One small notice from me - I would still use other agregative function
instead of sum to get binary FP:
np.reshape(fpa, (4, -1)).any(axis = 0)
I guess it doesn't change a thing with tanimoto, but if you try other
distances then you can get unexpected results (assuming there are crashes).
Pozd
Thanks, Greg,
Yes, sciket learn will automatically promote to arrays of float with
check_array()
function. What I am currently doing is
fpa = numpy.zeros((len(fp),),numpy.double)
DataStructs.ConvertToNumpyArray(fp,fpa)
np.sum(np.reshape(fpa, (4, -1)), axis = 0)
Is this the same as FoldFingerpr
If that doesn't help (and it may not since some Scikit-Learn functions
automatically promote their arguments to arrays of doubles), you can always
just generate a shorter fingerprint from the beginning (all the
fingerprinting functions take an optional argument for this) or fold the
existing finger
Hi Jing,
Most fingerprints are binary, thus can be stored as np.bool_, which
compared to double should be 64 times more memory efficient.
Best,
Maciej
Pozdrawiam, | Best regards,
Maciek Wójcikowski
mac...@wojcikowski.pl
2015-08-27 16:15 GMT+02:00 Jing Lu :
> Hi Greg,
>
> Thanks! It work
Hi Greg,
Thanks! It works! But, is that possible to fold the fingerprint to smaller
size? np.zeros((100,2048)) still takes a lot of memory...
Best,
Jing
On Wed, Aug 26, 2015 at 11:02 PM, Greg Landrum
wrote:
>
> On Thu, Aug 27, 2015 at 3:00 AM, Jing Lu wrote:
>
>>
>> So, I wonder is there
On Thu, Aug 27, 2015 at 3:00 AM, Jing Lu wrote:
>
> So, I wonder is there any way to convert fingerprint to a numpy vector?
>
Indeed there is:
In [11]: from rdkit import Chem
In [12]: from rdkit import DataStructs
In [13]: import numpy
In [14]: m =Chem.MolFromSmiles('C1CCC1')
In [15]: fp =
Sorry to bother again...
Now, the most time consuming part is clustering. The process getting the
fingerprints only takes less than 1h. But, the process for clustering has
already taken more than 30h, and I am not sure when it will finish.
Currently, I use scikit learn DBSCAN, which has time comp
On Aug 23, 2015, at 6:38 PM, Jing Lu wrote:
> I hope the memory issue won't be a problem.
That's up to you and your choice of threshold.
> Most AgglomerativeClustering algorithms have time complexity with N^2. Will
> that be a problem?
You have to decided for yourself what counts as a problem.
On 08/23/2015 11:38 AM, Jing Lu wrote:
> Thanks, Andrew!
>
> Yes, I was thinking about using scikit-learn also. But I guess I need to
> use a data structure for sparse matrix and define a function for
> connectivity. I hope the memory issue won't be a problem.
> Most AgglomerativeClustering algori
Thanks, Takayuki,
For both Repeated Bisection clustering and K-means clustering, they all
need the number of clusters as input, right?
Best,
Jing
On Sun, Aug 23, 2015 at 1:17 AM, Taka Seri wrote:
> Dear Jing,
>
> How about your trying using bayon ?
> https://code.google.com/p/bayon/
> It's no
Thanks, Andrew!
Yes, I was thinking about using scikit-learn also. But I guess I need to
use a data structure for sparse matrix and define a function for
connectivity. I hope the memory issue won't be a problem.
Most AgglomerativeClustering algorithms have time complexity with N^2. Will
that be a
On Aug 23, 2015, at 3:43 AM, Jing Lu wrote:
> If I want to cluster more than 1M molecules by ECFP4. How could I do it? If I
> calculate the distance between every pair of molecules, the size of distance
> matrix will be too big. Does RDKit support any heuristic clustering algorithm
> without cal
Dear Jing,
How about your trying using bayon ?
https://code.google.com/p/bayon/
It's not function of RDKit, but I think the library can cluster molecules
using ECFP4.
Unfortunately, input file format of bayon is not distance matrix but easy
to prepare the format.
Best regards.
Takayuki
2015年8
Currently, I prefer fingerprint based clustering, because it's hard to set
the cutoff for scaffold based clustering. Does RDKit have scaffold based
clustering?
On Sat, Aug 22, 2015 at 10:56 PM, wrote:
> Hi, how about scaffold based clustering . You extract the scaffolds and
> then cluster it and
Hi, how about scaffold based clustering . You extract the scaffolds and then
cluster it and then put the respective scaffold compounds inside the cluster .
Sent from my iPhone
> On Aug 22, 2015, at 8:43 PM, Jing Lu wrote:
>
> Dear RDKit users,
>
> If I want to cluster more than 1M molecules
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