Support Series and Python primitives in inplace_predict and QDM (#8547)

python-package/xgboost/core.py

@@ -2220,17 +2220,15 @@ class Booster:
         preds = ctypes.POINTER(ctypes.c_float)()
 
         # once caching is supported, we can pass id(data) as cache id.
-        args = {
-            "type": 0,
-            "training": False,
-            "iteration_begin": iteration_range[0],
-            "iteration_end": iteration_range[1],
-            "missing": missing,
-            "strict_shape": strict_shape,
-            "cache_id": 0,
-        }
-        if predict_type == "margin":
-            args["type"] = 1
+        args = make_jcargs(
+            type=1 if predict_type == "margin" else 0,
+            training=False,
+            iteration_begin=iteration_range[0],
+            iteration_end=iteration_range[1],
+            missing=missing,
+            strict_shape=strict_shape,
+            cache_id=0,
+        )
         shape = ctypes.POINTER(c_bst_ulong)()
         dims = c_bst_ulong()
 
@@ -2243,6 +2241,29 @@ class Booster:
             proxy = None
             p_handle = ctypes.c_void_p()
         assert proxy is None or isinstance(proxy, _ProxyDMatrix)
+
+        from .data import (
+            _array_interface,
+            _is_cudf_df,
+            _is_cupy_array,
+            _is_list,
+            _is_pandas_df,
+            _is_pandas_series,
+            _is_tuple,
+            _transform_pandas_df,
+        )
+
+        enable_categorical = True
+        if _is_pandas_series(data):
+            import pandas as pd
+            data = pd.DataFrame(data)
+        if _is_pandas_df(data):
+            data, fns, _ = _transform_pandas_df(data, enable_categorical)
+            if validate_features:
+                self._validate_features(fns)
+        if _is_list(data) or _is_tuple(data):
+            data = np.array(data)
+
         if validate_features:
             if not hasattr(data, "shape"):
                 raise TypeError(
@@ -2254,20 +2275,6 @@ class Booster:
                     f"got {data.shape[1]}"
                 )
 
-        from .data import (
-            _array_interface,
-            _is_cudf_df,
-            _is_cupy_array,
-            _is_pandas_df,
-            _transform_pandas_df,
-        )
-
-        enable_categorical = True
-        if _is_pandas_df(data):
-            data, fns, _ = _transform_pandas_df(data, enable_categorical)
-            if validate_features:
-                self._validate_features(fns)
-
         if isinstance(data, np.ndarray):
             from .data import _ensure_np_dtype
 
@@ -2276,7 +2283,7 @@ class Booster:
                 _LIB.XGBoosterPredictFromDense(
                     self.handle,
                     _array_interface(data),
-                    from_pystr_to_cstr(json.dumps(args)),
+                    args,
                     p_handle,
                     ctypes.byref(shape),
                     ctypes.byref(dims),
@@ -2293,7 +2300,7 @@ class Booster:
                     _array_interface(csr.indices),
                     _array_interface(csr.data),
                     c_bst_ulong(csr.shape[1]),
-                    from_pystr_to_cstr(json.dumps(args)),
+                    args,
                     p_handle,
                     ctypes.byref(shape),
                     ctypes.byref(dims),
@@ -2310,7 +2317,7 @@ class Booster:
                 _LIB.XGBoosterPredictFromCudaArray(
                     self.handle,
                     interface_str,
-                    from_pystr_to_cstr(json.dumps(args)),
+                    args,
                     p_handle,
                     ctypes.byref(shape),
                     ctypes.byref(dims),
@@ -2331,7 +2338,7 @@ class Booster:
                 _LIB.XGBoosterPredictFromCudaColumnar(
                     self.handle,
                     interfaces_str,
-                    from_pystr_to_cstr(json.dumps(args)),
+                    args,
                     p_handle,
                     ctypes.byref(shape),
                     ctypes.byref(dims),

python-package/xgboost/data.py

@@ -958,12 +958,12 @@ def dispatch_data_backend(
         return _from_list(data, missing, threads, feature_names, feature_types)
     if _is_tuple(data):
         return _from_tuple(data, missing, threads, feature_names, feature_types)
-    if _is_pandas_df(data):
-        return _from_pandas_df(data, enable_categorical, missing, threads,
-                               feature_names, feature_types)
     if _is_pandas_series(data):
-        return _from_pandas_series(
-            data, missing, threads, enable_categorical, feature_names, feature_types
+        import pandas as pd
+        data = pd.DataFrame(data)
+    if _is_pandas_df(data):
+        return _from_pandas_df(
+            data, enable_categorical, missing, threads, feature_names, feature_types
         )
     if _is_cudf_df(data) or _is_cudf_ser(data):
         return _from_cudf_df(
@@ -1205,6 +1205,9 @@ def _proxy_transform(
         return data, None, feature_names, feature_types
     if _is_scipy_csr(data):
         return data, None, feature_names, feature_types
+    if _is_pandas_series(data):
+        import pandas as pd
+        data = pd.DataFrame(data)
     if _is_pandas_df(data):
         arr, feature_names, feature_types = _transform_pandas_df(
             data, enable_categorical, feature_names, feature_types

python-package/xgboost/testing/data.py

@@ -40,6 +40,7 @@ def np_dtypes(
     for dtype in dtypes:
         X = np.array(orig, dtype=dtype)
         yield orig, X
+        yield orig.tolist(), X.tolist()
 
     for dtype in dtypes:
         X = np.array(orig, dtype=dtype)
@@ -101,6 +102,11 @@ def pd_dtypes() -> Generator:
                 {"f0": [1.0, 2.0, Null, 3.0], "f1": [3.0, 2.0, Null, 1.0]}, dtype=dtype
             )
             yield orig, df
+            ser_orig = orig["f0"]
+            ser = df["f0"]
+            assert isinstance(ser, pd.Series)
+            assert isinstance(ser_orig, pd.Series)
+            yield ser_orig, ser
 
     # Categorical
     orig = orig.astype("category")