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ValueError: array must not contain infs or NaNs
Hey,
import diffxpy.api as de
de_w_test = de.test.wald(
data=leukocytes_only_raw.X,
formula_loc="~ 1 + condition",
factor_loc_totest="condition",
gene_names=leukocytes_only_raw.var_names,
sample_description=leukocytes_only_raw.obs,
)
results in
╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮
│ <ipython-input-75-467c1e316174>:3 in <module> │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/diffxpy/test │
│ ing/tests.py:717 in wald │
│ │
│ 714 │ col_indices = np.array([np.where(constraints_loc_temp[x, :] == 1)[0][0] for x │
│ 715 │ │
│ 716 │ # Fit model. │
│ ❱ 717 │ model = _fit( │
│ 718 │ │ noise_model=noise_model, │
│ 719 │ │ data=data, │
│ 720 │ │ design_loc=design_loc, │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/diffxpy/test │
│ ing/tests.py:222 in _fit │
│ │
│ 219 │ else: │
│ 220 │ │ raise ValueError('backend="%s" not recognized.' % backend) │
│ 221 │ │
│ ❱ 222 │ estim = Estimator( │
│ 223 │ │ input_data=input_data, │
│ 224 │ │ init_a=init_a, │
│ 225 │ │ init_b=init_b, │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/batchglm/tra │
│ in/numpy/glm_nb/estimator.py:59 in __init__ │
│ │
│ 56 │ │ │ Useful in scenarios where fitting the exact `scale` is not absolutely n │
│ 57 │ │ :param dtype: Numerical precision. │
│ 58 │ │ """ │
│ ❱ 59 │ │ init_a, init_b, train_loc, train_scale = init_par( │
│ 60 │ │ │ input_data=input_data, │
│ 61 │ │ │ init_a=init_a, │
│ 62 │ │ │ init_b=init_b, │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/batchglm/mod │
│ els/glm_nb/utils.py:129 in init_par │
│ │
│ 126 │ │ │ │ ) │
│ 127 │ │ │ │ │
│ 128 │ │ │ │ # train mu, if the closed-form solution is inaccurate │
│ ❱ 129 │ │ │ │ train_loc = not (np.all(np.abs(rmsd_a) < 1e-20) or rmsd_a.size == 0 │
│ 130 │ │ │ │ │
│ 131 │ │ │ │ if input_data.size_factors is not None: │
│ 132 │ │ │ │ │ if np.any(input_data.size_factors != 1): │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/array/c │
│ ore.py:1622 in __bool__ │
│ │
│ 1619 │ │ │ │ "Use a.any() or a.all().".format(self.__class__.__name__) │
│ 1620 │ │ │ ) │
│ 1621 │ │ else: │
│ ❱ 1622 │ │ │ return bool(self.compute()) │
│ 1623 │ │
│ 1624 │ __nonzero__ = __bool__ # python 2 │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/base.py │
│ :283 in compute │
│ │
│ 280 │ │ -------- │
│ 281 │ │ dask.base.compute │
│ 282 │ │ """ │
│ ❱ 283 │ │ (result,) = compute(self, traverse=False, **kwargs) │
│ 284 │ │ return result │
│ 285 │ │
│ 286 │ def __await__(self): │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/base.py │
│ :565 in compute │
│ │
│ 562 │ │ keys.append(x.__dask_keys__()) │
│ 563 │ │ postcomputes.append(x.__dask_postcompute__()) │
│ 564 │ │
│ ❱ 565 │ results = schedule(dsk, keys, **kwargs) │
│ 566 │ return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/threade │
│ d.py:76 in get │
│ │
│ 73 │ │ │ │ atexit.register(pool.close) │
│ 74 │ │ │ │ pools[thread][num_workers] = pool │
│ 75 │ │
│ ❱ 76 │ results = get_async( │
│ 77 │ │ pool.apply_async, │
│ 78 │ │ len(pool._pool), │
│ 79 │ │ dsk, │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/local.p │
│ y:487 in get_async │
│ │
│ 484 │ │ │ │ │ │ task = dsk[key] │
│ 485 │ │ │ │ │ │ _execute_task(task, data) # Re-execute locally │
│ 486 │ │ │ │ │ else: │
│ ❱ 487 │ │ │ │ │ │ raise_exception(exc, tb) │
│ 488 │ │ │ │ res, worker_id = loads(res_info) │
│ 489 │ │ │ │ state["cache"][key] = res │
│ 490 │ │ │ │ finish_task(dsk, key, state, results, keyorder.get) │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/local.p │
│ y:317 in reraise │
│ │
│ 314 def reraise(exc, tb=None): │
│ 315 │ if exc.__traceback__ is not tb: │
│ 316 │ │ raise exc.with_traceback(tb) │
│ ❱ 317 │ raise exc │
│ 318 │
│ 319 │
│ 320 def identity(x): │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/local.p │
│ y:222 in execute_task │
│ │
│ 219 │ """ │
│ 220 │ try: │
│ 221 │ │ task, data = loads(task_info) │
│ ❱ 222 │ │ result = _execute_task(task, data) │
│ 223 │ │ id = get_id() │
│ 224 │ │ result = dumps((result, id)) │
│ 225 │ │ failed = False │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/dask/core.py │
│ :121 in _execute_task │
│ │
│ 118 │ │ # Note: Don't assign the subtask results to a variable. numpy detects │
│ 119 │ │ # temporaries by their reference count and can execute certain │
│ 120 │ │ # operations in-place. │
│ ❱ 121 │ │ return func(*(_execute_task(a, cache) for a in args)) │
│ 122 │ elif not ishashable(arg): │
│ 123 │ │ return arg │
│ 124 │ elif arg in cache: │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/scipy/linalg │
│ /basic.py:334 in solve_triangular │
│ │
│ 331 │ │ │ 'versions of SciPy.', DeprecationWarning, stacklevel=2) │
│ 332 │ │
│ 333 │ a1 = _asarray_validated(a, check_finite=check_finite) │
│ ❱ 334 │ b1 = _asarray_validated(b, check_finite=check_finite) │
│ 335 │ if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]: │
│ 336 │ │ raise ValueError('expected square matrix') │
│ 337 │ if a1.shape[0] != b1.shape[0]: │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/scipy/_lib/_ │
│ util.py:262 in _asarray_validated │
│ │
│ 259 │ │ if np.ma.isMaskedArray(a): │
│ 260 │ │ │ raise ValueError('masked arrays are not supported') │
│ 261 │ toarray = np.asarray_chkfinite if check_finite else np.asarray │
│ ❱ 262 │ a = toarray(a) │
│ 263 │ if not objects_ok: │
│ 264 │ │ if a.dtype is np.dtype('O'): │
│ 265 │ │ │ raise ValueError('object arrays are not supported') │
│ │
│ /home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/numpy/lib/fu │
│ nction_base.py:485 in asarray_chkfinite │
│ │
│ 482 │ """ │
│ 483 │ a = asarray(a, dtype=dtype, order=order) │
│ 484 │ if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all(): │
│ ❱ 485 │ │ raise ValueError( │
│ 486 │ │ │ "array must not contain infs or NaNs") │
│ 487 │ return a │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: array must not contain infs or NaNs
I already checked that leukocytes_raw.X does not contain any infs or nans.
Got any idea?
Maybe a result of:
/home/lukas/miniconda3/envs/single_cell_analysis/lib/python3.8/site-packages/diffxpy/testing/det.py:1620: RuntimeWarning: divide by zero encountered in log
self._logfc = np.log(mean_x1) - np.log(mean_x0)
?