PAGA_Tree: 'int' object has no attribute 'encode' #168
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I read that this error was provoked by warnings thrown by numba compilation and a solution was to add: in the code. |
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I would like to try a method simple and fast like PAGA_tree, but this error reoccured multiple times.
/usr/local/lib/python3.7/site-packages/umap/rp_tree.py:450: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "make_euclidean_tree" failed type inference due to: Cannot unify RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none) and RandomProjectionTreeNode(none, bool, array(float32, 1d, C), float64, RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none), RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none)) for '$14.16', defined at /usr/local/lib/python3.7/site-packages/umap/rp_tree.py (457)
File "../../usr/local/lib/python3.7/site-packages/umap/rp_tree.py", line 457:
def make_euclidean_tree(data, indices, rng_state, leaf_size=30):
[1] During: resolving callee type: recursive(type(CPUDispatcher(<function make_euclidean_tree at 0x7f9192912f28>)))
[2] During: typing of call at /usr/local/lib/python3.7/site-packages/umap/rp_tree.py (457)
File "../../usr/local/lib/python3.7/site-packages/umap/rp_tree.py", line 457:
def make_euclidean_tree(data, indices, rng_state, leaf_size=30):
@numba.jit()
/usr/local/lib/python3.7/site-packages/numba/compiler.py:725: NumbaWarning: Function "make_euclidean_tree" was compiled in object mode without forceobj=True.
File "../../usr/local/lib/python3.7/site-packages/umap/rp_tree.py", line 451:
@numba.jit()
def make_euclidean_tree(data, indices, rng_state, leaf_size=30):
^
self.func_ir.loc))
/usr/local/lib/python3.7/site-packages/umap/nndescent.py:92: NumbaPerformanceWarning:
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.
el.html#diagnostics for help.on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parall
File "../../usr/local/lib/python3.7/site-packages/umap/utils.py", line 409:
@numba.njit(parallel=True)
def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):
^
current_graph, n_vertices, n_neighbors, max_candidates, rng_state
/usr/local/lib/python3.7/site-packages/numba/compiler.py:588: NumbaPerformanceWarning:
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.
To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.
File "../../usr/local/lib/python3.7/site-packages/umap/nndescent.py", line 47:
@numba.njit(parallel=True)
def nn_descent(
^
self.func_ir.loc))
/usr/local/lib/python3.7/site-packages/umap/umap_.py:349: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "fuzzy_simplicial_set" failed type inference due to: Untyped global name 'nearest_neighbors': cannot determine Numba type of <class 'function'>
File "../../usr/local/lib/python3.7/site-packages/umap/umap_.py", line 467:
def fuzzy_simplicial_set(
if knn_indices is None or knn_dists is None:
knn_indices, knn_dists, _ = nearest_neighbors(
^
@numba.jit()
/usr/local/lib/python3.7/site-packages/numba/compiler.py:725: NumbaWarning: Function "fuzzy_simplicial_set" was compiled in object mode without forceobj=True.
File "../../usr/local/lib/python3.7/site-packages/umap/umap_.py", line 350:
@numba.jit()
def fuzzy_simplicial_set(
^
self.func_ir.loc))
/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py:62: UserWarning: Error while trying to convert the column "group_id". Fall back to string conversion. The error is: Converting pandas "Category" series to R factor is only possible when categories are strings.
