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eds_scikit.io.hive

HiveData

Source code in eds_scikit/io/hive.py 
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class HiveData:  # pragma: no cover
    def __init__(
        self,
        database_name: str,
        spark_session: Optional[SparkSession] = None,
        person_ids: Optional[Iterable[int]] = None,
        tables_to_load: Optional[
            Union[Dict[str, Optional[List[str]]], List[str]]
        ] = None,
        columns_to_load: Optional[
            Union[Dict[str, Optional[List[str]]], List[str]]
        ] = None,
        database_type: Optional[str] = "OMOP",
    ):
        """Spark interface for OMOP data stored in a Hive database.

        This class provides a simple access to data stored in Hive.
        Data is returned as koalas dataframes that match the tables
        stored in Hive.

        Parameters
        ----------
        database_name : str
            The name of you database in Hive. Ex: "cse_82727572"
        spark_session : pyspark.sql.SparkSession
            If None, a SparkSession will be retrieved or  created via `SparkSession.builder.enableHiveSupport().getOrCreate()`
        person_ids : Optional[Iterable[int]]
            An iterable of `person_id` that is used to define a subset of the database.
        tables_to_load : Optional[Union[Dict[str, Optional[List[str]]], List[str]]]
            By default (i.e. if ``tables_to_load is None``), loaded tables and columns loaded in each table are those listed
            [here][eds_scikit.io.settings.tables_to_load].
            A dictionnary can be provided to complement those default settings. Keys should be table names to load,
            and values should be:
            - ``None`` to load all columns
            - A list of columns to load (or to add to the default loaded columns if the table is already loaded by default)
            A list of the tables names can also be provided to load all columns of each table.
        columns_to_load : Optional[Union[Dict[str, Optional[List[str]]], List[str]]]
            *deprecated*
        database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2'
            Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.

        Attributes
        ----------
        person : koalas dataframe
            Hive data for table `person` as a koalas dataframe.
            Other OMOP tables can also be accessed as attributes
        available_tables : list of str
            names of OMOP tables that can be accessed as attributes with this
            HiveData object.

        Examples
        --------

        ```python
        data = HiveData(database_name="edsomop_prod_a")
        data.available_tables
        # Out: ["person", "care_site", "condition_occurrence", ... ]

        person = data.person
        type(person)
        # Out: databricks.koalas.frame.DataFrame

        person["person_id"].count()
        # Out: 12670874
        ```

        This class can be used to create a subset of data for a given
        list of `person_id`. This is useful because the smaller dataset
        can then be used to prototype more rapidly.

        ```python
        my_person_ids = [9226726, 2092082, .... ]
        data = HiveData(spark_session=spark, database_name="edsomop_prod_a",
                            person_ids=my_person_ids)
        data.person["person_id"].count()
        # Out: 1000

        tables_to_save = ["person", "visit_occurrence"]
        data.persist_tables_to_folder(
            "./cohort_sample_1000",
            table_names=tables_to_save
        )
        # Out: writing /export/home/USER/cohort_sample_1000/person.parquet
        # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
        # Out: ...
        ```

        """
        if columns_to_load and not tables_to_load:
            tables_to_load = columns_to_load
            # TODO: Deprecated since which version? Will be removed in which version?
            logger.warning(
                "'columns_to_load' is a deprecated argument. Please use 'tables_to_load' instead."
            )
        self.spark_session = (
            spark_session or SparkSession.builder.enableHiveSupport().getOrCreate()
        )
        self.database_name = database_name
        if database_type not in ["I2B2", "OMOP"]:
            raise ValueError(
                f"`database_type` must be either 'I2B2' or 'OMOP'. Got {database_type}"
            )
        self.database_type = database_type

