ENH preserve dtype and type when providing a dataframe with sparse dtype

doc/whats_new/v0.12.rst

@@ -42,3 +42,9 @@ Deprecations
 - Deprecate `kind_sel` in :class:`~imblearn.under_sampling.NeighbourhoodCleaningRule.
   It will be removed in 0.14. The parameter does not have any effect.
   :pr:`1012` by :user:`Guillaume Lemaitre <glemaitre>`.
+
+Enhancements
+............
+
+- Allows to output dataframe with sparse format if provided as input.
+  :pr:`1059` by :user:`ts2095 <ts2095>`.

imblearn/utils/_validation.py

@@ -10,6 +10,7 @@
 from numbers import Integral, Real
 
 import numpy as np
+from scipy.sparse import issparse
 from sklearn.base import clone
 from sklearn.neighbors import NearestNeighbors
 from sklearn.utils import check_array, column_or_1d
@@ -61,7 +62,10 @@ def _transfrom_one(self, array, props):
         elif type_ == "dataframe":
             import pandas as pd
 
-            ret = pd.DataFrame(array, columns=props["columns"])
+            if issparse(array):
+                ret = pd.DataFrame.sparse.from_spmatrix(array, columns=props["columns"])
+            else:
+                ret = pd.DataFrame(array, columns=props["columns"])
             ret = ret.astype(props["dtypes"])
         elif type_ == "series":
             import pandas as pd

imblearn/utils/estimator_checks.py

@@ -108,6 +108,7 @@ def _yield_sampler_checks(sampler):
         yield check_samplers_sparse
     if "dataframe" in tags["X_types"]:
         yield check_samplers_pandas
+        yield check_samplers_pandas_sparse
     if "string" in tags["X_types"]:
         yield check_samplers_string
     if tags["allow_nan"]:
@@ -312,6 +313,34 @@ def check_samplers_sparse(name, sampler_orig):
     assert_allclose(y_res_sparse, y_res)
 
 
+def check_samplers_pandas_sparse(name, sampler_orig):
+    pd = pytest.importorskip("pandas")
+    sampler = clone(sampler_orig)
+    # Check that the samplers handle pandas dataframe and pandas series
+    X, y = sample_dataset_generator()
+    X_df = pd.DataFrame(
+        X, columns=[str(i) for i in range(X.shape[1])], dtype=pd.SparseDtype(float, 0)
+    )
+    y_s = pd.Series(y, name="class")
+
+    X_res_df, y_res_s = sampler.fit_resample(X_df, y_s)
+    X_res, y_res = sampler.fit_resample(X, y)
+
+    # check that we return the same type for dataframes or series types
+    assert isinstance(X_res_df, pd.DataFrame)
+    assert isinstance(y_res_s, pd.Series)
+
+    for column_dtype in X_res_df.dtypes:
+        assert isinstance(column_dtype, pd.SparseDtype)
+
+    assert X_df.columns.tolist() == X_res_df.columns.tolist()
+    assert y_s.name == y_res_s.name
+
+    # FIXME: we should use to_numpy with pandas >= 0.25
+    assert_allclose(X_res_df.values, X_res)
+    assert_allclose(y_res_s.values, y_res)
+
+
 def check_samplers_pandas(name, sampler_orig):
     pd = pytest.importorskip("pandas")
     sampler = clone(sampler_orig)