'can only concatenate str (not "float") to str' while all dtypes are float64, int64

[GrigoriiTarasov]

Just runed as in example

from boostaroota import BoostARoota

br = BoostARoota(metric='logloss')
br.fit(X, y)

for classification task

getting

[06:31:00] WARNING: ../src/learner.cc:767: 
Parameters: { "silent" } are not used.

Round:  1  iteration:  10
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[22], line 4
      1 br = BoostARoota(metric='logloss')
      3 #Fit the model for the subset of variables
----> 4 br.fit(X, y)

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/boostaroota/boostaroota.py:46, in BoostARoota.fit(self, x, y)
     45 def fit(self, x, y):
---> 46     self.keep_vars_ = _BoostARoota(x, y,
     47                                    metric=self.metric,
     48                                    clf = self.clf,
     49                                    cutoff=self.cutoff,
     50                                    iters=self.iters,
     51                                    max_rounds=self.max_rounds,
     52                                    delta=self.delta,
     53                                    silent=self.silent)
     54     return self

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/boostaroota/boostaroota.py:219, in _BoostARoota(x, y, metric, clf, cutoff, iters, max_rounds, delta, silent)
    217 i += 1
    218 if clf is None:
--> 219     crit, keep_vars = _reduce_vars_xgb(new_x,
    220                                        y,
    221                                        metric=metric,
    222                                        this_round=i,
    223                                        cutoff=cutoff,
    224                                        n_iterations=iters,
    225                                        delta=delta,
    226                                        silent=silent)
    227 else:
    228     crit, keep_vars = _reduce_vars_sklearn(new_x,
    229                                            y,
    230                                            clf=clf,
   (...)
    234                                            delta=delta,
    235                                            silent=silent)

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/boostaroota/boostaroota.py:130, in _reduce_vars_xgb(x, y, metric, this_round, cutoff, n_iterations, delta, silent)
    127     if not silent:
    128         print("Round: ", this_round, " iteration: ", i)
--> 130 df['Mean'] = df.mean(axis=1)
    131 #Split them back out
    132 real_vars = df[~df['feature'].isin(shadow_names)]

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/generic.py:11556, in NDFrame._add_numeric_operations.<locals>.mean(self, axis, skipna, numeric_only, **kwargs)
  11539 @doc(
  11540     _num_doc,
  11541     desc="Return the mean of the values over the requested axis.",
   (...)
  11554     **kwargs,
  11555 ):
> 11556     return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/generic.py:11201, in NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
  11194 def mean(
  11195     self,
  11196     axis: Axis | None = 0,
   (...)
  11199     **kwargs,
  11200 ) -> Series | float:
> 11201     return self._stat_function(
  11202         "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
  11203     )

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/generic.py:11158, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs)
  11154     nv.validate_stat_func((), kwargs, fname=name)
  11156 validate_bool_kwarg(skipna, "skipna", none_allowed=False)
> 11158 return self._reduce(
  11159     func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
  11160 )

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/frame.py:10524, in DataFrame._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
  10520     df = df.T
  10522 # After possibly _get_data and transposing, we are now in the
  10523 #  simple case where we can use BlockManager.reduce
> 10524 res = df._mgr.reduce(blk_func)
  10525 out = df._constructor(res).iloc[0]
  10526 if out_dtype is not None:

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/internals/managers.py:1534, in BlockManager.reduce(self, func)
   1532 res_blocks: list[Block] = []
   1533 for blk in self.blocks:
-> 1534     nbs = blk.reduce(func)
   1535     res_blocks.extend(nbs)
   1537 index = Index([None])  # placeholder

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/internals/blocks.py:339, in Block.reduce(self, func)
    333 @final
    334 def reduce(self, func) -> list[Block]:
    335     # We will apply the function and reshape the result into a single-row
    336     #  Block with the same mgr_locs; squeezing will be done at a higher level
    337     assert self.ndim == 2
--> 339     result = func(self.values)
    341     if self.values.ndim == 1:
    342         # TODO(EA2D): special case not needed with 2D EAs
    343         res_values = np.array([[result]])

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/frame.py:10487, in DataFrame._reduce.<locals>.blk_func(values, axis)
  10485     return values._reduce(name, skipna=skipna, **kwds)
  10486 else:
> 10487     return op(values, axis=axis, skipna=skipna, **kwds)

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/nanops.py:96, in disallow.__call__.<locals>._f(*args, **kwargs)
     94 try:
     95     with np.errstate(invalid="ignore"):
---> 96         return f(*args, **kwargs)
     97 except ValueError as e:
     98     # we want to transform an object array
     99     # ValueError message to the more typical TypeError
    100     # e.g. this is normally a disallowed function on
    101     # object arrays that contain strings
    102     if is_object_dtype(args[0]):

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/nanops.py:158, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
    156         result = alt(values, axis=axis, skipna=skipna, **kwds)
    157 else:
--> 158     result = alt(values, axis=axis, skipna=skipna, **kwds)
    160 return result

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/nanops.py:421, in _datetimelike_compat.<locals>.new_func(values, axis, skipna, mask, **kwargs)
    418 if datetimelike and mask is None:
    419     mask = isna(values)
--> 421 result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
    423 if datetimelike:
    424     result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/pandas/core/nanops.py:727, in nanmean(values, axis, skipna, mask)
    724     dtype_count = dtype
    726 count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
--> 727 the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
    729 if axis is not None and getattr(the_sum, "ndim", False):
    730     count = cast(np.ndarray, count)

File ~/anaconda3/envs/filter/lib/python3.11/site-packages/numpy/core/_methods.py:48, in _sum(a, axis, dtype, out, keepdims, initial, where)
     43 def _amin(a, axis=None, out=None, keepdims=False,
     44           initial=_NoValue, where=True):
     45     return umr_minimum(a, axis, None, out, keepdims, initial, where)
     47 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
---> 48          initial=_NoValue, where=True):
     49     return umr_sum(a, axis, dtype, out, keepdims, initial, where)
     51 def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
     52           initial=_NoValue, where=True):

TypeError: can only concatenate str (not "float") to str```


When I calculate `df.mean(axis=1)` it outputs correct answer without failing.