From b36346574709e33dbfe2231181985888bae7bb4b Mon Sep 17 00:00:00 2001 From: Milton Pividori Date: Mon, 11 Sep 2023 12:43:28 -0600 Subject: [PATCH] run profiling again to ensure new CCC implementation works as expected --- .../12_cm_optimized/06-cm_many_genes.txt | 96 +++--- .../12_cm_optimized/06-many_genes.ipynb | 234 ++++++------- ...ny_samples-default_internal_n_clusters.txt | 88 ++--- ..._many_samples-less_internal_n_clusters.txt | 82 ++--- .../12_cm_optimized/07-many_samples.ipynb | 284 ++++++++-------- .../12_cm_optimized/08-cm_many_genes.txt | 84 ++--- .../12_cm_optimized/08-many_genes.ipynb | 260 +++++++-------- ...ny_samples-default_internal_n_clusters.txt | 80 ++--- ..._many_samples-less_internal_n_clusters.txt | 80 ++--- .../12_cm_optimized/09-many_samples.ipynb | 314 +++++++++--------- .../12_cm_optimized/10-cm_many_genes.txt | 104 +++--- .../12_cm_optimized/10-many_genes.ipynb | 232 ++++++------- ...ny_samples-default_internal_n_clusters.txt | 88 ++--- ..._many_samples-less_internal_n_clusters.txt | 82 ++--- .../12_cm_optimized/11-many_samples.ipynb | 284 ++++++++-------- .../12_cm_optimized/py/06-many_genes.py | 2 +- .../12_cm_optimized/py/07-many_samples.py | 2 +- .../12_cm_optimized/py/08-many_genes.py | 2 +- .../12_cm_optimized/py/09-many_samples.py | 2 +- .../12_cm_optimized/py/10-many_genes.py | 2 +- .../12_cm_optimized/py/11-many_samples.py | 2 +- 21 files changed, 1202 insertions(+), 1202 deletions(-) diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-cm_many_genes.txt b/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-cm_many_genes.txt index 40e63589..21f49ec0 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-cm_many_genes.txt +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-cm_many_genes.txt @@ -1,56 +1,56 @@ - 21956760 function calls (21956754 primitive calls) in 102.486 seconds + 21092455 function calls (21092449 primitive calls) in 99.101 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 167 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 102.486 102.486 {built-in method builtins.exec} - 1 0.000 0.000 102.486 102.486 :1() - 1 0.000 0.000 102.486 102.486 1375007101.py:1(func) - 1 0.005 0.005 102.486 102.486 impl.py:307(ccc) - 2 1.512 0.756 102.174 51.087 impl.py:508(compute_coef) - 124750 25.803 0.000 99.395 0.001 impl.py:154(cdist_parts_basic) - 10104750 45.819 0.000 73.540 0.000 metrics.py:107(adjusted_rand_index) - 10104750 27.722 0.000 27.722 0.000 metrics.py:72(get_pair_confusion_matrix) - 124750 0.071 0.000 1.030 0.000 <__array_function__ internals>:2(amax) - 124758 0.066 0.000 0.944 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} - 124750 0.106 0.000 0.878 0.000 fromnumeric.py:2638(amax) - 124750 0.213 0.000 0.772 0.000 fromnumeric.py:69(_wrapreduction) - 124750 0.460 0.000 0.460 0.000 {method 'reduce' of 'numpy.ufunc' objects} - 2 0.000 0.000 0.305 0.153 impl.py:476(compute_parts) - 2 0.001 0.001 0.300 0.150 impl.py:478() - 500 0.299 0.001 0.299 0.001 impl.py:110(get_parts) - 124750 0.129 0.000 0.129 0.000 {method 'argmax' of 'numpy.ndarray' objects} - 124750 0.069 0.000 0.069 0.000 core.py:85(unravel_index_2d) - 124754 0.052 0.000 0.052 0.000 {built-in method numpy.zeros} - 124750 0.047 0.000 0.047 0.000 fromnumeric.py:70() - 124750 0.039 0.000 0.039 0.000 impl.py:218(get_coords_from_index) - 124751 0.037 0.000 0.037 0.000 {built-in method builtins.getattr} - 124750 0.015 0.000 0.015 0.000 {method 'items' of 'dict' objects} - 124750 0.015 0.000 0.015 0.000 fromnumeric.py:2633(_amax_dispatcher) - 2 0.005 0.003 0.005 0.003 {built-in method numpy.array} - 1 0.000 0.000 0.001 0.001 {method 'seed' of 'numpy.random.mtrand.RandomState' objects} - 1 0.000 0.000 0.000 0.000 random.py:791(getrandbits) - 1 0.000 0.000 0.000 0.000 {built-in method posix.urandom} - 7 0.000 0.000 0.000 0.000 numeric.py:289(full) - 7 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(copyto) - 2 0.000 0.000 0.000 0.000 impl.py:242(get_chunks) + 1 0.000 0.000 99.101 99.101 {built-in method builtins.exec} + 1 0.000 0.000 99.101 99.101 :1() + 1 0.000 0.000 99.101 99.101 1375007101.py:1(func) + 1 0.008 0.008 99.101 99.101 impl.py:511(ccc) + 1 0.913 0.913 98.744 98.744 impl.py:405(compute_coef) + 124750 0.324 0.000 97.798 0.001 impl.py:344(compute_ccc) + 124750 24.820 0.000 97.238 0.001 impl.py:191(cdist_parts_basic) + 10104750 44.939 0.000 72.368 0.000 metrics.py:107(adjusted_rand_index) + 10104750 27.430 0.000 27.430 0.000 metrics.py:72(get_pair_confusion_matrix) + 1 0.016 0.016 0.343 0.343 impl.py:154(get_feature_parts) + 4500 0.324 0.000 0.324 0.000 impl.py:110(get_parts) + 124750 0.114 0.000 0.114 0.000 {method 'argmax' of 'numpy.ndarray' objects} + 124750 0.072 0.000 0.072 0.000 core.py:85(unravel_index_2d) + 124750 0.050 0.000 0.050 0.000 {built-in method builtins.max} + 124754 0.050 0.000 0.050 0.000 {built-in method numpy.zeros} + 124750 0.032 0.000 0.032 0.000 impl.py:255(get_coords_from_index) + 4500 0.003 0.000 0.003 0.000 {built-in method numpy.array} + 1 0.003 0.003 0.003 0.003 impl.py:688() + 1 0.000 0.000 0.001 0.001 impl.py:699() + 1 0.001 0.001 0.001 0.001 impl.py:700() + 5 0.000 0.000 0.001 0.000 numeric.py:289(full) + 6 0.001 0.000 0.001 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} + 5 0.000 0.000 0.001 0.000 <__array_function__ internals>:2(copyto) + 1 0.000 0.000 0.000 0.000 process.py:573(__init__) + 2 0.000 0.000 0.000 0.000 impl.py:279(get_chunks) + 1 0.000 0.000 0.000 0.000 {built-in method numpy.arange} + 5 0.000 0.000 0.000 0.000 synchronize.py:50(__init__) + 1 0.000 0.000 0.000 0.000 impl.py:713() + 4 0.000 0.000 0.000 0.000 context.py:65(Lock) + 1 0.000 0.000 0.000 0.000 process.py:160(__init__) + 4 0.000 0.000 0.000 0.000 synchronize.py:161(__init__) + 1 0.000 0.000 0.000 0.000 queues.py:37(__init__) + 1 0.000 0.000 0.000 0.000 {method 'seed' of 'numpy.random.mtrand.RandomState' objects} + 2 0.000 0.000 0.000 0.000 _base.py:636(__exit__) + 1 0.000 0.000 0.000 0.000 process.py:731(shutdown) + 1 0.000 0.000 0.000 0.000 impl.py:714() 9 0.000 0.000 0.000 0.000 typedlist.py:341(append) 1 0.000 0.000 0.000 0.000 typedlist.py:298(_initialise_list) + 1 0.000 0.000 0.000 0.000 context.py:110(SimpleQueue) 1 0.000 0.000 0.000 0.000 contextlib.py:76(inner) - 2 0.000 0.000 0.000 0.000 {built-in method numpy.arange} - 1 0.000 0.000 0.000 0.000 {function