% (name, str(e)))
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 57, in py2rpy_pandasdataframe
od[name] = conversion.py2rpy(values)
File "/usr/local/lib/python3.7/functools.py", line 824, in wrapper
return dispatch(args[0].class)(*args, **kw)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 104, in py2rpy_pandasseries
res = py2rpy_categoryseries(obj)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 84, in py2rpy_categoryseries
raise ValueError('Converting pandas "Category" series to '
ValueError: Converting pandas "Category" series to R factor is only possible when categories are strings.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 361, in from_object
mv = memoryview(obj)
TypeError: memoryview: a bytes-like object is required, not 'Series'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/code/run.py", line 157, in
branch_network = branch_network
File "/usr/local/lib/python3.7/site-packages/dynclipy/dataset.py", line 19, in adder
self.call_function_add(function, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/dynclipy/dataset.py", line 12, in call_function_add
call_function(self.id, function, True, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/dynclipy/dataset.py", line 30, in call_function
ro.globalenv["kwargs"] = ro.ListVector([[name, x] for name, x in kwargs.items()])
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/vectors.py", line 630, in init
kv = [(str(k), conversion.py2rpy(v)) for k, v in tlist]
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/vectors.py", line 630, in
k, v in tlist](k), conversion.py2rpy(v)) for
File "/usr/local/lib/python3.7/functools.py", line 824, in wrapper
return dispatch(args[0].class)(*args, **kw)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 63, in py2rpy_pandasdataframe
od[name] = StrVector(values)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/vectors.py", line 379, in init
super().init(obj)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 288, in init
super().init(type(self).from_object(obj).sexp)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 365, in from_object
res = cls.from_iterable(obj)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/conversion.py", line 28, in _
cdata = function(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 314, in from_iterable
cast_in)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 239, in _populate_r_vector
set_elt(r_vector, i, cast_value(v))
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 424, in _as_charsxp_cdata
return conversion._str_to_charsxp(x)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/conversion.py", line 120, in _str_to_charsxp
cchar = _str_to_cchar(val)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/conversion.py", line 99, in _str_to_cchar
b = s.encode(encoding)
AttributeError: 'int' object has no attribute 'encode'
Error: /usr/local/lib/python3.7/site-packages/umap/rp_tree.py:450: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "make_euclidean_tree" failed type inference due to: Cannot unify RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none) and RandomProjectionTreeNode(none, bool, array(float32, 1d, C), float64, RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none), RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none)) for '$14.16', defined at /usr/local/lib/python3.7/site-packages/umap/rp_tree.py (457)
File "../../usr/local/lib/python3.7/site-packages/umap/rp_tree.py", line 457:
def make_euclidean_tree(data, indices, rng_state, leaf_size=30):
[1] During: resolving callee type: recursive(type(CPUDispatcher(<function make_euclidean_tree at 0x7f9192912f28>)))
[2] During: typing of call at /usr/local/lib/python3.7/site-packages/umap/rp_tree.py (457)
File "../../usr/local/lib/python3.7/site-packages/umap/rp_tree.py", line 457:
def make_euclidean_tree(data, indices, rng_state, leaf_size=30):
@numba.jit()
/usr/local/lib/python3.7/site-packages/numba/compiler.py:725: NumbaWarning: Function "make_euclidean_tree" was compiled in object mode without forceobj=True.
File "../../usr/local/lib/python3.7/site-packages/umap/rp_tree.py", line 451:
@numba.jit()
def make_euclidean_tree(data, indices, rng_state, leaf_size=30):
^
self.func_ir.loc))
/usr/local/lib/python3.7/site-packages/umap/nndescent.py:92: NumbaPerformanceWarning:
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.
To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.
File "../../usr/local/lib/python3.7/site-packages/umap/utils.py", line 409:
@numba.njit(parallel=True)
def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):
^
current_graph, n_vertices, n_neighbors, max_candidates, rng_state
/usr/local/lib/python3.7/site-packages/numba/compiler.py:588: NumbaPerformanceWarning:
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.
To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.
File "../../usr/local/lib/python3.7/site-packages/umap/nndescent.py", line 47:
@numba.njit(parallel=True)
def nn_descent(
^
self.func_ir.loc))
/usr/local/lib/python3.7/site-packages/umap/umap_.py:349: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "fuzzy_simplicial_set" failed type inference due to: Untyped global name 'nearest_neighbors': cannot determine Numba type of <class 'function'>
File "../../usr/local/lib/python3.7/site-packages/umap/umap_.py", line 467:
def fuzzy_simplicial_set(
if knn_indices is None or knn_dists is None:
knn_indices, knn_dists, _ = nearest_neighbors(
^
@numba.jit()
/usr/local/lib/python3.7/site-packages/numba/compiler.py:725: NumbaWarning: Function "fuzzy_simplicial_set" was compiled in object mode without forceobj=True.
