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29 if label_column and label_column in out.columns:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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30 label_counts = out.loc[idx_train, label_column].value_counts()
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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31 if label_counts.size > 1:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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32 # Force stratify even with fewer samples - adjust validation_size if needed
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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33 min_samples_per_class = label_counts.min()
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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34 if min_samples_per_class * validation_size < 1:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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35 # Adjust validation_size to ensure at least 1 sample per class, but do not exceed original validation_size
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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36 adjusted_validation_size = min(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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37 validation_size, 1.0 / min_samples_per_class
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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38 )
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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39 if adjusted_validation_size != validation_size:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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40 validation_size = adjusted_validation_size
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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41 logger.info(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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42 f"Adjusted validation_size to {validation_size:.3f} to ensure at least one sample per class in validation"
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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43 )
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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44 stratify_arr = out.loc[idx_train, label_column]
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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45 logger.info("Using stratified split for validation set")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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46 else:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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47 logger.warning("Only one label class found; cannot stratify")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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48 if validation_size <= 0:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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49 logger.info("validation_size <= 0; keeping all as train.")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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50 return out
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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51 if validation_size >= 1:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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52 logger.info("validation_size >= 1; moving all train → validation.")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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53 out.loc[idx_train, split_column] = 1
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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54 return out
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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55 # Always try stratified split first
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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56 try:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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57 train_idx, val_idx = train_test_split(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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58 idx_train,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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59 test_size=validation_size,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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60 random_state=random_state,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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61 stratify=stratify_arr,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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62 )
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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63 logger.info("Successfully applied stratified split")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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64 except ValueError as e:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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65 logger.warning(f"Stratified split failed ({e}); falling back to random split.")
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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66 train_idx, val_idx = train_test_split(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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67 idx_train,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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68 test_size=validation_size,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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69 random_state=random_state,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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70 stratify=None,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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71 )
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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72 out.loc[train_idx, split_column] = 0
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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73 out.loc[val_idx, split_column] = 1
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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74 out[split_column] = out[split_column].astype(int)
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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75 return out
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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76
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77
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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78 def create_stratified_random_split(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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79 df: pd.DataFrame,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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80 split_column: str,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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81 split_probabilities: list = [0.7, 0.1, 0.2],
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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82 random_state: int = 42,
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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83 label_column: Optional[str] = None,
20
64872c48a21f planemo upload for repository https://github.com/goeckslab/gleam.git commit d4b122527a2402e43512f9b4bda00c7bff0ec9e9
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84 group_column: Optional[str] = None,
12
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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85 ) -> pd.DataFrame:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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86 """Create a stratified random split when no split column exists."""
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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87 out = df.copy()
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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88
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89 # initialize split column
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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90 out[split_column] = 0
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
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91
20
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92 if group_column and group_column not in out.columns:
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93 logger.warning(
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94 "Group column '%s' not found in data; proceeding without group-aware split.",
64872c48a21f planemo upload for repository https://github.com/goeckslab/gleam.git commit d4b122527a2402e43512f9b4bda00c7bff0ec9e9
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95 group_column,
64872c48a21f planemo upload for repository https://github.com/goeckslab/gleam.git commit d4b122527a2402e43512f9b4bda00c7bff0ec9e9
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96 )
64872c48a21f planemo upload for repository https://github.com/goeckslab/gleam.git commit d4b122527a2402e43512f9b4bda00c7bff0ec9e9
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97 group_column = None
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98
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99 def _allocate_split_counts(n_total: int, probs: list) -> list:
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100 """Allocate exact split counts using largest remainder rounding."""