        if self.database_type == "I2B2":
            self.database_source = "cse" if "cse" in self.database_name else "edsprod"
            self.omop_to_i2b2 = settings.i2b2_tables[self.database_source]
            self.i2b2_to_omop = defaultdict(list)
            for omop_table, i2b2_table in self.omop_to_i2b2.items():
                self.i2b2_to_omop[i2b2_table].append(omop_table)

        self.person_ids = self._prepare_person_ids(person_ids)

        tmp_tables_to_load = settings.tables_to_load
        if isinstance(tables_to_load, dict):
            for table_name, columns in tables_to_load.items():
                if columns is None:
                    tmp_tables_to_load[table_name] = None
                else:
                    tmp_tables_to_load[table_name] = list(
                        set(tmp_tables_to_load.get(table_name, []) + columns)
                    )
        elif isinstance(tables_to_load, list):
            for table_name in tables_to_load:
                tmp_tables_to_load[table_name] = None

        self.tables_to_load = tmp_tables_to_load
        self.available_tables = self.list_available_tables()
        self._tables = {}

    def list_available_tables(self) -> List[str]:
        tables_df = self.spark_session.sql(
            f"SHOW TABLES IN {self.database_name}"
        ).toPandas()
        available_tables = set()
        session_tables = tables_df["tableName"].drop_duplicates().to_list()
        session_tables = list(set(session_tables) & set(self.tables_to_load))
        for table_name in session_tables:
            if self.database_type == "OMOP":
                available_tables.add(table_name)
            elif self.database_type == "I2B2":
                for omop_table in self.i2b2_to_omop.get(table_name, []):
                    available_tables.add(omop_table)
        if self.database_type == "I2B2":
            available_tables |= set(self.omop_to_i2b2) - {None}
        return list(available_tables)

    def rename_table(self, old_table_name: str, new_table_name: str) -> None:
        # TODO: use _tables dict instead of self to store tables?
        if old_table_name in self.available_tables:
            setattr(self, new_table_name, getattr(self, old_table_name))
            self.available_tables.remove(old_table_name)
            self.available_tables.append(new_table_name)
            logger.info("Table {} has been renamed {}", old_table_name, new_table_name)
        else:
            logger.info("Table {} is not available", old_table_name)

    def add_table(self, table_name: str, columns: List[str]) -> None:
        tables_df = self.spark_session.sql(
            f"SHOW TABLES IN {self.database_name}"
        ).toPandas()
        if table_name in tables_df["tableName"].drop_duplicates().to_list():
            self.tables_to_load[table_name] = list(
                set(self.tables_to_load.get(table_name, []) + columns)
            )
            self.available_tables = self.list_available_tables()
            logger.info("Table {} has been added", table_name)

    def _prepare_person_ids(self, list_of_person_ids) -> Optional[SparkDataFrame]:

        if list_of_person_ids is None:
            return None
        elif hasattr(list_of_person_ids, "to_list"):
            # Useful when list_of_person_ids are Koalas (or Pandas) Series
            unique_ids = set(list_of_person_ids.to_list())
        else:
            unique_ids = set(list_of_person_ids)

        schema = StructType([StructField("person_id", LongType(), True)])

        filtering_df = self.spark_session.createDataFrame(
            [(int(p),) for p in unique_ids], schema=schema
        ).cache()

        print(f"Number of unique patients: {filtering_df.count()}")

        return filtering_df

    def _read_table(self, table_name, person_ids=None) -> DataFrame:
        if table_name not in self.available_tables:
            raise ValueError(
                f"{table_name} is not available. "
                f"Available tables are: {self.available_tables}"
            )

        if self.database_type == "OMOP":
            df = self.spark_session.sql(
                f"select * from {self.database_name}.{table_name}"
            )
        else:
            df = get_i2b2_table(
                spark_session=self.spark_session,
                db_name=self.database_name,
                db_source=self.database_source,
                table=table_name,
            )

        desired_columns = self.tables_to_load[table_name] or df.columns
        selected_columns = list(set(df.columns) & set(desired_columns))
        df = df.select(*selected_columns)

        person_ids = person_ids or self.person_ids
        if "person_id" in df.columns and person_ids is not None:
            df = df.join(person_ids, on="person_id", how="inner")

        df = clean_dates(df)

        return df.cache().to_koalas()

    def persist_tables_to_folder(
        self,
        folder: str,
        person_ids: Optional[Iterable[int]] = None,
        tables: List[str] = None,
    ) -> None:
        """Save OMOP tables as parquet files in a given folder.