File "../../usr/local/lib/python3.7/site-packages/umap/umap_.py", line 350:
@numba.jit()
def fuzzy_simplicial_set(
^
self.func_ir.loc))
/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py:62: UserWarning: Error while trying to convert the column "group_id". Fall back to string conversion. The error is: Converting pandas "Category" series to R factor is only possible when categories are strings.
% (name, str(e)))
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 57, in py2rpy_pandasdataframe
od[name] = conversion.py2rpy(values)
File "/usr/local/lib/python3.7/functools.py", line 824, in wrapper
return dispatch(args[0].class)(*args, **kw)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 104, in py2rpy_pandasseries
res = py2rpy_categoryseries(obj)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 84, in py2rpy_categoryseries
raise ValueError('Converting pandas "Category" series to '
ValueError: Converting pandas "Category" series to R factor is only possible when categories are strings.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 361, in from_object
mv = memoryview(obj)
TypeError: memoryview: a bytes-like object is required, not 'Series'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/code/run.py", line 157, in
branch_network = branch_network
File "/usr/local/lib/python3.7/site-packages/dynclipy/dataset.py", line 19, in adder
self.call_function_add(function, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/dynclipy/dataset.py", line 12, in call_function_add
call_function(self.id, function, True, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/dynclipy/dataset.py", line 30, in call_function
ro.globalenv["kwargs"] = ro.ListVector([[name, x] for name, x in kwargs.items()])
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/vectors.py", line 630, in init
kv = [(str(k), conversion.py2rpy(v)) for k, v in tlist]
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/vectors.py", line 630, in
kv = [(str(k), conversion.py2rpy(v)) for k, v in tlist]
File "/usr/local/lib/python3.7/functools.py", line 824, in wrapper
return dispatch(args[0].class)(*args, **kw)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py", line 63, in py2rpy_pandasdataframe
od[name] = StrVector(values)
File "/usr/local/lib/python3.7/site-packages/rpy2/robjects/vectors.py", line 379, in init
super().init(obj)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 288, in init
super().init(type(self).from_object(obj).sexp)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 365, in from_object
res = cls.from_iterable(obj)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/conversion.py", line 28, in _
cdata = function(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 314, in from_iterable
cast_in)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 239, in _populate_r_vector
set_elt(r_vector, i, cast_value(v))
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/sexp.py", line 424, in _as_charsxp_cdata
return conversion._str_to_charsxp(x)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/conversion.py", line 120, in _str_to_charsxp
cchar = _str_to_cchar(val)
File "/usr/local/lib/python3.7/site-packages/rpy2/rinterface_lib/conversion.py", line 99, in _str_to_cchar
b = s.encode(encoding)
AttributeError: 'int' object has no attribute 'encode'
