formatting issues

splink/clustering.py

@@ -1,3 +1,9 @@
-from .internals.clustering import cluster_pairwise_predictions_at_threshold, cluster_pairwise_predictions_at_multiple_thresholds
+from .internals.clustering import (
+    cluster_pairwise_predictions_at_multiple_thresholds, 
+    cluster_pairwise_predictions_at_threshold
+)
 
-__all__ = ["cluster_pairwise_predictions_at_threshold", "cluster_pairwise_predictions_at_multiple_thresholds"]
+__all__ = [
+    "cluster_pairwise_predictions_at_threshold", 
+    "cluster_pairwise_predictions_at_multiple_thresholds"
+    ]

splink/internals/linker_components/clustering.py

@@ -358,24 +358,25 @@ def cluster_pairwise_predictions_at_multiple_thresholds(
 
         Records with an estimated `match_probability` at or above each of the values in
         `threshold_match_probabilities` (or records with a `match_weight` at or above
-        each of the values in `threshold_match_weights`) are considered to be a match 
+        each of the values in `threshold_match_weights`) are considered to be a match
         (i.e. they represent the same entity).
 
         This function efficiently computes clusters for multiple thresholds by starting
-        with the lowest threshold and incrementally updating clusters for higher thresholds.
+        with the lowest threshold and incrementally updating clusters for higher
+         thresholds.
 
         Args:
             df_predict (SplinkDataFrame): The results of `linker.predict()`
-            threshold_match_probabilities (list[float] | None): List of match probability
-                thresholds to compute clusters for
-            threshold_match_weights (list[float] | None): List of match weight thresholds
-                to compute clusters for
+            threshold_match_probabilities (list[float] | None): List of match
+                probability thresholds to compute clusters for
+            threshold_match_weights (list[float] | None): List of match weight
+                 thresholds to compute clusters for
             output_cluster_summary_stats (bool): If True, output summary statistics
                 for each threshold instead of full cluster information
 
         Returns:
             SplinkDataFrame: A SplinkDataFrame containing a list of all IDs, clustered
-                into groups for each of the desired match thresholds. 
+                into groups for each of the desired match thresholds.
                 If output_cluster_summary_stats is True, it contains summary
                 statistics (number of clusters, max cluster size, avg cluster size) for
                 each threshold.
@@ -428,19 +429,21 @@ def cluster_pairwise_predictions_at_multiple_thresholds(
         ]
 
         is_match_weight = (
-            threshold_match_weights is not None and threshold_match_probabilities is None
+            threshold_match_weights is not None 
+            and threshold_match_probabilities is None
         )
 
         threshold_match_probabilities = threshold_args_to_match_prob_list(
             threshold_match_probabilities, threshold_match_weights
         )
 
-        if threshold_match_probabilities is None or len(threshold_match_probabilities) == 0:
+        if (threshold_match_probabilities is None 
+            or len(threshold_match_probabilities) == 0):
             raise ValueError(
                 "Must provide either threshold_match_probabilities "
                 "or threshold_match_weights"
             )
-        
+
         if not has_match_prob_col and threshold_match_probabilities is not None:
             raise ValueError(
                 "df_predict must have a column called 'match_probability' if "
@@ -607,7 +610,9 @@ def cluster_pairwise_predictions_at_multiple_thresholds(
         left join __splink__df_concat
         on co.node_id = {uid_concat_nodes}
         """
-        pipeline.enqueue_sql(sql, "__splink__clusters_at_all_thresholds_with_input_data")
+        pipeline.enqueue_sql(sql, 
+                             "__splink__clusters_at_all_thresholds_with_input_data"
+                             )
 
         df_clustered_with_input_data = db_api.sql_pipeline_to_splink_dataframe(pipeline)
 
@@ -618,7 +623,7 @@ def cluster_pairwise_predictions_at_multiple_thresholds(
         df_clustered_with_input_data.metadata["threshold_match_probabilities"] = (
                 [initial_threshold] + threshold_match_probabilities
             )
-        
+
         return df_clustered_with_input_data
 
     def _compute_metrics_nodes(