        Parameters
        ----------
        folder : str
            path to folder where the tables will be written.
        person_ids : iterable
            person_ids to keep in the subcohort
        tables : list of str, default None
            list of table names to save. Default value is
            :py:data:`~eds_scikit.io.settings.default_tables_to_save`

        """
        if tables is None:
            tables = settings.default_tables_to_save

        unknown_tables = [
            table for table in tables if table not in self.available_tables
        ]
        if unknown_tables:
            raise ValueError(
                f"The following tables are not available : {str(unknown_tables)}"
            )

        folder = os.path.abspath(folder)

        os.makedirs(folder, mode=0o766, exist_ok=False)

        assert os.path.exists(folder) and os.path.isdir(
            folder
        ), f"Folder {folder} not found."

        # TODO: remove everything in this folder that is a valid
        # omop table. This prevents a user from having a
        # folder containing datasets generated from different
        # patient subsets.

        # TODO: maybe check how much the user wants to persist
        # to disk. Set a limit on the number of patients in the cohort ?

        if person_ids is not None:
            person_ids = self._prepare_person_ids(person_ids)

        for table in tables:
            filepath = os.path.join(folder, f"{table}.parquet")
            df = self._read_table(table, person_ids=person_ids)
            self._write_df_to_parquet(df, filepath)

    def _write_df_to_parquet(
        self,
        df: DataFrame,
        filepath: str,
    ) -> None:
        assert os.path.isabs(filepath)
        print(f"writing {filepath}")
        df.to_pandas().to_parquet(filepath)

    def __getattr__(self, table_name: str) -> DataFrame:
        if table_name in self._tables:
            return self._tables[table_name]
        elif table_name in self.available_tables:
            # Add to cache dictionnary during the first call.
            table = self._read_table(table_name)
            self._tables[table_name] = table
            return table
        else:
            raise AttributeError(f"Table '{table_name}' unknown")

    def __dir__(self) -> List[str]:
        return list(set(list(super().__dir__()) + self.available_tables))

__init__ 

__init__(database_name: str, spark_session: Optional[SparkSession] = None, person_ids: Optional[Iterable[int]] = None, tables_to_load: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] = None, columns_to_load: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] = None, database_type: Optional[str] = 'OMOP')

Spark interface for OMOP data stored in a Hive database.

This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive.

PARAMETER DESCRIPTION
database_name

The name of you database in Hive. Ex: "cse_82727572"

TYPE: str

spark_session

If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate()

TYPE: pyspark.sql.SparkSession DEFAULT: None

person_ids

An iterable of person_id that is used to define a subset of the database.

TYPE: Optional[Iterable[int]] DEFAULT: None

tables_to_load

By default (i.e. if tables_to_load is None), loaded tables and columns loaded in each table are those listed here. A dictionnary can be provided to complement those default settings. Keys should be table names to load, and values should be: - None to load all columns - A list of columns to load (or to add to the default loaded columns if the table is already loaded by default) A list of the tables names can also be provided to load all columns of each table.

TYPE: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] DEFAULT: None

columns_to_load

deprecated

TYPE: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] DEFAULT: None

database_type

Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.

TYPE: Optional[str] DEFAULT: 'OMOP'

ATTRIBUTE DESCRIPTION
person

Hive data for table person as a koalas dataframe. Other OMOP tables can also be accessed as attributes

TYPE: koalas dataframe

available_tables

names of OMOP tables that can be accessed as attributes with this HiveData object.

