build: make NumPy, pandas, and Arrow deps optional (ibis-project#152)

Co-authored-by: Deepyaman Datta <[email protected]>

ibis_ml/__init__.py

@@ -44,11 +44,12 @@ def _auto_patch_skorch() -> None:
         return
 
     import ibis.expr.types as ir
-    import numpy as np
 
     old_fit = skorch.net.NeuralNet.fit
 
     def fit(self, X, y=None, **fit_params):
+        import numpy as np
+
         if isinstance(y, ir.Column):
             y = np.asarray(y)
 

ibis_ml/core.py

@@ -12,14 +12,14 @@
 import ibis
 import ibis.expr.operations as ops
 import ibis.expr.types as ir
-import numpy as np
-import pandas as pd
-import pyarrow as pa
 from ibis.common.dispatch import lazy_singledispatch
 
 if TYPE_CHECKING:
     import dask.dataframe as dd
+    import numpy as np
+    import pandas as pd
     import polars as pl
+    import pyarrow as pa
     import xgboost as xgb
     from sklearn.utils._estimator_html_repr import _VisualBlock
 
@@ -45,6 +45,9 @@ def _ibis_table_to_numpy(table: ir.Table) -> np.ndarray:
 
 def _y_as_dataframe(y: Any) -> pd.DataFrame:
     """Coerce `y` to a pandas dataframe"""
+    import numpy as np
+    import pandas as pd
+
     if isinstance(y, pd.DataFrame):
         return y
     elif isinstance(y, pd.Series):
@@ -144,8 +147,11 @@ def _(X, y=None, maintain_order=False):
     return table, tuple(y.columns), None
 
 
-@normalize_table.register(pd.DataFrame)
+@normalize_table.register("pd.DataFrame")
 def _(X, y=None, maintain_order=False):
+    import numpy as np
+    import pandas as pd
+
     if y is not None:
         y = _y_as_dataframe(y)
         table = pd.concat([X, y], axis=1)
@@ -162,8 +168,11 @@ def _(X, y=None, maintain_order=False):
     return ibis.memtable(table), targets, index
 
 
-@normalize_table.register(np.ndarray)
+@normalize_table.register("np.ndarray")
 def _(X, y=None, maintain_order=False):
+    import numpy as np
+    import pandas as pd
+
     X = pd.DataFrame(X, columns=[f"x{i}" for i in range(X.shape[-1])])
     if y is not None:
         y = _y_as_dataframe(y)
@@ -181,8 +190,11 @@ def _(X, y=None, maintain_order=False):
     return ibis.memtable(table), targets, index
 
 
-@normalize_table.register(pa.Table)
+@normalize_table.register("pa.Table")
 def _(X, y=None, maintain_order=False):
+    import numpy as np
+    import pyarrow as pa
+
     if y is not None:
         if isinstance(y, (pa.ChunkedArray, pa.Array)):
             y = pa.Table.from_pydict({"y": y})
@@ -246,6 +258,8 @@ def get_categories(self, column: str) -> pa.Array | None:
         return self.categories.get(column)
 
     def set_categories(self, column: str, values: pa.Array | list[Any]) -> None:
+        import pyarrow as pa
+
         self.categories[column] = pa.array(values)
 
     def drop_categories(self, column: str) -> None:
@@ -255,6 +269,8 @@ def drop_categories(self, column: str) -> None:
 def _categorize_wrap_reader(
     reader: pa.RecordBatchReader, categories: dict[str, pa.Array]
 ) -> Iterable[pa.RecordBatch]:
+    import pyarrow as pa
+
     for batch in reader:
         out = {}
         for name, col in zip(batch.schema.names, batch.columns):
@@ -620,6 +636,8 @@ def _categorize_pandas(self, df: pd.DataFrame) -> pd.DataFrame:
         return df
 
     def _categorize_pyarrow(self, table: pa.Table) -> pa.Table:
+        import pyarrow as pa
+
         if not self.metadata_.categories:
             return table
 
@@ -645,6 +663,8 @@ def _categorize_dask_dataframe(self, ddf: dd.DataFrame) -> dd.DataFrame:
     def _categorize_pyarrow_batches(
         self, reader: pa.RecordBatchReader
     ) -> pa.RecordBatchReader:
+        import pyarrow as pa
+
         if not self.metadata_.categories:
             return reader
 

ibis_ml/steps/_discretize.py

@@ -4,7 +4,6 @@
 
 import ibis
 import ibis.expr.types as ir
-import numpy as np
 
 from ibis_ml.core import Metadata, Step
 from ibis_ml.select import SelectionType, selector
@@ -94,6 +93,8 @@ def fit_table(self, table: ir.Table, metadata: Metadata) -> None:
     def _fit_uniform_strategy(
         self, table: ir.Table, columns: list[str]
     ) -> dict[str, list[float]]:
+        import numpy as np
+
         aggs = []
         for col_name in columns:
             col = table[col_name]
@@ -117,6 +118,8 @@ def _fit_uniform_strategy(
     def _fit_quantile_strategy(
         self, table: ir.Table, columns: list[str]
     ) -> dict[str, list[float]]:
+        import numpy as np
+
         aggs = []
         percentiles = np.linspace(0, 1, self.n_bins + 1)
         for col_name in columns:

tests/test_optional_dependencies.py

@@ -0,0 +1,15 @@
+import sys
+from importlib import import_module, reload
+from unittest.mock import patch
+
+import pytest
+
+
+# https://stackoverflow.com/a/65163627
+@pytest.mark.parametrize("optional_dependency", ["numpy", "pandas", "pyarrow"])
+def test_without_dependency(optional_dependency):
+    with patch.dict(sys.modules, {optional_dependency: None}):
+        if "ibis_ml" in sys.modules:
+            reload(sys.modules["ibis_ml"])
+        else:
+            import_module("ibis_ml")