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101 if n_total <= 0:
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102 return [0 for _ in probs]
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103
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104 counts = [0 for _ in probs]
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105 active = [i for i, p in enumerate(probs) if p > 0]
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106 remainder = n_total
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107
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108 if active and n_total >= len(active):
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109 for i in active:
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110 counts[i] = 1
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111 remainder -= len(active)
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112
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113 if remainder > 0:
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114 probs_arr = np.array(probs, dtype=float)
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115 probs_arr = probs_arr / probs_arr.sum()
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116 raw = remainder * probs_arr
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117 floors = np.floor(raw).astype(int)
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118 for i, value in enumerate(floors.tolist()):
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119 counts[i] += value
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120 leftover = remainder - int(floors.sum())
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121 if leftover > 0 and active:
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122 frac = raw - floors
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123 order = sorted(active, key=lambda i: (-frac[i], i))
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124 for i in range(leftover):
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125 counts[order[i % len(order)]] += 1
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126
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127 return counts
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128
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129 def _choose_split(counts: list, targets: list, active: list) -> int:
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130 remaining = [targets[i] - counts[i] for i in range(len(targets))]
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131 best = max(active, key=lambda i: (remaining[i], -counts[i], -targets[i]))
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132 if remaining[best] <= 0:
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133 best = min(active, key=lambda i: counts[i])
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134 return best
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135
12
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136 if not label_column or label_column not in out.columns:
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137 logger.warning(
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138 "No label column found; using random split without stratification"
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139 )
20
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140 # fall back to random assignment (group-aware if requested)
12
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141 indices = out.index.tolist()
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142 rng = np.random.RandomState(random_state)
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143
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144 if group_column:
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145 group_series = out[group_column].astype(object)
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146 missing_mask = group_series.isna()
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147 if missing_mask.any():
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148 group_series = group_series.copy()
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149 group_series.loc[missing_mask] = [
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150 f"__missing__{idx}" for idx in group_series.index[missing_mask]
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151 ]
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152 group_to_indices = {}
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153 for idx, group_id in group_series.items():
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154 group_to_indices.setdefault(group_id, []).append(idx)
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155 group_ids = sorted(group_to_indices.keys(), key=lambda x: str(x))
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156 rng.shuffle(group_ids)
12
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157
20
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158 targets = _allocate_split_counts(len(indices), split_probabilities)
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159 counts = [0 for _ in split_probabilities]
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160 active = [i for i, p in enumerate(split_probabilities) if p > 0]
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161 train_idx = []
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162 val_idx = []
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163 test_idx = []
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164
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165 for group_id in group_ids:
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166 size = len(group_to_indices[group_id])
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167 split_idx = _choose_split(counts, targets, active)
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168 counts[split_idx] += size
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169 if split_idx == 0:
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170 train_idx.extend(group_to_indices[group_id])
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171 elif split_idx == 1:
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172 val_idx.extend(group_to_indices[group_id])
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173 else:
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174 test_idx.extend(group_to_indices[group_id])
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175
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176 out.loc[train_idx, split_column] = 0
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177 out.loc[val_idx, split_column] = 1
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178 out.loc[test_idx, split_column] = 2
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179 return out.astype({split_column: int})
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180
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181 rng.shuffle(indices)
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182
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183 targets = _allocate_split_counts(len(indices), split_probabilities)
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184 n_train, n_val, n_test = targets
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185
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186 out.loc[indices[:n_train], split_column] = 0
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187 out.loc[indices[n_train:n_train + n_val], split_column] = 1
20
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188 out.loc[indices[n_train + n_val:n_train + n_val + n_test], split_column] = 2
12
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189
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190 return out.astype({split_column: int})
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191
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192 # check if stratification is possible
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193 label_counts = out[label_column].value_counts()
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194 min_samples_per_class = label_counts.min()
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195
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196 # ensure we have enough samples for stratification:
20
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197 # Each class must have at least as many samples as the number of nonzero splits,
12
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198 # so that each split can receive at least one sample per class.
