First pass at unblocking dask-expr issues - replace _Frame usage

dask-contrib · Mar 22, 2024 · 0fb485d · 0fb485d
1 parent 61513d3
commit 0fb485d
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Showing 3 changed files with 11 additions and 11 deletions.
diff --git a/dask_sql/physical/rel/custom/wrappers.py b/dask_sql/physical/rel/custom/wrappers.py
@@ -207,7 +207,7 @@ def transform(self, X):
                 estimator=self._postfit_estimator,
                 meta=output_meta,
             )
-        elif isinstance(X, dd._Frame):
+        elif isinstance(X, dd.DataFrame):
             if output_meta is None:
                 output_meta = _transform(X._meta_nonempty, self._postfit_estimator)
             try:
@@ -305,7 +305,7 @@ def predict(self, X):
             )
             return result
 
-        elif isinstance(X, dd._Frame):
+        elif isinstance(X, dd.DataFrame):
             if output_meta is None:
                 # dask-dataframe relies on dd.core.no_default
                 # for infering meta
@@ -364,7 +364,7 @@ def predict_proba(self, X):
                 meta=output_meta,
                 chunks=(X.chunks[0], len(self._postfit_estimator.classes_)),
             )
-        elif isinstance(X, dd._Frame):
+        elif isinstance(X, dd.DataFrame):
             if output_meta is None:
                 # dask-dataframe relies on dd.core.no_default
                 # for infering meta

diff --git a/dask_sql/physical/utils/filter.py b/dask_sql/physical/utils/filter.py
@@ -304,10 +304,10 @@ def combine(self, other: DNF | _And | _Or | list | tuple | None) -> DNF:
 # Specify functions that must be generated with
 # a different API at the dataframe-collection level
 _special_op_mappings = {
-    M.fillna: dd._Frame.fillna,
-    M.isin: dd._Frame.isin,
-    M.isna: dd._Frame.isna,
-    M.astype: dd._Frame.astype,
+    M.fillna: dd.DataFrame.fillna,
+    M.isin: dd.DataFrame.isin,
+    M.isna: dd.DataFrame.isna,
+    M.astype: dd.DataFrame.astype,
 }
 
 # Convert _pass_through_ops to respect "special" mappings
@@ -316,7 +316,7 @@ def combine(self, other: DNF | _And | _Or | list | tuple | None) -> DNF:
 
 def _preprocess_layers(input_layers):
     # NOTE: This is a Layer-specific work-around to deal with
-    # the fact that `dd._Frame.isin(values)` will add a distinct
+    # the fact that `dd.DataFrame.isin(values)` will add a distinct
     # `MaterializedLayer` for the `values` argument.
     # See: https://github.com/dask-contrib/dask-sql/issues/607
     skip = set()
@@ -418,9 +418,9 @@ def _dnf_filter_expression(self, dsk):
             func = _blockwise_logical_dnf
         elif op == operator.getitem:
             func = _blockwise_getitem_dnf
-        elif op == dd._Frame.isin:
+        elif op == dd.DataFrame.isin:
             func = _blockwise_isin_dnf
-        elif op == dd._Frame.isna:
+        elif op == dd.DataFrame.isna:
             func = _blockwise_isna_dnf
         elif op == operator.inv:
             func = _blockwise_inv_dnf

diff --git a/tests/unit/test_ml_utils.py b/tests/unit/test_ml_utils.py
@@ -98,7 +98,7 @@ def make_classification(
 
 def _assert_eq(l, r, name=None, **kwargs):
     array_types = (np.ndarray, da.Array)
-    frame_types = (pd.core.generic.NDFrame, dd._Frame)
+    frame_types = (pd.core.generic.NDFrame, dd.DataFrame)
     if isinstance(l, array_types):
         assert_eq_ar(l, r, **kwargs)
     elif isinstance(l, frame_types):