TypeError: unhashable type: 'numpy.ndarray' when running perturbation_space

[ernesto-iacucci]

Report

Hi, when I am running the code in "perturbation_space.ipynb" and I get the to section on logistic regression classifier space:

ps = pt.tl.LRClassifierSpace()
psadata = ps.compute(adata, embedding_key="X_pca", target_col="perturbation_name")
psadata

I get the following error:

---

TypeError Traceback (most recent call last)
Cell In[24], line 2
1 ps = pt.tl.LRClassifierSpace()
----> 2 psadata = ps.compute(adata, embedding_key="X_pca", target_col="perturbation_name")
3 psadata

File ~/anaconda3/lib/python3.10/site-packages/pertpy/tools/_perturbation_space/_discriminator_classifiers.py:83, in LRClassifierSpace.compute(self, adata, target_col, layer_key, embedding_key, test_split_size, max_iter)
81 # Save adata observations for embedding annotations in get_embeddings
82 adata_obs = adata.obs.reset_index(drop=True)
---> 83 adata_obs = adata_obs.groupby(target_col).agg(
84 lambda pert_group: np.nan if len(set(pert_group)) != 1 else list(set(pert_group))[0]
85 )
87 # Fit a logistic regression model for each perturbation
88 regression_model = LogisticRegression(max_iter=max_iter, class_weight="balanced")

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/generic.py:1482, in DataFrameGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
1480 gba = GroupByApply(self, [func], args=(), kwargs={})
1481 try:
-> 1482 result = gba.agg()
1484 except ValueError as err:
1485 if "No objects to concatenate" not in str(err):

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py:193, in Apply.agg(self)
190 return self.agg_dict_like()
191 elif is_list_like(func):
192 # we require a list, but not a 'str'
--> 193 return self.agg_list_like()
195 if callable(func):
196 f = com.get_cython_func(func)

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py:326, in Apply.agg_list_like(self)
318 def agg_list_like(self) -> DataFrame | Series:
319 """
320 Compute aggregation in the case of a list-like argument.
321
(...)
324 Result of aggregation.
325 """
--> 326 return self.agg_or_apply_list_like(op_name="agg")

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py:1571, in GroupByApply.agg_or_apply_list_like(self, op_name)
1566 # Only set as_index=True on groupby objects, not Window or Resample
1567 # that inherit from this class.
1568 with com.temp_setattr(
1569 obj, "as_index", True, condition=hasattr(obj, "as_index")
1570 ):
-> 1571 keys, results = self.compute_list_like(op_name, selected_obj, kwargs)
1572 result = self.wrap_results_list_like(keys, results)
1573 return result

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py:385, in Apply.compute_list_like(self, op_name, selected_obj, kwargs)
379 colg = obj._gotitem(col, ndim=1, subset=selected_obj.iloc[:, index])
380 args = (
381 [self.axis, *self.args]
382 if include_axis(op_name, colg)
383 else self.args
384 )
--> 385 new_res = getattr(colg, op_name)(func, *args, **kwargs)
386 results.append(new_res)
387 indices.append(index)

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/generic.py:257, in SeriesGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
255 kwargs["engine"] = engine
256 kwargs["engine_kwargs"] = engine_kwargs
--> 257 ret = self._aggregate_multiple_funcs(func, *args, **kwargs)
258 if relabeling:
259 # columns is not narrowed by mypy from relabeling flag
260 assert columns is not None # for mypy

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/generic.py:362, in SeriesGroupBy._aggregate_multiple_funcs(self, arg, *args, **kwargs)
360 for idx, (name, func) in enumerate(arg):
361 key = base.OutputKey(label=name, position=idx)
--> 362 results[key] = self.aggregate(func, *args, **kwargs)
364 if any(isinstance(x, DataFrame) for x in results.values()):
365 from pandas import concat

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/generic.py:294, in SeriesGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
291 return self._python_agg_general(func, *args, **kwargs)
293 try:
--> 294 return self._python_agg_general(func, *args, **kwargs)
295 except KeyError:
296 # KeyError raised in test_groupby.test_basic is bc the func does
297 # a dictionary lookup on group.name, but group name is not
298 # pinned in _python_agg_general, only in _aggregate_named
299 result = self._aggregate_named(func, *args, **kwargs)

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/generic.py:327, in SeriesGroupBy._python_agg_general(self, func, *args, **kwargs)
324 f = lambda x: func(x, *args, **kwargs)
326 obj = self._obj_with_exclusions
--> 327 result = self._grouper.agg_series(obj, f)
328 res = obj._constructor(result, name=obj.name)
329 return self._wrap_aggregated_output(res)

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/ops.py:864, in BaseGrouper.agg_series(self, obj, func, preserve_dtype)
857 if not isinstance(obj._values, np.ndarray):
858 # we can preserve a little bit more aggressively with EA dtype
859 # because maybe_cast_pointwise_result will do a try/except
860 # with _from_sequence. NB we are assuming here that _from_sequence
861 # is sufficiently strict that it casts appropriately.
862 preserve_dtype = True
--> 864 result = self._aggregate_series_pure_python(obj, func)
866 npvalues = lib.maybe_convert_objects(result, try_float=False)
867 if preserve_dtype:

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/ops.py:885, in BaseGrouper._aggregate_series_pure_python(self, obj, func)
882 splitter = self._get_splitter(obj, axis=0)
884 for i, group in enumerate(splitter):
--> 885 res = func(group)
886 res = extract_result(res)
888 if not initialized:
889 # We only do this validation on the first iteration

File ~/anaconda3/lib/python3.10/site-packages/pandas/core/groupby/generic.py:324, in SeriesGroupBy._python_agg_general..(x)
322 alias = com._builtin_table_alias[func]
323 warn_alias_replacement(self, orig_func, alias)
--> 324 f = lambda x: func(x, *args, **kwargs)
326 obj = self._obj_with_exclusions
327 result = self._grouper.agg_series(obj, f)

File ~/anaconda3/lib/python3.10/site-packages/pertpy/tools/_perturbation_space/_discriminator_classifiers.py:84, in LRClassifierSpace.compute..(pert_group)
81 # Save adata observations for embedding annotations in get_embeddings
82 adata_obs = adata.obs.reset_index(drop=True)
83 adata_obs = adata_obs.groupby(target_col).agg(
---> 84 lambda pert_group: np.nan if len(set(pert_group)) != 1 else list(set(pert_group))[0]
85 )
87 # Fit a logistic regression model for each perturbation
88 regression_model = LogisticRegression(max_iter=max_iter, class_weight="balanced")

TypeError: unhashable type: 'numpy.ndarray'

Version information

---

anndata 0.10.8
pandas 2.2.3
pertpy 0.9.4
scanpy 1.10.3
session_info 1.0.0

Cython 3.0.10
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Python 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0]
Linux-5.10.226-214.879.amzn2.x86_64-x86_64-with-glibc2.26

Session information updated at 2024-10-14 22:51

[Lilly-May]

Hi @ernesto-iacucci! Thanks for reporting this issue. I just tried, but I couldn’t reproduce your error. Are you using the dataset from the tutorial (pt.dt.norman_2019()) or your own data?

[ernesto-iacucci]

Hi, I am using: adata = pt.dt.norman_2019()