Parallelized time series k means barycenter update procedure #321

enricofallacara · 2020-12-15T14:16:44Z

First of all I want to thank you for the amazing job that you have done creating this python package which is awesome and very useful. I have used it in my final thesis for a master degree in which I had to cluster some type of players based on their behaviors. The problem in using Time series k-means wiht my dataset derived from the fact that the actual implementation performs parallel computations only in distance matrices (DTW in my case),thus i re-wrote the code related to the MM algorithm and to the barycenter update proceudre in the following way: the barceynter update procedure creates a process for each cluster of the algorithm (equal to the value of k) and then each process creates a pool of threads (15 in my implementation), that are used to perform the barycenter update procedure in a parallel and efficient way.

enricofallacara · 2020-12-15T14:19:20Z

I forgot to mention that also some redundant calls to the functions to_time_series_dataset() were removed because they caused memory allocation problems and they were basically not useful

kushalkolar · 2020-12-15T15:51:31Z

Hi @enricofallacara , I'm not the maintainer for tslearn but I thought I'd share some comments since this would be useful for me.

tests are failing since you're importing psutil which is not in the requirements.txt, however you do not appear to use psutil.
n_jobs for joblib is hard-coded to 15, would probably be better have it as a keyword argument

enricofallacara · 2020-12-15T20:02:07Z

Hi @enricofallacara , I'm not the maintainer for tslearn but I thought I'd share some comments since this would be useful for me.

tests are failing since you're importing psutil which is not in the requirements.txt, however you do not appear to use psutil.

n_jobs for joblib is hard-coded to 15, would probably be better have it as a keyword argument

You are completly right, sorry for the psutil mistake. I have used it for some stuffs and i forgot to remove it. Also, I know that I had hard-coded the number of threads, but I was in a hurry and I loaded my implementation. Tomorrow I will fix this stuffs, thanks!

rtavenar · 2021-01-05T10:00:41Z

Hi @enricofallacara

Your PR is now in conflict with the master branch since we have refactored the code. I can take care of the merge to resolve the conflicts if you want, after which I could do a review of your code.

enricofallacara · 2021-01-05T10:20:43Z

Hello, No problem with that. Sorry, I didn't have time to fix the code due to work problems, I really appreciate your help. Il giorno mar 5 gen 2021 alle ore 11:00 Romain Tavenard < notifications@github.com> ha scritto:

…

Hi @enricofallacara <https://github.com/enricofallacara> Your PR is now in conflict with the master branch since we have refactored the code. I can take care of the merge to resolve the conflicts if you want, after which I could do a review of your code. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#321 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKOARI3KO75OW4YPXDB54JTSYLPNTANCNFSM4U4M42ZQ> .

# Conflicts: # tslearn/barycenters/dba.py # tslearn/clustering/kmeans.py # tslearn/metrics/dtw_variants.py Co-authored-by: enricofallacara <enrico.fallacara@gmail.com>

rtavenar · 2021-01-05T11:15:19Z

tslearn/barycenters/dba.py

@@ -148,7 +149,8 @@ def dtw_barycenter_averaging_petitjean(X, barycenter_size=None,
       for dynamic time warping, with applications to clustering. Pattern
       Recognition, Elsevier, 2011, Vol. 44, Num. 3, pp. 678-693
    """
-    X_ = to_time_series_dataset(X)


I understand that you want to avoid a copy here, but making sure that the data is in the right format is important. Maybe we shoud tweak the to_time_series_dataset function to add an avoid_copy_if_possible parameter, WDYT?

rtavenar · 2021-01-05T11:16:05Z

tslearn/barycenters/dba.py

@@ -176,7 +178,7 @@ def dtw_barycenter_averaging_petitjean(X, barycenter_size=None,
    return barycenter


-def _mm_assignment(X, barycenter, weights, metric_params=None):
+def _mm_assignment(X, barycenter, weights, metric_params=None, n_treads=15):


We use n_jobs everywhere as a parameter name, and None as its default, we should stick to the same convention here, I think.

rtavenar · 2021-01-05T11:17:15Z

tslearn/barycenters/dba.py

+    #with parallel_backend('threading'):
+    res = Parallel(backend='threading',prefer="threads",require='sharedmem',n_jobs=n_treads,verbose=10)(delayed(dtw_path)(barycenter, X[i], global_constraint="sakoe_chiba",sakoe_chiba_radius=1) for i in range(n))
+    paths, dists = zip(*res)
+    paths = list(paths)


Do you really need these 2 casts?

rtavenar · 2021-01-05T11:18:51Z

tslearn/barycenters/dba.py

-        cost += dist_i ** 2 * weights[i]
-        list_p_k.append(path)
+    #with parallel_backend('threading'):
+    res = Parallel(backend='threading',prefer="threads",require='sharedmem',n_jobs=n_treads,verbose=10)(delayed(dtw_path)(barycenter, X[i], global_constraint="sakoe_chiba",sakoe_chiba_radius=1) for i in range(n))


Should we define a cdist_dtw_path function instead?

