Documentation for Linker object methods related to exploratory analysis

The Linker object manages the data linkage process and holds the data linkage model.

Most of Splink's functionality can be accessed by calling methods (functions) on the linker, such as linker.predict(), linker.profile_columns() etc.

The Linker class is intended for subclassing for specific backends, e.g. a DuckDBLinker.

Source code in splink/linker.py

class Linker:
    """The Linker object manages the data linkage process and holds the data linkage
    model.

    Most of Splink's functionality can  be accessed by calling methods (functions)
    on the linker, such as `linker.predict()`, `linker.profile_columns()` etc.

    The Linker class is intended for subclassing for specific backends, e.g.
    a `DuckDBLinker`.
    """

    def __init__(
        self,
        input_table_or_tables: str | list,
        settings_dict: dict | Path,
        accepted_df_dtypes,
        set_up_basic_logging: bool = True,
        input_table_aliases: str | list = None,
        validate_settings: bool = True,
    ):
        """Initialise the linker object, which manages the data linkage process and
        holds the data linkage model.

        Examples:
            === ":simple-duckdb: DuckDB"
                Dedupe
                ```py
                df = pd.read_csv("data_to_dedupe.csv")
                linker = DuckDBLinker(df, settings_dict)
                ```
                Link
                ```py
                df_1 = pd.read_parquet("table_1/")
                df_2 = pd.read_parquet("table_2/")
                linker = DuckDBLinker(
                    [df_1, df_2],
                    settings_dict,
                    input_table_aliases=["customers", "contact_center_callers"]
                    )
                ```
                Dedupe with a pre-trained model read from a json file
                ```py
                df = pd.read_csv("data_to_dedupe.csv")
                linker = DuckDBLinker(df, "model.json")
                ```
            === ":simple-apachespark: Spark"
                Dedupe
                ```py
                df = spark.read.csv("data_to_dedupe.csv")
                linker = SparkLinker(df, settings_dict)
                ```
                Link
                ```py
                df_1 = spark.read.parquet("table_1/")
                df_2 = spark.read.parquet("table_2/")
                linker = SparkLinker(
                    [df_1, df_2],
                    settings_dict,
                    input_table_aliases=["customers", "contact_center_callers"]
                    )
                ```
                Dedupe with a pre-trained model read from a json file
                ```py
                df = spark.read.csv("data_to_dedupe.csv")
                linker = SparkLinker(df, "model.json")
                ```

        Args:
            input_table_or_tables (Union[str, list]): Input data into the linkage model.
                Either a single string (the name of a table in a database) for
                deduplication jobs, or a list of strings  (the name of tables in a
                database) for link_only or link_and_dedupe.  For some linkers, such as
                the DuckDBLinker and the SparkLinker, it's also possible to pass in
                dataframes (Pandas and Spark respectively) rather than strings.
            settings_dict (dict | Path, optional): A Splink settings dictionary, or a
                path to a json defining a settingss dictionary or pre-trained model.
                If not provided when the object is created, can later be added using
                `linker.load_settings()` or `linker.load_model()` Defaults to None.
            set_up_basic_logging (bool, optional): If true, sets ups up basic logging
                so that Splink sends messages at INFO level to stdout. Defaults to True.
            input_table_aliases (Union[str, list], optional): Labels assigned to
                input tables in Splink outputs.  If the names of the tables in the
                input database are long or unspecific, this argument can be used
                to attach more easily readable/interpretable names. Defaults to None.
            validate_settings (bool, optional): When True, check your settings
                dictionary for any potential errors that may cause splink to fail.
        """
        self._db_schema = "splink"
        if set_up_basic_logging:
            logging.basicConfig(
                format="%(message)s",
            )
            splink_logger = logging.getLogger("splink")
            splink_logger.setLevel(logging.INFO)

        self._pipeline = SQLPipeline()

        self._intermediate_table_cache: dict = CacheDictWithLogging()

        if not isinstance(settings_dict, (dict, type(None))):
            # Run if you've entered a filepath
            # feed it a blank settings dictionary
            self._setup_settings_objs(None)
            self.load_settings(settings_dict)
        else:
            self._validate_settings_components(settings_dict)
            settings_dict = deepcopy(settings_dict)
            self._setup_settings_objs(settings_dict)

        homogenised_tables, homogenised_aliases = self._register_input_tables(
            input_table_or_tables,
            input_table_aliases,
            accepted_df_dtypes,
        )

        self._input_tables_dict = self._get_input_tables_dict(
            homogenised_tables, homogenised_aliases
        )

        self._validate_input_dfs()
        self._validate_settings(validate_settings)
        self._em_training_sessions = []

        self._find_new_matches_mode = False
        self._train_u_using_random_sample_mode = False
        self._compare_two_records_mode = False
        self._self_link_mode = False
        self._analyse_blocking_mode = False
        self._deterministic_link_mode = False

        self.debug_mode = False

    def _input_columns(
        self,
        include_unique_id_col_names=True,
        include_additional_columns_to_retain=True,
    ) -> list[InputColumn]:
        """Retrieve the column names from the input dataset(s) as InputColumns

        Args:
            include_unique_id_col_names (bool, optional): Whether to include unique ID
                column names. Defaults to True.
            include_additional_columns_to_retain (bool, optional): Whether to include
                additional columns to retain. Defaults to True.

        Raises:
            SplinkException: If the input frames have different sets of columns.

        Returns:
            list[InputColumn]
        """

        input_dfs = self._input_tables_dict.values()

        # get a list of the column names for each input frame
        # sort it for consistent ordering, and give each frame's
        # columns as a tuple so we can hash it
        column_names_by_input_df = [
            tuple(sorted([col.name for col in input_df.columns]))
            for input_df in input_dfs
        ]
        # check that the set of input columns is the same for each frame,
        # fail if the sets are different
        if len(set(column_names_by_input_df)) > 1:
            common_cols = set.intersection(
                *(set(col_names) for col_names in column_names_by_input_df)
            )
            problem_names = {
                col
                for frame_col_names in column_names_by_input_df
                for col in frame_col_names
                if col not in common_cols
            }
            raise SplinkException(
                "All linker input frames must have the same set of columns.  "
                "The following columns were not found in all input frames: "
                + ", ".join(problem_names)
            )

        columns = next(iter(input_dfs)).columns

        remove_columns = []
        if not include_unique_id_col_names:
            remove_columns.extend(self._settings_obj._unique_id_input_columns)
        if not include_additional_columns_to_retain:
            remove_columns.extend(self._settings_obj._additional_columns_to_retain)

        remove_id_cols = [c.unquote().name for c in remove_columns]
        columns = [col for col in columns if col.unquote().name not in remove_id_cols]

        return columns

    @property
    def _source_dataset_column_already_exists(self):
        if self._settings_obj_ is None:
            return False
        input_cols = [c.unquote().name for c in self._input_columns()]
        return self._settings_obj._source_dataset_column_name in input_cols

    @property
    def _cache_uid(self):
        if self._settings_dict:
            return self._settings_obj._cache_uid
        else:
            return self._cache_uid_no_settings

    @_cache_uid.setter
    def _cache_uid(self, value):
        if self._settings_dict:
            self._settings_obj._cache_uid = value
        else:
            self._cache_uid_no_settings = value

    @property
    def _settings_obj(self) -> Settings:
        if self._settings_obj_ is None:
            raise ValueError(
                "You did not provide a settings dictionary when you "
                "created the linker.  To continue, you need to provide a settings "
                "dictionary using the `load_settings()` method on your linker "
                "object. i.e. linker.load_settings(settings_dict)"
            )
        return self._settings_obj_

    @property
    def _input_tablename_l(self):
        if self._find_new_matches_mode:
            return "__splink__df_concat_with_tf"

        if self._self_link_mode:
            return "__splink__df_concat_with_tf"

        if self._compare_two_records_mode:
            return "__splink__compare_two_records_left_with_tf"

        if self._train_u_using_random_sample_mode:
            if self._two_dataset_link_only:
                return "__splink__df_concat_with_tf_sample_left"
            else:
                return "__splink__df_concat_with_tf_sample"

        if self._analyse_blocking_mode:
            return "__splink__df_concat"

        if self._two_dataset_link_only:
            return "__splink__df_concat_with_tf_left"

        return "__splink__df_concat_with_tf"

    @property
    def _input_tablename_r(self):
        if self._find_new_matches_mode:
            return "__splink__df_new_records_with_tf"

        if self._self_link_mode:
            return "__splink__df_concat_with_tf"

        if self._compare_two_records_mode:
            return "__splink__compare_two_records_right_with_tf"

        if self._train_u_using_random_sample_mode:
            if self._two_dataset_link_only:
                return "__splink__df_concat_with_tf_sample_right"
            else:
                return "__splink__df_concat_with_tf_sample"

        if self._analyse_blocking_mode:
            return "__splink__df_concat"

        if self._two_dataset_link_only:
            return "__splink__df_concat_with_tf_right"
        return "__splink__df_concat_with_tf"

    @property
    def _two_dataset_link_only(self):
        # Two dataset link only join is a special case where an inner join of the
        # two datasets is much more efficient than self-joining the vertically
        # concatenation of all input datasets
        if self._find_new_matches_mode:
            return True

        if self._compare_two_records_mode:
            return True

        if self._analyse_blocking_mode:
            return False

        if (
            len(self._input_tables_dict) == 2
            and self._settings_obj._link_type == "link_only"
        ):
            return True
        else:
            return False

    @property
    def _sql_dialect(self):
        if self._sql_dialect_ is None:
            raise NotImplementedError(
                f"No SQL dialect set on object of type {type(self)}. "
                "Did you make sure to create a dialect-specific Linker?"
            )
        return self._sql_dialect_

    @property
    def _infinity_expression(self):
        raise NotImplementedError(
            f"infinity sql expression not available for {type(self)}"
        )

    def _random_sample_sql(
        self, proportion, sample_size, seed=None, table=None, unique_id=None
    ):
        raise NotImplementedError("Random sample sql not implemented for this linker")

    def _register_input_tables(self, input_tables, input_aliases, accepted_df_dtypes):
        # 'homogenised' means all entries are strings representing tables
        homogenised_tables = []
        homogenised_aliases = []
        accepted_df_dtypes = ensure_is_tuple(accepted_df_dtypes)

        existing_tables = []
        for alias in input_aliases:
            # Check if alias is a string (indicating a table name) and that it is not
            # a file path.
            if not isinstance(alias, str) or re.match(pattern=r".*", string=alias):
                continue
            exists = self._table_exists_in_database(alias)
            if exists:
                existing_tables.append(f"'{alias}'")
        if existing_tables:
            input_tables = ", ".join(existing_tables)
            raise ValueError(
                f"Table(s): {input_tables} already exists in database. "
                "Please remove or rename it/them before retrying"
            )

        for i, (table, alias) in enumerate(zip(input_tables, input_aliases)):
            if isinstance(alias, accepted_df_dtypes):
                alias = f"__splink__input_table_{i}"

            if isinstance(table, accepted_df_dtypes):
                self._table_registration(table, alias)
                table = alias

            homogenised_tables.append(table)
            homogenised_aliases.append(alias)

        return homogenised_tables, homogenised_aliases

    def _setup_settings_objs(self, settings_dict):
        # Setup the linker class's required settings
        self._settings_dict = settings_dict

        # if settings_dict is passed, set sql_dialect on it if missing, and make sure
        # incompatible dialect not passed
        if settings_dict is not None and settings_dict.get("sql_dialect", None) is None:
            settings_dict["sql_dialect"] = self._sql_dialect

        if settings_dict is None:
            self._cache_uid_no_settings = ascii_uid(8)
        else:
            uid = settings_dict.get("linker_uid", ascii_uid(8))
            settings_dict["linker_uid"] = uid

        if settings_dict is None:
            self._settings_obj_ = None
        else:
            self._settings_obj_ = Settings(settings_dict)

    def _check_for_valid_settings(self):
        if (
            # no settings to check
            self._settings_obj_ is None
            or
            # raw tables don't yet exist in db
            not hasattr(self, "_input_tables_dict")
        ):
            return False
        else:
            return True

    def _validate_settings_components(self, settings_dict):
        # Vaidate our settings after plugging them through
        # `Settings(<settings>)`
        if settings_dict is None:
            return

        log_comparison_errors(
            # null if not in dict - check using value is ignored
            settings_dict.get("comparisons", None),
            self._sql_dialect,
        )

    def _validate_settings(self, validate_settings):
        # Vaidate our settings after plugging them through
        # `Settings(<settings>)`
        if not self._check_for_valid_settings():
            return

        # Run miscellaneous checks on our settings dictionary.
        _validate_dialect(
            settings_dialect=self._settings_obj._sql_dialect,
            linker_dialect=self._sql_dialect,
            linker_type=self.__class__.__name__,
        )

        # Constructs output logs for our various settings inputs
        cleaned_settings = SettingsColumnCleaner(
            settings_object=self._settings_obj,
            input_columns=self._input_tables_dict,
        )
        InvalidColumnsLogger(cleaned_settings).construct_output_logs(validate_settings)

    def _initialise_df_concat(self, materialise=False):
        cache = self._intermediate_table_cache
        concat_df = None
        if "__splink__df_concat" in cache:
            concat_df = cache.get_with_logging("__splink__df_concat")
        elif "__splink__df_concat_with_tf" in cache:
            concat_df = cache.get_with_logging("__splink__df_concat_with_tf")
            concat_df.templated_name = "__splink__df_concat"
        else:
            if materialise:
                # Clear the pipeline if we are materialising
                # There's no reason not to do this, since when
                # we execute the pipeline, it'll get cleared anyway
                self._pipeline.reset()
            sql = vertically_concatenate_sql(self)
            self._enqueue_sql(sql, "__splink__df_concat")
            if materialise:
                concat_df = self._execute_sql_pipeline()
                cache["__splink__df_concat"] = concat_df

        return concat_df

    def _initialise_df_concat_with_tf(self, materialise=True):
        cache = self._intermediate_table_cache
        nodes_with_tf = None
        if "__splink__df_concat_with_tf" in cache:
            nodes_with_tf = cache.get_with_logging("__splink__df_concat_with_tf")

        else:
            # In duckdb, calls to random() in a CTE pipeline cause problems:
            # https://gist.github.com/RobinL/d329e7004998503ce91b68479aa41139
            if self._settings_obj.salting_required:
                materialise = True

            if materialise:
                # Clear the pipeline if we are materialising
                # There's no reason not to do this, since when
                # we execute the pipeline, it'll get cleared anyway
                self._pipeline.reset()

            sql = vertically_concatenate_sql(self)
            self._enqueue_sql(sql, "__splink__df_concat")

            sqls = compute_all_term_frequencies_sqls(self)
            for sql in sqls:
                self._enqueue_sql(sql["sql"], sql["output_table_name"])

            if materialise:
                nodes_with_tf = self._execute_sql_pipeline()
                cache["__splink__df_concat_with_tf"] = nodes_with_tf

        return nodes_with_tf

    def _table_to_splink_dataframe(
        self, templated_name, physical_name
    ) -> SplinkDataFrame:
        """Create a SplinkDataframe from a table in the underlying database called
        `physical_name`.

        Associate a `templated_name` with this table, which signifies the purpose
        or 'meaning' of this table to splink. (e.g. `__splink__df_blocked`)

        Args:
            templated_name (str): The purpose of the table to Splink
            physical_name (str): The name of the table in the underlying databse
        """
        raise NotImplementedError(
            "_table_to_splink_dataframe not implemented on this linker"
        )

    def _enqueue_sql(self, sql, output_table_name):
        """Add sql to the current pipeline, but do not execute the pipeline."""
        self._pipeline.enqueue_sql(sql, output_table_name)

    def _execute_sql_pipeline(
        self,
        input_dataframes: list[SplinkDataFrame] = [],
        use_cache=True,
    ) -> SplinkDataFrame:
        """Execute the SQL queued in the current pipeline as a single statement
        e.g. `with a as (), b as , c as (), select ... from c`, then execute the
        pipeline, returning the resultant table as a SplinkDataFrame

        Args:
            input_dataframes (List[SplinkDataFrame], optional): A 'starting point' of
                SplinkDataFrames if needed. Defaults to [].
            use_cache (bool, optional): If true, look at whether the SQL pipeline has
                been executed before, and if so, use the existing result. Defaults to
                True.

        Returns:
            SplinkDataFrame: An abstraction representing the table created by the sql
                pipeline
        """

        if not self.debug_mode:
            sql_gen = self._pipeline._generate_pipeline(input_dataframes)

            output_tablename_templated = self._pipeline.queue[-1].output_table_name

            try:
                dataframe = self._sql_to_splink_dataframe_checking_cache(
                    sql_gen,
                    output_tablename_templated,
                    use_cache,
                )
            except Exception as e:
                raise e
            finally:
                self._pipeline.reset()

            return dataframe
        else:
            # In debug mode, we do not pipeline the sql and print the
            # results of each part of the pipeline
            for task in self._pipeline._generate_pipeline_parts(input_dataframes):
                start_time = time.time()
                output_tablename = task.output_table_name
                sql = task.sql
                print("------")  # noqa: T201
                print(  # noqa: T201
                    f"--------Creating table: {output_tablename}--------"
                )

                dataframe = self._sql_to_splink_dataframe_checking_cache(
                    sql,
                    output_tablename,
                    use_cache=False,
                )
                run_time = parse_duration(time.time() - start_time)
                print(f"Step ran in: {run_time}")  # noqa: T201
            self._pipeline.reset()
            return dataframe

    def _execute_sql_against_backend(
        self, sql: str, templated_name: str, physical_name: str
    ) -> SplinkDataFrame:
        """Execute a single sql SELECT statement, returning a SplinkDataFrame.

        Subclasses should implement this, using _log_and_run_sql_execution() within
        their implementation, maybe doing some SQL translation or other prep/cleanup
        work before/after.
        """
        raise NotImplementedError(
            f"_execute_sql_against_backend not implemented for {type(self)}"
        )

    def _run_sql_execution(
        self, final_sql: str, templated_name: str, physical_name: str
    ) -> SplinkDataFrame:
        """**Actually** execute the sql against the backend database.

        This is intended to be implemented by a subclass, but not actually called
        directly. Instead, call _log_and_run_sql_execution, and that will call
        this method.

        This could return something, or not. It's up to the Linker subclass to decide.
        """
        raise NotImplementedError(
            f"_run_sql_execution not implemented for {type(self)}"
        )

    def _log_and_run_sql_execution(
        self, final_sql: str, templated_name: str, physical_name: str
    ) -> SplinkDataFrame:
        """Log the sql, then call _run_sql_execution(), wrapping any errors"""
        logger.debug(execute_sql_logging_message_info(templated_name, physical_name))
        logger.log(5, log_sql(final_sql))
        try:
            return self._run_sql_execution(final_sql, templated_name, physical_name)
        except Exception as e:
            # Parse our SQL through sqlglot to pretty print
            try:
                final_sql = sqlglot.parse_one(
                    final_sql,
                    read=self._sql_dialect,
                ).sql(pretty=True)
                # if sqlglot produces any errors, just report the raw SQL
            except Exception:
                pass

            raise SplinkException(
                f"Error executing the following sql for table "
                f"`{templated_name}`({physical_name}):\n{final_sql}"
                f"\n\nError was: {e}"
            ) from e

    def register_table(self, input, table_name, overwrite=False):
        """
        Register a table to your backend database, to be used in one of the
        splink methods, or simply to allow querying.

        Tables can be of type: dictionary, record level dictionary,
        pandas dataframe, pyarrow table and in the spark case, a spark df.

        Examples:
            ```py
            test_dict = {"a": [666,777,888],"b": [4,5,6]}
            linker.register_table(test_dict, "test_dict")
            linker.query_sql("select * from test_dict")
            ```

        Args:
            input: The data you wish to register. This can be either a dictionary,
                pandas dataframe, pyarrow table or a spark dataframe.
            table_name (str): The name you wish to assign to the table.
            overwrite (bool): Overwrite the table in the underlying database if it
                exists

        Returns:
            SplinkDataFrame: An abstraction representing the table created by the sql
                pipeline
        """

        raise NotImplementedError(f"register_table not implemented for {type(self)}")

    def _table_registration(self, input, table_name):
        """
        Register a table to your backend database, to be used in one of the
        splink methods, or simply to allow querying.

        Tables can be of type: dictionary, record level dictionary,
        pandas dataframe, pyarrow table and in the spark case, a spark df.

        This function is contains no overwrite functionality, so it can be used
        where we don't want to allow for overwriting.

        Args:
            input: The data you wish to register. This can be either a dictionary,
                pandas dataframe, pyarrow table or a spark dataframe.
            table_name (str): The name you wish to assign to the table.

        Returns:
            None
        """

        raise NotImplementedError(
            f"_table_registration not implemented for {type(self)}"
        )

    def query_sql(self, sql, output_type="pandas"):
        """
        Run a SQL query against your backend database and return
        the resulting output.

        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                linker = DuckDBLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
                ```
            === ":simple-apachespark: Spark"
                ```py
                linker = SparkLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
                ```
            === ":simple-amazonaws: Athena"
                ```py
                linker = AthenaLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
                ```
            === ":simple-sqlite: SQLite"
                ```py
                linker = SQLiteLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
            ```

        Args:
            sql (str): The SQL to be queried.
            output_type (str): One of splink_df/splinkdf or pandas.
                This determines the type of table that your results are output in.
        """

        output_tablename_templated = "__splink__df_sql_query"

        splink_dataframe = self._sql_to_splink_dataframe_checking_cache(
            sql,
            output_tablename_templated,
            use_cache=False,
        )

        if output_type in ("splink_df", "splinkdf"):
            return splink_dataframe
        elif output_type == "pandas":
            out = splink_dataframe.as_pandas_dataframe()
            # If pandas, drop the table to cleanup the db
            splink_dataframe.drop_table_from_database_and_remove_from_cache()
            return out
        else:
            raise ValueError(
                f"output_type '{output_type}' is not supported.",
                "Must be one of 'splink_df'/'splinkdf' or 'pandas'",
            )

    def _sql_to_splink_dataframe_checking_cache(
        self,
        sql,
        output_tablename_templated,
        use_cache=True,
    ) -> SplinkDataFrame:
        """Execute sql, or if identical sql has been run before, return cached results.

        This function
            - is used by _execute_sql_pipeline to to execute SQL
            - or can be used directly if you have a single SQL statement that's
              not in a pipeline

        Return a SplinkDataFrame representing the results of the SQL
        """

        to_hash = (sql + self._cache_uid).encode("utf-8")
        hash = hashlib.sha256(to_hash).hexdigest()[:9]
        # Ensure hash is valid sql table name
        table_name_hash = f"{output_tablename_templated}_{hash}"

        if use_cache:
            # Certain tables are put in the cache using their templated_name
            # An example is __splink__df_concat_with_tf
            # These tables are put in the cache when they are first calculated
            # e.g. with _initialise_df_concat_with_tf()
            # But they can also be put in the cache manually using
            # e.g. register_table_input_nodes_concat_with_tf()

            # Look for these 'named' tables in the cache prior
            # to looking for the hashed version

            if output_tablename_templated in self._intermediate_table_cache:
                return self._intermediate_table_cache.get_with_logging(
                    output_tablename_templated
                )

            if table_name_hash in self._intermediate_table_cache:
                return self._intermediate_table_cache.get_with_logging(table_name_hash)

            # If not in cache, fall back on checking the database
            if self._table_exists_in_database(table_name_hash):
                logger.debug(
                    f"Found cache for {output_tablename_templated} "
                    f"in database using table name with physical name {table_name_hash}"
                )
                return self._table_to_splink_dataframe(
                    output_tablename_templated, table_name_hash
                )

        if self.debug_mode:
            print(sql)  # noqa: T201
            splink_dataframe = self._execute_sql_against_backend(
                sql,
                output_tablename_templated,
                output_tablename_templated,
            )

            self._intermediate_table_cache.executed_queries.append(splink_dataframe)

            df_pd = splink_dataframe.as_pandas_dataframe()
            try:
                from IPython.display import display

                display(df_pd)
            except ModuleNotFoundError:
                print(df_pd)  # noqa: T201

        else:
            splink_dataframe = self._execute_sql_against_backend(
                sql, output_tablename_templated, table_name_hash
            )
            self._intermediate_table_cache.executed_queries.append(splink_dataframe)

        splink_dataframe.created_by_splink = True
        splink_dataframe.sql_used_to_create = sql

        physical_name = splink_dataframe.physical_name

        self._intermediate_table_cache[physical_name] = splink_dataframe

        return splink_dataframe

    def __deepcopy__(self, memo):
        """When we do EM training, we need a copy of the linker which is independent
        of the main linker e.g. setting parameters on the copy will not affect the
        main linker.  This method implements ensures linker can be deepcopied.
        """
        new_linker = copy(self)
        new_linker._em_training_sessions = []
        new_settings = deepcopy(self._settings_obj_)
        new_linker._settings_obj_ = new_settings
        return new_linker

    def _ensure_aliases_populated_and_is_list(
        self, input_table_or_tables, input_table_aliases
    ):
        if input_table_aliases is None:
            input_table_aliases = input_table_or_tables

        input_table_aliases = ensure_is_list(input_table_aliases)

        return input_table_aliases

    def _get_input_tables_dict(self, input_table_or_tables, input_table_aliases):
        input_table_or_tables = ensure_is_list(input_table_or_tables)

        input_table_aliases = self._ensure_aliases_populated_and_is_list(
            input_table_or_tables, input_table_aliases
        )

        d = {}
        for table_name, table_alias in zip(input_table_or_tables, input_table_aliases):
            d[table_alias] = self._table_to_splink_dataframe(table_alias, table_name)
        return d

    def _get_input_tf_dict(self, df_dict):
        d = {}
        for df_name, df_value in df_dict.items():
            renamed = colname_to_tf_tablename(df_name)
            d[renamed] = self._table_to_splink_dataframe(renamed, df_value)
        return d

    def _predict_warning(self):
        if not self._settings_obj._is_fully_trained:
            msg = (
                "\n -- WARNING --\n"
                "You have called predict(), but there are some parameter "
                "estimates which have neither been estimated or specified in your "
                "settings dictionary.  To produce predictions the following"
                " untrained trained parameters will use default values."
            )
            messages = self._settings_obj._not_trained_messages()

            warn_message = "\n".join([msg] + messages)

            logger.warning(warn_message)

    def _table_exists_in_database(self, table_name):
        raise NotImplementedError(
            f"table_exists_in_database not implemented for {type(self)}"
        )

    def _validate_input_dfs(self):
        if not hasattr(self, "_input_tables_dict"):
            # This is only triggered where a user loads a settings dict from a
            # given file path.
            return

        for df in self._input_tables_dict.values():
            df.validate()

        if self._settings_obj_ is not None:
            if self._settings_obj._link_type == "dedupe_only":
                if len(self._input_tables_dict) > 1:
                    raise ValueError(
                        'If link_type = "dedupe only" then input tables must contain '
                        "only a single input table",
                    )

    def _populate_probability_two_random_records_match_from_trained_values(self):
        recip_prop_matches_estimates = []

        logger.log(
            15,
            (
                "---- Using training sessions to compute "
                "probability two random records match ----"
            ),
        )
        for em_training_session in self._em_training_sessions:
            training_lambda = (
                em_training_session._settings_obj._probability_two_random_records_match
            )
            training_lambda_bf = prob_to_bayes_factor(training_lambda)
            reverse_levels = (
                em_training_session._comparison_levels_to_reverse_blocking_rule
            )

            logger.log(
                15,
                "\n"
                f"Probability two random records match from trained model blocking on "
                f"{em_training_session._blocking_rule_for_training.blocking_rule_sql}: "
                f"{training_lambda:,.3f}",
            )

            for reverse_level in reverse_levels:
                # Get comparison level on current settings obj
                cc = self._settings_obj._get_comparison_by_output_column_name(
                    reverse_level.comparison._output_column_name
                )

                cl = cc._get_comparison_level_by_comparison_vector_value(
                    reverse_level._comparison_vector_value
                )

                if cl._has_estimated_values:
                    bf = cl._trained_m_median / cl._trained_u_median
                else:
                    bf = cl._bayes_factor

                logger.log(
                    15,
                    f"Reversing comparison level {cc._output_column_name}"
                    f" using bayes factor {bf:,.3f}",
                )

                training_lambda_bf = training_lambda_bf / bf

                as_prob = bayes_factor_to_prob(training_lambda_bf)

                logger.log(
                    15,
                    (
                        "This estimate of probability two random records match now: "
                        f" {as_prob:,.3f} "
                        f"with reciprocal {(1/as_prob):,.3f}"
                    ),
                )
            logger.log(15, "\n---------")
            p = bayes_factor_to_prob(training_lambda_bf)
            recip_prop_matches_estimates.append(1 / p)

        prop_matches_estimate = 1 / median(recip_prop_matches_estimates)

        self._settings_obj._probability_two_random_records_match = prop_matches_estimate
        logger.log(
            15,
            "\nMedian of prop of matches estimates: "
            f"{self._settings_obj._probability_two_random_records_match:,.3f} "
            "reciprocal "
            f"{1/self._settings_obj._probability_two_random_records_match:,.3f}",
        )

    def _populate_m_u_from_trained_values(self):
        ccs = self._settings_obj.comparisons

        for cc in ccs:
            for cl in cc._comparison_levels_excluding_null:
                if cl._has_estimated_u_values:
                    cl.u_probability = cl._trained_u_median
                if cl._has_estimated_m_values:
                    cl.m_probability = cl._trained_m_median

    def delete_tables_created_by_splink_from_db(self):
        for splink_df in list(self._intermediate_table_cache.values()):
            if splink_df.created_by_splink:
                splink_df.drop_table_from_database_and_remove_from_cache()

    def _raise_error_if_necessary_waterfall_columns_not_computed(self):
        ricc = self._settings_obj._retain_intermediate_calculation_columns
        rmc = self._settings_obj._retain_matching_columns
        if not (ricc and rmc):
            raise ValueError(
                "retain_intermediate_calculation_columns and "
                "retain_matching_columns must both be set to True in your settings"
                " dictionary to use this function, because otherwise the necessary "
                "columns will not be available in the input records."
                f" Their current values are {ricc} and {rmc}, respectively. "
                "Please re-run your linkage with them both set to True."
            )

    def _raise_error_if_necessary_accuracy_columns_not_computed(self):
        rmc = self._settings_obj._retain_matching_columns
        if not (rmc):
            raise ValueError(
                "retain_matching_columns must be set to True in your settings"
                " dictionary to use this function, because otherwise the necessary "
                "columns will not be available in the input records."
                f" Its current value is {rmc}. "
                "Please re-run your linkage with it set to True."
            )

    def load_settings(
        self,
        settings_dict: dict | str | Path,
        validate_settings: str = True,
    ):
        """Initialise settings for the linker.  To be used if settings were
        not passed to the linker on creation. This can either be in the form
        of a settings dictionary or a filepath to a json file containing a
        valid settings dictionary.

