Add typehint to rabit module. (#7240)

Makefile

@@ -92,6 +92,7 @@ endif
 mypy:
 	cd python-package; \
 	mypy ./xgboost/dask.py && \
+	mypy ./xgboost/rabit.py && \
 	mypy ../demo/guide-python/external_memory.py && \
 	mypy ../tests/python-gpu/test_gpu_with_dask.py && \
 	mypy ../tests/python/test_data_iterator.py && \

python-package/xgboost/core.py

@@ -7,7 +7,7 @@ import collections
 from collections.abc import Mapping
 from typing import List, Optional, Any, Union, Dict, TypeVar
 # pylint: enable=no-name-in-module,import-error
-from typing import Callable, Tuple
+from typing import Callable, Tuple, cast
 import ctypes
 import os
 import re
@@ -2008,7 +2008,8 @@ class Booster(object):
         dims = c_bst_ulong()
 
         if base_margin is not None:
-            proxy = _ProxyDMatrix()
+            proxy: Optional[_ProxyDMatrix] = _ProxyDMatrix()
+            assert proxy is not None
             proxy.set_info(base_margin=base_margin)
             p_handle = proxy.handle
         else:
@@ -2274,8 +2275,8 @@ class Booster(object):
         return self.get_score(fmap, importance_type='weight')
 
     def get_score(
-        self, fmap: os.PathLike = '', importance_type: str = 'weight'
-    ) -> Dict[str, float]:
+        self, fmap: Union[str, os.PathLike] = '', importance_type: str = 'weight'
+    ) -> Dict[str, Union[float, List[float]]]:
         """Get feature importance of each feature.
         For tree model Importance type can be defined as:
 
@@ -2332,7 +2333,7 @@ class Booster(object):
         features_arr = from_cstr_to_pystr(features, n_out_features)
         scores_arr = _prediction_output(shape, out_dim, scores, False)
 
-        results = {}
+        results: Dict[str, Union[float, List[float]]] = {}
         if len(scores_arr.shape) > 1 and scores_arr.shape[1] > 1:
             for feat, score in zip(features_arr, scores_arr):
                 results[feat] = [float(s) for s in score]
@@ -2481,7 +2482,7 @@ class Booster(object):
         fmap: Union[os.PathLike, str] = '',
         bins: Optional[int] = None,
         as_pandas: bool = True
-    ):
+    ) -> Union[np.ndarray, DataFrame]:
         """Get split value histogram of a feature
 
         Parameters
@@ -2526,7 +2527,7 @@ class Booster(object):
                 fn = [f"f{i}" for i in range(self.num_features())]
             try:
                 index = fn.index(feature)
-                feature_t = ft[index]
+                feature_t: Optional[str] = cast(List[str], ft)[index]
             except (ValueError, AttributeError, TypeError):
                 # None.index: attr err, None[0]: type err, fn.index(-1): value err
                 feature_t = None

python-package/xgboost/rabit.py

@@ -3,12 +3,14 @@
 """Distributed XGBoost Rabit related API."""
 import ctypes
 import pickle
+from typing import Any, TypeVar, Callable, Optional, cast, List, Union
+
 import numpy as np
 
 from .core import _LIB, c_str, STRING_TYPES, _check_call
 
 
-def _init_rabit():
+def _init_rabit() -> None:
     """internal library initializer."""
     if _LIB is not None:
         _LIB.RabitGetRank.restype = ctypes.c_int
@@ -17,21 +19,21 @@ def _init_rabit():
         _LIB.RabitVersionNumber.restype = ctypes.c_int
 
 
-def init(args=None):
+def init(args: Optional[List[bytes]] = None) -> None:
     """Initialize the rabit library with arguments"""
     if args is None:
         args = []
     arr = (ctypes.c_char_p * len(args))()
-    arr[:] = args
+    arr[:] = cast(List[Union[ctypes.c_char_p, bytes, None, int]], args)
     _LIB.RabitInit(len(arr), arr)
 
 
-def finalize():
+def finalize() -> None:
     """Finalize the process, notify tracker everything is done."""
     _LIB.RabitFinalize()
 
 
-def get_rank():
+def get_rank() -> int:
     """Get rank of current process.
 
     Returns
@@ -43,7 +45,7 @@ def get_rank():
     return ret
 
 
-def get_world_size():
+def get_world_size() -> int:
     """Get total number workers.
 
     Returns
@@ -55,13 +57,13 @@ def get_world_size():
     return ret
 
 
-def is_distributed():
+def is_distributed() -> int:
     '''If rabit is distributed.'''
     is_dist = _LIB.RabitIsDistributed()
     return is_dist
 
 
-def tracker_print(msg):
+def tracker_print(msg: Any) -> None:
     """Print message to the tracker.
 
     This function can be used to communicate the information of
@@ -81,7 +83,7 @@ def tracker_print(msg):
         print(msg.strip(), flush=True)
 
 
-def get_processor_name():
+def get_processor_name() -> bytes:
     """Get the processor name.
 
     Returns
@@ -96,7 +98,10 @@ def get_processor_name():
     return buf.value
 
 
-def broadcast(data, root):
+T = TypeVar("T")
+
+
+def broadcast(data: T, root: int) -> T:
     """Broadcast object from one node to all other nodes.
 
     Parameters
@@ -155,7 +160,9 @@ class Op:                     # pylint: disable=too-few-public-methods
     OR = 3
 
 
-def allreduce(data, op, prepare_fun=None):
+def allreduce(
+    data: np.ndarray, op: int, prepare_fun: Optional[Callable[[np.ndarray], None]] = None
+) -> np.ndarray:
     """Perform allreduce, return the result.
 
     Parameters
@@ -193,16 +200,17 @@ def allreduce(data, op, prepare_fun=None):
     else:
         func_ptr = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
 
-        def pfunc(_):
+        def pfunc(_: Any) -> None:
             """prepare function."""
-            prepare_fun(data)
+            fn = cast(Callable[[np.ndarray], None], prepare_fun)
+            fn(data)
         _check_call(_LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
                                         buf.size, DTYPE_ENUM__[buf.dtype],
                                         op, func_ptr(pfunc), None))
     return buf
 
 
-def version_number():
+def version_number() -> int:
     """Returns version number of current stored model.
 
     This means how many calls to CheckPoint we made so far.

python-package/xgboost/sklearn.py

@@ -23,14 +23,9 @@ from .compat import (
     XGBClassifierBase,
     XGBRegressorBase,
     XGBoostLabelEncoder,
-    DataFrame,
-    scipy_csr,
 )
 
-# Actually XGBoost supports a lot more data types including `scipy.sparse.csr_matrix` and
-# many others.  See `data.py` for a complete list.  The `array_like` here is just for
-# easier type checks.
-array_like = TypeVar("array_like", bound=Union[np.ndarray, DataFrame, scipy_csr])
+array_like = Any
 
 
 class XGBRankerMixIn:  # pylint: disable=too-few-public-methods