File size: 4,697 Bytes
4947683 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | """Copyright (c) Meta Platforms, Inc. and affiliates."""
from __future__ import annotations
import io
from abc import ABC, abstractmethod
from contextlib import redirect_stdout
from copy import deepcopy
from dataclasses import dataclass
from pathlib import Path
from typing import Literal, Tuple, get_args
import joblib
import pandas as pd
from pymatgen.core import Composition, Structure
from flowmm.pandas_ import maybe_get_missing_columns
from flowmm.pymatgen_ import COLUMNS_COMPUTATIONS
ValidStages = Literal["train", "val", "test"]
VALID_STAGES: Tuple[ValidStages, ...] = get_args(ValidStages)
ValidTabularDatasets = Literal["diffcsp_mp20", "cdvae_mp20"]
VALID_TABULAR_DATASETS: Tuple[ValidTabularDatasets, ...] = get_args(
ValidTabularDatasets
)
trap = io.StringIO()
def get_tabular_dataset(name: ValidTabularDatasets) -> TabularDataset:
if name == "diffcsp_mp20":
return DiffCSP_MP20()
elif name == "cdvae_mp20":
return CDVAE_MP20()
else:
raise ValueError()
@dataclass
class TabularDataset(ABC):
root: Path
train_unprocessed_path: Path
val_unprocessed_path: Path
test_unprocessed_path: Path
train_processed_json: Path
val_processed_json: Path
test_processed_json: Path
method: str
@abstractmethod
def process(
self,
stage: ValidStages,
) -> None:
raise NotImplementedError()
def process_all(self) -> None:
for stage in VALID_STAGES:
self.process(stage)
def get_df(self, stage: ValidStages) -> pd.DataFrame:
json = self.root / getattr(self, f"{stage}_processed_json")
if not json.exists():
self.process(stage)
df = pd.read_json(json)
df["source"] = stage
df["method"] = self.method
return df
@property
def train_df(self) -> pd.DataFrame:
return self.get_df("train")
@property
def val_df(self) -> pd.DataFrame:
return self.get_df("val")
@property
def test_df(self) -> pd.DataFrame:
return self.get_df("test")
@dataclass
class DiffCSP_MP20(TabularDataset):
root: Path = (Path(__file__).parents[2] / "remote/DiffCSP-official/data").resolve()
train_unprocessed_path: Path = Path("mp_20/train.csv")
val_unprocessed_path: Path = Path("mp_20/val.csv")
test_unprocessed_path: Path = Path("mp_20/test.csv")
train_processed_json: Path = Path("mp_20/train.json")
val_processed_json: Path = Path("mp_20/val.json")
test_processed_json: Path = Path("mp_20/test.json")
method: str = "diffcsp_mp20"
def process(
self,
stage: ValidStages,
) -> None:
unprocessed_path = self.root / getattr(self, f"{stage}_unprocessed_path")
processed_json = self.root / getattr(self, f"{stage}_processed_json")
df = pd.read_csv(unprocessed_path)
df["mp_id"] = df["material_id"]
our_columns_computations = deepcopy(COLUMNS_COMPUTATIONS)
our_columns_computations["composition"] = lambda ddf: ddf["pretty_formula"].map(
lambda x: Composition(x).as_dict()
)
df = maybe_get_missing_columns(df, our_columns_computations)
# save
df.to_json(processed_json, default_handler=str)
print("done!")
@dataclass
class CDVAE_MP20(TabularDataset):
root: Path = (Path(__file__).parents[2] / "remote/cdvae/data").resolve()
train_unprocessed_path: Path = Path("mp_20/train.joblib")
val_unprocessed_path: Path = Path("mp_20/val.joblib")
test_unprocessed_path: Path = Path("mp_20/test.joblib")
train_processed_json: Path = Path("mp_20/train.json")
val_processed_json: Path = Path("mp_20/val.json")
test_processed_json: Path = Path("mp_20/test.json")
method: str = "cdvae_mp20"
def process(
self,
stage: ValidStages,
) -> None:
unprocessed_path = self.root / getattr(self, f"{stage}_unprocessed_path")
processed_json = self.root / getattr(self, f"{stage}_processed_json")
print("computing df...")
cached_data = joblib.load(unprocessed_path)
rows = []
for row in cached_data:
with redirect_stdout(trap):
structure = Structure.from_str(row["cif"], fmt="cif")
rows.append(
{
"mp_id": row["mp_id"],
"num_sites": structure.num_sites,
}
)
df = pd.DataFrame.from_records(rows)
df = maybe_get_missing_columns(df, COLUMNS_COMPUTATIONS)
# save
df.to_json(processed_json)
print("done!")
if __name__ == "__main__":
tds = DiffCSP_MP20()
tds.process_all()
|