        Examples:
            ```py
            linker = DuckDBLinker(df)
            linker.profile_columns(["first_name", "surname"])
            linker.load_settings(settings_dict, validate_settings=True)
            ```

        Args:
            settings_dict (dict | str | Path): A Splink settings dictionary or
                the path to your settings json file.
            validate_settings (bool, optional): When True, check your settings
                dictionary for any potential errors that may cause splink to fail.
        """

        if not isinstance(settings_dict, dict):
            p = Path(settings_dict)
            settings_dict = json.loads(p.read_text())

        # Store the cache ID so it can be reloaded after cache invalidation
        cache_id = self._cache_uid
        # So we don't run into any issues with generated tables having
        # invalid columns as settings have been tweaked, invalidate
        # the cache and allow these tables to be recomputed.

        # This is less efficient, but triggers infrequently and ensures we don't
        # run into issues where the defaults used conflict with the actual values
        # supplied in settings.

        # This is particularly relevant with `source_dataset`, which appears within
        # concat_with_tf.
        self.invalidate_cache()

        # If a uid already exists in your settings object, prioritise this
        settings_dict["linker_uid"] = settings_dict.get("linker_uid", cache_id)
        settings_dict["sql_dialect"] = settings_dict.get(
            "sql_dialect", self._sql_dialect
        )
        self._settings_dict = settings_dict
        self._settings_obj_ = Settings(settings_dict)
        self._validate_input_dfs()
        self._validate_settings(validate_settings)

    def load_model(self, model_path: Path):
        """
        Load a pre-defined model from a json file into the linker.
        This is intended to be used with the output of
        `save_model_to_json()`.

        Examples:
            ```py
            linker.load_model("my_settings.json")
            ```

        Args:
            model_path (Path): A path to your model settings json file.
        """

        return self.load_settings(model_path)

    def initialise_settings(self, settings_dict: dict):
        """*This method is now deprecated. Please use `load_settings`
        when loading existing settings or `load_model` when loading
         a pre-trained model.*

        Initialise settings for the linker.  To be used if settings were
        not passed to the linker on creation.
        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                linker = DuckDBLinker(df)
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
            === ":simple-apachespark: Spark"
                ```py
                linker = SparkLinker(df)
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
            === ":simple-amazonaws: Athena"
                ```py
                linker = AthenaLinker(df)
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
            === ":simple-sqlite: SQLite"
                ```py
                linker = SQLiteLinker(df)
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
        Args:
            settings_dict (dict): A Splink settings dictionary
        """
        # If a uid already exists in your settings object, prioritise this
        settings_dict["linker_uid"] = settings_dict.get("linker_uid", self._cache_uid)
        settings_dict["sql_dialect"] = settings_dict.get(
            "sql_dialect", self._sql_dialect
        )
        self._settings_dict = settings_dict
        self._settings_obj_ = Settings(settings_dict)
        self._validate_input_dfs()
        self._validate_dialect()

        warnings.warn(
            "`initialise_settings` is deprecated. We advise you use "
            "`linker.load_settings()` when loading in your settings or a previously "
            "trained model.",
            SplinkDeprecated,
            stacklevel=2,
        )

    def load_settings_from_json(self, in_path: str | Path):
        """*This method is now deprecated. Please use `load_settings`
        when loading existing settings or `load_model` when loading
         a pre-trained model.*

        Load settings from a `.json` file.
        This `.json` file would usually be the output of
        `linker.save_model_to_json()`
        Examples:
            ```py
            linker.load_settings_from_json("my_settings.json")
            ```
        Args:
            in_path (str): Path to settings json file
        """
        self.load_settings(in_path)

        warnings.warn(
            "`load_settings_from_json` is deprecated. We advise you use "
            "`linker.load_settings()` when loading in your settings or a previously "
            "trained model.",
            SplinkDeprecated,
            stacklevel=2,
        )

    def compute_tf_table(self, column_name: str) -> SplinkDataFrame:
        """Compute a term frequency table for a given column and persist to the database

        This method is useful if you want to pre-compute term frequency tables e.g.
        so that real time linkage executes faster, or so that you can estimate
        various models without having to recompute term frequency tables each time

        Examples:
            === ":simple-duckdb: DuckDB"
                Real time linkage
                ```py
                linker = DuckDBLinker(df)
                linker.load_settings("saved_settings.json")
                linker.compute_tf_table("surname")
                linker.compare_two_records(record_left, record_right)
                ```
                Pre-computed term frequency tables
                ```py
                linker = DuckDBLinker(df)
                df_first_name_tf = linker.compute_tf_table("first_name")
                df_first_name_tf.write.parquet("folder/first_name_tf")
                >>>
                # On subsequent data linking job, read this table rather than recompute
                df_first_name_tf = pd.read_parquet("folder/first_name_tf")
                df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")
                ```
            === ":simple-apachespark: Spark"
                Real time linkage
                ```py
                linker = SparkLinker(df)
                linker.load_settings("saved_settings.json")
                linker.compute_tf_table("surname")
                linker.compare_two_records(record_left, record_right)
                ```
                Pre-computed term frequency tables
                ```py
                linker = SparkLinker(df)
                df_first_name_tf = linker.compute_tf_table("first_name")
                df_first_name_tf.write.parquet("folder/first_name_tf")
                >>>
                # On subsequent data linking job, read this table rather than recompute
                df_first_name_tf = spark.read.parquet("folder/first_name_tf")
                df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")
                ```

        Args:
            column_name (str): The column name in the input table

        Returns:
            SplinkDataFrame: The resultant table as a splink data frame
        """

        input_col = InputColumn(column_name, settings_obj=self._settings_obj)
        tf_tablename = colname_to_tf_tablename(input_col)
        cache = self._intermediate_table_cache
        concat_tf_tables = [
            tf_col.unquote().name
            for tf_col in self._settings_obj._term_frequency_columns
        ]

        if tf_tablename in cache:
            tf_df = cache.get_with_logging(tf_tablename)
        elif "__splink__df_concat_with_tf" in cache and column_name in concat_tf_tables:
            self._pipeline.reset()
            # If our df_concat_with_tf table already exists, use backwards inference to
            # find a given tf table
            colname = InputColumn(column_name)
            sql = term_frequencies_from_concat_with_tf(colname)
            self._enqueue_sql(sql, colname_to_tf_tablename(colname))
            tf_df = self._execute_sql_pipeline([cache["__splink__df_concat_with_tf"]])
            self._intermediate_table_cache[tf_tablename] = tf_df
        else:
            # Clear the pipeline if we are materialising
            self._pipeline.reset()
            df_concat = self._initialise_df_concat()
            input_dfs = []
            if df_concat:
                input_dfs.append(df_concat)
            sql = term_frequencies_for_single_column_sql(input_col)
            self._enqueue_sql(sql, tf_tablename)
            tf_df = self._execute_sql_pipeline(input_dfs)
            self._intermediate_table_cache[tf_tablename] = tf_df

        return tf_df

    def deterministic_link(self) -> SplinkDataFrame:
        """Uses the blocking rules specified by
        `blocking_rules_to_generate_predictions` in the settings dictionary to
        generate pairwise record comparisons.

        For deterministic linkage, this should be a list of blocking rules which
        are strict enough to generate only true links.

        Deterministic linkage, however, is likely to result in missed links
        (false negatives).

        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                from splink.duckdb.linker import DuckDBLinker

                settings = {
                    "link_type": "dedupe_only",
                    "blocking_rules_to_generate_predictions": [
                        "l.first_name = r.first_name",
                        "l.surname = r.surname",
                    ],
                    "comparisons": []
                }
                >>>
                linker = DuckDBLinker(df, settings)
                df = linker.deterministic_link()
                ```
            === ":simple-apachespark: Spark"
                ```py
                from splink.spark.linker import SparkLinker

                settings = {
                    "link_type": "dedupe_only",
                    "blocking_rules_to_generate_predictions": [
                        "l.first_name = r.first_name",
                        "l.surname = r.surname",
                    ],
                    "comparisons": []
                }
                >>>
                linker = SparkLinker(df, settings)
                df = linker.deterministic_link()
                ```
            === ":simple-amazonaws: Athena"
                ```py
                from splink.athena.linker import AthenaLinker

                settings = {
                    "link_type": "dedupe_only",
                    "blocking_rules_to_generate_predictions": [
                        "l.first_name = r.first_name",
                        "l.surname = r.surname",
                    ],
                    "comparisons": []
                }
                >>>
                linker = AthenaLinker(df, settings)
                df = linker.deterministic_link()
                ```
            === ":simple-sqlite: SQLite"
                ```py
                from splink.sqlite.linker import SQLiteLinker

                settings = {
                    "link_type": "dedupe_only",
                    "blocking_rules_to_generate_predictions": [
                        "l.first_name = r.first_name",
                        "l.surname = r.surname",
                    ],
                    "comparisons": []
                }
                >>>
                linker = SQLiteLinker(df, settings)
                df = linker.deterministic_link()
                ```

        Returns:
            SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons.  This
                represents a table materialised in the database. Methods on the
                SplinkDataFrame allow you to access the underlying data.
        """

        # Allows clustering during a deterministic linkage.
        # This is used in `cluster_pairwise_predictions_at_threshold`
        # to set the cluster threshold to 1
        self._deterministic_link_mode = True

        concat_with_tf = self._initialise_df_concat_with_tf()
        sqls = block_using_rules_sqls(self)
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])
        return self._execute_sql_pipeline([concat_with_tf])

    def estimate_u_using_random_sampling(
        self, max_pairs: int = None, seed: int = None, *, target_rows=None
    ):
        """Estimate the u parameters of the linkage model using random sampling.

        The u parameters represent the proportion of record comparisons that fall
        into each comparison level amongst truly non-matching records.

        This procedure takes a sample of the data and generates the cartesian
        product of pairwise record comparisons amongst the sampled records.
        The validity of the u values rests on the assumption that the resultant
        pairwise comparisons are non-matches (or at least, they are very unlikely to be
        matches). For large datasets, this is typically true.

        The results of estimate_u_using_random_sampling, and therefore an entire splink
        model, can be made reproducible by setting the seed parameter. Setting the seed
        will have performance implications as additional processing is required.

        Args:
            max_pairs (int): The maximum number of pairwise record comparisons to
            sample. Larger will give more accurate estimates
            but lead to longer runtimes.  In our experience at least 1e9 (one billion)
            gives best results but can take a long time to compute. 1e7 (ten million)
            is often adequate whilst testing different model specifications, before
            the final model is estimated.
            seed (int): Seed for random sampling. Assign to get reproducible u
            probabilities. Note, seed for random sampling is only supported for
            DuckDB and Spark, for Athena and SQLite set to None.

        Examples:
            ```py
            linker.estimate_u_using_random_sampling(1e8)
            ```

        Returns:
            None: Updates the estimated u parameters within the linker object
            and returns nothing.
        """
        # TODO: Remove this compatibility code in a future release once we drop
        # support for "target_rows". Deprecation warning added in 3.7.0
        if max_pairs is not None and target_rows is not None:
            # user supplied both
            raise TypeError("Just use max_pairs")
        elif max_pairs is not None:
            # user is doing it correctly
            pass
        elif target_rows is not None:
            # user is using deprecated argument
            warnings.warn(
                "target_rows is deprecated; use max_pairs",
                SplinkDeprecated,
                stacklevel=2,
            )
            max_pairs = target_rows
        else:
            raise TypeError("Missing argument max_pairs")

        estimate_u_values(self, max_pairs, seed)
        self._populate_m_u_from_trained_values()

        self._settings_obj._columns_without_estimated_parameters_message()

    def estimate_m_from_label_column(self, label_colname: str):
        """Estimate the m parameters of the linkage model from a label (ground truth)
        column in the input dataframe(s).

        The m parameters represent the proportion of record comparisons that fall
        into each comparison level amongst truly matching records.

        The ground truth column is used to generate pairwise record comparisons
        which are then assumed to be matches.

        For example, if the entity being matched is persons, and your input dataset(s)
        contain social security number, this could be used to estimate the m values
        for the model.

        Note that this column does not need to be fully populated.  A common case is
        where a unique identifier such as social security number is only partially
        populated.

        Args:
            label_colname (str): The name of the column containing the ground truth
                label in the input data.

        Examples:
            ```py
            linker.estimate_m_from_label_column("social_security_number")
            ```

        Returns:
            Updates the estimated m parameters within the linker object
            and returns nothing.
        """

        # Ensure this has been run on the main linker so that it can be used by
        # training linked when it checks the cache
        self._initialise_df_concat_with_tf()
        estimate_m_values_from_label_column(
            self,
            self._input_tables_dict,
            label_colname,
        )
        self._populate_m_u_from_trained_values()

        self._settings_obj._columns_without_estimated_parameters_message()

    def estimate_parameters_using_expectation_maximisation(
        self,
        blocking_rule: str,
        comparisons_to_deactivate: list[str | Comparison] = None,
        comparison_levels_to_reverse_blocking_rule: list[ComparisonLevel] = None,
        estimate_without_term_frequencies: bool = False,
        fix_probability_two_random_records_match: bool = False,
        fix_m_probabilities=False,
        fix_u_probabilities=True,
        populate_probability_two_random_records_match_from_trained_values=False,
    ) -> EMTrainingSession:
        """Estimate the parameters of the linkage model using expectation maximisation.

        By default, the m probabilities are estimated, but not the u probabilities,
        because good estimates for the u probabilities can be obtained from
        `linker.estimate_u_using_random_sampling()`.  You can change this by setting
        `fix_u_probabilities` to False.

        The blocking rule provided is used to generate pairwise record comparisons.
        Usually, this should be a blocking rule that results in a dataframe where
        matches are between about 1% and 99% of the comparisons.

        By default, m parameters are estimated for all comparisons except those which
        are included in the blocking rule.

        For example, if the blocking rule is `l.first_name = r.first_name`, then
        parameter esimates will be made for all comparison except those which use
        `first_name` in their sql_condition

        By default, the probability two random records match is estimated for the
        blocked data, and then the m and u parameters for the columns specified in the
        blocking rules are used to estiamte the global probability two random records
        match.

        To control which comparisons should have their parameter estimated, and the
        process of 'reversing out' the global probability two random records match, the
        user may specify `comparisons_to_deactivate` and
        `comparison_levels_to_reverse_blocking_rule`.   This is useful, for example
        if you block on the dmetaphone of a column but match on the original column.

        Examples:
            Default behaviour
            ```py
            br_training = "l.first_name = r.first_name and l.dob = r.dob"
            linker.estimate_parameters_using_expectation_maximisation(br_training)
            ```
            Specify which comparisons to deactivate
            ```py
            br_training = "l.dmeta_first_name = r.dmeta_first_name"
            settings_obj = linker._settings_obj
            comp = settings_obj._get_comparison_by_output_column_name("first_name")
            dmeta_level = comp._get_comparison_level_by_comparison_vector_value(1)
            linker.estimate_parameters_using_expectation_maximisation(
                br_training,
                comparisons_to_deactivate=["first_name"],
                comparison_levels_to_reverse_blocking_rule=[dmeta_level],
            )
            ```

        Args:
            blocking_rule (BlockingRule | str): The blocking rule used to generate
                pairwise record comparisons.
            comparisons_to_deactivate (list, optional): By default, splink will
                analyse the blocking rule provided and estimate the m parameters for
                all comaprisons except those included in the blocking rule.  If
                comparisons_to_deactivate are provided, spink will instead
                estimate m parameters for all comparison except those specified
                in the comparisons_to_deactivate list.  This list can either contain
                the output_column_name of the Comparison as a string, or Comparison
                objects.  Defaults to None.
            comparison_levels_to_reverse_blocking_rule (list, optional): By default,
                splink will analyse the blocking rule provided and adjust the
                global probability two random records match to account for the matches
                specified in the blocking rule. If provided, this argument will overrule
                this default behaviour. The user must provide a list of ComparisonLevel
                objects.  Defaults to None.
            estimate_without_term_frequencies (bool, optional): If True, the iterations
                of the EM algorithm ignore any term frequency adjustments and only
                depend on the comparison vectors. This allows the EM algorithm to run
                much faster, but the estimation of the parameters will change slightly.
            fix_probability_two_random_records_match (bool, optional): If True, do not
                update the probability two random records match after each iteration.
                Defaults to False.
            fix_m_probabilities (bool, optional): If True, do not update the m
                probabilities after each iteration. Defaults to False.
            fix_u_probabilities (bool, optional): If True, do not update the u
                probabilities after each iteration. Defaults to True.
            populate_probability_two_random_records_match_from_trained_values
                (bool, optional): If True, derive this parameter from
                the blocked value. Defaults to False.

        Examples:
            ```py
            blocking_rule = "l.first_name = r.first_name and l.dob = r.dob"
            linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
            ```
            or using pre-built rules
            ```py
            from splink.duckdb.blocking_rule_library import block_on
            blocking_rule = block_on(["first_name", "surname"])
            linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
            ```

        Returns:
            EMTrainingSession:  An object containing information about the training
                session such as how parameters changed during the iteration history

        """
        # Ensure this has been run on the main linker so that it's in the cache
        # to be used by the training linkers
        self._initialise_df_concat_with_tf()

        # Extract the blocking rule
        blocking_rule = blocking_rule_to_obj(blocking_rule).blocking_rule_sql

        if comparisons_to_deactivate:
            # If user provided a string, convert to Comparison object
            comparisons_to_deactivate = [
                self._settings_obj._get_comparison_by_output_column_name(n)
                if isinstance(n, str)
                else n
                for n in comparisons_to_deactivate
            ]
            if comparison_levels_to_reverse_blocking_rule is None:
                logger.warning(
                    "\nWARNING: \n"
                    "You have provided comparisons_to_deactivate but not "
                    "comparison_levels_to_reverse_blocking_rule.\n"
                    "If comparisons_to_deactivate is provided, then "
                    "you usually need to provide corresponding "
                    "comparison_levels_to_reverse_blocking_rule "
                    "because each comparison to deactivate is effectively treated "
                    "as an exact match."
                )

        em_training_session = EMTrainingSession(
            self,
            blocking_rule,
            fix_u_probabilities=fix_u_probabilities,
            fix_m_probabilities=fix_m_probabilities,
            fix_probability_two_random_records_match=fix_probability_two_random_records_match,  # noqa 501
            comparisons_to_deactivate=comparisons_to_deactivate,
            comparison_levels_to_reverse_blocking_rule=comparison_levels_to_reverse_blocking_rule,  # noqa 501
            estimate_without_term_frequencies=estimate_without_term_frequencies,
        )

        em_training_session._train()

        self._populate_m_u_from_trained_values()

        if populate_probability_two_random_records_match_from_trained_values:
            self._populate_probability_two_random_records_match_from_trained_values()

        self._settings_obj._columns_without_estimated_parameters_message()

        return em_training_session

    def predict(
        self,
        threshold_match_probability: float = None,
        threshold_match_weight: float = None,
        materialise_after_computing_term_frequencies=True,
    ) -> SplinkDataFrame:
        """Create a dataframe of scored pairwise comparisons using the parameters
        of the linkage model.

        Uses the blocking rules specified in the
        `blocking_rules_to_generate_predictions` of the settings dictionary to
        generate the pairwise comparisons.

        Args:
            threshold_match_probability (float, optional): If specified,
                filter the results to include only pairwise comparisons with a
                match_probability above this threshold. Defaults to None.
            threshold_match_weight (float, optional): If specified,
                filter the results to include only pairwise comparisons with a
                match_weight above this threshold. Defaults to None.
            materialise_after_computing_term_frequencies (bool): If true, Splink
                will materialise the table containing the input nodes (rows)
                joined to any term frequencies which have been asked
                for in the settings object.  If False, this will be
                computed as part of one possibly gigantic CTE
                pipeline.   Defaults to True

        Examples:
            ```py
            linker = DuckDBLinker(df)
            linker.load_settings("saved_settings.json")
            df = linker.predict(threshold_match_probability=0.95)
            df.as_pandas_dataframe(limit=5)
            ```
        Returns:
            SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons.  This
                represents a table materialised in the database. Methods on the
                SplinkDataFrame allow you to access the underlying data.

        """

        # If materialise_after_computing_term_frequencies=False and the user only
        # calls predict, it runs as a single pipeline with no materialisation
        # of anything.

        # _initialise_df_concat_with_tf returns None if the table doesn't exist
        # and only SQL is queued in this step.
        nodes_with_tf = self._initialise_df_concat_with_tf(
            materialise=materialise_after_computing_term_frequencies
        )

        input_dataframes = []
        if nodes_with_tf:
            input_dataframes.append(nodes_with_tf)

        sqls = block_using_rules_sqls(self)
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        repartition_after_blocking = getattr(self, "repartition_after_blocking", False)

        # repartition after blocking only exists on the SparkLinker
        if repartition_after_blocking:
            df_blocked = self._execute_sql_pipeline(input_dataframes)
            input_dataframes.append(df_blocked)

        sql = compute_comparison_vector_values_sql(self._settings_obj)
        self._enqueue_sql(sql, "__splink__df_comparison_vectors")

        sqls = predict_from_comparison_vectors_sqls(
            self._settings_obj,
            threshold_match_probability,
            threshold_match_weight,
            sql_infinity_expression=self._infinity_expression,
        )
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        predictions = self._execute_sql_pipeline(input_dataframes)
        self._predict_warning()
        return predictions

    def find_matches_to_new_records(
        self,
        records_or_tablename,
        blocking_rules=[],
        match_weight_threshold=-4,
    ) -> SplinkDataFrame:
        """Given one or more records, find records in the input dataset(s) which match
        and return in order of the Splink prediction score.

        This effectively provides a way of searching the input datasets
        for given record(s)

        Args:
            records_or_tablename (List[dict]): Input search record(s) as list of dict,
                or a table registered to the database.
            blocking_rules (list, optional): Blocking rules to select
                which records to find and score. If [], do not use a blocking
                rule - meaning the input records will be compared to all records
                provided to the linker when it was instantiated. Defaults to [].
            match_weight_threshold (int, optional): Return matches with a match weight
                above this threshold. Defaults to -4.

