Implementation: add results from scaling benchmarks
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import matplotlib.pyplot as plt
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import seaborn as sns
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import json
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import sys
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with open('scaling-benchmarks.json', 'r') as fd:
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data = json.load(fd)
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#labels = list(data.keys())
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#labels = ['2', '3', '5', '10', '15', '20', 'hpav1']
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labels = ['5', '10', 'hpav1', 'hpav2', 'hpav3', 'hpav4', 'hpav5']
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expanded_labels = []
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config_run_times = []
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for label in labels:
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expanded_labels.append(label)
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exec_times = []
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for run in data[label]:
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exec_times += run['execution_times']
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config_run_times.append(exec_times)
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fig, axl = plt.subplots(nrows=1, ncols=1, figsize=(7, 3))
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axl.yaxis.grid(True, zorder=0.4)
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axl.set_ylabel('Configuration Run Time (in Seconds)')
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axl.set_xlabel('Scaling Setting')
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# Do a regular boxplot:
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# bplot2 = axl.boxplot(config_run_times,
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# notch=False, # notch shape
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# vert=True, # vertical box alignment
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# patch_artist=True, # fill with color
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# labels=["[" + l + "]" for l in expanded_labels], # will be used to label x-ticks
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# )
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# axl.set_xticks(range(1, len(labels) + 1))
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# axl.set_xticklabels(labels)
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# Do a violin plot
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plot = sns.violinplot(data=config_run_times,
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ax=axl,
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scale="count",
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)
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axl.set_xticks(range(0, len(labels)))
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axl.set_xticklabels(labels)
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# Add median values at the top:
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def flatten(list_of_lists):
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return [val for sublist in list_of_lists for val in sublist]
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def median(alist):
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return "%.1f" % (sum(alist)/len(alist),)
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#axl.set_yticks([i*10 for i in range(0,200,25)])
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#plot_y_max = max(flatten(config_run_times))+600
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#plot_y_min = min(flatten(config_run_times))-200
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#offset = 200
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axl.set_yticks(range(0,500,100))
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plot_y_max = max(flatten(config_run_times))+150
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plot_y_min = min(flatten(config_run_times))-100
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offset = 50
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axl.set_ylim([plot_y_min,plot_y_max])
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for i, label in enumerate(labels):
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axl.text(i, plot_y_max-offset, median(config_run_times[i]),
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horizontalalignment='center',color='b')
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fig.tight_layout()
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fig.savefig('config-run-variance-autoscaling.pdf',
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format='pdf',
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# dpi=100,
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# bbox_inches='tight',
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)
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import matplotlib.pyplot as plt
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import seaborn as sns
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import json
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import sys
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with open('scaling-benchmarks.json', 'r') as fd:
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data = json.load(fd)
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#labels = list(data.keys())
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labels = ['2', '3', '5', '10', '15', '20', 'hpav1']
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#labels = ['5', '10', 'hpav1', 'hpav2', 'hpav3', 'hpav4', 'hpav5']
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fig, axr = plt.subplots(nrows=1, ncols=1, figsize=(7, 3.5))
