Skip clustering if k > n_topics #65
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I get a `ValueError` if the number of topics after density filtering is less than the number of desired components.
If a single component is all zeroes (which can be the case now that k too large doesn't throw an exception), this introduces a div0 which turns the results into NaNs.
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@dylkot would you please take a look and consider merging this PR? |
If density_threshold is too low, a very confusing error is raised:
```
Traceback (most recent call last):
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/sklearn/utils/_param_validation.py", line 192, in wrapper
return func(*args, **kwargs)
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py", line 1106, in non_negative_factorization
est._validate_params()
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/sklearn/base.py", line 600, in _validate_params
validate_parameter_constraints(
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/sklearn/utils/_param_validation.py", line 97, in validate_parameter_constraints
raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'n_components' parameter of NMF must be an int in the range [1, inf) or None. Got 0 instead.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/cnmf/cnmf.py", line 739, in consensus
rf_usages = self.refit_usage(norm_counts.X, median_spectra)
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/cnmf/cnmf.py", line 658, in refit_usage
_, rf_usages = self._nmf(X, nmf_kwargs=refit_nmf_kwargs)
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/cnmf/cnmf.py", line 551, in _nmf
(usages, spectra, niter) = non_negative_factorization(X, **nmf_kwargs)
File "/Users/scottgigante/envs/immunaISR/lib/python3.8/site-packages/sklearn/utils/_param_validation.py", line 203, in wrapper
raise InvalidParameterError(msg) from e
sklearn.utils._param_validation.InvalidParameterError: The 'n_components' parameter of non_negative_factorization must be an int in the range [1, inf) or None. Got 0 instead.
```
This PR makes the error more comprehensible.
Skip clustering if k > n_topics
Raise error if zero components meet density threshold
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I get a
ValueErrorif the number of topics after density filtering is less than the number of desired components.This PR solves that issue.