data/lmdb/PMT/preprocess/qmof_preprocessor.py

"""

MOFTransformer Preprocessor — LMDB Edition



Prepares raw MOF dataset for training by processing CIF files and packing

the results into three LMDB files (train / val / test), one file per split.



All numeric target columns from id_prop.csv are stored in the LMDB.

The target variable to predict is chosen at training time, not here.



LMDB schema per file:

    b'__metadata__'  → pickle dict  {target_columns: [...], n_samples: int}

    b'__keys__'      → pickle list  [cif_id, ...]

    b'__targets__'   → pickle dict  {cif_id: {col: float_or_nan, ...}}

    b'{cif_id}'      → pickle dict  {cif_id,

                                     atom_num, nbr_idx, nbr_dist,

                                     uni_idx, uni_count,

                                     grid_header, griddata16}



Author: MOFTransformer Team

Date: 2026-03-16

"""

import os
import sys
import json
import shutil
import pickle
import argparse
from pathlib import Path
from typing import Optional, Tuple, List

import pandas as pd
import numpy as np
import lmdb

# Use the pip-installed moftransformer for preprocessing (has compiled GRIDAY).
# The local MOFTransformer copy is only used by trainer.py (patched modules).
from moftransformer.utils.prepare_data import prepare_data
from moftransformer.utils.install_griday import install_griday

# Dummy downstream name used internally for prepare_data splitting.
_DUMMY_DOWNSTREAM = "lmdb_split"


# ---------------------------------------------------------------------------
# GRIDAY helpers
# ---------------------------------------------------------------------------

def verify_griday_installation() -> None:
    """Verify that GRIDAY is installed, install if necessary."""
    try:
        from moftransformer import __root_dir__
        griday_path = os.path.join(__root_dir__, "libs/GRIDAY/scripts/grid_gen")
        if not os.path.exists(griday_path):
            print("GRIDAY not found. Installing GRIDAY...")
            install_griday()
    except ImportError as e:
        print(f"Error importing GRIDAY: {e}")
        print("Attempting to install GRIDAY...")
        install_griday()


# ---------------------------------------------------------------------------
# CSV / CIF validation helpers
# ---------------------------------------------------------------------------

def load_all_targets(data_dir: Path) -> Tuple[pd.DataFrame, List[str]]:
    """

    Load id_prop.csv and return a DataFrame with all numeric target columns.



    Returns

    -------

    Tuple[pd.DataFrame, List[str]]

        DataFrame with columns [cif_id, col1, col2, ...] and list of target

        column names.  Values may be NaN where data is missing.

    """
    id_prop_path = data_dir / "id_prop.csv"
    if not id_prop_path.exists():
        raise FileNotFoundError(f"id_prop.csv not found at {id_prop_path}")

    df = pd.read_csv(id_prop_path)
    if df.empty:
        raise ValueError("id_prop.csv is empty")
    if df.shape[1] < 2:
        raise ValueError(
            f"id_prop.csv must have at least 2 columns. Found {df.shape[1]}."
        )

    cif_id_col = df.columns[0]

    # Detect numeric columns (excluding the cif_id column)
    numeric_cols = []
    for col in df.columns[1:]:
        converted = pd.to_numeric(df[col], errors="coerce")
        if converted.notna().sum() > 0:
            numeric_cols.append(col)
            df[col] = converted  # ensure numeric dtype

    if not numeric_cols:
        raise ValueError("No numeric target columns found in id_prop.csv")

    result = df[[cif_id_col] + numeric_cols].copy()
    result = result.rename(columns={cif_id_col: "cif_id"})
    result["cif_id"] = result["cif_id"].astype(str)
    result = result.set_index("cif_id")

    print(f"Loaded {len(result)} rows from id_prop.csv")
    print(f"Found {len(numeric_cols)} numeric target columns:")
    for col in numeric_cols:
        n_valid = result[col].notna().sum()
        print(f"  {col:50s}  {n_valid}/{len(result)} non-NaN")

    return result, numeric_cols


def verify_cif_files(data_dir: Path, df: pd.DataFrame) -> pd.DataFrame:
    """Drop rows whose CIF file is missing; return updated DataFrame."""
    raw_dir = data_dir / "raw"
    if not raw_dir.exists():
        raise FileNotFoundError(f"Raw CIF directory not found at {raw_dir}")

    cif_files = {f.stem for f in raw_dir.glob("*.cif")}
    print(f"\nFound {len(cif_files)} CIF files in raw/")

    valid_ids = set(df.index) & cif_files
    missing = set(df.index) - cif_files
    if missing:
        print(f"Warning: {len(missing)} entries have no CIF file and will be skipped")

    filtered = df.loc[list(valid_ids)].copy()
    print(f"Valid samples: {len(filtered)}")

    if len(filtered) == 0:
        raise ValueError("No valid samples found after CIF verification.")
    return filtered


def create_dummy_raw_json(df: pd.DataFrame, raw_dir: Path) -> None:
    """

    Write raw_{DUMMY}.json  (cif_id → 0.0) so prepare_data can split the data.