        Examples:
            ```py
            linker = DuckDBLinker(df)
            linker.load_settings("saved_settings.json")
            # Pre-compute tf tables for any tables with
            # term frequency adjustments
            linker.compute_tf_table("first_name")
            record = {'unique_id': 1,
                'first_name': "John",
                'surname': "Smith",
                'dob': "1971-05-24",
                'city': "London",
                'email': "john@smith.net"
                }
            df = linker.find_matches_to_new_records([record], blocking_rules=[])
            ```

        Returns:
            SplinkDataFrame: The pairwise comparisons.
        """

        original_blocking_rules = (
            self._settings_obj._blocking_rules_to_generate_predictions
        )
        original_link_type = self._settings_obj._link_type

        blocking_rules = ensure_is_list(blocking_rules)

        if not isinstance(records_or_tablename, str):
            uid = ascii_uid(8)
            new_records_tablename = f"__splink__df_new_records_{uid}"
            self.register_table(
                records_or_tablename, new_records_tablename, overwrite=True
            )

        else:
            new_records_tablename = records_or_tablename

        new_records_df = self._table_to_splink_dataframe(
            "__splink__df_new_records", new_records_tablename
        )

        cache = self._intermediate_table_cache
        input_dfs = []
        # If our df_concat_with_tf table already exists, derive the term frequency
        # tables from df_concat_with_tf rather than computing them
        if "__splink__df_concat_with_tf" in cache:
            concat_with_tf = cache["__splink__df_concat_with_tf"]
            tf_tables = compute_term_frequencies_from_concat_with_tf(self)
            # This queues up our tf tables, rather materialising them
            for tf in tf_tables:
                # if tf is a SplinkDataFrame, then the table already exists
                if isinstance(tf, SplinkDataFrame):
                    input_dfs.append(tf)
                else:
                    self._enqueue_sql(tf["sql"], tf["output_table_name"])
        else:
            # This queues up our cols_with_tf and df_concat_with_tf tables.
            concat_with_tf = self._initialise_df_concat_with_tf(materialise=False)

        if concat_with_tf:
            input_dfs.append(concat_with_tf)

        blocking_rules = [blocking_rule_to_obj(br) for br in blocking_rules]
        for n, br in enumerate(blocking_rules):
            br.add_preceding_rules(blocking_rules[:n])

        self._settings_obj._blocking_rules_to_generate_predictions = blocking_rules

        self._find_new_matches_mode = True

        sql = _join_tf_to_input_df_sql(self)
        sql = sql.replace("__splink__df_concat", new_records_tablename)
        self._enqueue_sql(sql, "__splink__df_new_records_with_tf_before_uid_fix")

        add_unique_id_and_source_dataset_cols_if_needed(self, new_records_df)

        sqls = block_using_rules_sqls(self)
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        sql = compute_comparison_vector_values_sql(self._settings_obj)
        self._enqueue_sql(sql, "__splink__df_comparison_vectors")

        sqls = predict_from_comparison_vectors_sqls(
            self._settings_obj,
            sql_infinity_expression=self._infinity_expression,
        )
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        sql = f"""
        select * from __splink__df_predict
        where match_weight > {match_weight_threshold}
        """

        self._enqueue_sql(sql, "__splink__find_matches_predictions")

        predictions = self._execute_sql_pipeline(
            input_dataframes=input_dfs, use_cache=False
        )

        self._settings_obj._blocking_rules_to_generate_predictions = (
            original_blocking_rules
        )
        self._settings_obj._link_type = original_link_type
        self._find_new_matches_mode = False

        return predictions

    def compare_two_records(self, record_1: dict, record_2: dict):
        """Use the linkage model to compare and score a pairwise record comparison
        based on the two input records provided

        Args:
            record_1 (dict): dictionary representing the first record.  Columns names
                and data types must be the same as the columns in the settings object
            record_2 (dict): dictionary representing the second record.  Columns names
                and data types must be the same as the columns in the settings object

        Examples:
            ```py
            linker = DuckDBLinker(df)
            linker.load_settings("saved_settings.json")
            linker.compare_two_records(record_left, record_right)
            ```

        Returns:
            SplinkDataFrame: Pairwise comparison with scored prediction
        """
        original_blocking_rules = (
            self._settings_obj._blocking_rules_to_generate_predictions
        )
        original_link_type = self._settings_obj._link_type

        self._compare_two_records_mode = True
        self._settings_obj._blocking_rules_to_generate_predictions = []

        uid = ascii_uid(8)
        df_records_left = self.register_table(
            [record_1], f"__splink__compare_two_records_left_{uid}", overwrite=True
        )
        df_records_left.templated_name = "__splink__compare_two_records_left"

        df_records_right = self.register_table(
            [record_2], f"__splink__compare_two_records_right_{uid}", overwrite=True
        )
        df_records_right.templated_name = "__splink__compare_two_records_right"

        sql_join_tf = _join_tf_to_input_df_sql(self)

        sql_join_tf = sql_join_tf.replace(
            "__splink__df_concat", "__splink__compare_two_records_left"
        )
        self._enqueue_sql(sql_join_tf, "__splink__compare_two_records_left_with_tf")

        sql_join_tf = sql_join_tf.replace(
            "__splink__compare_two_records_left", "__splink__compare_two_records_right"
        )

        self._enqueue_sql(sql_join_tf, "__splink__compare_two_records_right_with_tf")

        sqls = block_using_rules_sqls(self)
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        sql = compute_comparison_vector_values_sql(self._settings_obj)
        self._enqueue_sql(sql, "__splink__df_comparison_vectors")

        sqls = predict_from_comparison_vectors_sqls(
            self._settings_obj,
            sql_infinity_expression=self._infinity_expression,
        )
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        predictions = self._execute_sql_pipeline(
            [df_records_left, df_records_right], use_cache=False
        )

        self._settings_obj._blocking_rules_to_generate_predictions = (
            original_blocking_rules
        )
        self._settings_obj._link_type = original_link_type
        self._compare_two_records_mode = False

        return predictions

    def _self_link(self) -> SplinkDataFrame:
        """Use the linkage model to compare and score all records in our input df with
            themselves.

        Returns:
            SplinkDataFrame: Scored pairwise comparisons of the input records to
                themselves.
        """

        original_blocking_rules = (
            self._settings_obj._blocking_rules_to_generate_predictions
        )
        original_link_type = self._settings_obj._link_type

        # Changes our sql to allow for a self link.
        # This is used in `_sql_gen_where_condition` in blocking.py
        # to remove any 'where' clauses when blocking (normally when blocking
        # we want to *remove* self links!)
        self._self_link_mode = True

        # Block on uid i.e. create pairwise record comparisons where the uid matches
        uid_cols = self._settings_obj._unique_id_input_columns
        uid_l = _composite_unique_id_from_edges_sql(uid_cols, None, "l")
        uid_r = _composite_unique_id_from_edges_sql(uid_cols, None, "r")

        self._settings_obj._blocking_rules_to_generate_predictions = [
            BlockingRule(f"{uid_l} = {uid_r}", sqlglot_dialect=self._sql_dialect)
        ]

        nodes_with_tf = self._initialise_df_concat_with_tf()

        sqls = block_using_rules_sqls(self)
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        sql = compute_comparison_vector_values_sql(self._settings_obj)

        self._enqueue_sql(sql, "__splink__df_comparison_vectors")

        sqls = predict_from_comparison_vectors_sqls(
            self._settings_obj,
            sql_infinity_expression=self._infinity_expression,
        )
        for sql in sqls:
            output_table_name = sql["output_table_name"]
            output_table_name = output_table_name.replace("predict", "self_link")
            self._enqueue_sql(sql["sql"], output_table_name)

        predictions = self._execute_sql_pipeline(
            input_dataframes=[nodes_with_tf], use_cache=False
        )

        self._settings_obj._blocking_rules_to_generate_predictions = (
            original_blocking_rules
        )
        self._settings_obj._link_type = original_link_type
        self._self_link_mode = False

        return predictions

    def cluster_pairwise_predictions_at_threshold(
        self,
        df_predict: SplinkDataFrame,
        threshold_match_probability: float = None,
        pairwise_formatting: bool = False,
        filter_pairwise_format_for_clusters: bool = True,
    ) -> SplinkDataFrame:
        """Clusters the pairwise match predictions that result from `linker.predict()`
        into groups of connected record using the connected components graph clustering
        algorithm

        Records with an estimated `match_probability` at or above
        `threshold_match_probability` are considered to be a match (i.e. they represent
        the same entity).

        Args:
            df_predict (SplinkDataFrame): The results of `linker.predict()`
            threshold_match_probability (float): Filter the pairwise match predictions
                to include only pairwise comparisons with a match_probability at or
                above this threshold. This dataframe is then fed into the clustering
                algorithm.
            pairwise_formatting (bool): Whether to output the pairwise match predictions
                from linker.predict() with cluster IDs.
                If this is set to false, the output will be a list of all IDs, clustered
                into groups based on the desired match threshold.
            filter_pairwise_format_for_clusters (bool): If pairwise formatting has been
                selected, whether to output all columns found within linker.predict(),
                or just return clusters.

        Returns:
            SplinkDataFrame: A SplinkDataFrame containing a list of all IDs, clustered
                into groups based on the desired match threshold.

        """

        # Feeding in df_predict forces materiailisation, if it exists in your database
        concat_with_tf = self._initialise_df_concat_with_tf(df_predict)

        edges_table = _cc_create_unique_id_cols(
            self,
            concat_with_tf.physical_name,
            df_predict.physical_name,
            threshold_match_probability,
        )

        cc = solve_connected_components(
            self,
            edges_table,
            df_predict,
            concat_with_tf,
            pairwise_formatting,
            filter_pairwise_format_for_clusters,
        )

        return cc

    def _compute_cluster_metrics(
        self,
        df_predict: SplinkDataFrame,
        df_clustered: SplinkDataFrame,
        threshold_match_probability: float,
    ):
        """Generates a table containing cluster metrics and returns a Splink dataframe

        Args:
            df_predict (SplinkDataFrame): The results of `linker.predict()`
            df_clustered (SplinkDataFrame): The outputs of
                `linker.cluster_pairwise_predictions_at_threshold()`
            threshold_match_probability (float): Filter the pairwise match predictions
                to include only pairwise comparisons with a match_probability above this
                threshold.

        Returns:
            SplinkDataFrame: A SplinkDataFrame containing cluster IDs and selected
            cluster metrics

        """

        # Get unique id columns
        uid_cols = self._settings_obj._unique_id_input_columns
        # Create unique id for left-hand edges
        composite_uid_edges_l = _composite_unique_id_from_edges_sql(uid_cols, "l")
        # Create unique id for clusters
        composite_uid_clusters = _composite_unique_id_from_nodes_sql(uid_cols)

        sqls = _size_density_sql(
            df_predict,
            df_clustered,
            threshold_match_probability,
            composite_uid_edges_l,
            composite_uid_clusters,
        )

        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        df_cluster_metrics = self._execute_sql_pipeline()

        return df_cluster_metrics

    def profile_columns(
        self, column_expressions: str | list[str] = None, top_n=10, bottom_n=10
    ):
        """
        Profiles the specified columns of the dataframe initiated with the linker.

        This can be computationally expensive if the dataframe is large.

        For the provided columns with column_expressions (or for all columns if
         left empty) calculate:
        - A distribution plot that shows the count of values at each percentile.
        - A top n chart, that produces a chart showing the count of the top n values
        within the column
        - A bottom n chart, that produces a chart showing the count of the bottom
        n values within the column

        This should be used to explore the dataframe, determine if columns have
        sufficient completeness for linking, analyse the cardinality of columns, and
        identify the need for standardisation within a given column.

        Args:
            linker (object): The initiated linker.
            column_expressions (list, optional): A list of strings containing the
                specified column names.
                If left empty this will default to all columns.
            top_n (int, optional): The number of top n values to plot.
            bottom_n (int, optional): The number of bottom n values to plot.

        Returns:
            altair.Chart or dict: A visualization or JSON specification describing the
            profiling charts.

        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                linker = DuckDBLinker(df)
                linker.profile_columns()
                ```
            === ":simple-apachespark: Spark"
                ```py
                linker = SparkLinker(df)
                linker.profile_columns()
                ```
            === ":simple-amazonaws: Athena"
                ```py
                linker = AthenaLinker(df)
                linker.profile_columns()
                ```
            === ":simple-sqlite: SQLite"
                ```py
                linker = SQLiteLinker(df)
                linker.profile_columns()
                ```

        Note:
            - The `linker` object should be an instance of the initiated linker.
            - The provided `column_expressions` can be a list of column names to
                profile. If left empty, all columns will be profiled.
            - The `top_n` and `bottom_n` parameters determine the number of top and
                 bottom values to display in the respective charts.
        """

        return profile_columns(
            self, column_expressions=column_expressions, top_n=top_n, bottom_n=bottom_n
        )

    def _get_labels_tablename_from_input(
        self, labels_splinkdataframe_or_table_name: str | SplinkDataFrame
    ):
        if isinstance(labels_splinkdataframe_or_table_name, SplinkDataFrame):
            labels_tablename = labels_splinkdataframe_or_table_name.physical_name
        elif isinstance(labels_splinkdataframe_or_table_name, str):
            labels_tablename = labels_splinkdataframe_or_table_name
        else:
            raise ValueError(
                "The 'labels_splinkdataframe_or_table_name' argument"
                " must be of type SplinkDataframe or a string representing a tablename"
                " in the input database"
            )
        return labels_tablename

    def estimate_m_from_pairwise_labels(self, labels_splinkdataframe_or_table_name):
        """Estimate the m parameters of the linkage model from a dataframe of pairwise
        labels.

        The table of labels should be in the following format, and should
        be registered with your database:
        |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|
        |----------------|-----------|----------------|-----------|
        |df_1            |1          |df_2            |2          |
        |df_1            |1          |df_2            |3          |

        Note that `source_dataset` and `unique_id` should correspond to the
        values specified in the settings dict, and the `input_table_aliases`
        passed to the `linker` object. Note that at the moment, this method does
        not respect values in a `clerical_match_score` column.  If provided, these
        are ignored and it is assumed that every row in the table of labels is a score
        of 1, i.e. a perfect match.

        Args:
          labels_splinkdataframe_or_table_name (str): Name of table containing labels
            in the database or SplinkDataframe

        Examples:
            ```py
            pairwise_labels = pd.read_csv("./data/pairwise_labels_to_estimate_m.csv")
            linker.register_table(pairwise_labels, "labels", overwrite=True)
            linker.estimate_m_from_pairwise_labels("labels")
            ```
        """
        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )
        estimate_m_from_pairwise_labels(self, labels_tablename)

    def truth_space_table_from_labels_table(
        self,
        labels_splinkdataframe_or_table_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
    ) -> SplinkDataFrame:
        """Generate truth statistics (false positive etc.) for each threshold value of
        match_probability, suitable for plotting a ROC chart.

        The table of labels should be in the following format, and should be registered
        with your database:

        |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
        |----------------|-----------|----------------|-----------|--------------------|
        |df_1            |1          |df_2            |2          |0.99                |
        |df_1            |1          |df_2            |3          |0.2                 |

        Note that `source_dataset` and `unique_id` should correspond to the values
        specified in the settings dict, and the `input_table_aliases` passed to the
        `linker` object.

        For `dedupe_only` links, the `source_dataset` columns can be ommitted.

        Args:
            labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
                containing labels in the database
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the ROC chart. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.truth_space_table_from_labels_table("labels")
                ```
            === ":simple-apachespark: Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.truth_space_table_from_labels_table("labels")
                ```
        Returns:
            SplinkDataFrame:  Table of truth statistics
        """
        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )

        self._raise_error_if_necessary_accuracy_columns_not_computed()
        return truth_space_table_from_labels_table(
            self,
            labels_tablename,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )

    def roc_chart_from_labels_table(
        self,
        labels_splinkdataframe_or_table_name: str | SplinkDataFrame,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
    ):
        """Generate a ROC chart from labelled (ground truth) data.

        The table of labels should be in the following format, and should be registered
        with your database:

        |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
        |----------------|-----------|----------------|-----------|--------------------|
        |df_1            |1          |df_2            |2          |0.99                |
        |df_1            |1          |df_2            |3          |0.2                 |

        Note that `source_dataset` and `unique_id` should correspond to the values
        specified in the settings dict, and the `input_table_aliases` passed to the
        `linker` object.

        For `dedupe_only` links, the `source_dataset` columns can be ommitted.

        Args:
            labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
                containing labels in the database
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the ROC chart. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.roc_chart_from_labels_table("labels")
                ```
            === ":simple-apachespark: Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.roc_chart_from_labels_table("labels")
                ```

        Returns:
            altair.Chart: An altair chart
        """
        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )

        self._raise_error_if_necessary_accuracy_columns_not_computed()
        df_truth_space = truth_space_table_from_labels_table(
            self,
            labels_tablename,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )
        recs = df_truth_space.as_record_dict()
        return roc_chart(recs)

    def precision_recall_chart_from_labels_table(
        self,
        labels_splinkdataframe_or_table_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
    ):
        """Generate a precision-recall chart from labelled (ground truth) data.

        The table of labels should be in the following format, and should be registered
        as a table with your database:

        |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
        |----------------|-----------|----------------|-----------|--------------------|
        |df_1            |1          |df_2            |2          |0.99                |
        |df_1            |1          |df_2            |3          |0.2                 |

        Note that `source_dataset` and `unique_id` should correspond to the values
        specified in the settings dict, and the `input_table_aliases` passed to the
        `linker` object.

        For `dedupe_only` links, the `source_dataset` columns can be ommitted.

        Args:
            labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
                containing labels in the database
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the ROC chart. Defaults to None.
        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.precision_recall_chart_from_labels_table("labels")
                ```
            === ":simple-apachespark: Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.precision_recall_chart_from_labels_table("labels")
                ```

        Returns:
            altair.Chart: An altair chart
        """
        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )
        self._raise_error_if_necessary_accuracy_columns_not_computed()
        df_truth_space = truth_space_table_from_labels_table(
            self,
            labels_tablename,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )
        recs = df_truth_space.as_record_dict()
        return precision_recall_chart(recs)

    def accuracy_chart_from_labels_table(
        self,
        labels_splinkdataframe_or_table_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
        add_metrics: list = [],
    ):
        """Generate an accuracy measure chart from labelled (ground truth) data.

        The table of labels should be in the following format, and should be registered
        as a table with your database:

        |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
        |----------------|-----------|----------------|-----------|--------------------|
        |df_1            |1          |df_2            |2          |0.99                |
        |df_1            |1          |df_2            |3          |0.2                 |

        Note that `source_dataset` and `unique_id` should correspond to the values
        specified in the settings dict, and the `input_table_aliases` passed to the
        `linker` object.

        For `dedupe_only` links, the `source_dataset` columns can be ommitted.

        Args:
            labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
                containing labels in the database
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the chart. Defaults to None.
            add_metrics (list(str), optional): Precision and recall metrics are always
                included. Where provided, `add_metrics` specifies additional metrics
                to show, with the following options:

                - `"specificity"`: specificity, selectivity, true negative rate (TNR)
                - `"npv"`: negative predictive value (NPV)
                - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
                - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03B2=1 (balanced), \u03B2=2
                (emphasis on recall) and \u03B2=0.5 (emphasis on precision)
                - `"p4"` -  an extended F1 score with specificity and NPV included
                - `"phi"` - \u03C6 coefficient or Matthews correlation coefficient (MCC)
        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.accuracy_chart_from_labels_table("labels", add_metrics=["f1"])
                ```
            === ":simple-apachespark: Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.accuracy_chart_from_labels_table("labels", add_metrics=['f1'])
                ```

        Returns:
            altair.Chart: An altair chart
        """
        allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]

        if not isinstance(add_metrics, list):
            raise Exception(
                "add_metrics must be a list containing one or more of the following:",
                allowed,
            )

        # Silently filter out invalid entries (except case errors - e.g. ["NPV", "F1"])
        add_metrics = list(set(map(str.lower, add_metrics)).intersection(allowed))

        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )
        self._raise_error_if_necessary_accuracy_columns_not_computed()
        df_truth_space = truth_space_table_from_labels_table(
            self,
            labels_tablename,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )
        recs = df_truth_space.as_record_dict()
        return accuracy_chart(recs, add_metrics=add_metrics)

    def confusion_matrix_from_labels_table(
        self,
        labels_splinkdataframe_or_table_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
        match_weight_range=[-15, 15],
    ):
        """Generate an interactive confusion matrix from labelled (ground truth) data.

        The table of labels should be in the following format, and should be registered
        as a table with your database:

        |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
        |----------------|-----------|----------------|-----------|--------------------|
        |df_1            |1          |df_2            |2          |0.99                |
        |df_1            |1          |df_2            |3          |0.2                 |

        Note that `source_dataset` and `unique_id` should correspond to the values
        specified in the settings dict, and the `input_table_aliases` passed to the
        `linker` object.

        For `dedupe_only` links, the `source_dataset` columns can be ommitted.

        Args:
            labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
                containing labels in the database
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the chart. Defaults to None.
            match_weight_range (list(float), optional): minimum and maximum thresholds
                to include in chart output. Defaults to [-15,15].
        Examples:
            === ":simple-duckdb: DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.confusion_matrix_from_labels_table("labels")
                ```
            === ":simple-apachespark: Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.confusion_matrix_from_labels_table("labels")
                ```

        Returns:
            altair.Chart: An altair chart
        """

        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )
        self._raise_error_if_necessary_accuracy_columns_not_computed()
        df_truth_space = truth_space_table_from_labels_table(
            self,
            labels_tablename,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )

        recs = df_truth_space.as_record_dict()
        a, b = match_weight_range
        recs = [r for r in recs if a < r["truth_threshold"] < b]
        return confusion_matrix_chart(recs, match_weight_range=match_weight_range)

    def prediction_errors_from_labels_table(
        self,
        labels_splinkdataframe_or_table_name,
        include_false_positives=True,
        include_false_negatives=True,
        threshold=0.5,
    ):
        """Generate a dataframe containing false positives and false negatives
        based on the comparison between the clerical_match_score in the labels
        table compared with the splink predicted match probability

        Args:
            labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
                containing labels in the database
            include_false_positives (bool, optional): Defaults to True.
            include_false_negatives (bool, optional): Defaults to True.
            threshold (float, optional): Threshold above which a score is considered
                to be a match. Defaults to 0.5.

        Returns:
            SplinkDataFrame:  Table containing false positives and negatives
        """
        labels_tablename = self._get_labels_tablename_from_input(
            labels_splinkdataframe_or_table_name
        )
        return prediction_errors_from_labels_table(
            self,
            labels_tablename,
            include_false_positives,
            include_false_negatives,
            threshold,
        )

    def truth_space_table_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
    ):
        """Generate truth statistics (false positive etc.) for each threshold value of
        match_probability, suitable for plotting a ROC chart.

        Your labels_column_name should include the ground truth cluster (unique
        identifier) that groups entities which are the same

        Args:
            labels_tablename (str): Name of table containing labels in the database
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the ROC chart. Defaults to None.

        Examples:
            ```py
            linker.truth_space_table_from_labels_column("cluster")
            ```

        Returns:
            SplinkDataFrame:  Table of truth statistics
        """

        return truth_space_table_from_labels_column(
            self, labels_column_name, threshold_actual, match_weight_round_to_nearest
        )

    def roc_chart_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
    ):
        """Generate a ROC chart from ground truth data, whereby the ground truth
        is in a column in the input dataset called `labels_column_name`

        Args:
            labels_column_name (str): Column name containing labels in the input table
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the ROC chart. Defaults to None.

        Examples:
            ```py
            linker.roc_chart_from_labels_column("labels")
            ```

        Returns:
            altair.Chart: An altair chart
        """

        df_truth_space = truth_space_table_from_labels_column(
            self,
            labels_column_name,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )
        recs = df_truth_space.as_record_dict()
        return roc_chart(recs)

    def precision_recall_chart_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
    ):
        """Generate a precision-recall chart from ground truth data, whereby the ground
        truth is in a column in the input dataset called `labels_column_name`

        Args:
            labels_column_name (str): Column name containing labels in the input table
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the ROC chart. Defaults to None.
        Examples:
            ```py
            linker.precision_recall_chart_from_labels_column("ground_truth")
            ```

        Returns:
            altair.Chart: An altair chart
        """

        df_truth_space = truth_space_table_from_labels_column(
            self,
            labels_column_name,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )
        recs = df_truth_space.as_record_dict()
        return precision_recall_chart(recs)

    def accuracy_chart_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
        add_metrics: list = [],
    ):
        """Generate an accuracy chart from ground truth data, whereby the ground
        truth is in a column in the input dataset called `labels_column_name`

        Args:
            labels_column_name (str): Column name containing labels in the input table
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the chart. Defaults to None.
            add_metrics (list(str), optional): Precision and recall metrics are always
                included. Where provided, `add_metrics` specifies additional metrics
                to show, with the following options:

                - `"specificity"`: specificity, selectivity, true negative rate (TNR)
                - `"npv"`: negative predictive value (NPV)
                - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
                - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03B2=1 (balanced), \u03B2=2
                (emphasis on recall) and \u03B2=0.5 (emphasis on precision)
                - `"p4"` -  an extended F1 score with specificity and NPV included
                - `"phi"` - \u03C6 coefficient or Matthews correlation coefficient (MCC)
        Examples:
            ```py
            linker.accuracy_chart_from_labels_column("ground_truth", add_metrics=["f1"])
            ```

        Returns:
            altair.Chart: An altair chart
        """

        allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]

        if not isinstance(add_metrics, list):
            raise Exception(
                "add_metrics must be a list containing one or more of the following:",
                allowed,
            )

        # Silently filter out invalid entries (except case errors - e.g. ["NPV", "F1"])
        add_metrics = list(set(map(str.lower, add_metrics)).intersection(allowed))

        df_truth_space = truth_space_table_from_labels_column(
            self,
            labels_column_name,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )
        recs = df_truth_space.as_record_dict()
        return accuracy_chart(recs, add_metrics=add_metrics)

    def confusion_matrix_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=0.5,
        match_weight_round_to_nearest: float = None,
        match_weight_range=[-15, 15],
    ):
        """Generate an accuracy chart from ground truth data, whereby the ground
        truth is in a column in the input dataset called `labels_column_name`

        Args:
            labels_column_name (str): Column name containing labels in the input table
            threshold_actual (float, optional): Where the `clerical_match_score`
                provided by the user is a probability rather than binary, this value
                is used as the threshold to classify `clerical_match_score`s as binary
                matches or non matches. Defaults to 0.5.
            match_weight_round_to_nearest (float, optional): When provided, thresholds
                are rounded.  When large numbers of labels are provided, this is
                sometimes necessary to reduce the size of the ROC table, and therefore
                the number of points plotted on the chart. Defaults to None.
            match_weight_range (list(float), optional): minimum and maximum thresholds
                to include in chart output. Defaults to [-15,15].
        Examples:
            ```py
            linker.confusion_matrix_from_labels_column("ground_truth")
            ```

        Returns:
            altair.Chart: An altair chart
        """

        df_truth_space = truth_space_table_from_labels_column(
            self,
            labels_column_name,
            threshold_actual=threshold_actual,
            match_weight_round_to_nearest=match_weight_round_to_nearest,
        )

        recs = df_truth_space.as_record_dict()
        a, b = match_weight_range
        recs = [r for r in recs if a < r["truth_threshold"] < b]
        return confusion_matrix_chart(recs, match_weight_range=match_weight_range)

    def prediction_errors_from_labels_column(
        self,
        label_colname,
        include_false_positives=True,
        include_false_negatives=True,
        threshold=0.5,
    ):
        """Generate a dataframe containing false positives and false negatives
        based on the comparison between the splink match probability and the
        labels column.  A label column is a column in the input dataset that contains
        the 'ground truth' cluster to which the record belongs

        Args:
            label_colname (str): Name of labels column in input data
            include_false_positives (bool, optional): Defaults to True.
            include_false_negatives (bool, optional): Defaults to True.
            threshold (float, optional): Threshold above which a score is considered
                to be a match. Defaults to 0.5.

        Returns:
            SplinkDataFrame:  Table containing false positives and negatives
        """
        return prediction_errors_from_label_column(
            self,
            label_colname,
            include_false_positives,
            include_false_negatives,
            threshold,
        )

    def match_weights_histogram(
        self, df_predict: SplinkDataFrame, target_bins: int = 30, width=600, height=250
    ):
        """Generate a histogram that shows the distribution of match weights in
        `df_predict`

        Args:
            df_predict (SplinkDataFrame): Output of `linker.predict()`
            target_bins (int, optional): Target number of bins in histogram. Defaults to
                30.
            width (int, optional): Width of output. Defaults to 600.
            height (int, optional): Height of output chart. Defaults to 250.


        Returns:
            altair.Chart: An altair chart

        """
        df = histogram_data(self, df_predict, target_bins)
        recs = df.as_record_dict()
        return match_weights_histogram(recs, width=width, height=height)

    def waterfall_chart(self, records: list[dict], filter_nulls=True):
        """Visualise how the final match weight is computed for the provided pairwise
        record comparisons.

        Records must be provided as a list of dictionaries. This would usually be
        obtained from `df.as_record_dict(limit=n)` where `df` is a SplinkDataFrame.

        Examples:
            ```py
            df = linker.predict(threshold_match_weight=2)
            records = df.as_record_dict(limit=10)
            linker.waterfall_chart(records)
            ```

        Args:
            records (List[dict]): Usually be obtained from `df.as_record_dict(limit=n)`
                where `df` is a SplinkDataFrame.
            filter_nulls (bool, optional): Whether the visualiation shows null
                comparisons, which have no effect on final match weight. Defaults to
                True.


        Returns:
            altair.Chart: An altair chart

        """
        self._raise_error_if_necessary_waterfall_columns_not_computed()

        return waterfall_chart(records, self._settings_obj, filter_nulls)

    def unlinkables_chart(
        self,
        x_col="match_weight",
        source_dataset=None,
        as_dict=False,
    ):
        """Generate an interactive chart displaying the proportion of records that
        are "unlinkable" for a given splink score threshold and model parameters.

        Unlinkable records are those that, even when compared with themselves, do not
        contain enough information to confirm a match.

        Args:
            x_col (str, optional): Column to use for the x-axis.
                Defaults to "match_weight".
            source_dataset (str, optional): Name of the source dataset to use for
                the title of the output chart.
            as_dict (bool, optional): If True, return a dict version of the chart.

        Examples:
            For the simplest code pipeline, load a pre-trained model
            and run this against the test data.
            ```py
            from splink.datasets import splink_datasets
            df = splink_datasets.fake_1000
            linker = DuckDBLinker(df)
            linker.load_settings("saved_settings.json")
            linker.unlinkables_chart()
            ```
            For more complex code pipelines, you can run an entire pipeline
            that estimates your m and u values, before `unlinkables_chart().