I think we should design a high-efficiency cdist_dtw_path function instead. The main bottlenecks are _mm_assignment function and _subgradient_valence_warping from my view. However, as DBA is communication-intensive, we require a good tune on parallelizing DBA's implementation. Perhaps, a good target is to ensure that all the available computing units are at the desired utilization level.

rtavenar · 2021-01-05T11:19:33Z

tslearn/barycenters/dba.py

    return list_p_k, cost


-def _subgradient_valence_warping(list_p_k, barycenter_size, weights):
+def _subgradient_valence_warping(list_p_k, barycenter_size, weights, n_treads):


We use n_jobs everywhere as a parameter name, and None as its default, we should stick to the same convention here, I think.

rtavenar · 2021-01-05T11:21:32Z

tslearn/barycenters/dba.py

-        list_w_k.append(w_k * weights[k])
-        list_v_k.append(w_k.sum(axis=1) * weights[k])
+        return w_k
+    w_k_ones = Parallel(backend='threading',prefer='threads',require='sharedmem', n_jobs=n_treads, verbose = True)(delayed(create_w_k)(p_k,barycenter_size) for p_k in list_p_k)


Is this part really crucial to parallelize? I would expect that it would be neglectible in terms of running times.

This function cannot fully utilize CPU cores, even if we force to parallelize dba on different clusters and then becomes the bottleneck.

rtavenar · 2021-01-05T11:21:40Z

tslearn/barycenters/dba.py

    return list_v_k, list_w_k


-def _mm_valence_warping(list_p_k, barycenter_size, weights):
+def _mm_valence_warping(list_p_k, barycenter_size, weights, n_treads):


We use n_jobs everywhere as a parameter name, and None as its default, we should stick to the same convention here, I think.

rtavenar · 2021-01-05T11:22:13Z

tslearn/barycenters/dba.py

+    #diag_sum_v_k = numpy.zeros(list_v_k[0].shape)
+    #for v_k in list_v_k:
+    #    diag_sum_v_k += v_k
+    diag_sum_v_k = Parallel(backend='threading',prefer='threads',require='sharedmem', n_jobs=n_treads, verbose = True)(delayed(numpy.sum)(x) for x in zip(*numpy.array(list_v_k)))


Once again, I'm not sure we need to parallelize this part.

rtavenar · 2021-01-05T11:22:20Z

tslearn/barycenters/dba.py

    return diag_sum_v_k, list_w_k


-def _mm_update_barycenter(X, diag_sum_v_k, list_w_k):
+def _mm_update_barycenter(X, diag_sum_v_k, list_w_k, n_treads):


We use n_jobs everywhere as a parameter name, and None as its default, we should stick to the same convention here, I think.

rtavenar · 2021-01-05T11:22:44Z

tslearn/barycenters/dba.py

+    #sum_w_x = numpy.zeros((barycenter_size, d))
+    #for k, (w_k, x_k) in enumerate(zip(list_w_k, X)):
+    #    sum_w_x += w_k.dot(x_k[:ts_size(x_k)])
+    sum_w_x = Parallel(backend='threading',prefer='threads',require='sharedmem', n_jobs=n_treads, verbose = True)(delayed(numpy.dot)(w_k,x_k[:ts_size(x_k)]) for (w_k, x_k) in zip(list_w_k, X))


Once again, I'm not sure we need to parallelize this part.

rtavenar · 2021-01-05T11:22:56Z

tslearn/barycenters/dba.py

@@ -392,7 +412,7 @@ def _subgradient_update_barycenter(X, list_diag_v_k, list_w_k, weights_sum,


 def dtw_barycenter_averaging(X, barycenter_size=None, init_barycenter=None,
-                             max_iter=30, tol=1e-5, weights=None,
+                             max_iter=30, tol=1e-5, weights=None, n_treads=15,


We use n_jobs everywhere as a parameter name, and None as its default, we should stick to the same convention here, I think.