SeedSequence.generate_state at 0x7fd0685e59d0} + 1 0.000 0.000 0.000 0.000 queues.py:340(__init__) + 5 0.000 0.000 0.000 0.000 synchronize.py:114(_make_name) + 11 0.000 0.000 0.000 0.000 {built-in method builtins.next} + 5 0.000 0.000 0.000 0.000 tempfile.py:149(__next__) + 1 0.000 0.000 0.000 0.000 {function SeedSequence.generate_state at 0x7f75101d79d0} + 5 0.000 0.000 0.000 0.000 tempfile.py:152() + 40 0.000 0.000 0.000 0.000 random.py:343(choice) 2 0.000 0.000 0.000 0.000 abstract.py:60(__call__) - 2 0.000 0.000 0.000 0.000 typeof.py:25(typeof) - 1 0.000 0.000 0.000 0.000 typedlist.py:270(_parse_arg) - 2 0.000 0.000 0.000 0.000 functools.py:883(wrapper) - 1 0.000 0.000 0.000 0.000 dispatcher.py:677(typeof_pyval) - 2 0.000 0.000 0.000 0.000 abstract.py:48(_intern) - 1 0.000 0.000 0.000 0.000 thread.py:123(__init__) - 2 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects} - 1 0.000 0.000 0.000 0.000 containers.py:618(__init__) - 1 0.000 0.000 0.000 0.000 typeof.py:257(_typeof_nb_type) - 7 0.000 0.000 0.000 0.000 {built-in method numpy.empty} - 1 0.000 0.000 0.000 0.000 _base.py:636(__exit__) - 2 0.000 0.000 0.000 0.000 _ufunc_config.py:32(seterr) - 1 0.000 0.000 0.000 0.000 _ufunc_config.py:429(__enter__) \ No newline at end of file + 1 0.000 0.000 0.000 0.000 context.py:85(BoundedSemaphore) + 1 0.000 0.000 0.000 0.000 random.py:791(getrandbits) \ No newline at end of file diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-many_genes.ipynb b/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-many_genes.ipynb index e379bb9c..1572ae43 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-many_genes.ipynb +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/06-many_genes.ipynb @@ -5,10 +5,10 @@ "id": "507e9319-381b-4934-987c-2958e7f6ace7", "metadata": { "papermill": { - "duration": 0.0043, - "end_time": "2023-09-09T18:11:26.957147", + "duration": 0.004312, + "end_time": "2023-09-11T16:10:20.837194", "exception": false, - "start_time": "2023-09-09T18:11:26.952847", + "start_time": "2023-09-11T16:10:20.832882", "status": "completed" }, "tags": [] @@ -22,10 +22,10 @@ "id": "9a88f2a7-3773-459c-8fdc-c69c1b0bb338", "metadata": { "papermill": { - "duration": 0.003479, - "end_time": "2023-09-09T18:11:26.964420", + "duration": 0.003972, + "end_time": "2023-09-11T16:10:20.851357", "exception": false, - "start_time": "2023-09-09T18:11:26.960941", + "start_time": "2023-09-11T16:10:20.847385", "status": "completed" }, "tags": [] @@ -39,10 +39,10 @@ "id": "72de9f6f-dc00-435e-9dba-871db859a78a", "metadata": { "papermill": { - "duration": 0.003437, - "end_time": "2023-09-09T18:11:26.971460", + "duration": 0.003412, + "end_time": "2023-09-11T16:10:20.859437", "exception": false, - "start_time": "2023-09-09T18:11:26.968023", + "start_time": "2023-09-11T16:10:20.856025", "status": "completed" }, "tags": [] @@ -57,16 +57,16 @@ "id": "88d32361-f1b5-4cf0-9a2c-7ab927d14b42", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:11:26.980713Z", - 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8190 function calls (8184 primitive calls) in 0.487 seconds + 8491 function calls (8485 primitive calls) in 0.469 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 167 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.487 0.487 {built-in method builtins.exec} - 1 0.000 0.000 0.487 0.487 :1() - 1 0.001 0.001 0.487 0.487 1375007101.py:1(func) - 1 0.001 0.001 0.486 0.486 impl.py:307(ccc) - 2 0.000 0.000 0.325 0.162 impl.py:476(compute_parts) - 2 0.000 0.000 0.322 0.161 impl.py:478() - 10 0.322 0.032 0.322 0.032 impl.py:110(get_parts) - 2 0.001 0.000 0.159 0.080 impl.py:508(compute_coef) - 45 0.009 0.000 0.158 0.004 impl.py:154(cdist_parts_basic) - 3645 0.017 0.000 0.149 0.000 metrics.py:107(adjusted_rand_index) - 3645 0.132 0.000 0.132 0.000 metrics.py:72(get_pair_confusion_matrix) - 2 0.003 0.002 0.003 0.002 {built-in method numpy.array} - 45 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(amax) - 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"iopub.execute_input": "2023-09-09T18:11:42.135479Z", - "iopub.status.busy": "2023-09-09T18:11:42.135335Z", - "iopub.status.idle": "2023-09-09T18:11:42.137782Z", - "shell.execute_reply": "2023-09-09T18:11:42.137440Z" + "iopub.execute_input": "2023-09-11T16:33:10.318027Z", + "iopub.status.busy": "2023-09-11T16:33:10.317860Z", + "iopub.status.idle": "2023-09-11T16:33:10.320284Z", + "shell.execute_reply": "2023-09-11T16:33:10.319942Z" }, "papermill": { - "duration": 0.008705, - "end_time": "2023-09-09T18:11:42.138937", + "duration": 0.006089, + "end_time": "2023-09-11T16:33:10.321090", "exception": false, - "start_time": "2023-09-09T18:11:42.130232", + "start_time": "2023-09-11T16:33:10.315001", "status": "completed" }, "tags": [] @@ -544,16 +544,16 @@ "id": "11259d8c-3bf3-4299-b47b-211556c3bc08", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:11:42.148699Z", - "iopub.status.busy": "2023-09-09T18:11:42.148561Z", - "iopub.status.idle": "2023-09-09T18:11:57.706017Z", - "shell.execute_reply": "2023-09-09T18:11:57.705528Z" + "iopub.execute_input": "2023-09-11T16:33:10.326630Z", + "iopub.status.busy": "2023-09-11T16:33:10.326368Z", + "iopub.status.idle": "2023-09-11T16:33:25.559458Z", + "shell.execute_reply": "2023-09-11T16:33:25.559004Z" }, "papermill": { - "duration": 15.563719, - "end_time": "2023-09-09T18:11:57.707332", + "duration": 15.237224, + "end_time": "2023-09-11T16:33:25.560816", "exception": false, - "start_time": "2023-09-09T18:11:42.143613", + "start_time": "2023-09-11T16:33:10.323592", "status": "completed" }, "tags": [] @@ -563,7 +563,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "173 ms ± 336 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "170 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -578,16 +578,16 @@ "id": "42d9e6e0-3c01-46e1-a409-52ff26cb78f4", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:11:57.723261Z", - "iopub.status.busy": "2023-09-09T18:11:57.723170Z", - "iopub.status.idle": "2023-09-09T18:11:57.901877Z", - "shell.execute_reply": "2023-09-09T18:11:57.901376Z" + "iopub.execute_input": "2023-09-11T16:33:25.576677Z", + "iopub.status.busy": "2023-09-11T16:33:25.576575Z", + "iopub.status.idle": "2023-09-11T16:33:25.751572Z", + "shell.execute_reply": "2023-09-11T16:33:25.751115Z" }, "papermill": { - "duration": 