        Returns:
            altair.Chart: An altair chart
        """

        # Link our initial df on itself and calculate the % of unlinkable entries
        records = unlinkables_data(self)
        return unlinkables_chart(records, x_col, source_dataset, as_dict)

    def comparison_viewer_dashboard(
        self,
        df_predict: SplinkDataFrame,
        out_path: str,
        overwrite=False,
        num_example_rows=2,
        return_html_as_string=False,
    ):
        """Generate an interactive html visualization of the linker's predictions and
        save to `out_path`.  For more information see
        [this video](https://www.youtube.com/watch?v=DNvCMqjipis)


        Args:
            df_predict (SplinkDataFrame): The outputs of `linker.predict()`
            out_path (str): The path (including filename) to save the html file to.
            overwrite (bool, optional): Overwrite the html file if it already exists?
                Defaults to False.
            num_example_rows (int, optional): Number of example rows per comparison
                vector. Defaults to 2.
            return_html_as_string: If True, return the html as a string

        Examples:
            ```py
            df_predictions = linker.predict()
            linker.comparison_viewer_dashboard(df_predictions, "scv.html", True, 2)
            ```

            Optionally, in Jupyter, you can display the results inline
            Otherwise you can just load the html file in your browser
            ```py
            from IPython.display import IFrame
            IFrame(src="./scv.html", width="100%", height=1200)
            ```

        """
        self._raise_error_if_necessary_waterfall_columns_not_computed()

        sql = comparison_vector_distribution_sql(self)
        self._enqueue_sql(sql, "__splink__df_comparison_vector_distribution")

        sqls = comparison_viewer_table_sqls(self, num_example_rows)
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        df = self._execute_sql_pipeline([df_predict])

        rendered = render_splink_comparison_viewer_html(
            df.as_record_dict(),
            self._settings_obj._as_completed_dict(),
            out_path,
            overwrite,
        )
        if return_html_as_string:
            return rendered

    def parameter_estimate_comparisons_chart(self, include_m=True, include_u=False):
        """Show a chart that shows how parameter estimates have differed across
        the different estimation methods you have used.

        For example, if you have run two EM estimation sessions, blocking on
        different variables, and both result in parameter estimates for
        first_name, this chart will enable easy comparison of the different
        estimates

        Args:
            include_m (bool, optional): Show different estimates of m values. Defaults
                to True.
            include_u (bool, optional): Show different estimates of u values. Defaults
                to False.

        """
        records = self._settings_obj._parameter_estimates_as_records

        to_retain = []
        if include_m:
            to_retain.append("m")
        if include_u:
            to_retain.append("u")

        records = [r for r in records if r["m_or_u"] in to_retain]

        return parameter_estimate_comparisons(records)

    def missingness_chart(self, input_dataset: str = None):
        """Generate a summary chart of the missingness (prevalence of nulls) of
        columns in the input datasets.  By default, missingness is assessed across
        all input datasets

        Args:
            input_dataset (str, optional): Name of one of the input tables in the
                database.  If provided, missingness will be computed for
                this table alone.
                Defaults to None.

        Examples:
            ```py
            linker.missingness_chart()
            ```
            To view offline (if you don't have an internet connection):
            ```py
            from splink.charts import save_offline_chart
            c = linker.missingness_chart()
            save_offline_chart(c.to_dict(), "test_chart.html")
            ```
            View resultant html file in Jupyter (or just load it in your browser)
            ```py
            from IPython.display import IFrame
            IFrame(src="./test_chart.html", width=1000, height=500
            ```

        Returns:
            altair.Chart: An altair chart
        """
        records = missingness_data(self, input_dataset)
        return missingness_chart(records)

    def completeness_chart(self, input_dataset: str = None, cols: list[str] = None):
        """Generate a summary chart of the completeness (proportion of non-nulls) of
        columns in each of the input datasets. By default, completeness is assessed for
        all column in the input data.

        Args:
            input_dataset (str, optional): Name of one of the input tables in the
                database.  If provided, completeness will be computed for this table
                alone. Defaults to None.
            cols (List[str], optional): List of column names to calculate completeness.
                Default to None.

        Examples:
            ```py
            linker.completeness_chart()
            ```
            To view offline (if you don't have an internet connection):
            ```py
            from splink.charts import save_offline_chart
            c = linker.completeness_chart()
            save_offline_chart(c.to_dict(), "test_chart.html")
            ```
            View resultant html file in Jupyter (or just load it in your browser)
            ```py
            from IPython.display import IFrame
            IFrame(src="./test_chart.html", width=1000, height=500
            ```
        """
        records = completeness_data(self, input_dataset, cols)
        return completeness_chart(records)

    def count_num_comparisons_from_blocking_rule(
        self,
        blocking_rule: str | BlockingRule,
    ) -> int:
        """Compute the number of pairwise record comparisons that would be generated by
        a blocking rule

        Args:
            blocking_rule (str | BlockingRule): The blocking rule to analyse
            link_type (str, optional): The link type.  This is needed only if the
                linker has not yet been provided with a settings dictionary.  Defaults
                to None.
            unique_id_column_name (str, optional):  This is needed only if the
                linker has not yet been provided with a settings dictionary.  Defaults
                to None.

        Examples:
            ```py
            br = "l.surname = r.surname"
            linker.count_num_comparisons_from_blocking_rule(br)
            ```
            > 19387

            ```py
            br = "l.name = r.name and substr(l.dob,1,4) = substr(r.dob,1,4)"
            linker.count_num_comparisons_from_blocking_rule(br)
            ```
            > 394
            Alternatively, you can use the blocking rule library functions
            ```py
            import splink.duckdb.blocking_rule_library as brl
            br = brl.exact_match_rule("surname")
            linker.count_num_comparisons_from_blocking_rule(br)
            ```
            > 3167

        Returns:
            int: The number of comparisons generated by the blocking rule
        """

        blocking_rule = blocking_rule_to_obj(blocking_rule).blocking_rule_sql

        sql = vertically_concatenate_sql(self)
        self._enqueue_sql(sql, "__splink__df_concat")

        sql = number_of_comparisons_generated_by_blocking_rule_post_filters_sql(
            self, blocking_rule
        )
        self._enqueue_sql(sql, "__splink__analyse_blocking_rule")
        res = self._execute_sql_pipeline().as_record_dict()[0]
        return res["count_of_pairwise_comparisons_generated"]

    def _count_num_comparisons_from_blocking_rule_pre_filter_conditions(
        self,
        blocking_rule: str,
    ) -> int:
        """Compute the number of pairwise record comparisons that would be generated by
        a blocking rule, prior to any filters (non equi-join conditions) being applied
        by the SQL engine.

        For more information on what this means, see
        https://github.com/moj-analytical-services/splink/discussions/1391

        Args:
            blocking_rule (str): The blocking rule to analyse

        Returns:
            int: The number of comparisons generated by the blocking rule
        """

        input_dataframes = []
        df_concat = self._initialise_df_concat()

        if df_concat:
            input_dataframes.append(df_concat)

        sqls = count_comparisons_from_blocking_rule_pre_filter_conditions_sqls(
            self, blocking_rule
        )
        for sql in sqls:
            self._enqueue_sql(sql["sql"], sql["output_table_name"])

        res = self._execute_sql_pipeline(input_dataframes).as_record_dict()[0]
        return int(res["count_of_pairwise_comparisons_generated"])

    def cumulative_comparisons_from_blocking_rules_records(
        self,
        blocking_rules: str | BlockingRule | list = None,
    ):
        """Output the number of comparisons generated by each successive blocking rule.

        This is equivalent to the output size of df_predict and details how many
        comparisons each of your individual blocking rules will contribute to the
        total.

        Args:
            blocking_rules (str or list): The blocking rule(s) to compute comparisons
                for. If null, the rules set out in your settings object will be used.

        Examples:
            Generate total comparisons from Blocking Rules defined in settings
            dictionary
            ```py
            linker_settings = DuckDBLinker(df, settings)
            # Compute the cumulative number of comparisons generated by the rules
            # in your settings object.
            linker_settings.cumulative_comparisons_from_blocking_rules_records()
            ```

            Generate total comparisons with custom blocking rules.
            ```py
            blocking_rules = [
               "l.surname = r.surname",
               "l.first_name = r.first_name
                and substr(l.dob,1,4) = substr(r.dob,1,4)"
            ]

            linker_settings.cumulative_comparisons_from_blocking_rules_records(
                blocking_rules
             )
            ```

        Returns:
            List: A list of blocking rules and the corresponding number of
                comparisons it is forecast to generate.
        """
        if blocking_rules:
            blocking_rules = ensure_is_list(blocking_rules)

        records = cumulative_comparisons_generated_by_blocking_rules(
            self, blocking_rules, output_chart=False
        )

        return records

    def cumulative_num_comparisons_from_blocking_rules_chart(
        self,
        blocking_rules: str | BlockingRule | list = None,
    ):
        """Display a chart with the cumulative number of comparisons generated by a
        selection of blocking rules.

        This is equivalent to the output size of df_predict and details how many
        comparisons each of your individual blocking rules will contribute to the
        total.

        Args:
            blocking_rules (str or list): The blocking rule(s) to compute comparisons
                for. If null, the rules set out in your settings object will be used.

        Examples:
            ```py
            linker_settings = DuckDBLinker(df, settings)
            # Compute the cumulative number of comparisons generated by the rules
            # in your settings object.
            linker_settings.cumulative_num_comparisons_from_blocking_rules_chart()
            >>>
            # Generate total comparisons with custom blocking rules.
            blocking_rules = [
               "l.surname = r.surname",
               "l.first_name = r.first_name
                and substr(l.dob,1,4) = substr(r.dob,1,4)"
            ]
            >>>
            linker_settings.cumulative_num_comparisons_from_blocking_rules_chart(
                blocking_rules
             )
            ```

        Returns:
            altair.Chart: An altair chart
        """

        if blocking_rules:
            blocking_rules = ensure_is_list(blocking_rules)

        records = cumulative_comparisons_generated_by_blocking_rules(
            self, blocking_rules, output_chart=True
        )

        return cumulative_blocking_rule_comparisons_generated(records)

    def count_num_comparisons_from_blocking_rules_for_prediction(self, df_predict):
        """Counts the marginal number of edges created from each of the blocking rules
        in `blocking_rules_to_generate_predictions`

        This is different to `count_num_comparisons_from_blocking_rule`
        because it (a) analyses multiple blocking rules rather than a single rule, and
        (b) deduplicates any comparisons that are generated, to tell you the
        marginal effect of each entry in `blocking_rules_to_generate_predictions`

        Args:
            df_predict (SplinkDataFrame): SplinkDataFrame with match weights
            and probabilities of rows matching

        Examples:
            ```py
            linker = DuckDBLinker(df)
            linker.load_model("settings.json")
            df_predict = linker.predict(threshold_match_probability=0.95)
            count_pairwise = linker.count_num_comparisons_from_blocking_rules_for_prediction(df_predict)
            count_pairwise.as_pandas_dataframe(limit=5)
            ```

        Returns:
            SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons and
                estimated pairwise comparisons generated by the blocking rules.
        """  # noqa: E501
        sql = count_num_comparisons_from_blocking_rules_for_prediction_sql(
            self, df_predict
        )
        match_key_analysis = self._sql_to_splink_dataframe_checking_cache(
            sql, "__splink__match_key_analysis"
        )
        return match_key_analysis

    def match_weights_chart(self):
        """Display a chart of the (partial) match weights of the linkage model

        Examples:
            ```py
            linker.match_weights_chart()
            ```
            To view offline (if you don't have an internet connection):
            ```py
            from splink.charts import save_offline_chart
            c = linker.match_weights_chart()
            save_offline_chart(c.to_dict(), "test_chart.html")
            ```
            View resultant html file in Jupyter (or just load it in your browser)
            ```py
            from IPython.display import IFrame
            IFrame(src="./test_chart.html", width=1000, height=500)
            ```

        Returns:
            altair.Chart: An altair chart
        """
        return self._settings_obj.match_weights_chart()

    def tf_adjustment_chart(
        self,
        output_column_name: str,
        n_most_freq: int = 10,
        n_least_freq: int = 10,
        vals_to_include: str | list = None,
        as_dict: bool = False,
    ):
        """Display a chart showing the impact of term frequency adjustments on a
        specific comparison level.
        Each value

        Args:
            output_column_name (str): Name of an output column for which term frequency
                 adjustment has been applied.
            n_most_freq (int, optional): Number of most frequent values to show. If this
                 or `n_least_freq` set to None, all values will be shown.
                Default to 10.
            n_least_freq (int, optional): Number of least frequent values to show. If
                this or `n_most_freq` set to None, all values will be shown.
                Default to 10.
            vals_to_include (list, optional): Specific values for which to show term
                sfrequency adjustments.
                Defaults to None.

        Returns:
            altair.Chart: An altair chart
        """

        # Comparisons with TF adjustments
        tf_comparisons = [
            c._output_column_name
            for c in self._settings_obj.comparisons
            if any([cl._has_tf_adjustments for cl in c.comparison_levels])
        ]
        if output_column_name not in tf_comparisons:
            raise ValueError(
                f"{output_column_name} is not a valid comparison column, or does not"
                f" have term frequency adjustment activated"
            )

        vals_to_include = ensure_is_list(vals_to_include)

        return tf_adjustment_chart(
            self,
            output_column_name,
            n_most_freq,
            n_least_freq,
            vals_to_include,
            as_dict,
        )

    def m_u_parameters_chart(self):
        """Display a chart of the m and u parameters of the linkage model

        Examples:
            ```py
            linker.m_u_parameters_chart()
            ```
            To view offline (if you don't have an internet connection):
            ```py
            from splink.charts import save_offline_chart
            c = linker.match_weights_chart()
            save_offline_chart(c.to_dict(), "test_chart.html")
            ```
            View resultant html file in Jupyter (or just load it in your browser)
            ```py
            from IPython.display import IFrame
            IFrame(src="./test_chart.html", width=1000, height=500)
            ```

        Returns:
            altair.Chart: An altair chart
        """

        return self._settings_obj.m_u_parameters_chart()

    def cluster_studio_dashboard(
        self,
        df_predict: SplinkDataFrame,
        df_clustered: SplinkDataFrame,
        out_path: str,
        sampling_method="random",
        sample_size: int = 10,
        cluster_ids: list = None,
        cluster_names: list = None,
        overwrite: bool = False,
        return_html_as_string=False,
    ):
        """Generate an interactive html visualization of the predicted cluster and
        save to `out_path`.

        Args:
            df_predict (SplinkDataFrame): The outputs of `linker.predict()`
            df_clustered (SplinkDataFrame): The outputs of
                `linker.cluster_pairwise_predictions_at_threshold()`
            out_path (str): The path (including filename) to save the html file to.
            sampling_method (str, optional): `random` or `by_cluster_size`. Defaults to
                `random`.
            sample_size (int, optional): Number of clusters to show in the dahboard.
                Defaults to 10.
            cluster_ids (list): The IDs of the clusters that will be displayed in the
                dashboard.  If provided, ignore the `sampling_method` and `sample_size`
                arguments. Defaults to None.
            overwrite (bool, optional): Overwrite the html file if it already exists?
                Defaults to False.
            cluster_names (list, optional): If provided, the dashboard will display
                these names in the selection box. Ony works in conjunction with
                `cluster_ids`.  Defaults to None.
            return_html_as_string: If True, return the html as a string

        Examples:
            ```py
            df_p = linker.predict()
            df_c = linker.cluster_pairwise_predictions_at_threshold(df_p, 0.5)
            linker.cluster_studio_dashboard(
                df_p, df_c, [0, 4, 7], "cluster_studio.html"
            )
            ```
            Optionally, in Jupyter, you can display the results inline
            Otherwise you can just load the html file in your browser
            ```py
            from IPython.display import IFrame
            IFrame(src="./cluster_studio.html", width="100%", height=1200)
            ```
        """
        self._raise_error_if_necessary_waterfall_columns_not_computed()

        rendered = render_splink_cluster_studio_html(
            self,
            df_predict,
            df_clustered,
            out_path,
            sampling_method=sampling_method,
            sample_size=sample_size,
            cluster_ids=cluster_ids,
            overwrite=overwrite,
            cluster_names=cluster_names,
        )

        if return_html_as_string:
            return rendered

    def save_model_to_json(
        self, out_path: str | None = None, overwrite: bool = False
    ) -> dict:
        """Save the configuration and parameters of the linkage model to a `.json` file.

        The model can later be loaded back in using `linker.load_model()`.
        The settings dict is also returned in case you want to save it a different way.

        Examples:
            ```py
            linker.save_model_to_json("my_settings.json", overwrite=True)
            ```
        Args:
            out_path (str, optional): File path for json file. If None, don't save to
                file. Defaults to None.
            overwrite (bool, optional): Overwrite if already exists? Defaults to False.

        Returns:
            dict: The settings as a dictionary.
        """
        model_dict = self._settings_obj.as_dict()
        if out_path:
            if os.path.isfile(out_path) and not overwrite:
                raise ValueError(
                    f"The path {out_path} already exists. Please provide a different "
                    "path or set overwrite=True"
                )
            with open(out_path, "w", encoding="utf-8") as f:
                json.dump(model_dict, f, indent=4)
        return model_dict

    def save_settings_to_json(
        self, out_path: str | None = None, overwrite: bool = False
    ) -> dict:
        """
        This function is deprecated. Use save_model_to_json() instead.
        """
        warnings.warn(
            "This function is deprecated. Use save_model_to_json() instead.",
            SplinkDeprecated,
            stacklevel=2,
        )
        return self.save_model_to_json(out_path, overwrite)

    def estimate_probability_two_random_records_match(
        self, deterministic_matching_rules, recall
    ):
        """Estimate the model parameter `probability_two_random_records_match` using
        a direct estimation approach.

        See [here](https://github.com/moj-analytical-services/splink/issues/462)
        for discussion of methodology

        Args:
            deterministic_matching_rules (list): A list of deterministic matching
                rules that should be designed to admit very few (none if possible)
                false positives
            recall (float): A guess at the recall the deterministic matching rules
                will attain.  i.e. what proportion of true matches will be recovered
                by these deterministic rules
        """

        if (recall > 1) or (recall <= 0):
            raise ValueError(
                f"Estimated recall must be greater than 0 "
                f"and no more than 1. Supplied value {recall}."
            )

        # If user, by error, provides a single rule as a string
        if isinstance(deterministic_matching_rules, str):
            deterministic_matching_rules = [deterministic_matching_rules]

        records = cumulative_comparisons_generated_by_blocking_rules(
            self,
            deterministic_matching_rules,
        )

        summary_record = records[-1]
        num_observed_matches = summary_record["cumulative_rows"]
        num_total_comparisons = summary_record["cartesian"]

        if num_observed_matches > num_total_comparisons * recall:
            raise ValueError(
                f"Deterministic matching rules led to more "
                f"observed matches than is consistent with supplied recall. "
                f"With these rules, recall must be at least "
                f"{num_observed_matches/num_total_comparisons:,.2f}."
            )

        num_expected_matches = num_observed_matches / recall
        prob = num_expected_matches / num_total_comparisons

        # warn about boundary values, as these will usually be in error
        if num_observed_matches == 0:
            logger.warning(
                f"WARNING: Deterministic matching rules led to no observed matches! "
                f"This means that no possible record pairs are matches, "
                f"and no records are linked to one another.\n"
                f"If this is truly the case then you do not need "
                f"to run the linkage model.\n"
                f"However this is usually in error; "
                f"expected rules to have recall of {100*recall:,.0f}%. "
                f"Consider revising rules as they may have an error."
            )
        if prob == 1:
            logger.warning(
                "WARNING: Probability two random records match is estimated to be 1.\n"
                "This means that all possible record pairs are matches, "
                "and all records are linked to one another.\n"
                "If this is truly the case then you do not need "
                "to run the linkage model.\n"
                "However, it is more likely that this estimate is faulty. "
                "Perhaps your deterministic matching rules include "
                "too many false positives?"
            )

        self._settings_obj._probability_two_random_records_match = prob

        reciprocal_prob = "Infinity" if prob == 0 else f"{1/prob:,.2f}"
        logger.info(
            f"Probability two random records match is estimated to be  {prob:.3g}.\n"
            f"This means that amongst all possible pairwise record comparisons, one in "
            f"{reciprocal_prob} are expected to match.  "
            f"With {num_total_comparisons:,.0f} total"
            " possible comparisons, we expect a total of around "
            f"{num_expected_matches:,.2f} matching pairs"
        )

    def invalidate_cache(self):
        """Invalidate the Splink cache.  Any previously-computed tables
        will be recomputed.
        This is useful, for example, if the input data tables have changed.
        """
        # Before Splink executes a SQL command, it checks the cache to see
        # whether a table already exists with the name of the output table

        # This function has the effect of changing the names of the output tables
        # to include a different unique id

        # As a result, any previously cached tables will not be found
        self._cache_uid = ascii_uid(8)

        # Drop any existing splink tables from the database
        # Note, this is not actually necessary, it's just good housekeeping
        self.delete_tables_created_by_splink_from_db()

        # As a result, any previously cached tables will not be found
        self._intermediate_table_cache.invalidate_cache()

    def register_table_input_nodes_concat_with_tf(self, input_data, overwrite=False):
        """Register a pre-computed version of the input_nodes_concat_with_tf table that
        you want to re-use e.g. that you created in a previous run

        This method allowed you to register this table in the Splink cache
        so it will be used rather than Splink computing this table anew.

        Args:
            input_data: The data you wish to register. This can be either a dictionary,
                pandas dataframe, pyarrow table or a spark dataframe.
            overwrite (bool): Overwrite the table in the underlying database if it
                exists
        """

        table_name_physical = "__splink__df_concat_with_tf_" + self._cache_uid
        splink_dataframe = self.register_table(
            input_data, table_name_physical, overwrite=overwrite
        )
        splink_dataframe.templated_name = "__splink__df_concat_with_tf"

        self._intermediate_table_cache["__splink__df_concat_with_tf"] = splink_dataframe
        return splink_dataframe

    def register_table_predict(self, input_data, overwrite=False):
        table_name_physical = "__splink__df_predict_" + self._cache_uid
        splink_dataframe = self.register_table(
            input_data, table_name_physical, overwrite=overwrite
        )
        self._intermediate_table_cache["__splink__df_predict"] = splink_dataframe
        splink_dataframe.templated_name = "__splink__df_predict"
        return splink_dataframe

    def register_term_frequency_lookup(self, input_data, col_name, overwrite=False):
        input_col = InputColumn(col_name, settings_obj=self._settings_obj)
        table_name_templated = colname_to_tf_tablename(input_col)
        table_name_physical = f"{table_name_templated}_{self._cache_uid}"
        splink_dataframe = self.register_table(
            input_data, table_name_physical, overwrite=overwrite
        )
        self._intermediate_table_cache[table_name_templated] = splink_dataframe
        splink_dataframe.templated_name = table_name_templated
        return splink_dataframe

    def register_labels_table(self, input_data, overwrite=False):
        table_name_physical = "__splink__df_labels_" + ascii_uid(8)
        splink_dataframe = self.register_table(
            input_data, table_name_physical, overwrite=overwrite
        )
        splink_dataframe.templated_name = "__splink__df_labels"
        return splink_dataframe

    def labelling_tool_for_specific_record(
        self,
        unique_id,
        source_dataset=None,
        out_path="labelling_tool.html",
        overwrite=False,
        match_weight_threshold=-4,
        view_in_jupyter=False,
        show_splink_predictions_in_interface=True,
    ):
        """Create a standalone, offline labelling dashboard for a specific record
        as identified by its unique id

        Args:
            unique_id (str): The unique id of the record for which to create the
                labelling tool
            source_dataset (str, optional): If there are multiple datasets, to
                identify the record you must also specify the source_dataset. Defaults
                to None.
            out_path (str, optional): The output path for the labelling tool. Defaults
                to "labelling_tool.html".
            overwrite (bool, optional): If true, overwrite files at the output
                path if they exist. Defaults to False.
            match_weight_threshold (int, optional): Include possible matches in the
                output which score above this threshold. Defaults to -4.
            view_in_jupyter (bool, optional): If you're viewing in the Jupyter
                html viewer, set this to True to extract your labels. Defaults to False.
            show_splink_predictions_in_interface (bool, optional): Whether to
                show information about the Splink model's predictions that could
                potentially bias the decision of the clerical labeller. Defaults to
                True.
        """

        df_comparisons = generate_labelling_tool_comparisons(
            self,
            unique_id,
            source_dataset,
            match_weight_threshold=match_weight_threshold,
        )

        render_labelling_tool_html(
            self,
            df_comparisons,
            show_splink_predictions_in_interface=show_splink_predictions_in_interface,
            out_path=out_path,
            view_in_jupyter=view_in_jupyter,
            overwrite=overwrite,
        )

    def _remove_splinkdataframe_from_cache(self, splink_dataframe: SplinkDataFrame):
        keys_to_delete = set()
        for key, df in self._intermediate_table_cache.items():
            if df.physical_name == splink_dataframe.physical_name:
                keys_to_delete.add(key)

        for k in keys_to_delete:
            del self._intermediate_table_cache[k]

    def _find_blocking_rules_below_threshold(
        self, max_comparisons_per_rule, blocking_expressions=None
    ):
        return find_blocking_rules_below_threshold_comparison_count(
            self, max_comparisons_per_rule, blocking_expressions
        )

    def _detect_blocking_rules_for_prediction(
        self,
        max_comparisons_per_rule,
        blocking_expressions=None,
        min_freedom=1,
        num_runs=200,
        num_equi_join_weight=0,
        field_freedom_weight=1,
        num_brs_weight=10,
        num_comparison_weight=10,
        return_as_df=False,
    ):
        """Find blocking rules for prediction below some given threshold of the
        maximum number of comparisons that can be generated per blocking rule
        (max_comparisons_per_rule).
        Uses a heuristic cost algorithm to identify the 'best' set of blocking rules
        Args:
            max_comparisons_per_rule (int): The maximum number of comparisons that
                each blocking rule is allowed to generate
            blocking_expressions: By default, blocking rules will be equi-joins
                on the columns used by the Splink model.  This allows you to manually
                specify sql expressions from which combinations will be created. For
                example, if you specify ["substr(dob, 1,4)", "surname", "dob"]
                blocking rules will be chosen by blocking on combinations
                of those expressions.
            min_freedom (int, optional): The minimum amount of freedom any column should
                be allowed.
            num_runs (int, optional): Each run selects rows using a heuristic and costs
                them. The more runs, the more likely you are to find the best rule.
                Defaults to 5.
            num_equi_join_weight (int, optional): Weight allocated to number of equi
                joins in the blocking rules.
                Defaults to 0 since this is cost better captured by other criteria.
            field_freedom_weight (int, optional): Weight given to the cost of
                having individual fields which don't havem much flexibility.  Assigning
                a high weight here makes it more likely you'll generate combinations of
                blocking rules for which most fields are allowed to vary more than
                the minimum. Defaults to 1.
            num_brs_weight (int, optional): Weight assigned to the cost of
                additional blocking rules.  Higher weight here will result in a
                 preference for fewer blocking rules. Defaults to 10.
            num_comparison_weight (int, optional): Weight assigned to the cost of
                larger numbers of comparisons, which happens when more of the blocking
                rules are close to the max_comparisons_per_rule.  A higher
                 weight here prefers sets of rules which generate lower total
                comparisons. Defaults to 10.
            return_as_df (bool, optional): If false, assign recommendation to settings.
                If true, return a dataframe containing details of the weights.
                Defaults to False.
        """

        df_br_below_thres = find_blocking_rules_below_threshold_comparison_count(
            self, max_comparisons_per_rule, blocking_expressions
        )

        blocking_rule_suggestions = suggest_blocking_rules(
            df_br_below_thres,
            min_freedom=min_freedom,
            num_runs=num_runs,
            num_equi_join_weight=num_equi_join_weight,
            field_freedom_weight=field_freedom_weight,
            num_brs_weight=num_brs_weight,
            num_comparison_weight=num_comparison_weight,
        )

        if return_as_df:
            return blocking_rule_suggestions
        else:
            if blocking_rule_suggestions is None or len(blocking_rule_suggestions) == 0:
                logger.warning("No set of blocking rules found within constraints")
            else:
                suggestion = blocking_rule_suggestions[
                    "suggested_blocking_rules_as_splink_brs"
                ].iloc[0]
                self._settings_obj._blocking_rules_to_generate_predictions = suggestion

                suggestion_str = blocking_rule_suggestions[
                    "suggested_blocking_rules_for_prediction"
                ].iloc[0]
                msg = (
                    "The following blocking_rules_to_generate_predictions were "
                    "automatically detected and assigned to your settings:\n"
                )
                logger.info(f"{msg}{suggestion_str}")

    def _detect_blocking_rules_for_em_training(
        self,
        max_comparisons_per_rule,
        min_freedom=1,
        num_runs=200,
        num_equi_join_weight=0,
        field_freedom_weight=1,
        num_brs_weight=20,
        num_comparison_weight=10,
        return_as_df=False,
    ):
        """Find blocking rules for EM training below some given threshold of the
        maximum number of comparisons that can be generated per blocking rule
        (max_comparisons_per_rule).
        Uses a heuristic cost algorithm to identify the 'best' set of blocking rules
        Args:
            max_comparisons_per_rule (int): The maximum number of comparisons that
                each blocking rule is allowed to generate
            min_freedom (int, optional): The minimum amount of freedom any column should
                be allowed.
            num_runs (int, optional): Each run selects rows using a heuristic and costs
                them.  The more runs, the more likely you are to find the best rule.
                Defaults to 5.
            num_equi_join_weight (int, optional): Weight allocated to number of equi
                joins in the blocking rules.
                Defaults to 0 since this is cost better captured by other criteria.
                Defaults to 0 since this is cost better captured by other criteria.
            field_freedom_weight (int, optional): Weight given to the cost of
                having individual fields which don't havem much flexibility.  Assigning
                a high weight here makes it more likely you'll generate combinations of
                blocking rules for which most fields are allowed to vary more than
                the minimum. Defaults to 1.
            num_brs_weight (int, optional): Weight assigned to the cost of
                additional blocking rules.  Higher weight here will result in a
                 preference for fewer blocking rules. Defaults to 10.
            num_comparison_weight (int, optional): Weight assigned to the cost of
                larger numbers of comparisons, which happens when more of the blocking
                rules are close to the max_comparisons_per_rule.  A higher
                 weight here prefers sets of rules which generate lower total
                comparisons. Defaults to 10.
            return_as_df (bool, optional): If false, return just the recommendation.
                If true, return a dataframe containing details of the weights.
                Defaults to False.
        """

        df_br_below_thres = find_blocking_rules_below_threshold_comparison_count(
            self, max_comparisons_per_rule
        )

        blocking_rule_suggestions = suggest_blocking_rules(
            df_br_below_thres,
            min_freedom=min_freedom,
            num_runs=num_runs,
            num_equi_join_weight=num_equi_join_weight,
            field_freedom_weight=field_freedom_weight,
            num_brs_weight=num_brs_weight,
            num_comparison_weight=num_comparison_weight,
        )

        if return_as_df:
            return blocking_rule_suggestions
        else:
            if blocking_rule_suggestions is None or len(blocking_rule_suggestions) == 0:
                logger.warning("No set of blocking rules found within constraints")
                return None
            else:
                suggestion_str = blocking_rule_suggestions[
                    "suggested_EM_training_statements"
                ].iloc[0]
                msg = "The following EM training strategy was detected:\n"
                msg = f"{msg}{suggestion_str}"
                logger.info(msg)
                suggestion = blocking_rule_suggestions[
                    "suggested_blocking_rules_as_splink_brs"
                ].iloc[0]
                return suggestion

