Documentation for Linker object methods related to QA

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

        Examples:
            === "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")
                ```
            === "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.
        """

        if set_up_basic_logging:
            logging.basicConfig(
                format="%(message)s",
            )
            splink_logger = logging.getLogger("splink")
            splink_logger.setLevel(logging.INFO)

        self._pipeline = SQLPipeline()

        self._names_of_tables_created_by_splink: set = set()
        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:
            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._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

    @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:
            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:
            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 _source_dataset_column_name(self):
        if self._settings_obj_ is None:
            return None

        # Used throughout the scripts to feed our SQL
        if self._settings_obj._source_dataset_column_name_is_required:
            df_obj = next(iter(self._input_tables_dict.values()))
            columns = df_obj.columns_escaped

            input_column, src_ds_col = self._settings_obj_._source_dataset_col
            return "__splink_source_dataset" if src_ds_col in columns else input_column
        else:
            return None

    @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

        # in u-train sample mode we are joining the concatenated table mixing
        # both data sets - hence if we inner join on True we will end up with
        # samples which both originate from the same dataset
        if self._train_u_using_random_sample_mode:
            return False

        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)}"
        )

    @property
    def _verify_link_only_job(self):
        cache = self._intermediate_table_cache
        if "__splink__df_concat_with_tf" not in cache:
            return

        if self._settings_obj._link_type == "link_only":
            # if input datasets > 1 then skip
            if len(self._input_tables_dict) > 1:
                return

            # else, check if source dataset column is populated...
            src_ds = self._source_dataset_column_name
            if src_ds == "__splink_source_dataset":
                _, src_ds = self._settings_obj_._source_dataset_col

            sql = find_unique_source_dataset(src_ds)
            self._enqueue_sql(sql, "source_ds_distinct")
            src_ds_distinct = self._execute_sql_pipeline(
                [cache["__splink__df_concat_with_tf"]]
            )
            if len(src_ds_distinct.as_record_dict()) == 1:
                raise SplinkException(
                    "if `link_type` is `link_only`, it should have at least two "
                    "input dataframes, or one dataframe with a `source_dataset` "
                    "column outlining which dataset each record belongs to."
                )

    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)

            self._validate_dialect()

    def _initialise_df_concat(self, materialise=False):
        cache = self._intermediate_table_cache
        concat_df = None
        if "__splink__df_concat" in cache:
            concat_df = cache["__splink__df_concat"]
        elif "__splink__df_concat_with_tf" in cache:
            concat_df = cache["__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["__splink__df_concat_with_tf"]

        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")

            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

        # verify the link job
        if self._settings_obj_ is not None:
            self._verify_link_only_job

        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] = [],
        materialise_as_hash=True,
        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 [].
            materialise_as_hash (bool, optional): If true, the output tablename will end
                in a unique identifer. Defaults to True.
            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,
                    materialise_as_hash,
                    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):
                output_tablename = task.output_table_name
                sql = task.sql
                print("------")
                print(f"--------Creating table: {output_tablename}--------")

                dataframe = self._sql_to_splink_dataframe_checking_cache(
                    sql,
                    output_tablename,
                    materialise_as_hash=False,
                    use_cache=False,
                )
            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}"
            ) 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:
            === "DuckDB"
                ```py
                linker = DuckDBLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
                ```
            === "Spark"
                ```py
                linker = SparkLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
                ```
            === "Athena"
                ```py
                linker = AthenaLinker(df, settings)
                df_predict = linker.predict()
                linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
                ```
            === "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,
            materialise_as_hash=False,
            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()
            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,
        materialise_as_hash=True,
        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:
            if self._table_exists_in_database(output_tablename_templated):
                logger.debug(f"Using existing table {output_tablename_templated}")
                return self._table_to_splink_dataframe(
                    output_tablename_templated, output_tablename_templated
                )

            if self._table_exists_in_database(table_name_hash):
                logger.debug(
                    f"Using cache for {output_tablename_templated}"
                    f" with physical name {table_name_hash}"
                )
                return self._table_to_splink_dataframe(
                    output_tablename_templated, table_name_hash
                )

        if self.debug_mode:
            print(sql)

        if materialise_as_hash:
            splink_dataframe = self._execute_sql_against_backend(
                sql, output_tablename_templated, table_name_hash
            )
        else:
            splink_dataframe = self._execute_sql_against_backend(
                sql,
                output_tablename_templated,
                output_tablename_templated,
            )

        self._names_of_tables_created_by_splink.add(splink_dataframe.physical_name)

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

                display(df_pd)
            except ModuleNotFoundError:
                print(df_pd)

        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 _validate_dialect(self):
        settings_dialect = self._settings_obj._sql_dialect
        if settings_dialect != self._sql_dialect:
            raise ValueError(
                f"Incompatible SQL dialect! `settings` dictionary uses "
                f"dialect {settings_dialect}, but expecting "
                f"'{self._sql_dialect}' for Linker of type {type(self)}"
            )