0.190986, - "end_time": "2023-09-09T18:11:57.903134", + "duration": 0.187281, + "end_time": "2023-09-11T16:33:25.752918", "exception": false, - "start_time": "2023-09-09T18:11:57.712148", + "start_time": "2023-09-11T16:33:25.565637", "status": "completed" }, "tags": [] @@ -613,10 +613,10 @@ "id": "e2556204-1c10-4e01-8c6c-ea63ddb37530", "metadata": { "papermill": { - "duration": 0.010557, - "end_time": "2023-09-09T18:11:57.918574", + "duration": 0.010346, + "end_time": "2023-09-11T16:33:25.768137", "exception": false, - "start_time": "2023-09-09T18:11:57.908017", + "start_time": "2023-09-11T16:33:25.757791", "status": "completed" }, "tags": [] @@ -648,14 +648,14 @@ }, "papermill": { "default_parameters": {}, - "duration": 32.253249, - "end_time": "2023-09-09T18:11:58.239170", + "duration": 31.460592, + "end_time": "2023-09-11T16:33:26.088092", "environment_variables": {}, "exception": null, "input_path": "nbs/others/05_clustermatch_profiling/12_cm_optimized/07-many_samples.ipynb", "output_path": "nbs/others/05_clustermatch_profiling/12_cm_optimized/07-many_samples.run.ipynb", "parameters": {}, - "start_time": "2023-09-09T18:11:25.985921", + "start_time": "2023-09-11T16:32:54.627500", "version": "2.3.3" } }, diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-cm_many_genes.txt b/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-cm_many_genes.txt index 1070f138..be06229a 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-cm_many_genes.txt +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-cm_many_genes.txt @@ -1,56 +1,56 @@ - 5410850 function calls in 53.982 seconds + 5405565 function calls in 52.444 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 174 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 53.982 53.982 {built-in method builtins.exec} - 1 0.000 0.000 53.982 53.982 :1() - 1 0.000 0.000 53.982 53.982 1375007101.py:1(func) - 1 0.000 0.000 53.982 53.982 impl.py:307(ccc) - 2 0.010 0.005 53.927 26.964 impl.py:508(compute_coef) - 1225 0.287 0.000 53.889 0.044 impl.py:154(cdist_parts_basic) - 99225 0.399 0.000 53.602 0.001 metrics.py:107(adjusted_rand_index) - 99225 0.828 0.000 53.203 0.001 metrics.py:72(get_pair_confusion_matrix) - 99225 49.747 0.001 50.793 0.001 metrics.py:42(get_contingency_matrix) - 697025 1.204 0.000 1.204 0.000 {method 'reduce' of 'numpy.ufunc' objects} - 400833 0.135 0.000 1.173 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} - 496125 0.162 0.000 1.021 0.000 {method 'sum' of 'numpy.ndarray' objects} - 199675 0.086 0.000 1.006 0.000 <__array_function__ internals>:2(amax) - 496125 0.097 0.000 0.859 0.000 _methods.py:46(_sum) - 199675 0.131 0.000 0.824 0.000 fromnumeric.py:2638(amax) - 199675 0.175 0.000 0.693 0.000 fromnumeric.py:69(_wrapreduction) - 198900 0.078 0.000 0.348 0.000 <__array_function__ internals>:2(ravel) - 198900 0.104 0.000 0.189 0.000 fromnumeric.py:1718(ravel) + 1 0.000 0.000 52.444 52.444 {built-in method builtins.exec} + 1 0.000 0.000 52.444 52.444 :1() + 1 0.000 0.000 52.444 52.444 1375007101.py:1(func) + 1 0.000 0.000 52.444 52.444 impl.py:511(ccc) + 1 0.008 0.008 52.380 52.380 impl.py:405(compute_coef) + 1225 0.002 0.000 52.363 0.043 impl.py:344(compute_ccc) + 1225 0.275 0.000 52.351 0.043 impl.py:191(cdist_parts_basic) + 99225 0.404 0.000 52.075 0.001 metrics.py:107(adjusted_rand_index) + 99225 0.827 0.000 51.672 0.001 metrics.py:72(get_pair_confusion_matrix) + 99225 48.239 0.000 49.266 0.000 metrics.py:42(get_contingency_matrix) + 695800 1.187 0.000 1.187 0.000 {method 'reduce' of 'numpy.ufunc' objects} + 399606 0.132 0.000 1.159 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} + 496125 0.161 0.000 1.014 0.000 {method 'sum' of 'numpy.ndarray' objects} + 198450 0.083 0.000 0.980 0.000 <__array_function__ internals>:2(amax) + 496125 0.096 0.000 0.854 0.000 _methods.py:46(_sum) + 198450 0.125 0.000 0.805 0.000 fromnumeric.py:2638(amax) + 198450 0.175 0.000 0.679 0.000 fromnumeric.py:69(_wrapreduction) + 198900 0.076 0.000 0.351 0.000 <__array_function__ internals>:2(ravel) + 198900 0.105 0.000 0.194 0.000 fromnumeric.py:1718(ravel) 198450 0.161 0.000 0.161 0.000 {method 'dot' of 'numpy.ndarray' objects} - 199675 0.059 0.000 0.059 0.000 fromnumeric.py:70() - 2 0.000 0.000 0.054 0.027 impl.py:476(compute_parts) - 2 0.000 0.000 0.054 0.027 impl.py:478() - 50 0.001 0.000 0.054 0.001 impl.py:110(get_parts) + 1 0.001 0.001 0.062 0.062 impl.py:154(get_feature_parts) + 450 0.004 0.000 0.061 0.000 impl.py:110(get_parts) + 198450 0.058 0.000 0.058 0.000 fromnumeric.py:70() + 450 0.006 0.000 0.046 0.000 impl.py:36(run_quantile_clustering) 198900 0.044 0.000 0.044 0.000 {method 'ravel' of 'numpy.ndarray' objects} - 450 0.006 0.000 0.043 0.000 impl.py:36(run_quantile_clustering) - 101404 0.038 0.000 0.038 0.000 {built-in method numpy.zeros} - 100582 0.033 0.000 0.033 0.000 {built-in method numpy.empty} - 199675 0.023 0.000 0.023 0.000 fromnumeric.py:2633(_amax_dispatcher) + 101804 0.038 0.000 0.038 0.000 {built-in method numpy.zeros} + 100580 0.032 0.000 0.032 0.000 {built-in method numpy.empty} + 198904 0.024 0.000 0.024 0.000 {built-in method builtins.isinstance} + 99225 0.023 0.000 0.023 0.000 {method 'transpose' of 'numpy.ndarray' objects} + 198450 0.022 0.000 0.022 0.000 fromnumeric.py:2633(_amax_dispatcher) 198900 0.021 0.000 0.021 0.000 fromnumeric.py:1714(_ravel_dispatcher) - 198904 0.021 0.000 0.021 0.000 {built-in method builtins.isinstance} 199800 0.020 0.000 0.020 0.000 {built-in method numpy.asanyarray} - 99225 0.020 0.000 0.020 0.000 {method 'transpose' of 'numpy.ndarray' objects} - 199675 0.019 0.000 0.019 0.000 {method 'items' of 'dict' objects} - 1350 0.001 0.000 0.016 0.000 fromnumeric.py:51(_wrapfunc) + 198450 0.019 0.000 0.019 0.000 {method 'items' of 'dict' objects} + 1350 0.001 0.000 0.019 0.000 fromnumeric.py:51(_wrapfunc) + 450 0.000 0.000 0.017 0.000 <__array_function__ internals>:2(argsort) + 450 0.000 0.000 0.017 0.000 fromnumeric.py:1006(argsort) + 450 0.016 0.000 0.016 0.000 {method 'argsort' of 'numpy.ndarray' objects} 450 0.008 0.000 0.015 0.000 stats.py:65(rank) - 450 0.000 0.000 0.015 0.000 <__array_function__ internals>:2(argsort) - 450 0.000 0.000 0.014 0.000 fromnumeric.py:1006(argsort) - 450 0.014 0.000 0.014 0.000 {method 'argsort' of 'numpy.ndarray' objects} - 102808 0.012 0.000 0.012 0.000 {built-in method builtins.len} - 