__deepcopy__(memo)

When we do EM training, we need a copy of the linker which is independent of the main linker e.g. setting parameters on the copy will not affect the main linker. This method implements ensures linker can be deepcopied.

Source code in splink/linker.py

def __deepcopy__(self, memo):
    """When we do EM training, we need a copy of the linker which is independent
    of the main linker e.g. setting parameters on the copy will not affect the
    main linker.  This method implements ensures linker can be deepcopied.
    """
    new_linker = copy(self)
    new_linker._em_training_sessions = []
    new_settings = deepcopy(self._settings_obj_)
    new_linker._settings_obj_ = new_settings
    return new_linker

__init__(input_table_or_tables, settings_dict, accepted_df_dtypes, set_up_basic_logging=True, input_table_aliases=None, validate_settings=True)

Initialise the linker object, which manages the data linkage process and holds the data linkage model.

Examples:

DuckDB Spark

Dedupe

df = pd.read_csv("data_to_dedupe.csv")
linker = DuckDBLinker(df, settings_dict)

Link

df_1 = pd.read_parquet("table_1/")
df_2 = pd.read_parquet("table_2/")
linker = DuckDBLinker(
    [df_1, df_2],
    settings_dict,
    input_table_aliases=["customers", "contact_center_callers"]
    )

Dedupe with a pre-trained model read from a json file

df = pd.read_csv("data_to_dedupe.csv")
linker = DuckDBLinker(df, "model.json")

Dedupe

df = spark.read.csv("data_to_dedupe.csv")
linker = SparkLinker(df, settings_dict)

Link

df_1 = spark.read.parquet("table_1/")
df_2 = spark.read.parquet("table_2/")
linker = SparkLinker(
    [df_1, df_2],
    settings_dict,
    input_table_aliases=["customers", "contact_center_callers"]
    )

Dedupe with a pre-trained model read from a json file

df = spark.read.csv("data_to_dedupe.csv")
linker = SparkLinker(df, "model.json")

Parameters:

Name	Type	Description	Default
input_table_or_tables	Union[str, list]	Input data into the linkage model. Either a single string (the name of a table in a database) for deduplication jobs, or a list of strings (the name of tables in a database) for link_only or link_and_dedupe. For some linkers, such as the DuckDBLinker and the SparkLinker, it's also possible to pass in dataframes (Pandas and Spark respectively) rather than strings.	required
settings_dict	dict | Path	A Splink settings dictionary, or a path to a json defining a settingss dictionary or pre-trained model. If not provided when the object is created, can later be added using linker.load_settings() or linker.load_model() Defaults to None.	required
set_up_basic_logging	bool	If true, sets ups up basic logging so that Splink sends messages at INFO level to stdout. Defaults to True.	True
input_table_aliases	Union[str, list]	Labels assigned to input tables in Splink outputs. If the names of the tables in the input database are long or unspecific, this argument can be used to attach more easily readable/interpretable names. Defaults to None.	None
validate_settings	bool	When True, check your settings dictionary for any potential errors that may cause splink to fail.	True

Source code in splink/linker.py

def __init__(
    self,
    input_table_or_tables: str | list,
    settings_dict: dict | Path,
    accepted_df_dtypes,
    set_up_basic_logging: bool = True,
    input_table_aliases: str | list = None,
    validate_settings: bool = True,
):
    """Initialise the linker object, which manages the data linkage process and
    holds the data linkage model.

    Examples:
        === ":simple-duckdb: DuckDB"
            Dedupe
            ```py
            df = pd.read_csv("data_to_dedupe.csv")
            linker = DuckDBLinker(df, settings_dict)
            ```
            Link
            ```py
            df_1 = pd.read_parquet("table_1/")
            df_2 = pd.read_parquet("table_2/")
            linker = DuckDBLinker(
                [df_1, df_2],
                settings_dict,
                input_table_aliases=["customers", "contact_center_callers"]
                )
            ```
            Dedupe with a pre-trained model read from a json file
            ```py
            df = pd.read_csv("data_to_dedupe.csv")
            linker = DuckDBLinker(df, "model.json")
            ```
        === ":simple-apachespark: Spark"
            Dedupe
            ```py
            df = spark.read.csv("data_to_dedupe.csv")
            linker = SparkLinker(df, settings_dict)
            ```
            Link
            ```py
            df_1 = spark.read.parquet("table_1/")
            df_2 = spark.read.parquet("table_2/")
            linker = SparkLinker(
                [df_1, df_2],
                settings_dict,
                input_table_aliases=["customers", "contact_center_callers"]
                )
            ```
            Dedupe with a pre-trained model read from a json file
            ```py
            df = spark.read.csv("data_to_dedupe.csv")
            linker = SparkLinker(df, "model.json")
            ```

    Args:
        input_table_or_tables (Union[str, list]): Input data into the linkage model.
            Either a single string (the name of a table in a database) for
            deduplication jobs, or a list of strings  (the name of tables in a
            database) for link_only or link_and_dedupe.  For some linkers, such as
            the DuckDBLinker and the SparkLinker, it's also possible to pass in
            dataframes (Pandas and Spark respectively) rather than strings.
        settings_dict (dict | Path, optional): A Splink settings dictionary, or a
            path to a json defining a settingss dictionary or pre-trained model.
            If not provided when the object is created, can later be added using
            `linker.load_settings()` or `linker.load_model()` Defaults to None.
        set_up_basic_logging (bool, optional): If true, sets ups up basic logging
            so that Splink sends messages at INFO level to stdout. Defaults to True.
        input_table_aliases (Union[str, list], optional): Labels assigned to
            input tables in Splink outputs.  If the names of the tables in the
            input database are long or unspecific, this argument can be used
            to attach more easily readable/interpretable names. Defaults to None.
        validate_settings (bool, optional): When True, check your settings
            dictionary for any potential errors that may cause splink to fail.
    """
    self._db_schema = "splink"
    if set_up_basic_logging:
        logging.basicConfig(
            format="%(message)s",
        )
        splink_logger = logging.getLogger("splink")
        splink_logger.setLevel(logging.INFO)

    self._pipeline = SQLPipeline()

    self._intermediate_table_cache: dict = CacheDictWithLogging()

    if not isinstance(settings_dict, (dict, type(None))):
        # Run if you've entered a filepath
        # feed it a blank settings dictionary
        self._setup_settings_objs(None)
        self.load_settings(settings_dict)
    else:
        self._validate_settings_components(settings_dict)
        settings_dict = deepcopy(settings_dict)
        self._setup_settings_objs(settings_dict)

    homogenised_tables, homogenised_aliases = self._register_input_tables(
        input_table_or_tables,
        input_table_aliases,
        accepted_df_dtypes,
    )

    self._input_tables_dict = self._get_input_tables_dict(
        homogenised_tables, homogenised_aliases
    )

    self._validate_input_dfs()
    self._validate_settings(validate_settings)
    self._em_training_sessions = []

    self._find_new_matches_mode = False
    self._train_u_using_random_sample_mode = False
    self._compare_two_records_mode = False
    self._self_link_mode = False
    self._analyse_blocking_mode = False
    self._deterministic_link_mode = False

    self.debug_mode = False

accuracy_chart_from_labels_column(labels_column_name, threshold_actual=0.5, match_weight_round_to_nearest=None, add_metrics=[])

Generate an accuracy chart from ground truth data, whereby the ground truth is in a column in the input dataset called labels_column_name

Parameters:

Name	Type	Description	Default
labels_column_name	str	Column name containing labels in the input table	required
threshold_actual	float	Where the clerical_match_score provided by the user is a probability rather than binary, this value is used as the threshold to classify clerical_match_scores as binary matches or non matches. Defaults to 0.5.	0.5
match_weight_round_to_nearest	float	When provided, thresholds are rounded. When large numbers of labels are provided, this is sometimes necessary to reduce the size of the ROC table, and therefore the number of points plotted on the chart. Defaults to None.	None
add_metrics	list(str)	Precision and recall metrics are always included. Where provided, add_metrics specifies additional metrics to show, with the following options: "specificity": specificity, selectivity, true negative rate (TNR) "npv": negative predictive value (NPV) "accuracy": overall accuracy (TP+TN)/(P+N) "f1"/"f2"/"f0_5": F-scores for β=1 (balanced), β=2 (emphasis on recall) and β=0.5 (emphasis on precision) "p4" - an extended F1 score with specificity and NPV included "phi" - φ coefficient or Matthews correlation coefficient (MCC)	[]

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def accuracy_chart_from_labels_column(
    self,
    labels_column_name,
    threshold_actual=0.5,
    match_weight_round_to_nearest: float = None,
    add_metrics: list = [],
):
    """Generate an accuracy chart from ground truth data, whereby the ground
    truth is in a column in the input dataset called `labels_column_name`

    Args:
        labels_column_name (str): Column name containing labels in the input table
        threshold_actual (float, optional): Where the `clerical_match_score`
            provided by the user is a probability rather than binary, this value
            is used as the threshold to classify `clerical_match_score`s as binary
            matches or non matches. Defaults to 0.5.
        match_weight_round_to_nearest (float, optional): When provided, thresholds
            are rounded.  When large numbers of labels are provided, this is
            sometimes necessary to reduce the size of the ROC table, and therefore
            the number of points plotted on the chart. Defaults to None.
        add_metrics (list(str), optional): Precision and recall metrics are always
            included. Where provided, `add_metrics` specifies additional metrics
            to show, with the following options:

            - `"specificity"`: specificity, selectivity, true negative rate (TNR)
            - `"npv"`: negative predictive value (NPV)
            - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
            - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03B2=1 (balanced), \u03B2=2
            (emphasis on recall) and \u03B2=0.5 (emphasis on precision)
            - `"p4"` -  an extended F1 score with specificity and NPV included
            - `"phi"` - \u03C6 coefficient or Matthews correlation coefficient (MCC)
    Examples:
        ```py
        linker.accuracy_chart_from_labels_column("ground_truth", add_metrics=["f1"])
        ```

    Returns:
        altair.Chart: An altair chart
    """

    allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]

    if not isinstance(add_metrics, list):
        raise Exception(
            "add_metrics must be a list containing one or more of the following:",
            allowed,
        )

    # Silently filter out invalid entries (except case errors - e.g. ["NPV", "F1"])
    add_metrics = list(set(map(str.lower, add_metrics)).intersection(allowed))

    df_truth_space = truth_space_table_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=threshold_actual,
        match_weight_round_to_nearest=match_weight_round_to_nearest,
    )
    recs = df_truth_space.as_record_dict()
    return accuracy_chart(recs, add_metrics=add_metrics)

accuracy_chart_from_labels_table(labels_splinkdataframe_or_table_name, threshold_actual=0.5, match_weight_round_to_nearest=None, add_metrics=[])

Generate an accuracy measure chart from labelled (ground truth) data.

The table of labels should be in the following format, and should be registered as a table with your database:

source_dataset_l	unique_id_l	source_dataset_r	unique_id_r	clerical_match_score
df_1	1	df_2	2	0.99
df_1	1	df_2	3	0.2

Note that source_dataset and unique_id should correspond to the values specified in the settings dict, and the input_table_aliases passed to the linker object.

For dedupe_only links, the source_dataset columns can be ommitted.

Parameters:

Name	Type	Description	Default
labels_splinkdataframe_or_table_name	str | SplinkDataFrame	Name of table containing labels in the database	required
threshold_actual	float	Where the clerical_match_score provided by the user is a probability rather than binary, this value is used as the threshold to classify clerical_match_scores as binary matches or non matches. Defaults to 0.5.	0.5
match_weight_round_to_nearest	float	When provided, thresholds are rounded. When large numbers of labels are provided, this is sometimes necessary to reduce the size of the ROC table, and therefore the number of points plotted on the chart. Defaults to None.	None
add_metrics	list(str)	Precision and recall metrics are always included. Where provided, add_metrics specifies additional metrics to show, with the following options: "specificity": specificity, selectivity, true negative rate (TNR) "npv": negative predictive value (NPV) "accuracy": overall accuracy (TP+TN)/(P+N) "f1"/"f2"/"f0_5": F-scores for β=1 (balanced), β=2 (emphasis on recall) and β=0.5 (emphasis on precision) "p4" - an extended F1 score with specificity and NPV included "phi" - φ coefficient or Matthews correlation coefficient (MCC)	[]

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def accuracy_chart_from_labels_table(
    self,
    labels_splinkdataframe_or_table_name,
    threshold_actual=0.5,
    match_weight_round_to_nearest: float = None,
    add_metrics: list = [],
):
    """Generate an accuracy measure chart from labelled (ground truth) data.

    The table of labels should be in the following format, and should be registered
    as a table with your database:

    |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
    |----------------|-----------|----------------|-----------|--------------------|
    |df_1            |1          |df_2            |2          |0.99                |
    |df_1            |1          |df_2            |3          |0.2                 |

    Note that `source_dataset` and `unique_id` should correspond to the values
    specified in the settings dict, and the `input_table_aliases` passed to the
    `linker` object.

    For `dedupe_only` links, the `source_dataset` columns can be ommitted.

    Args:
        labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
            containing labels in the database
        threshold_actual (float, optional): Where the `clerical_match_score`
            provided by the user is a probability rather than binary, this value
            is used as the threshold to classify `clerical_match_score`s as binary
            matches or non matches. Defaults to 0.5.
        match_weight_round_to_nearest (float, optional): When provided, thresholds
            are rounded.  When large numbers of labels are provided, this is
            sometimes necessary to reduce the size of the ROC table, and therefore
            the number of points plotted on the chart. Defaults to None.
        add_metrics (list(str), optional): Precision and recall metrics are always
            included. Where provided, `add_metrics` specifies additional metrics
            to show, with the following options:

            - `"specificity"`: specificity, selectivity, true negative rate (TNR)
            - `"npv"`: negative predictive value (NPV)
            - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
            - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03B2=1 (balanced), \u03B2=2
            (emphasis on recall) and \u03B2=0.5 (emphasis on precision)
            - `"p4"` -  an extended F1 score with specificity and NPV included
            - `"phi"` - \u03C6 coefficient or Matthews correlation coefficient (MCC)
    Examples:
        === ":simple-duckdb: DuckDB"
            ```py
            labels = pd.read_csv("my_labels.csv")
            linker.register_table(labels, "labels")
            linker.accuracy_chart_from_labels_table("labels", add_metrics=["f1"])
            ```
        === ":simple-apachespark: Spark"
            ```py
            labels = spark.read.csv("my_labels.csv", header=True)
            labels.createDataFrame("labels")
            linker.accuracy_chart_from_labels_table("labels", add_metrics=['f1'])
            ```

    Returns:
        altair.Chart: An altair chart
    """
    allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]

    if not isinstance(add_metrics, list):
        raise Exception(
            "add_metrics must be a list containing one or more of the following:",
            allowed,
        )

    # Silently filter out invalid entries (except case errors - e.g. ["NPV", "F1"])
    add_metrics = list(set(map(str.lower, add_metrics)).intersection(allowed))

    labels_tablename = self._get_labels_tablename_from_input(
        labels_splinkdataframe_or_table_name
    )
    self._raise_error_if_necessary_accuracy_columns_not_computed()
    df_truth_space = truth_space_table_from_labels_table(
        self,
        labels_tablename,
        threshold_actual=threshold_actual,
        match_weight_round_to_nearest=match_weight_round_to_nearest,
    )
    recs = df_truth_space.as_record_dict()
    return accuracy_chart(recs, add_metrics=add_metrics)

cluster_pairwise_predictions_at_threshold(df_predict, threshold_match_probability=None, pairwise_formatting=False, filter_pairwise_format_for_clusters=True)

Clusters the pairwise match predictions that result from linker.predict() into groups of connected record using the connected components graph clustering algorithm

Records with an estimated match_probability at or above threshold_match_probability are considered to be a match (i.e. they represent the same entity).

Parameters:

Name	Type	Description	Default
df_predict	SplinkDataFrame	The results of linker.predict()	required
threshold_match_probability	float	Filter the pairwise match predictions to include only pairwise comparisons with a match_probability at or above this threshold. This dataframe is then fed into the clustering algorithm.	None
pairwise_formatting	bool	Whether to output the pairwise match predictions from linker.predict() with cluster IDs. If this is set to false, the output will be a list of all IDs, clustered into groups based on the desired match threshold.	False
filter_pairwise_format_for_clusters	bool	If pairwise formatting has been selected, whether to output all columns found within linker.predict(), or just return clusters.	True

Returns:

Name	Type	Description
SplinkDataFrame	SplinkDataFrame	A SplinkDataFrame containing a list of all IDs, clustered into groups based on the desired match threshold.

Source code in splink/linker.py

def cluster_pairwise_predictions_at_threshold(
    self,
    df_predict: SplinkDataFrame,
    threshold_match_probability: float = None,
    pairwise_formatting: bool = False,
    filter_pairwise_format_for_clusters: bool = True,
) -> SplinkDataFrame:
    """Clusters the pairwise match predictions that result from `linker.predict()`
    into groups of connected record using the connected components graph clustering
    algorithm

    Records with an estimated `match_probability` at or above
    `threshold_match_probability` are considered to be a match (i.e. they represent
    the same entity).

    Args:
        df_predict (SplinkDataFrame): The results of `linker.predict()`
        threshold_match_probability (float): Filter the pairwise match predictions
            to include only pairwise comparisons with a match_probability at or
            above this threshold. This dataframe is then fed into the clustering
            algorithm.
        pairwise_formatting (bool): Whether to output the pairwise match predictions
            from linker.predict() with cluster IDs.
            If this is set to false, the output will be a list of all IDs, clustered
            into groups based on the desired match threshold.
        filter_pairwise_format_for_clusters (bool): If pairwise formatting has been
            selected, whether to output all columns found within linker.predict(),
            or just return clusters.

    Returns:
        SplinkDataFrame: A SplinkDataFrame containing a list of all IDs, clustered
            into groups based on the desired match threshold.

    """

    # Feeding in df_predict forces materiailisation, if it exists in your database
    concat_with_tf = self._initialise_df_concat_with_tf(df_predict)

    edges_table = _cc_create_unique_id_cols(
        self,
        concat_with_tf.physical_name,
        df_predict.physical_name,
        threshold_match_probability,
    )

    cc = solve_connected_components(
        self,
        edges_table,
        df_predict,
        concat_with_tf,
        pairwise_formatting,
        filter_pairwise_format_for_clusters,
    )

    return cc

cluster_studio_dashboard(df_predict, df_clustered, out_path, sampling_method='random', sample_size=10, cluster_ids=None, cluster_names=None, overwrite=False, return_html_as_string=False)

Generate an interactive html visualization of the predicted cluster and save to out_path.

Parameters:

Name	Type	Description	Default
df_predict	SplinkDataFrame	The outputs of linker.predict()	required
df_clustered	SplinkDataFrame	The outputs of linker.cluster_pairwise_predictions_at_threshold()	required
out_path	str	The path (including filename) to save the html file to.	required
sampling_method	str	random or by_cluster_size. Defaults to random.	'random'
sample_size	int	Number of clusters to show in the dahboard. Defaults to 10.	10
cluster_ids	list	The IDs of the clusters that will be displayed in the dashboard. If provided, ignore the sampling_method and sample_size arguments. Defaults to None.	None
overwrite	bool	Overwrite the html file if it already exists? Defaults to False.	False
cluster_names	list	If provided, the dashboard will display these names in the selection box. Ony works in conjunction with cluster_ids. Defaults to None.	None
return_html_as_string		If True, return the html as a string	False

Examples:

df_p = linker.predict()
df_c = linker.cluster_pairwise_predictions_at_threshold(df_p, 0.5)
linker.cluster_studio_dashboard(
    df_p, df_c, [0, 4, 7], "cluster_studio.html"
)

Optionally, in Jupyter, you can display the results inline Otherwise you can just load the html file in your browser

from IPython.display import IFrame
IFrame(src="./cluster_studio.html", width="100%", height=1200)

Source code in splink/linker.py

def cluster_studio_dashboard(
    self,
    df_predict: SplinkDataFrame,
    df_clustered: SplinkDataFrame,
    out_path: str,
    sampling_method="random",
    sample_size: int = 10,
    cluster_ids: list = None,
    cluster_names: list = None,
    overwrite: bool = False,
    return_html_as_string=False,
):
    """Generate an interactive html visualization of the predicted cluster and
    save to `out_path`.