    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}: "
                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, retain_term_frequency=True, retain_df_concat_with_tf=True
    ):
        to_remove = set()
        for name in self._names_of_tables_created_by_splink:
            # Only delete tables explicitly marked as having been created by splink
            if "__splink__" not in name:
                continue
            if name == "__splink__df_concat_with_tf":
                if not retain_df_concat_with_tf:
                    self._delete_table_from_database(name)
                    to_remove.add(name)
            elif name.startswith("__splink__df_tf_"):
                if not retain_term_frequency:
                    self._delete_table_from_database(name)
                    to_remove.add(name)
            else:
                self._delete_table_from_database(name)
                to_remove.add(name)

        self._names_of_tables_created_by_splink = (
            self._names_of_tables_created_by_splink - to_remove
        )

    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):
        """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)
            ```

        Args:
            settings_dict (dict | str | Path): A Splink settings dictionary or
                the path to your settings json file.
        """

        if not isinstance(settings_dict, dict):
            p = Path(settings_dict)
            if not p.is_file():  # check if it's a valid file/filepath
                raise FileNotFoundError(
                    "The filepath you have provided is either not a valid file "
                    "or doesn't exist along the path provided."
                )
            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_dialect()

    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:
            === "DuckDB"
                ```py
                linker = DuckDBLinker(df")
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
            === "Spark"
                ```py
                linker = SparkLinker(df")
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
            === "Athena"
                ```py
                linker = AthenaLinker(df")
                linker.profile_columns(["first_name", "surname"])
                linker.initialise_settings(settings_dict)
                ```
            === "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.",
            DeprecationWarning,
            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.",
            DeprecationWarning,
            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:
            === "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")
                ```
            === "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 = [
            remove_quotes_from_identifiers(tf_col.input_name_as_tree).sql()
            for tf_col in self._settings_obj._term_frequency_columns
        ]

        if tf_tablename in cache:
            tf_df = cache[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"]], materialise_as_hash=True
            )
            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, materialise_as_hash=True)
            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:
            === "DuckDB"
            ```py
            from splink.duckdb.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()
            ```
            === "Spark"
            ```py
            from splink.spark.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()
            ```
            === "Athena"
            ```py
            from splink.athena.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()
            ```
            === "SQLite"
            ```py
            from splink.sqlite.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()
        sql = block_using_rules_sql(self)
        self._enqueue_sql(sql, "__splink__df_blocked")
        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",
                DeprecationWarning,
                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,
        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 (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.
            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)
            ```

        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()

        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
        )

        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)

        sql = block_using_rules_sql(self)
        self._enqueue_sql(sql, "__splink__df_blocked")

        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

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

        cache = self._intermediate_table_cache
        input_dfs = []
        # If our df_concat_with_tf table already exists, use backwards inference to
        # find all underlying term frequency tables.
        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)

        rules = []
        for r in blocking_rules:
            br_as_obj = BlockingRule(r) if not isinstance(r, BlockingRule) else r
            br_as_obj.preceding_rules = rules.copy()
            rules.append(br_as_obj)
        blocking_rules = rules

        self._settings_obj._blocking_rules_to_generate_predictions = blocking_rules

        self._settings_obj._link_type = "link_only_find_matches_to_new_records"
        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")

        sql = block_using_rules_sql(self)
        self._enqueue_sql(sql, "__splink__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,
            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")

        sql = block_using_rules_sql(self)
        self._enqueue_sql(sql, "__splink__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,
            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}")
        ]

        nodes_with_tf = self._initialise_df_concat_with_tf()

        sql = block_using_rules_sql(self)

        self._enqueue_sql(sql, "__splink__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,
            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` 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 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 profile_columns(
        self, column_expressions: str | list[str], top_n=10, bottom_n=10
    ):
        return profile_columns(self, 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:
            === "DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.truth_space_table_from_labels_table("labels")
                ```
            === "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:
            === "DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.roc_chart_from_labels_table("labels")
                ```
            === "Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.roc_chart_from_labels_table("labels")
                ```

        Returns:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """
        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:
            === "DuckDB"
                ```py
                labels = pd.read_csv("my_labels.csv")
                linker.register_table(labels, "labels")
                linker.precision_recall_chart_from_labels_table("labels")
                ```
            === "Spark"
                ```py
                labels = spark.read.csv("my_labels.csv", header=True)
                labels.createDataFrame("labels")
                linker.precision_recall_chart_from_labels_table("labels")
                ```

        Returns:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """
        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 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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """

        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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """

        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 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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.

        """
        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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.

        """
        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
            df = pd.read_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
            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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """

        # 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=True):
        """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 True.