1225 0.010 0.000 0.010 0.000 impl.py:218(get_coords_from_index) - 50 0.000 0.000 0.009 0.000 impl.py:144() - 450 0.000 0.000 0.009 0.000 <__array_function__ internals>:2(unique) + 103651 0.012 0.000 0.012 0.000 {built-in method builtins.len} + 450 0.001 0.000 0.011 0.000 impl.py:144() + 450 0.000 0.000 0.010 0.000 <__array_function__ internals>:2(unique) + 1225 0.010 0.000 0.010 0.000 impl.py:255(get_coords_from_index) 450 0.000 0.000 0.009 0.000 arraysetops.py:138(unique) - 1225 0.004 0.000 0.009 0.000 core.py:85(unravel_index_2d) - 450 0.002 0.000 0.008 0.000 arraysetops.py:320(_unique1d) + 450 0.002 0.000 0.009 0.000 arraysetops.py:320(_unique1d) + 1225 0.004 0.000 0.008 0.000 core.py:85(unravel_index_2d) 450 0.006 0.000 0.006 0.000 {method 'sort' of 'numpy.ndarray' objects} 450 0.000 0.000 0.005 0.000 impl.py:19(get_perc_from_k) 450 0.004 0.000 0.004 0.000 impl.py:33() 1225 0.000 0.000 0.003 0.000 {method 'prod' of 'numpy.ndarray' objects} - 1225 0.000 0.000 0.002 0.000 _methods.py:50(_prod) \ No newline at end of file + 2126 0.002 0.000 0.002 0.000 {built-in method numpy.array} \ No newline at end of file diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-many_genes.ipynb b/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-many_genes.ipynb index dd649557..199b7650 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-many_genes.ipynb +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/08-many_genes.ipynb @@ -5,10 +5,10 @@ "id": "507e9319-381b-4934-987c-2958e7f6ace7", "metadata": { "papermill": { - "duration": 0.004792, - "end_time": "2023-09-09T18:11:26.950635", + "duration": 0.004674, + "end_time": "2023-09-11T16:33:28.412595", "exception": false, - "start_time": "2023-09-09T18:11:26.945843", + "start_time": "2023-09-11T16:33:28.407921", "status": "completed" }, "tags": [] @@ -22,10 +22,10 @@ "id": "9a88f2a7-3773-459c-8fdc-c69c1b0bb338", "metadata": { "papermill": { - "duration": 0.003866, - "end_time": "2023-09-09T18:11:26.958656", + "duration": 0.003816, + "end_time": "2023-09-11T16:33:28.426626", "exception": false, - "start_time": "2023-09-09T18:11:26.954790", + "start_time": "2023-09-11T16:33:28.422810", "status": "completed" }, "tags": [] @@ -41,10 +41,10 @@ "id": "61443ae0-70fe-42c4-adf2-76b8a7b22217", "metadata": { "papermill": { - "duration": 0.003869, - "end_time": "2023-09-09T18:11:26.967614", + "duration": 0.003819, + "end_time": "2023-09-11T16:33:28.435469", "exception": false, - "start_time": "2023-09-09T18:11:26.963745", + "start_time": "2023-09-11T16:33:28.431650", "status": "completed" }, "tags": [] @@ -59,16 +59,16 @@ "id": "9495a29b-3ba6-4a51-99b0-780fa5c1d9c7", "metadata": { "execution": { - 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"52.7 s ± 120 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "52.6 s ± 198 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -517,16 +517,16 @@ "id": "51c7a416-064a-4669-a09f-16f837d32475", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:25:32.229397Z", - "iopub.status.busy": "2023-09-09T18:25:32.229308Z", - "iopub.status.idle": "2023-09-09T18:26:26.215198Z", - "shell.execute_reply": "2023-09-09T18:26:26.214686Z" + "iopub.execute_input": "2023-09-11T16:47:30.220620Z", + "iopub.status.busy": "2023-09-11T16:47:30.220534Z", + "iopub.status.idle": "2023-09-11T16:48:22.669818Z", + "shell.execute_reply": "2023-09-11T16:48:22.669222Z" }, "papermill": { - "duration": 53.996671, - "end_time": "2023-09-09T18:26:26.220868", + "duration": 52.459945, + "end_time": "2023-09-11T16:48:22.675609", "exception": false, - "start_time": "2023-09-09T18:25:32.224197", + "start_time": "2023-09-11T16:47:30.215664", "status": "completed" }, "tags": [] @@ -551,10 +551,10 @@ "id": "664b37eb-ead5-4b74-af40-41f1e257a5f3", "metadata": { "papermill": { - 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"start_time": "2023-09-09T18:11:25.989176", + "start_time": "2023-09-11T16:33:27.459459", "version": "2.3.3" } }, diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-default_internal_n_clusters.txt b/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-default_internal_n_clusters.txt index 5e983ce0..4d874118 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-default_internal_n_clusters.txt +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-default_internal_n_clusters.txt @@ -1,56 +1,56 @@ - 203270 function calls in 53.259 seconds + 203954 function calls in 53.085 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 174 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 53.259 53.259 {built-in method builtins.exec} - 1 0.000 0.000 53.259 53.259 :1() - 1 0.001 0.001 53.259 53.259 1375007101.py:1(func) - 1 0.003 0.003 53.258 53.258 impl.py:307(ccc) - 2 0.000 0.000 53.000 26.500 impl.py:508(compute_coef) - 45 0.012 0.000 52.998 1.178 impl.py:154(cdist_parts_basic) - 3645 0.017 0.000 52.986 0.015 metrics.py:107(adjusted_rand_index) - 3645 0.035 0.000 52.969 0.015 metrics.py:72(get_pair_confusion_matrix) - 3645 52.671 0.014 52.867 0.015 metrics.py:42(get_contingency_matrix) - 15173 0.006 0.000 0.415 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} - 2 0.000 0.000 0.255 0.128 impl.py:476(compute_parts) - 2 0.000 0.000 0.255 0.128 impl.py:478() - 10 0.001 0.000 0.255 0.026 impl.py:110(get_parts) - 90 0.009 0.000 0.213 0.002 impl.py:36(run_quantile_clustering) - 25605 0.204 0.000 0.204 0.000 {method 'reduce' of 'numpy.ufunc' objects} - 7335 0.003 0.000 0.194 0.000 <__array_function__ internals>:2(amax) - 7335 0.005 0.000 0.187 0.000 fromnumeric.py:2638(amax) - 7335 0.007 0.000 0.182 0.000 fromnumeric.py:69(_wrapreduction) - 270 0.000 0.000 0.173 0.001 fromnumeric.py:51(_wrapfunc) - 90 0.000 0.000 0.172 0.002 <__array_function__ internals>:2(argsort) - 90 0.000 0.000 0.171 0.002 fromnumeric.py:1006(argsort) - 90 0.171 0.002 0.171 0.002 {method 'argsort' of 'numpy.ndarray' objects} - 18225 0.007 0.000 0.043 0.000 {method 'sum' of 'numpy.ndarray' objects} - 10 0.000 0.000 0.041 0.004 impl.py:144() + 1 0.000 0.000 53.085 53.085 {built-in method builtins.exec} + 1 0.000 0.000 53.085 53.085 :1() + 1 0.001 0.001 53.085 53.085 1375007101.py:1(func) + 1 0.003 0.003 53.084 53.084 impl.py:511(ccc) + 1 0.000 0.000 52.826 52.826 impl.py:405(compute_coef) + 45 0.000 0.000 52.825 1.174 impl.py:344(compute_ccc) + 45 0.012 0.000 52.825 1.174 impl.py:191(cdist_parts_basic) + 