And here is my config:
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C
[3] LC_TIME=zh_CN.UTF-8 LC_COLLATE=zh_CN.UTF-8
[5] LC_MONETARY=zh_CN.UTF-8 LC_MESSAGES=zh_CN.UTF-8
[7] LC_PAPER=zh_CN.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] shiny_1.6.0 SeuratObject_4.0.2 Seurat_4.0.3
[4] Matrix_1.3-4 forcats_0.5.1 stringr_1.4.0
[7] dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[10] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5
[13] tidyverse_1.3.1 assertthat_0.2.1 dynutils_1.0.6
[16] dyno_0.1.2 dynwrap_1.2.2 dynplot_1.0.2.9000
[19] dynmethods_1.0.5 dynguidelines_1.0.1 dynfeature_1.0.0.9000
loaded via a namespace (and not attached):
[1] utf8_1.2.1 reticulate_1.20 tidyselect_1.1.1
[4] htmlwidgets_1.5.3 grid_4.1.0 ranger_0.12.1
[7] Rtsne_0.15 munsell_0.5.0 codetools_0.2-18
[10] ica_1.0-2 future_1.21.0 miniUI_0.1.1.1
[13] withr_2.4.2 colorspace_2.0-1 knitr_1.33
[16] rstudioapi_0.13 ROCR_1.0-11 tensor_1.5
[19] shinyWidgets_0.6.0 listenv_0.8.0 labeling_0.4.2
[22] polyclip_1.10-0 bit64_4.0.5 farver_2.1.0
[25] rprojroot_2.0.2 parallelly_1.26.0 vctrs_0.3.8
[28] generics_0.1.0 xfun_0.23 R6_2.5.1
[31] GA_3.2.1 graphlayouts_0.7.1 hdf5r_1.3.4
[34] cachem_1.0.5 spatstat.utils_2.2-0 promises_1.2.0.1
[37] scales_1.1.1 ggraph_2.0.5 lmds_0.1.0
[40] gtable_0.3.0 babelwhale_1.0.2 globals_0.14.0
[43] processx_3.5.2 goftest_1.2-2 tidygraph_1.2.0
[46] rlang_0.4.11 akima_0.6-2.1 splines_4.1.0
[49] lazyeval_0.2.2 hexbin_1.28.2 spatstat.geom_2.1-0
[52] broom_0.7.7 yaml_2.2.1 reshape2_1.4.4
[55] abind_1.4-5 modelr_0.1.8 backports_1.2.1
[58] httpuv_1.6.1 tools_4.1.0 ellipsis_0.3.2
[61] spatstat.core_2.1-2 jquerylib_0.1.4 RColorBrewer_1.1-2
[64] ggridges_0.5.3 Rcpp_1.0.6 plyr_1.8.6
[67] ps_1.6.0 rpart_4.1-15 deldir_0.2-10
[70] pbapply_1.4-3 viridis_0.6.1 cowplot_1.1.1
[73] dynparam_1.0.2 zoo_1.8-9 haven_2.4.1
[76] ggrepel_0.9.1 cluster_2.1.2 fs_1.5.0
[79] magrittr_2.0.1 data.table_1.14.0 scattermore_0.7
[82] carrier_0.1.0 lmtest_0.9-38 reprex_2.0.0
[85] RANN_2.6.1 fitdistrplus_1.1-5 matrixStats_0.59.0
[88] pkgload_1.2.1 shinyjs_2.0.0 hms_1.1.0
[91] patchwork_1.1.1 mime_0.10 xtable_1.8-4
[94] readxl_1.3.1 gridExtra_2.3 testthat_3.0.2
[97] compiler_4.1.0 KernSmooth_2.23-20 crayon_1.4.1
[100] rje_1.10.16 htmltools_0.5.1.1 proxyC_0.2.0
[103] mgcv_1.8-36 later_1.2.0 RcppParallel_5.1.4
[106] lubridate_1.7.10 DBI_1.1.1 tweenr_1.0.2
[109] dbplyr_2.1.1 MASS_7.3-54 cli_2.5.0
[112] parallel_4.1.0 igraph_1.2.6 pkgconfig_2.0.3
[115] sp_1.4-5 plotly_4.9.4 spatstat.sparse_2.0-0
[118] xml2_1.3.2 foreach_1.5.1 bslib_0.2.5.1
[121] rvest_1.0.0 digest_0.6.27 dyndimred_1.0.4
[124] sctransform_0.3.2 RcppAnnoy_0.0.18 spatstat.data_2.1-0
[127] cellranger_1.1.0 leiden_0.3.8 uwot_0.1.10
[130] lifecycle_1.0.0 nlme_3.1-152 jsonlite_1.7.2
[133] desc_1.3.0 viridisLite_0.4.0 fansi_0.5.0
[136] pillar_1.6.1 lattice_0.20-44 fastmap_1.1.0
[139] httr_1.4.2 survival_3.2-11 waldo_0.2.5
[142] glue_1.4.2 remotes_2.4.0 png_0.1-7
[145] iterators_1.0.13 bit_4.0.4 sass_0.4.0
[148] ggforce_0.3.3 stringi_1.6.2 irlba_2.3.3
[151] future.apply_1.7.0
I checked my docker and HDF5R that have already be installed and seems to work well. Is this error given rised by my mistakes, or some outmode package dependencies, or something with python?
Thanks,
John Hsiung
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