comparison image_learner_cli.py @ 7:801a8b6973fb draft

planemo upload for repository https://github.com/goeckslab/gleam.git commit 67df782ea551181e1d240d463764016ba528eba9

6:09904b1f61f5

import tempfile
import zipfile
from pathlib import Path
from typing import Any, Dict, Optional, Protocol, Tuple

import pandas as pd
import pandas.api.types as ptypes
import yaml
from constants import (
    IMAGE_PATH_COLUMN_NAME,

def split_data_0_2(
    df: pd.DataFrame,
    split_column: str,
    validation_size: float = 0.15,
    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()

    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 and (label_counts.min() * validation_size) >= 1:
            stratify_arr = out.loc[idx_train, label_column]
        else:
            logger.warning(
                "Cannot stratify (too few labels); splitting without 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
    try:
        train_idx, val_idx = train_test_split(
            idx_train,
            test_size=validation_size,
            random_state=random_state,
            stratify=stratify_arr,
        )
    except ValueError as e:
        logger.warning(f"Stratified split failed ({e}); retrying without stratify.")
        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


class Backend(Protocol):
    """Interface for a machine learning backend."""

            raise

        if SPLIT_COLUMN_NAME in df.columns:
            df, split_config, split_info = self._process_fixed_split(df)
        else:
            logger.info("No split column; using random split")
            split_config = {
                "type": "random",
                "probabilities": self.args.split_probabilities,
            }
            split_info = (
                f"No split column in CSV. Used random split: "
                f"{[int(p * 100) for p in self.args.split_probabilities]}% "
                f"for train/val/test."
            )

        final_csv = self.temp_dir / TEMP_CSV_FILENAME
        try:

                )
                split_info = (
                    "Detected a split column (with values 0 and 2) in the input CSV. "
                    f"Used this column as a base and reassigned "
                    f"{self.args.validation_size * 100:.1f}% "
                    "of the training set (originally labeled 0) to validation (labeled 1)."
                )
                logger.info("Applied custom 0/2 split.")
            elif unique.issubset({0, 1, 2}):
                split_info = "Used user-defined split column from CSV."
                logger.info("Using fixed split as-is.")

        help="Where to write outputs",
    )
    parser.add_argument(
        "--validation-size",
        type=float,
        default=0.15,
        help="Fraction for validation (0.0–1.0)",
    )
    parser.add_argument(
        "--preprocessing-num-processes",
        type=int,

7:801a8b6973fb

import tempfile
import zipfile
from pathlib import Path
from typing import Any, Dict, Optional, Protocol, Tuple

import numpy as np
import pandas as pd
import pandas.api.types as ptypes
import yaml
from constants import (
    IMAGE_PATH_COLUMN_NAME,

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()

    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

    # Always use stratify if possible
    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],
    )

    # 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 Backend(Protocol):
    """Interface for a machine learning backend."""

            raise

        if SPLIT_COLUMN_NAME in df.columns:
            df, split_config, split_info = self._process_fixed_split(df)
        else:
            logger.info("No split column; creating stratified random split")
            df = create_stratified_random_split(
                df=df,
                split_column=SPLIT_COLUMN_NAME,
                split_probabilities=self.args.split_probabilities,
                random_state=self.args.random_seed,
                label_column=LABEL_COLUMN_NAME,
            )
            split_config = {
                "type": "fixed",
                "column": SPLIT_COLUMN_NAME,
            }
            split_info = (
                f"No split column in CSV. Created stratified random split: "
                f"{[int(p * 100) for p in self.args.split_probabilities]}% "
                f"for train/val/test with balanced label distribution."
            )

        final_csv = self.temp_dir / TEMP_CSV_FILENAME
        try:

                )
                split_info = (
                    "Detected a split column (with values 0 and 2) in the input CSV. "
                    f"Used this column as a base and reassigned "
                    f"{self.args.validation_size * 100:.1f}% "
                    "of the training set (originally labeled 0) to validation (labeled 1) using stratified sampling."
                )
                logger.info("Applied custom 0/2 split.")
            elif unique.issubset({0, 1, 2}):
                split_info = "Used user-defined split column from CSV."
                logger.info("Using fixed split as-is.")

        help="Where to write outputs",
    )
    parser.add_argument(
        "--validation-size",
        type=float,
        default=0.1,
        help="Fraction for validation (0.0–1.0)",
    )
    parser.add_argument(
        "--preprocessing-num-processes",
        type=int,