    All CIF IDs present in the DataFrame are included.

    """
    dummy_data = {cif_id: 0.0 for cif_id in df.index}
    json_path = raw_dir / f"raw_{_DUMMY_DOWNSTREAM}.json"
    with open(json_path, "w") as f:
        json.dump(dummy_data, f, indent=2)
    print(f"Created dummy split JSON: {json_path}  ({len(dummy_data)} entries)")


def create_filtered_id_prop(df: pd.DataFrame, raw_dir: Path) -> None:
    """Write a minimal id_prop.csv (cif_id, dummy_target) for prepare_data."""
    csv_df = pd.DataFrame({"cif_id": df.index, _DUMMY_DOWNSTREAM: 0.0})
    csv_path = raw_dir / "id_prop.csv"
    csv_df.to_csv(csv_path, index=False)
    print(f"Created {csv_path}")


# ---------------------------------------------------------------------------
# Data preparation (calls MOFTransformer's prepare_data)
# ---------------------------------------------------------------------------

def run_data_preparation(

    raw_dir: Path,

    processed_dir: Path,

    train_fraction: float,

    test_fraction: float,

    seed: int,

) -> None:
    """Run MOFTransformer's prepare_data to build graph / grid embeddings."""
    print("\nStarting data preparation with MOFTransformer utilities...")
    print(f"  Raw directory : {raw_dir}")
    print(f"  Processed dir : {processed_dir}")
    print(f"  Downstream    : {_DUMMY_DOWNSTREAM}")

    prepare_data(
        root_cifs=raw_dir,
        root_dataset=processed_dir,
        downstream=_DUMMY_DOWNSTREAM,
        train_fraction=train_fraction,
        test_fraction=test_fraction,
        seed=seed,
    )

    expected = [
        processed_dir / f"train_{_DUMMY_DOWNSTREAM}.json",
        processed_dir / f"val_{_DUMMY_DOWNSTREAM}.json",
        processed_dir / f"test_{_DUMMY_DOWNSTREAM}.json",
    ]
    print("\nVerifying processed split files...")
    for p in expected:
        if p.exists() and p.stat().st_size > 0:
            print(f"  OK  {p.name}")
        else:
            raise RuntimeError(
                f"Data preparation did not produce expected file: {p}"
            )
    print("Data preparation completed!")


# ---------------------------------------------------------------------------
# LMDB packing
# ---------------------------------------------------------------------------

def _estimate_map_size(split_dir: Path, cif_ids: list) -> int:
    """Estimate LMDB map_size from file sizes × 4 safety margin (virtual mem)."""
    total = 0
    for cif_id in cif_ids:
        for ext in (".graphdata", ".griddata16", ".grid"):
            f = split_dir / f"{cif_id}{ext}"
            if f.exists():
                total += f.stat().st_size
    return max(int(total * 4), 1 << 30)


def pack_split_to_lmdb(

    processed_dir: Path,

    split: str,

    targets_df: pd.DataFrame,

    target_columns: List[str],

    output_path: Path,

) -> None:
    """

    Pack one split into an LMDB file.



    Parameters

    ----------

    processed_dir : Path

        Directory produced by prepare_data.

    split : str

        'train', 'val', or 'test'.

    targets_df : pd.DataFrame

        Index = cif_id, columns = all numeric target columns (may contain NaN).

    target_columns : List[str]

        Ordered list of target column names to store.

    output_path : Path

        Destination LMDB file path.