TYPE: list of str

Examples:

data = HiveData(database_name="edsomop_prod_a")
data.available_tables
# Out: ["person", "care_site", "condition_occurrence", ... ]

person = data.person
type(person)
# Out: databricks.koalas.frame.DataFrame

person["person_id"].count()
# Out: 12670874

This class can be used to create a subset of data for a given list of person_id. This is useful because the smaller dataset can then be used to prototype more rapidly.

my_person_ids = [9226726, 2092082, .... ]
data = HiveData(spark_session=spark, database_name="edsomop_prod_a",
                    person_ids=my_person_ids)
data.person["person_id"].count()
# Out: 1000

tables_to_save = ["person", "visit_occurrence"]
data.persist_tables_to_folder(
    "./cohort_sample_1000",
    table_names=tables_to_save
)
# Out: writing /export/home/USER/cohort_sample_1000/person.parquet
# Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
# Out: ...
Source code in eds_scikit/io/hive.py 
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def __init__(
    self,
    database_name: str,
    spark_session: Optional[SparkSession] = None,
    person_ids: Optional[Iterable[int]] = None,
    tables_to_load: Optional[
        Union[Dict[str, Optional[List[str]]], List[str]]
    ] = None,
    columns_to_load: Optional[
        Union[Dict[str, Optional[List[str]]], List[str]]
    ] = None,
    database_type: Optional[str] = "OMOP",
):
    """Spark interface for OMOP data stored in a Hive database.

    This class provides a simple access to data stored in Hive.
    Data is returned as koalas dataframes that match the tables
    stored in Hive.

    Parameters
    ----------
    database_name : str
        The name of you database in Hive. Ex: "cse_82727572"
    spark_session : pyspark.sql.SparkSession
        If None, a SparkSession will be retrieved or  created via `SparkSession.builder.enableHiveSupport().getOrCreate()`
    person_ids : Optional[Iterable[int]]
        An iterable of `person_id` that is used to define a subset of the database.
    tables_to_load : Optional[Union[Dict[str, Optional[List[str]]], List[str]]]
        By default (i.e. if ``tables_to_load is None``), loaded tables and columns loaded in each table are those listed
        [here][eds_scikit.io.settings.tables_to_load].
        A dictionnary can be provided to complement those default settings. Keys should be table names to load,
        and values should be:
        - ``None`` to load all columns
        - A list of columns to load (or to add to the default loaded columns if the table is already loaded by default)
        A list of the tables names can also be provided to load all columns of each table.
    columns_to_load : Optional[Union[Dict[str, Optional[List[str]]], List[str]]]
        *deprecated*
    database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2'
        Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.

    Attributes
    ----------
    person : koalas dataframe
        Hive data for table `person` as a koalas dataframe.
        Other OMOP tables can also be accessed as attributes
    available_tables : list of str
        names of OMOP tables that can be accessed as attributes with this
        HiveData object.

    Examples
    --------

    ```python
    data = HiveData(database_name="edsomop_prod_a")
    data.available_tables
    # Out: ["person", "care_site", "condition_occurrence", ... ]

    person = data.person
    type(person)
    # Out: databricks.koalas.frame.DataFrame

    person["person_id"].count()
    # Out: 12670874
    ```

    This class can be used to create a subset of data for a given
    list of `person_id`. This is useful because the smaller dataset
    can then be used to prototype more rapidly.