3645 0.017 0.000 52.813 0.014 metrics.py:107(adjusted_rand_index) + 3645 0.035 0.000 52.796 0.014 metrics.py:72(get_pair_confusion_matrix) + 3645 52.502 0.014 52.696 0.014 metrics.py:42(get_contingency_matrix) + 15126 0.006 0.000 0.410 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} + 1 0.002 0.002 0.254 0.254 impl.py:154(get_feature_parts) + 90 0.001 0.000 0.252 0.003 impl.py:110(get_parts) + 90 0.009 0.000 0.210 0.002 impl.py:36(run_quantile_clustering) + 25560 0.201 0.000 0.201 0.000 {method 'reduce' of 'numpy.ufunc' objects} + 7290 0.003 0.000 0.192 0.000 <__array_function__ internals>:2(amax) + 7290 0.005 0.000 0.185 0.000 fromnumeric.py:2638(amax) + 7290 0.007 0.000 0.179 0.000 fromnumeric.py:69(_wrapreduction) + 270 0.000 0.000 0.171 0.001 fromnumeric.py:51(_wrapfunc) + 90 0.000 0.000 0.170 0.002 <__array_function__ internals>:2(argsort) + 90 0.000 0.000 0.169 0.002 fromnumeric.py:1006(argsort) + 90 0.169 0.002 0.169 0.002 {method 'argsort' of 'numpy.ndarray' objects} + 18225 0.007 0.000 0.042 0.000 {method 'sum' of 'numpy.ndarray' objects} + 90 0.000 0.000 0.041 0.000 impl.py:144() 90 0.000 0.000 0.041 0.000 <__array_function__ internals>:2(unique) - 90 0.000 0.000 0.041 0.000 arraysetops.py:138(unique) - 90 0.001 0.000 0.041 0.000 arraysetops.py:320(_unique1d) - 90 0.040 0.000 0.040 0.000 {method 'sort' of 'numpy.ndarray' objects} - 18225 0.004 0.000 0.036 0.000 _methods.py:46(_sum) - 90 0.022 0.000 0.031 0.000 stats.py:65(rank) - 7380 0.003 0.000 0.015 0.000 <__array_function__ internals>:2(ravel) + 90 0.000 0.000 0.040 0.000 arraysetops.py:138(unique) + 90 0.001 0.000 0.040 0.000 arraysetops.py:320(_unique1d) + 90 0.039 0.000 0.039 0.000 {method 'sort' of 'numpy.ndarray' objects} + 18225 0.004 0.000 0.035 0.000 _methods.py:46(_sum) + 90 0.020 0.000 0.030 0.000 stats.py:65(rank) + 7380 0.003 0.000 0.014 0.000 <__array_function__ internals>:2(ravel) 7380 0.004 0.000 0.008 0.000 fromnumeric.py:1718(ravel) 7290 0.007 0.000 0.007 0.000 {method 'dot' of 'numpy.ndarray' objects} 90 0.006 0.000 0.006 0.000 {method 'cumsum' of 'numpy.ndarray' objects} - 7335 0.002 0.000 0.002 0.000 fromnumeric.py:70() - 3884 0.002 0.000 0.002 0.000 {built-in method numpy.zeros} + 7290 0.002 0.000 0.002 0.000 fromnumeric.py:70() + 3964 0.002 0.000 0.002 0.000 {built-in method numpy.zeros} 7380 0.002 0.000 0.002 0.000 {method 'ravel' of 'numpy.ndarray' objects} - 3922 0.002 0.000 0.002 0.000 {built-in method numpy.empty} 90 0.000 0.000 0.002 0.000 <__array_function__ internals>:2(nonzero) + 3920 0.002 0.000 0.002 0.000 {built-in method numpy.empty} 90 0.000 0.000 0.002 0.000 fromnumeric.py:1829(nonzero) 90 0.001 0.000 0.001 0.000 {method 'nonzero' of 'numpy.ndarray' objects} 90 0.000 0.000 0.001 0.000 impl.py:19(get_perc_from_k) 7384 0.001 0.000 0.001 0.000 {built-in method builtins.isinstance} 90 0.001 0.000 0.001 0.000 impl.py:33() - 7335 0.001 0.000 0.001 0.000 fromnumeric.py:2633(_amax_dispatcher) - 3645 0.001 0.000 0.001 0.000 {method 'transpose' of 'numpy.ndarray' objects} - 7380 0.001 0.000 0.001 0.000 fromnumeric.py:1714(_ravel_dispatcher) 7560 0.001 0.000 0.001 0.000 {built-in method numpy.asanyarray} - 92 0.001 0.000 0.001 0.000 {built-in method numpy.arange} - 7335 0.001 0.000 0.001 0.000 {method 'items' of 'dict' objects} \ No newline at end of file + 7290 0.001 0.000 0.001 0.000 fromnumeric.py:2633(_amax_dispatcher) + 7380 0.001 0.000 0.001 0.000 fromnumeric.py:1714(_ravel_dispatcher) + 3645 0.001 0.000 0.001 0.000 {method 'transpose' of 'numpy.ndarray' objects} + 91 0.001 0.000 0.001 0.000 {built-in method numpy.arange} + 7290 0.001 0.000 0.001 0.000 {method 'items' of 'dict' objects} \ No newline at end of file diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-less_internal_n_clusters.txt b/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-less_internal_n_clusters.txt index c96f35cd..10d936f4 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-less_internal_n_clusters.txt +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-cm_many_samples-less_internal_n_clusters.txt @@ -1,56 +1,56 @@ - 42365 function calls in 10.670 seconds + 42708 function calls in 10.601 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 174 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 10.670 10.670 {built-in method builtins.exec} - 1 0.000 0.000 10.670 10.670 :1() - 1 0.000 0.000 10.670 10.670 3466635110.py:1(func) - 1 0.000 0.000 10.670 10.670 impl.py:307(ccc) - 2 0.000 0.000 10.560 5.280 impl.py:508(compute_coef) - 45 0.003 0.000 10.558 0.235 impl.py:154(cdist_parts_basic) - 720 0.003 0.000 10.556 0.015 metrics.py:107(adjusted_rand_index) - 720 0.007 0.000 10.552 0.015 metrics.py:72(get_pair_confusion_matrix) - 720 10.493 0.015 10.532 0.015 metrics.py:42(get_contingency_matrix) - 3173 0.001 0.000 0.132 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} - 2 0.000 0.000 0.110 0.055 impl.py:476(compute_parts) - 2 0.000 0.000 0.110 0.055 impl.py:478() - 10 0.000 0.000 0.110 0.011 impl.py:110(get_parts) - 40 0.004 0.000 0.094 0.002 impl.py:36(run_quantile_clustering) - 120 0.000 0.000 0.077 0.001 fromnumeric.py:51(_wrapfunc) - 40 0.000 0.000 0.076 0.002 <__array_function__ internals>:2(argsort) - 40 0.000 0.000 0.076 0.002 fromnumeric.py:1006(argsort) - 40 0.076 0.002 0.076 0.002 {method 'argsort' of 'numpy.ndarray' objects} - 5130 0.040 0.000 0.040 0.000 {method 'reduce' of 'numpy.ufunc' objects} - 1485 0.001 0.000 0.039 0.000 <__array_function__ internals>:2(amax) - 1485 0.001 0.000 0.037 0.000 fromnumeric.py:2638(amax) - 1485 0.002 0.000 0.036 0.000 fromnumeric.py:69(_wrapreduction) - 10 0.000 0.000 0.016 0.002 impl.py:144() - 40 0.000 0.000 0.016 0.000 <__array_function__ internals>:2(unique) - 40 0.000 0.000 0.016 0.000 arraysetops.py:138(unique) - 40 0.000 0.000 0.016 0.000 arraysetops.py:320(_unique1d) + 1 0.000 0.000 10.601 10.601 {built-in method builtins.exec} + 1 0.000 0.000 10.601 10.601 :1() + 1 0.000 0.000 10.601 10.601 3466635110.py:1(func) + 1 0.000 0.000 10.601 10.601 impl.py:511(ccc) + 1 0.000 0.000 10.490 10.490 impl.py:405(compute_coef) + 45 0.000 0.000 10.489 0.233 impl.py:344(compute_ccc) + 45 0.003 0.000 10.489 0.233 impl.py:191(cdist_parts_basic) + 720 0.004 0.000 10.486 0.015 metrics.py:107(adjusted_rand_index) + 720 0.007 0.000 10.483 0.015 metrics.py:72(get_pair_confusion_matrix) + 