debug deeprank for models with no feature value #242
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manonreau
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CunliangGeng and
NicoRenaud
and removed request for
CunliangGeng
Pull Request Test Coverage Report for Build 1468853462
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| maxv = self.feature_mean[ic] + w * self.feature_std[ic] | ||
| if minv != maxv: | ||
| feature[ic] = np.clip(feature[ic], minv, maxv) | ||
| #feature[ic] = self._mad_based_outliers(feature[ic],minv,maxv) |
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why is that line commented ? should we simply remove it ?
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No idea, this line was already commented in the code
compute the norm values if `self.clip_features` exists
compute the norm values if `self.clip_features` exists
compute the norm values if `self.clip_features` exists
Remove the benchmarking mode: The prediction/benchmarking mode can be detected by simply checking the presence of target values for a given input data set when `plot == True` Added the different required controls Note that the hitrate function is only adapted for IRMSD input, should be modified
I modified the |
Modify Hitrate so that it can handle any type of target values (not limited to irmsd anymore)
deeprank/learn/NeuralNet.py
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| targ = self.data[l]['targets'].flatten() | ||
| try: | ||
| targ = self.data[l]['targets'] |
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Just to comfirm: the targets data does not need to be flatten(), right? I see the old code is using targ = self.data[l]['targets'].flatten()
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good catch ! It must be flattened
deeprank/learn/NeuralNet.py
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| for fname, mol in data['mol']: | ||
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| f5 = h5py.File(fname, 'r') | ||
| irmsd.append(f5[mol + '/targets/IRMSD'][()]) | ||
| targets.append(f5[mol + f'/targets/self.data_set.select_target'][()]) |
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need to change f'/targets/self.data_set.select_target' to f'/targets/{self.data_set.select_target}'
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Thanks @manonreau, the changes look good. I left a few comments for you to check.
1. Change field self.grid_shape to self._grid_shape 2. Update DataSet class docstring for grid_info 3. Update get_grid_shape method
1. Rename target_thr to hit_cutoff 2. Update hit_cutoff docstring 3. Replace print with logger 4. Shorten long lines
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Hi @manonreau I pushed two commits (since it's not easy to comment all the details), take a look please :-) |
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Thanks @CunliangGeng, everything looks fine, I will merge that PR once you approve it
deeprank/learn/NeuralNet.py
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| targ = self.data[l]['targets'].flatten() | ||
| try: | ||
| targ = self.data[l]['targets'] |
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good catch ! It must be flattened
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BTW, please also close the related issues after merging the PR :-) |
Compute grid_shape only if it is not provided as an input
Compute the feature_mean if
self.clip_features == Truefeature_mean was computed when
self.normalize_features == True, this is not required anymore.Format logger message in the
compute_normfunctionAllow feature clipping in the
_clip_featurefunction only if values exists for that featureAdd a condition not to transform features values when they correspond to an empty vector in the mapping process
set
save_hit_rateas False by default since it call the IRMSD target that may not be used by the usersAdded plots as optional in the
test()function since the users, in principle, have no target information for the test set, excepted in benchmark conditions