Unable to plot figures using the NK example files

[Troylimyj]

Thank you for developing this very interesting ML tool for cytof data analysis. I am new to Python and keen to utilise your algorithm for my dataset.

However when trying to run using the example NK cells data set, I keep getting this error code, I wonder if you could advise further.

run run_analysis.py -f NK_fcs_samples_with_labels.csv -m NK_markers.csv -i gated_NK/ -o outdir_NK --export_csv --group_a CMV- --group_b CMV+ --verbose 0

2021-02-23 13:44:11 - main:156 - INFO - Samples used for model training: ['a_001_NK.fcs', 'a_002_NK.fcs', 'a_003_NK.fcs', 'a_004_NK.fcs', 'a_006_NK.fcs', 'a_009_NK.fcs', 'a_010_NK.fcs', 'a_011_NK.fcs', 'a_012_NK.fcs', 'a_1a_NK.fcs', 'a_2a_NK.fcs', 'a_2b_NK.fcs', 'a_4a_NK.fcs', 'a_4b_NK.fcs', 'a_5a_NK.fcs']
2021-02-23 13:44:11 - main:157 - INFO - Samples used for validation: ['a_005_NK.fcs', 'a_007_NK.fcs', 'a_3a_NK.fcs', 'a_3b_NK.fcs', 'a_5b_NK.fcs']
2021-02-23 13:44:12 - cellCnn.model:320 - INFO - Generating multi-cell inputs...
2021-02-23 13:44:12 - cellCnn.model:390 - INFO - Done.
2021-02-23 13:44:12 - cellCnn.model:425 - INFO - Number of filters: 3
2021-02-23 13:44:12 - cellCnn.model:431 - INFO - Cells pooled: 1
63/63 [==============================] - 0s 1ms/step - loss: 0.6482 - accuracy: 0.6768
2021-02-23 13:44:16 - cellCnn.model:460 - INFO - Best validation accuracy: 0.68
2021-02-23 13:44:16 - cellCnn.model:425 - INFO - Number of filters: 6
2021-02-23 13:44:16 - cellCnn.model:431 - INFO - Cells pooled: 2
63/63 [==============================] - 0s 1ms/step - loss: 0.4967 - accuracy: 0.7944
2021-02-23 13:44:25 - cellCnn.model:460 - INFO - Best validation accuracy: 0.79
2021-02-23 13:44:25 - cellCnn.model:425 - INFO - Number of filters: 8
2021-02-23 13:44:25 - cellCnn.model:431 - INFO - Cells pooled: 10
63/63 [==============================] - 0s 1ms/step - loss: 0.3701 - accuracy: 0.8989
2021-02-23 13:44:34 - cellCnn.model:460 - INFO - Best validation accuracy: 0.90
2021-02-23 13:44:34 - cellCnn.model:425 - INFO - Number of filters: 4
2021-02-23 13:44:34 - cellCnn.model:431 - INFO - Cells pooled: 40
63/63 [==============================] - 0s 1ms/step - loss: 0.6090 - accuracy: 0.7579
2021-02-23 13:44:38 - cellCnn.model:460 - INFO - Best validation accuracy: 0.76
2021-02-23 13:44:38 - cellCnn.model:425 - INFO - Number of filters: 6
2021-02-23 13:44:38 - cellCnn.model:431 - INFO - Cells pooled: 1
63/63 [==============================] - 0s 1ms/step - loss: 0.5279 - accuracy: 0.7724
2021-02-23 13:44:45 - cellCnn.model:460 - INFO - Best validation accuracy: 0.77
2021-02-23 13:44:45 - cellCnn.model:425 - INFO - Number of filters: 7
2021-02-23 13:44:45 - cellCnn.model:431 - INFO - Cells pooled: 2
63/63 [==============================] - 0s 1ms/step - loss: 0.6276 - accuracy: 0.7114
2021-02-23 13:44:50 - cellCnn.model:460 - INFO - Best validation accuracy: 0.71
2021-02-23 13:44:50 - cellCnn.model:425 - INFO - Number of filters: 9
2021-02-23 13:44:50 - cellCnn.model:431 - INFO - Cells pooled: 10