    Args:
        df_predict (SplinkDataFrame): The outputs of `linker.predict()`
        df_clustered (SplinkDataFrame): The outputs of
            `linker.cluster_pairwise_predictions_at_threshold()`
        out_path (str): The path (including filename) to save the html file to.
        sampling_method (str, optional): `random` or `by_cluster_size`. Defaults to
            `random`.
        sample_size (int, optional): Number of clusters to show in the dahboard.
            Defaults to 10.
        cluster_ids (list): The IDs of the clusters that will be displayed in the
            dashboard.  If provided, ignore the `sampling_method` and `sample_size`
            arguments. Defaults to None.
        overwrite (bool, optional): Overwrite the html file if it already exists?
            Defaults to False.
        cluster_names (list, optional): If provided, the dashboard will display
            these names in the selection box. Ony works in conjunction with
            `cluster_ids`.  Defaults to None.
        return_html_as_string: If True, return the html as a string

    Examples:
        ```py
        df_p = linker.predict()
        df_c = linker.cluster_pairwise_predictions_at_threshold(df_p, 0.5)
        linker.cluster_studio_dashboard(
            df_p, df_c, [0, 4, 7], "cluster_studio.html"
        )
        ```
        Optionally, in Jupyter, you can display the results inline
        Otherwise you can just load the html file in your browser
        ```py
        from IPython.display import IFrame
        IFrame(src="./cluster_studio.html", width="100%", height=1200)
        ```
    """
    self._raise_error_if_necessary_waterfall_columns_not_computed()

    rendered = render_splink_cluster_studio_html(
        self,
        df_predict,
        df_clustered,
        out_path,
        sampling_method=sampling_method,
        sample_size=sample_size,
        cluster_ids=cluster_ids,
        overwrite=overwrite,
        cluster_names=cluster_names,
    )

    if return_html_as_string:
        return rendered

compare_two_records(record_1, record_2)

Use the linkage model to compare and score a pairwise record comparison based on the two input records provided

Parameters:

Name	Type	Description	Default
record_1	dict	dictionary representing the first record. Columns names and data types must be the same as the columns in the settings object	required
record_2	dict	dictionary representing the second record. Columns names and data types must be the same as the columns in the settings object	required

Examples:

linker = DuckDBLinker(df)
linker.load_settings("saved_settings.json")
linker.compare_two_records(record_left, record_right)

Returns:

Name	Type	Description
SplinkDataFrame		Pairwise comparison with scored prediction

Source code in splink/linker.py

def compare_two_records(self, record_1: dict, record_2: dict):
    """Use the linkage model to compare and score a pairwise record comparison
    based on the two input records provided

    Args:
        record_1 (dict): dictionary representing the first record.  Columns names
            and data types must be the same as the columns in the settings object
        record_2 (dict): dictionary representing the second record.  Columns names
            and data types must be the same as the columns in the settings object

    Examples:
        ```py
        linker = DuckDBLinker(df)
        linker.load_settings("saved_settings.json")
        linker.compare_two_records(record_left, record_right)
        ```

    Returns:
        SplinkDataFrame: Pairwise comparison with scored prediction
    """
    original_blocking_rules = (
        self._settings_obj._blocking_rules_to_generate_predictions
    )
    original_link_type = self._settings_obj._link_type

    self._compare_two_records_mode = True
    self._settings_obj._blocking_rules_to_generate_predictions = []

    uid = ascii_uid(8)
    df_records_left = self.register_table(
        [record_1], f"__splink__compare_two_records_left_{uid}", overwrite=True
    )
    df_records_left.templated_name = "__splink__compare_two_records_left"

    df_records_right = self.register_table(
        [record_2], f"__splink__compare_two_records_right_{uid}", overwrite=True
    )
    df_records_right.templated_name = "__splink__compare_two_records_right"

    sql_join_tf = _join_tf_to_input_df_sql(self)

    sql_join_tf = sql_join_tf.replace(
        "__splink__df_concat", "__splink__compare_two_records_left"
    )
    self._enqueue_sql(sql_join_tf, "__splink__compare_two_records_left_with_tf")

    sql_join_tf = sql_join_tf.replace(
        "__splink__compare_two_records_left", "__splink__compare_two_records_right"
    )

    self._enqueue_sql(sql_join_tf, "__splink__compare_two_records_right_with_tf")

    sqls = block_using_rules_sqls(self)
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    sql = compute_comparison_vector_values_sql(self._settings_obj)
    self._enqueue_sql(sql, "__splink__df_comparison_vectors")

    sqls = predict_from_comparison_vectors_sqls(
        self._settings_obj,
        sql_infinity_expression=self._infinity_expression,
    )
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    predictions = self._execute_sql_pipeline(
        [df_records_left, df_records_right], use_cache=False
    )

    self._settings_obj._blocking_rules_to_generate_predictions = (
        original_blocking_rules
    )
    self._settings_obj._link_type = original_link_type
    self._compare_two_records_mode = False

    return predictions

comparison_viewer_dashboard(df_predict, out_path, overwrite=False, num_example_rows=2, return_html_as_string=False)

Generate an interactive html visualization of the linker's predictions and save to out_path. For more information see this video

Parameters:

Name	Type	Description	Default
df_predict	SplinkDataFrame	The outputs of linker.predict()	required
out_path	str	The path (including filename) to save the html file to.	required
overwrite	bool	Overwrite the html file if it already exists? Defaults to False.	False
num_example_rows	int	Number of example rows per comparison vector. Defaults to 2.	2
return_html_as_string		If True, return the html as a string	False

Examples:

df_predictions = linker.predict()
linker.comparison_viewer_dashboard(df_predictions, "scv.html", True, 2)

Optionally, in Jupyter, you can display the results inline Otherwise you can just load the html file in your browser

from IPython.display import IFrame
IFrame(src="./scv.html", width="100%", height=1200)

Source code in splink/linker.py

def comparison_viewer_dashboard(
    self,
    df_predict: SplinkDataFrame,
    out_path: str,
    overwrite=False,
    num_example_rows=2,
    return_html_as_string=False,
):
    """Generate an interactive html visualization of the linker's predictions and
    save to `out_path`.  For more information see
    [this video](https://www.youtube.com/watch?v=DNvCMqjipis)


    Args:
        df_predict (SplinkDataFrame): The outputs of `linker.predict()`
        out_path (str): The path (including filename) to save the html file to.
        overwrite (bool, optional): Overwrite the html file if it already exists?
            Defaults to False.
        num_example_rows (int, optional): Number of example rows per comparison
            vector. Defaults to 2.
        return_html_as_string: If True, return the html as a string

    Examples:
        ```py
        df_predictions = linker.predict()
        linker.comparison_viewer_dashboard(df_predictions, "scv.html", True, 2)
        ```

        Optionally, in Jupyter, you can display the results inline
        Otherwise you can just load the html file in your browser
        ```py
        from IPython.display import IFrame
        IFrame(src="./scv.html", width="100%", height=1200)
        ```

    """
    self._raise_error_if_necessary_waterfall_columns_not_computed()

    sql = comparison_vector_distribution_sql(self)
    self._enqueue_sql(sql, "__splink__df_comparison_vector_distribution")

    sqls = comparison_viewer_table_sqls(self, num_example_rows)
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    df = self._execute_sql_pipeline([df_predict])

    rendered = render_splink_comparison_viewer_html(
        df.as_record_dict(),
        self._settings_obj._as_completed_dict(),
        out_path,
        overwrite,
    )
    if return_html_as_string:
        return rendered

completeness_chart(input_dataset=None, cols=None)

Generate a summary chart of the completeness (proportion of non-nulls) of columns in each of the input datasets. By default, completeness is assessed for all column in the input data.

Parameters:

Name	Type	Description	Default
input_dataset	str	Name of one of the input tables in the database. If provided, completeness will be computed for this table alone. Defaults to None.	None
cols	List[str]	List of column names to calculate completeness. Default to None.	None

Examples:

linker.completeness_chart()

To view offline (if you don't have an internet connection):

from splink.charts import save_offline_chart
c = linker.completeness_chart()
save_offline_chart(c.to_dict(), "test_chart.html")

View resultant html file in Jupyter (or just load it in your browser)

from IPython.display import IFrame
IFrame(src="./test_chart.html", width=1000, height=500

Source code in splink/linker.py

def completeness_chart(self, input_dataset: str = None, cols: list[str] = None):
    """Generate a summary chart of the completeness (proportion of non-nulls) of
    columns in each of the input datasets. By default, completeness is assessed for
    all column in the input data.

    Args:
        input_dataset (str, optional): Name of one of the input tables in the
            database.  If provided, completeness will be computed for this table
            alone. Defaults to None.
        cols (List[str], optional): List of column names to calculate completeness.
            Default to None.

    Examples:
        ```py
        linker.completeness_chart()
        ```
        To view offline (if you don't have an internet connection):
        ```py
        from splink.charts import save_offline_chart
        c = linker.completeness_chart()
        save_offline_chart(c.to_dict(), "test_chart.html")
        ```
        View resultant html file in Jupyter (or just load it in your browser)
        ```py
        from IPython.display import IFrame
        IFrame(src="./test_chart.html", width=1000, height=500
        ```
    """
    records = completeness_data(self, input_dataset, cols)
    return completeness_chart(records)

compute_tf_table(column_name)

Compute a term frequency table for a given column and persist to the database

This method is useful if you want to pre-compute term frequency tables e.g. so that real time linkage executes faster, or so that you can estimate various models without having to recompute term frequency tables each time

Examples:

DuckDB Spark

Real time linkage

linker = DuckDBLinker(df)
linker.load_settings("saved_settings.json")
linker.compute_tf_table("surname")
linker.compare_two_records(record_left, record_right)

Pre-computed term frequency tables

linker = DuckDBLinker(df)
df_first_name_tf = linker.compute_tf_table("first_name")
df_first_name_tf.write.parquet("folder/first_name_tf")
>>>
# On subsequent data linking job, read this table rather than recompute
df_first_name_tf = pd.read_parquet("folder/first_name_tf")
df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")

Real time linkage

linker = SparkLinker(df)
linker.load_settings("saved_settings.json")
linker.compute_tf_table("surname")
linker.compare_two_records(record_left, record_right)

Pre-computed term frequency tables

linker = SparkLinker(df)
df_first_name_tf = linker.compute_tf_table("first_name")
df_first_name_tf.write.parquet("folder/first_name_tf")
>>>
# On subsequent data linking job, read this table rather than recompute
df_first_name_tf = spark.read.parquet("folder/first_name_tf")
df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")

Parameters:

Name	Type	Description	Default
column_name	str	The column name in the input table	required

Returns:

Name	Type	Description
SplinkDataFrame	SplinkDataFrame	The resultant table as a splink data frame

Source code in splink/linker.py

def compute_tf_table(self, column_name: str) -> SplinkDataFrame:
    """Compute a term frequency table for a given column and persist to the database

    This method is useful if you want to pre-compute term frequency tables e.g.
    so that real time linkage executes faster, or so that you can estimate
    various models without having to recompute term frequency tables each time

    Examples:
        === ":simple-duckdb: DuckDB"
            Real time linkage
            ```py
            linker = DuckDBLinker(df)
            linker.load_settings("saved_settings.json")
            linker.compute_tf_table("surname")
            linker.compare_two_records(record_left, record_right)
            ```
            Pre-computed term frequency tables
            ```py
            linker = DuckDBLinker(df)
            df_first_name_tf = linker.compute_tf_table("first_name")
            df_first_name_tf.write.parquet("folder/first_name_tf")
            >>>
            # On subsequent data linking job, read this table rather than recompute
            df_first_name_tf = pd.read_parquet("folder/first_name_tf")
            df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")
            ```
        === ":simple-apachespark: Spark"
            Real time linkage
            ```py
            linker = SparkLinker(df)
            linker.load_settings("saved_settings.json")
            linker.compute_tf_table("surname")
            linker.compare_two_records(record_left, record_right)
            ```
            Pre-computed term frequency tables
            ```py
            linker = SparkLinker(df)
            df_first_name_tf = linker.compute_tf_table("first_name")
            df_first_name_tf.write.parquet("folder/first_name_tf")
            >>>
            # On subsequent data linking job, read this table rather than recompute
            df_first_name_tf = spark.read.parquet("folder/first_name_tf")
            df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")
            ```

    Args:
        column_name (str): The column name in the input table

    Returns:
        SplinkDataFrame: The resultant table as a splink data frame
    """

    input_col = InputColumn(column_name, settings_obj=self._settings_obj)
    tf_tablename = colname_to_tf_tablename(input_col)
    cache = self._intermediate_table_cache
    concat_tf_tables = [
        tf_col.unquote().name
        for tf_col in self._settings_obj._term_frequency_columns
    ]

    if tf_tablename in cache:
        tf_df = cache.get_with_logging(tf_tablename)
    elif "__splink__df_concat_with_tf" in cache and column_name in concat_tf_tables:
        self._pipeline.reset()
        # If our df_concat_with_tf table already exists, use backwards inference to
        # find a given tf table
        colname = InputColumn(column_name)
        sql = term_frequencies_from_concat_with_tf(colname)
        self._enqueue_sql(sql, colname_to_tf_tablename(colname))
        tf_df = self._execute_sql_pipeline([cache["__splink__df_concat_with_tf"]])
        self._intermediate_table_cache[tf_tablename] = tf_df
    else:
        # Clear the pipeline if we are materialising
        self._pipeline.reset()
        df_concat = self._initialise_df_concat()
        input_dfs = []
        if df_concat:
            input_dfs.append(df_concat)
        sql = term_frequencies_for_single_column_sql(input_col)
        self._enqueue_sql(sql, tf_tablename)
        tf_df = self._execute_sql_pipeline(input_dfs)
        self._intermediate_table_cache[tf_tablename] = tf_df

    return tf_df

confusion_matrix_from_labels_column(labels_column_name, threshold_actual=0.5, match_weight_round_to_nearest=None, match_weight_range=[-15, 15])

Generate an accuracy chart from ground truth data, whereby the ground truth is in a column in the input dataset called labels_column_name

Parameters:

Name	Type	Description	Default
labels_column_name	str	Column name containing labels in the input table	required
threshold_actual	float	Where the clerical_match_score provided by the user is a probability rather than binary, this value is used as the threshold to classify clerical_match_scores as binary matches or non matches. Defaults to 0.5.	0.5
match_weight_round_to_nearest	float	When provided, thresholds are rounded. When large numbers of labels are provided, this is sometimes necessary to reduce the size of the ROC table, and therefore the number of points plotted on the chart. Defaults to None.	None
match_weight_range	list(float)	minimum and maximum thresholds to include in chart output. Defaults to [-15,15].	[-15, 15]

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def confusion_matrix_from_labels_column(
    self,
    labels_column_name,
    threshold_actual=0.5,
    match_weight_round_to_nearest: float = None,
    match_weight_range=[-15, 15],
):
    """Generate an accuracy chart from ground truth data, whereby the ground
    truth is in a column in the input dataset called `labels_column_name`

    Args:
        labels_column_name (str): Column name containing labels in the input table
        threshold_actual (float, optional): Where the `clerical_match_score`
            provided by the user is a probability rather than binary, this value
            is used as the threshold to classify `clerical_match_score`s as binary
            matches or non matches. Defaults to 0.5.
        match_weight_round_to_nearest (float, optional): When provided, thresholds
            are rounded.  When large numbers of labels are provided, this is
            sometimes necessary to reduce the size of the ROC table, and therefore
            the number of points plotted on the chart. Defaults to None.
        match_weight_range (list(float), optional): minimum and maximum thresholds
            to include in chart output. Defaults to [-15,15].
    Examples:
        ```py
        linker.confusion_matrix_from_labels_column("ground_truth")
        ```

    Returns:
        altair.Chart: An altair chart
    """

    df_truth_space = truth_space_table_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=threshold_actual,
        match_weight_round_to_nearest=match_weight_round_to_nearest,
    )

    recs = df_truth_space.as_record_dict()
    a, b = match_weight_range
    recs = [r for r in recs if a < r["truth_threshold"] < b]
    return confusion_matrix_chart(recs, match_weight_range=match_weight_range)

confusion_matrix_from_labels_table(labels_splinkdataframe_or_table_name, threshold_actual=0.5, match_weight_round_to_nearest=None, match_weight_range=[-15, 15])

Generate an interactive confusion matrix from labelled (ground truth) data.

The table of labels should be in the following format, and should be registered as a table with your database:

source_dataset_l	unique_id_l	source_dataset_r	unique_id_r	clerical_match_score
df_1	1	df_2	2	0.99
df_1	1	df_2	3	0.2

Note that source_dataset and unique_id should correspond to the values specified in the settings dict, and the input_table_aliases passed to the linker object.

For dedupe_only links, the source_dataset columns can be ommitted.

Parameters:

Name	Type	Description	Default
labels_splinkdataframe_or_table_name	str | SplinkDataFrame	Name of table containing labels in the database	required
threshold_actual	float	Where the clerical_match_score provided by the user is a probability rather than binary, this value is used as the threshold to classify clerical_match_scores as binary matches or non matches. Defaults to 0.5.	0.5
match_weight_round_to_nearest	float	When provided, thresholds are rounded. When large numbers of labels are provided, this is sometimes necessary to reduce the size of the ROC table, and therefore the number of points plotted on the chart. Defaults to None.	None
match_weight_range	list(float)	minimum and maximum thresholds to include in chart output. Defaults to [-15,15].	[-15, 15]

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def confusion_matrix_from_labels_table(
    self,
    labels_splinkdataframe_or_table_name,
    threshold_actual=0.5,
    match_weight_round_to_nearest: float = None,
    match_weight_range=[-15, 15],
):
    """Generate an interactive confusion matrix from labelled (ground truth) data.

    The table of labels should be in the following format, and should be registered
    as a table with your database:

    |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
    |----------------|-----------|----------------|-----------|--------------------|
    |df_1            |1          |df_2            |2          |0.99                |
    |df_1            |1          |df_2            |3          |0.2                 |

    Note that `source_dataset` and `unique_id` should correspond to the values
    specified in the settings dict, and the `input_table_aliases` passed to the
    `linker` object.

    For `dedupe_only` links, the `source_dataset` columns can be ommitted.

    Args:
        labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
            containing labels in the database
        threshold_actual (float, optional): Where the `clerical_match_score`
            provided by the user is a probability rather than binary, this value
            is used as the threshold to classify `clerical_match_score`s as binary
            matches or non matches. Defaults to 0.5.
        match_weight_round_to_nearest (float, optional): When provided, thresholds
            are rounded.  When large numbers of labels are provided, this is
            sometimes necessary to reduce the size of the ROC table, and therefore
            the number of points plotted on the chart. Defaults to None.
        match_weight_range (list(float), optional): minimum and maximum thresholds
            to include in chart output. Defaults to [-15,15].
    Examples:
        === ":simple-duckdb: DuckDB"
            ```py
            labels = pd.read_csv("my_labels.csv")
            linker.register_table(labels, "labels")
            linker.confusion_matrix_from_labels_table("labels")
            ```
        === ":simple-apachespark: Spark"
            ```py
            labels = spark.read.csv("my_labels.csv", header=True)
            labels.createDataFrame("labels")
            linker.confusion_matrix_from_labels_table("labels")
            ```

    Returns:
        altair.Chart: An altair chart
    """

    labels_tablename = self._get_labels_tablename_from_input(
        labels_splinkdataframe_or_table_name
    )
    self._raise_error_if_necessary_accuracy_columns_not_computed()
    df_truth_space = truth_space_table_from_labels_table(
        self,
        labels_tablename,
        threshold_actual=threshold_actual,
        match_weight_round_to_nearest=match_weight_round_to_nearest,
    )

    recs = df_truth_space.as_record_dict()
    a, b = match_weight_range
    recs = [r for r in recs if a < r["truth_threshold"] < b]
    return confusion_matrix_chart(recs, match_weight_range=match_weight_range)

count_num_comparisons_from_blocking_rule(blocking_rule)

Compute the number of pairwise record comparisons that would be generated by a blocking rule

Parameters:

Name	Type	Description	Default
blocking_rule	str | BlockingRule	The blocking rule to analyse	required
link_type	str	The link type. This is needed only if the linker has not yet been provided with a settings dictionary. Defaults to None.	required
unique_id_column_name	str	This is needed only if the linker has not yet been provided with a settings dictionary. Defaults to None.	required

Examples:

br = "l.surname = r.surname"
linker.count_num_comparisons_from_blocking_rule(br)

19387

br = "l.name = r.name and substr(l.dob,1,4) = substr(r.dob,1,4)"
linker.count_num_comparisons_from_blocking_rule(br)

394 Alternatively, you can use the blocking rule library functions

import splink.duckdb.blocking_rule_library as brl
br = brl.exact_match_rule("surname")
linker.count_num_comparisons_from_blocking_rule(br)

3167

Returns:

Name	Type	Description
int	int	The number of comparisons generated by the blocking rule

Source code in splink/linker.py

def count_num_comparisons_from_blocking_rule(
    self,
    blocking_rule: str | BlockingRule,
) -> int:
    """Compute the number of pairwise record comparisons that would be generated by
    a blocking rule

    Args:
        blocking_rule (str | BlockingRule): The blocking rule to analyse
        link_type (str, optional): The link type.  This is needed only if the
            linker has not yet been provided with a settings dictionary.  Defaults
            to None.
        unique_id_column_name (str, optional):  This is needed only if the
            linker has not yet been provided with a settings dictionary.  Defaults
            to None.

    Examples:
        ```py
        br = "l.surname = r.surname"
        linker.count_num_comparisons_from_blocking_rule(br)
        ```
        > 19387

        ```py
        br = "l.name = r.name and substr(l.dob,1,4) = substr(r.dob,1,4)"
        linker.count_num_comparisons_from_blocking_rule(br)
        ```
        > 394
        Alternatively, you can use the blocking rule library functions
        ```py
        import splink.duckdb.blocking_rule_library as brl
        br = brl.exact_match_rule("surname")
        linker.count_num_comparisons_from_blocking_rule(br)
        ```
        > 3167

    Returns:
        int: The number of comparisons generated by the blocking rule
    """

    blocking_rule = blocking_rule_to_obj(blocking_rule).blocking_rule_sql

    sql = vertically_concatenate_sql(self)
    self._enqueue_sql(sql, "__splink__df_concat")

    sql = number_of_comparisons_generated_by_blocking_rule_post_filters_sql(
        self, blocking_rule
    )
    self._enqueue_sql(sql, "__splink__analyse_blocking_rule")
    res = self._execute_sql_pipeline().as_record_dict()[0]
    return res["count_of_pairwise_comparisons_generated"]

count_num_comparisons_from_blocking_rules_for_prediction(df_predict)

Counts the marginal number of edges created from each of the blocking rules in blocking_rules_to_generate_predictions

This is different to count_num_comparisons_from_blocking_rule because it (a) analyses multiple blocking rules rather than a single rule, and (b) deduplicates any comparisons that are generated, to tell you the marginal effect of each entry in blocking_rules_to_generate_predictions

Parameters:

Name	Type	Description	Default
df_predict	SplinkDataFrame	SplinkDataFrame with match weights	required

Examples:

linker = DuckDBLinker(df)
linker.load_model("settings.json")
df_predict = linker.predict(threshold_match_probability=0.95)
count_pairwise = linker.count_num_comparisons_from_blocking_rules_for_prediction(df_predict)
count_pairwise.as_pandas_dataframe(limit=5)

Returns:

Name	Type	Description
SplinkDataFrame		A SplinkDataFrame of the pairwise comparisons and estimated pairwise comparisons generated by the blocking rules.

Source code in splink/linker.py

def count_num_comparisons_from_blocking_rules_for_prediction(self, df_predict):
    """Counts the marginal number of edges created from each of the blocking rules
    in `blocking_rules_to_generate_predictions`

    This is different to `count_num_comparisons_from_blocking_rule`
    because it (a) analyses multiple blocking rules rather than a single rule, and
    (b) deduplicates any comparisons that are generated, to tell you the
    marginal effect of each entry in `blocking_rules_to_generate_predictions`

    Args:
        df_predict (SplinkDataFrame): SplinkDataFrame with match weights
        and probabilities of rows matching

    Examples:
        ```py
        linker = DuckDBLinker(df)
        linker.load_model("settings.json")
        df_predict = linker.predict(threshold_match_probability=0.95)
        count_pairwise = linker.count_num_comparisons_from_blocking_rules_for_prediction(df_predict)
        count_pairwise.as_pandas_dataframe(limit=5)
        ```

    Returns:
        SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons and
            estimated pairwise comparisons generated by the blocking rules.
    """  # noqa: E501
    sql = count_num_comparisons_from_blocking_rules_for_prediction_sql(
        self, df_predict
    )
    match_key_analysis = self._sql_to_splink_dataframe_checking_cache(
        sql, "__splink__match_key_analysis"
    )
    return match_key_analysis

cumulative_comparisons_from_blocking_rules_records(blocking_rules=None)

Output the number of comparisons generated by each successive blocking rule.

This is equivalent to the output size of df_predict and details how many comparisons each of your individual blocking rules will contribute to the total.

Parameters:

Name	Type	Description	Default
blocking_rules	str or list	The blocking rule(s) to compute comparisons for. If null, the rules set out in your settings object will be used.	None

Examples:

Generate total comparisons from Blocking Rules defined in settings dictionary

linker_settings = DuckDBLinker(df, settings)
# Compute the cumulative number of comparisons generated by the rules
# in your settings object.
linker_settings.cumulative_comparisons_from_blocking_rules_records()

Generate total comparisons with custom blocking rules.

blocking_rules = [
   "l.surname = r.surname",
   "l.first_name = r.first_name
    and substr(l.dob,1,4) = substr(r.dob,1,4)"
]

linker_settings.cumulative_comparisons_from_blocking_rules_records(
    blocking_rules
 )

Returns:

Name	Type	Description
List		A list of blocking rules and the corresponding number of comparisons it is forecast to generate.

Source code in splink/linker.py

def cumulative_comparisons_from_blocking_rules_records(
    self,
    blocking_rules: str | BlockingRule | list = None,
):
    """Output the number of comparisons generated by each successive blocking rule.

    This is equivalent to the output size of df_predict and details how many
    comparisons each of your individual blocking rules will contribute to the
    total.

    Args:
        blocking_rules (str or list): The blocking rule(s) to compute comparisons
            for. If null, the rules set out in your settings object will be used.

    Examples:
        Generate total comparisons from Blocking Rules defined in settings
        dictionary
        ```py
        linker_settings = DuckDBLinker(df, settings)
        # Compute the cumulative number of comparisons generated by the rules
        # in your settings object.
        linker_settings.cumulative_comparisons_from_blocking_rules_records()
        ```

        Generate total comparisons with custom blocking rules.
        ```py
        blocking_rules = [
           "l.surname = r.surname",
           "l.first_name = r.first_name
            and substr(l.dob,1,4) = substr(r.dob,1,4)"
        ]

        linker_settings.cumulative_comparisons_from_blocking_rules_records(
            blocking_rules
         )
        ```

    Returns:
        List: A list of blocking rules and the corresponding number of
            comparisons it is forecast to generate.
    """
    if blocking_rules:
        blocking_rules = ensure_is_list(blocking_rules)

    records = cumulative_comparisons_generated_by_blocking_rules(
        self, blocking_rules, output_chart=False
    )

    return records

cumulative_num_comparisons_from_blocking_rules_chart(blocking_rules=None)

Display a chart with the cumulative number of comparisons generated by a selection of blocking rules.

This is equivalent to the output size of df_predict and details how many comparisons each of your individual blocking rules will contribute to the total.

Parameters:

Name	Type	Description	Default
blocking_rules	str or list	The blocking rule(s) to compute comparisons for. If null, the rules set out in your settings object will be used.	None

Examples:

linker_settings = DuckDBLinker(df, settings)
# Compute the cumulative number of comparisons generated by the rules
# in your settings object.
linker_settings.cumulative_num_comparisons_from_blocking_rules_chart()
>>>
# Generate total comparisons with custom blocking rules.
blocking_rules = [
   "l.surname = r.surname",
   "l.first_name = r.first_name
    and substr(l.dob,1,4) = substr(r.dob,1,4)"
]
>>>
linker_settings.cumulative_num_comparisons_from_blocking_rules_chart(
    blocking_rules
 )

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def cumulative_num_comparisons_from_blocking_rules_chart(
    self,
    blocking_rules: str | BlockingRule | list = None,
):
    """Display a chart with the cumulative number of comparisons generated by a
    selection of blocking rules.

    This is equivalent to the output size of df_predict and details how many
    comparisons each of your individual blocking rules will contribute to the
    total.

    Args:
        blocking_rules (str or list): The blocking rule(s) to compute comparisons
            for. If null, the rules set out in your settings object will be used.

    Examples:
        ```py
        linker_settings = DuckDBLinker(df, settings)
        # Compute the cumulative number of comparisons generated by the rules
        # in your settings object.
        linker_settings.cumulative_num_comparisons_from_blocking_rules_chart()
        >>>
        # Generate total comparisons with custom blocking rules.
        blocking_rules = [
           "l.surname = r.surname",
           "l.first_name = r.first_name
            and substr(l.dob,1,4) = substr(r.dob,1,4)"
        ]
        >>>
        linker_settings.cumulative_num_comparisons_from_blocking_rules_chart(
            blocking_rules
         )
        ```

    Returns:
        altair.Chart: An altair chart
    """

    if blocking_rules:
        blocking_rules = ensure_is_list(blocking_rules)

    records = cumulative_comparisons_generated_by_blocking_rules(
        self, blocking_rules, output_chart=True
    )

    return cumulative_blocking_rule_comparisons_generated(records)

deterministic_link()

Uses the blocking rules specified by blocking_rules_to_generate_predictions in the settings dictionary to generate pairwise record comparisons.

For deterministic linkage, this should be a list of blocking rules which are strict enough to generate only true links.

Deterministic linkage, however, is likely to result in missed links (false negatives).

Examples:

DuckDB Spark Athena SQLite

from splink.duckdb.linker import DuckDBLinker

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name",
        "l.surname = r.surname",
    ],
    "comparisons": []
}
>>>
linker = DuckDBLinker(df, settings)
df = linker.deterministic_link()

from splink.spark.linker import SparkLinker

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name",
        "l.surname = r.surname",
    ],
    "comparisons": []
}
>>>
linker = SparkLinker(df, settings)
df = linker.deterministic_link()

from splink.athena.linker import AthenaLinker

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name",
        "l.surname = r.surname",
    ],
    "comparisons": []
}
>>>
linker = AthenaLinker(df, settings)
df = linker.deterministic_link()

from splink.sqlite.linker import SQLiteLinker

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name",
        "l.surname = r.surname",
    ],
    "comparisons": []
}
>>>
linker = SQLiteLinker(df, settings)
df = linker.deterministic_link()

Returns:

Name	Type	Description
SplinkDataFrame	SplinkDataFrame	A SplinkDataFrame of the pairwise comparisons. This represents a table materialised in the database. Methods on the SplinkDataFrame allow you to access the underlying data.

Source code in splink/linker.py

def deterministic_link(self) -> SplinkDataFrame:
    """Uses the blocking rules specified by
    `blocking_rules_to_generate_predictions` in the settings dictionary to
    generate pairwise record comparisons.

    For deterministic linkage, this should be a list of blocking rules which
    are strict enough to generate only true links.

    Deterministic linkage, however, is likely to result in missed links
    (false negatives).

