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Merge pull request #2498 from moj-analytical-services/improve_compare…
…_two_records Improve compare two records
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,142 @@ | ||
| from __future__ import annotations | ||
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| from pathlib import Path | ||
| from typing import Any | ||
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| from splink.internals.accuracy import _select_found_by_blocking_rules | ||
| from splink.internals.database_api import AcceptableInputTableType, DatabaseAPISubClass | ||
| from splink.internals.misc import ascii_uid | ||
| from splink.internals.pipeline import CTEPipeline | ||
| from splink.internals.predict import ( | ||
| predict_from_comparison_vectors_sqls_using_settings, | ||
| ) | ||
| from splink.internals.settings_creator import SettingsCreator | ||
| from splink.internals.splink_dataframe import SplinkDataFrame | ||
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| class SQLCache: | ||
| def __init__(self): | ||
| self._cache: dict[int, tuple[str, str | None]] = {} | ||
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| def get(self, settings_id: int, new_uid: str) -> str | None: | ||
| if settings_id not in self._cache: | ||
| return None | ||
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| sql, cached_uid = self._cache[settings_id] | ||
| if cached_uid: | ||
| sql = sql.replace(cached_uid, new_uid) | ||
| return sql | ||
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| def set(self, settings_id: int, sql: str | None, uid: str | None) -> None: | ||
| if sql is not None: | ||
| self._cache[settings_id] = (sql, uid) | ||
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| _sql_cache = SQLCache() | ||
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| def compare_records( | ||
| record_1: dict[str, Any] | AcceptableInputTableType, | ||
| record_2: dict[str, Any] | AcceptableInputTableType, | ||
| settings: SettingsCreator | dict[str, Any] | Path | str, | ||
| db_api: DatabaseAPISubClass, | ||
| use_sql_from_cache: bool = True, | ||
| include_found_by_blocking_rules: bool = False, | ||
| ) -> SplinkDataFrame: | ||
| """Compare two records and compute similarity scores without requiring a Linker. | ||
| Assumes any required term frequency values are provided in the input records. | ||
| Args: | ||
| record_1 (dict): First record to compare | ||
| record_2 (dict): Second record to compare | ||
| db_api (DatabaseAPISubClass): Database API to use for computations | ||
| Returns: | ||
| SplinkDataFrame: Comparison results | ||
| """ | ||
| global _sql_cache | ||
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| uid = ascii_uid(8) | ||
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| if isinstance(record_1, dict): | ||
| to_register_left: AcceptableInputTableType = [record_1] | ||
| else: | ||
| to_register_left = record_1 | ||
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| if isinstance(record_2, dict): | ||
| to_register_right: AcceptableInputTableType = [record_2] | ||
| else: | ||
| to_register_right = record_2 | ||
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| df_records_left = db_api.register_table( | ||
| to_register_left, | ||
| f"__splink__compare_records_left_{uid}", | ||
| overwrite=True, | ||
| ) | ||
| df_records_left.templated_name = "__splink__compare_records_left" | ||
|
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| df_records_right = db_api.register_table( | ||
| to_register_right, | ||
| f"__splink__compare_records_right_{uid}", | ||
| overwrite=True, | ||
| ) | ||
| df_records_right.templated_name = "__splink__compare_records_right" | ||
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| settings_id = id(settings) | ||
| if use_sql_from_cache: | ||
| if cached_sql := _sql_cache.get(settings_id, uid): | ||
| return db_api._sql_to_splink_dataframe( | ||
| cached_sql, | ||
| templated_name="__splink__realtime_compare_records", | ||
| physical_name=f"__splink__realtime_compare_records_{uid}", | ||
| ) | ||
|
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| if not isinstance(settings, SettingsCreator): | ||
| settings_creator = SettingsCreator.from_path_or_dict(settings) | ||
| else: | ||
| settings_creator = settings | ||
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| settings_obj = settings_creator.get_settings(db_api.sql_dialect.sql_dialect_str) | ||
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| settings_obj._retain_matching_columns = True | ||
| settings_obj._retain_intermediate_calculation_columns = True | ||
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| pipeline = CTEPipeline([df_records_left, df_records_right]) | ||
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| cols_to_select = settings_obj._columns_to_select_for_blocking | ||
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| select_expr = ", ".join(cols_to_select) | ||
| sql = f""" | ||
| select {select_expr}, 0 as match_key | ||
| from __splink__compare_records_left as l | ||
| cross join __splink__compare_records_right as r | ||
| """ | ||
| pipeline.enqueue_sql(sql, "__splink__compare_two_records_blocked") | ||
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| cols_to_select = settings_obj._columns_to_select_for_comparison_vector_values | ||
| select_expr = ", ".join(cols_to_select) | ||
| sql = f""" | ||
| select {select_expr} | ||
| from __splink__compare_two_records_blocked | ||
| """ | ||
| pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors") | ||
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| sqls = predict_from_comparison_vectors_sqls_using_settings( | ||
| settings_obj, | ||
| sql_infinity_expression=db_api.sql_dialect.infinity_expression, | ||
| ) | ||
| pipeline.enqueue_list_of_sqls(sqls) | ||
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| if include_found_by_blocking_rules: | ||
| br_col = _select_found_by_blocking_rules(settings_obj) | ||
| sql = f""" | ||
| select *, {br_col} | ||
| from __splink__df_predict | ||
| """ | ||
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| pipeline.enqueue_sql(sql, "__splink__found_by_blocking_rules") | ||
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| predictions = db_api.sql_pipeline_to_splink_dataframe(pipeline) | ||
| _sql_cache.set(settings_id, predictions.sql_used_to_create, uid) | ||
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| return predictions |
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