63/63 [==============================] - 0s 1ms/step - loss: 0.4519 - accuracy: 0.8374
2021-02-23 13:44:58 - cellCnn.model:460 - INFO - Best validation accuracy: 0.84
2021-02-23 13:44:58 - cellCnn.model:425 - INFO - Number of filters: 4
2021-02-23 13:44:58 - cellCnn.model:431 - INFO - Cells pooled: 40
63/63 [==============================] - 0s 1ms/step - loss: 0.3997 - accuracy: 0.9390
2021-02-23 13:45:03 - cellCnn.model:460 - INFO - Best validation accuracy: 0.94
2021-02-23 13:45:03 - cellCnn.model:425 - INFO - Number of filters: 5
2021-02-23 13:45:03 - cellCnn.model:431 - INFO - Cells pooled: 1
63/63 [==============================] - 0s 2ms/step - loss: 0.6324 - accuracy: 0.8139
2021-02-23 13:45:07 - cellCnn.model:460 - INFO - Best validation accuracy: 0.81
2021-02-23 13:45:07 - cellCnn.model:425 - INFO - Number of filters: 8
2021-02-23 13:45:07 - cellCnn.model:431 - INFO - Cells pooled: 2
63/63 [==============================] - 0s 1ms/step - loss: 0.4493 - accuracy: 0.8229
2021-02-23 13:45:17 - cellCnn.model:460 - INFO - Best validation accuracy: 0.82
2021-02-23 13:45:17 - cellCnn.model:425 - INFO - Number of filters: 5
2021-02-23 13:45:17 - cellCnn.model:431 - INFO - Cells pooled: 10
63/63 [==============================] - 0s 1ms/step - loss: 0.6025 - accuracy: 0.7044
2021-02-23 13:45:22 - cellCnn.model:460 - INFO - Best validation accuracy: 0.70
2021-02-23 13:45:22 - cellCnn.model:425 - INFO - Number of filters: 7
2021-02-23 13:45:22 - cellCnn.model:431 - INFO - Cells pooled: 40
63/63 [==============================] - 0s 1ms/step - loss: 0.4441 - accuracy: 0.8574
2021-02-23 13:45:29 - cellCnn.model:460 - INFO - Best validation accuracy: 0.86
2021-02-23 13:45:29 - cellCnn.model:425 - INFO - Number of filters: 7
2021-02-23 13:45:29 - cellCnn.model:431 - INFO - Cells pooled: 1
63/63 [==============================] - 0s 1ms/step - loss: 0.4593 - accuracy: 0.8219
2021-02-23 13:45:37 - cellCnn.model:460 - INFO - Best validation accuracy: 0.82
2021-02-23 13:45:37 - cellCnn.model:425 - INFO - Number of filters: 4
2021-02-23 13:45:37 - cellCnn.model:431 - INFO - Cells pooled: 2
63/63 [==============================] - 0s 1ms/step - loss: 0.5350 - accuracy: 0.7729
2021-02-23 13:45:42 - cellCnn.model:460 - INFO - Best validation accuracy: 0.77
2021-02-23 13:45:42 - cellCnn.model:425 - INFO - Number of filters: 6
2021-02-23 13:45:42 - cellCnn.model:431 - INFO - Cells pooled: 10
63/63 [==============================] - 0s 1ms/step - loss: 0.2961 - accuracy: 0.9450
2021-02-23 13:45:51 - cellCnn.model:460 - INFO - Best validation accuracy: 0.94
2021-02-23 13:45:53 - cellCnn.plotting:144 - INFO - Loading the weights of consensus filters.
2021-02-23 13:45:53 - cellCnn.plotting:168 - INFO - Computing t-SNE projection...
C:\Users\yeong\AppData\Local\Programs\Python\Python37\Scripts\CellCnn-python3\cellCnn\plotting.py:582: MatplotlibDeprecationWarning:
The 'add_all' parameter of init() was deprecated in Matplotlib 3.3 and will be removed two minor releases later. If any parameter follows 'add_all', they should be passed as keyword, not positionally.
cbar_pad="5%",