    ```python
    my_person_ids = [9226726, 2092082, .... ]
    data = HiveData(spark_session=spark, database_name="edsomop_prod_a",
                        person_ids=my_person_ids)
    data.person["person_id"].count()
    # Out: 1000

    tables_to_save = ["person", "visit_occurrence"]
    data.persist_tables_to_folder(
        "./cohort_sample_1000",
        table_names=tables_to_save
    )
    # Out: writing /export/home/USER/cohort_sample_1000/person.parquet
    # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
    # Out: ...
    ```

    """
    if columns_to_load and not tables_to_load:
        tables_to_load = columns_to_load
        # TODO: Deprecated since which version? Will be removed in which version?
        logger.warning(
            "'columns_to_load' is a deprecated argument. Please use 'tables_to_load' instead."
        )
    self.spark_session = (
        spark_session or SparkSession.builder.enableHiveSupport().getOrCreate()
    )
    self.database_name = database_name
    if database_type not in ["I2B2", "OMOP"]:
        raise ValueError(
            f"`database_type` must be either 'I2B2' or 'OMOP'. Got {database_type}"
        )
    self.database_type = database_type

    if self.database_type == "I2B2":
        self.database_source = "cse" if "cse" in self.database_name else "edsprod"
        self.omop_to_i2b2 = settings.i2b2_tables[self.database_source]
        self.i2b2_to_omop = defaultdict(list)
        for omop_table, i2b2_table in self.omop_to_i2b2.items():
            self.i2b2_to_omop[i2b2_table].append(omop_table)

    self.person_ids = self._prepare_person_ids(person_ids)

    tmp_tables_to_load = settings.tables_to_load
    if isinstance(tables_to_load, dict):
        for table_name, columns in tables_to_load.items():
            if columns is None:
                tmp_tables_to_load[table_name] = None
            else:
                tmp_tables_to_load[table_name] = list(
                    set(tmp_tables_to_load.get(table_name, []) + columns)
                )
    elif isinstance(tables_to_load, list):
        for table_name in tables_to_load:
            tmp_tables_to_load[table_name] = None

    self.tables_to_load = tmp_tables_to_load
    self.available_tables = self.list_available_tables()
    self._tables = {}

persist_tables_to_folder 

persist_tables_to_folder(folder: str, person_ids: Optional[Iterable[int]] = None, tables: List[str] = None) -> None

Save OMOP tables as parquet files in a given folder.

PARAMETER DESCRIPTION
folder

path to folder where the tables will be written.

TYPE: str

person_ids

person_ids to keep in the subcohort

TYPE: iterable DEFAULT: None

tables

list of table names to save. Default value is :py:data:~eds_scikit.io.settings.default_tables_to_save

TYPE: list of str, default None DEFAULT: None

Source code in eds_scikit/io/hive.py 
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def persist_tables_to_folder(
    self,
    folder: str,
    person_ids: Optional[Iterable[int]] = None,
    tables: List[str] = None,
) -> None:
    """Save OMOP tables as parquet files in a given folder.

    Parameters
    ----------
    folder : str
        path to folder where the tables will be written.
    person_ids : iterable
        person_ids to keep in the subcohort
    tables : list of str, default None
        list of table names to save. Default value is
        :py:data:`~eds_scikit.io.settings.default_tables_to_save`

    """
    if tables is None:
        tables = settings.default_tables_to_save

    unknown_tables = [
        table for table in tables if table not in self.available_tables
    ]
    if unknown_tables:
        raise ValueError(
            f"The following tables are not available : {str(unknown_tables)}"
        )

    folder = os.path.abspath(folder)

    os.makedirs(folder, mode=0o766, exist_ok=False)

    assert os.path.exists(folder) and os.path.isdir(
        folder
    ), f"Folder {folder} not found."

    # TODO: remove everything in this folder that is a valid
    # omop table. This prevents a user from having a
    # folder containing datasets generated from different
    # patient subsets.

    # TODO: maybe check how much the user wants to persist
    # to disk. Set a limit on the number of patients in the cohort ?

    if person_ids is not None:
        person_ids = self._prepare_person_ids(person_ids)

    for table in tables:
        filepath = os.path.join(folder, f"{table}.parquet")
        df = self._read_table(table, person_ids=person_ids)
        self._write_df_to_parquet(df, filepath)