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199 active_splits = [p for p in split_probabilities if p > 0]
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200 min_samples_required = len(active_splits)
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201 if min_samples_per_class < min_samples_required:
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202 logger.warning(
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203 f"Insufficient samples per class for stratification (min: {min_samples_per_class}, required: {min_samples_required}); using random split"
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204 )
20
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205 # fall back to simple random assignment (group-aware if requested)
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206 indices = out.index.tolist()
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207 rng = np.random.RandomState(random_state)
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208
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209 if group_column:
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210 group_series = out[group_column].astype(object)
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211 missing_mask = group_series.isna()
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212 if missing_mask.any():
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213 group_series = group_series.copy()
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214 group_series.loc[missing_mask] = [
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215 f"__missing__{idx}" for idx in group_series.index[missing_mask]
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216 ]
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217 group_to_indices = {}
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218 for idx, group_id in group_series.items():
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219 group_to_indices.setdefault(group_id, []).append(idx)
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220 group_ids = sorted(group_to_indices.keys(), key=lambda x: str(x))
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221 rng.shuffle(group_ids)
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222
20
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223 targets = _allocate_split_counts(len(indices), split_probabilities)
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224 counts = [0 for _ in split_probabilities]
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225 active = [i for i, p in enumerate(split_probabilities) if p > 0]
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226 train_idx = []
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227 val_idx = []
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228 test_idx = []
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229
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230 for group_id in group_ids:
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231 size = len(group_to_indices[group_id])
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232 split_idx = _choose_split(counts, targets, active)
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233 counts[split_idx] += size
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234 if split_idx == 0:
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235 train_idx.extend(group_to_indices[group_id])
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236 elif split_idx == 1:
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237 val_idx.extend(group_to_indices[group_id])
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238 else:
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239 test_idx.extend(group_to_indices[group_id])
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240
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241 out.loc[train_idx, split_column] = 0
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242 out.loc[val_idx, split_column] = 1
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243 out.loc[test_idx, split_column] = 2
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244 return out.astype({split_column: int})
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245
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246 rng.shuffle(indices)
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247
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248 targets = _allocate_split_counts(len(indices), split_probabilities)
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249 n_train, n_val, n_test = targets
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250
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251 out.loc[indices[:n_train], split_column] = 0
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252 out.loc[indices[n_train:n_train + n_val], split_column] = 1
20
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253 out.loc[indices[n_train + n_val:n_train + n_val + n_test], split_column] = 2
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254
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255 return out.astype({split_column: int})
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256
20
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257 if group_column:
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258 logger.info(
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259 "Using stratified random split for train/validation/test sets (grouped by '%s')",
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260 group_column,
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261 )
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262 rng = np.random.RandomState(random_state)
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263
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264 group_series = out[group_column].astype(object)
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265 missing_mask = group_series.isna()
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266 if missing_mask.any():
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267 group_series = group_series.copy()
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268 group_series.loc[missing_mask] = [
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269 f"__missing__{idx}" for idx in group_series.index[missing_mask]
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270 ]
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271
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272 group_to_indices = {}
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273 for idx, group_id in group_series.items():
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274 group_to_indices.setdefault(group_id, []).append(idx)
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275
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276 group_ids = sorted(group_to_indices.keys(), key=lambda x: str(x))
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277 group_labels = {}
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278 mixed_groups = []
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279 label_series = out[label_column]
12
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280
20
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281 for group_id in group_ids:
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282 labels = label_series.loc[group_to_indices[group_id]].dropna().unique()
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283 if len(labels) == 1:
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284 group_labels[group_id] = labels[0]
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285 elif len(labels) == 0:
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286 group_labels[group_id] = None
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287 else:
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288 mode_vals = label_series.loc[group_to_indices[group_id]].mode(dropna=True)
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289 group_labels[group_id] = mode_vals.iloc[0] if not mode_vals.empty else labels[0]
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290 mixed_groups.append(group_id)
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291
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292 if mixed_groups:
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293 logger.warning(
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294 "Detected %d groups with multiple labels; using the most common label per group for stratification.",
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295 len(mixed_groups),
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296 )
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297
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298 train_idx = []
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299 val_idx = []
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300 test_idx = []
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301 active = [i for i, p in enumerate(split_probabilities) if p > 0]
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302
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303 for label_value in sorted(label_counts.index.tolist(), key=lambda x: str(x)):
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304 label_groups = [g for g in group_ids if group_labels.get(g) == label_value]
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305 if not label_groups:
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306 continue
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307 rng.shuffle(label_groups)
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308 label_total = sum(len(group_to_indices[g]) for g in label_groups)
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309 targets = _allocate_split_counts(label_total, split_probabilities)
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310 counts = [0 for _ in split_probabilities]
12
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311
20
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312 for group_id in label_groups:
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313 size = len(group_to_indices[group_id])
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314 split_idx = _choose_split(counts, targets, active)
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315 counts[split_idx] += size
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316 if split_idx == 0:
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317 train_idx.extend(group_to_indices[group_id])
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318 elif split_idx == 1:
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319 val_idx.extend(group_to_indices[group_id])
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320 else:
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321 test_idx.extend(group_to_indices[group_id])
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322
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323 # Assign groups without a label (or missing labels) using overall targets.