    """
    json_path = processed_dir / f"{split}_{_DUMMY_DOWNSTREAM}.json"
    if not json_path.exists():
        raise FileNotFoundError(f"Split JSON not found: {json_path}")

    with open(json_path) as f:
        split_cif_ids: list = list(json.load(f).keys())

    split_dir = processed_dir / split
    if not split_dir.exists():
        raise FileNotFoundError(f"Split directory not found: {split_dir}")

    n = len(split_cif_ids)
    print(f"\n  Packing '{split}' split → {output_path.name}")
    print(f"  Samples in split: {n}")

    # Build targets lookup: {cif_id: {col: float_or_nan}}
    all_targets: dict = {}
    for cif_id in split_cif_ids:
        if cif_id in targets_df.index:
            row = targets_df.loc[cif_id]
            all_targets[cif_id] = {
                col: float(row[col]) if pd.notna(row[col]) else float("nan")
                for col in target_columns
            }
        else:
            all_targets[cif_id] = {col: float("nan") for col in target_columns}

    # Coverage stats
    for col in target_columns[:5]:
        n_valid = sum(1 for v in all_targets.values() if not np.isnan(v[col]))
        print(f"    {col[:50]:50s}  {n_valid}/{n} non-NaN")
    if len(target_columns) > 5:
        print(f"    ... ({len(target_columns) - 5} more columns)")

    map_size = _estimate_map_size(split_dir, split_cif_ids)
    print(f"  LMDB map_size  : {map_size / 1e9:.2f} GB (virtual)")

    metadata = {
        "target_columns": target_columns,
        "n_samples": n,
    }

    env = lmdb.open(
        str(output_path),
        map_size=map_size,
        subdir=False,
        readonly=False,
        meminit=False,
        map_async=True,
    )

    missing_files = []
    written = 0

    with env.begin(write=True) as txn:
        txn.put(b"__metadata__", pickle.dumps(metadata, protocol=4))
        txn.put(b"__keys__", pickle.dumps(split_cif_ids, protocol=4))
        txn.put(b"__targets__", pickle.dumps(all_targets, protocol=4))

        for cif_id in split_cif_ids:
            graph_path = split_dir / f"{cif_id}.graphdata"
            grid_path = split_dir / f"{cif_id}.grid"
            griddata_path = split_dir / f"{cif_id}.griddata16"

            missing = [p for p in (graph_path, grid_path, griddata_path) if not p.exists()]
            if missing:
                missing_files.extend(str(p) for p in missing)
                continue

            with open(graph_path, "rb") as fh:
                graphdata = pickle.load(fh)

            grid_header = grid_path.read_text()

            with open(griddata_path, "rb") as fh:
                griddata16 = pickle.load(fh)

            sample = {
                "cif_id": cif_id,
                "atom_num": graphdata[1],
                "nbr_idx": graphdata[2],
                "nbr_dist": graphdata[3],
                "uni_idx": graphdata[4],
                "uni_count": graphdata[5],
                "grid_header": grid_header,
                "griddata16": griddata16,
            }

            txn.put(cif_id.encode(), pickle.dumps(sample, protocol=4))
            written += 1

    env.sync()
    env.close()

    if missing_files:
        print(f"  WARNING: {len(missing_files)} files missing, samples skipped:")
        for mf in missing_files[:10]:
            print(f"    {mf}")

    lmdb_size_mb = output_path.stat().st_size / 1e6
    print(f"  Written {written}/{n} samples  ({lmdb_size_mb:.1f} MB on disk)")


def pack_all_splits_to_lmdb(

    processed_dir: Path,

    targets_df: pd.DataFrame,

    target_columns: List[str],

    output_prefix: str,

) -> dict:
    """Pack train / val / test into separate LMDB files."""
    prefix = Path(output_prefix)
    prefix.parent.mkdir(parents=True, exist_ok=True)

    paths = {}
    for split in ("train", "val", "test"):
        out = prefix.parent / f"{prefix.name}_{split}.lmdb"
        pack_split_to_lmdb(processed_dir, split, targets_df, target_columns, out)
        paths[split] = out

    return paths


# ---------------------------------------------------------------------------
# Main preprocessing pipeline
# ---------------------------------------------------------------------------

def preprocess_dataset(

    data_dir: str,

    output_prefix: str,

    train_fraction: float = 0.8,

    test_fraction: float = 0.1,

    seed: int = 42,

) -> dict:
    """

    Main preprocessing function.



    Reads ALL numeric columns from id_prop.csv and stores them in the LMDB.

    The target variable is chosen at training time via --target-column.



