incremental typos
@@ -77,14 +77,14 @@ class Linker:
self._validate_input_dfs()
self.em_training_sessions = []
- self.incremental_linkage_mode = False
+ self.find_new_matches_mode = False
self.train_u_using_random_sample_mode = False
self.compare_two_records_mode = False
@property
def _input_tablename_l(self):
- if self.incremental_linkage_mode:
+ if self.find_new_matches_mode:
return "__splink__df_concat_with_tf"
if self.compare_two_records_mode:
@@ -100,7 +100,7 @@ class Linker:
@property
def _input_tablename_r(self):
- if self.incremental_linkage_mode:
+ if self.find_new_matches_mode:
return "__splink__df_new_records_with_tf"
if self.compare_two_records_mode:
@@ -117,7 +117,7 @@ class Linker:
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.incremental_linkage_mode:
+ if self.find_new_matches_mode:
return True
if self.compare_two_records_mode:
@@ -404,7 +404,7 @@ class Linker:
if blocking_rules is not None:
self.settings_obj._blocking_rules_to_generate_predictions = blocking_rules
self.settings_obj._link_type = "link_only"
- self.incremental_linkage_mode = True
+ self.find_new_matches_mode = True
sql = join_tf_to_input_df(self.settings_obj)
sql = sql.replace("__splink__df_concat", "__splink__df_new_records")
@@ -425,7 +425,7 @@ class Linker:
where match_weight > {match_weight_threshold}
"""
- self.enqueue_sql(sql, "__splink_incremental_predictions")
+ self.enqueue_sql(sql, "__splink_find_matches_predictions")
predictions = self.execute_sql_pipeline(use_cache=False)
@@ -433,7 +433,7 @@ class Linker:
original_blocking_rules
)
self.settings_obj._link_type = original_link_type
- self.incremental_linkage_mode = False
+ self.find_new_matches_mode = False
return predictions