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324 unlabeled_groups = [g for g in group_ids if group_labels.get(g) is None]
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325 if unlabeled_groups:
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326 rng.shuffle(unlabeled_groups)
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327 total_unlabeled = sum(len(group_to_indices[g]) for g in unlabeled_groups)
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328 targets = _allocate_split_counts(total_unlabeled, split_probabilities)
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329 counts = [0 for _ in split_probabilities]
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330 for group_id in unlabeled_groups:
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331 size = len(group_to_indices[group_id])
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332 split_idx = _choose_split(counts, targets, active)
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333 counts[split_idx] += size
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334 if split_idx == 0:
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335 train_idx.extend(group_to_indices[group_id])
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336 elif split_idx == 1:
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337 val_idx.extend(group_to_indices[group_id])
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338 else:
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339 test_idx.extend(group_to_indices[group_id])
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340
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341 out.loc[train_idx, split_column] = 0
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342 out.loc[val_idx, split_column] = 1
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343 out.loc[test_idx, split_column] = 2
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344
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345 logger.info("Successfully applied stratified random split")
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346 logger.info(
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347 f"Split counts: Train={len(train_idx)}, Val={len(val_idx)}, Test={len(test_idx)}"
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348 )
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349 return out.astype({split_column: int})
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350
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351 logger.info("Using stratified random split for train/validation/test sets (per-class allocation)")
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352
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353 rng = np.random.RandomState(random_state)
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354 label_values = sorted(label_counts.index.tolist(), key=lambda x: str(x))
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355 train_idx = []
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356 val_idx = []
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357 test_idx = []
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358
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359 for label_value in label_values:
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360 label_indices = out.index[out[label_column] == label_value].tolist()
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361 rng.shuffle(label_indices)
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362 n_train, n_val, n_test = _allocate_split_counts(len(label_indices), split_probabilities)
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363 train_idx.extend(label_indices[:n_train])
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364 val_idx.extend(label_indices[n_train:n_train + n_val])
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365 test_idx.extend(label_indices[n_train + n_val:n_train + n_val + n_test])
12
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366
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367 # assign split values
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368 out.loc[train_idx, split_column] = 0
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369 out.loc[val_idx, split_column] = 1
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370 out.loc[test_idx, split_column] = 2
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371
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372 logger.info("Successfully applied stratified random split")
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diff changeset
373 logger.info(
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374 f"Split counts: Train={len(train_idx)}, Val={len(val_idx)}, Test={len(test_idx)}"
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375 )
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376 return out.astype({split_column: int})
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377
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378
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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379 class SplitProbAction(argparse.Action):
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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380 def __call__(self, parser, namespace, values, option_string=None):
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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381 train, val, test = values
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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382 total = train + val + test
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
parents:
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383 if abs(total - 1.0) > 1e-6:
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
parents:
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384 parser.error(
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
parents:
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385 f"--split-probabilities must sum to 1.0; "
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
parents:
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386 f"got {train:.3f} + {val:.3f} + {test:.3f} = {total:.3f}"
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
parents:
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387 )
bcfa2e234a80 planemo upload for repository https://github.com/goeckslab/gleam.git commit 96bab8325992d16fcaad8e0a4dc4c62b00e2abc2
goeckslab
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388 setattr(namespace, self.dest, values)