From 8924cc2f07a36def424ea90434e4b6a18ed4680d Mon Sep 17 00:00:00 2001
From: Jim Crist-Harif <jcristharif@gmail.com>
Date: Wed, 20 Mar 2024 10:48:32 -0500
Subject: [PATCH] Rename `to_table` to `to_ibis`

This better matches the other `to_*` method conventions.
---
 ibisml/core.py     | 14 +++++++-------
 tests/test_core.py |  2 +-
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/ibisml/core.py b/ibisml/core.py
index b060d37..6b6f966 100644
--- a/ibisml/core.py
+++ b/ibisml/core.py
@@ -366,7 +366,7 @@ def _categorize_pyarrow_batches(
             _categorize_wrap_reader(reader, self.metadata_.categories),
         )
 
-    def to_table(self, X) -> ir.Table:
+    def to_ibis(self, X) -> ir.Table:
         """Transform X and return an ibis table.
 
         Parameters
@@ -394,7 +394,7 @@ def to_pandas(self, X: Any, categories: bool = False) -> pd.DataFrame:
             as numeric columns containing only their integral categorical
             codes.
         """
-        df = self.to_table(X).to_pandas()
+        df = self.to_ibis(X).to_pandas()
         if categories:
             return self._categorize_pandas(df)
         return df
@@ -407,7 +407,7 @@ def to_numpy(self, X: Any) -> np.ndarray:
         X : table-like
             The input data to transform.
         """
-        table = self.to_table(X)
+        table = self.to_ibis(X)
         if not all(t.is_numeric() for t in table.schema().types):
             raise ValueError(
                 "Not all output columns are numeric, cannot convert to a numpy array"
@@ -422,7 +422,7 @@ def to_polars(self, X: Any) -> pl.DataFrame:
         X : table-like
             The input data to transform.
         """
-        return self.to_table(X).to_polars()
+        return self.to_ibis(X).to_polars()
 
     def to_pyarrow(self, X: Any, categories: bool = False) -> pa.Table:
         """Transform X and return a ``pyarrow.Table``.
@@ -437,7 +437,7 @@ def to_pyarrow(self, X: Any, categories: bool = False) -> pa.Table:
             as numeric columns containing only their integral categorical
             codes.
         """
-        table = self.to_table(X).to_pyarrow()
+        table = self.to_ibis(X).to_pyarrow()
         if categories:
             table = self._categorize_pyarrow(table)
         return table
@@ -457,7 +457,7 @@ def to_pyarrow_batches(
             as numeric columns containing only their integral categorical
             codes.
         """
-        reader = self.to_table(X).to_pyarrow_batches()
+        reader = self.to_ibis(X).to_pyarrow_batches()
         if categories:
             return self._categorize_pyarrow_batches(reader)
         return reader
@@ -477,7 +477,7 @@ def to_dask_dataframe(self, X: Any, categories: bool = False) -> dd.DataFrame:
         """
         import dask.dataframe as dd
 
-        table = self.to_table(X)
+        table = self.to_ibis(X)
 
         con = ibis.get_backend(table)
         if hasattr(con, "to_dask"):
diff --git a/tests/test_core.py b/tests/test_core.py
index 09c07b9..9caed4b 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -51,7 +51,7 @@ def test_sklearn_clone(table):
     assert not r2.is_fitted()
 
     r2.fit(table)
-    assert r1.to_table(table).equals(r2.to_table(table))
+    assert r1.to_ibis(table).equals(r2.to_ibis(table))
 
 
 def test_in_memory_workflow(df):