TypeError Traceback (most recent call last)
~\AppData\Local\Programs\Python\Python37\Scripts\CellCnn-python3\run_analysis.py in
240 if name == 'main':
241 try:
--> 242 main()
243 except KeyboardInterrupt:
244 sys.stderr.write("User interrupt!\n")

~\AppData\Local\Programs\Python\Python37\Scripts\CellCnn-python3\run_analysis.py in main()
209 tsne_ncell=args.tsne_ncell,
210 regression=args.regression,
--> 211 show_filters=False)
212 _v = plot_results(results, valid_samples, valid_phenotypes,
213 marker_names, os.path.join(plotdir, 'validation_plots'),

~\AppData\Local\Programs\Python\Python37\Scripts\CellCnn-python3\cellCnn\plotting.py in plot_results(results, samples, phenotypes, labels, outdir, filter_diff_thres, filter_response_thres, response_grad_cutoff, stat_test, log_yscale, group_a, group_b, group_names, tsne_ncell, regression, show_filters)
176 fig_path = os.path.join(outdir, 'tsne_all_cells')
177 plot_tsne_grid(x_tsne, x_for_tsne, fig_path, labels=labels, fig_size=(20, 20),
--> 178 point_size=5)
179
180 return_filters = []

~\AppData\Local\Programs\Python\Python37\Scripts\CellCnn-python3\cellCnn\plotting.py in plot_tsne_grid(z, x, fig_path, labels, fig_size, g_j, suffix, point_size)
580 cbar_mode="each",
581 cbar_size="8%",
--> 582 cbar_pad="5%",
583 )
584 for seq_index in range(ncol):

c:\users\yeong\appdata\local\programs\python\python37\lib\site-packages\matplotlib\cbook\deprecation.py in wrapper(*inner_args, **inner_kwargs)
409 else deprecation_addendum,
410 **kwargs)
--> 411 return func(*inner_args, **inner_kwargs)
412
413 return wrapper

c:\users\yeong\appdata\local\programs\python\python37\lib\site-packages\mpl_toolkits\axes_grid1\axes_grid.py in init(self, fig, rect, nrows_ncols, ngrids, direction, axes_pad, add_all, share_all, aspect, label_mode, cbar_mode, cbar_location, cbar_pad, cbar_size, cbar_set_cax, axes_class)
434 direction=direction, axes_pad=axes_pad,
435 share_all=share_all, share_x=True, share_y=True, aspect=aspect,
--> 436 label_mode=label_mode, axes_class=axes_class)
437 else: # Only show deprecation in that case.
438 super().init(

c:\users\yeong\appdata\local\programs\python\python37\lib\site-packages\matplotlib\cbook\deprecation.py in wrapper(*inner_args, **inner_kwargs)
409 else deprecation_addendum,
410 **kwargs)
--> 411 return func(*inner_args, **inner_kwargs)
412
413 return wrapper

c:\users\yeong\appdata\local\programs\python\python37\lib\site-packages\mpl_toolkits\axes_grid1\axes_grid.py in init(self, fig, rect, nrows_ncols, ngrids, direction, axes_pad, add_all, share_all, share_x, share_y, label_mode, axes_class, aspect)
210 if add_all:
211 for ax in self.axes_all:
--> 212 fig.add_axes(ax)
213
214 self.set_label_mode(label_mode)

c:\users\yeong\appdata\local\programs\python\python37\lib\site-packages\matplotlib\figure.py in add_axes(self, *args, **kwargs)
1234 else:
1235 rect = args[0]
-> 1236 if not np.isfinite(rect).all():
1237 raise ValueError('all entries in rect must be finite '
1238 'not {}'.format(rect))

TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

[eleniel-mocna]

I've had the same issue and solved it by downgrading matplotlib to version 2.2.
Rewriting pipfile with locked versions would probably solve this issue...

[Z-Forest]

@Troylimyj @eleniel-mocna
Hello, I just learned Python and found that the data set website of this project is no longer accessible. Could you please share a relevant data set with me? Thank you very much!