    Examples:
        === ":simple-duckdb: DuckDB"
            ```py
            from splink.duckdb.linker import DuckDBLinker

            settings = {
                "link_type": "dedupe_only",
                "blocking_rules_to_generate_predictions": [
                    "l.first_name = r.first_name",
                    "l.surname = r.surname",
                ],
                "comparisons": []
            }
            >>>
            linker = DuckDBLinker(df, settings)
            df = linker.deterministic_link()
            ```
        === ":simple-apachespark: Spark"
            ```py
            from splink.spark.linker import SparkLinker

            settings = {
                "link_type": "dedupe_only",
                "blocking_rules_to_generate_predictions": [
                    "l.first_name = r.first_name",
                    "l.surname = r.surname",
                ],
                "comparisons": []
            }
            >>>
            linker = SparkLinker(df, settings)
            df = linker.deterministic_link()
            ```
        === ":simple-amazonaws: Athena"
            ```py
            from splink.athena.linker import AthenaLinker

            settings = {
                "link_type": "dedupe_only",
                "blocking_rules_to_generate_predictions": [
                    "l.first_name = r.first_name",
                    "l.surname = r.surname",
                ],
                "comparisons": []
            }
            >>>
            linker = AthenaLinker(df, settings)
            df = linker.deterministic_link()
            ```
        === ":simple-sqlite: SQLite"
            ```py
            from splink.sqlite.linker import SQLiteLinker

            settings = {
                "link_type": "dedupe_only",
                "blocking_rules_to_generate_predictions": [
                    "l.first_name = r.first_name",
                    "l.surname = r.surname",
                ],
                "comparisons": []
            }
            >>>
            linker = SQLiteLinker(df, settings)
            df = linker.deterministic_link()
            ```

    Returns:
        SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons.  This
            represents a table materialised in the database. Methods on the
            SplinkDataFrame allow you to access the underlying data.
    """

    # Allows clustering during a deterministic linkage.
    # This is used in `cluster_pairwise_predictions_at_threshold`
    # to set the cluster threshold to 1
    self._deterministic_link_mode = True

    concat_with_tf = self._initialise_df_concat_with_tf()
    sqls = block_using_rules_sqls(self)
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])
    return self._execute_sql_pipeline([concat_with_tf])

estimate_m_from_label_column(label_colname)

Estimate the m parameters of the linkage model from a label (ground truth) column in the input dataframe(s).

The m parameters represent the proportion of record comparisons that fall into each comparison level amongst truly matching records.

The ground truth column is used to generate pairwise record comparisons which are then assumed to be matches.

For example, if the entity being matched is persons, and your input dataset(s) contain social security number, this could be used to estimate the m values for the model.

Note that this column does not need to be fully populated. A common case is where a unique identifier such as social security number is only partially populated.

Parameters:

Name	Type	Description	Default
label_colname	str	The name of the column containing the ground truth label in the input data.	required

Examples:

linker.estimate_m_from_label_column("social_security_number")

Returns:

Type	Description
	Updates the estimated m parameters within the linker object
	and returns nothing.

Source code in splink/linker.py

def estimate_m_from_label_column(self, label_colname: str):
    """Estimate the m parameters of the linkage model from a label (ground truth)
    column in the input dataframe(s).

    The m parameters represent the proportion of record comparisons that fall
    into each comparison level amongst truly matching records.

    The ground truth column is used to generate pairwise record comparisons
    which are then assumed to be matches.

    For example, if the entity being matched is persons, and your input dataset(s)
    contain social security number, this could be used to estimate the m values
    for the model.

    Note that this column does not need to be fully populated.  A common case is
    where a unique identifier such as social security number is only partially
    populated.

    Args:
        label_colname (str): The name of the column containing the ground truth
            label in the input data.

    Examples:
        ```py
        linker.estimate_m_from_label_column("social_security_number")
        ```

    Returns:
        Updates the estimated m parameters within the linker object
        and returns nothing.
    """

    # Ensure this has been run on the main linker so that it can be used by
    # training linked when it checks the cache
    self._initialise_df_concat_with_tf()
    estimate_m_values_from_label_column(
        self,
        self._input_tables_dict,
        label_colname,
    )
    self._populate_m_u_from_trained_values()

    self._settings_obj._columns_without_estimated_parameters_message()

estimate_m_from_pairwise_labels(labels_splinkdataframe_or_table_name)

Estimate the m parameters of the linkage model from a dataframe of pairwise labels.

The table of labels should be in the following format, and should be registered with your database: |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r| |----------------|-----------|----------------|-----------| |df_1 |1 |df_2 |2 | |df_1 |1 |df_2 |3 |

Note that source_dataset and unique_id should correspond to the values specified in the settings dict, and the input_table_aliases passed to the linker object. Note that at the moment, this method does not respect values in a clerical_match_score column. If provided, these are ignored and it is assumed that every row in the table of labels is a score of 1, i.e. a perfect match.

Parameters:

Name	Type	Description	Default
labels_splinkdataframe_or_table_name	str	Name of table containing labels in the database or SplinkDataframe	required

Examples:

pairwise_labels = pd.read_csv("./data/pairwise_labels_to_estimate_m.csv")
linker.register_table(pairwise_labels, "labels", overwrite=True)
linker.estimate_m_from_pairwise_labels("labels")

Source code in splink/linker.py

def estimate_m_from_pairwise_labels(self, labels_splinkdataframe_or_table_name):
    """Estimate the m parameters of the linkage model from a dataframe of pairwise
    labels.

    The table of labels should be in the following format, and should
    be registered with your database:
    |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|
    |----------------|-----------|----------------|-----------|
    |df_1            |1          |df_2            |2          |
    |df_1            |1          |df_2            |3          |

    Note that `source_dataset` and `unique_id` should correspond to the
    values specified in the settings dict, and the `input_table_aliases`
    passed to the `linker` object. Note that at the moment, this method does
    not respect values in a `clerical_match_score` column.  If provided, these
    are ignored and it is assumed that every row in the table of labels is a score
    of 1, i.e. a perfect match.

    Args:
      labels_splinkdataframe_or_table_name (str): Name of table containing labels
        in the database or SplinkDataframe

    Examples:
        ```py
        pairwise_labels = pd.read_csv("./data/pairwise_labels_to_estimate_m.csv")
        linker.register_table(pairwise_labels, "labels", overwrite=True)
        linker.estimate_m_from_pairwise_labels("labels")
        ```
    """
    labels_tablename = self._get_labels_tablename_from_input(
        labels_splinkdataframe_or_table_name
    )
    estimate_m_from_pairwise_labels(self, labels_tablename)

estimate_parameters_using_expectation_maximisation(blocking_rule, comparisons_to_deactivate=None, comparison_levels_to_reverse_blocking_rule=None, estimate_without_term_frequencies=False, fix_probability_two_random_records_match=False, fix_m_probabilities=False, fix_u_probabilities=True, populate_probability_two_random_records_match_from_trained_values=False)

Estimate the parameters of the linkage model using expectation maximisation.

By default, the m probabilities are estimated, but not the u probabilities, because good estimates for the u probabilities can be obtained from linker.estimate_u_using_random_sampling(). You can change this by setting fix_u_probabilities to False.

The blocking rule provided is used to generate pairwise record comparisons. Usually, this should be a blocking rule that results in a dataframe where matches are between about 1% and 99% of the comparisons.

By default, m parameters are estimated for all comparisons except those which are included in the blocking rule.

For example, if the blocking rule is l.first_name = r.first_name, then parameter esimates will be made for all comparison except those which use first_name in their sql_condition

By default, the probability two random records match is estimated for the blocked data, and then the m and u parameters for the columns specified in the blocking rules are used to estiamte the global probability two random records match.

To control which comparisons should have their parameter estimated, and the process of 'reversing out' the global probability two random records match, the user may specify comparisons_to_deactivate and comparison_levels_to_reverse_blocking_rule. This is useful, for example if you block on the dmetaphone of a column but match on the original column.

Examples:

Default behaviour

br_training = "l.first_name = r.first_name and l.dob = r.dob"
linker.estimate_parameters_using_expectation_maximisation(br_training)

Specify which comparisons to deactivate

br_training = "l.dmeta_first_name = r.dmeta_first_name"
settings_obj = linker._settings_obj
comp = settings_obj._get_comparison_by_output_column_name("first_name")
dmeta_level = comp._get_comparison_level_by_comparison_vector_value(1)
linker.estimate_parameters_using_expectation_maximisation(
    br_training,
    comparisons_to_deactivate=["first_name"],
    comparison_levels_to_reverse_blocking_rule=[dmeta_level],
)

Parameters:

Name	Type	Description	Default
blocking_rule	BlockingRule | str	The blocking rule used to generate pairwise record comparisons.	required
comparisons_to_deactivate	list	By default, splink will analyse the blocking rule provided and estimate the m parameters for all comaprisons except those included in the blocking rule. If comparisons_to_deactivate are provided, spink will instead estimate m parameters for all comparison except those specified in the comparisons_to_deactivate list. This list can either contain the output_column_name of the Comparison as a string, or Comparison objects. Defaults to None.	None
comparison_levels_to_reverse_blocking_rule	list	By default, splink will analyse the blocking rule provided and adjust the global probability two random records match to account for the matches specified in the blocking rule. If provided, this argument will overrule this default behaviour. The user must provide a list of ComparisonLevel objects. Defaults to None.	None
estimate_without_term_frequencies	bool	If True, the iterations of the EM algorithm ignore any term frequency adjustments and only depend on the comparison vectors. This allows the EM algorithm to run much faster, but the estimation of the parameters will change slightly.	False
fix_probability_two_random_records_match	bool	If True, do not update the probability two random records match after each iteration. Defaults to False.	False
fix_m_probabilities	bool	If True, do not update the m probabilities after each iteration. Defaults to False.	False
fix_u_probabilities	bool	If True, do not update the u probabilities after each iteration. Defaults to True.	True

Examples:

blocking_rule = "l.first_name = r.first_name and l.dob = r.dob"
linker.estimate_parameters_using_expectation_maximisation(blocking_rule)

or using pre-built rules

from splink.duckdb.blocking_rule_library import block_on
blocking_rule = block_on(["first_name", "surname"])
linker.estimate_parameters_using_expectation_maximisation(blocking_rule)

Returns:

Name	Type	Description
EMTrainingSession	EMTrainingSession	An object containing information about the training session such as how parameters changed during the iteration history

Source code in splink/linker.py

def estimate_parameters_using_expectation_maximisation(
    self,
    blocking_rule: str,
    comparisons_to_deactivate: list[str | Comparison] = None,
    comparison_levels_to_reverse_blocking_rule: list[ComparisonLevel] = None,
    estimate_without_term_frequencies: bool = False,
    fix_probability_two_random_records_match: bool = False,
    fix_m_probabilities=False,
    fix_u_probabilities=True,
    populate_probability_two_random_records_match_from_trained_values=False,
) -> EMTrainingSession:
    """Estimate the parameters of the linkage model using expectation maximisation.

    By default, the m probabilities are estimated, but not the u probabilities,
    because good estimates for the u probabilities can be obtained from
    `linker.estimate_u_using_random_sampling()`.  You can change this by setting
    `fix_u_probabilities` to False.

    The blocking rule provided is used to generate pairwise record comparisons.
    Usually, this should be a blocking rule that results in a dataframe where
    matches are between about 1% and 99% of the comparisons.

    By default, m parameters are estimated for all comparisons except those which
    are included in the blocking rule.

    For example, if the blocking rule is `l.first_name = r.first_name`, then
    parameter esimates will be made for all comparison except those which use
    `first_name` in their sql_condition

    By default, the probability two random records match is estimated for the
    blocked data, and then the m and u parameters for the columns specified in the
    blocking rules are used to estiamte the global probability two random records
    match.

    To control which comparisons should have their parameter estimated, and the
    process of 'reversing out' the global probability two random records match, the
    user may specify `comparisons_to_deactivate` and
    `comparison_levels_to_reverse_blocking_rule`.   This is useful, for example
    if you block on the dmetaphone of a column but match on the original column.

    Examples:
        Default behaviour
        ```py
        br_training = "l.first_name = r.first_name and l.dob = r.dob"
        linker.estimate_parameters_using_expectation_maximisation(br_training)
        ```
        Specify which comparisons to deactivate
        ```py
        br_training = "l.dmeta_first_name = r.dmeta_first_name"
        settings_obj = linker._settings_obj
        comp = settings_obj._get_comparison_by_output_column_name("first_name")
        dmeta_level = comp._get_comparison_level_by_comparison_vector_value(1)
        linker.estimate_parameters_using_expectation_maximisation(
            br_training,
            comparisons_to_deactivate=["first_name"],
            comparison_levels_to_reverse_blocking_rule=[dmeta_level],
        )
        ```

    Args:
        blocking_rule (BlockingRule | str): The blocking rule used to generate
            pairwise record comparisons.
        comparisons_to_deactivate (list, optional): By default, splink will
            analyse the blocking rule provided and estimate the m parameters for
            all comaprisons except those included in the blocking rule.  If
            comparisons_to_deactivate are provided, spink will instead
            estimate m parameters for all comparison except those specified
            in the comparisons_to_deactivate list.  This list can either contain
            the output_column_name of the Comparison as a string, or Comparison
            objects.  Defaults to None.
        comparison_levels_to_reverse_blocking_rule (list, optional): By default,
            splink will analyse the blocking rule provided and adjust the
            global probability two random records match to account for the matches
            specified in the blocking rule. If provided, this argument will overrule
            this default behaviour. The user must provide a list of ComparisonLevel
            objects.  Defaults to None.
        estimate_without_term_frequencies (bool, optional): If True, the iterations
            of the EM algorithm ignore any term frequency adjustments and only
            depend on the comparison vectors. This allows the EM algorithm to run
            much faster, but the estimation of the parameters will change slightly.
        fix_probability_two_random_records_match (bool, optional): If True, do not
            update the probability two random records match after each iteration.
            Defaults to False.
        fix_m_probabilities (bool, optional): If True, do not update the m
            probabilities after each iteration. Defaults to False.
        fix_u_probabilities (bool, optional): If True, do not update the u
            probabilities after each iteration. Defaults to True.
        populate_probability_two_random_records_match_from_trained_values
            (bool, optional): If True, derive this parameter from
            the blocked value. Defaults to False.

    Examples:
        ```py
        blocking_rule = "l.first_name = r.first_name and l.dob = r.dob"
        linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
        ```
        or using pre-built rules
        ```py
        from splink.duckdb.blocking_rule_library import block_on
        blocking_rule = block_on(["first_name", "surname"])
        linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
        ```

    Returns:
        EMTrainingSession:  An object containing information about the training
            session such as how parameters changed during the iteration history

    """
    # Ensure this has been run on the main linker so that it's in the cache
    # to be used by the training linkers
    self._initialise_df_concat_with_tf()

    # Extract the blocking rule
    blocking_rule = blocking_rule_to_obj(blocking_rule).blocking_rule_sql

    if comparisons_to_deactivate:
        # If user provided a string, convert to Comparison object
        comparisons_to_deactivate = [
            self._settings_obj._get_comparison_by_output_column_name(n)
            if isinstance(n, str)
            else n
            for n in comparisons_to_deactivate
        ]
        if comparison_levels_to_reverse_blocking_rule is None:
            logger.warning(
                "\nWARNING: \n"
                "You have provided comparisons_to_deactivate but not "
                "comparison_levels_to_reverse_blocking_rule.\n"
                "If comparisons_to_deactivate is provided, then "
                "you usually need to provide corresponding "
                "comparison_levels_to_reverse_blocking_rule "
                "because each comparison to deactivate is effectively treated "
                "as an exact match."
            )

    em_training_session = EMTrainingSession(
        self,
        blocking_rule,
        fix_u_probabilities=fix_u_probabilities,
        fix_m_probabilities=fix_m_probabilities,
        fix_probability_two_random_records_match=fix_probability_two_random_records_match,  # noqa 501
        comparisons_to_deactivate=comparisons_to_deactivate,
        comparison_levels_to_reverse_blocking_rule=comparison_levels_to_reverse_blocking_rule,  # noqa 501
        estimate_without_term_frequencies=estimate_without_term_frequencies,
    )

    em_training_session._train()

    self._populate_m_u_from_trained_values()

    if populate_probability_two_random_records_match_from_trained_values:
        self._populate_probability_two_random_records_match_from_trained_values()

    self._settings_obj._columns_without_estimated_parameters_message()

    return em_training_session

estimate_probability_two_random_records_match(deterministic_matching_rules, recall)

Estimate the model parameter probability_two_random_records_match using a direct estimation approach.

See here for discussion of methodology

Parameters:

Name	Type	Description	Default
deterministic_matching_rules	list	A list of deterministic matching rules that should be designed to admit very few (none if possible) false positives	required
recall	float	A guess at the recall the deterministic matching rules will attain. i.e. what proportion of true matches will be recovered by these deterministic rules	required

Source code in splink/linker.py

def estimate_probability_two_random_records_match(
    self, deterministic_matching_rules, recall
):
    """Estimate the model parameter `probability_two_random_records_match` using
    a direct estimation approach.

    See [here](https://github.com/moj-analytical-services/splink/issues/462)
    for discussion of methodology

    Args:
        deterministic_matching_rules (list): A list of deterministic matching
            rules that should be designed to admit very few (none if possible)
            false positives
        recall (float): A guess at the recall the deterministic matching rules
            will attain.  i.e. what proportion of true matches will be recovered
            by these deterministic rules
    """

    if (recall > 1) or (recall <= 0):
        raise ValueError(
            f"Estimated recall must be greater than 0 "
            f"and no more than 1. Supplied value {recall}."
        )

    # If user, by error, provides a single rule as a string
    if isinstance(deterministic_matching_rules, str):
        deterministic_matching_rules = [deterministic_matching_rules]

    records = cumulative_comparisons_generated_by_blocking_rules(
        self,
        deterministic_matching_rules,
    )

    summary_record = records[-1]
    num_observed_matches = summary_record["cumulative_rows"]
    num_total_comparisons = summary_record["cartesian"]

    if num_observed_matches > num_total_comparisons * recall:
        raise ValueError(
            f"Deterministic matching rules led to more "
            f"observed matches than is consistent with supplied recall. "
            f"With these rules, recall must be at least "
            f"{num_observed_matches/num_total_comparisons:,.2f}."
        )

    num_expected_matches = num_observed_matches / recall
    prob = num_expected_matches / num_total_comparisons

    # warn about boundary values, as these will usually be in error
    if num_observed_matches == 0:
        logger.warning(
            f"WARNING: Deterministic matching rules led to no observed matches! "
            f"This means that no possible record pairs are matches, "
            f"and no records are linked to one another.\n"
            f"If this is truly the case then you do not need "
            f"to run the linkage model.\n"
            f"However this is usually in error; "
            f"expected rules to have recall of {100*recall:,.0f}%. "
            f"Consider revising rules as they may have an error."
        )
    if prob == 1:
        logger.warning(
            "WARNING: Probability two random records match is estimated to be 1.\n"
            "This means that all possible record pairs are matches, "
            "and all records are linked to one another.\n"
            "If this is truly the case then you do not need "
            "to run the linkage model.\n"
            "However, it is more likely that this estimate is faulty. "
            "Perhaps your deterministic matching rules include "
            "too many false positives?"
        )

    self._settings_obj._probability_two_random_records_match = prob

    reciprocal_prob = "Infinity" if prob == 0 else f"{1/prob:,.2f}"
    logger.info(
        f"Probability two random records match is estimated to be  {prob:.3g}.\n"
        f"This means that amongst all possible pairwise record comparisons, one in "
        f"{reciprocal_prob} are expected to match.  "
        f"With {num_total_comparisons:,.0f} total"
        " possible comparisons, we expect a total of around "
        f"{num_expected_matches:,.2f} matching pairs"
    )

estimate_u_using_random_sampling(max_pairs=None, seed=None, *, target_rows=None)

Estimate the u parameters of the linkage model using random sampling.

The u parameters represent the proportion of record comparisons that fall into each comparison level amongst truly non-matching records.

This procedure takes a sample of the data and generates the cartesian product of pairwise record comparisons amongst the sampled records. The validity of the u values rests on the assumption that the resultant pairwise comparisons are non-matches (or at least, they are very unlikely to be matches). For large datasets, this is typically true.

The results of estimate_u_using_random_sampling, and therefore an entire splink model, can be made reproducible by setting the seed parameter. Setting the seed will have performance implications as additional processing is required.

Parameters:

Name	Type	Description	Default
max_pairs	int	The maximum number of pairwise record comparisons to	None
seed	int	Seed for random sampling. Assign to get reproducible u	None

Examples:

linker.estimate_u_using_random_sampling(1e8)

Returns:

Name	Type	Description
None		Updates the estimated u parameters within the linker object
		and returns nothing.

Source code in splink/linker.py

def estimate_u_using_random_sampling(
    self, max_pairs: int = None, seed: int = None, *, target_rows=None
):
    """Estimate the u parameters of the linkage model using random sampling.

    The u parameters represent the proportion of record comparisons that fall
    into each comparison level amongst truly non-matching records.

    This procedure takes a sample of the data and generates the cartesian
    product of pairwise record comparisons amongst the sampled records.
    The validity of the u values rests on the assumption that the resultant
    pairwise comparisons are non-matches (or at least, they are very unlikely to be
    matches). For large datasets, this is typically true.

    The results of estimate_u_using_random_sampling, and therefore an entire splink
    model, can be made reproducible by setting the seed parameter. Setting the seed
    will have performance implications as additional processing is required.

    Args:
        max_pairs (int): The maximum number of pairwise record comparisons to
        sample. Larger will give more accurate estimates
        but lead to longer runtimes.  In our experience at least 1e9 (one billion)
        gives best results but can take a long time to compute. 1e7 (ten million)
        is often adequate whilst testing different model specifications, before
        the final model is estimated.
        seed (int): Seed for random sampling. Assign to get reproducible u
        probabilities. Note, seed for random sampling is only supported for
        DuckDB and Spark, for Athena and SQLite set to None.

    Examples:
        ```py
        linker.estimate_u_using_random_sampling(1e8)
        ```

    Returns:
        None: Updates the estimated u parameters within the linker object
        and returns nothing.
    """
    # TODO: Remove this compatibility code in a future release once we drop
    # support for "target_rows". Deprecation warning added in 3.7.0
    if max_pairs is not None and target_rows is not None:
        # user supplied both
        raise TypeError("Just use max_pairs")
    elif max_pairs is not None:
        # user is doing it correctly
        pass
    elif target_rows is not None:
        # user is using deprecated argument
        warnings.warn(
            "target_rows is deprecated; use max_pairs",
            SplinkDeprecated,
            stacklevel=2,
        )
        max_pairs = target_rows
    else:
        raise TypeError("Missing argument max_pairs")

    estimate_u_values(self, max_pairs, seed)
    self._populate_m_u_from_trained_values()

    self._settings_obj._columns_without_estimated_parameters_message()

find_matches_to_new_records(records_or_tablename, blocking_rules=[], match_weight_threshold=-4)

Given one or more records, find records in the input dataset(s) which match and return in order of the Splink prediction score.

This effectively provides a way of searching the input datasets for given record(s)

Parameters:

Name	Type	Description	Default
records_or_tablename	List[dict]	Input search record(s) as list of dict, or a table registered to the database.	required
blocking_rules	list	Blocking rules to select which records to find and score. If [], do not use a blocking rule - meaning the input records will be compared to all records provided to the linker when it was instantiated. Defaults to [].	[]
match_weight_threshold	int	Return matches with a match weight above this threshold. Defaults to -4.	-4

Examples:

linker = DuckDBLinker(df)
linker.load_settings("saved_settings.json")
# Pre-compute tf tables for any tables with
# term frequency adjustments
linker.compute_tf_table("first_name")
record = {'unique_id': 1,
    'first_name': "John",
    'surname': "Smith",
    'dob': "1971-05-24",
    'city': "London",
    'email': "john@smith.net"
    }
df = linker.find_matches_to_new_records([record], blocking_rules=[])

Returns:

Name	Type	Description
SplinkDataFrame	SplinkDataFrame	The pairwise comparisons.

Source code in splink/linker.py

def find_matches_to_new_records(
    self,
    records_or_tablename,
    blocking_rules=[],
    match_weight_threshold=-4,
) -> SplinkDataFrame:
    """Given one or more records, find records in the input dataset(s) which match
    and return in order of the Splink prediction score.

    This effectively provides a way of searching the input datasets
    for given record(s)

    Args:
        records_or_tablename (List[dict]): Input search record(s) as list of dict,
            or a table registered to the database.
        blocking_rules (list, optional): Blocking rules to select
            which records to find and score. If [], do not use a blocking
            rule - meaning the input records will be compared to all records
            provided to the linker when it was instantiated. Defaults to [].
        match_weight_threshold (int, optional): Return matches with a match weight
            above this threshold. Defaults to -4.

    Examples:
        ```py
        linker = DuckDBLinker(df)
        linker.load_settings("saved_settings.json")
        # Pre-compute tf tables for any tables with
        # term frequency adjustments
        linker.compute_tf_table("first_name")
        record = {'unique_id': 1,
            'first_name': "John",
            'surname': "Smith",
            'dob': "1971-05-24",
            'city': "London",
            'email': "john@smith.net"
            }
        df = linker.find_matches_to_new_records([record], blocking_rules=[])
        ```

    Returns:
        SplinkDataFrame: The pairwise comparisons.
    """

    original_blocking_rules = (
        self._settings_obj._blocking_rules_to_generate_predictions
    )
    original_link_type = self._settings_obj._link_type

    blocking_rules = ensure_is_list(blocking_rules)

    if not isinstance(records_or_tablename, str):
        uid = ascii_uid(8)
        new_records_tablename = f"__splink__df_new_records_{uid}"
        self.register_table(
            records_or_tablename, new_records_tablename, overwrite=True
        )

    else:
        new_records_tablename = records_or_tablename

    new_records_df = self._table_to_splink_dataframe(
        "__splink__df_new_records", new_records_tablename
    )

    cache = self._intermediate_table_cache
    input_dfs = []
    # If our df_concat_with_tf table already exists, derive the term frequency
    # tables from df_concat_with_tf rather than computing them
    if "__splink__df_concat_with_tf" in cache:
        concat_with_tf = cache["__splink__df_concat_with_tf"]
        tf_tables = compute_term_frequencies_from_concat_with_tf(self)
        # This queues up our tf tables, rather materialising them
        for tf in tf_tables:
            # if tf is a SplinkDataFrame, then the table already exists
            if isinstance(tf, SplinkDataFrame):
                input_dfs.append(tf)
            else:
                self._enqueue_sql(tf["sql"], tf["output_table_name"])
    else:
        # This queues up our cols_with_tf and df_concat_with_tf tables.
        concat_with_tf = self._initialise_df_concat_with_tf(materialise=False)

    if concat_with_tf:
        input_dfs.append(concat_with_tf)

    blocking_rules = [blocking_rule_to_obj(br) for br in blocking_rules]
    for n, br in enumerate(blocking_rules):
        br.add_preceding_rules(blocking_rules[:n])

    self._settings_obj._blocking_rules_to_generate_predictions = blocking_rules

    self._find_new_matches_mode = True

    sql = _join_tf_to_input_df_sql(self)
    sql = sql.replace("__splink__df_concat", new_records_tablename)
    self._enqueue_sql(sql, "__splink__df_new_records_with_tf_before_uid_fix")

    add_unique_id_and_source_dataset_cols_if_needed(self, new_records_df)

    sqls = block_using_rules_sqls(self)
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    sql = compute_comparison_vector_values_sql(self._settings_obj)
    self._enqueue_sql(sql, "__splink__df_comparison_vectors")

    sqls = predict_from_comparison_vectors_sqls(
        self._settings_obj,
        sql_infinity_expression=self._infinity_expression,
    )
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    sql = f"""
    select * from __splink__df_predict
    where match_weight > {match_weight_threshold}
    """

    self._enqueue_sql(sql, "__splink__find_matches_predictions")

    predictions = self._execute_sql_pipeline(
        input_dataframes=input_dfs, use_cache=False
    )

    self._settings_obj._blocking_rules_to_generate_predictions = (
        original_blocking_rules
    )
    self._settings_obj._link_type = original_link_type
    self._find_new_matches_mode = False

    return predictions

initialise_settings(settings_dict)

This method is now deprecated. Please use load_settings when loading existing settings or load_model when loading a pre-trained model.