720 10.423 0.014 10.462 0.015 metrics.py:42(get_contingency_matrix) + 3126 0.001 0.000 0.131 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} + 1 0.000 0.000 0.109 0.109 impl.py:154(get_feature_parts) + 40 0.000 0.000 0.109 0.003 impl.py:110(get_parts) + 40 0.004 0.000 0.092 0.002 impl.py:36(run_quantile_clustering) + 120 0.000 0.000 0.076 0.001 fromnumeric.py:51(_wrapfunc) + 40 0.000 0.000 0.075 0.002 <__array_function__ internals>:2(argsort) + 40 0.000 0.000 0.075 0.002 fromnumeric.py:1006(argsort) + 40 0.075 0.002 0.075 0.002 {method 'argsort' of 'numpy.ndarray' objects} + 5085 0.040 0.000 0.040 0.000 {method 'reduce' of 'numpy.ufunc' objects} + 1440 0.001 0.000 0.038 0.000 <__array_function__ internals>:2(amax) + 1440 0.001 0.000 0.037 0.000 fromnumeric.py:2638(amax) + 1440 0.001 0.000 0.036 0.000 fromnumeric.py:69(_wrapreduction) + 40 0.000 0.000 0.016 0.000 impl.py:144() + 40 0.000 0.000 0.015 0.000 <__array_function__ internals>:2(unique) + 40 0.000 0.000 0.015 0.000 arraysetops.py:138(unique) + 40 0.000 0.000 0.015 0.000 arraysetops.py:320(_unique1d) 40 0.015 0.000 0.015 0.000 {method 'sort' of 'numpy.ndarray' objects} 40 0.009 0.000 0.013 0.000 stats.py:65(rank) - 3600 0.001 0.000 0.008 0.000 {method 'sum' of 'numpy.ndarray' objects} + 3600 0.001 0.000 0.009 0.000 {method 'sum' of 'numpy.ndarray' objects} 3600 0.001 0.000 0.007 0.000 _methods.py:46(_sum) 1480 0.001 0.000 0.003 0.000 <__array_function__ internals>:2(ravel) 40 0.003 0.000 0.003 0.000 {method 'cumsum' of 'numpy.ndarray' objects} 1480 0.001 0.000 0.002 0.000 fromnumeric.py:1718(ravel) 1440 0.001 0.000 0.001 0.000 {method 'dot' of 'numpy.ndarray' objects} - 859 0.001 0.000 0.001 0.000 {built-in method numpy.zeros} + 889 0.001 0.000 0.001 0.000 {built-in method numpy.zeros} 40 0.000 0.000 0.001 0.000 <__array_function__ internals>:2(nonzero) 40 0.000 0.000 0.001 0.000 fromnumeric.py:1829(nonzero) + 1 0.000 0.000 0.001 0.001 process.py:573(__init__) 40 0.001 0.000 0.001 0.000 {method 'nonzero' of 'numpy.ndarray' objects} - 1485 0.000 0.000 0.000 0.000 fromnumeric.py:70() - 1 0.000 0.000 0.000 0.000 {method 'seed' of 'numpy.random.mtrand.RandomState' objects} - 847 0.000 0.000 0.000 0.000 {built-in method numpy.empty} - 45 0.000 0.000 0.000 0.000 impl.py:218(get_coords_from_index) + 1440 0.000 0.000 0.000 0.000 fromnumeric.py:70() 1480 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects} - 42 0.000 0.000 0.000 0.000 {built-in method numpy.arange} + 45 0.000 0.000 0.000 0.000 impl.py:255(get_coords_from_index) + 845 0.000 0.000 0.000 0.000 {built-in method numpy.empty} + 5 0.000 0.000 0.000 0.000 synchronize.py:50(__init__) 45 0.000 0.000 0.000 0.000 core.py:85(unravel_index_2d) - 40 0.000 0.000 0.000 0.000 impl.py:19(get_perc_from_k) - 40 0.000 0.000 0.000 0.000 impl.py:33() - 1 0.000 0.000 0.000 0.000 random.py:791(getrandbits) - 1 0.000 0.000 0.000 0.000 {built-in method posix.urandom} - 40 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(searchsorted) \ No newline at end of file + 4 0.000 0.000 0.000 0.000 context.py:65(Lock) + 41 0.000 0.000 0.000 0.000 {built-in method numpy.arange} + 4 0.000 0.000 0.000 0.000 synchronize.py:161(__init__) + 1 0.000 0.000 0.000 0.000 process.py:160(__init__) + 1 0.000 0.000 0.000 0.000 queues.py:37(__init__) \ No newline at end of file diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-many_samples.ipynb b/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-many_samples.ipynb index 0a18f7bb..7385e272 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-many_samples.ipynb +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/09-many_samples.ipynb @@ -5,10 +5,10 @@ "id": "fd52facb-2470-4824-83d4-7c9fd042ecfb", "metadata": { "papermill": { - "duration": 0.005324, - "end_time": "2023-09-09T18:11:26.968978", + "duration": 0.005423, + "end_time": "2023-09-11T16:48:25.302285", "exception": false, - "start_time": "2023-09-09T18:11:26.963654", + "start_time": "2023-09-11T16:48:25.296862", "status": "completed" }, "tags": [] @@ -22,10 +22,10 @@ "id": "7e0a6af6-c10e-45a5-9d8a-8849bf5ce82d", "metadata": { "papermill": { - "duration": 0.00561, - "end_time": "2023-09-09T18:11:26.979366", + "duration": 0.005571, + "end_time": "2023-09-11T16:48:25.318071", "exception": false, - "start_time": "2023-09-09T18:11:26.973756", + "start_time": "2023-09-11T16:48:25.312500", "status": "completed" }, "tags": [] @@ -39,10 +39,10 @@ "id": "874ffd00-afe1-4b51-bb22-350cbd9479f1", "metadata": { "papermill": { - "duration": 0.004574, - "end_time": "2023-09-09T18:11:26.988575", + "duration": 0.005007, + "end_time": "2023-09-11T16:48:25.327713", "exception": false, - "start_time": "2023-09-09T18:11:26.984001", + "start_time": "2023-09-11T16:48:25.322706", "status": "completed" }, "tags": [] @@ -57,16 +57,16 @@ "id": "502fe9ff-d27d-43bd-aa37-73edf7ba4f24", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:11:26.999033Z", - "iopub.status.busy": "2023-09-09T18:11:26.998799Z", - "iopub.status.idle": "2023-09-09T18:11:27.007946Z", - "shell.execute_reply": "2023-09-09T18:11:27.007430Z" + "iopub.execute_input": "2023-09-11T16:48:25.338233Z", + "iopub.status.busy": "2023-09-11T16:48:25.337847Z", + "iopub.status.idle": "2023-09-11T16:48:25.346386Z", + "shell.execute_reply": "2023-09-11T16:48:25.345918Z" }, "papermill": { - "duration": 0.016071, - "end_time": "2023-09-09T18:11:27.009362", + "duration": 0.015416, + "end_time": "2023-09-11T16:48:25.347830", "exception": false, - "start_time": "2023-09-09T18:11:26.993291", + "start_time": "2023-09-11T16:48:25.332414", "status": "completed" }, "tags": [] @@ -89,10 +89,10 @@ "id": "325cb06a-df7f-43e8-be3e-f704aaf015b0", "metadata": { "papermill": { - "duration": 0.004575, - "end_time": "2023-09-09T18:11:27.018748", + "duration": 0.004569, + "end_time": "2023-09-11T16:48:25.357282", "exception": false, - "start_time": "2023-09-09T18:11:27.014173", + "start_time": "2023-09-11T16:48:25.352713", "status": "completed" }, "tags": [] @@ -107,16 +107,16 @@ "id": "73f954a6-1776-4b92-bd0e-fc3caf5df081", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:11:27.029722Z", - "iopub.status.busy": "2023-09-09T18:11:27.029327Z", - "iopub.status.idle": "2023-09-09T18:11:27.275531Z", - "shell.execute_reply": "2023-09-09T18:11:27.275092Z" + "iopub.execute_input": "2023-09-11T16:48:25.367590Z", + "iopub.status.busy": "2023-09-11T16:48:25.367299Z", + "iopub.status.idle": "2023-09-11T16:48:25.613365Z", + "shell.execute_reply": "2023-09-11T16:48:25.612814Z" }, "papermill": { - "duration": 