Dear Niko.
Thank you for your quick reply.
Your suggestion was right.
I tried PCA with 500 mols, and it took very long time. So, i could not
wait....
Next, I tried PCA with 10 mols, then the calculation was finished. Thanks!
I understood this script was not sufficient for do PCA with lots of
molecules.
So, i will change the script that do PCA using molecular fingerprint to
using molecular descriptors.
Thanks.
Taka
2013/1/19 Nikolas Fechner <m...@fechner.cc>
> Dear Takayuki,
> I tested in a fairly recent svn checkout as well as on 2012.03.1, which
> is the version I also used for this example (the Stats.py has not changed
> since 2009). I ran it on a set of public serineprotease inhibitors (
> http://cheminformatics.org/datasets/bohm/bohm-test.3d.sdf, chosen because
> rather small and publicly available) in newly installed python notebook.
> The res[1] I got are:
>
> [[ 0.00000000e+00 0.00000000e+00 7.67554438e-02 ..., 0.00000000e+00
> 0.00000000e+00 0.00000000e+00]
> [ 0.00000000e+00 0.00000000e+00 -1.60075292e-02 ..., 0.00000000e+00
> 0.00000000e+00 0.00000000e+00]
> [ 0.00000000e+00 0.00000000e+00 -8.20590478e-02 ..., 0.00000000e+00
> 0.00000000e+00 0.00000000e+00]
> ...,
> [ 0.00000000e+00 0.00000000e+00 -2.85344502e-16 ..., 0.00000000e+00
> 0.00000000e+00 0.00000000e+00]
> [ 0.00000000e+00 0.00000000e+00 -2.12793777e-15 ..., 0.00000000e+00
> 0.00000000e+00 0.00000000e+00]
> [ 0.00000000e+00 0.00000000e+00 -2.12793777e-15 ..., 0.00000000e+00
> 0.00000000e+00 0.00000000e+00]]
>
>
> The error you posted looks a bit like the script was manually stopped
> (ctrl-c for instance) or did the program terminate with error independent
> from any user interaction? The PCA can take a very long time for feature
> vectors with such high dimensionality like fingerprints (several minutes in
> the case of my very small example dataset). How many compounds are you
> using for your example? In the case that it just ran forever in your case,
> could you try it with a tiny subset (e.g. 10 compounds) just to see wether
> it terminates.
>
> Best,
> Niko
>
> On Jan 18, 2013, at 11:48 PM, Taka Seri <serit...@gmail.com> wrote:
>
> Dear Greg and Niko.
>
> Thank you for your quick repry.
> >To Greg, thanks for your recommendation.
> I tried PCA with matplotlib and it worked with no problem. Thanks.
> But Matplotlib returned view that was different from R.
>
> >To Niko.
> I using RDKit, version RDKit_2012_06_1.
> And, when I tried PCA with the code, no response was returned.
>
> KeyboardInterrupt, following message was returned.
>
> Traceback (most recent call last):
> File "mol_pca.py", line 33, in <module>
> res=Stats.PrincipalComponents(matrix)
> File "C:\RDKit_2012_06_1\rdkit\ML\Data\Stats.py", line 82, in
> PrincipalComponents
> covMat = FormCorrelationMatrix(mat)
> File "C:\RDKit_2012_06_1\rdkit\ML\Data\Stats.py", line 66, in
> FormCorrelationMatrix
> sumY = sum(y)
>
> So, what version of RDKit are you using?
> And if you don't care, could you show me some results ?
>
> Thanks.
> Takayuki
>
> 2013/1/18 Nikolas Fechner <m...@fechner.cc>
>
>> Hi Takayuki,
>> I was able to run your code snippet without any errors (with different
>> example molecules of course). Could possible explain in more detail what is
>> not working for you? What version of RDKit are you using (from rdkit import
>> rdBase;print rdBase.rdkitVersion) ?
>>
>> Niko
>>
>> On Jan 17, 2013, at 11:08 AM, Taka Seri <serit...@gmail.com> wrote:
>>
>> Dear All.
>>
>> I want to do PCA with molecular fingerprint .
>> So, I wrote following code.
>> But, this code did not work .
>> Does anyone have a suggestion?
>> Thanks.
>>
>> Takayuki
>>
>>
>> 1 from rdkit import Chem
>> 2 from rdkit.Chem import AllChem
>> 3 from rdkit.ML.Data import Stats
>> 4 import numpy
>> 5 import sys
>> 6
>> 7
>> 8 mols = [mol for mol in Chem.SDMolSupplier(sys.argv[1])]
>> 9 fps = [AllChem.GetMorganFingerprintAsBitVect(mol,2) for mol in mols]
>> 10
>> 11 mat = []
>> 12 for fp in fps:
>> 13 bits = fp.ToBitString()
>> 14 bitsvec = [int(bit) for bit in bits]
>> 15 mat.append(bitsvec)
>> 16
>> 17 mat=numpy.array(mat)
>> 18 res = Stats.PrincpalComponents(mat)
>> 19 print res[1]
>>
>>
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