From 6a93aae5fa610e5ac38b2a108e1481dfbe9abdf0 Mon Sep 17 00:00:00 2001 From: entodi Date: Fri, 23 Feb 2024 03:34:34 +0000 Subject: [PATCH 1/5] Add MongoDBCreator and BaseDBCreator class --- mindfultensors/creator/__init__.py | 0 .../creator/base_db_creator/__init__.py | 6 + .../base_db_creator/base_db_creator.py | 41 +++ .../creator/mongo_creator/__init__.py | 6 + .../creator/mongo_creator/mongo_creator.py | 318 ++++++++++++++++++ .../test_data/Template-T1-U8-RALPFH-BR.nii.gz | Bin 0 -> 13265077 bytes .../test_data/Template-T2-U8-RALPFH-BR.nii.gz | Bin 0 -> 6134099 bytes 7 files changed, 371 insertions(+) create mode 100644 mindfultensors/creator/__init__.py create mode 100644 mindfultensors/creator/base_db_creator/__init__.py create mode 100644 mindfultensors/creator/base_db_creator/base_db_creator.py create mode 100644 mindfultensors/creator/mongo_creator/__init__.py create mode 100644 mindfultensors/creator/mongo_creator/mongo_creator.py create mode 100644 mindfultensors/creator/mongo_creator/test_data/Template-T1-U8-RALPFH-BR.nii.gz create mode 100644 mindfultensors/creator/mongo_creator/test_data/Template-T2-U8-RALPFH-BR.nii.gz diff --git a/mindfultensors/creator/__init__.py b/mindfultensors/creator/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mindfultensors/creator/base_db_creator/__init__.py b/mindfultensors/creator/base_db_creator/__init__.py new file mode 100644 index 0000000..79d2508 --- /dev/null +++ b/mindfultensors/creator/base_db_creator/__init__.py @@ -0,0 +1,6 @@ +from .base_db_creator import BaseDBCreator + + +__all__ = [ + "BaseDBCreator", +] diff --git a/mindfultensors/creator/base_db_creator/base_db_creator.py b/mindfultensors/creator/base_db_creator/base_db_creator.py new file mode 100644 index 0000000..ab8f9b5 --- /dev/null +++ b/mindfultensors/creator/base_db_creator/base_db_creator.py @@ -0,0 +1,41 @@ +import abc + + +class BaseDBCreator(abc.ABC): + """ + Base class for database creators + """ + def __init__(self): + """ + Base class for database creators + """ + super().__init__() + + @abc.abstractmethod + def connect(self): + """ + Connects to the database + """ + pass + + @abc.abstractmethod + def write(self, *args, **kwargs): + """ + Writes the data + """ + pass + + @abc.abstractmethod + def close(self): + """ + Closes the database connection + """ + pass + + @abc.abstractmethod + def clean(self): + """ + Cleans the database + """ + pass + diff --git a/mindfultensors/creator/mongo_creator/__init__.py b/mindfultensors/creator/mongo_creator/__init__.py new file mode 100644 index 0000000..9bb7556 --- /dev/null +++ b/mindfultensors/creator/mongo_creator/__init__.py @@ -0,0 +1,6 @@ +from .mongo_creator import MongoDBCreator + + +__all__ = [ + "MongoDBCreator", +] diff --git a/mindfultensors/creator/mongo_creator/mongo_creator.py b/mindfultensors/creator/mongo_creator/mongo_creator.py new file mode 100644 index 0000000..5e9b048 --- /dev/null +++ b/mindfultensors/creator/mongo_creator/mongo_creator.py @@ -0,0 +1,318 @@ +import bson +import io +import numpy as np +import os +import pandas as pd +from typing import Dict, List, Callable, Optional, Any, Tuple +from tqdm import tqdm + +import nibabel as nib +from pymongo import MongoClient, ASCENDING +import torch +from torch import Tensor + + +from mindfultensors.utils import unit_interval_normalize as normalize +from mindfultensors.creator.base_db_creator import BaseDBCreator + + +class MongoDBCreator(BaseDBCreator): + """ + MongoDB creator class + """ + _meta_prefix = "meta" + _label_prefix = "label" + _input_prefix = "input" + + def __init__( + self, + database_name: str, + collection_name: str, + host: str, + port: int, + preprocessing_functions: Optional[Dict[str, Callable]] = None, + chunk_size: int = 10, + ) -> BaseDBCreator: + """ + Constructor + + Args: + database_name: str: name of the database + collection_name: str: name of the collection + host: str: host name + port: int: port number + preprocessing_functions: Optional[Dict[str, Callable]]: dictionary of preprocessing functions + chunk_size: int: size of the chunk + + Returns: + BaseDBCreator: an object of MongoDBCreator class + """ + super().