0.253497, - "end_time": "2023-09-09T18:11:27.276932", + "duration": 0.252719, + "end_time": "2023-09-11T16:48:25.614709", "exception": false, - "start_time": "2023-09-09T18:11:27.023435", + "start_time": "2023-09-11T16:48:25.361990", "status": "completed" }, "tags": [] @@ -140,16 +140,16 @@ "id": "d17492bb-34fe-4c34-a693-419180ba068e", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:11:27.287299Z", - "iopub.status.busy": "2023-09-09T18:11:27.287179Z", - "iopub.status.idle": "2023-09-09T18:11:27.534275Z", - 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"iopub.execute_input": "2023-09-09T18:26:39.502452Z", - "iopub.status.busy": "2023-09-09T18:26:39.502360Z", - "iopub.status.idle": "2023-09-09T18:29:31.181542Z", - "shell.execute_reply": "2023-09-09T18:29:31.181103Z" + "iopub.execute_input": "2023-09-11T17:03:18.806931Z", + "iopub.status.busy": "2023-09-11T17:03:18.806838Z", + "iopub.status.idle": "2023-09-11T17:06:07.645545Z", + "shell.execute_reply": "2023-09-11T17:06:07.645239Z" }, "papermill": { - "duration": 171.717392, - "end_time": "2023-09-09T18:29:31.213771", + "duration": 168.876975, + "end_time": "2023-09-11T17:06:07.678287", "exception": false, - "start_time": "2023-09-09T18:26:39.496379", + "start_time": "2023-09-11T17:03:18.801312", "status": "completed" }, "tags": [] @@ -630,7 +630,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.7 s ± 72.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "10.6 s ± 50.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -645,16 +645,16 @@ "id": "42d9e6e0-3c01-46e1-a409-52ff26cb78f4", "metadata": { "execution": { - 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52519760 function calls (51771260 primitive calls) in 277.174 seconds + 112301721 function calls (111553221 primitive calls) in 352.048 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 167 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 277.174 277.174 {built-in method builtins.exec} - 1 0.000 0.000 277.174 277.174 :1() - 1 0.486 0.486 277.174 277.174 1514735963.py:1(func) - 124750 4.967 0.000 276.689 0.002 impl.py:307(ccc) - 249500 0.287 0.000 140.640 0.001 impl.py:476(compute_parts) - 249500 1.098 0.000 139.520 0.001 impl.py:478() - 249500 138.422 0.001 138.422 0.001 impl.py:110(get_parts) - 124750 2.239 0.000 101.929 0.001 impl.py:508(compute_coef) - 124750 24.627 0.000 97.255 0.001 impl.py:154(cdist_parts_basic) - 10104750 45.114 0.000 72.588 0.000 metrics.py:107(adjusted_rand_index) - 10104750 27.474 0.000 27.474 0.000 metrics.py:72(get_pair_confusion_matrix) - 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"duration": 4260.956524, - "end_time": "2023-09-09T19:23:00.612824", + "duration": 5243.536906, + "end_time": "2023-09-11T18:33:43.500406", "environment_variables": {}, "exception": null, "input_path": "nbs/others/05_clustermatch_profiling/12_cm_optimized/10-many_genes.ipynb", "output_path": "nbs/others/05_clustermatch_profiling/12_cm_optimized/10-many_genes.run.ipynb", "parameters": {}, - "start_time": "2023-09-09T18:11:59.656300", + "start_time": "2023-09-11T17:06:19.963500", "version": "2.3.3" } }, diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/11-cm_many_samples-default_internal_n_clusters.txt b/nbs/others/05_clustermatch_profiling/12_cm_optimized/11-cm_many_samples-default_internal_n_clusters.txt index 8dd3545a..cccfc879 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/11-cm_many_samples-default_internal_n_clusters.txt +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/11-cm_many_samples-default_internal_n_clusters.txt @@ -1,56 +1,56 @@ - 18955 function calls (18685 primitive calls) in 3.127 seconds + 40520 function calls (40250 primitive calls) in 3.071 seconds Ordered by: cumulative time - List reduced from 106 to 50 due to restriction <50> + List reduced from 167 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 3.127 3.127 {built-in method builtins.exec} - 1 0.000 0.000 3.127 3.127 :1() - 1 0.010 0.010 3.127 3.127 1514735963.py:1(func) - 45 0.009 0.000 3.117 0.069 impl.py:307(ccc) - 90 0.000 0.000 2.927 0.033 impl.py:476(compute_parts) - 90 0.001 0.000 2.889 0.032 impl.py:478() - 90 2.889 0.032 2.889 0.032 impl.py:110(get_parts) - 45 0.001 0.000 0.164 0.004 impl.py:508(compute_coef) - 45 0.009 0.000 0.161 0.004 impl.py:154(cdist_parts_basic) - 3645 0.017 0.000 0.152 0.000 metrics.py:107(adjusted_rand_index) - 3645 0.135 0.000 0.135 0.000 metrics.py:72(get_pair_confusion_matrix) - 90 0.038 0.000 0.038 0.000 {built-in method numpy.array} - 45 0.002 0.000 0.007 0.000 {method 'seed' of 'numpy.random.mtrand.RandomState' objects} - 405 0.001 0.000 0.005 0.000 typedlist.py:341(append) + 1 0.000 0.000 3.071 3.071 {built-in method builtins.exec} + 1 0.000 0.000 3.071 3.071 :1() + 1 0.005 0.005 3.071 3.071 1514735963.py:1(func) + 45 0.019 0.000 3.066 0.068 impl.py:516(ccc) + 45 0.021 0.000 2.858 0.064 impl.py:154(get_feature_parts) + 810 2.835 0.004 2.835 0.004 impl.py:110(get_parts) + 45 0.001 0.000 0.159 0.004 impl.py:410(compute_coef) + 45 0.000 0.000 0.158 0.004 impl.py:349(compute_ccc) + 45 0.009 0.000 0.157 0.003 impl.py:196(cdist_parts_basic) + 3645 0.017 0.000 0.148 0.000 metrics.py:107(adjusted_rand_index) + 3645 0.131 0.000 0.131 0.000 metrics.py:72(get_pair_confusion_matrix) + 45 0.000 0.000 0.013 0.000 process.py:573(__init__) + 225 0.005 0.000 0.009 0.000 synchronize.py:50(__init__) + 180 0.000 0.000 0.008 0.000 context.py:65(Lock) + 180 0.000 0.000 0.008 0.000 synchronize.py:161(__init__) + 45 0.000 0.000 0.007 0.000 process.py:160(__init__) + 45 0.000 0.000 0.007 0.000 queues.py:37(__init__) + 45 0.002 0.000 0.006 0.000 {method 'seed' of 'numpy.random.mtrand.RandomState' objects} + 405 0.001 0.000 0.004 0.000 typedlist.py:341(append) + 45 0.000 0.000 0.004 0.000 context.py:110(SimpleQueue) + 45 0.000 0.000 0.004 0.000 queues.py:340(__init__) 45 0.000 0.000 0.004 0.000 contextlib.py:76(inner) + 90 0.000 0.000 0.003 0.000 _base.py:636(__exit__) + 225 0.000 0.000 0.003 0.000 synchronize.py:114(_make_name) + 45 0.002 0.000 0.003 0.000 process.py:731(shutdown) + 495 0.000 0.000 0.003 0.000 {built-in method builtins.next} + 45 0.003 0.000 0.003 