    Produces:

        {output_prefix}_train.lmdb

        {output_prefix}_val.lmdb

        {output_prefix}_test.lmdb

    """
    print("=" * 60)
    print("MOFTransformer Preprocessor — LMDB Edition")
    print("=" * 60)

    data_dir = Path(data_dir).resolve()
    output_prefix = str(Path(output_prefix).resolve())

    print(f"\nData directory : {data_dir}")
    print(f"Output prefix  : {output_prefix}")
    print(f"Train / Test   : {train_fraction} / {test_fraction}  seed={seed}")

    # Step 1
    print("\nStep 1: Verifying GRIDAY installation...")
    verify_griday_installation()

    # Step 2
    print("\nStep 2: Loading all numeric targets from id_prop.csv...")
    targets_df, target_columns = load_all_targets(data_dir)

    # Step 3
    print("\nStep 3: Verifying CIF files...")
    targets_df = verify_cif_files(data_dir, targets_df)

    # Step 4
    print("\nStep 4: Setting up working directory...")
    work_dir = data_dir / "preprocessed_work"
    raw_dir = work_dir / "raw"
    processed_dir = work_dir / "processed"

    if work_dir.exists():
        shutil.rmtree(work_dir)
    for d in (work_dir, raw_dir, processed_dir):
        d.mkdir(parents=True, exist_ok=True)

    source_raw = data_dir / "raw"
    for cif_id in targets_df.index:
        shutil.copy2(source_raw / f"{cif_id}.cif", raw_dir / f"{cif_id}.cif")
    print(f"Copied {len(targets_df)} CIF files")

    # Step 5
    print("\nStep 5: Creating dummy split JSON for prepare_data...")
    create_dummy_raw_json(targets_df, raw_dir)
    create_filtered_id_prop(targets_df, raw_dir)

    # Step 6
    print("\nStep 6: Running data preparation (graph + grid embeddings)...")
    run_data_preparation(
        raw_dir=raw_dir,
        processed_dir=processed_dir,
        train_fraction=train_fraction,
        test_fraction=test_fraction,
        seed=seed,
    )

    # Step 7
    print("\nStep 7: Packing into LMDB files...")
    lmdb_paths = pack_all_splits_to_lmdb(
        processed_dir=processed_dir,
        targets_df=targets_df,
        target_columns=target_columns,
        output_prefix=output_prefix,
    )

    # Step 8
    print("\nStep 8: Cleaning up working directory...")
    shutil.rmtree(work_dir)
    print("Working directory removed")

    print("\n" + "=" * 60)
    print("Preprocessing completed!")
    print(f"Stored {len(target_columns)} target columns:")
    for col in target_columns:
        print(f"  {col}")
    print("\nOutput LMDB files:")
    for split, path in lmdb_paths.items():
        size_mb = path.stat().st_size / 1e6
        print(f"  {split:5s}: {path}  ({size_mb:.1f} MB)")
    print("=" * 60)

    return lmdb_paths


# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------

def parse_arguments() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description=(
            "Preprocess MOF dataset and store ALL numeric targets in LMDB files. "
            "The target variable to predict is chosen at training time."
        ),
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""

Examples:

  python preprocessor.py \\

      --data-dir      ./qmof_cif/               \\

      --output-prefix ./output/qmof_pmt_lmdb    \\

      --train-fraction 0.8 --test-fraction 0.1



  # Produces:

  #   ./output/qmof_pmt_lmdb_train.lmdb

  #   ./output/qmof_pmt_lmdb_val.lmdb

  #   ./output/qmof_pmt_lmdb_test.lmdb

  #

  # Each LMDB stores ALL numeric columns from id_prop.csv.

  # Choose which target to train on via --target-column in trainer.py.

        """,
    )
    parser.add_argument(
        "--data-dir", type=str, required=True,
        help="Dataset directory with id_prop.csv and raw/ folder",
    )
    parser.add_argument(
        "--output-prefix", type=str, required=True,
        help="Base path prefix for output LMDB files (no extension)",
    )
    parser.add_argument(
        "--train-fraction", type=float, default=0.8,
        help="Fraction for training (default: 0.8)",
    )
    parser.add_argument(
        "--test-fraction", type=float, default=0.1,
        help="Fraction for testing (default: 0.1)",
    )
    parser.add_argument(
        "--seed", type=int, default=42,
        help="Random seed (default: 42)",
    )
    return parser.parse_args()


def main():
    args = parse_arguments()
    try:
        preprocess_dataset(
            data_dir=args.data_dir,
            output_prefix=args.output_prefix,
            train_fraction=args.train_fraction,
            test_fraction=args.test_fraction,
            seed=args.seed,
        )
    except Exception as e:
        print(f"\nERROR: Preprocessing failed: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)


if __name__ == "__main__":
    main()