rtavenar · 2021-01-05T11:23:05Z

tslearn/barycenters/dba.py

@@ -514,6 +534,7 @@ def dtw_barycenter_averaging(X, barycenter_size=None, init_barycenter=None,
 def dtw_barycenter_averaging_one_init(X, barycenter_size=None,
                                      init_barycenter=None,
                                      max_iter=30, tol=1e-5, weights=None,
+									  n_treads=15,


We use n_jobs everywhere as a parameter name, and None as its default, we should stick to the same convention here, I think.

rtavenar · 2021-01-05T11:23:27Z

tslearn/barycenters/dba.py

@@ -576,23 +597,23 @@ def dtw_barycenter_averaging_one_init(X, barycenter_size=None,
       for Averaging in Dynamic Time Warping Spaces.
       Pattern Recognition, 74, 340-358.
    """
-    X_ = to_time_series_dataset(X)


See comment above about the array copy issue.

rtavenar · 2021-01-05T11:24:28Z

tslearn/clustering/kmeans.py

@@ -692,23 +693,37 @@ def _assign(self, X, update_class_attributes=True):

    def _update_centroids(self, X):
        metric_params = self._get_metric_params()
+        X_list = []


Here, I guess we could use a comprehension list for better readability.

rtavenar · 2021-01-05T11:25:22Z

tslearn/clustering/kmeans.py

-                self.cluster_centers_[k] = euclidean_barycenter(
-                    X=X[self.labels_ == k])
+           X_list.append(X[self.labels_ == k])
+        cluster_centers = Parallel(backend='threading', prefer='threads', require='sharedmem', n_jobs=self.n_clusters, verbose=5)(delayed(dtw_barycenter_averaging)(


Take care that here, the function to be called depends on the value of self.metric

Simply parallelize DBA over the clusters only speedup less than 0.5x times. More effort is required.

rtavenar · 2021-01-05T11:26:28Z

tslearn/metrics/utils.py

@@ -9,48 +9,39 @@ def _cdist_generic(dist_fun, dataset1, dataset2, n_jobs, verbose,
                   compute_diagonal=True, dtype=numpy.float, *args, **kwargs):
    """Compute cross-similarity matrix with joblib parallelization for a given
    similarity function.
-


Could you please revert all these line suppressions? They tend to pack the docstrings.

rtavenar · 2021-01-05T11:27:22Z

tslearn/metrics/utils.py

@@ -60,7 +51,7 @@ def _cdist_generic(dist_fun, dataset1, dataset2, n_jobs, verbose,
                                     k=0 if compute_diagonal else 1,
                                     m=len(dataset1))
        matrix[indices] = Parallel(n_jobs=n_jobs,
-                                   prefer="threads",


Should we always prefer this? Or should it be specified when calling the function?

rtavenar · 2021-01-05T11:28:54Z

tslearn/metrics/utils.py

-                dataset1[i], dataset2[j],
-                *args, **kwargs
+        #dataset2 = to_time_series_dataset(dataset2, dtype=dtype)
+        with parallel_backend('loky'):


Is it a good idea to specify the parallel_backend here? Will loky always be a better alternative or could it depend on some specificities or one's data?

rtavenar · 2021-01-05T11:30:02Z

I have left a few comments. Maybe the main point would be to assess the improvement (in terms of running times) that these changes offer on some benchmark.

rtavenar · 2021-01-05T11:31:43Z

Also, tests seem to fail at the moment (let me know if it's due to a bug in my merge).

Parallelized time series k means barycenter update procedure

5e0fa46

fixed number of threads parameter

f7bb66b

rtavenar and others added 2 commits January 5, 2021 12:08

Merge branch 'master' into parallel_time_series_k_means

6c13bf4

# Conflicts: # tslearn/barycenters/dba.py # tslearn/clustering/kmeans.py # tslearn/metrics/dtw_variants.py Co-authored-by: enricofallacara <enrico.fallacara@gmail.com>

Merge branch 'master' into parallel_time_series_k_means

6e2ce6b

# Conflicts: # tslearn/barycenters/dba.py # tslearn/clustering/kmeans.py # tslearn/metrics/dtw_variants.py Co-authored-by: enricofallacara <enrico.fallacara@gmail.com>

rtavenar reviewed Jan 5, 2021

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Base automatically changed from master to main January 26, 2021 12:41

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Parallelized time series k means barycenter update procedure #321

Parallelized time series k means barycenter update procedure #321

enricofallacara commented Dec 15, 2020

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Parallelized time series k means barycenter update procedure #321

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Parallelized time series k means barycenter update procedure #321

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enricofallacara commented Dec 15, 2020

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rtavenar commented Jan 5, 2021

rtavenar commented Jan 5, 2021