Initialise settings for the linker. To be used if settings were not passed to the linker on creation. Examples: === " DuckDB"

linker = DuckDBLinker(df)
linker.profile_columns(["first_name", "surname"])
linker.initialise_settings(settings_dict)

=== " Spark"

linker = SparkLinker(df)
linker.profile_columns(["first_name", "surname"])
linker.initialise_settings(settings_dict)

=== " Athena"

linker = AthenaLinker(df)
linker.profile_columns(["first_name", "surname"])
linker.initialise_settings(settings_dict)

=== " SQLite"

linker = SQLiteLinker(df)
linker.profile_columns(["first_name", "surname"])
linker.initialise_settings(settings_dict)

Args: settings_dict (dict): A Splink settings dictionary

Source code in splink/linker.py

def initialise_settings(self, settings_dict: dict):
    """*This method is now deprecated. Please use `load_settings`
    when loading existing settings or `load_model` when loading
     a pre-trained model.*

    Initialise settings for the linker.  To be used if settings were
    not passed to the linker on creation.
    Examples:
        === ":simple-duckdb: DuckDB"
            ```py
            linker = DuckDBLinker(df)
            linker.profile_columns(["first_name", "surname"])
            linker.initialise_settings(settings_dict)
            ```
        === ":simple-apachespark: Spark"
            ```py
            linker = SparkLinker(df)
            linker.profile_columns(["first_name", "surname"])
            linker.initialise_settings(settings_dict)
            ```
        === ":simple-amazonaws: Athena"
            ```py
            linker = AthenaLinker(df)
            linker.profile_columns(["first_name", "surname"])
            linker.initialise_settings(settings_dict)
            ```
        === ":simple-sqlite: SQLite"
            ```py
            linker = SQLiteLinker(df)
            linker.profile_columns(["first_name", "surname"])
            linker.initialise_settings(settings_dict)
            ```
    Args:
        settings_dict (dict): A Splink settings dictionary
    """
    # If a uid already exists in your settings object, prioritise this
    settings_dict["linker_uid"] = settings_dict.get("linker_uid", self._cache_uid)
    settings_dict["sql_dialect"] = settings_dict.get(
        "sql_dialect", self._sql_dialect
    )
    self._settings_dict = settings_dict
    self._settings_obj_ = Settings(settings_dict)
    self._validate_input_dfs()
    self._validate_dialect()

    warnings.warn(
        "`initialise_settings` is deprecated. We advise you use "
        "`linker.load_settings()` when loading in your settings or a previously "
        "trained model.",
        SplinkDeprecated,
        stacklevel=2,
    )

invalidate_cache()

Invalidate the Splink cache. Any previously-computed tables will be recomputed. This is useful, for example, if the input data tables have changed.

Source code in splink/linker.py

def invalidate_cache(self):
    """Invalidate the Splink cache.  Any previously-computed tables
    will be recomputed.
    This is useful, for example, if the input data tables have changed.
    """
    # Before Splink executes a SQL command, it checks the cache to see
    # whether a table already exists with the name of the output table

    # This function has the effect of changing the names of the output tables
    # to include a different unique id

    # As a result, any previously cached tables will not be found
    self._cache_uid = ascii_uid(8)

    # Drop any existing splink tables from the database
    # Note, this is not actually necessary, it's just good housekeeping
    self.delete_tables_created_by_splink_from_db()

    # As a result, any previously cached tables will not be found
    self._intermediate_table_cache.invalidate_cache()

labelling_tool_for_specific_record(unique_id, source_dataset=None, out_path='labelling_tool.html', overwrite=False, match_weight_threshold=-4, view_in_jupyter=False, show_splink_predictions_in_interface=True)

Create a standalone, offline labelling dashboard for a specific record as identified by its unique id

Parameters:

Name	Type	Description	Default
unique_id	str	The unique id of the record for which to create the labelling tool	required
source_dataset	str	If there are multiple datasets, to identify the record you must also specify the source_dataset. Defaults to None.	None
out_path	str	The output path for the labelling tool. Defaults to "labelling_tool.html".	'labelling_tool.html'
overwrite	bool	If true, overwrite files at the output path if they exist. Defaults to False.	False
match_weight_threshold	int	Include possible matches in the output which score above this threshold. Defaults to -4.	-4
view_in_jupyter	bool	If you're viewing in the Jupyter html viewer, set this to True to extract your labels. Defaults to False.	False
show_splink_predictions_in_interface	bool	Whether to show information about the Splink model's predictions that could potentially bias the decision of the clerical labeller. Defaults to True.	True

Source code in splink/linker.py

def labelling_tool_for_specific_record(
    self,
    unique_id,
    source_dataset=None,
    out_path="labelling_tool.html",
    overwrite=False,
    match_weight_threshold=-4,
    view_in_jupyter=False,
    show_splink_predictions_in_interface=True,
):
    """Create a standalone, offline labelling dashboard for a specific record
    as identified by its unique id

    Args:
        unique_id (str): The unique id of the record for which to create the
            labelling tool
        source_dataset (str, optional): If there are multiple datasets, to
            identify the record you must also specify the source_dataset. Defaults
            to None.
        out_path (str, optional): The output path for the labelling tool. Defaults
            to "labelling_tool.html".
        overwrite (bool, optional): If true, overwrite files at the output
            path if they exist. Defaults to False.
        match_weight_threshold (int, optional): Include possible matches in the
            output which score above this threshold. Defaults to -4.
        view_in_jupyter (bool, optional): If you're viewing in the Jupyter
            html viewer, set this to True to extract your labels. Defaults to False.
        show_splink_predictions_in_interface (bool, optional): Whether to
            show information about the Splink model's predictions that could
            potentially bias the decision of the clerical labeller. Defaults to
            True.
    """

    df_comparisons = generate_labelling_tool_comparisons(
        self,
        unique_id,
        source_dataset,
        match_weight_threshold=match_weight_threshold,
    )

    render_labelling_tool_html(
        self,
        df_comparisons,
        show_splink_predictions_in_interface=show_splink_predictions_in_interface,
        out_path=out_path,
        view_in_jupyter=view_in_jupyter,
        overwrite=overwrite,
    )

load_model(model_path)

Load a pre-defined model from a json file into the linker. This is intended to be used with the output of save_model_to_json().

Examples:

linker.load_model("my_settings.json")

Parameters:

Name	Type	Description	Default
model_path	Path	A path to your model settings json file.	required

Source code in splink/linker.py

def load_model(self, model_path: Path):
    """
    Load a pre-defined model from a json file into the linker.
    This is intended to be used with the output of
    `save_model_to_json()`.

    Examples:
        ```py
        linker.load_model("my_settings.json")
        ```

    Args:
        model_path (Path): A path to your model settings json file.
    """

    return self.load_settings(model_path)

load_settings(settings_dict, validate_settings=True)

Initialise settings for the linker. To be used if settings were not passed to the linker on creation. This can either be in the form of a settings dictionary or a filepath to a json file containing a valid settings dictionary.

Examples:

linker = DuckDBLinker(df)
linker.profile_columns(["first_name", "surname"])
linker.load_settings(settings_dict, validate_settings=True)

Parameters:

Name	Type	Description	Default
settings_dict	dict | str | Path	A Splink settings dictionary or the path to your settings json file.	required
validate_settings	bool	When True, check your settings dictionary for any potential errors that may cause splink to fail.	True

Source code in splink/linker.py

def load_settings(
    self,
    settings_dict: dict | str | Path,
    validate_settings: str = True,
):
    """Initialise settings for the linker.  To be used if settings were
    not passed to the linker on creation. This can either be in the form
    of a settings dictionary or a filepath to a json file containing a
    valid settings dictionary.

    Examples:
        ```py
        linker = DuckDBLinker(df)
        linker.profile_columns(["first_name", "surname"])
        linker.load_settings(settings_dict, validate_settings=True)
        ```

    Args:
        settings_dict (dict | str | Path): A Splink settings dictionary or
            the path to your settings json file.
        validate_settings (bool, optional): When True, check your settings
            dictionary for any potential errors that may cause splink to fail.
    """

    if not isinstance(settings_dict, dict):
        p = Path(settings_dict)
        settings_dict = json.loads(p.read_text())

    # Store the cache ID so it can be reloaded after cache invalidation
    cache_id = self._cache_uid
    # So we don't run into any issues with generated tables having
    # invalid columns as settings have been tweaked, invalidate
    # the cache and allow these tables to be recomputed.

    # This is less efficient, but triggers infrequently and ensures we don't
    # run into issues where the defaults used conflict with the actual values
    # supplied in settings.

    # This is particularly relevant with `source_dataset`, which appears within
    # concat_with_tf.
    self.invalidate_cache()

    # If a uid already exists in your settings object, prioritise this
    settings_dict["linker_uid"] = settings_dict.get("linker_uid", cache_id)
    settings_dict["sql_dialect"] = settings_dict.get(
        "sql_dialect", self._sql_dialect
    )
    self._settings_dict = settings_dict
    self._settings_obj_ = Settings(settings_dict)
    self._validate_input_dfs()
    self._validate_settings(validate_settings)

load_settings_from_json(in_path)

This method is now deprecated. Please use load_settings when loading existing settings or load_model when loading a pre-trained model.

Load settings from a .json file. This .json file would usually be the output of linker.save_model_to_json() Examples:

linker.load_settings_from_json("my_settings.json")

Args: in_path (str): Path to settings json file

Source code in splink/linker.py

def load_settings_from_json(self, in_path: str | Path):
    """*This method is now deprecated. Please use `load_settings`
    when loading existing settings or `load_model` when loading
     a pre-trained model.*

    Load settings from a `.json` file.
    This `.json` file would usually be the output of
    `linker.save_model_to_json()`
    Examples:
        ```py
        linker.load_settings_from_json("my_settings.json")
        ```
    Args:
        in_path (str): Path to settings json file
    """
    self.load_settings(in_path)

    warnings.warn(
        "`load_settings_from_json` is deprecated. We advise you use "
        "`linker.load_settings()` when loading in your settings or a previously "
        "trained model.",
        SplinkDeprecated,
        stacklevel=2,
    )

m_u_parameters_chart()

Display a chart of the m and u parameters of the linkage model

Examples:

linker.m_u_parameters_chart()

To view offline (if you don't have an internet connection):

from splink.charts import save_offline_chart
c = linker.match_weights_chart()
save_offline_chart(c.to_dict(), "test_chart.html")

View resultant html file in Jupyter (or just load it in your browser)

from IPython.display import IFrame
IFrame(src="./test_chart.html", width=1000, height=500)

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def m_u_parameters_chart(self):
    """Display a chart of the m and u parameters of the linkage model

    Examples:
        ```py
        linker.m_u_parameters_chart()
        ```
        To view offline (if you don't have an internet connection):
        ```py
        from splink.charts import save_offline_chart
        c = linker.match_weights_chart()
        save_offline_chart(c.to_dict(), "test_chart.html")
        ```
        View resultant html file in Jupyter (or just load it in your browser)
        ```py
        from IPython.display import IFrame
        IFrame(src="./test_chart.html", width=1000, height=500)
        ```

    Returns:
        altair.Chart: An altair chart
    """

    return self._settings_obj.m_u_parameters_chart()

match_weights_chart()

Display a chart of the (partial) match weights of the linkage model

Examples:

linker.match_weights_chart()

To view offline (if you don't have an internet connection):

from splink.charts import save_offline_chart
c = linker.match_weights_chart()
save_offline_chart(c.to_dict(), "test_chart.html")

View resultant html file in Jupyter (or just load it in your browser)

from IPython.display import IFrame
IFrame(src="./test_chart.html", width=1000, height=500)

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def match_weights_chart(self):
    """Display a chart of the (partial) match weights of the linkage model

    Examples:
        ```py
        linker.match_weights_chart()
        ```
        To view offline (if you don't have an internet connection):
        ```py
        from splink.charts import save_offline_chart
        c = linker.match_weights_chart()
        save_offline_chart(c.to_dict(), "test_chart.html")
        ```
        View resultant html file in Jupyter (or just load it in your browser)
        ```py
        from IPython.display import IFrame
        IFrame(src="./test_chart.html", width=1000, height=500)
        ```

    Returns:
        altair.Chart: An altair chart
    """
    return self._settings_obj.match_weights_chart()

match_weights_histogram(df_predict, target_bins=30, width=600, height=250)

Generate a histogram that shows the distribution of match weights in df_predict

Parameters:

Name	Type	Description	Default
df_predict	SplinkDataFrame	Output of linker.predict()	required
target_bins	int	Target number of bins in histogram. Defaults to 30.	30
width	int	Width of output. Defaults to 600.	600
height	int	Height of output chart. Defaults to 250.	250

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def match_weights_histogram(
    self, df_predict: SplinkDataFrame, target_bins: int = 30, width=600, height=250
):
    """Generate a histogram that shows the distribution of match weights in
    `df_predict`

    Args:
        df_predict (SplinkDataFrame): Output of `linker.predict()`
        target_bins (int, optional): Target number of bins in histogram. Defaults to
            30.
        width (int, optional): Width of output. Defaults to 600.
        height (int, optional): Height of output chart. Defaults to 250.


    Returns:
        altair.Chart: An altair chart

    """
    df = histogram_data(self, df_predict, target_bins)
    recs = df.as_record_dict()
    return match_weights_histogram(recs, width=width, height=height)

missingness_chart(input_dataset=None)

Generate a summary chart of the missingness (prevalence of nulls) of columns in the input datasets. By default, missingness is assessed across all input datasets

Parameters:

Name	Type	Description	Default
input_dataset	str	Name of one of the input tables in the database. If provided, missingness will be computed for this table alone. Defaults to None.	None

Examples:

linker.missingness_chart()

To view offline (if you don't have an internet connection):

from splink.charts import save_offline_chart
c = linker.missingness_chart()
save_offline_chart(c.to_dict(), "test_chart.html")

View resultant html file in Jupyter (or just load it in your browser)

from IPython.display import IFrame
IFrame(src="./test_chart.html", width=1000, height=500

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def missingness_chart(self, input_dataset: str = None):
    """Generate a summary chart of the missingness (prevalence of nulls) of
    columns in the input datasets.  By default, missingness is assessed across
    all input datasets

    Args:
        input_dataset (str, optional): Name of one of the input tables in the
            database.  If provided, missingness will be computed for
            this table alone.
            Defaults to None.

    Examples:
        ```py
        linker.missingness_chart()
        ```
        To view offline (if you don't have an internet connection):
        ```py
        from splink.charts import save_offline_chart
        c = linker.missingness_chart()
        save_offline_chart(c.to_dict(), "test_chart.html")
        ```
        View resultant html file in Jupyter (or just load it in your browser)
        ```py
        from IPython.display import IFrame
        IFrame(src="./test_chart.html", width=1000, height=500
        ```

    Returns:
        altair.Chart: An altair chart
    """
    records = missingness_data(self, input_dataset)
    return missingness_chart(records)

parameter_estimate_comparisons_chart(include_m=True, include_u=False)

Show a chart that shows how parameter estimates have differed across the different estimation methods you have used.

For example, if you have run two EM estimation sessions, blocking on different variables, and both result in parameter estimates for first_name, this chart will enable easy comparison of the different estimates

Parameters:

Name	Type	Description	Default
include_m	bool	Show different estimates of m values. Defaults to True.	True
include_u	bool	Show different estimates of u values. Defaults to False.	False

Source code in splink/linker.py

def parameter_estimate_comparisons_chart(self, include_m=True, include_u=False):
    """Show a chart that shows how parameter estimates have differed across
    the different estimation methods you have used.

    For example, if you have run two EM estimation sessions, blocking on
    different variables, and both result in parameter estimates for
    first_name, this chart will enable easy comparison of the different
    estimates

    Args:
        include_m (bool, optional): Show different estimates of m values. Defaults
            to True.
        include_u (bool, optional): Show different estimates of u values. Defaults
            to False.

    """
    records = self._settings_obj._parameter_estimates_as_records

    to_retain = []
    if include_m:
        to_retain.append("m")
    if include_u:
        to_retain.append("u")

    records = [r for r in records if r["m_or_u"] in to_retain]

    return parameter_estimate_comparisons(records)

precision_recall_chart_from_labels_column(labels_column_name, threshold_actual=0.5, match_weight_round_to_nearest=None)

Generate a precision-recall chart from ground truth data, whereby the ground truth is in a column in the input dataset called labels_column_name

Parameters:

Name	Type	Description	Default
labels_column_name	str	Column name containing labels in the input table	required
threshold_actual	float	Where the clerical_match_score provided by the user is a probability rather than binary, this value is used as the threshold to classify clerical_match_scores as binary matches or non matches. Defaults to 0.5.	0.5
match_weight_round_to_nearest	float	When provided, thresholds are rounded. When large numbers of labels are provided, this is sometimes necessary to reduce the size of the ROC table, and therefore the number of points plotted on the ROC chart. Defaults to None.	None

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def precision_recall_chart_from_labels_column(
    self,
    labels_column_name,
    threshold_actual=0.5,
    match_weight_round_to_nearest: float = None,
):
    """Generate a precision-recall chart from ground truth data, whereby the ground
    truth is in a column in the input dataset called `labels_column_name`

    Args:
        labels_column_name (str): Column name containing labels in the input table
        threshold_actual (float, optional): Where the `clerical_match_score`
            provided by the user is a probability rather than binary, this value
            is used as the threshold to classify `clerical_match_score`s as binary
            matches or non matches. Defaults to 0.5.
        match_weight_round_to_nearest (float, optional): When provided, thresholds
            are rounded.  When large numbers of labels are provided, this is
            sometimes necessary to reduce the size of the ROC table, and therefore
            the number of points plotted on the ROC chart. Defaults to None.
    Examples:
        ```py
        linker.precision_recall_chart_from_labels_column("ground_truth")
        ```

    Returns:
        altair.Chart: An altair chart
    """

    df_truth_space = truth_space_table_from_labels_column(
        self,
        labels_column_name,
        threshold_actual=threshold_actual,
        match_weight_round_to_nearest=match_weight_round_to_nearest,
    )
    recs = df_truth_space.as_record_dict()
    return precision_recall_chart(recs)

precision_recall_chart_from_labels_table(labels_splinkdataframe_or_table_name, threshold_actual=0.5, match_weight_round_to_nearest=None)

Generate a precision-recall chart from labelled (ground truth) data.

The table of labels should be in the following format, and should be registered as a table with your database:

source_dataset_l	unique_id_l	source_dataset_r	unique_id_r	clerical_match_score
df_1	1	df_2	2	0.99
df_1	1	df_2	3	0.2

Note that source_dataset and unique_id should correspond to the values specified in the settings dict, and the input_table_aliases passed to the linker object.

For dedupe_only links, the source_dataset columns can be ommitted.

Parameters:

Name	Type	Description	Default
labels_splinkdataframe_or_table_name	str | SplinkDataFrame	Name of table containing labels in the database	required
threshold_actual	float	Where the clerical_match_score provided by the user is a probability rather than binary, this value is used as the threshold to classify clerical_match_scores as binary matches or non matches. Defaults to 0.5.	0.5
match_weight_round_to_nearest	float	When provided, thresholds are rounded. When large numbers of labels are provided, this is sometimes necessary to reduce the size of the ROC table, and therefore the number of points plotted on the ROC chart. Defaults to None.	None

Returns:

Type	Description
	altair.Chart: An altair chart

Source code in splink/linker.py

def precision_recall_chart_from_labels_table(
    self,
    labels_splinkdataframe_or_table_name,
    threshold_actual=0.5,
    match_weight_round_to_nearest: float = None,
):
    """Generate a precision-recall chart from labelled (ground truth) data.

    The table of labels should be in the following format, and should be registered
    as a table with your database:

    |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
    |----------------|-----------|----------------|-----------|--------------------|
    |df_1            |1          |df_2            |2          |0.99                |
    |df_1            |1          |df_2            |3          |0.2                 |

    Note that `source_dataset` and `unique_id` should correspond to the values
    specified in the settings dict, and the `input_table_aliases` passed to the
    `linker` object.

    For `dedupe_only` links, the `source_dataset` columns can be ommitted.

    Args:
        labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
            containing labels in the database
        threshold_actual (float, optional): Where the `clerical_match_score`
            provided by the user is a probability rather than binary, this value
            is used as the threshold to classify `clerical_match_score`s as binary
            matches or non matches. Defaults to 0.5.
        match_weight_round_to_nearest (float, optional): When provided, thresholds
            are rounded.  When large numbers of labels are provided, this is
            sometimes necessary to reduce the size of the ROC table, and therefore
            the number of points plotted on the ROC chart. Defaults to None.
    Examples:
        === ":simple-duckdb: DuckDB"
            ```py
            labels = pd.read_csv("my_labels.csv")
            linker.register_table(labels, "labels")
            linker.precision_recall_chart_from_labels_table("labels")
            ```
        === ":simple-apachespark: Spark"
            ```py
            labels = spark.read.csv("my_labels.csv", header=True)
            labels.createDataFrame("labels")
            linker.precision_recall_chart_from_labels_table("labels")
            ```

    Returns:
        altair.Chart: An altair chart
    """
    labels_tablename = self._get_labels_tablename_from_input(
        labels_splinkdataframe_or_table_name
    )
    self._raise_error_if_necessary_accuracy_columns_not_computed()
    df_truth_space = truth_space_table_from_labels_table(
        self,
        labels_tablename,
        threshold_actual=threshold_actual,
        match_weight_round_to_nearest=match_weight_round_to_nearest,
    )
    recs = df_truth_space.as_record_dict()
    return precision_recall_chart(recs)

predict(threshold_match_probability=None, threshold_match_weight=None, materialise_after_computing_term_frequencies=True)

Create a dataframe of scored pairwise comparisons using the parameters of the linkage model.

Uses the blocking rules specified in the blocking_rules_to_generate_predictions of the settings dictionary to generate the pairwise comparisons.

Parameters:

Name	Type	Description	Default
threshold_match_probability	float	If specified, filter the results to include only pairwise comparisons with a match_probability above this threshold. Defaults to None.	None
threshold_match_weight	float	If specified, filter the results to include only pairwise comparisons with a match_weight above this threshold. Defaults to None.	None
materialise_after_computing_term_frequencies	bool	If true, Splink will materialise the table containing the input nodes (rows) joined to any term frequencies which have been asked for in the settings object. If False, this will be computed as part of one possibly gigantic CTE pipeline. Defaults to True	True

Examples:

linker = DuckDBLinker(df)
linker.load_settings("saved_settings.json")
df = linker.predict(threshold_match_probability=0.95)
df.as_pandas_dataframe(limit=5)

Source code in splink/linker.py

def predict(
    self,
    threshold_match_probability: float = None,
    threshold_match_weight: float = None,
    materialise_after_computing_term_frequencies=True,
) -> SplinkDataFrame:
    """Create a dataframe of scored pairwise comparisons using the parameters
    of the linkage model.

    Uses the blocking rules specified in the
    `blocking_rules_to_generate_predictions` of the settings dictionary to
    generate the pairwise comparisons.

    Args:
        threshold_match_probability (float, optional): If specified,
            filter the results to include only pairwise comparisons with a
            match_probability above this threshold. Defaults to None.
        threshold_match_weight (float, optional): If specified,
            filter the results to include only pairwise comparisons with a
            match_weight above this threshold. Defaults to None.
        materialise_after_computing_term_frequencies (bool): If true, Splink
            will materialise the table containing the input nodes (rows)
            joined to any term frequencies which have been asked
            for in the settings object.  If False, this will be
            computed as part of one possibly gigantic CTE
            pipeline.   Defaults to True

    Examples:
        ```py
        linker = DuckDBLinker(df)
        linker.load_settings("saved_settings.json")
        df = linker.predict(threshold_match_probability=0.95)
        df.as_pandas_dataframe(limit=5)
        ```
    Returns:
        SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons.  This
            represents a table materialised in the database. Methods on the
            SplinkDataFrame allow you to access the underlying data.

    """

    # If materialise_after_computing_term_frequencies=False and the user only
    # calls predict, it runs as a single pipeline with no materialisation
    # of anything.

    # _initialise_df_concat_with_tf returns None if the table doesn't exist
    # and only SQL is queued in this step.
    nodes_with_tf = self._initialise_df_concat_with_tf(
        materialise=materialise_after_computing_term_frequencies
    )

    input_dataframes = []
    if nodes_with_tf:
        input_dataframes.append(nodes_with_tf)

    sqls = block_using_rules_sqls(self)
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    repartition_after_blocking = getattr(self, "repartition_after_blocking", False)

    # repartition after blocking only exists on the SparkLinker
    if repartition_after_blocking:
        df_blocked = self._execute_sql_pipeline(input_dataframes)
        input_dataframes.append(df_blocked)

    sql = compute_comparison_vector_values_sql(self._settings_obj)
    self._enqueue_sql(sql, "__splink__df_comparison_vectors")

    sqls = predict_from_comparison_vectors_sqls(
        self._settings_obj,
        threshold_match_probability,
        threshold_match_weight,
        sql_infinity_expression=self._infinity_expression,
    )
    for sql in sqls:
        self._enqueue_sql(sql["sql"], sql["output_table_name"])

    predictions = self._execute_sql_pipeline(input_dataframes)
    self._predict_warning()
    return predictions

prediction_errors_from_labels_column(label_colname, include_false_positives=True, include_false_negatives=True, threshold=0.5)

Generate a dataframe containing false positives and false negatives based on the comparison between the splink match probability and the labels column. A label column is a column in the input dataset that contains the 'ground truth' cluster to which the record belongs

Parameters:

Name	Type	Description	Default
label_colname	str	Name of labels column in input data	required
include_false_positives	bool	Defaults to True.	True
include_false_negatives	bool	Defaults to True.	True
threshold	float	Threshold above which a score is considered to be a match. Defaults to 0.5.	0.5

Returns:

Name	Type	Description
SplinkDataFrame		Table containing false positives and negatives

Source code in splink/linker.py

def prediction_errors_from_labels_column(
    self,
    label_colname,
    include_false_positives=True,
    include_false_negatives=True,
    threshold=0.5,
):
    """Generate a dataframe containing false positives and false negatives
    based on the comparison between the splink match probability and the
    labels column.  A label column is a column in the input dataset that contains
    the 'ground truth' cluster to which the record belongs

    Args:
        label_colname (str): Name of labels column in input data
        include_false_positives (bool, optional): Defaults to True.
        include_false_negatives (bool, optional): Defaults to True.
        threshold (float, optional): Threshold above which a score is considered
            to be a match. Defaults to 0.5.

    Returns:
        SplinkDataFrame:  Table containing false positives and negatives
    """
    return prediction_errors_from_label_column(
        self,
        label_colname,
        include_false_positives,
        include_false_negatives,
        threshold,
    )

prediction_errors_from_labels_table(labels_splinkdataframe_or_table_name, include_false_positives=True, include_false_negatives=True, threshold=0.5)

Generate a dataframe containing false positives and false negatives based on the comparison between the clerical_match_score in the labels table compared with the splink predicted match probability

Parameters:

Name	Type	Description	Default
labels_splinkdataframe_or_table_name	str | SplinkDataFrame	Name of table containing labels in the database	required
include_false_positives	bool	Defaults to True.	True
include_false_negatives	bool	Defaults to True.	True
threshold	float	Threshold above which a score is considered to be a match. Defaults to 0.5.	0.5

Returns:

Name	Type	Description
SplinkDataFrame		Table containing false positives and negatives

Source code in splink/linker.py

def prediction_error