0.000 {function SeedSequence.generate_state at 0x7f65546659d0} + 225 0.000 0.000 0.003 0.000 tempfile.py:149(__next__) + 270 0.003 0.000 0.003 0.000 {built-in method numpy.zeros} 45 0.000 0.000 0.003 0.000 typedlist.py:298(_initialise_list) - 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45 0.000 0.000 0.001 0.000 <__array_function__ internals>:2(amax) + 135 0.000 0.000 0.001 0.000 connection.py:521(Pipe) 90 0.000 0.000 0.001 0.000 abstract.py:48(_intern) - 45 0.000 0.000 0.001 0.000 fromnumeric.py:2638(amax) + 45 0.000 0.000 0.001 0.000 process.py:67(__init__) + 45 0.000 0.000 0.001 0.000 process.py:77(wakeup) 90 0.000 0.000 0.001 0.000 {method 'get' of 'dict' objects} - 45 0.000 0.000 0.001 0.000 typeof.py:257(_typeof_nb_type) - 45 0.000 0.000 0.001 0.000 fromnumeric.py:69(_wrapreduction) - 226 0.000 0.000 0.001 0.000 <__array_function__ internals>:2(copyto) - 45 0.000 0.000 0.001 0.000 random.py:791(getrandbits) - 45 0.000 0.000 0.001 0.000 containers.py:618(__init__) - 45 0.000 0.000 0.001 0.000 thread.py:123(__init__) - 90 0.000 0.000 0.000 0.000 _ufunc_config.py:32(seterr) - 45 0.000 0.000 0.000 0.000 _ufunc_config.py:429(__enter__) - 45 0.000 0.000 0.000 0.000 {built-in method posix.urandom} - 405 0.000 0.000 0.000 0.000 typedlist.py:81(_append) - 405 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance} - 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"iopub.execute_input": "2023-09-09T18:27:28.731477Z", - "iopub.status.busy": "2023-09-09T18:27:28.731370Z", - "iopub.status.idle": "2023-09-09T18:27:28.734663Z", - "shell.execute_reply": "2023-09-09T18:27:28.734287Z" + "iopub.execute_input": "2023-09-11T18:34:44.867137Z", + "iopub.status.busy": "2023-09-11T18:34:44.867032Z", + "iopub.status.idle": "2023-09-11T18:34:44.869922Z", + "shell.execute_reply": "2023-09-11T18:34:44.869660Z" }, "papermill": { - "duration": 0.009526, - "end_time": "2023-09-09T18:27:28.735860", + "duration": 0.009061, + "end_time": "2023-09-11T18:34:44.871059", "exception": false, - "start_time": "2023-09-09T18:27:28.726334", + "start_time": "2023-09-11T18:34:44.861998", "status": "completed" }, "tags": [] @@ -556,16 +556,16 @@ "id": "11259d8c-3bf3-4299-b47b-211556c3bc08", "metadata": { "execution": { - "iopub.execute_input": "2023-09-09T18:27:28.746096Z", - "iopub.status.busy": "2023-09-09T18:27:28.745799Z", - "iopub.status.idle": "2023-09-09T18:27:49.502104Z", - 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"duration": 83.311396, - "end_time": "2023-09-09T18:27:51.166886", + "duration": 82.301789, + "end_time": "2023-09-11T18:35:07.212398", "environment_variables": {}, "exception": null, "input_path": "nbs/others/05_clustermatch_profiling/12_cm_optimized/11-many_samples.ipynb", "output_path": "nbs/others/05_clustermatch_profiling/12_cm_optimized/11-many_samples.run.ipynb", "parameters": {}, - "start_time": "2023-09-09T18:26:27.855490", + "start_time": "2023-09-11T18:33:44.910609", "version": "2.3.3" } }, diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/06-many_genes.py b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/06-many_genes.py index 0b4e08d2..14051965 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/06-many_genes.py +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/06-many_genes.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: all,-execution,-papermill,-trusted +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version # text_representation: # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.11.5 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/07-many_samples.py b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/07-many_samples.py index aaf93102..265d5df0 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/07-many_samples.py +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/07-many_samples.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: all,-execution,-papermill,-trusted +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version # text_representation: # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.11.5 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/08-many_genes.py b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/08-many_genes.py index 7e948ba0..56b13d70 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/08-many_genes.py +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/08-many_genes.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: all,-execution,-papermill,-trusted +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version # text_representation: # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.11.5 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/09-many_samples.py b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/09-many_samples.py index e9e58127..69643a29 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/09-many_samples.py +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/09-many_samples.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: all,-execution,-papermill,-trusted +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version # text_representation: # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.11.5 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/10-many_genes.py b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/10-many_genes.py index b2d8b7f2..cd85d022 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/10-many_genes.py +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/10-many_genes.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: all,-execution,-papermill,-trusted +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version # text_representation: # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.11.5 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/11-many_samples.py b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/11-many_samples.py index 26e4ab0d..3900d272 100644 --- a/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/11-many_samples.py +++ b/nbs/others/05_clustermatch_profiling/12_cm_optimized/py/11-many_samples.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: all,-execution,-papermill,-trusted +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version # text_representation: # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.11.5 # kernelspec: # display_name: Python 3 (ipykernel) # language: python