__init__() + self._database_name = database_name + self._collection_name = collection_name + self._host = host + self._port = port + self._url = f"mongodb://{self._host}:{self._port}" + self._preprocessing_functions = preprocessing_functions + self._chunk_size = chunk_size + self._client = None + + def connect(self) -> None: + """ + Connects to the database + """ + self._client = MongoClient(self._url) + self._database = self._client[self._database_name] + self._collection_bin = self._database[f"{self._collection_name}.bin"] + self._collection_meta = self._database[f"{self._collection_name}.meta"] + + def write( + self, + data: pd.DataFrame, + input_columns: List[str], + label_columns: List[str], + meta_columns: List[str], + label_description: Optional[Dict[str, str]] = None, + *args, **kwargs + ) -> None: + """ + Writes the data + + Args: + data: pd.DataFrame: data + input_columns: List[str]: list of input columns + label_columns: List[str]: list of label columns + meta_columns: List[str]: list of meta columns + label_description: Optional[Dict[str, str]: dictionary of label description + + Returns: + None + """ + data = self._prepare_dataframe( + data, input_columns, label_columns, meta_columns + ) + self._insert_samples(data, label_description=label_description) + index_name_meta = self._collection_meta.create_index([("id", ASCENDING)]) + index_name_bin = self._collection_bin.create_index([("id", ASCENDING)]) + + def clean(self) -> None: + """ + Cleans the database + + Args: + None + + Returns: + None + """ + self._collection_bin.drop() + self._collection_meta.drop() + + def close(self) -> None: + """ + Closes the database connection + + Args: + None + + Returns: + None + """ + self._client.close() + + def _prepare_dataframe( + self, + data: pd.DataFrame, + input_columns: List[str], + label_columns: List[str], + meta_columns: List[str], + ) -> pd.DataFrame: + """ + Prepare dataframe + + Args: + data: pd.DataFrame: data + input_columns: List[str]: list of input columns + label_columns: List[str]: list of label columns + meta_columns: List[str]: list of meta columns + + Returns: + pd.DataFrame: prepared dataframe + """ + new_columns = { + **{col: f"{self._input_prefix}_{col}" for col in input_columns}, + **{col: f"{self._label_prefix}_{col}" for col in label_columns}, + **{col: f"{self._meta_prefix}_{col}" for col in meta_columns}, + } + data = data.rename( + columns=new_columns + ) + selected_columns = new_columns.values() + return data[selected_columns] + + def _insert_samples( + self, + data: pd.DataFrame, + label_description: Optional[Dict[str, str]] = None, + ) -> None: + """ + Insert samples + + Args: + data: pd.DataFrame: data + label_description: Optional[Dict[str, str]]: dictionary of label description + + Returns: + None + """ + for index in tqdm(data.index): + meta_data = {"id": index, "labels": {}} + for column in data.columns: + shape = None + value = data[column].iloc[index] + if column.startswith(self._meta_prefix): + meta_data[column] = str(value) + elif ( + column.startswith(self._input_prefix) or \ + column.startswith(self._label_prefix) + ): + shape = self._insert_data(column, value, index) + if "shape" not in meta_data: + meta_data["shape"] = shape + else: + assert meta_data["shape"] == shape + if column.startswith(self._label_prefix): + if column in label_description: + meta_data["labels"][column] = label_description[column] + else: + meta_data["labels"][column] = "Label is not described" + else: + raise NotImplementedError(f"Column {column} is missing prefix.") + self._collection_meta.insert_one(meta_data) + + def _insert_data( + self, + column: str, + filename: str, + index: int + ) -> Tuple[int]: + """ + Insert data + + Args: + column: str: column + filename: str: filename + index: int: index + + Returns: + shape: Tuple[int]: shape + """ + tensor_data = self._data_filename_2_tensor(filename) + shape = tensor_data.shape + if column in self._preprocessing_functions: + tensor_data = self._preprocessing_functions[column](tensor_data) + tensor_data = self._tensor_2_bin(tensor_data) + # write data + for chunk in self._chunk_binobj(tensor_data, index, column, self._chunk_size): + self._collection_bin.insert_one(chunk) + return shape + + @staticmethod + def _data_filename_2_tensor(filename: str) -> Tensor: + assert os.path.exists(filename) + return torch.from_numpy(np.asanyarray(nib.load(filename).dataobj)) + + @staticmethod + def _tensor_2_bin(tensor: Tensor) -> bytes: + """ + Convert tensor to binary + + Args: + tensor: Tensor: tensor + + Returns: + tensor_binary: binary + """ + tensor_1d = tensor.to(torch.uint8) + # Serialize tensor and get binary + buffer = io.BytesIO() + torch.save(tensor_1d, buffer) + tensor_binary = buffer.getvalue() + return tensor_binary + + @staticmethod + def _chunk_binobj( + tensor_compressed: Tensor, + id: int, + kind: str, + chunksize: int + ) -> Dict[str, Any]: + """ + Chunk the binary object + + Args: + tensor_compressed: Tensor: compressed tensor + id: int: id + kind: str: kind + chunksize: int: chunk size + + Returns: + Dict[str, Any]: dictionary of chunk + """ + # Convert chunksize from megabytes to bytes + chunksize_bytes = chunksize * 1024 * 1024 + + # Calculate the number of chunks + num_chunks = len(tensor_compressed) // chunksize_bytes + if len(tensor_compressed) % chunksize_bytes != 0: + num_chunks += 1 + + # Yield chunks + for i in range(num_chunks): + start = i * chunksize_bytes + end = min((i + 1) * chunksize_bytes, len(tensor_compressed)) + chunk = tensor_compressed[start:end] + yield { + "id": id, + "chunk_id": i, + "kind": kind, + "chunk": bson.Binary(chunk), + } + + +if __name__ == "__main__": + + # Example usage + database_name = "mydatabase" + collection_name = "mycollection" + mongo_host = "10.245.12.58" + mongo_port = "27017" + metadata = pd.DataFrame( + columns=['t1', 't2', 'subject_id', 'age', 'gender'] + ) + metadata.loc[metadata.shape[0]] = [ + './test_data/Template-T1-U8-RALPFH-BR.nii.gz', + './test_data/Template-T2-U8-RALPFH-BR.nii.gz', + 1, 25, 'M' + ] + creator = MongoDBCreator( + database_name=database_name, + collection_name=collection_name, + host=mongo_host, + port=mongo_port, + preprocessing_functions={ + 't1': lambda x: normalize(x) * 256, + 't2': lambda x: normalize(x) * 256, + }, + chunk_size=10, + ) + creator.connect() + creator.write( + data=metadata, + input_columns=['t1'], + label_columns=['t2'], + meta_columns=['subject_id', 'age', 'gender'], + label_description={"mask": "Lesion mask"} + ) + creator.clean() + creator.close() diff --git a/mindfultensors/creator/mongo_creator/test_data/Template-T1-U8-RALPFH-BR.nii.gz b/mindfultensors/creator/mongo_creator/test_data/Template-T1-U8-RALPFH-BR.nii.gz new 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