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axr.grid(True, zorder=1.0)
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for label in labels:
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x_vals = [run['time_to_completion'] for run in data[label]]
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y_vals = [run['replica_seconds'] for run in data[label]]
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sns.scatterplot(
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x=x_vals,
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y=y_vals,
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zorder=2.0, # make sure it's on top of the grid
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s=70, # controls size of dots
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label=label,
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)
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# text_x = max(x_vals) + 25
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# text_y = max(y_vals) + 25
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# if label == "2":
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# text_x -= 15
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# text_y += 0.1*text_y
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# if label == "20":
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# text_y -= 0.01*text_y
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# axr.text(text_x, text_y, "[" + label + "]")
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axr.legend(title='Scaling setting:')
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axr.set_xlabel('Performance: Time to Completion (in Seconds)')
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axr.set_ylabel('Cost: Replica Seconds')
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fig.tight_layout()
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# save the plot as a file
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fig.savefig('performance-cost-benchmark-static.pdf',
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format='pdf',
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)
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{
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"2": [
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"3": [
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"10": [
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"15": [
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"20": [
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"hpav1": [
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"hpav2": [
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"hpav3": [
|
||||
{"start_time":"2021-04-23 18:39:53.021749796 +0300 EEST m=+268.578466646","end_time":"2021-04-23 18:51:25.630831025 +0300 EEST m=+961.187547862","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[190.188,135.591,98.83,87.461,86.326,91.335,82.933,92.991,89.044,86.378],"time_to_completion":692.609,"replica_seconds":9498},
|
||||
{"start_time":"2021-04-24 08:57:38.217776346 +0300 EEST m=+531.077181864","end_time":"2021-04-24 09:08:59.498112288 +0300 EEST m=+1212.357517858","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[180.65,122.402,77.415,85.846,91.755,95.715,122.821,90.041,106.447,79.22],"time_to_completion":681.28,"replica_seconds":8451},
|
||||
{"start_time":"2021-04-24 09:37:25.145724546 +0300 EEST m=+543.827686903","end_time":"2021-04-24 09:49:08.822249496 +0300 EEST m=+1247.504211829","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[192.955,169.764,154.476,84.2,114.964,86.121,93.016,126.221,111.792,95.622],"time_to_completion":703.676,"replica_seconds":8531}],
|
||||
"hpav4": [
|
||||
{"start_time":"2021-04-23 15:03:46.785710656 +0300 EEST m=+539.494768426","end_time":"2021-04-23 15:15:30.774566433 +0300 EEST m=+1243.483624180","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[199.135,134.694,91.468,92.563,119.402,114.288,128.775,97.409,133.912,96.254],"time_to_completion":703.988,"replica_seconds":7625},
|
||||
{"start_time":"2021-04-24 10:17:42.917707044 +0300 EEST m=+527.895535414","end_time":"2021-04-24 10:29:23.889262334 +0300 EEST m=+1228.867090746","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[186.515,184.394,139.319,81.976,88.858,121.758,103.404,108.905,117.381,90.656],"time_to_completion":700.971,"replica_seconds":7475},
|
||||
{"start_time":"2021-04-24 10:58:07.309716212 +0300 EEST m=+529.258708644","end_time":"2021-04-24 11:09:49.056091439 +0300 EEST m=+1231.005083795","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[191.107,123.687,92.064,89.977,89.825,135.715,93.445,130.762,121.788,91.918],"time_to_completion":701.746,"replica_seconds":7632}],
|
||||
"hpav5": [
|
||||
{"start_time":"2021-04-23 19:19:23.933701222 +0300 EEST m=+527.603123457","end_time":"2021-04-23 19:31:37.871511594 +0300 EEST m=+1261.540933913","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[245.727,215.634,83.668,146.78,121.145,146.502,105.206,134.187,142.139,123.075],"time_to_completion":733.937,"replica_seconds":6577},
|
||||
{"start_time":"2021-04-24 11:37:39.813729687 +0300 EEST m=+529.310820611","end_time":"2021-04-24 11:49:41.252677515 +0300 EEST m=+1250.749768499","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[184.476,103.858,136.93,88.774,132.856,199.417,157.243,148.596,165.462,116.995],"time_to_completion":721.438,"replica_seconds":6796},
|
||||
{"start_time":"2021-04-24 14:00:37.365651384 +0300 EEST m=+537.136091772","end_time":"2021-04-24 14:12:39.554796933 +0300 EEST m=+1259.325237298","config_runs":10,"mock_settings":"python3 ci/tools/scripts/setup_mock_data.py \\\n --no-threat --no-execution-run --no-compliance-run --no-configuration-run --no-failing \\\n -c 20 --assets-per-group 5 --tasklists-per-group 2 --policies-per-group 2","execution_times":[193.955,90.22,109.771,86.624,142.376,144.37,140.194,145.807,148.949,113.994],"time_to_completion":722.189,"replica_seconds":6629}]
|
||||
}
|
Loading…
Reference in New Issue