UPDATED: In the end, the solution I opted to use for clustering my large dataset was one suggested by Anony-Mousse below. That is, using ELKI's DBSCAN implimentation to do my clustering rather than scikit-learn's. It can be run from the command line and with proper indexing, performs this task within a few hours. Use the GUI and small sample datasets to work out the options you want to use and then go to town. Worth looking into. Anywho, read on for a description of my original problem and some interesting discussion.
I have a dataset with ~2.5 million samples, each with 35 features (floating point values) that I'm trying to cluster. I've been trying to do this with scikit-learn's implementation of DBSCAN, using the Manhattan distance metric and a value of epsilon estimated from some small random samples drawn from the data. So far, so good. (here is the snippet, for reference)
db = DBSCAN(eps=40, min_samples=10, metric='cityblock').fit(mydata)
My issue at the moment is that I easily run out of memory. (I'm currently working on a machine with 16 GB of RAM)
My question is, is DBSCAN calculating the pairwise distance matrix on the fly as it runs, and that's what's gobbling up my memory? (2.5 million ^ 2) * 8 bytes is obviously stupidly large, I would understand that. Should I not be using the fit()
method? And more generally, is there a way around this issue, or am I generally barking up the wrong tree here?
Apologies if the answer winds up being obvious. I've been puzzling over this for a few days. Thanks!
Addendum: Also if anyone could explain the difference between fit(X)
and fit_predict(X)
to me more explicitly I'd also appreciate that--I'm afraid I just don't quite get it.
Addendum #2: To be sure, I just tried this on a machine with ~550 GB of RAM and it still blew up, so I feel like DBSCAN is likely trying to make a pairwise distance matrix or something I clearly don't want it to do. I guess now the big question is how to stop that behavior, or find other methods that might suit my needs more. Thanks for bearing with me here.
Addendum #3(!): I forgot to attach the traceback, here it is,
Traceback (most recent call last):
File "tDBSCAN.py", line 34, in <module>
db = DBSCAN(eps=float(sys.argv[2]), min_samples=10, metric='cityblock').fit(mydata)
File "/home/jtownsend/.local/lib/python2.6/site-packages/sklearn/base.py", line 329, in fit_predict
self.fit(X)
File "/home/jtownsend/.local/lib/python2.6/site-packages/sklearn/cluster/dbscan_.py", line 186, in fit
**self.get_params())
File "/home/jtownsend/.local/lib/python2.6/site-packages/sklearn/cluster/dbscan_.py", line 69, in dbscan
D = pairwise_distances(X, metric=metric)
File "/home/jtownsend/.local/lib/python2.6/site-packages/sklearn/metrics/pairwise.py", line 651, in pairwise_distances
return func(X, Y, **kwds)
File "/home/jtownsend/.local/lib/python2.6/site-packages/sklearn/metrics/pairwise.py", line 237, in manhattan_distances
D = np.abs(X[:, np.newaxis, :] - Y[np.newaxis, :, :])
MemoryError