        """
        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.spec, "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 = 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.spec, "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,
    ) -> int:
        """Compute the number of pairwise record comparisons that would be generated by
        a blocking rule

        Args:
            blocking_rule (str): 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.first_name = r.first_name"
            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

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

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

        sql = number_of_comparisons_generated_by_blocking_rule_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 cumulative_comparisons_from_blocking_rules_records(
        self,
        blocking_rules: str or 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:
            ```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.
            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 or 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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """

        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 maginal 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, connection=":memory:")
            linker.load_settings("saved_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.spec, "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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """
        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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """

        # 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.spec, "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:
            VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
                The vegalite spec is available as a dictionary using the `spec`
                attribute.
        """

        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.",
            DeprecationWarning,
            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)

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

        # Also 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()

    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
        )
        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
        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
        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
        )
        return splink_dataframe

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

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

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.spec, "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:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

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.spec, "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:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """

    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.spec, "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:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

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.spec, "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:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """
    return self._settings_obj.match_weights_chart()

parameter_estimate_comparisons_chart(include_m=True, include_u=True)

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 True.	True

Source code in splink/linker.py

def parameter_estimate_comparisons_chart(self, include_m=True, include_u=True):
    """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 True.

    """
    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

Examples:

linker.precision_recall_chart_from_labels_column("ground_truth")

Returns:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

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:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """

    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

Examples:

DuckDBSpark

labels = pd.read_csv("my_labels.csv")
linker.register_table(labels, "labels")
linker.precision_recall_chart_from_labels_table("labels")

labels = spark.read.csv("my_labels.csv", header=True)
labels.createDataFrame("labels")
linker.precision_recall_chart_from_labels_table("labels")

Returns:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

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:
        === "DuckDB"
            ```py
            labels = pd.read_csv("my_labels.csv")
            linker.register_table(labels, "labels")
            linker.precision_recall_chart_from_labels_table("labels")
            ```
        === "Spark"
            ```py
            labels = spark.read.csv("my_labels.csv", header=True)
            labels.createDataFrame("labels")
            linker.precision_recall_chart_from_labels_table("labels")
            ```

    Returns:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """
    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)

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_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,
    )

roc_chart_from_labels_column(labels_column_name, threshold_actual=0.5, match_weight_round_to_nearest=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

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

Examples:

linker.roc_chart_from_labels_column("labels")

Returns:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

Source code in splink/linker.py

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:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """

    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)

roc_chart_from_labels_table(labels_splinkdataframe_or_table_name, threshold_actual=0.5, match_weight_round_to_nearest=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.

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

Examples:

DuckDBSpark

labels = pd.read_csv("my_labels.csv")
linker.register_table(labels, "labels")
linker.roc_chart_from_labels_table("labels")

labels = spark.read.csv("my_labels.csv", header=True)
labels.createDataFrame("labels")
linker.roc_chart_from_labels_table("labels")

Returns:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

Source code in splink/linker.py

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

    Returns:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """
    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)

truth_space_table_from_labels_column(labels_column_name, threshold_actual=0.5, match_weight_round_to_nearest=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

Parameters:

Name	Type	Description	Default
labels_tablename	str	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

Examples:

linker.truth_space_table_from_labels_column("cluster")

Returns:

Name	Type	Description
SplinkDataFrame		Table of truth statistics

Source code in splink/linker.py

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
    )

truth_space_table_from_labels_table(labels_splinkdataframe_or_table_name, threshold_actual=0.5, match_weight_round_to_nearest=None)

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.

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

Examples:

DuckDBSpark

labels = pd.read_csv("my_labels.csv")
linker.register_table(labels, "labels")
linker.truth_space_table_from_labels_table("labels")

labels = spark.read.csv("my_labels.csv", header=True)
labels.createDataFrame("labels")
linker.truth_space_table_from_labels_table("labels")

Returns:

Name	Type	Description
SplinkDataFrame	SplinkDataFrame	Table of truth statistics

Source code in splink/linker.py

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:
        === "DuckDB"
            ```py
            labels = pd.read_csv("my_labels.csv")
            linker.register_table(labels, "labels")
            linker.truth_space_table_from_labels_table("labels")
            ```
        === "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,
    )

unlinkables_chart(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.

Parameters:

Name	Type	Description	Default
x_col	str	Column to use for the x-axis. Defaults to "match_weight".	'match_weight'
source_dataset	str	Name of the source dataset to use for the title of the output chart.	None
as_dict	bool	If True, return a dict version of the chart.	False

Examples:

For the simplest code pipeline, load a pre-trained model and run this against the test data.

df = pd.read_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
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:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

Source code in splink/linker.py

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
        df = pd.read_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
        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:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.
    """

    # 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)

waterfall_chart(records, 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:

df = linker.predict(threshold_match_weight=2)
records = df.as_record_dict(limit=10)
linker.waterfall_chart(records)

Parameters:

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

Returns:

Name	Type	Description
VegaLite		A VegaLite chart object. See altair.vegalite.v4.display.VegaLite. The vegalite spec is available as a dictionary using the spec attribute.

Source code in splink/linker.py

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:
        VegaLite: A VegaLite chart object. See altair.vegalite.v4.display.VegaLite.
            The vegalite spec is available as a dictionary using the `spec`
            attribute.

    """
    self._raise_error_if_necessary_waterfall_columns_not_computed()

    return waterfall_chart(records, self._settings_obj, filter_nulls)