Update some docs to remove old backend-specific linkers

moj-analytical-services · Jul 23, 2024 · e66600e · e66600e
1 parent 0545ce7
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Showing 5 changed files with 15 additions and 12 deletions.
diff --git a/docs/api_docs/datasets.md b/docs/api_docs/datasets.md
@@ -17,16 +17,18 @@ df = splink_datasets.fake_1000
 which you can then use to set up a linker:
 ```py
 from splink splink_datasets, Linker, DuckDBAPI, SettingsCreator
+
 df = splink_datasets.fake_1000
-linker = DuckDBLinker(
+linker = Linker(
     df,
     SettingsCreator(
         link_type="dedupe_only",
         comparisons=[
             cl.exact_match("first_name"),
             cl.exact_match("surname"),
         ],
-    )
+    ),
+    db_api=DuckDBAPI()
 )
 ```
 

diff --git a/docs/dev_guides/debug_modes.md b/docs/dev_guides/debug_modes.md
@@ -44,7 +44,7 @@ Note that by default Splink sets the [logging level to `INFO` on initialisation]
 
 ```python
 import logging
-linker = DuckDBLinker(df, settings, set_up_basic_logging=False)
+linker = Linker(df, settings, db_api, set_up_basic_logging=False)
 
 # This must come AFTER the linker is intialised, because the logging level
 # will be set to INFO
@@ -61,5 +61,5 @@ logging.basicConfig(format="%(message)s")
 splink_logger = logging.getLogger("splink")
 splink_logger.setLevel(logging.INFO)
 
-linker = DuckDBLinker(df, settings, set_up_basic_logging=False)
+linker = Linker(df, settings, db_api, set_up_basic_logging=False)
 ```
diff --git a/docs/topic_guides/data_preparation/feature_engineering.md b/docs/topic_guides/data_preparation/feature_engineering.md
@@ -194,7 +194,7 @@ For a more detailed explanation on phonetic transformation algorithms, see the [
 
 ### Example
 
-There are a number of python packages which support phonetic transformations that can be applied to a pandas dataframe, which can then be loaded into the `DuckDBLinker`. For example, creating a [Double Metaphone](../comparisons/phonetic.md#double-metaphone) column with the [phonetics](https://pypi.org/project/phonetics/) python library:
+There are a number of python packages which support phonetic transformations that can be applied to a pandas dataframe, which can then be loaded into the `Linker`. For example, creating a [Double Metaphone](../comparisons/phonetic.md#double-metaphone) column with the [phonetics](https://pypi.org/project/phonetics/) python library:
 
 ```python
 import pandas as pd

diff --git a/docs/topic_guides/performance/optimising_duckdb.md b/docs/topic_guides/performance/optimising_duckdb.md
@@ -99,17 +99,17 @@ Use the special `:temporary:` connection built into Splink that creates a tempor
 
 ```python
 
-linker = DuckDBLinker(
-    df, settings, connection=":temporary:"
+linker = Linker(
+    df, settings, DuckDBAPI(connection=":temporary:")
 )
 ```
 
 Use an on-disk database:
 
 ```python
 con = duckdb.connect(database='my-db.duckdb')
-linker = DuckDBLinker(
-    df, settings, connection=con
+linker = Linker(
+    df, settings, DuckDBAPI(connection=con)
 )
 ```
 
@@ -119,8 +119,8 @@ Use an in-memory database, but ensure it can spill to disk:
 con = duckdb.connect(":memory:")
 
 con.execute("SET temp_directory='/path/to/temp';")
-linker = DuckDBLinker(
-    df, settings, connection=con
+linker = Linker(
+    df, settings, DuckDBAPI(connection=con)
 )
 ```
 

diff --git a/docs/topic_guides/splink_fundamentals/settings.md b/docs/topic_guides/splink_fundamentals/settings.md
@@ -416,7 +416,8 @@ where the `m_probability` and `u_probability` values here are then used to gener
 When using a pre-trained model, you can read in the model from a json and recreate the linker object to make new pairwise predictions. For example:
 
 ```py
-linker = DuckDBLinker(new_df,
+linker = Linker(
+    new_df,
     settings="./path/to/model.json",
     db_api=db_api
 )